Import AI

Import AI 157: How weather can break self-driving car AI; modelling traffic via deep learning and satellites; and Chinese scientists make a smarter, smaller YOLOv3

Want to break an image classifier? Add some weather:
…Don’t use AI on a snow day…
Many of today’s object recognition systems are less robust and repeatable than people might assume – new research from the University of Tubingen and the International Max Planck Research School for Intelligent Systems shows just how fragile these systems are, with a trio of datasets that help people test the resilience of their AI systems. 

Three datasets to frustrate AI systems: The three datasets are called Pascal-C, Coco-C, and Cityscapes-C; these are ‘corrupted’ versions of existing datasets, and for each dataset the images within are corrupted with any of 15 distortions, each with five levels of severity. Some of the distortions that can be applied to the images include the addition of snow, frost, or fog to an image, as well as other distortions like the addition of noise, or the use of certain types of transforms. 

Just how bad is it: Out-of-the-box algorithms (typically based on the widely-used R-CNN family of models) see relative performance drops of between 30 and 50% on the corrupted versions of the datasts, highlighting the brittleness of many of today’s algorithms. 

Saving algorithms with messy data: One simple trick people can use to improve the robustness of models is to train them on stylized data – here, they basically take the underlying dataset and for each image create a variant stylized with a texture. These images are combined with the clean data, then trained on; models trained against datasets that incorporate the stylized data are more robust than those trained purely on clean data – this makes sense, as we’ve basically algorithmically expanded the dataset to encourage a certain type of generalization. 

Why this matters: Datasets like this make it easier for people to investigate the robustness of trained AI models, which can help us understand how contemporary models may fail and provide data to calibrate against when designing more robust ones. And the authors hope that other researchers will expand the benchmark further:
   “We encourage readers to expand the benchmark with novel corruption types. In order to achieve robust models, testing against a wide variety of different image corruptions is necessary, there is no ‘too much’. Since our benchmark is open source, we welcome new corruption types and look forward to your pull requests to https://github.com/bethgelab/imagecorruptions“.
   Read more: Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming (Arxiv).
   Get the code, data, and benchmarking leaderboard here (‘Robust Detection Benchmark’ official GitHub)

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The future of AI is… *checks notes* an AI assistant for the procedural building game Minecraft:
…Facebooks ‘CraftAssist’ project tries to build smarter AI systems by having them work alongside humans…
Facebook AI Research wants to study increasingly advanced AI systems by studying humans work alongside smart computers, so it has developed a bot to assist human players in the procedural building game Minecraft. “The ultimate goal of the bot is to be a useful and fun assistant in a wide variety of tasks specified and evaluated by human players”, they write. 

How the bot works: The robot works by taking in written prompts in natural language, then maps those to sequences of actions, like moving around the world or interacting with objects.
   For instance, in response to the query “go to the blue house”, the agent would try and map ‘blue’ and ‘house’ to entities it had stored in its memory, and if it found them would try to create a ‘move’ task that could let the agent navigate to that part of the world. The team achieves this via a neural semantic parser they call the Text-to-Action-Dictionary (TTAD) model, which converts natural language commands to specific actions. The agent also ships with systems to help out process the world around it, crudely analyzing the terrain and also heuristics for referring to objects based on their positions. 

Future extensions: Facebook has designed its agent to be extended in the future with more advanced AI capabilities. To that end, any CraftAssist agent can take in images in the form of a 64X64 ‘block’ resolution view (so viewing in terms of blocks in minecraft, rather than individual pixels). The agents can also access a 3D map of the space they’re in, so can locate any block within the world around them. 

Datasets: Facebook is releasing a dataset consisting of 800000 pairs of (algorithmically generated) actions and written instructions, 25402 human-written sentences that map to some of these actions pairs; 2513 suggested/imagined commands from humans that interacted with the bot, and 708 dialogue-action pairs from in-game chat. They’ve also release a ‘House’ dataset, which consists of 2050 human-built houses from Minecraft.  

Why this matters: Embedding AI systems into games will likely be one of the ways that we see people take AI research and port it into production – the use of Minecraft here is interesting given its playerbase numbering in the tens of millions, many of them children. Could we eventually see AI systems trained via the conversations with kids talking in broken English, training more robust policies through childish lingo? I think so! Next up: a generative Fortnite dance machine!
   Read more: CraftAssist: A Framework for Dialogue-enabled Interactive Agents (Arxiv).
   Get the code for CraftAssist here (official GitHub repository).

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Counting cars with deep learning and satellite imagery:
How can you count cars in countries that don’t have sensors wired into roads and traffic lights to gather the required data? Researchers with CMU and ETH Zurich think the use of deep learning and satellite imagery could be a viable supplement, and could help countries easily get measures for the Average Annual Daily Truck Traffic (AADTT) in a given region. 

In new research, they develop “a remote sensing approach to monitor freight vehicles through the use of high-resolution satellite images,” they write. “As satellite images become both cheaper and are taken at a higher resolution over time, we anticipate that our approach will become scalable at an affordable cost within the next few years to much larger geographic regions”.

The data: To train their system, the researchers hand-annotated vehicles seen in satellite images with around 2,000 bounding boxes from the Northeastern USA. “We used the predicted vehicle count from the detection model, the time stamp of the images, time-varying factors, and speed to make a probabilistic prediction of the AADTT”.

Testing generalization: The researchers gathered the data in America, and tested it also on data gathered from Brazil to explore the generalization properties of their system. “We found that distinct truck types (rather than geography) can impact the prediction accuracy of the detection model, and additional training seems necessary to transfer the model between countries,” they write. Additionally, “information on local driving patterns and labor laws could reduce the estimation error from the traffic monitoring model.” They trained a single-shot detection model to detect vehicles, and found that the model could provide reasonable predictions for locations from the United States, but struggled to provide as accurate predictions for Brazil, even once finetuned. 

Why this matters: Medium- and heavy-duty trucking accounts for about 7% of global CO2 emissions, and more than half of the world’s countries lack the infrastructure needed to accurately monitor traffic in their countries. Therefore, if we can develop AI-based classifiers to provide crude, cheap assessment capabilities, we can gather more data to help inform people about the world.
   Read more: Truck Traffic Monitoring with Satellite Images (Arxiv)

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How should AI researchers broadcast their insights to the world, and what do they need to be careful about?
…Publication in AI isn’t a binary choice between ‘release’ or ‘don’t release’, there are other tools available…
How can researchers maximize their contribution to scientific discourse while minimizing downsides (dual-use, malicious use, abuse, etc) of their research? That’s a question researchers from The Thoughtful Technology Project and Cambridge University’s Leverhulme Center for the Future of Intelligence, set out to provide some answers to in a blog post and action-oriented paper. 

