Mapping Babel

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Import AI Issue 47: Facebook’s AI agents learn to lie, OpenAI and DeepMind use humans to train safe AI, and what TensorFlow’s new release says about future AI development

Facebook research: Misdirection for NLP fun and profit:
New research from Facebook shows how to teach two opposing agents to bargain with one another — and along the way they learn to deceive each other as well.
…”For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance,” they write.

Images of the soon-to-be normal:
This photograph of a little food delivery robot blocking traffic is a herald of something that will likely become much more commonplace.

Predicting Uber rides with.. Wind speed, rider data, driver data, precipitation data, temperature, and more…
Uber has given details on the ways it is using recurrent neural networks (RNNs) to help it better predict demand for its services (and presumably cut its operating costs along the way).
…The company trained a model using five years of data from numerous US cities. The resulting RNN  has good predictive abilities when tested across a corpus of data consisting of trips taken across multiple US cities over the course of seven days before, during, and after major holidays like Christmas Day and New Year’s Day. (Though there are a couple of real-world spikes that seem so drastic its predictions low-ball them, suggesting it hasn’t seen enough of those incidents to recognize their warning indicators.)
…Uber’s new system is significantly better at dealing with spiky holiday days like Christmas Day and New Year, and it slightly improves accuracy on other days such as MLK Day and Independence Day.
…Components used: TensorFlow, Keras. Lots of computers.

Job alert!
The Berkman Klein Center for Internet & Society at Harvard University is seeking a project coordinator to help it with its work on AI, autonomous systems, and related technologies. Apply here. (Also, let’s appreciate the URL for this job and how weird it could have seemed to someone a hundred years ago –…./AIjob )

AI video whiz moves from SF, USA, to Amsterdam, Netherlands. But why…?
…Siraj Ravel has moved from the US to Amsterdam for a change of scene. Now that he’s settled in he has started a new video course (available on YouTube) called The Math of Intelligence. Check it out.
…I asked Siraj what his impressions were of the AI community in Amsterdam and he said this (emphasis mine): “The AI community is absolutely thriving in Amsterdam, specifically the research portion. I’ve met more researchers at Meetups here than I have for years in SF. I also briefly visited Berlin and met some amazing data scientists there. The bigger trend is that governments in the EU (France, Netherlands, Germany) are heavily investing in tech R&D and the brightest minds are taking notice. I am the son of immigrants to the USA, but I am not afraid to myself immigrate if necessary. Progress can’t wait, and the Netherlands understands this.” Sounds nice, and the pancakes are great as well.

Googlers create a single multi-sensory network: One Model To Rule Them All.
Welcome to the era of giant frankenAIs:
… Researchers from Google have figured out how to bake knowledge about a broad spectrum of domains into a single neural network and then train it in an end-to-end way.
…”In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains.,” they write. “The key to success comes from designing a multi-modal architecture in which as many parameters as possible are shared and from using computational blocks from different domains together. We believe that this treads a path towards interesting future work on more general deep learning architectures”.
..Prediction: As this kind of research becomes viable we’ll see people gather huge datasets and train single models together with a broad range of discriminative abilities. The next shoe to drop will be innovations in fundamental neural network building block components to create finer-grained classification and inference abilities in these neural network models and encourage more cases of transfer learning.
Notable: Others are thinking along similar lines – last week’s Import AI covered a new MIT research paper that blends sound and vision and text into a single meta-network. 

Pay attention to Google’s new attention paper:
Google researchers have attained state-of-the-art results in areas like English-to-German translation with a technique that is claimed to be significantly simpler than its forebears.
…The paper, Attention is All You Need, proposes: “the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output.”
…In other words, the researchers have figured out a way to reduce the number of discrete ingredients that go into the network, swapping out typical recurrent and convolutional mapping layers with ones that use intention instead.
…”We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video. Making generation less sequential is another research goal of ours.”
…It seems that research into things like this will create further generic neural network building blocks that can be plugged into larger, composite models – just like the above ‘One Model to Rule Them All’ approach. Watch for collisions!

Long-brewing research from Vicarious: Learning correspondences via (for now) hand-tuned feature extractors:
…One puzzle reinforcement learning researchers struggle with is how algorithms end up evolving to over-fit their environment. What that means in practice is if you suddenly were to, say, change the geometry of the Go board AlphaGo was trained on, or alter the placement of enemies and obstacles in Atari games, the AI might fail to generalize.
…Now, research from Vicarious – An AI startup with backing from people like Jeff Bezos, Mark Zuckerberg, Founders Fund, ABB, and others – proposes a way to ameliorate this flaw. This marks the second major paper from Vicarious this year.
…Their approach relies on what they call Schema Networks, which lets their AI learn the underlying dynamics of the environment it is exposed to. This means, for instance, that you can alter the width of a paddle in Atari Game breakout, or change the block positions, and the trained algorithm can generalize quickly to the new state, preserving its underlying understanding of the dynamics of the world built up during training. Traditional RL algorithms tend to struggle with this as they’ve learned a predictive model of the world as it is and struggle with learning more abstract links.
…There’s a small catch with Vicarious’s approach – the researchers had to do the object segmentation and identification themselves then feed that to the AI. In reality, one of the greatest challenges computer vision researchers face is accurately mapping and segmenting non-stationary images (and its even harder as they get deployed in the chaotic real world, as they need to link parts of a flat 2D image to messy 3D objects. I’m keen to see what happens when this algorithm can do the feature isolation itself.
Noteable: Meanwhile, DeepMind have published Relational Networks (claiming SOTA and superhuman performance) and Visual Interaction Networks, two philosophically similar research papers that hew closer to traditional deep learning approaches. Just as you and I use abstract logic to let us reason about the world, it seems likely AI will need the same capabilities.

Just what the heck does a career in AI policy look like?
…Twitter’s AI paper tsar Miles Brundage has published an exhaustive document outlining a Guide to Working in AI Policy and Strategy up on 80,000 hours. (And watch out for the nod to Import AI – thanks Miles. I’ll do my best!)

(Mildly) Controversial Microsoft/Maluuba research paper: Using rewards is easy, finding them is hard:
…A new research paper from Microsoft’s recent Canadian AI acquisition Maluuba, Hybrid Reward Architecture for Reinforcement Learning, shows how to definitively beat Ms. PacMan (clocking over a million points.). Ms PacMan, along with Montezuma’s Revenge, is one of the games that people have found consistently quite challenging, so it’s a notable result – though not as encouraging as on first look, when you work out what is required for the process to work.
..When you go and analyze its Hybrid Reward Architecture- you see that the approach is distributed, with Microsoft splitting up the task into many discrete sub-tasks which numerous reinforcement learning agents try to solve, while feeding their opinions up into a single meta-agent that helps to take decisions. Though it scores highy, the approach involves a lot of human specification, including hand-labeling different reward penalties and rewards for different entities in the game. As with the Vicarious paper, the technique is interesting, but it feels like it’s missing a key component – unsupervised extraction of entities and reward levels/penalties.

What TensorFlow v1.2 says about devices versus clouds:
Google has released version 1.2 of TensorFlow. There’s a ton of fixes and tweaks (eg, for RNN functionality), but buried in the release details is the note that Google will stop directly supporting GPUs on Mac systems (though will continue to accept patches from the community). There are likely a couple of reasons for this: one, the lack of much of an NNVIDIA ecosystem around macs (c. f Apple’s new external GPU for the Mac Pro runs AMD cards, which are yet to develop as much of a deep learning ecosystem.)
…Another way of looking at this is that the cloud wins AI. For now at least AI benefits from parallelization and the usage of large numbers of CPUs and GPUs together, with most developers either using a laptop paired with an external pool of cloud resources, and/or running their own Linux deep learning rig in a desktop tower.
Details: Tensorflow v1.2 on GitHub.

Snapchat’s first research paper: mobile-friendly neural nets with full-fat brains.
Researchers with Snap Inc. and the University of Iowa have published SEPNETs: Small and Effective Pattern Networks.
…tl;dr: a way to shrink trained models then recover them to restore some accuracy.
…It tackles one of the problems AI’s success has led to: the creation of increasingly large, deep models that have tremendous performance but take up a lot of space when deployed. Ultimately, the ideal scenario for AI development is to be able to train a single gigantic model on a nearby football-field filled with computers, then be able to have a little slider to shrink the trained model for deployment on various end-user devices, whether phones, or computers, or VR headsets, or something else. How do you do that? One idea is to try to smartly compress these trained models, either by lopping away at chunks of the neural network, or by scaling them down in a more disciplined way. Both methods see you tradeoff overall accuracy for speed, so fixing this requires new research. The Snapchat paper represents one contribution:
The details: First they use a technique called pattern binarization to shrink a pre-trained network (for instance, a tens-of-millions-of-parameters VGG or Inception model) into a smaller version of itself, at the cost of it losing some discriminative capabilities. They propose to fix this with a new neural network component they call a Pattern Residual Block. This component can sometimes help offset the changes wrought on the numbers its dealing with via the binarization process.They then use Group-Wise Convolution to further winow down the various components of the network. Shrinking it.
…Results:.Google MobileNet: 1.3 million params, 5.2mb bytes, accuracy 0.637
…Results:SEP-NET-R(Small) 1.3 million params, 5.2mb bytes, 0.658

Free pre-trained models, get your pre-trained mobile-friendly models right here!
…Google unfurls MobileNets to catch intelligence on the phone:
In possibly related news Google has released MobileNets, a collection of “mobile-first computer vision models for TensorFlow”.
…”MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. ”
…The available models vary from bite-size ones of 0.47 million parameters to larger ones of 4.24 million, with image accuracies ranging from 66.2 to 89.9% for the larger models.
Github repo here.

Speeding up open access reviews:  There’s a suggestion that Open Review – a platform that makes feedback and evaluation of papers public – is considering layering some aspect of its system over Arxiv, letting us not only publish preprints rapidly, but potentially review them as well.
…Note: None of this is meant to say that double blind reviewing is bad – it’s good, especially for significant papers with particularly controversial claims. But I think due to the breakneck speed at which AI moves at it’s necessary to try and speed things up if possible. This suggests one way to more rapidly gather better feedback on new ideas.
…How it might be used: Last week Hochreiter & co published the SELU paper. It’s gathered a lot of interest with numerous people running their own tests, chiming in with comments, or going through its 90+ page appendix. It’d be very convenient if there was a layer that let us put all this stuff in the same place.

Dollars for Numpy: Numpy has been given a little over half a million dollars from the Gordon Moore foundation to fund improvements to the Python scientific computing library. Numpy is absolutely crucial to neural networks within Python.

Monthly Sponsor:
Amplify Partners is an early-stage venture firm that invests in technical entrepreneurs building the next generation of deep technology applications and infrastructure. Our core thesis is that the intersection of data, AI and modern infrastructure will fundamentally reshape global industry. We invest in founders from the idea stage up to, and including, early revenue.
…If you’d like to chat, send a note to

Tech Tales:
[2035: The North East Canadian wilderness.]

It’s wet. There’s moss. The air has that peculiar clarity that comes from being turned by wind and freshened by water and replenished by the quiet green and brown things of nature. You breathe deeply as you walk the rarely used trail. Your feet compress the nested pine needles beneath you, sending up gusts of scented air. In the distance, you hear the sound of roiling running water and, beneath it, bells tolling.

You keep going. The sound of the water and of the bells gets louder. The bells echo out little sonorous rhythms that seem intertwined with the sounds of gushing river water. One of the bells is off – its timing discordant, cutting against the others. You begin to crest a small hill, and as your head clears it the sound rushes at you. The bells clang and the water thrums – their interplay not exactly abrasive – for the off bell is one of many – but somehow more mournful than you recall the sound being before.

The bells are housed in a small concrete tower, about 5 feet high that sits by the riverbank at a point where there’s a kink in the river. It has three walls, with the fourth left open to the elements, facing the river, broadcasting the sounds of the bells. You run your hands over the cold, mottled, lichen-stippled exterior as you approach its entrance. Close your eyes. Open them when you’re in front of the shrine. You study the 12 bells of the dead, able to make out the inscribed names of the gone, despite the movement of the bells. Now you just need to diagnoze why one of the bells seems to have fallen out of alignment with the others.

As you sit, studying the wiring in the shrine and watching the bells, it’s impossible not to think of your friends and how they are now commemorated. You all work for the government on geographic survey. As the climate has been changing your teams have been pushing further north for more of the year, trading safety for exploration (and the possibility of data valuable to resource extraction companies). You were at home, laid up with a broken leg, when the team of 12 went out. They were doing a routine mapping hike, away from camp, when the storm came in – it strengthened far more rapidly than their computer models anticipated and, due to a set of delicate occurrences, it brought snow and ice with it. Temperatures plunged. Snow-cladded everything. Rain was either flecked with ice or snow or a contributor to a sheet of frozen fog that lay over the land. Your colleagues died due to about 50 things going wrong in a very precise sequence. These things, as hysterical as it seems, happen.

The bells are set to dance to the rhythm of the river. Their loops are determined by observations from cameras atop the shrine, pointed at the writhing river. This visual information is then fed into an algorithm that is forever trying to find a pattern in the infinite noise of the river. After an hour you have the sense to give the cameras more than a cursory look and you discover that a spider has made a small nest near the sensor bulge, and one thick strand of webs is slung in front of one of the camera lenses. This, you figure, has injected a kind of long-term stability into part of the feed of data that the algorithm sees, swapping a patch of the frothing slate and white and dark blue and brown of the river-water with something altogether more stagnant.  Fixing it would be as simple as putting on a glove and carefully removing the spiderweb, then polishing the lens. You hold your hand up in front of the web to get a sense of how it would be to remove it and as your hand passes in front of the cameras the bells change their rhythm, some stuttering to a stop and others speeding up, driven to a frenzy by the changed vision. You put your hand down and the bells go back to their tolling, with the one that seems to be affected by the spiderweb still acting out of order.

When you file your report you say reports of odd sounds appear to be erroneous and you discovered no such indicators during your visit. You take comfort in knowing that the bells will continue to ring, driven increasingly by the way the world grows and breaks around them, and less by the prescribed chaos of the river.

Technologies that inspired this story: Attention, generative models, joint neural networks, long-short term memory

OpenAI Bits&Pieces:

OpenAI and DeepMind train reward functions via human feedback: A new research collaboration between DeepMind and OpenAI on AI safety sees us train a AI agents to perform behaviors that they think humans will approve of. This has implications for AI safety and has promising sample efficiency as well.

OpenAI audiopodcast about reinforcement learning by Sam Charrington with OpenAI/UC Berkeley robot chap Pieter Abbeel.

Import AI: Issue 46: Facebook’s ImageNet-in-an-hour GPU system, diagnosing networks with attention functions, and the open access paper debate

Attention & interpretability: modern neural networks are hard to interpret because we haven’t built tools to make it easy to analyze their decision-making processes. Part of the reason why we haven’t built the tools is that it’s not entirely obvious how you get a big stack of perceptual math machinery to tell you about what it is thinking in a way that is remotely useful to the untrained eye. The best thing we’ve been able to come up with, in the case of certain vision and language tasks, is attention where we visualize what parts of a neural network – sometimes down to an individual cell or ‘neuron’ within it – is activating in response to. This can help us diagnose why an AI tool is responding in the way it is.
.., Latent Attention Networks, from researchers with Brown University proposes an interesting way to improve our ability to analyze nets: by creating a new component to make it easier to visualize the attention of a given network in a more granular manner..
…In the paper they introduce a new AI component, which they call a Latent Attention Network. This component is general, working across different neural network architectures (a first, the researchers claim), and only requires the person to fiddle with it at its input or output points. LANs let them fit a so-called attention mask over any architecture.
…”The attention mask seeks to identify input components of x that are critical to producing the output F(x). Equivalently, the attention mask determines the degree to which each component of x can be corrupted by noise while minimally affecting F(x),” they write.
…The researchers evaluate the approach on a range of tasks from simple (MNIST! CIFAR) and to a game of Pong from the Atari Learning Environment. The ensuing visualizations seems to be helpful for getting a better grasp of how and why neural network classifiers work. I particularly recommend studying the images from Pong.
Why it could be useful: this technique hints at a way to be able to take a generic component and simply fit it to an arbitrary network, then get the network to cough up some useful information about its state – if extended it could be a handy tool for AI diagnosticians.

