Import AI: Issue 28: What one quadrillion dollars pays for, research paper archaeology, and AI modules for drones

by Jack Clark

Cost of automating the entire global economy? One quadrillion dollars.
Requirements for the resulting system to be able to perfectly replace all human labor:
…Computation: 10^26 operations per second
…Memory: 10^25 bits
…I/O: 10^19 input-output bits per second
…Knowledge ingestion: 7 bits per person per second
and many more marvelous numbers in this essay by data compression expert Matt Mahoney on ‘the cost of AI”. A virtuoso performance of extrapolation and (with apologies to Mitchell & Webb) numberwang-ery.

Google self-driving cars, report card (PDF):
…Miles driven in 2015: 424,331
…Miles driven in 2016: 635,868
…Disengagements per 1,000 miles, 2015: 0.80
…Disengagements per 1,000 miles, 2016: 0.20
… now let’s see how they do with hard training situations for which there is little good training data, like navigating a sandstorm-ridden road in the Middle East.

How much is an AI worth? In which Google’s head of M&A, Don Harrison, says Google is happy to throw large quantities of cash at AI companies. “It’s very hard to apply valuation metrics to AI. These acquisitions are driven by key talent — really smart people. It’s an area I’m focused on and our team is focused on. The valuations are part and parcel of the promise of the technology. We pay attention to it but don’t necessarily worry about it,” he says. (Emphasis mine.)

Your organization and public data: a message to Import AI readers: most organizations gather some form of data which can be safely published, and the world is richer for it. Case in point: Backblaze its latest report on hard drive reliability. These reports should factor into any HDD buyer’s decision, as they represent good, statistically significant real-world data of drive performance. If you work at an organization that may have similar data that can be externalized, please try to make this happen – I’ll be happy to help, so feel free to email me.

Measurement: besides Atari, what are other good measures for the progression of reinforcement learning techniques? As we move into an era dominated by dynamic environments supplied by tools like Universe, DeepMind Lab, Malmo, Torchcraft, and others, how do we effectively  model the progress of agents in a way that captures their full spectrum of their growing capabilities?

AI for researching AI: the Allen Institute for AI has released Citeomatic, a tool that uses deep learning to predict citations for a given paper. To test out the system I fed it OpenAI’s RL^2 paper and it gave me back over 30 papers that it recommended we consider citing. Many of these seem reasonable, eg ‘solving partially observable reinforcement learning problems with rnns’, etc…
…Most of all, this seems like a great tool to help researchers find papers they should be reading. AI has a large literature and researchers frequently find themselves stumbling on good ideas from the previous decade. Any tool that can make this form of intellectual archaeology more efficient is likely to aid in science.

From the Dept. of Recursive Education: Tutorial from Arthur Juliani outlines how to build agents that learn how to learn, with code inspired by the DeepMind paper “Learning to reinforcement learn”, and the OpenAI paper “RL^2”.

Explanations as cognitive maps: the act of explaining situations lets us deal with the chaotic novelty of the world, and create useful abstractions we can use to reason about it. More detail, with many great research references, in this blog from Shakir at DeepMind.

Executive Order strikes a chill in math, AI community: President Trump’s executive order banning people from seven predominantly muslim countries from coming to the US will have significant effects on academia, according to mathematician Terry Tao. “This is already affecting upcoming or ongoing mathematical conferences or programs in the US, with many international speakers (including those from countries not directly affected by the order) now cancelling their visit, either in protest or in concern about their ability to freely enter and leave the country,” he writes. “It is still possible for this sort of long-term damage to the mathematical community (both within the US and abroad) to be reversed or at least contained, but at present there is a real risk of the damage becoming permanent.”…
… another illustration of the law of unintended consequences when politics runs amok. Reminds me of one of the more subtle and chilling consequences of the UK’s decisions to leave the European Union, which was that it reduced collaboration between EU and UK scientists as EU researchers worried that, because their grants were contingent on EU funding, collaboration with UK scientists could violate funding causes. Scientists need to collaborate across international borders.

“Give it the latest personality module, we’re wheels up in five minutes!” – autonomous drones are going to operate in such a huge possibility space that today’s if-this, then-that rule systems will be insufficient, according to this research paper from the University of Texas at Austin and SparkCognition. Eventually, scientists may use a combination of simulators and real world data to train different drone brains for different missions, then swap bits of them in and out as needed. “We propose delinking control networks from the ensembler RNN so that individual control RNNs may be evolved and trained to execute differing mission profiles optimally, and these “personalities” may be easily uploaded into the autonomous asset with no hardware changes necessary,” they write.

Language as the link between us and the machines: CommAI: Facebook AI researchers believe language will be crucial to the development of general purpose AI, and have outlined a platform named CommAI (short for communication-based AI) that uses language to train and communicate agents..
…The idea is that the AI will operate in a world attempting to complete tasks and it’s only major point of input/output with the operator will be a language interface. “In a CommAI-mini task, the environment presents a (simplified) regular expression to the learner. It then asks it to either recognize or produce a string matching the expression. The environment listens to the learner response and it provides linguistic feedback on the learner’s performance (possibly assigning reward). All exchanges take place at the bit level,” they write.
… whether this solves the language ‘chicken and egg’ problem remains to be seen. Language is hard because it represents a high level abstraction to refer to a bunch of low-level inputs. “Horse”, is our mental shorthand for the flood of sensory data that coincides with our experience of the creature. Ideally, we want our AIs to learn similar associations between the words in their language model and their experience of the world. CommAI is structured to encourage this sort of grounding.
…“We hope the CommAI-mini challenge is at the right level of complexity to stimulate researchers to develop genuinely new models,” they write.