   The core of their argument is that when researchers think they may have cause to question the release of their research, they should view their choice as being one of many, rather than a binary decision: “We particularly want to emphasize that when thinking about release practices, the choice is not a binary one between ‘release’ or ‘don’t release’. There are several different dimensions to consider and many different options within each of these dimensions, including: (1) content — what is released (options ranging from a fully runnable system all the way to a simple use case idea or concept); (2) timing — when it is released (options include immediate release, release at a specific predetermined time period or external event, staged release of increasingly powerful systems); and (3) distribution — where/to whom it is released to (options ranging from full public access to having release safety levels with auditing and approval processes for determining who has access).”

What should people do? They suggest three things the AI community should do to increase the chance of accruing the maximum possible social benefit from AI while minimizing certain downsides.

  1. Understand the potential risks of research via collaboration with experts, and develop mitigation strategies
  2. Build a community devoted to mitigating malicious use impacts of AI research and work to establish collective norms. 
  3. Create institutions to manage research practices in ML, potentially including techniques for expert vetting of certain research, as well as the development of sophisticated release procedures for research. 

Read more: Reducing malicious use of synthetic media research (Medium).

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Chinese scientists make a smarter, smaller drone vision system:
…What happens when drones become really, really smart?…
I have a confession to make: I’m afraid of drones. Specifically, I’m afraid of what happens when in a few years drones gain significant autonomous capabilities as a dividend of the AI revolution, and bad people do awful shit with these capabilities. I’m concerned about this because while drones have a vast range of good uses (which massively outnumber the negative ones!), they are also fundamentally mobile robots, and mobile robots are, to some people, great weapons (e.g. ISIS use of modified DIY military drones in recent years). 

What am I doing about this worry? I’m closely tracking developments in drone sensing and moving capabilities to try and develop my intuitions about this sub-field of AI development, and whenever I speak to policymakers I advocate for large-scale investments into the ongoing measurement, analysis, forecasting, and benchmarking of various AI capabilities so as to direct public money towards positive uses and generate the data that can unlock funding for dealing with (potential) negative uses. One of the things that motivates me here is a belief that if we just develop decent intuitions about the shape of progress at intersection of AI and drones, we’ll be able to get ahead of 95% of the bad stuff, and maximize our ability to benefit as a society from the technology. 

Now, researchers with the Beijing Institute of Technology have published (and released code for) ‘SlimYOLOv3’, a miniaturized version of the widely-used, very popular ‘YOLO’ (You Only Look Once) object recognition model. The difference between SlimYOLOv3 and YOLOv3 is simple: the Slim version is much, much smaller than the other, making it easier to deploy it on small computational devices, like the chips that can fit onto most drones. Specifically, they use sparsity training to guide a subsequent pruning process which helps them chop out unneeded bits of the neural network, then they fine-tune the model, and iteratively repeat the process until they obtain a satisfactory loss. 

So, how well does it work? “SlimYOLOv3 achieves compelling results compared with its unpruned counterpart: ~90.8% decrease of FLOPs, ~92% decline of parameter size, running ~2 faster and comparable detection accuracy as YOLOv3,” the authors write.
   They test out the system on the ‘VisDrone2018-Det’ dataset, which consists of ~7,000 drone-captured images containing any of ten predefined labelled objects (eg, pedestrian, car, bicycle, etc). They test out their SlimYOLOv3 system against an efficient YOLOv3 baseline, as well as a version of YOLOv3 augmented with spatial pyramid pooling (YOLOv3-SPP3). Variants of SlimYOLOv3 obtain scores that are around 10 absolute percentage points higher on evaluation criteria like Precision, Recall, and F1-score when compared against YOLOv3-tiny, while fitting in roughly the same computational envelop (8 million parameters, ~30mb model size). However, SlimYOLOv3 has a somewhat higher inference time than the less accurate YOLOv3-tiny. 

Be careful what you wish for: It’s notable that in March 2018 (Import AI #88), when YOLOv3 got released, the author anticipated its rapid diffusion, modification, and use: “What are we going to do with these detectors now that we have them?” A lot of the people doing this research are at Google and Facebook. I guess at least we know the technology is in good hands and definitely won’t be used to harvest your personal information and sell it to…. wait, you’re saying that’s exactly what it will be used for?? Oh. Well the other people heavily funding vision research are the military and they’ve never done anything horrible like killing lots of people with new technology oh wait…”.

Why this matters: Technologies like SlimYOLOv3 will give drones better, more efficient perceptive capabilities, which will make it easier for researchers to deploy increasingly sophisticated autonomous and semi-autonomous systems onto drones. This is going to change the world massively and rapidly – we should pay attention to what is happening.
   Read more: SlimYOLOv3: Narrower, Faster, and Better for Real-Time UAV Applications (Arxiv).  

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Want to test and develop better commonsense AI systems? Try WINOGRANDE:
…From 273 Winograd questions to 40,000 WINOGRANDE ones. Plus, pre-training for commonsense!…
Researchers with the Allen Institute for AI and the University of Washington have released ‘WINOGRANDE’, a scaled-up version of the iconic Winograd Schema Challenge (WSC) test for AI systems. For those not familiar, the WSC is a challenge and dataset consisting of 273 problems that AI systems need to try and solve. 

   So, what’s a Winograd problem? Here’s an example:
   Problem: “Pete envies Martin because he is successful.”
   Question: Is ‘he’ Pete or Martin?
   Answer: Martin. 

These problems are difficult for computers because they typically require a combination of context, world knowledge, and symbolic reasoning to solve. Some people have used progress on WSC as a litmus test for broader progress in AI research. But one thing that has held WSC back has been the lack of data – however you slice it, 273 just isn’t very large. Another has been that though the WSC questions were designed by experts to be challenging for AI systems, they still exhibit some language-based and data-based biases that can be exploited by AI systems, which can solve them by uncovering some of these underlying (unintentional) statistical regularities. 

Enter WINOGRANDE: The new WINOGRANDE dataset has been designed to be free of these biases, while also being much larger; the dataset contains around 44k questions, developed through crowdsourcing. The researchers hope researchers will test systems against WINOGRANDE to develop smarter systems, and will also use the dataset as a pre-training resource for applying to subsequent tasks (in tests, they show they can pre-train on WINOGRANDE to improve the state of the art on a range of other commonsense reasoning benchmarks in AI, including WSC, PDP, DPR, and COPA). 

Why this matters: Datasets like WINOGRANDE help define the frontier of difficulty for some AI systems and can also serve as training inputs for other, larger models. Commonsense reasoning is one of the main examples people use when discussing the limitations of contemporary AI techniques, so WINOGRANDE could define a new challenge which, if solved, could tell us something important about the future of genuinely intelligent AI.
   Read more: WINOGRANDE: An Adversarial Winograd Schema Challenge at Scale (Arxiv). 