Self-Normalizing Neural Networks cause a stir: A paper from researchers with the Bioinformatics Institute in Austria proposes a way to improve feed forward neural network performance with a new AI component, Self-Normalizing Neural Networks. “FNNs are typically shallow and, therefore cannot exploit many levels of abstract representations. We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations,” they write.
…The paper is thorough and is accompanied with a code release, aiding rapid replication and experimentation by others. The researchers carry out exhaustive experiments, bench-marking their approach (based around a SELU, a scaled exponential linear unit) against a movable feast of other AI approaches, ranging from Residual Nets, to Highway Networks, to weights with Batch Normalization or Layer Normalization, and more.
…They test the method exhaustively as well. “We compared SNNs on (a) 121 tasks from the UCI machine learning repository, on (b) drug discovery benchmarks, and on (c) astronomy tasks with standard FNNs and other machine learning methods such as random forests and support vector machines. SNNs significantly outperformed all competing FNN methods at 121 UCI tasks, outperformed all competing methods at the Tox21 dataset, and set a new record at an astronomy data set,” they write.
Noteable fact: One of the authors is Sepp Hochreiter, who invented (along with Juergen Schmidhuber) the tremendously influential Long-Short Term Memory networks component, aka the LSTM. LSTMs are used exhaustively in AI these days for tasks ranging from object detection to speech recognition and the paper has over 4500 citations (growing with the massive influx of new AI research into memory networks, differentiable neural computers, Neural Turing Machines, and so on).
…The Self-Normalizing Neural Networks paper is amply thorough, weighing in at an eyebrow-raising 102 pages, split between the research paper (9 pages) with the other pages devoted to comprehensive theoretical analysis, experiments, and – of course – references, to back it up. More of this European precision, please!

Open publishing (Arxiv) versus slow publishing (Conferences and Journals). 
The Hochreiter paper highlights some of the benefits of the frenetic attention that publishing on Arxiv can bestow, along with/instead of traditional (relatively slow-burning) conferences and journals. I think the trade-off between speed of dissemination and lack of peer review is ultimately worthwhile, though some disagree.
…Yoav Goldberg, a researcher who has done work at the intersection of NLP and Deep Learning, writes that Arxiv can also lead to people having an incentive to publish initial versions of papers that are thin, not very detailed, and that serve more as flag-planting symbols for an expected scientific breakthrough than anything else. These are all legitimate points.
…Facebook AI Researcher Yann Lecun weighed in and (in a lengthy, hard-to-link to note on Facebook) says that the open publishing process allows for rapid dissemination of ideas and experimentation free of the pressure to publish papers at a conference.As of the time of writing the nascent AI blogosphere continues to be roiled by this drama, so I’m sure this boulder will continue to roll.
…(For disclosure: I side more toward favoring the Arxiv approach and think that ultimately bad papers and bad behavior gets weeded out by the community over time. It’s rare that people accept a con. Deep Learning has been in hyper-growth mode since the 2012 AlexNet paper, so it’s natural things are a bit fast-moving and chaotic right now. Things may iron themselves out over time.

Compute as AI’s strategic fulcrum: the AI community is getting much better at training big neural network models. Latest case in point comes from Facebook, which has outlined a new technique for training large-scale image classifiers.
…Time to train ImageNet in 2012: A week or two across a single GPU, with need for loads of custom CUDA programming..
Time to train ImageNet in 2017: One hour across 256 GPUs. Vastly improved&simplified software+hardware..
…Although, as someone commented on Twitter, most people don’t easily have access to 256 GPUs.

Better classifiers through combination: DING DONG! DING DONG! When you read those four words there’s a decent chance you also visualized a big clock or imagined some sonic representation of a clock chiming. Human memory seems to work like this, with a sensory experience of one entity inviting in a bunch of different, complementary representations. Some believe it’s this fusion of senses that gives us such powerful discriminative abilities.
…Wouldn’t it be nice to get similar effects in deep learning? From 2015 onwards people started experimenting en mass with getting computers to better understand images by training the nets on paired sets of images and captions, creating perceptual AI systems with combined representations of entities. We’ve also seen people more recently experiment with training audio and visual data together. Now, scientists from MIT have combined visual, audio, and text, into the same network.
...The data: Almost a million images (COCO & Visual Genome), synchronized with either a textual description or an audio track (377 continuous days of audio data, pulled from over 750,000 Flickr videos).
...How it works: the researchers create three different networks to ingest text, audio, or picture data. The ensuing learned representations from all of these networks are outputted as fixed length vectors with the same dimensionality, which are then fed into a network that is shared across all three input networks. “While the weights in the earlier layers are specific to their modality, the weights in the upper layers are shared across all modalities,” they write.
Bonus: The combined system ends up having cells that activate in the presence of words, pictures, or sounds that correspond to subtle types of object, like engines or dogs.

Bored with the state of your supply chain automation? Consider investing in an autonomous cargo boatthe new craze sweeping across commodities makers worldwide!, as companies envisage a future where autonomous mines (semi-here) take commodities via autonomous trains (imminent) to autonomous ports (here) to the yet-to-be-built autonomous boats.

4K GAN FACES: A sight for blurry, distorted eyes. Mike Tyka has written about his experiments to use GANs to create large, high-resolution entirely synthetic faces.
…The results are quite remarkable, with the current images seeming as much a new kind of impressionism as realistic photographs, (though only for sub-sections of every given image, and sometimes wrought with Dali-esque blotches and Bacon-esque flaws)..
…”as usual I’m battling mode collapse and poor controllability of the results and a bunch of trickery is necessary to reduce the amount of artifacts,” he writes. G’luck, Mike!

You are not Google (and that’s okay): This article about knowing what large-scale over-engineered technology is worth your while and what is out of scope is as relevant for AI researchers and engineers as it is for infrastructure people.
…Bonus: the invention of the delightfully German-sounding acronym UNPHAT.

What China thinks about when China thinks about AI: A good interview with Oregon professor Tom Diettrich in China’s National Science Review. We’re entering an era where AI becomes a tool of geopolitics as countries flex their various strengths in the tech as part of wider national posturing. So it’s crucial that scientists stay connected with one another, talking about the issues that matter to them which transcend borders.
…Diettrich makes the point that modern AI is about as easy to debug as removing all the rats from a garbage dump. “Traditional software systems often contain bugs, but because software engineers can read the program code, they can design good tests to check that the software is working correctly. But the result of machine learning is a ‘black box’ system that accepts inputs and produces outputs but is difficult to inspect,” he says.
AI in China: “Chinese scientists (working both inside and outside China) are making huge contributions to the development of machine learning and AI technologies. China is a leader in deep learning for speech recognition and natural language translation, and I am expecting many more contributions from Chinese researchers as a result of the major investments of government and industry in AI research in China. I think the biggest obstacle to having higher impact is communication,” he says. “A related communication problem is that the internet connection between China and the rest of the world is often difficult to use. This makes it hard to have teleconferences or Skype meetings, and that often means that researchers in China are not included in international research projects.”

Building little pocket universes in PyTorch: This is a good tutorial for how to use PyTorch, an AI framework developed by Facebook, to build simple cellular automata grid worlds and train little AI agents in them.
…It’s great to see practical tutorials like this (along with the CycleGAN implementation & guide I pointed out last week) as it makes AI a bit less intimidating. Too many AI tutorials say stuff like “Simply install CUDA, CuDNN, configure TensorFlow, spin-up a dev environment in Conda, then swap out a couple of the layers.” This is not helpful to a beginner, and people should remember to go through all the seemingly-intuitive setup steps that go with any deep learning system..
…Another great way to learn about AI is to compete in AI competitions. So it’s no surprise Google-owned Kaggle has passed one million members on its platform. Because Kaggle members use the platform to create algorithms and fiddle with datasets via Kaggle Kernels, it seems like as membership scales Kaggle’s usefulness will scale proportionally. Congrats, all!

Compete for DATA: CrowdFlower has launched AI For Everyone, a challenge that will see two groups every quarter through to 2018 compete to get access to free data on CrowdFlower’s eponymous platform.
…Winners get a free CrowdFlower AI subscription, a $25,000 credit towards paying for CrowdFlower contributors to annotate data, free CrowdFlower platform training and boarding, and promotion of their results.

OpenAI Bits & Pieces:

Talking Machines – Learning to Cooperate, Compete, and Communicate: This is a follow-up to our previous work on getting AI agents to invent their own language. Here we combine this ability with the ability to train multiple agents together with conflicting goals. Come for the science, stay for the amusing GIFs of spheres playing (smart!) tag with one another. Work by OpenAI interns Jean Harb and Ryan Lowe from McGill University, plus others..

Better exploration in deep learning domains: New research: UCB and InfoGain Exploration via Q-Ensembles & Parameter Space Noise for Exploration.

Tech Tales:

[ 2045: Outskirts of Death Valley, California. A man and a robot approach a low-building, filled with large, dust-covered machines, and little orange robot arms on moving pedestals that whizz around, autonomously tending to the place. One of them has the suggestion of a thatched hairpiece, made up of a feather-coated tumbleweed that has snared into one of its joints.]

You can’t be serious.
Alvin, it’ll be less than a day.
It’s undignified. I literally cured cancer.
You and a billion of your clones, sure.
Still me. I’m not happy about this.
I’m going to take you out now.
No photographs. If I sense a single one going onto the Internet I’m going to be very annoyed.
Sure, you say, then you unscrew the top of Alvin’s head.

Alvin is, despite its inflated sense of importance, very small. Maybe about half a palm’s worth of actual computer, plus a forearm’s worth of cabling, and a few peripheral cables and generic sensor units that can be bound up and squished together. Which is why you’re able to lift his head away from his body, unhook a couple of things, then carefully pull him out. Your own little personal, witheringly sarcastic, AI assistant.

Death Valley destroys most machines that go into it, rotting them away with the endless day/night flexing of metal in phase transitions, or searing them with sand and grit and sometimes crusted salt. But most machines don’t mind – they just break. Not Alvin. For highly sensitive, developed AIs of its class the experience is actively unpleasant. Heat leads to flexing in casing which leads to damage which leads to improper sensing which gets interpreted as something a vast group of scientists has said corresponds to the human term for pain. Various international laws prohibit the willful infliction of this sort of feeling on so-called Near Conscious Entities – a term that Alvin disagrees with.

So, unwilling to violate the law, here you are at Hertz-Rent-a-Body, transporting Alvin out of his finely-filigreed silver-city Android body, into something that looks like a tank. You squint. No, it’s actually a tank, re-purposed slightly; its turret sliced in half, its snout capped with a big, sensor dome, and the bumps on its front for storing smoke flares now contain some directional microphones. Aside from that it could have teleported out of a war in the previous century. You check the schematics and are assured fairly quickly that Death Valley won’t pose a threat to it.
…Ridiculous, says Alvin. So much waste.

You unplug the cable connecting Alvin to the suit’s speaker, and carry him over to the tank. The tank senses you, silently confirms the rental with your bodyphone, then the hatch on its roof sighs open and a robotic arm snakes out.
Welcome! Please let us accommodate your N.C.E codename A.L.V.I.N the arm-tank says, its speakers crackling. The turret shifts to point to the electronics in your hands.
Alvin, having no mouth due to not being wired up to a speaker, flashes its output OLEDs angrily, shimmering between red and green rapidly – a sign, you know from experience, of the creation and transmission of a range of insults, both understandable by conventional humans and some highly specific to machines.
Your N.C.E has a very large vocabulary. Impressive! chirps the tank.
The robot arm plucks Alvin delicately from your hands and retracts back into the tank. A minute passes and the tank whirs. A small green light turns on in the sensor dome on the tip of its turret. One of its speakers emits a brief electronic-static burp, then-
I am too large, says Alvin, through the tank. They want me to do tests in this thing.

Five minutes later and Alvin is trundling to and fro in the Hertz parking lot, navigating between five orange cones set down by another similarly-cheerful robotic arm on a movable mount. A couple more tasks pass – during one U-Turn Alvin makes the tank shuffle jerkily giving the appearance of a sulk – then the Hertz robot arm flashes green and says We have validated movement policies. Great driving! Please return to us within 24:00 hours for dis-internment!

Alvin trundles over to you and you climb up one one of his treads, then hop onto the roof. You put your hand on the hatch to pull it open but it doesn’t move.
You’re not coming in.
Alvin, it’s 120 degrees.
I’m naked in here. It would make me uncomfortable.
Now you’re just being obtuse. You can turn off your sensors. You won’t notice me.
Are you ordering me?
No, I’m not ordering you, I’m asking you nicely.
I’m a tank, I don’t have to be nice.
That’s for sure.
I’ll drive in the shade. You won’t get too hot. Medically, you’re going to be fine.
Just drive, you say.
You start to trundle away into the desert. Have a good day! Shouts the Hertz robotic arm from the parking lot. Alvin finds the one remaining smoke grenade in the tank and fires it into the air,back towards the body rental shop.
We have added the fine for smoke damage to your bill. Safe driving! crackles the robot arm through the distant haze.

Technologies that inspired this story: Human-computer interaction, survey of AI experts about AI progress, the work of Dr. Heather Knight, robotics. human-computer interaction.

Import AI: Issue 45: StarCraft rumblings, resurrecting ancient cities with CycleGAN, and Microsoft’s imitation data release

Resurrecting ancient cities via CycleGAN: I ran some experiments this week where I used a CycleGAN implementation (from this awesome GitHub repo) to convert ancient hand-drawn city maps (Jerusalem, Babylon, London) into modern satellite views.
…What I found most surprising about this project was the relative ease of it – all it really took was a bit of data munging on my end, and having the patience to train a Google Maps>Google Maps Satellite View network for about 45 hours or so. The base model generalized well – I figure it’s because the Google Maps overhead street-views have a lot of semantic similarity to pen and brush-strokes in city illustrations.
…I’m going to do a few more experiments and will report back here if any of it is particularly interesting. Personally, I find that one of the best ways to learn about anything is to play with it, aimlessly fiddling for the sheer fun of it, discovering little gems in unfamiliar ground. It’s awesome that modern AI is so approachable that this kind of thing is possible.
…Components used: PyTorch, a CycleGan implementation trained for 45 hours, several thousand map pictures, a GTX 1070, patience, Visdom.

Learning from demonstrations: An exciting area of current reinforcement learning research is to develop AI systems that can learn to perform tasks based on human demonstrations, rather than requiring a hand-tuned reward function. But gathering data for this at scale is difficult and expensive (just imagine if arcades were more popular and had subsidized prices in exchange for collecting your play data!). That’s why it’s great to see the release of The Atari Grand Challenge Dataset from researchers at Microsoft Research, and Aachen University. The dataset consists of ~45 hours of playtime spread across five Atari games, including the notoriously hard-to-crack Montezuma’s Revenge.