Reinforcement learning goes from controlling Atari games, to robots, to… freeway onramps?  “Expert level control of Ramp Metering based on Multi-Task deep reinforcement learning” shows how RL methods can be extended to the control systems for the traffic lights that filter cars onto freeways. In tests, the researchers’ system is able to learn an effective policy for controlling traffic across a 20 mile-long section of the 210 freeway in Southern California. Their technique beats traditional reinforcement learning algorithms, as well as a baseline system in which no control occurs at all…
…“By eliminating the need for calibration, our method addresses one of the critical challenges and dominant causes of controller failure making our approach particularly promising in the field of traffic management,” they write.

Soft robots for hard work: UK online supermarket Ocado has tested a new robotic hand, created as part of a European Union ‘Horizon 2020’ research initiative for soft robots. The hand can pick up objects of varying sizes and textures, and is shown deftly handling tricky items like limes and apples. It uses a dextrous gripper called ‘RBO Hand 2’ with developed by the technical university of Berlin. The approach is reminiscent of that of SF-based Otherlab, which is using soft materials and air to build more flexible robots and exoskeletons.

Sizing up deep learning frameworks: the AI community is bad at two things: reproducibility and comparability.  The research paper “Benchmarking state-of-the-art deep learning software tools” asses the varying properties of frameworks like TensorFlow, Caffe, Theano, CNTK, and MXNet, comparing their performance on a wide variety of tasks and hardware substates. Worth reading to get an idea of the different capabilities of this software.

Import AI administrative note:

The riddle of the missing research paper: Last week I profiled some new research from MIT that involved automatically tying spoken words and sections of imagery together. However, due to a clerical error I did not link to the paper. “Learning Word-Like Units from Joint Audio-Visual Analysis

OpenAI bits & pieces:

23 principles to rule them all, 23 principles to bind them: earlier this month a bunch of people involved in the development, analysis, and study of artificial intelligence gathered at Asilomar for the “Beneficial AI” conference, a sequel to a 2015 gathering in Puerto Rico. Many people from OpenAI attended, including myself. There, the attendees helped hash out a set of 23 principles for the development of AI that signatories shall attempt to abide by.

Ian Goodfellow (OpenAI) and Richard Mallah (FLI), in conversation: podcast between Ian and Richard, in which they talk about some of the big AI breakthroughs that happened in 2016, and look ahead to some of the things that may define 2017 (machine learning security! Further development of neural translation systems! Work on OpenAI Universe!, etc).

Inverse autoregressive flow 2.0: Durk Kingma et al have posted a substantial update to the paper: “Improving Variational Inference with Inverse Autoregressive Flow”.

Do fake galaxies dream of the GANs that created them? Ian Goodfellow interview for this article in Nature about how scientists are starting to use AI-generated images to create training datasets to teach computers to spot real galaxies.

Tech Tales:

[2023, a cybercafe in Ankara]

When you were young you studied ants, staring at their nests as they grew, spreading tendrils through the dirt, sometimes brushing their antenna against the perspex walls sandwiching their captured colony. But you liked them best outside – crawling from a crack in the steps by the garage and charting a path along the sidewalk, carrying blades of grass and pebbles into some other nest. Your house was full of the signs of ants; each blob of silicone gel and mortared over holes testifying some pitched battle.

Modern spambots feel a lot like ants to you. After the first AI systems went online around 2018 the bots gained the ability to learn from the conversations with people they engaged on the internet. After this, their skills improved rapidly and their manners became more convincing.

Information started to flow between people and the bots, improving the AI’s ability to gain trust and effectively launder ideas, viruses, links, and eventually outright fraud. Spend a year arguing on the internet with someone and, stranger or no, there’s a good chance you’ll click on a link they post, seeing if it’s one of their nutty websites or something else to confirm your beliefs about them. And all your talking has taught them a lot about you.

The attacks mounted by the AIs destroyed the value of numerous publicly traded social companies. People changed their internet habits, becoming more cautious, better at security, more effective at uploading the sorts of words and images and videos to persuade people that they were real humans in the real world. And the AIs learned from this to.

So now you have to hunt them out, trace their paths and links to find the nests from which they emanate. Like the ants, you don’t get much insight from imprisoning them in display cases; synthetic social networks, where the AI bots are studied as they interact with your own simulated people bots. You feed data to their control systems and try to simulate the experience of the real internet, but soon your little model world goes out of sync with reality. It fails to keep up with those of its peers roaming wild, cut off from the links on the real internet where it gets its software updates – the few bits of code still pushed by humans.

So now you hunt these controllers through the internet and in real life, switching between VPNs and ethereal internet sites, and dusty internet cafes in the baltics and, now, Ankara. But recently you’ve been having trouble finding the humans, and you wonder if some of the swarms you are tracking have stopped taking orders from people. You’ll find out soon enough – there’s an election next year.