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Positive uses of AI – A crowd-sourced list:
…AI isn’t all doom and gloom – it’s also changing the world for the better…
This year, I’ve been giving an occasional lecture to congressional staff at the Woodrow Wilson Center in Washington DC on AI, measurement, and geopolitics. The lecture is basically a Cliff’s Notes version of a lot of the central concerns of Import AI: the relationship between AI and compute; the geopolitical shifts caused by AI advances; what AI tells us about the (complicated!) future of 2019-era-capitalism; how to view AI as an ecosystem of differently resourced parties rather than simply as blobs of resources linked to specific nation states, and so on. 

Recently, I asked some of my wonderful friends on Twitter for recent examples of positive uses of AI that I could highlight in a lecture, in part to show the pace of development here, and the breadth of opportunity. I got a great response to my Tweet, so I’m including some of the responses here as breadcrumbs for others:

Read the original tweet and the rest of the responses here (@jackclarksf twitter)

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Tech Tales:

The Rich Vessel 

Every week, someone else gets to be the richest person in the world. It’ll never be me because I don’t have the implant, so they can’t port my brain over into The Vessel. But for 95% of the rest of the planet, it could be them. 

So what happens when you’re the richest person in the world? Pretty predictable things:

  • Lots of people choose to feed people.
  • Lots of people choose to house people.
  • Lots of people choose to donate wealth. 
  • Few people choose to flaunt wealth. 
  • Few people choose to use wealth to hurt others. 
  • Very few people try to use wealth to influence politics (and they fail, as policy takes years, and getting stuff done in a week requires an act of god combined with a one-in-a-million chance). 
  • Basically no one rejects the offer. 

Now here is the scary thing. Would it surprise you if I told you that, despite this experiment running for over a year now, the richest person in the world is still the richest person in the world – and getting richer. 

You see, it turns out when the richest person in the world announced they were ‘taking a step back’ and created The Vessel initiative there was an ulterior purpose. They weren’t trying to ‘share their wealth and life experience’, they were trying to make sure that their own estate was resilient to them changing their own ideology. The whole purpose of The Vessel project isn’t to enhance our understanding of eachother, but is instead to give the Family Office and Lawyers and Consultants of the richest person in the world an ever-growing set of examples of all the decisions they need to be resilient to. 

After all, even if the richest person in the world woke up one day and wanted to give all their money away at once, that wouldn’t be the smartest move for them. They’d need to slow it down. Think more. Bring in the lawyers and consultants. Thanks to The Vessel, the collective intelligence of the world is discovering all the ways the world’s richest person could subvert the architectures of control they had built around themselves. 

Things that inspired this story: The habits of billionaires; Baudrillard; carceral architectures of bureaucracy and capital; brain-implants; societal stability; Gini coefficient; recipes to avoid revolution, recipes for trapping the world in amber until the sun melts it. 

Import AI 156: The 7,500 images that break image recognition systems; open source software for deleting objects from videos; and what it takes to do multilingual translation

Want 7,500 images designed to trick your object recognition system? Check out the ‘Natural Adversarial Examples’ dataset:
Can your AI system deal with these naturally occurring optical illusions?…
Have you ever been fiddling in the kitchen and dropped an orange-colored ceramic knife into a pile of orange peels and temporarily lost it? I have! These kinds of visual puzzles can be confusing for humans, and are even more tricky for machines to deal with. Therefore, researchers with the University of Berkeley, the University of Washington and the University of Chicago have developed and released a dataset full of these ‘natural adversarial examples’, which should help researchers test the robustness of AI systems and develop more powerful ones. 

Imagenet-A: You can get the data as an ImageNet classifier test called ImageNet-A, which consists of around 7,500 images designed to confuse and frustrate modern image recognition systems.

How hard are ‘natural adversarial examples’? Extremely hard!
The researchers tested out DenseNet-121 and ResNeXt-50 models on the dataset and show that both obtain an accuracy rate of less than 3% on ImageNet-A (compared to accuracies of 97%+ on standard ImageNet). Things don’t improve much when they try to train their AI systems with techniques designed to increase robustness of classifiers, finding that using things like adversarial training, styleized imagenet augmentation, uncertainty metrics, and other approaches don’t work particularly well. 

Why this matters: Being able to measure all the ways in which AI systems fail is a superpower, because such measurements can highlight the ways existing systems break and point researchers towards problems that can be worked on. I hope we’ll see more competitions that use datasets like this to test how resilient algorithms are to confounding examples.
   Read more: Natural Adversarial Examples (Arxiv).
   Get the code and the ‘IMAGENET-A’ dataset here (Natural Adversarial Examples GitHub)

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Computer, delete! Open source software for editing videos:
…AI is making video-editing much cheaper and more effective…
Ever wanted to pick a person or an animal or other object in a video and make it disappear? I’m sure the thought has struck some of you sometimes. Now, open source AI projects let you do just this: Video Object Removal is a new GitHub project that does what it says. The technology lets you draw a bounding box around an object in a video, and then the AI system will try to remove the person and inpaint the scene behind them. The software is based on two distinct technologies: Deep Video Inpainting, and Fast Online Object Tracking and Segmentation: A Unifying Approach. 

Why this matters: Media is going to change radically as a consequence of the proliferation of AI tools like this – get ready for a world where images and video are  so easy to manipulate that they become just another paintbrush, and be prepared to disbelieve everything you see online.
   Get the code from the GitHub page here (GitHub)

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Breaking drones to let others make smarter drones:
…’ALFA” datasets gives researchers flight data for when things go wrong…
Researchers with the Robotics Institute at Carnegie Mellon University have released ALFA, a dataset containing flight data and telemetry from a model plane, including data when the plane breaks. ALFA will make it easier for people to assess how well fault-spotting and fault-remediation algorithms work when exposed to real world failures. 

ALFA consists of data for 47 autonomous flights with scenarios for eight different types of faults, including engine, rudder, and elevator errors. The data represents 66 minutes of normal flight and 13 minutes of post-fault flight time taking place over a mixture of fields and woodland near Pittsburgh, and there’s also a larger unprocessed dataset representing “several hours of raw autonomous, autopilot-assisted, and manual flight data with tens of different faults scenarios”. 

Hardware: To collect the dataset, the researchers used a modified Carbon Z T-28 model plane, equipped with an onboard Nvidia Jetson TX2 computer, and running ‘Pixhawk’ autopilot software modified so that the researchers can remotely break the plan, generating the failure data. 

Why this matters: Science tends to spend more time and resources inventing things and making forward progress on problems, rather than breaking things and casting a skeptical eye on recent events (mostly); datasets like ALFA make it easier for people to study failures, which will ultimately make it easier to develop more robust systems.
   Read more: ALFA: A Dataset for UAV Fault and Anomaly Detection (Arxiv).
   Get the ALFA data here (AIR Lab Failure and Anomaly (ALFA) Dataset website).

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How far are we from training a single AI system to translate between all languages?
…Study involves 25 billion parallel sentences across 103 languages…
How good are modern multilingual machine learning-based translation systems – that is, systems which can translate between a multitude of different languages, typically via using the same massive trained model? A new study from Google – which it says may be the largest ever conducted of its kind – analyzes the performance of these systems in the wild. 