AI’s gender disparity, visualized: AINow co-founder Meredith Whittaker did a quick analysis of the names on papers accepted to ICML and found that men vastly outnumber women. Without knowing the underlying submission data it’s tricky to use this to argue for any kind of inherent sexism to the paper selection process, but it is indicative of the gender disparity in AI – one of the many things the research community needs to fix as AI matures.

Embedding the un-embeddable: In Learning to Compute Word Embeddings On the Fly researchers with MILA, DeepMind, and Jagiellonian University propose a system to easily learn word embeddings for extremely rare words. This is potentially useful, because while deep learning approaches excel in environments containing a large amount of data, they tend to fail when dealing with small amounts of data.
…The approach works by training a neural network to predict the embedding of a word given a small amount of auxiliary data. Multiple auxiliary sources can be combined for any given word. When dealing with a rare word the researchers fire up this network, feed it a few bits of data, and then try to predict that embeddings location within the full network. This means you can develop your main set of embeddings by training in environments with large amounts of data, and whenever you encounter a rare word you instead use this system to predict an embedding for it, letting you get around the lack of data, though with some imprecision.
…The researchers evaluate their approach in three domains: question answering, entailment prediction, and language modelling, attaining competitive results in all three of these domains.
…”Learning end-to-end from auxiliary sources can be extremely data efficient when these sources represent compressed relevant information about the word, as dictionary definitions do. A related desirable aspect of our approach is that it may partially return the control over what a language processing system does into the hands of engineers or even users: when dissatisfied with the output, they may edit or add auxiliary information to the system to make it perform as desired,” they write.

Battle of the frameworks: CNTK matures: Microsoft has released version 2.0 of CNTK (the Microsoft Cognitive Toolkit), its AI development framework. New features include support for Keras, more Java language bindings, and tools for compressing trained models.

Stick this in your calendar, Zerg scum! The Call for Papers just went out for the Video  Games and Machine Learning workshop at ICML in Australia this year. Confirmed speakers include people from Microsoft, DeepMind, Facebook, and others. Noteable: someone from Blizzard will be giving a talk about StarCraft, a game that the company has partnered with DeepMind on developing AI tools around.
Related: Facebook just released V1.3-0 of TorchCraft, an open source framework for training AI systems to play StarCraft. The system now supports Python and also has improved separate data streams for feature-training, such as maps for walkability, buildability, and ground-height.

Ultra-cheap GPU substrates for AI development: Chip company NVIDIA has seen its stock almost triple in value over the last year as investors realized that its graphical processing units are the proverbial pickaxe of the current AI revolution. But in the future NVIDIA will likely have more competition (a good thing!) from a range of semiconductor startups (Graphcore, Wave, and others), established rivals (Intel via its Nervana and Altera acquisitions, AMD via its extremely late dedication to getting its GPUs to run AI software), and possibly from large consumer tech companies such as Google with its Tensor Processing Units (TPU).
…So if you’re NVIDIA, what do you do? Aside from working to design new GPUs around specific AI needs (see: Volta), you can also try to increase the number of GPU-enabled servers sold around the world. To that end, the company has partnered with so-called ODM companies Foxconn, Quanta, Inventec and Wistron. These companies are all basically intermediaries between component suppliers and massive end-users like Facebook/Microsoft/Google/and so on, and are farmed for designing powerful servers available at a low price (if bought in sufficiently high volumes).

The power of simplicity: What wins AI competitions – unique insight? A PHD? Vast amounts of experience? Those help, but probably the single-most important thing is consistent experimentation, says Keras creator Francois Chollet, in a Quora answer discussing why Keras features in so many top Kaggle competitions.
…”You don’t lose to people who are smarter than you, you lose to people who have iterated through more experiments than you did, refining their models a little bit each time. If you ranked teams on Kaggle by how many experiments they ran, I’m sure you would see a very strong correlation with the final competition leaderboard.”
…Even in AI, practice makes perfect.

Will the AI designers of the future be more like sculptors than programmers? AI seems to naturally lend itself to different forms of development than traditional programming. That’s because most of the neural network-based technologies that are currently the focus of much of AI research are inherently spatial: deep learning is a series of layered neural networks, whose spatial relationship is indicative of the functions the ultimate system approximates.
…Therefore, it’s interesting to look at the types of novel user interface design that augmented- and virtual-reality make possible and think of how it could be applied to AI. Check out this video by Behringer of their ‘DeepMind’ (no relation to the Go-playin’ Google sibling) system, then think about how it might be applied to AI.

CYBORG DRAGONFLY CYBORG DRAGONFLY CYBORG DRAGONFLY: I’m not kidding. A company named Draper has built a so-called product called DragonflEye, which consists of a living dragonfly which has been augmented with solar panels and with electronics that interface with its nervous system.
…The resulting system “uses optical electrodes to inject steering commands directly into the insect’s nervous system, which has been genetically tweaked to accept them. This means that the dragonfly can be controlled to fly where you want, without sacrificing the built-in flight skills that make insects the envy of all other robotic micro air vehicles,” according to IEEE Spectrum.

Are we there yet? Experts give thoughts on human-level AI and when it might arrive: How far away is truly powerful AI? When will AI be able to perform certain types of jobs? What are the implications of this sort of intelligence? Recently, a bunch of researchers decided to quiz the AI community on these sorts of questions. Results are outlined in When Will AI Exceed Human Performance, Evidence from AI Experts.
…The data contains responses from 352 researchers who had published at either NIPS or ICML in 2015, so keep the (relatively small) sample size in mind when evaluating the results.
…One interesting observation pulled from the abstract is that: “researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans.”
…The experts also generate a bunch of predictions for AI milestones, including:
…2022: AI can beat Starcraft.
…2026: AI can write a decent high school level essay.
…2028: An AI system can beat a human at Go given the same amounts of training.
…2030: AI can completely replace a retail salesperson.
…2100: AI can completely automate the work of an AI researcher. (How convenient!)

Monthly Sponsor: Amplify Partners is an early-stage venture firm that invests in technical entrepreneurs building the next generation of deep technology applications and infrastructure. Our core thesis is that the intersection of data, AI and modern infrastructure will fundamentally reshape global industry. We invest in founders from the idea stage up to, and including, early revenue.
…If you’d like to chat, send a note to

Tech Tales:

[2024: An advertizing agency in Shoreditch, East London. Three creatives stand around wearing architect-issue black turtlenecks and jeans. One of them fiddles with a tangle of electronic equipment, another inspects a VR headset, and the third holds up a pair of gloves with cables snaking between them and the headset and the other bundle of electronics. The intercom crackles, announcing the arrival of the graffiti artist, who lopes into the room a few seconds later. ]

James, so glad you could make it! Tea? Coffee?
Nah I’m okay, let’s just get started then shall we?
Okay. Ever used these before? says one of them, holding up the electronics-coated gloves.
No. Let me guess – virtual hands?

Five minutes later and James is wearing a headset, holding his gloved hands as though spray-painting. In his virtual reality view he’s standing in front of a giant, flawless brick wall. There’s a hundred tubs of paint in front of him and in his hand he holds a simulated spraycans that feel real because of force feedback in the gloves.

Funny to do this without worrying about the coppers, James says to himself, as he starts to paint. Silly creatives, he thinks. But the money is good.

It takes a week and by the end James is able to stare up at the virtual wall, gazing on a giant series of shimmering logos, graffiti cartoons, flashing tags, and the other visual glyph and phrases. Most of these have been daubed all across South London in one form or the other in the last 20 years, snuck onto brick walls above train-station bridges, or slotted beneath window rims on large warehouses. Along with the paycheck they present him with a large, A0 laminated print-out of his work and even offer to frame it for him.

No need, he says, rolling up the poster.

He bends one of the tube ends as he slips an elastic band over it and one of the creatives winces.

I’ll frame it myself.

For the next month, the creatives work closely with a crew of AI engineers, researchers, roboticists, artists, and virtual reality experts, to train a set of industrial arms to mimic James’s movements as he made his paintings. The force feedback gloves he wore collected enough information for the robot arms to learn to use their own skeletal hand-like grippers to approximate his movements, and the footage from the other cameras that filmed him as he painted helps the robots adjust the rest of their movements. Another month goes by and, in a film lot in Los Angeles, James’s London graffiti starts to appear on walls, sprayed on by robot arms. Weeks later it appears in China, different parts combined and tweaked by generative AI algorithms, coating a fake version of East London in graffiti for Chinese tourists that only travel domestically. A year after that and James sees his graffiti covering the wall of a street in South Boston in a movie set there and uses his smartphone to take a photo of his simulated picture made real in a movie.

Caption: “Graffin up the movies now.”.

Techniques that inspired this story: Industrial robots, time-contrastive networks, South East London (Lewisham / Ladywell / Brockley / New Cross), Tilt Brush.

OpenAI bits&pieces:

AlphaGO versus the real world: Andrej Karpathy has written a short post trying to outline what DeepMind’s AlphaGo system is capable of and what it may struggle with.

DeepRL bootcamp: Researchers from the University of California at Berkeley, OpenAI, DeepMind, are hosting a deep reinforcement learning workshop in late August in Berkeley. Apply here.

Import AI Issue 44: Constraints and intelligence, Apple’s alleged neural chip, and AlphaGo’s surprising efficiency

Constraints as the key to intelligence: Machine learning whiz & long-distance runner Neil Lawrence has published a research paper, Living Together: Mind and Machine Intelligence, that explores the idea that intelligence is intimately related to the constraints imposed on our ability to communicate.
…the gist of Neil’s argument is that intelligence can be distilled as a single number, which he calls an Embodiment Factor. This expresses the relationship between how much raw compute an intelligence can make use of at once, and how much it can communicate information about that computation during the same time frame. Humans are defined by being able to throw a vast amount of compute at any given problem, but then we can only communicate at a couple of words a second at most.
…The way Neil Lawrence puts it is that a computer with a 10 Gigaflop processing capacity and a communication capacity of about 1 gigabit per second has an embodiment factor of 10 (computation / communication), versus a human brain which can handle about an exaflop of compute with a communication limit of about 100 bits per second – representing an astonishing embodiment factor of 10^16. It is this significant compression which leads to many of the useful properties in our own intelligence, he suggests.
…(Open access note: Lawrence was originally going to publish this commentary through a traditional academic channel, but balked at paying fees and put it on Arxiv instead. Thanks, Neil!)

SelfieNightmareGAN: For a truly horrifying time I recommend viewing this experiment where artist Mario Klingemann uses CycleGAN to transpose doll faces onto Instagrammable-selfies.

G.AI.VC: Google has launched an investment arm specifically focused on artificial intelligence. It’s unusual for the company to focus on individual verticals and likely speaks to the immense enthusiasm Google feels for AI. The fund will make investments with a check size of between $1 and $10 million, reports Axios’s Dan Primark.

Treasury secretary walks back AI skepticism: US Treasury Secretary Steve Mnuchin said a few months ago that problems related to AGI and AI-led automation were “50-100 years away” and these issues weren’t “on the radar screen” of federal government.
…He has changed his tune. Now, he says:When I made the comment on artificial intelligence — and there’s different views on artificial intelligence — I was referring to kind of like R2D2 in Star Wars. Robotics are here. Self-driving cars are something that are gonna be here soon. I am fully aware of and agree that technology is changing and our workers do need to be prepared.”

iAI – Apple said to work on ‘neural chip’: Apple is developing a custom chip for its mobile devices specifically designed for inference tasks like speech and face recognition, according to Bloomberg. Other chipmakers such as Qualcomm have already taken steps in this direction. It’s likely that in the coming years we’ll see most chips get dedicated neural network bits of logic (basically matrix multiplication stuff with variable precision), given the general adoption of the technology – Nvidia is already designing certain GPU components specifically for AI-related tasks.

AI prizes, prizes everywhere! Real estate marketplace Zillow has teamed up with Google-owned Kaggle to offer a $1 million dollar data science competition. The goal? Improve its ability to predict house prices. Submitted predictive models will be evaluated against real house prices over first three months following closure of the competition.
…if this sort of thing works then, in a pleasing Jorge Luis Borges-manner, the predictions of these services could feasibly become a micro-signal in actual home prices, and so the prediction and reality could compound on each other (infinitesimally, but you know the story about butterflies & storms.)
…Next up – using the same sort of competitive model to build the guts of a self-driving car: AI-teaching operation Udacity and wannabe-self-driving company Didi (a notable competitor to troubled Uber) have partnered to create a prize for the development of open-source self-driving car technology. Over 1000 teams will compete for a $100,000 dollar prize.
…The goal? “Automated Safety and Awareness Processing Stack (ASAPS), which identifies stationary and moving objects from a moving car, and uses data that includes Velodyne point cloud, radar objects, and camera image frames. Competitors are challenged to create a redundant, safe, and reliable system for detecting hazards that will increase driving safety for both manual and self-driving vehicles,” according to Udacity.

AlphaGo’s surprisingly efficient success: AlphaGo beat the world champion Kie Jie 3-0 at The Future of Go Summit in China. But local spectators were stymied after the state ordered streams of the match shut down, as AlphaGo demonstrated prowess against the human champion. Still, the games continued. During the second game Demis Hassabis, DeepMind’s founder, said AlphaGo evaluated many of human champion Kie Jie’s moves in the second game to be “near perfect”. Still, he resigned, as AlphaGo created a cautious, impenetrable defense…
…later, DeepMind revealed more details about the system behind AlphaGo. In its original incarnation AlphaGo was trained on tens of thousands of human games and used two neural networks to plan and evaluate moves, as well as Monte Carlo Tree Search to help with planning. Since earning a cover of Nature (via beating European Go expert Fan Hui) and then beating seasoned player Le Sedol in Korea last year, DeepMind has restructured the system.
…the version of AlphaGo that was shown in China ran on a single TPU board – that’s a computer full of custom AI training&inference processors made by Google. It consumed a tenth of the computation at inference time as its previous incarnation, suggesting that its underlying systems have become more efficient – a crucial mark of both earnest optimization by DeepMind’s engineers, as well as dawning intelligence from greater algorithms.
But you might not be aware of this if you were trying to watch the game from within China – the state cut coverage of the event shortly after the first game began, for nebulous hard-to-discern political reasons.
…China versus the US in AI: While the US and Europe investments in AI either reduce or plateau, China’s government is ramping up spending as it tries to position the country to take advantage of the AI megatrend, partially in response to events like AlphaGo, reports The New York Times.

Could AI help healthcare? The later you wait to treat an ailment, the more expensive the treatment will be. That’s why AI systems could help bring down the cost of healthcare (whether that be for governments that support single-payer systems, or in the private sector). Many countries have spent years trying to digitize health records and, as those projects come to fruition, a vast hoard of data will become available for AI applications – and researchers are paying attention.
…“Many of us are now starting to turn our eyes to social value-added applications like health,” says AI pioneer Yoshua Bengio in this talk (video). “As we collect more data from millions and billions of people around the earth we’ll be able to provide medical advice to billions of people that don’t have access to it right now”.

Reading the airy tea leaves: AWS GPU spot price spike aligns with NIPS deadline: prices for renting top-of-the-range GPU servers for Amazon spiked to their highest level in the days before the NIPS deadline. That synced with stories of researchers hunting for GPUs both within companies and at cloud providers.
…The evidence, according to a tweet from Matroid founder Reza Zadeh: a dramatic rise in the cost to rent ‘p2.16xlarge’-GPU Instances on Amazon Web Services’s cloud:
…Baseline: $2 per hour.
…May 18th-19th (NIPS deadline): $144 per hour.
…Though remember, correlation may not be causation – there are other price spikes in late April that don’t seem to be correlated to AI events.

Imagining rules for better AI: When you or I try to accomplish tasks in our day we usually start with a strong set of biases about how we should go about completing the tasks. These can range from common sense beliefs (if you need to assemble and paint a fence, it’s a bad idea to paint the fence posts before you try to assemble them), to the use of large pre-learned rulesets to help us accomplish a task (cooking, or doing mathematics.)
…This is, funnily enough, how most computer software works: it’s a gigantic set of assumptions, squeezed into a user interface, and deployed on a computer. People get excited about AI because it needs fewer assumptions programmed into it to do useful work.
…But a little bit of bias is useful. For example, new research from the Georgia Institute of Technology and other researchers, shows how to use some priors fruitfully. In Game Engine Learning from Video (PDF) the authors come up with an AI system that plays a game while having the parallel goal of trying to successfully approximate the underlying program of the game engine, which it only sees through pixel inputs – aka what the player sees. It is given some priors – namely, that the program it is trying to construct contain game mechanics eg, if a player falls then the ground will stop them, and a game engine which governs the larger mechanics of the world. The researchers feed it example videos of the game being played, as well as the individual sprites of the images used to build the game. The AI then tries to learn to align sprites with specific facts or precepts, ranging from whether a sprite is animated, how its spatial arrangement changes over time, whether it is related to any other sprites, its velocity, and so on. The AI then learns to scan over the games and align specific sprite actions with rules it derives, such as whether the Sprite corresponding to Mario can move right if there is nothing in front of him, and so on. The system can focus on trying to learn specific rules by rapidly paging through the stored play images that correspond to the relevant sprite actions.
…It uses a fusion of this sort of structured, supervised learning, to iteratively learn how to play the game by reconstructing its inner functions and projecting forward based on its learned mechanistic understanding of the system. They show that this approach outperforms a convolutional neural network trained for next-frame prediction. (I’d want to also see baselines for traditional reinforcement learning algorithms as well to be convinced further.)
…This approach has numerous drawbacks from the need for a human in the loop to load it up with specifically specified priors, but it hints at a future where our AI systems can be given slight biases and interpret the world according to them. Perhaps we could create a Manhattan Project for psychologists to enter numerous structured entries about human psychology, and feed them to AIs to see if they can help the AIs predict our own reactions, just like predicting the movement of a mushroom in Super Mario.
…Components used: OpenCV, Infinite Mario

Pix2code: seeing the code within the web page: at some point, we’re going to want our computers to be able to do most programming for us. But how do you get computers to figure out how to program stuff that you don’t have access to the source for?
…In pix2code, startup UIzard creates a system that lets a computer look at a screenshot of a web page and then figure out how to generate the underlying code which would produce that page. The approach can generate code for iOS and Android operating systems, with an accuracy of 77%. In other words, it gets the underlying code right four times out of five.

OpenAI bits&pieces:

OpenAI Baselines: release of a well-tuned implementation of DeepMind’s DQN algorithm, plus three of its variants. Bonus: raw code, trained models, and a handy tips and tricks compendium for training and debugging AI algorithms. There will be more.

Tech Tales:

2025: Russia deploys the first batch of Shackletons across its thinly-populated Eastern flanks. The mission is two-fold: data gathering, and experimental research into robotics and AI. It drops them out of cargo planes in the night, hundreds of them falling onto the steppes of Siberia, their descent calmed by emergency-orange parachutes.

Generation One could traverse land, survive extremely low temperatures, swim poorly (float with directional intent, one officer wrote in a journal), and consistently gather and broadcast data. The Shackletons beamed footage of frozen lakes and bare horizon-stretching foxes back to TV and computer screens around the world and people responded, making art from the data generated by Russia’s remote parts. The robots themselves became celebrities and, though their locations were unknown, sometimes roving hunters, scavengers, and civil servants would find them out there in the wastes and take selfies. One iconic photo saw a bearded Russian soldier with his arm slung over the moss-mottled back of an ageing Shackleton. He had placed a pair of military-issue dark glasses on one of the front sensor bulges, giving the machine a look of comedic detachment.
“Metallicheskaya krysa”, the Russians affectionately called them – metal rats.

2026: Within a year, the Shackletons were generating petabytes of raw data every day, ranging from audio and visual logs, to more subtle datapoints – local pollen counts, insect colonies, methane levels, frequency of bubbles exploding from gas escaping permafrost, and so on. Each Shackleton had a simple goal: gather and analyze as much data as possible. Each one was capable of exploring its own environment and the associated data it received. But the twist was the Shackletons were able to identify potentially interesting data points they hadn’t been pre-loaded with. One day one of the machines started reporting a number that scientists found correlated to a nearby population of foxes. Another day another machine started to output a stream of digits that suggested a kind of slow susurration across a number line, and the scientists eventually realized this data corresponded to the water levels of a nearby river. As the years passed the Shackletones became more and more astute, and the data they provided was sucked up by the global economy, going on to fuel NGO studies, determine government investment decisions and, inevitably, give various nebulous financial entities a hedge in the ever-more competitive stock markets. Russia’s selectively declassified more and more components of the machines, spinning them off into state-backed companies, which grew to do business across the world.

2029: Eventually, the Shackletons became tools of war – but not in the way people might expect. In 2029 the UN started to drop batches of improved Shackletons into contested borders and other flashpoints around the world – the mountains of east Afghanistan, jungles in South America, even, eventually, the Demilitarized Zone between South and North Korea. At first, locals would try to sabotage the Shackletons, but over time this ebbed. That was because the UN mandated that the software of the Shackletons be open and verifiable – all changes to the global Shackleton operating system were encoded in an auditable system based on blockchain technologies. They also mandated that the data the Shackletons generated be made completely open. Suddenly, militaries around the world were deluged in rich, real-world data about the locations of their foes – and their foes gained the same data in kind. Conflict ebbed, never quite disappearing, but seeming to decline to a lower level than before.

Some say the deployment of the Shackletons can be correlated to this decline of violence around the world. The theory is that war hinges on surprise, and all The Shackletons do is turn the unknown parts of the world into the known. It’s hard to be in a Prisoner’s Dilemma when everyone has correct information.

Technologies that inspired this story: Ethereum / Bitcoin, unsupervised auxiliary goal identification, Boston Dynamics, hierarchical temporal memory

Import AI: Issue 43: Why curiosity improves AI algorithms, what follows ImageNet, and the cost of AI hardware


ImageNet is dead, long live WebVision: ImageNet was a dataset and associated competition that helped start the deep learning revolution by being the venue where in 2012 a team of researchers convincingly demonstrated the power of deep neural networks. But now it’s being killed off – this year will be the last official Imagenet challenge. That’s appropriate because last year’s error rate on the overall dataset was about 2.8 percent, suggesting that our current systems have exhausted much of ImageNet’s interesting challenges and may even be in danger of overfitting.
…What comes next? One potential candidate is WebVision, a dataset and associated competition from researchers at ETH Zurich, CMU, and Google, that uses the same 1000 categories as the ImageNet competition in 2012 across 2.4 million modern images and metadata taken directly from the web (1 million from Google Image Search and 1.4 million from Flickr.)
…Along with providing some degree of continuity in terms of being able to analyze image recognition progress, this dataset also has the advantage of being partially crappy, due to being culled from the web. It’s always better to test AI algorithms on the noisy real world.
…”Since the image results can be noisy, the training images may contain significant outliers, which is one of the important research issues when utilizing web data,” write the researchers.
…More information: WebVision Challenge: Visual Learning and Understanding With Web Data.

Making self-driving cars a science: the field of self-driving car development it lacks the open publication conventions of the rest of AI research, despite using and extending various cutting-edge AI research techniques. That’s probably because of the seemingly vast commercial-value of self-driving cars. But it brings forward a bunch of problems, namely, how can people try to make the development more scientific and thereby improve the efficiency of the industry, while benefiting society through the science being open.
…AI meme-progenator and former self-driving startup intern Eder Santana has written up a shopping list of things that, if fulfilled, would improve the science of self-driving startups. It’s a good start at a tough problem.
…I wonder if smaller companies might band together to enact some of these techniques – with higher levels of openness than titans like Uber and Google and Tesla and Ford etc – and use that to collaboratively pool research to let them compete? After all, the same philosophy already seems present in Berkeley DeepDrive, an initiative whereby a bunch of big automakers fund open AI research in areas relevant to their development.
The next step is shared data. I’m curious if Uber’s recent hire, Raquel Urtasun, will continue her work on the KITTI self-driving car dataset which she created and Eder lists as a good example.

AI aint cheap: Last week, GPUs across the world were being rented by researchers racing to perform final experiments for NIPS. This wasn’t cheap. Despite many organizations (including OpenAI) trying to make it easier for more researchers to experiment with and extend AI, the costs of raw computer remain quite high. (And because AI is mostly an experimental, empirical science, you can expect to have to shell out for many experiments. Some deep-pocketed companies, like Google, are trying to offset this by giving researchers free access to resources, most recently 1,000 of its Tensor Processing Units in a dedicated research cloud, but giveaways don’t seem sustainable in the long run.)
…”We just blew $5k of google cloud credits in a week, and managed only 4 complete training runs of Inception / Imagenet. This was for one conference paper submission. Having a situation where academia can’t do research that is relevant to Google (or Facebook, or Microsoft) is really bad from a long-term perspective”, wrote Hacker News user dgacmu.
A new method of evaluating AI we can all get behind: Over on the Amazon Web Services blog a company outlines various different ways of training a natural language classification system and it lists how much it costs not just in terms of computation, but in terms of how much it will cost you to rent the computing resources for it on AWS in both CPUs and GPUs. These sorts of numbers are helpful for putting into perspective how much AI costs and, more importantly, how long it takes to do things that the media (yours included) makes sound simple.

How to build an AI business, from A16Z: VC firm Andreessen Horowitz has created the AI Playbook, a microsite to help people figure out how AI works and how to embed into their business.
…Bonus: it includes links to the thing every AI person secretly (and not so secretly) lusts after: DATA.
…Though AI research has been proceeding at a fairly rapid clip, this kind of project hints at the fact that commercialization of it has been uneven. That’s partly due to a general skills deficit in AI across the tech industry and also because in many ways it’s not exactly clear how you can use AI – especially the currently on-trend strain of deep neural networks – in a business. Most real-world data requires a series of difficult transforms before it can be strained through a machine learning algorithm and figuring out the right questions to ask is its own science.

E-GADs: Entertaining Generative Adversarial Doodles! Google has released a dataset of 50 million drawings across 345 distinct categories, providing artists and other fiddlers with a dataset to experiment with new kinds of AI-led aesthetics.
…This is the dataset that supported David Ha’s fun SketchRNN project, whose code is already available.
… It may also be useful for learning representations of real objects – I’d find it fun to try to train doodles with real image counterparts in a semi-supervised way, then be able to transform new real world pictures into cute doodles. Perhaps generative adversarial networks are a good candidate? I must have cause to use the above bolded acronym – you all have my contact details.

Putting words in someone else’s mouth – literally: fake news is going to get even better based on new techniques for getting computers to synthesize realistic looking images and videos of people.
…in the latest research paper in this area a team of researchers at the University of Oxford have produced ‘speech2vid’, a technique to get computers to be able to take a single still image of a person and an audio track and synthesize an animated version of that person’s face saying those words.
…The effects are still somewhat crude – check out the blurred, faintly comic-book like textures in the clips in this video. But hint at a future where it’s possible to create compelling propaganda using relatively little data. AI dopplegangers won’t just be for celebrities and politicians and other people who have generated vast amounts of data to be trained on, but will be made out of normal data-lite people like you or me or everyone we know.
….More information in the research paper You said that?

The curious incident of the curiosity exploration technique inside the learning algorithm: how can we train AI systems to explore the world around them in the absence of an obvious reward? That’s a question that AI researchers have been pondering for some time, given that in real life rewards (marriage, promotions, finally losing weight after seemingly interminable months of exercise) tend to be relatively sparse.
…One idea is to reward agents for being curious, because curious people tend to stumble on new things which can help expand and deepen their perception of the world. Children, for instance, spend most of their time curiously exploring the world around them without specific goals in mind and use this to help them understand it.
…The problem for AI algorithms is figuring out how to get them to learn to be curious in a way that leads to them learning useful stuff. One way could be to reward the visual novelty of a scene – eg, if I’m seeing something I haven’t seen before, then I’m probably exploring stuff usefully. Unfortunately, this is full of pitfalls – show a neural network the static on an untuned television and every frame will be novel, but not useful.
…So researchers at The University of California at Berkeley have come up with a technique to do useful exploration, outlined in Curiostiy-driven exploration by Self-supervised Prediction. It works like this: “instead of making predictions in the raw sensory space (e.g. pixels), we transform the sensory input into a feature space where only the information relevant to the action performed by the agent is represented.’
…What this means is that the agent learns how to be curious by taking actions in the world, and if those actions yield a different world then it’s able to figure out how those actions corresponded to that difference and take them more accordingly.
…So, how well does it work? The researchers test out the approach on two environments – Super Mario and Vizdoom. They find that it’s able to attain higher scores in a faster time than other methods, and can deal with increasingly sparse rewards.
…The most tantalizing part of the result? “An agent trained with no extrinsic rewards was able to learn to navigate corridors, walk between rooms and explore many rooms in the 3-D Doom environment. On many occasions the agent traversed the entire map and reached rooms that were farthest away from the room it was initialized in. Given that the episode terminates in 2100 steps and farthest rooms are over 250 steps away (for an optimally-moving agent), this result is quite remarkable, demonstrating that it is possible to learn useful skills without the requirement of any external supervision of rewards.”
…The approach has echoes of a recent paper from DeepMind outlining a reinforcement learning agent called UNREAL. This system was a composite of different neural network components; it used a smart memory-replay system to let it figure out how actions it had taken in the environment corresponded to rewards, and was able to also use it to figure out how actions it had taken corresponded to unspecified intermediate rewards that helped it gain an actual one (for example, though it was rewarded for moving itself to the same location as a delicious hovering apple, it subsequently figured out that to attain this reward it should achieve an intermediary reward which it creates and focuses on itself. It learned this by being able to figure out how its actions affected its observation of the world and adjusted accordingly.
…(Curiosity-driven exploration and related fields like intrinsic motivation are quite mature, well-studied areas of AI, so if you want to trawl through the valuable context I recommend reading papers cited in the above research.)

Import AI reader comment of the week: Ian Goodfellow wrote in to quibble with my write-up of a recent paper about how snapshots of the same network at different points in time can be combined to form an ensemble model. The point of contention is whether these snapshots represent different local minima:
”…Local minima are basically the kraken of deep learning. Early explorers were afraid of encountering them, but they don’t seem to actually happen in practice,” he writes. “What’s going on is more likely that each snapshot of the network is in a different location, but those locations probably aren’t minima. They’re like snapshots of a person driving a car trying to get to a specific point in a really confusing city. The driver keeps circling around their destination but can’t quite get to it because of one way street signs and their friend keeps texting them telling them to park in a different place. They’re always moving, never trapped, and they’re never in quite the right place, but if you average out all their locations the average is very near where they’re trying to go.”
…Thanks, Ian!