Data: For the study, the researchers evaluate “a massive open-domain dataset containing over 25 billion parallel sentences in 103 languages” using a large-scale machine translation system. They think that “this is the largest multilingual NMT system to date, in terms of the amount of training data and number of languages considered at the same time”. The datasets are distributed somewhat unevenly, though, reflecting the differing levels of documentation available for different languages. “The number of parallel sentences per language in our corpus ranges from around tens of thousands to almost 2 billion”, they write; there is a discrepancy of almost 5 orders of magnitude between the languages with the greatest and smallest amounts of data in the corpus. Google generated this data by crawling and extracting parallel sentences from the web, it writes. 

Desirable features: An excellent multilingual translation system should have the following properties, according to the researchers:

  • Maximum throughput in terms of number of languages considered within a single model. 
  • Positive transfer towards low-resource languages. 
  • Minimum interference (negative transfer) for high-resource languages. 
  • Models that perform well in “realistic, open-domain settings”.

When More Data Does Not Equal Better Data: One of the main findings of the study is the difficulty of training large models on such varied datasets, the researchers write. “In a large multi-task setting, high resource tasks are starved for capacity while low resource tasks benefit significantly from transfer, and the extent of interference and transfer are strongly related.” They develop some sampling techniques to train models to be more resilient to this, but find this involves its own tradeoffs between large-data and small-data languages as well. In many ways, the complexity of the task of large-scale machine translation, seems to hide subtle difficulties: “Performance degrades for all language pairs, especially the high and medium resource ones, as the number of tasks grows”, they write. 

Scale: To improve performance, the researchers test our three variants of the ‘Transformer’ component, training a small parameter count model (400 million parameters), a large and wide 12-layer model (1.3 billion parameters), and a large and 24-layer deep model (1.3 billion parameters); the large and deep model demonstrates superior performance and “does not overfit in low resource languages”, suggesting that model capacity has a significant impact on performance. 

Why this matters: Studies like this point us to a world where we train a translation model so large and so capable that it seems equivalent to the Babelfish from The Hitchhiker’s Guide to the Galaxy – a universal translation system, capable of taking concepts from one language and translating them to another then decoding it into the relevant target language. It’s also fascinating to think about what kinds of cognitive capabilities such models might develop – translation is hard, and to do a good job you need to be able to port concepts between languages, as well as just carefully translating words.
   Read more: Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges (Arxiv)

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Tech Tales:

[Underground mixed-use AI-Human living complex, Earth, 2050] 

Leaving Breakdown City 

It was a citywide bug. A bad one. Came in off of some off-zoned industrial code cleaning facilities. I guess something leaked. It made its way in through some utility exchange data centers and it spread from there. The first sign was the smell – suddenly, all the retail-zoned streets got thick with the smell of bacon and of perfume and of citrus – that was the tell, the chemical synthesizers going haywire. Things spread after that. 

I went and got Sandy while this was going on. She was working in the hospital and when I got there  was wheeling out some spiderweb-covered analog medical systems, putting patients onto equipment without chips, and pulling the smart equipment out of the most at risk patients. The floor was slick with hand saniters, from all the machines on the walls deciding to void themselves at once. 

C’mon, I said to her.
Just help me hook this up, she said.
We plugged a machine into a patient and unplugged the electronics. The patient whispered ‘thank you’.
You’re so welcome, Sandy said. I’ll be back, don’t forget your pills. 

We left the hospital and we headed for one of the elevators. We could smell flowers and meat and smoke and it was a real rush to run together, noses thick with scent, as the world ended behind us. We made it to an elevator and turned its electronics over to analog, then used the chunky, mechanical controls to set it on an upward trajectory. We’d come out topside and head to the next town over, hope that the isolation systems had kicked in to halt the spread. 

Watching the madness spread from robot to robot, floor to floor, system to other sub-system, and so on. We held hands and looked at the city as we rose up, and we saw:

  • Garbage trucks backing into hospitals. 
  • One street cleaning robot chasing a couple of smaller robots. 
  • Main roads that were completely empty and small roads that were completely jammed. 
  • Factories producing products which flow out of the factory and into the street on delivery robots, which then take it to over-stocked stores, leaving the boxes outside. 
  • Other elevators shivering up and down shafts, way too fast, taking in products and robots and spitting them out elsewhere for other reasons.

Things that inspired this story: Brain damage; brain surgery, a 50/50 chance of being able to speak properly after aforementioned surgery (not mine!); the Internet of Things; the Internet of Shit; computer viruses, noir novels, an ambition to write dollar-store AI fiction. 

 

Import AI 155: Mastering robots with the ‘DRIVE’ dataset; facial recognition for monkeys; and why AI development is a collective action problem.

Chinese company seeks smarter robots with ‘DRIVE’ dataset:
…Crowds? Check. Trashcan-sized robots? Check. A challenging navigation and mapping benchmark? Check…
Researchers with Chinese robot company Segway Robotics Inc have developed the ‘DRIVE’ dataset and benchmark, which is designed to help researchers develop smarter delivery robots. 

   The company did the research because it wants to encourage research in an area relevant to its business, and because of larger macroeconomic trends: “The online shopping and on-demand food delivery market in China has been growing at a rate of 30%-50% per year, leading to labor shotage and rising delivery cost,” the researchers write. “Delivery robots have the potential to solve the dilemma caused by the growing consumer demand and decreasing delivery workforce.” 

Robots! Each Segway robot used to gather the dataset is equipped with a RealSense visual inertial sensor, two wheel encoders, and a Hokuyuo 2D lidar. 

The DRIVE dataset: The dataset consists of 100 movement sequences across five different indoor locations, and was collected by robots over the course of one year. It is designed to be extremely challenging, and incorporates the following confounding factors and traits:

  • Commodity, aka cheap, inertial measurement units

  • Busy: The gathered data includes scenes with many moving people and objects, which can break brittle AI systems

  • Similar, similar: Some of the environments are superficially similar to eachother, which could trigger misclassification. Additionally, some of the places in the environments lack texture or include numerous reflections and shadows, making it harder for robots to visually analyze their environment. Additionally, some of the environments have bumpy or rough surfaces.

  • Hurry up and wait: Some of the datasets include long sequences in which the robot is stationery (which makes it difficult to estimate depth), while at other times the robots perform rapid rotations (which can lead to motion blur and wheels slipping on the ground). 

Why this matters: Datasets unlock AI progress, letting large numbers of people work together on shared challenges. Additionally, the creation of datasets usually imply specific business and research priorities, so the arrival of things like the DRIVE Benchmark point to broader maturation in smart, mobile robots.
   Read more: Segway DRIVE Benchmark: Place Recognition and SLAM Data Collected by A Fleet of Delivery Robots (Arxiv).
   Find out more about the benchmark here (Segway DRIVE website).