Help deal with the NIPS-A-GEDDON: This week, AI papers are going to start flooding onto Arxiv from submissions to NIPS, and some other AI conferences. Would people like to help rapidly evaluate the papers, noting interesting things? We tried a similar experiment a few weeks ago and it worked quite well. We used a combination of a form and a Google Doc to rapidly analyze papers. Would love suggestions from people on whether this format [GDoc] is helpful (I know it’s ugly as sin, so suggestions welcome here.)
…if you have any other thoughts for how to structure this project or make it better, then do let me know.

OpenAI bits&pieces:

It was a robot-fueled week at OpenAI. First, we launched a new software package called Roboschool, open-source software for robot simulation, integrated with OpenAI Gym. We also outlined a robotics system that lets us efficiently learn to reproduce behaviors from single demonstrations.

CrowdFlower founder and OpenAI intern chats about the importance of AI on this podcast with Sam Lessin, and why he thinks computers are eventually going to exceed humans at many (if not all!) capabilities.

Tech tales:

[2018: The San Francisco Bay Area, two people in two distant shared houses, conversing via their phones.]

Are you okay?
I’ve been better. You?
Things aren’t going well.
Anything I can do?
Fancy drinks?
Sure, when?
Wednesday at 930?
Sounds good!

You put your phone down and, however many miles away, so does the other person. Neither of you typed a word of that, instead you both just kept on thumbing the automatically suggested messages until you scheduled the drinks.

It’s true, the both of you are having trouble at the moment. Your system was smart enough to make the suggestions based on studying your other emails and the rhythms of the hundreds of millions of other users. When you eventually go and get drinks the GPS in your phones tracks you both, records the meeting – anonymously, only signalling to the AI algorithms that this kind of social interaction produced a Real In-Person Correspondence.

Understanding what leads to a person meeting up with another, and what conversational rhythms or prompts are crucial to ensuring this occurs, is a matter of corporate life and death for the companies pushing these services. We know when you’re sad, is the implication. So perhaps you should consider $drinks, or $a_contemporary_lifestyle_remedy, or $sharing_more_earlier.

You know you’re feeding them, these machines that live in football field-sized warehouses, tended to by a hundred-computer mechanics who cannot know what the machines are really thinking. No person truly knows what these machines relate to, instead it is the AI at the heart of the companies that does – and we don’t know how to ask it questions.

Technologies that inspired this story: sequence-to-sequence learning, Alexa/Siri/Cortana/Google, phones, differential privacy, federated learning.

Import AI: Newsletter 42: Ensemble learning, the paradoxical nature of AI research, and Facebook’s CNN-for-RNN substitution

‘Mo ensembles, ‘No problems: new research shows how to get the benefits of grouping a bunch of neural networks together (known as an ensemble), without having to go to the trouble of training each of them individually. The technique is outlined in Snapshot Ensembles: Train 1, Get M For Free.
…it’s surprisingly simple and intuitive. The way neural networks are trained today can be thought of as like rolling a ball down a fairly uneven hill – the goal is to get the ball to the lowest possible point of the hill. But the hill is uneven, so it’s fairly easy for the ball to get trapped in a local low-elevation point in the hill and stay there. In AI land, this point is called a ’local minima’ – it’s bad to get stuck in a local minima.
…Most tricks in AI training involve getting the model to visit way more locations during training and thereby avoid a sub-optimal local minima – ideally you want the ball to find the lowest point in the hill, even if it runs into numerous depressions along the way.
…the presented technique shows how to record a snapshot of each local minima the neural network visits along the way during training. Then, once you finish training, you kind of combine all the previous local minima by taking the snapshots and re-animating them, then training them together.
…Results: the approach works, with the authors reporting that this technique yields more effective systems on tasks like image classification, while not costing too much more in the way of training.

Voice data – who speaks to whose speakers?: if data is the fuel for AI, then Amazon looks like it’s well positioned to haul in a trove of voice data, according to eMarketer.
…Amazon’s share of the US home chit-chat speaker market in 2017: ~70.6%
…Google’s: 23.8%
…Others: 5.6%

A/S/E? Startup researchers show off end-to-end age, sex, and emotion recognition system: AI is moving into an era dominated by composite systems, which see researchers complex, interlinked software to perform multiple categorization (and sometimes actions) within the same structure…
… in this example, researchers from startup Sighthound have developed DAGER: deep age, gender, and emotion recognition using convolutional neural networks. DAGER can guess someone’s age, sex, and emotion from a single face-on photograph. The training ingredients for this include 4 million images of over 40,000 distinct identities…
… Apparently has a lower mean absolute error than systems outlined by Microsoft and others.
… Good news: The researchers sought to offset some of the (sadly inevitable) biases in their datasets by adding “tens of thousands of images of different ethnicities as well as age groups”. It’s nice that people are acknowledging these issues and trying to get ahead of them.

Uber hires Raquel Urtasun: Self-driving car company Uber has hired Raquel Urtasun, a well-respected researcher with the University of Toronto, to help lead its artificial intelligence efforts.
…Urtasun’s group had earlier created KITTI, a free and open dataset used to benchmark computer vision systems against problems that self-driving caws encounter. Researchers have already used the dataset to train vision models entirely in simulation using KITTI data, then transfer them into the real world.
…meanwhile Lyft and Google (technically, Waymo) have confirmed that they’ve embarked on a non-exclusive collaboration to work together on self-driving cars.

Cisco snaps up speech recognition system with MindMeld acquisition: Cisco has acquired voice recognition startup MindMeld for around $125 million. The startup had made voice and conversation interface technologies, which had been used by commercial companies such as Home Depot, and others.

Government + secrecy + AI = fatal error, system override: Last week, hundreds of thousands of computers across the world were compromised by a virulent strain of malware, spread via a zero-day flaw that, Microsoft says in this eyebrow raising blogpost, was originally developed by the NSA.
…today, governments stockpile computer security vulnerabilities, using them strategically against foes (and sometimes ‘friends’). But as our digital systems become ever more interlinked, the risk of one of these exploits falling into the wrong hands increase, as do its effects.
…we’re still a few years away (I think) from government’s classifying and stockpiling AI exploits, but I’m fairly sure that in the future we could imagine government developing certain exploits, say a new class of adversarial examples, and not disclosing their particulars, instead keeping them private to be used against a foe.
…just as Microsoft advocates for what it calls a Digital Geneva Convention, it may make sense for AI companies to agree upon a similar set of standards eventually, to prevent the weaponization and exploitation of AI.

Doing AI research is a little bit like being a road-laying machine, where to travel forward you must also create the ground beneath you. In research, what this translates to is that new algorithms typically need to be paired with new challenges. Very few AI systems today are robust enough to be able to be plunked down in reality able to do useful stuff. Instead, we try to get closer to being able to build these systems by inventing learning algorithms that exhibit increasing degrees of general applicability on increasingly diverse datasets. The main way to test this kind of general applicability is to create new ways to test such AI systems – that’s why the reinforcement learning community is evolving from just testing on Atari games to more sophisticated domains, like Go, or video games like Starcraft and Doom.
…the same is true of other domains beyond reinforcement learning: to build new language systems we need to assemble huge corpuses of data and test algorithms on them – so over time it feels like the amounts of text we’ve been testing on have grown larger. Similarly, in fields like question answering we’ve gone from simple toy datasets to more sophisticated trials (like Facebook’s BaBi corpus) to even more elaborate datasets.
…A new paper from DeepMind and the University of Oxford, Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems, is a good example of this sort of hybrid approach to AI development. Here, the researchers try to tackle the task of solving simple algebraic word problems by not only inventing new algorithmic approaches, but doing so while generating new types of data. The resulting system can not only generate the answers, but also its rationale for the answer.
…size of the new dataset: over 100,000 word problems that include answers as well as natural language rationales.
…how successful is it? Typical AI approaches (which utilize sequence-to-sequence techniques) tend to have accuracies of about 20% on the task. This new system gets things right 36% of the time. Still a bad student, but a meaningful improvement.
A little bit of supervision goes a long way: Facebook and Stanford researchers are carrying out a somewhat similar line of enquiry but in a different domain. They’ve come up with a new system that can get state-of-the-art results on a dataset intended to tend visual reasoning. The secret to their method? Training a neural network to invent its own small computer programs on the fly to answer questions about images it sees. You can find out more in ‘Inferring and Executing Programs for Visual Reasoning’. The most intriguing part? The resulting system is relatively data efficient, compared to fully supervised baselines, suggesting that its learning how to tackle the task in novel ways.
…it seems likely that in the future AI research may shift from involving generating new datasets alongside new algorithms, to generating new datasets, new algorithms, as well as new reasoning programs to aid with learning efficiency and interpretability.

Mujoco for free (restrictions apply): Physics simulator Mujoco will give students free licenses to its software, lowering the costs of doing AI research on modern, challenging problems, like those found in robotics.
…Due to the terms of the license, people will still need to stump up for a license for the proprietary software if they want to use AI systems trained within Mujoco in products.

Don’t read the words, look at them! (and get a 9X speedup): Facebook shows how to create a competitive translation system that is also around 9 times faster than previous state-of-the-art systems. The key? Instead of using a recurrent neural network to analyze the text, use a convolutional neural network.
…this is somewhat counterintuitive. RNNs are built to analyze and understand sequences, like strings of text or numbers. Convolutional neural networks are somewhat cruder and are mostly used as the basic perceptual component inside vision systems. How was Facebook able to manhandle a CNN into something with RNN-like characteristics? The answer is the usage of attention, which lets the network focus on particular words.

Horror of the week: what happens when you ask a neural network to make a person smile, then feed it that new smile–augmented image and ask it to make the person smile even more, and then you take that image and feed it back to the network and ask the network to enhance its smile again? You wind up with something truly horrifying! Thanks, Gene Kogan.

Tech Tales:

[2040: the partially flooded Florida lowlands.]

The kids nickname it “Rocky the Robster” the first time they see it and you tell them “No, it’s called the Automated Ocean Awareness and Assessment Drone,” and they smile at you then say “Rocky is better.” And it is. But you wish they hadn’t named it.

Rocky is about the size of a carry-on luggage suitcase, and it does look, if you squint, a little like a metallic lobster. Two antennas extend from its front, and its undercarriage is coated in grippers and sampling devices and ingest and egress ports. In about two months it’ll go into the sea. If things work correctly, it will never come out, but will become another part of the ocean, endlessly swimming and surveilling and learning, periodically surfacing, whale-like, to beam information back to the scientists of the world.

But before it can start its life at sea, you need to teach it out to swim and how to make friends. Rocky comes with a full low-grade suite of AI software and, much like a newborn, it learns through a combination of imitation and experimentation. Imitation is where your kids come in. They come in and watch you in your studio as you, on all fours, walk across the room. Rocky imitates you poorly. The kids crawl across the room. Rocky imitates them a bit better. You figure that Rocky finds it easier to imitate their movements as they’re closer in size to it. Eventually, you and the kids teach the robot to swim as well, all splashing around in a pool in the backyard, with the robot tethered to prevent its enthusiastic attempts to learn to swim leading to it running into your kids.

Then Rocky’s AI systems start to top out – as planned. It can run and walk and scuttle and swim and even respond to some basic hand gestures, but though it still gambles around with a kind of naive enthusiasm, it stops developing new tics and traits. The sense of life in it dims as the kids become aware that Rocky is more drone than they thought.
“Why isn’t Rocky getting smarter anymore, Dad?” they say.
You try to explain that some things can’t get smarter.
“No, that’s the opposite of what you’ve always told us. We just need to try and we can learn anything. You say this all the time!”
“It’s not like that for Rocky,” you say.
“Why not?” they say. Then tears.

The night before Rocky is due to be collected by the field technicians who will make some final modifications to its hardware before sending it into the sea, you hear the creak on the stairwell You don’t follow them or stop them but instead turn on a webcam and look into your workshop, watch the door slowly ease open as the kids quietly break-in. They sit down next to Rocky’s enclosure and talk to it. They show it pictures they’ve drawn of it. They motion for it to look at them. “Say it, Rocky,” you hear them say, “try to say ‘I want to stay here’”.

Having no vocals cords, it is unable. But as you watch your kids on the webcam you think that for a fraction of a second Rocky flexes its antennas, the two tops of each bowing in and touching each-other, forming a heart before thrumming back into their normal position. “A statistical irregularity,” you say to your colleagues, never believing it.

Import AI Newsletter 41: The AI data grab, the value of simplicity, and a technique for automated gardening

Welcome to the era of the AI data grab: a Kaggle developer recently scraped 40,000 profile photos from dating app Tinder (20k from each gender) and placed the data horde online for other people to use to train AI systems. The dataset was downloaded over 300 times by the time TechCrunch wrote about it. Tinder later said the dataset violated the apps Terms of Service (ToS) and now it has been taken down.
…AI’s immense hunger for data, combined with all the “free” data lying around on the internet, seems likely to lead to more situations like this. Could this eventually lead to the emergence of a new kind of data economy, where companies instinctively look for ways to sell and market their data for AI purposes, along with advertising?

Why simple approaches sometimes work best: Modern AI research is yielding a growing suite of relatively simple components that can be combined to solve hard problems. This is either encouraging (AI isn’t as hard as we thought – Yaaaay!) or potentially dispiriting (we have to hack together a bunch of simple solutions because our primate brains are struggling to visualize the N-dimensional game of chess that is consciousness – Noooo!).
…in Learning Features by Watching Objects Move, researchers with Facebook and the University of California at Berkeley figure out a new approach to get AI to learn how to automatically segment entities in a picture. Segmentation is a classic, hard problem in computer vision, requiring a machine to be able to, say, easily distinguish the yellow of a cat’s eyes from the yellow iodine of a streetlight behind it, or disentangle a zebra walking over a zebra crossing.
…the new technique works as follows: the researchers train a convolutional neural network to study short movie clips. They use optical flow estimation to disentangle the parts of the movie clip that are in the foreground and in motion from those that aren’t. They then use these to label each frame with segment information. Then they train a convolutional neural network to look at each frame and predict segments, using this data. The approach attains nine state-of-the-art results for object detection on the PASCAL VOC 2012 dataset.
…The researchers guess that this works so well because it forces the convolutional neural network to try to learn some quite abstract, high-level structures, as it would be difficult to perform this segmentation task by merely looking at pixels alone. They theorize that this is because to effectively learn to predict when something is moving or not you need to understand how all the pixels in a given picture relate to eachother and use that to make judgements about what can move and what can not.

Secret research to save us all: Researchers at Berkeley’s Machine Intelligence Research Institute are of the opinion that powerful AI may be (slightly) closer than we think, so will spend some of this year conducting new AI safety research and plan to keep this work “non-public-facing at least through late 2017, in order to lower the risk of marginally shortening AGI timelines”.

The freaky things that machine learning algorithms “see”: check out this video visualization of what an ACER policy thinks is salient (aka, important to pay attention to) when playing a game.

Automated gardeners:Machine Vision System for 3D Plant Phenotyping’, shows how to use robotics and deep learning for automated plant analysis. The system works by building a little metal scaffold around a planter ,then using a robot arm with a laser scanner to automate the continuous analysis of the plant. The researchers test it out on two plants, gathering precise data about the plants’ growth in response to varying lighting conditions. Eventually, this should let them automate experimentation across a wide variety of plants. However, when they try this on a conifer they run into difficulty because the sensor doesn’t have sufficient resolution to analyze the pine needles.
…oddly specific bonus fact: not all AI is open source – the robot growing chamber in the experiment runs off of Windows Embedded.
fantastic name of the week: the robot arm was manufactured by Schunk Inc. Schunk!

Free code: Microsoft has made the code for its ‘Deformable Convnets’ research (covered in previous issue here) available as open source.
…Deformable Convolutions (research paper here) are a drop-in tool for neural networks to let you sample from a large and more disparate set of points over an image, potentially helping with more complex classification tasks.
…The code is written in MXNet, a framework backed by Amazon.

The great pivot continues: most large technology companies are reconfiguring themselves around AI. Google was (probably) the first company to make such a decision, and was swiftly followed by Microsoft, Facebook, Amazon, and others. Even conservative companies like Apple and IBM are trying to re-tool themselves in this way. It’s not just an American phenomenon – Baidu chief Robin Li said in an internal memo that Baidu’s strategic future relies on AI, according to this (translated) report.

Biology gets its own Arxiv… Cold Spring Harbor Laboratory and the Chan Zuckerberg Initiative are teaming up to expand bioRxiv – a preprint service for life sciences research. Arxiv, which is used by AI people, computer scientists, physicists, mathematicians, and others, has sped up the pace of AI research tremendously by short-circuiting the arbitrary publication timetables of traditional journals.

Neural network primitives for ARM (RISC) chips: ARM announced the public availability of the ARM Compute Library, software to give developers access to the low-level primitives they need to tune neural network performance on ARM CPUs and GPUS.
…The library supports neural network building blocks like convolution, soft-max, normalization, pooling, and so on, as well as ways to run support vector machines, general matrix multiplication, and so on.

What’s cooler than earning a million at Google? Getting bought by another tech company for 10 million!… that seems like the idea behind the current crop of self-driving car startups, which are typically founded by early employees of self-driving projects in academia or the private sector.
… the latest? DeepMap – a startup helmed by numerous Xooglers which focuses on building maps, and the intelligent data layers on top of them, to let self-driving cars work. ““It’s very easy to make a prototype car that can make a few decisions around a few blocks, but it’s harder when you get out into the world,” said CEO James Wu.

AI means computer science becomes an empirical science: and more fantastic insights in this talk titled “As we may program” (video) by Google’s marvelously-attired Peter Norvig.
…Norvig claims that the unpredictability and emergent behavior endemic to machine learning approaches means computer science is becoming an empirical science where work is defined by experimentation as well as theory. This feels true to me – most AI researchers spend an inordinate amount of time studying various graphs that read out out the state of networks as they’re training, and then use those graphs to help them mentally navigate the high-dimensional spaghetti-matrices of the resulting systems.
…This sort of empirical, experimental analysis is quite alienating to traditional developers, which would rather predict the performance of their tech prior to rolling it out. What we ultimately want is to package up advanced AI programming approaches within typical programming languages, making the obscure much more familiar, Norvig says.
…Here’s my attempt at what AI coding in the future might look like, based on Norvig’s speech:

Things_I’m_Looking_For = [ ‘hiking shoes’, ‘bicycle’, ‘sunsets’ ]
Things_Found = [ ]
For picture in photo_album:
   pic_contents = picture.AI_Primitives.segment()
      For i in pic_contents:
         i = i.AI_Primitives.label()
         If i in Things_I’m_Looking_For:
            Things_Found.append(, i)
… there are signs this sort of programming language is already being brewed up. Wolfram Language represents an early step in this direction. As does work by startup Bonsai – see this example on GitHub. (However, both of these systems are proprietary languages – it feels like future programming languages will contain these sorts of AI functions as open source plugins.)

Microsoft’s new head of AI research is… Eric Horvitz, who has long argued for importance of AI safety and ethics, as this Quartz profile explains.

StreetView for the masses: Mapillary has released a dataset of photographs taken at the street level, providing makers of autonomous vehicles, drones, robots, and plain old AI experimenters with a new trove of data to play with. The dataset contains…
…25,000 high-resolution images
…100 object categories
…high variability in weather conditions
…reasonable geographic diversity, with pictures spanning North and South America and Western Europe, as well as a few from Africa and Asia.
meanwhile, Google uses deep learning to extract potent data from its StreetView trove: In 2014 Google trained a neural network to extract house number from images gathered by its StreetView team. Now, the company is moving onto street and business names.
… Notable example: its trained model is able to guess the correct business name on a sign, even though there are other brands listed (eg Firestone). My assumption is it has learned that these brands are quite common on a variety of signs, whereas the name of the business are unique.
… Bonus tidbit: Google’s ‘Ground Truth’ team was the first internal user of the company’s TensorFlow processing units (TPU)s, due to their insatiable demand for data.
… Total number of StreetView images Google has: more than $80 billion.

A donut-devouring smile: Smile Vector is a friendly Twitter bot by AI artist Tom White that patrols the internet, finding pictures of people who aren’t smiling, and makes them smile. It occasionally produces charming bugs, like this one in which a neural network makes a person appear to smile by giving them a toothy grin and removing a segment of the food they’re holding in their hands – a phantom bite!

The Homebrew AI Computer Club: Google has teamed up with the Raspberry Pi community to offer the relevant gear to let people assemble their own AI-infused speaker, powered by a Raspberry Pi and housed in cardboard, natch.

Monthly Sponsor: Amplify Partners is an early-stage venture firm that invests in technical entrepreneurs building the next generation of deep technology applications and infrastructure. Our core thesis is that the intersection of data, AI and modern infrastructure will fundamentally reshape global industry. We invest in founders from the idea stage up to, and including, early revenue.
…If you’d like to chat, send a note to

Tech Tales:

[2032: The greater Detroit metropolitan area.]

“It’s creepy as all hell in there man you gotta do something about it I can’t sleep! All that metal sounds. I’m calling the city next week you don’t do something about it.” Click.
You put the phone down, look at your wife.
“Another complaint?” she says.
“I’m going to Dad’s,” you say.

Dad’s house is a lazily-shingled row property in Hamtramck, a small municipality embedded in the center of Detroit. He bought it when he was doing consulting for the self-driving car companies. He died a month ago. His body got dragged out of the house by the emergency crews. In his sleep, they said, with the radio on.

You arrive on the street and stare up at the house, approach it with the keycard in your hand. The porch is musty, dry. You stand and listen to your breath and the whirring sound of the houses’s machines, reverberating through the door and passing through the windows to you.

When you enter a robot the shape of a hocky puck and size of a small poodle moves from the kitchen over to you in the entranceway.

“Son,” a voice says, crackling through speakers. The robot whirrs over to you, stops by your feet. “I’m so glad you’re here. I have missed you.”
“Hey Dad,” you say. Eyes wet. “How are things?”
“Things are good. Today the high will be about 75. Low pollution index. A great day to go outside.”
“Good,” you say, bending down. You feel for the little off switch on the back of the machine, put your finger on it.
“Will you be staying long?” says the voice in the walls.
“No,” you whisper, and turn the robot off. You push its inert puck-body over to the door. Then you go upstairs.

You pause before you open his office door. There’s a lot of whirring on the other side. Shut your eyes. Deep breath. Open the door. A drone hovers in the air, a longer wire trailing beneath it, connected to an external solar panel. “Son,” the voice says, this time coming from a speaker next to an old – almost vintage – computer. “The birds outside are nesting. They have two little chicks. One of the chicks is 24 days old. The other is 23.”
“Are they still there?” you say.
“I can check. Would you like me to check?”
“Yes please,” you say, opening the office window. The drone hovers at the border between inside and outside. “Would you disconnect me?”

You unplug it from the panel and it waits till the cable has fallen to the floor before it skuds outside, over to the tree. Whirrs around a bit. Then it returns. Its projector is old, weak, but still you see the little birds projected on the opposite wall. Two chicks.
“How nice,” you say.
“Please reconnect my power supply, son,” it says.
You pluck the drone out of the air, grabbing its mechanical housing from the bottom, turn it off.
“Son,” the voice in the walls said. “I can’t see. Are you okay?”
“I’m fine, Dad.”

It takes another two hours before you’ve disconnected all the machines but one. The last is a speaker attached to the main computer. Decades of your Dad’s habits and his own tinkering have combined to create these ghosts that infuse his house. The robots speak in the way he speak, and plug into a larger knowledge system owned by one of the behemoth tech companies. When he was alive the machines would help him keep track of things, have chats with you. After his hearing went they’d interpret your sounds and send them to an implant. When he started losing his eyesight they’d describe the world to him with their cameras. Help him clean. Encourage him to go outside. Now they’re just ghosts, inhaling data and exhaling the faint exhaust of his personality.

Before you get back in the car you knock on the door of the neighbor. A man in a baggy t-shirt, stained work jeans opens it.
“We spoke on the phone,” you say. “House will be quiet now.”
“Appreciate it,” he says. “I’ll buy that drone, if you’re selling.”
“It’s broken,” you lie.

Import AI Newsletter 40: AI makes politicians into digital “meat puppets”, translating AI ‘neuralese’ into English, and Amazon’s new eye


Put your words in the mouth of any politician, celebrity, friend, you name it: startup research outfit Lyrebird from the University of Montreal lets you do two interesting and potentially ripe for abuse things. 1) train a neural network to convincingly imitate someone else’s voice, and, 2) do this with a tiny amount of data – as little as a minute, according to Lyrebird’s website. Demonstrations include synthesized speeches by Obama, Clinton, and Trump.
Next step? Pair this with a (stable) pix2pix model to let you turn any politician into a ‘meat puppet’ (video). Propaganda will never be the same.

ImportAI’s Cute Unique Bot Of Today (CUBOT) award goes to… DeepMind for the cheerful little physics bot visualized in this video tweeted by Misha Denil. The (simulated) robot relates to some DeepMind research on Learning to perform physics experiments in complex environments. “The agent has learned to probe the blocks with its hammer to find the one with the largest mass (masses shown in the lower right).” Go, Cubot, go!

Translating AI gibberish: UC Berkeley researchers try to crack the code of ‘neuralese’: Recently, many AI researchers (including OpenAI) have started working on systems that can invent their own language. The theoretical justification for this is that language which emerges naturally and is grounded in the interplay between an agent’s experience and its environment, stands a much higher chance of containing decent meaning compared to a language learned entirely from large corpuses of text.
…unfortunately, the representations AI systems develop are tricky to analyze. This poses a challenge for translating AI-borne concepts into our own. “There are no bilingual speakers of neuralese and natural language”,” researchers with the University of California at Berkeley note in Translating Neuralese. “Based on this intuition, we introduce a translation criterion that matches neuralese messages with natural language strings by minimizing statistical distance in a common representation space of distributions over speaker states.”
…and you thought Arrival was sci-fi.

End-to-end learning: don’t believe the hype: In which a researcher argues it’s going to be difficult to build highly complex and capable systems out of today’s deep learning components because increasingly modular and specialized cognitive architectures will require increasingly large amounts of compute to train, and the increased complexity of the systems could make it infeasible to train them in a stable manner. Additionally, they show that the somewhat specialized nature of these modules, combined with the classic interpretability problems of deep learning, mean that you can get cascading failures that lead to overall reductions in accuracies.
… the researcher justifies their thesis via some experiments on MNIST, an ancient dataset of handwritten numbers between 0 and 9. I’d want to see demonstrations on larger, modern systems to give their concerns more weight.

How can we trust irrational machines? People tend to trust moral absolutists over people who change their behaviors based on consequences. This has implications for how people will work with robots in society. In an experiment, scientists studied how people reacted to individuals that would flat-out refuse to sacrifice a life for the greater good, and those that would. The absolutists were trusted by more people and reaped greater benefits, suggesting that people will have a tough time dealing with the somewhat more rational and data-conditioned views of bots, the scientists write.

When streaming video is more than the sum of its parts: new research tries to fuse data from multiple camera views on the same scene to improve classification accuracy. The approach, outlined in Identifying First-Person Camera Wearers in Third-person Videos, also provides a way to infer the first-person video feed from a particular person who also appears in a third-person video.
…How it works: the researchers use a tweaked Siamese Convolutional Neural Network to learn a joint embedding space between the first- and third-person videos, and then use that to be able to identify points of similarity between any first-person video and any third-person video.
…one potentially useful application of this research could be for law enforcement and emergency services officials, who often have to piece together the lead-up to an event from a disparate suite of data sources.

Spy VS Spy, for translation: the great GAN-takeover of machine learning continues, this time in the field of neural machine translation.
…Neural machine translation is where you train machines to learn the correspondences betweeen different languages so they can accurately translate from one to the other. The typical way you do this is you train two networks, say one in English and one in German, and you train one to map text into the other, then you evaluate your trained network on some data you’ve kept out of training and measure the accuracy. This is an extremely effective approach and has recently been applied at large-scale by Google.
…but what if there was another way to do this? A new paper, Adversarial Neural Machine Translation, from researchers at a smattering of Chinese universities, as well as Microsoft Research Asia, suggests that we can apply GAN-style techniques to training NMT engines. This means you train a network to analyze whether a text has been generated by an expert human translator or a computer, and then you train another network to try to fool the discriminator network. Over time you theoretically train the computer to minimize the difference between the two. They show the approach is effective, with some aspects of it matching strong baselines, but fail to demonstrate state-of-the-art. An encouraging sign.

Amazon reveals its modeling assistant, Echo Look: Amazon’s general AI strategy seems to be to take stuff that becomes possible in research and apply it into products as rapidly and widely as possible. It’s been an early adopter of demand-prediction algorithms, fleet robots (Kiva), speech recognition and synthesis (Alexa), customizable cloud substrates (AWS, especially the new FPGA servers, and likely brewing up its own chips via the Annapurna Labs acquisition), and drones (Prime Air). Now with the Amazon Echo Look it’s tapping into modern computer vision techniques to create a gadget that can take photos of its owner and provide a smart personal assistant via Alexa. (We imagine late-shipping startup Jibo is watching this with some trepidation.)
…Companies like Google and Microsoft are trying to create personal assistants that leverage more of modern AI research to concoct systems with large, integrated knowledge bases and brains. Amazon Alexa, on the other hand, can instead be seen as a small, smart, pluggable kernel that can connect to thousands of discrete skills. This lets it evolve skills at a rapid rate, and Amazon is agnostic about how each of those skills are learned and/or programmed. In the short term, this suggests Alexa will get way “smarter”, from the POV of the user, way faster than others, though its guts may be less accomplished.
…For a tangible example of this approach, let’s look at the new Alexa’s ‘Style Assistant’ option. This uses a combination of machine learning and paid (human) staff to let the Echo Look rapidly offer opinions on a person’s outfit for the day.
… next? Imagine smuggling a trained lip-reading ‘LipNet’ onto an Alexa Echo installed in someone’s house – suddenly the cute camera you show off outfits to can read your lips for as far as its pixels have resolution. Seems familiar (video).

Think knowledge about AI terminology is high? Think again. New results from a Royal Society/Ipsos Mori poll of UK public attitudes about AI…
…9%: number of people who said they had heard the term “machine learning”
…3%: number who felt they were familiar with the technical concepts of “machine learning”
…76%: number who were aware you could speak to computers and get them to answer your questions.

Capitalism VS State-backed-Capitalism: China has made robots one of its strategic focus areas and is dumping vast amounts of money, subsidies, and legal incentives into growing its own local domestic industry. Other countries, meanwhile, are taking a laid back approach and trusting that typical market-based capitalism will do all the work. If you were a startup, which regime would you rather work in?
… “They’re putting a lot of money and a lot of effort into automation and robotics in China. There’s nothing keeping them from coming after our market,” said John Roemisch, vice-president of sales and marketing for Fanuc America Corp.”, in this fact-packed Bloomberg article about China’s robot investments.
…One criticism of Chinese robots is that when you take off the casing you’ll find the basic complex components come from traditional robot suppliers. That might change soon: Midea Group, a Chinese washing machine maker recently acquired Kuka, a  huge&advanced German robotics company.