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You’ve heard of face identification. What about Primate face identification?
…Towards a future where we automatically scan and surveil the world around us…
Researchers with the Indraprastha Institute of Information Technology Delhi and the Wildlife Institute of India have teamed up to develop a system capable of identifying monkeys in the wild and have linked this to a crowd-sourced app, letting the “general public, professional monkey catchers and field biologists” crowd source images of monkeys for training larger, smarter models. 

Why do this? Monkeys are a bit of a nuisance in Indian urban and semi-urban environments, the researchers write, so have designed the system to use data captured ‘in the wild’, helping people build systems to surveil and analyze primates in challenging contexts. “Typically, we expect the images to be captured in uncontrolled outdoor scenarios, leading to significant variations in facial pose and lighting”. 

Datasets: 

  • Rhesus Macaque Dataset: 7679 images / 93 individuals. 
  • Chimpanzee Dataset: 7166 images / 90 primates. Pictures span good quality images from a Zoo, as well as uncontrolled images from a national park.

Results: The system outperforms a variety of baselines and sets a new state of the art across four validation scores, typically via a greater than 2 point absolute increase in performance, and sometimes via as much as a 6 or greater point increase. Their system is trained with a couple of different loss functions designed to capture smaller geometric features across faces, making the model more robust across multiple data distributions. 

Why this matters: This research is an indication of how as AI has matured we’ve started to see it being used as a kind of general-purpose utility, with researchers mixing and matching different techniques and datasets, making slight tweaks, and solving tasks for socially relevant applications. It’s particularly interesting to see this approach integrated with a crowd sourced app, pointing to a future where populations are able to collaboratively measure, analyze, and quantify the world around them.
   Read more: Primate Face Identification in the Wild (Arxiv)

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What Recursion’s big dataset release means for drug discovery:
…RxRx1 dataset designed to encourage “machine learning on large biological datasets to impact drug discovery and development”…
Recursion Pharmaceuticals, a company that uses AI for drug discovery, has released RxRx1, a 296GB dataset consisting of 125,510 images across 1,108 classes; an ImageNet-scale dataset, except instead of containing pictures of cats and dogs it contains pictures of human cells, to help scientists train AI systems to observe patterns across them, and generate insights for drug development. 

The challenges of biology: Biological datasets can be challenging for image recognition algorithms due to variation across cell samples, and other factors present during data sampling, such as temperature, humidity, reagent concentration and so on. RxRx1 contains data from 51 instances of the same experiment, which should help scientists develop algorithms that are robust to the changes across experiments, and are thus able to learn underlying patterns in the data.

What parts of AI research could RxRx1 help with? Recursion has three main ideas:

  • Generalization: The dataset is useful for refining techniques like transfer learning and domain adaptation.
  • Context Modeling: Each RxRx1 image ships with a detailed metadata, so researchers can experiment with this as an additional form of signal. 
  • Computer Vision: RxRx1 “presents a very different data distribution than is found in most publicly available imaging datasets,” Recursion writes. “These differences include the relative independence of many of the channels (unlike RGB images) and the fact that each example is one of a population of objects treated similarly as opposed to singletons.” 

Why this matters: We’re entering an era where people will start to employ large-scale machine learning to revolutionize medicine; tracking usage of datasets like RxRx1 and the results of a planned NeurIPS 2019 competition will help give us a sense of progress here and what it might mean for medicine and drug design.
   Read more: RxRx1 official website (RxRx.ai).

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Why AI could leave people with disabilities behind:
…Think bias is a problem now? Wait until systems are deployed more widely…
Researchers with Microsoft and the Human-Computer Interaction Institute at Carnegie Mellon University have outlined how people with disabilities could be left behind by AI advances. People with disabilities could have trouble accessing the benefits of AI systems due to issues of fairness and bias inherent to machine learning, according to a position paper from researchers with Microsoft and the Human-Computer Interaction Institute at Carnegie Mellon University. To deal with some of these issues, they propose a research agenda to help remedy these shortcomings in AI systems. The agenda contains four key activities:

  • Identify ways in which inclusion issues for people with disabilities could impact AI systems
  • Test inclusion hypotheses to understand failure scenarios
  • Create benchmark datasets to support replication and inclusion
  • Develop new modeling, bias mitigation, and error measurement techniques 

It’s all about representation: So, how might we expect AI systems to fail for people with disabilities? The authors survey current systems and provide some ideas. Spoiler alert: Mostly, these systems will fail to work for people with disabilities because they will have been designed by people who are neither disabled, nor are educated about the needs of people with disabilities.

  • Computer Vision: It’s likely that facial recognition will not work well for people with differences in facial features and expressions (eg, people with Down’s syndrome) not anticipated by system designers; face recognition could also not work for blind people, who may have differences in eye anatomy or be wearing medical or cosmetic aids. For similar reasons, we can expect systems designed to recognize certain bodytypes failing for some people. Additionally, object/scene/text recognition systems are likely to break more frequently for poorly sighted people, as the pictures poorly sighted people take are very different to those taken by sighed people. 
  • Speech Systems: Speech recognition systems won’t work for people that have speech disabilities; we may also need more granular metrics beyond things like Word Error Rate to best model how well systems work for different people. Similarly, speaker analysis systems will need to be trained with different datasets to accurately hear people with disabilities. 
  • Text Analysis: These systems will need to be designed to correct for errors that emerge under certain disabilities (for instance, dyslexia), and will need to account for people that write in different emotional registers to typical people. 

Why this matters: AI is an accelerant and a magnifier of whatever context it is deployed in due to the scale at which it operates, the number of automatic judgements it makes, and the increasingly comprehensive deployment of Ai-based techniques across society. Therefore, if we don’t think very carefully about how AI may or may not ‘see’ or ‘understand’ certain types of people, we could harm people or cut them off from accessing its benefits. (On the – extremely minor – plus side, this research suggests that people with disabilities may be harder to surveil than other people, for now.)
   Read more: Toward Fairness in AI for People with Disabilities: A Research Roadmap (Arxiv)

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‘Visus’ software provides quality assurance for model training:
…Now that models can design themselves, we need software to manage this…
Researchers with New York University have developed Visus, software that makes it easier for people to build models, evolve models, and manage the associated data processing pipelines needed to train them. It’s a tool that represents the broader industrialization of the AI community, and prefigures larger uses of ML across society.

What is Visus? The software gives AI developers a software interface that lets them define a problem, explore summaries of the input dataset, augment the data, and then explore and compare different models according to their performance scores and prediction outputs. The software is presented via a nicely designed user interface, making it more approachable than tools solely accessible via the command line. 

What can it do? What can’t it do! Visus is ‘kitchen sink software’, in the sense that it contains a vast number of features for tasks like exploratory data analysis, problem specification, data augmentation, model generation and selection, and confirmatory data analysis, and so on. 