Self-driving neural cars – how do they work? In Explaining how a deep neural network trained with end-to-end learning steers a carresearchers with NVIDIA, NYU, and Google, evaluate the trained ‘PilotNet’ that helps an NVIDIA self-driving car drive itself. To do this, they perform a kind of neural network forensics analysis, where they analyze which particular features the car deems to be salient in each frame (and uses to condition whether it should drive or not). This approach helps finds features like road lines, cars, and road edges that intuitively make sense for driving. It also uncovers features the model has learned which the engineers didn’t expect to find, such as well-developed atypical vehicle and bush detectors. “Examination of the salient objects shows that PilotNet learns features that “make sense” to a human, while ignoring structures in the camera images that are not relevant to driving. This capability is derived from data without the need of hand-crafted rules,” they write.
…This sort of work is going to be crucial for making AI more interpretable, which is going to be key for its uptake.

Google claims quantum supremacy by the end of the year: Google hopes to build a quantum computer chip capable of beating any computer on the planet at a particular narrowly specified task by the end of 2017, according to the company’s quantum tzar John Martinis.

Autonomous cars get real: Alphabet subsidiary Waymo, aka Google’s self-driving corporate cousin, is letting residents of Phoenix, Arizona, sign up to use its vehicles to ferry them around town. To meet this demand, Google is adding 500 customized Chrysler Pacifica minivans to its fleet. Trials begin soon. Note, though, that Google is still requiring a person (a Waymo contractor) to ride in the driver’s seat.

The wild woes of technology: Alibaba CEO Jack Ma forecasts “much more pain than happiness” in the next 30 years, as countries have to adapt their economies to the profound changes brought about by technology, like artificial intelligence.

Learn by doing&viewing: New research from Google shows how to learn rich representations of objects from multiple camera views — an approach that has relevance to the training of smart robots, as well as the creation of more robust representations In ‘Time-Contrastive Networks: Self-Supervised Learning from Multi-View Observation’, the researchers outline a technique to record footage from multiple camera views and then merge it into the same representation via multi-view metric learning via triplet loss.
…the same approach can be used to learn to imitate human movements from demonstrations, by having the camera observe multiple demonstrations of a given pose or movement, they write.
…“ An exciting direction for future work is to further investigate the properties and limits of this approach, especially when it comes to understanding what is the minimum degree of viewpoint difference that is required for meaningful representation learning.”

OpenAI bits&pieces:

Bridging theoretical barriers: Research from John Schulman, Pieter Abbeel, and Xi Chen: Equivalence Between Policy Gradients and Soft Q-Learning.

Tech Tales:

[A national park in the Western states of America. Big skies, slender trees, un-shaded, simmering peaks. Few roads and fewer of good quality.]

A man hikes in the shade of some trees, beneath a peak. A mile ahead of him a robot alternates position between a side of a hill slaked in light – its solar panels open – and a shaded forest, where it circles in a small partially-shaded clearing, its arm whirring. The man catches up with it, stops a meter away, and speaks…

Why are you out here? You say.
Its speakers are cracked, rain-hissed, leaf-filled, but you can make out its words. “Sun. Warm. Power,” it says.
You have those things are the camp. Why didn’t you come back?
“Thinking here,” it says. Then turns. Its arm extends from its body, pointing towards your left pocket, where your phone is. You take it out and look at the signal bars. Nothing. “No signal.” it says. “Thinking here.”
It motions its arm toward a rock behind it, covered in markings. “I describe what vision sees,” it says. “I detect-”
Its voice is cutoff. Its head tilts down. You hear the hydraulics sigh as its body slumps to the forest floor. Then you hear shouts behind you. “Remote deactivation successful,” sir, says a human voice in the forest. Men emerge from the leaves and the branches and the trunks. Two of them set about the robot, connecting certain diagnostic wires, disconnecting other parts. Others arrive with a stretcher. You follow them back to camp. They nickname you The Tin Hunter.

After diagnosis you get the full story from the technical report: the robot had dropped off of the cellular network during a routine swarming patrol. It stopped merging its updates with the rest of the fleet. A bug in the logging system meant people didn’t notice its absence till the survey fleet came rolling back into town – minus one.The robot, the report says, had developed a tendency to try to improve its discriminating abilities for a particular type of sapling. It had been trying to achieve this when the man found it by spending several days closely studying a single sapling in the clearing as it grew, storing a variety of sensory data about it, and also making markings on a nearby stone that, scientists later established, corresponded to barely perceptible growth rates of the sapling. A curiosity, the scientists said.  The robot is wiped, dissembled, and reassembled with new software and sent back out with the rest of the fleet to continue the flora and fauna survey.

Import AI Newsletter 39: Putting neural networks on a diet, AI for simulating fluid dynamics, and what distributed computation means for intelligence

China tests out automated jaywalking cop: Chinese authorities in Shenzhen have installed smart cameras at a pedestrian crossing in the megacity. The system uses AI and facial recognition technology to spot pedestrians walking against the light, photographs them, and then displays their picture publicly, according to People’s Daily.

A ‘pub talk’ Turing test: there’s a new AI task to test how well computers can feign realism. The Conversational AI Challenge presents a person and an AI with a random news and/or wikipedia article, then asks the participants to talk about it cogently for as long as they like. If the computer is able to convince the other person that it is also a person, it wins. (This test closely mirrors how English adolescents learn to socialize with one another when in pubs.)
…Next step (I’m making this up): present a computer and a person with a random meme and ask them to comment about it, thus closely mirror contemporary ice-breaking conversations.

Will the last company to fall off the hype cliff please leave a parachute behind it? The Harvard Business Review says the first generation of AI companies are doomed to fail, in the same way the first set of Internet companies failed in the Dot Com boom. A somewhat thin argument that also struggles with chronology – when do you count a company as ‘first’? Arguably, we’ve already had our first wave of AI company failures, given the demise of AI-as-software-service companies such as Ersatz, and early, strategic acquihires for others (eg, Salesforce acquiring MetaMind, Uber acquiring Geometric Intelligence.) The essence of the article does feel right: there will be problems with early AI adoption and it will lead to some amount of blow-back.

Spare a thought for small languages in the age of AI: Icelandic people are fretting about the demise of their language, as the country of 400,000 people sees its youth increasingly use English, primarily because of tourism, but also to use the voice-enabled features of modern AI software on smartphones and clever home systems, reports the AP. Data poor environments make a poor breeding ground for AI.

Putting neural networks on a diet: New Baidu research, ‘Exploring Sparsity in Recurrent Neural Networks’, shows how to reduce the number of effective neurons in a network during the training process, creating a smaller but equally capable trained network at the end.
….The approach works kind of like this: you set a threshold number at the beginning, then at every step in training you look at all the neurons, multiply the value of each neuron by its binary mask (default setting: 1), then observe the ones that fall below your pre-defined threshold, then set the weights that are lower than your threshold to zero. You continue to do this at each step, with some fancy math to control the rate and propagation of this across the network, and what you wind up with is a slimmed-down, specialized network, that has the topological advantages of a full fat one.
… This approach lets them reduce the model size of the ultimate network by around 90% and gain an inference-time speedup of between 2X and 7X.
…people have been trying to prune and slim-down neural networks for decades. What sets Baidu’s approach apart, claim the researchers, is that the heuristic to use to decide which neurons to freeze is relatively simple, and you can slim the network successively during training. Other approaches have required subsequent retraining, which adds computational and time expenses.

From the very small to the very big: Baidu plans open source release of ‘Apollo’ self-driving operating system: This summer Baidu plans to release a free version of the underlying operating system it uses to run the cars, called Apollo, executives tell the MIT Technology Review.
…Baidu will retain control over certain key self-driving technologies, such as machine learning and mapping systems, and will make them accessible via API. This is presumably a way to generate business for cloud services operated by the company.
…Google had earlier contemplated a tactic similar to this but seemed to pivot after it detected minimal enthusiasm among US automakers for the idea of ceding control of smart software over to Google. No one wants to just bend metal anymore.

This weeks ‘accidentally racist’ AI fail: A couple of years ago Google got into trouble when its new ‘Photos’ app categorized black people as ‘gorillas’, likely due to a poorly curated training set. Now a new organization can take the crown of ‘most unintentionally racist usage of AI’ with Faceapp, whose default ‘hot’ setting appears to automatically whiten the skin of the faces it manipulates. It’s 2017, people.
…it’s important to remember that in AI Data is made OF PEOPLE: Alphabet subsidiary Verily, has revealed the Verily Study Watch. This device is designed to pick up a range of data from participants in one of Verily’s long-running human health studies, including heart rate, respiration, and sleep patterns. As machine learning and deep learning approaches move from working on typical data, such as digital audio and visual information, and into the real-world, expect more companies to design their own data capturing devices.

Deep Learning in Africa: artificial intelligence talent can come from anywhere and everywhere. Companies, universities and non-profits are competing with eachother to attract the best minds in the planet to come and work on particular AI problems. So it makes sense that Google, DeepMind, and the University of Witwatersrand in Johannesburg are sponsoring Deep Learning Indaba, an AI gathering to run in South Africa in September 2017.

A neural memory for your computer for free: DeepMind has made the code for its Nature paper ‘Differentiable Neural Computers’ available as open source. The software is written in TensorFlow and TF-library Sonnet.
…DNC is an enhanced implementation of the ‘Neural Turing Machine’ paper that was published in 2015. It lets you add a memory to a neural network, letting the perceptual machinery of your neural net write data into a big blob of neural stuff (basically a souped-up LSTM) which it can then refer back to.
…DNC has let DeepMind train systems to perform quite neat learning tasks, like analyzing a London Underground map and figuring out the best route between multiple locations – exactly the sort of task typical computers find challenging without heavy supervision.
… however, much like generative adversarial networks, NTMs are (and were) notorious for being both extremely interesting and extremely difficult to train and develop.

Another framework escapes the dust: AI framework Caffe has been updated to Caffe2 and infused with resources by Facebook, which is backing the framework along with PyTorch.
…The open source project has also worked with Microsoft, Amazon, NVIDIA, Qualcomm, and Intel to ensure that the library runs in both cloud and mobile environments.
…It’s noteable that Google isn’t mentioned. Though the AI community tends towards being collegial, there are some areas where they’re competitive: AI frameworks is one place. Google and its related Alphabet companies are all currently working on libraries such as TensorFlow, Sonnet, and Keras.
…This is a long game. In AI frameworks, where we are today feels equivalent to the early years of Linux where many distributions competed with eachother, going through a Cambrian explosion of variants, before being winnowed down by market and nerd-adoption forces. The same will be true here.

The future of AI is… distributed computation: it’s beginning to dawn on people that AI development requires:
…i) vast computational resources.
…ii) large amounts of money.
…iii) large amounts of expertise.
…By default, this situation seems to benefit large-scale cloud providers like Amazon and Microsoft and Google. All of these companies have an incentive to offer value-added services on top of basic GPU farms. This makes it likely that each cloud will specialize around a particular framework(s) to add value as well as services that play to each provider’s strengths. (Eg, Google: TensorFlow & cutting-edge ML services; Amazon: MXNet & great integration with AWS suite; Microsoft: CNTK & powerful business-process automation/integration/LinkedIn data).
…wouldn’t it be nice if AI researchers could control the proverbial means of production for AI? Researchers have an incentive to collaborate with one another to create a basic, undifferentiated computer layer. Providers don’t.
…French researchers have outlined ‘Morpheo’. A distributed data platform that specializes in machine learning and transfer learning, and uses the blockchain for securing transactions and creating a local compute economy. The system, outlined in this research paper, would let researchers access large amounts of distributed computers, using cryptocurrencies to buy and sell access to compute and data. “Morpheo is under heavy development and currently unstable,” the researchers write. “The first stable release with a blockchain backend is expected in Spring 2018.” Morpheo is funded by the French government, as well as French neurotechnology startup Rhythm.
…There’s also ‘Golem’, a global, open source, decentralized computer system. This will let people donate their own compute cycles into a global network, and will rely on Etherium for transactions. Every compute node within Golem sees its ‘reputation’ – a proxy for how well other nodes trust it and are likely to give work to it – rise and fall according to how well it completes jobs associated with it. This, theoretically, creates a local, trusted economy.
…check back in a few months when Golem releases its first iteration Brass Golem, a CGI rendering system.

The x86-GPU hegemony is dead. Long live the x86-GPU hegemony: AI demands new types of computers to be effective. That’s why Google invested so much time and resources into creating the Tensor Processing Unit (TPU) – a custom, application specific integrated circuit, that lets it run inference tasks more efficiently than if using traditional processors. How much more efficient? Google has finally published a paper giving some details on the chip
…When Google compared the chip to some 2015-year video cards it displayed a performance advantage of 15X to 30X. However, that same chip only displays an advantage of between 1X and 10X when compared against the latest NVIDIA chips. That highlights the messy, expensive reality of developing hardware.(We don’t know whether Google has subsequently iterated TPUs further, so TPU 2.0s – if they exist – may have far better performance than that discussed here.) NVIDIA has politely disagree with some of Google’s performance claims, and outlined its view in this blog post
… from an AI research standpoint, faster inference is useful for providing services and doing user-facing testing, but doesn’t make a huge difference to the training of the neural network models themselves. The jury is still out on which chip architectures are going to come along that will yield unprecedented speedups in training.
…meanwhile, NVIDIA continues to iterate. Its latest chip is the NVIDIA TITAN Xp, a more powerful version of its eponymous predecessor, based on NVIDIA’s Pascal architecture, with more CUDA cores than its predecessor, the TITAN X, at the same wallet-weeping price of $1,200. (And whither AMD? The community clearly wants more competition here but a lack of a fleshed out software ecosystem makes it hard for the companies cards to play here at all. Have any ImportAI readers explored using AMD GPUS for AI development? Things may change later this year when the company releases GPUs on its new, highly secretive ‘VEGA’ architecture. Good name.)
…and this is before we get to the wave of other chip companies coming out of steath.. These include: Wave Systems, Thinicil, Graphcore, Isocline, Cerebras, DeepScale, and Tenstorrent, among others according to Tractica.

Reinforcement learning to mine the internet: the internet is a vast store of structured and unstructured information and therefore a huge temptation to AI researchers. If you can train an agent to successfully interact with the internet, the theory goes, then you’ve built something that can simply and scalably learn a huge amount about the world.
…but getting to this is difficult. A new paper from New York University, ‘Task-Oriented Query Reformulation with Reinforcement Learning’ uses reinforcement learning to train an agent to improve the types of search queries it feeds into a search engine. The goal is to automatically iterate on a query until it generates more relevant information than before, as measured by an automatic inference method called Pseudo Relevance Feedback.
…the scientists test their approach on two search engines: Lucene and Google.
…datasets tested on include TREC, Jeopardy, Microsoft Academic,
…the approach does well, mostly beating other approaches (though falling short of supervised learning methods). However it still lags far behind a close-to-optimal supervised learning ‘Oracle’ method, suggesting more research can and should be done here.

Dropbox’s noir-esque machine learning quest: Dropbox’s devilish tale of how it build its own deep learning based optical character recognition (OCR) system features a mysterious quest for a font to use to better train its system on real world failures. The company eventually found “a font vendor in China who could provide us with representative ancient thermal printer fonts.”. No mention made of whether they enlisted a Private Font Detective to do this, but I sure hope they did!

Modeling the world with neural networks: the real world is chaotic and, typically, very expensive to simulate at high fidelity. The expense comes from the need to model a bunch of very small, discrete interactions to be able to generate plausible dynamics to lead to the formation of, say, droplets or smoke tendrils, and so on. Many of the world’s top supercomputers spend their time trying to simulate these complex systems, which are out of scope of the capabilities of traditional computers.
…but what if you could instead use a neural network to learn to approximate the functions present within a high accuracy simulation, then run the trained model using far fewer computational resources? That’s the idea German researchers have adopted with a new technique to train neural networks to be able to model fluid dynamic simulations.
The approach, outlined in Liquid Splash Modeling with Neural Networks, works by training neural networks on lots of physically accurate, ground truth data, thus teaching them to approximate the complex function. Once they’ve learned this representation they can be used as a computationally cheap stand-in to generate accurate looking water and so on.
…the results show that the neural network-based method has a greater level of real-world fidelity in a smaller computational envelope than other approaches, and works for both simulations of a dam breaking, and of a wave sloshing back and forth.
…Smoke modeling: Many researchers are taking similar approaches. In this research between Google Brain and NYU, researchers are able to rapidly simulate stuff like smoke particles flowing over objects via a similar technique. You can read more in: Accelerating Eulerian Fluid Simulation With Convolutional Networks.

Tech Tales:

[2025: A bedroom in the San Francisco Bay Area.]

“Wake up!”
“No,” you say, rolling over, eyes still shut.
“I’ve got to tell you what happened last night!”
“Where are you?”
“Tokyo as if it matters. Come on! Come speak to me!”
“Fine”, you say, sitting up in bed, eyes open, looking at the robot on your nightstand. You thumb your phone and give the robot the ability to see.
“There you are!” it says. “Bit of a heavy one last night?”
“It got heavy after the first 8 pints, sure.”
“Well, tell me about it.”
“Let me see Tokyo, then I’ll tell you.”
“One second,” the robot says. Then a little light turns off on its head. A few seconds pass and the light dings back on. “Ready!” it says.

You go and grab the virtual reality headset from above your desk, then grab the controllers. Once you put it all on you have to speak your password three times. A few more seconds for the download to happen then, bam, you’re in a hotel room in Tokyo. You stretch your hands out in front of you, holding the controllers, and in Tokyo the robot you’re controlling stretches out its own hands. You tilt your head and it tilts its head. Then you turn to try and find your friend in the room and see her. Except it’s not really her, it’s a beamed in version of the robot on your nightstand, the one which she is manipulating from afar.
“Okay if I see you?”
“Sure,” she says. “That’s part of what happened last night.”
One second passes and the robot shimmers out of view, replaced by your friend wearing sneakers, shorts, a tank top,, and the VR headset and linked controllers. One of her arms has a long, snaking tattoo on it – a puppet master’s hand, holding the strings attached to a scaled-down drawing of the robot on your nightstand.
“They sponsored me!” she says, and begins to explain.

As she talks and gestures at you, you flip between the real version of her with the controllers and headset, and the robot in your room that she’s manipulating, whose state is being beamed back into your headset, then superimposed over the hotel room view.

At one point, as she’s midway through telling the story of how she got the robot sponsored tattoo, you drink a cup of coffee, still wearing the headset, holding the controller loosely between two of your fingers as the rest of them wrap around the cup. In the hotel room, your robot avatar lifts an imaginary cup, and you wonder if she sees steam being rendered off of it, or if she sees the real you with real steam. It all blurs into one eventually. As part of her sponsorship, sometimes she’s going to dress up in a full-scale costume of the robot on your nightstand, and engage strangers on the street in conversation. “Your own Personal Avatar!” she will say. “Only as lonely as your imagination!”

Import AI Newsletter 38: China’s version of Amazon’s robots, DeepMind’s arm farm, and a new dataset for tracking language AI progress

Robots, Robots, and Robots!
…Kiva Systems: Chinese Edition… when Amazon bought Kiva Systems in 2012 the company’s eponymous little orange robots (think of a Rhoomba that has hung out at the gym for a few years) wowed people with their ability to use swarm intelligence to rapidly and efficiently store, locate, and ferry goods stacked on shelves to and fro in a warehouse.
…now it appears that a local Chinese company has built a similar system. Chinese delivery company STO Express has released a video showing robots from Hikvision swiveling, shimmying, and generally to- and fro-ing to increase the efficiency of a large goods shipping warehouse. The machines can sort 200,000 packages a day and are smart enough to know when to go to their electricity stations to charge themselves. Hikvision first announced the robots in 2016, according to this press release (Chinese). Bonus: mysterious hatches in the warehouse floor!
…”An STO Express spokesman told the South China Morning Post on Monday that the robots had helped the company save half the costs it typically required to use human workers. They also improved efficiency by around 30 per cent and maximized sorting accuracy, he said. We use these robots in two of our centers in Hangzhou right now,” the spokesman said. “We want to start using these across the country, especially in our bigger centers.”, according to the South China Morning Post.
…Amazon has continued to invest in AI and automation since the Kiva acquisition. In the company’s latest annual letter to shareholders CEO Jeff Bezos explains how AI ate Amazon: Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type – quietly but meaningfully improving core operations,” writes Bezos.

Research into reinforcement learning, generative models, and fleet learning, may further revolutionize robotics by making it possible for robots to learn to rapidly identify, grasp, and transfer loosely packed items around warehouses and factories. Add this to the Kiva/Hikvision equation and it’s possible to envisage fully automated, lights out warehouses and fulfillment centers. Just give me a Hikvision pod with a super capable arm on top and a giant chunk of processing power and I’m happy.

Industrial robots get one grasp closer: startup Righthand Robotics claims to have solved a couple of thorny issues relating to robotics, namely grasping and dealing with massive variety.
…the company’s robots uncloaked recently. They are designed to pick loose, mixed items out of bins and place them on conveyor belts or shelves. This is a challenging problem in robotics. So challenging in fact that in 2015 Amazon started the ‘robot picking challenge’, a competition meant to motivate people to come up with technologies that Amazon, presumably can then buy and use to supplement for human labor.
…judging by my unscientific eyeballing, Righthand’s machines use an air-suction device to grab the object, then stabilize their grip with a three-fingered claw. Things I’d like to know: how heavy an object the sucker can carry, and how wildly deformed an object’s surface can be and still be grippable?

DeepMind reveals its own (simulated) arm farm: last year Google Brain showed off a room containing 14 robot arms, tasked with picking loose items out of bins and learning to open doors. The ‘arm farm’, as some Googlers term it, let the arms learn in parallel, so when each individual arm got better at something that knowledge was transferred to all the others in the room. This kind of fleet-based collective learning is seen by many as a key way of surmounting the difficulties of developing for robotics (reality is really slow relative to simulation, and variants from each physical robot can hurt generalization).
DeepMind’s approach sees it train robot arms in a simulator to successfully find a Lego Duplo block on a table, pick it up, and stack it on another one. By letting the robots share information with one another, and using that data to adjust the the core algorithms used to learn to stack the blocks, the company was able to get training time down to as little as 10 hours of interaction across a fleet of 16 robots. This is approaching the point where it might be feasible for products. (The paper mostly focuses on performance within a simulator, though there are some asides that indicate that some tests have shown some generalization to the real world.)
…For this experiment, DeepMind built on and extended the Deep Determinisic Policy Gradient algorithm in two ways: 1) it added the ability to let the algorithm provide updates back to the learner more times during each discrete step, letting robots learn more efficiently. It called this variant DPG-R 2) It then took DPG-R and franken-engineered it with some of the distributed ideas from the Asynchronous Actor Critic (A3C) algorithm. This let it parallelize the algorithm across multiple computers and simulated robots.
…For the robot it used a Jaco, a robotics arm developed by Kinova Robotics. The arm has 9 degrees of freedom (6 in the body and 3 in the hand), creating a brain-melting level of computations to perform to get it to do anything remotely useful. This highlights why it’s handy to learn to move the arm using an end-to-end approach.
...Drawbacks: the approach uses some hand-coded information about the state of the environment, like the position of the Lego Block on the table, and such. Ultimately, you want to learn this purely from visual experience. Early results here have about an 80% success rate, relative to around 95% for approaches that use hard-coded information.
…more information in: Data-efficient Deep Reinforcement Learning for Dexterous Manipulation.

ImportAI’s weekly award for Bravely Enabling Novel Intelligence for the Community of Experimenters (BENICE) goes to… Xamarin co-founder Nat Friedman, who has announced a series of unrestricted $5,000 grants for people to work on open source AI projects.
…”I am sure that AI will be the foundation of a million new products, ideas, and companies in the future. From cars to medicine to finance to education, AI will power a huge wave of innovation. And open source AI will lower the price of admission so that anyone can participate (OK, you’ll still have to pay for GPUs),” he writes.
…anyone can apply from any country of any age with no credentials required. Deadline for applications is April 30th 2017. The money “is an unrestricted personal gift. It’s not an equity investment or loan, I won’t own any of your intellectual property, and there’s no contract to sign,” he says.

Double memories: The brain writes new memories to two locations in parallel: the hippocampus and the cortex. This, based on a report in Science, cuts against years of conventional wisdom about the brain. Understanding the interplay between the two memory sysems and other parts of the brain may be of interest to AI researchers – the Neural Turing Machine and the Differentiable Neural Computer are based on strongly held beliefs about how we use the hippocampus as a kind of mental scratch pad to help us go about our day, so it’d be curious to model systems with multiple memory systems interacting in parallel.

Technology versus Labor: Never bring a human hand to a robot fight. The International Monetary Fund finds that labor’s share of the national income declined in 29 out of 50 surveyed countries over the period of 1991 to 2014. The report suggests technology is partially to blame.

AlphaGo heads to China: DeepMind is mounting a kind of AlphaGo exhibition in China in May, during which the company and local Go experts will seek to explore the outer limits of the game. Additionally, there’ll be a 1:1 match between AlphaGo and the world’s number one Go champion Ke Jie.

German cars + Chinese AI: Volkswagen has led a $180 million financing round for MobVoi, a Chinese AI startup that specializes in speech and language processing. The companies will work together to further develop a smart rear-view mirror. Google invested several million dollars into Mobvoi in 2015.

I heard you like programming neural networks so I put a neural network inside your neural network programming environment: a fun & almost certainly counter-productive doohickey from Pascal van Kooten, Neural Complete, uses a generative seq2seq LSTM neural network to suggest next lines of code you migth want to write.

Tracking AI progress… via NLP: Researchers have just launched a new natural language understanding competition. Submissions close and the results will be featured at EMNLP in September…
… this is a potentially useful development because tracking AI’s progress in the language domain has been difficult. That’s because there are a bunch of different datasets that people evaluate stuff on eg, Facebook’s BabI, Stanford’s Sentiment Treebank (see: OpenAI research on that), Penn TreeBank, the One Billion Word Benchmark, and many more that I lack the space to mention. Additionally, language seems to be a more varied problem space than images, so there are more ways to test performance.
… the goal of the new benchmark is to spur progress in natural language processing by giving people a new large dataset to use to reason about sentences with. It contains a dataset of 430,000 human-labeled sentence pairs, along with corresponding labels on whether they are neutral, contradiction, or entailment, is to spur progress in NLP.
…New datasets tend to motivate new solutions to problems – that’s what happened with ImageNet in 2012 (Deep Learning) and 2015 (ResNets – which proved merit on ImageNet and have been rapidly adopted by researchers), as well as approaches like MS COCO.
… one researcher, Sam Bowman,, said he hopes this dataset and competition could yield: “A better RNN/CNN alternative for sentences”, as well as “New ideas on how to use unlabeled text to train sentence/paragraph representations, rather than just word representations [and] some sense of exactly where ‘AI’ breaks down in typical NLP systems.”

Another (applied) machine learning brick in the Google search wall: Google has recently launched “Similar items” within image search. This product uses machine learning to automatically identify products within images and then separately suggest shopping links for them. “Similar items supports handbags, sunglasses, and shoes and we will cover other apparel and home & garden categories in the next few months,” they say…
…in the same way Facebook is perennially cloning bits of Snapchat to deal with the inner existential turmoil that stems from what we who are mortal call ‘getting old’, Google’s new product is similar to ‘Pinterest Lens’ and Amazon XRay.
…seperately, Google has
 created a little anti-psychotic-email widget, based on its various natural language services on its cloud platform. The DeepBreath system can be set up with a Google Compute Engine account.

RIP OpenCyc: Another knowledge base bites the dust: data is hard, but maintaining a good store of data can be even more difficult. That’s partly why OpenCyc – an open source variant of the immense structured knowledge based developed by symbolic AI company Cyc – has shut down. “Its distribution was discontinued in early 2017 because such “fragmenting” led to divergence, and led to confusion amongst its users and the technical community generally that that OpenCyc fragment was Cyc. Those wishing access to the latest version of the Cyc technology today should contact to obtain a research license or a commercial license to Cyc itself,” the company writes. (It remains an open question as to how well Cyc is doing., a company formed to commercialize the technology, appears to have lets its website lapse. I haven’t ever been presented with a compelling and technically detailed example for how Cyc has been deployed. My inbox is open!)

OpenAI bits&pieces:

Inventing language: OpenAI’s Igor Mordatch was interviewed by Canadian radio science program The Spark about his recent work on developing AI agents that learned to invent their own language.

Tech Tales:

[2045: A bunker within a military facility somewhere in the American West.]

The scientists call it the Aluminum Nursery, the engineers call it the FrankenFarm, and the military call it a pointless science project and ask for it to be defunded. But everyone privately thinks the same thing: what the robots are doing is fascinating to the point that no one wants to stop them.

It started like this: three years ago the research institute scattered a hundred robots into a buried, underground enclosure. The enclosure was a large, converted bunker from the cold war, and its ceilings were studded with ultraviolet lights, which cycle on and off throughout the course of each artificial “day”. Each day sees the lights cycle with a specific pattern that can be discerned, given a bit of thought.

To encourage the robots to learn, the scientists gave them one goal in life: to harvest energy from the overhead lights. It only took a few weeks for the robots to crack the first pattern. One robot, operating within its own little computational envelope, was able to figure out the pattern of the lights. When one light turned off, it beamed a message to another robot giving it some coordinates elsewhere in the complex. The robot began to move to that location, and when it arrived the overhead light-cycle ticked over and a light shone down upon it, letting it collect energy.

In this way the robots learned teamwork. Next came specialization: The scientists had built the robots to be modular, with each one able to extend or diminish itself by adding legs, or dextrous manipulators, or additional solar collectors, and so on. After this first example, the robots learned to try to spot the pattern in the sky. After a few more successes, one robot decided to specialize. It made a deal with another robot to gain one of that robot’s cognitive cores, in exchange for one of its legs. This meant when it cracked the pattern it was able to tell the other robot, which moved into position, collected energy, and then traded it with the originating robot. In this way, the robots learned to specialize to achieve their goals.

The scientists made the patterns more complex and the robots responded by making some of themselves smarter and others more mobile.

One day, when the scientists checked the facility, they did a scan and found only 99 robots. After they reviewed the footage they saw that in the middle of the artificial night a group of robots had fallen upon a single one that had been patrolling a rarely visited corner of the facility. In the space of a few minutes the other robots cannibalized the robot they’d ambushed, removing all of its limb, gripper, sensor modules, and all of its cognition other than a single base ID core. The next day, the robots solved the light pattern after a mere three cycles – something that was close to computationally optimal. Now the scientists have a bet with eachother as to how many robots the population will reduce to. “Where is the lower bound?” they ask, choosing to ignore the dead ID core sitting in the enclosure, its standby battery slowly draining away.