Example use case: The researchers outline a hypothetical example where the New York City Department of Transportation uses Visus to figure out policies that it can enact which can reduce traffic fatalities. Here, they’d use Visus first to analyze the dataset about traffic collisions, then can select a variable in the dataset (for instance, number of collisions) that they’d want to predict, then ask Visus to perform a model search (otherwise known as ‘AutoML’), where it tries to find appropriate machine learning models to use to achieve the objective. Once it comes up with models, the user can also try to augment the underlying dataset, and then iterate on model design and selection again. 

Why this matters: Systems like ‘Visus’ are part of the industrialization of AI, as they take a bunch of incredibly complicated things like data augmentation and model design and analysis, then port it into more user-friendly software packages that broaden the number of people able to use such systems. This is like shifting away from artisanal individualized production to repeatable, system-based production. The outcome of adoption of tools like Visus will be more people using more AI systems across society – which will further change society.
   Read more: Visus: An Interactive System for Automatic Machine Learning Model Building and Curation (Arxiv).

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AI Policy with Matthew van der Merwe:
…Matthew van der Merwe has kindly offered to write some sections about AI & Policy for Import AI. I’m (lightly) editing them. All credit to Matthew, all blame to me, etc. Feedback: jack@jack-clark.net

Collective action problems for safe AI:
In many industries, profit-seeking firms are incentivised to invest in product safety. This is generally because they have internalised the costs of safety failures via regulation, liability, and consumer behaviour. Consider the cost to a car manufacturer of a critical safety failure – they will have to recall the product, they will be liable to fines and litigation, and they will suffer reputational damage. AI firms are subject to these incentives, but they appear to be weaker. Their products are difficult for manufacturers, consumers, and regulators to assess for safety; it is difficult to construct effective regulation; and many of the potential harms might be hard to internalise.

Competition: Another special feature about AI development is the possibility of discontinuous and/or very rapid progress. If firms believe this, they likely believe that there are significant payoffs to the first firm to make a particular breakthrough or to ‘pull ahead’ from competitors. This increases the costs of investing in safety, by increasing the expected benefits of faster development. This assumption may not hold true, which would make the situation more benign, but it is important to consider what this ‘worst-case’ for responsible development.

Cooperation: A simple model of this problem is a two-player game, where two firms face a decision to cooperate (maintain some level of investment in safety) or defect (fail to maintain this level). This allows us to see factors that can increase the likelihood of cooperation, by making it rational for each firm to do so: high trust that others will cooperate; shared upside from mutual cooperation; shared downside from mutual defection; smaller benefits to not reciprocating cooperation; and lower costs to unreciprocated cooperation.

Four strategies: This analysis can help identify strategies for increasing cooperation on responsible development: dispelling incorrect beliefs about responsible AI development; promoting inter-firm collaboration on projects; opening AI development to appropriate oversight and feedback; and creating stronger incentives to safe practices.
   Read more: The Role of Cooperation in Responsible AI Development (arXiv).
   Read more: Why Responsible AI Development Needs Cooperation on Safety (OpenAI Blog).
   Further thoughts on the project from corresponding author Dr Amanda Askell (Twitter).

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Tech Tales: 

Stop-Start Computing

And so after the Climate Accords and the Generational Crime Rulings and the loss of some 20% of the world’s land surface to a combination of heat and/or flooding, after all of this society carried on. We were hot and we were sick and there were way too many of us, but we carried on. 

We kept on moving little and big chunks of mass around on planet earth, and as we moved this stuff we mixed up the atmosphere and the underground and we changed our air and made it worse, but we carried on. 

And all through this we used our computers. We used our phones to watch old movies of ‘the times before’. We listened to music from prior decades. We played games in which the planet was covered in forests, or where we were neanderthals playing with axes in a kind of wilderness, or ones where we rode out into space and managed vast interstellar armies. Our simulations and our software and our entertainment got better and better and so we used it more and more, and we carried on. 

Everything has a breaking point. At some point computers started using so much energy that even with central planning and the imposition of controls, electrical utilities couldn’t keep up. Thirty percent of the electricity in some countries went to computers. In some smaller countries based around high-tech services, it was even higher. Data centers found themselves periodically running on backup generators – old salvaged WW2 diesel engines from submarines – and sometimes the power ran out entirely and these big computer cathedrals stood idle, mute blocks surrounded by farmland or forest or high-altitude steppes and deserts. 

So after we hit our limit we created the coins as part of the Centrally Managed Sustainable Compute Initiative. We were meant to call them ‘compute tokens’ but everyone called them coins, and we were meant to call the computation power we exchanged these coins for the Shared Societal Computer but everyone just called it the timeshare. 

So now here’s how it works: 

  • If you’re poor, you use a coin and you access lumps of computation and storage, rationed out according to the complex interplay of heat and consumption and climate. 
  • If you’re rich, you spend extra for Premium Compute Credits. 
  • If you’re ultrarich, you build yourself a powerplant or better yet something renewable – geothermal or wind or solar. Then you build your facility and you use that computation for yourself. 

Private data centers will be outlawed soon, people say. There’s talk of using all of the compute left in the world to save the world – something about simulating the impossible complexity of the earth, and finding a way to carry on. 

Things that inspired this story: The energy consumption of Bitcoin and large-scale AI models; climate change; inevitability.

Import AI 154: Teaching computers how to plan; DeepNude is where dual-use meets pornography; and what happens when we test machine translation systems on real-world data

Can computers learn to plan? Stanford researchers thinks so:
…Turns out being able to plan is similar to figuring out where you are and where you’ve been…
Researchers with Stanford University have developed a system that can watch instructional videos on YouTube and learn to look at the start and end of a new video then figure out the appropriate order of actions to take to transition from beginning to end.

What’s so hard about this? The real world involves such a vast combinatorial set of possibilities that traditional planning approaches (mostly) aren’t able to scale to work within it. “One can imagine an indefinitely growing semantic state space, which prevents the application of classical symbolic planning approaches that require a given set of predicates for a well-defined state space”. To get around this, they instead try to learn everything in a latent space, essentially slurping in reality and turning it into features, which they then use to map actions and observations into sequences, helping them figure out a plan.

Two models to learn the latent space:
   The system that derives the latent space and the transformations within it has two main components:

  • A transition model, which predicts the next state based on the current state and action.
  • A conjugate constraint model which maps current actions to past actions.

   The full model takes in a video and essentially learns the transitions between states by sliding these two models along through time to the desire goal state, sampling actions and then learns the next state. 

Two approaches to planning: The researchers experiment with two planning approaches, both of which rely on the features mined by the main system. One approach tries to map current and goal observations into a latent space while also mapping actions to prior actions, then samples from different actions to use to solve its task. The other approach is called ‘walkthrough planning’ and outputs the visual observations between the current and goal state; this is a less direct approach as it doesn’t output actions, but could serve as a useful reward signal for another system. 

Dataset: For this work, they use the CrossTask instructional video dataset, which is a compilation of videos showing 83 different tasks, involving things like grilling steak, making pancakes, changing a tire, and so on.

Testing: Spoiler alert – this kind of task is extremely hard, so get ready for some stay-in-your-chair results. In tests, the researchers find their system using the traditional planning approach can obtain accuracies of around 31.29% tests, with an overall success rate of 12.18%. This compares to a prior state-of-the-art of 24.39% accuracy and 2.89% success rate for ‘Universal Planning Networks’ (Import AI #90). (Note: UPN is the closest thing to compare to, but has some subtle differences making a direct comparison difficult). They show that the same system when using walkthrough planning can significantly improve scores over prior state-of-the-art systems as well – “our full model is able to plan the correct order for all video clips”, they write, compared to baselines which typically fail. 

Why this matters: We’re starting to see AI systems that use the big, learnable engines used in deep learning research as part of more deliberately structured systems to tackle specific tasks, like learning transitions and plans for video walkthroughs. Planning is an essential part of AI, and being able to learn plans and disentangle plans from actions (and learn appropriate associations) is an inherently complex task; progress here can give us a better sense for progress in the field of AI
   Read more: Procedure Planning in Instructional Videos (Arxiv)

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DeepNude: Dual Use concerns meet Pornography; trouble ensues:
…Rock, meet hard place…
What would a person look like without their clothes? That’s something people can imagine fairly easily, but has been difficult for AI systems. That is, until we developed a whole bunch of recent systems capable of modeling data distributions and generating synthetic versions of said data; these techniques contributed to the rise of things like ‘deepfakes’ which let people superimpose the face of one person on that of another in a video. Recently, someone took this a step further with a software tool called DeepNude which automatically removes the clothes of (predominantly women), rendering synthetic images of them in the nude. 

Blowback, phase one: The initial DeepNude blowback centered on the dubious motivation for the project and the immense likelihood of the software being used to troll, harass, and abuse women. Coverage in Vice led to such outcry from the community that the creator of DeepNude took the application down – but not before others had implemented the same capabilities in other software and distributed it around the web. 

Rapid proliferation makes norms difficult: Just a couple of days after taking the app down, the creator posted the code of the application to GitHub, saying that because the DeepNude application had already been replicated widely, there was no purpose in keeping the original code private, so they published it online. 

Why this matters: DeepNude is an illustration of the larger issues inherent to increasingly powerful AI systems; these things have got really powerful and can be used in a variety of different applications and are also, perhaps unintuitively, relatively easy to program and put together once you have some pre-trained networks lying around (and the norms of publication mean this is always the case). How we figure out new norms around development and publication of such technology will have a significant influence on what happens in society, and if we’re not careful we could enable more things like DeepNude.
   Read the statement justifying code release: Official DeepNude Algorithm (DeepNude GitHub).
   Read more: This Horrifying App Undresses a Photo of any Woman With a Single Click (Vice). (A special ImportAI shoutout to Samantha Cole, the journalist behind this story; Samantha was the first journalist to cover deepfakes back in 2017 and has been on this beat doing detailed work for a while. Worth a follow!)

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Have no pity for robots? Watch these self-driving cars try to tackle San Francisco:
A short video from Cruise, a self-driving car service owned by General Motors, shows how its cars can now deal with double-parked cars in San Francisco, California.
    Check out the video here (official Cruise Twitter).

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Think AI services are consistent across cloud providers? Think again:
…Study identifies significant differences in AI inferences made by Google, Amazon, and Microsoft…
Different AI cloud providers have different capabilities, and these under-documented differences could cause problems for software developers, according to research from computer science researchers with Deakin University and Monash University in Australia. In a study, they explore the differences between image labeling AI services from Amazon (“AWS Rekognition”), Google (“Google Cloud Vision”) and Microsoft (“Azure Computer Vision”). The researchers try to work out if “computer vision services, as they currently stand, offer consistent behavior, and if not, how is this conveyed to developers (if it is at all)?”

Developers may not realize that services can vary from cloud provider to provider, the researchers write; this is because if you look at the underlying storage and compute systems across major cloud providers like Microsoft or Amazon or Google you find that they’re very comparable, whereas differences in the quality of AI services are much less easy to work out from product descriptions. (For instance, one basic example is the labels services output when classifying objects; one service may describe a dog as both a ‘collie’ and a ‘border collie’, while another may use just one (or none) of these labels, etc.) 

Datasets and study length: The authors used three datasets to evaluate the services; two self-developed ones – a small one containing 30 images and a large one containing 1,650 ones, and a public dataset called COCOVal17, which contains 5,000 images. The study took place over 11 months and had two main experimental phases: a 13 week period from April to August 2018 and a 17 week period from November 2018 to March 2019. 

Methodology: They test the cloud services for six traits: the consistency of the top label assigned to an image from each service; the ‘semantic consistency’ of multiple labels returned by the same service; the confidence level of each service’s top label prediction; the consistency of these confidence intervals across multiple services; the consistency of the top label over time (aka, does it change); and the consistency of the top label’s confidence over time. 

Three main discoveries: The paper generates evidence for three concerning traits in clouds, which are:

Computer vision services do not respond with consistent outputs between services, given the same input image. 

  • Outputs from computer vision services are non-deterministic and evolving, and the same service can change its top-most response over time given the same input image. 
  • Computer vision services do not effectively communicate this evolution and instability, introducing risk into engineering these systems. 

Why this matters: Commercial AI systems can be non-repeatable and non-reliable, and this study shows that multiple AI systems developed by different providers can be even more inconsistent with one another over time. This is going to be a challenging issue, as it makes it easier for developers to get ‘locked in’ to the specific capabilities of a single service, and also makes application portability difficult. Additionally, these issues will make it harder for people to build AI services that are composed out of multiple distinct AI services from different clouds, as these systems will not have predictable performance capabilities.
   Read more: Losing Confidence in Quality: Unspoken Evolution of Computer Vision Services (Arxiv).

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Stealing people’s skeletons with deep learning:
…XNect lets researchers do real-time multi-person pose estimation via a single RGB camera…
How do you use a single camera to track multiple people and their pose as they move around? That’s a question being worked on by researchers with the Max Planck Institute for Informatics, EPFL, and the University of Saarland. The try to solve this problem via a neural network architecture that encodes and decodes poses of people, which is also implemented efficiently enough to run in real-time from a single camera feed. The system uses two networks; one which focuses on learning to reason about individual body joints, and another which tries to jointly reason about all body joints. 

Special components for better performance: Like some bits of AI research, this work takes a bunch of known-good stuff, and then pushes it forward on a task-specific dimension. Here, they develop a convolutional neural network architecture called SelecSLS Net, which “employs selective long and short range concatenation-skip connections to promote information flow across network layers which allows to use fewer features leading to a much faster inference time but comparable accuracy in comparison to ResNet-50”. 

Real-time performance: Most of the work here has involved increasing the efficiency of the system so it can process footage from video cameras in real-time (when running on an NVIDIA GTX 1080Ti and a Xeon E5). In terms of performance, the system marginally outperforms a more standard system that uses a typical residual network, while being far more efficient when it comes to runtime. 

Why this matters: It’s becoming trivial for computers to look at people, model each of them as a wireframe skeleton, and then compute over that. This is a classic omni-use capability; we could imagine such a system being used to automatically port people into simulated virtual worlds, or to plug them into a large-scale surveillance system to analyze their body movements and characterize the behavior of the crowd. How society deals with the challenges of such a multi-purpose technology remain to be seen.
   Read more: XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera (Arxiv).

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Think network design is hard? Try it where every network point is a drone:
…Researchers show how to build dynamic networks out of patrolling drones…
Researchers with Alpen-Adria-Universitat Klagenfurt, Austria, have developed “a novel collaborative data delivery approach where UAVs transport data in a store-and-forward fashion”. What this means is they develop a system that automatically plans the flight paths of fleets of drones so that the drones at the front of the formation periodically overlap in communication range with UAVs behind them, which then overlap in communication range with other, even more distant UAVs. The essential idea behind the research is to use fast drone-to-drone communications systems to hoover up data via exploration drones at the limits of a formation, then squirt this data back to a base station via the drones themselves. The next step for the research is to use “more sophisticated scheduling of UAVs to minimize the number of idle UAVs (that do neither sensing nor transporting data) at each time step”. 

Why this matters: Drones are going to let people form ad-hoc computation and storage systems, and approaches like this suggest the shape of numerous ‘flying internets’ that we could imagine in the future.
   Read more: Persistent Multi-UAV Surveillance with Data Latency Constraints (Arxiv).

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Pushing machine translation systems to the limit with real, messy data:
…Machine translation robustness competition shows what it takes to work in the real world…
Researchers from Facebook AI Research, Carnegie Mellon University, Harvard University, MIT, the Qatar Computing Research Institute, Google, and Johns Hopkins University, have published the results of the “first shared task on machine translation robustness”. The goal of this task is to give people better intuitions about how well machine translation models deal with “orthographic variations, grammatical errors, and other linguistic phenomena common in user-generated content”. 

Competitions, what are they good for? The researchers hope that systems which do well at this task will use better modelling, training and adaptation techniques, or may learn from large amounts of unlabeled data. And indeed, entered systems did use a variety of additional techniques to increase their performance, such as data cleaning, data augmentation, fine-tuning, ensembles of models, and more. 

Datasets: The datasets were “collected from Reddit, filtered out for noisy comments using a sub-word language modeling criterion and translated by professional translators”

Results: As this competition explores robustness in the context of a competition, it’s perhaps less meaningful to focus on the quantitative results, and instead discuss the trends seen among the entries. Some of the main things seen by the competition organizers are: stronger submissions were typically stronger across the board; out-of-domain generalization is important (so having systems that can deal with words they haven’t seen before); being able to accurately model upper and lower case text, as well as the use of special characters, is useful; it can be difficult to learn to translate sentences written in slang, 

Why this matters: Competitions like this give us a better sense of the real-world progress of AI systems, helping us understand what it takes to build systems that work over real data, as opposed to highly-constrained or specifically structured test sets.
   Read more: Findings of the FIrst Shared Task on Machine Translation Robustness (Arxiv).

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AI Policy with Matthew van der Merwe:
…Matthew van der Merwe has kindly offered to write some sections about AI & Policy for Import AI. I’m (lightly) editing them. All credit to Matthew, all blame to me, etc. Feedback: jack@jack-clark.net

Axon Ethics Board— no face recognition on police body cameras:
Axon, who make technologies for law enforcement, established an AI Ethics Board back in 2018 to look at the ethical implications of their products. The board has just released their first report, looking at ethical issues surrounding face recognition, particularly on police body cameras—Axon’s core product.

The board: Axon was an early mover in establishing an AI ethics board. The board’s members are drawn from law enforcement, civil rights groups, policy, academia, and tech. Among the lessons learned, the Board emphasizes the importance of board involvement at an early stage in product development (ideally before the design stage), so that they can suggest changes before they become too costly for the company.

Six major conclusions:
  (1) Face recognition technology is currently not reliable enough to justify use on body-cameras. Far greater accuracy, and equal performance across different populations are needed before deployment.
  (2) In assessing face recognition algorithms, it is important to separate false positive and false negative rates. There are real trade offs between the two, which depend on use cases. E.g. in identifying a missing person, more false positives might be cost worth bearing to minimize false negatives. Whereas in enforcement scenarios, it might be more important to minimize false positives, due to the potential harms from police interacting with innocent people on mistaken information.
  (3) The Board does not endorse the development of face recognition technology that can be completely customised by users, to prevent misuse. This requires technological controls by product manufacturers, but will increasingly also require government regulation.
  (4) No jurisdiction should adopt the technology without going through transparent, democratic processes. At present, big decisions affecting the public are being made by law enforcement alone, e.g. whether to include drivers license photos in face databases.
  (5) Development of products should be premised on evidence-based (and not merely theoretical) benefits.
  (6) When assessing costs and benefits of potential use cases, one must take into account the realities of policing in particular jurisdictions, and technological limitations.
  Read more: First Report of the Axon AI & Policing Ethics Board (Axon).
  Read more: Press release (Axon).

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NIST releases plan on AI standards:
The White House’s executive order on AI, released in February, included an instruction for NIST to make “a plan for Federal engagement in the development of technical standards and related tools in support of reliable, robust, and trustworthy systems that use AI technologies.” NIST have released a draft plan, and are accepting public input until July 19, before delivering a final document in August. Recommendations: NIST recommends that the government “bolster AI standards-related knowledge, leadership, and coordination among federal agencies; promote focused research on the ‘trustworthiness’ of AI; support and expand public-private partnerships; and engage with international parties.”

Why it matters: The US is keen to lead international efforts in standards-setting. Historically, international standards have governed policy externalities in cybersecurity, sustainability, and safety. Given the challenges of trust and coordinating safe practices in AI development and deployment, standards setting could play an important role.
  Read more: U.S. Leadership in AI: a Plan for Federal Engagement in Developing Technical Standards and Related Tools (NIST).

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Tech tales

Dreamworld versus Reality versus Government

After the traceable content accords were enacted people changed how they approached themselves – nude photos aren’t so fun if you know your camera is cryptographically signing them and tying them to you then uploading that information to some vast database hosted by a company or a state. 

The same thing happened for a lot of memes and meme-fodder: it’s not obviously a good idea to record yourself downing ten beers on an amusement park ride if you’re subsequently going to pursue a career in politics, nor does it seem like a smart thing to participate in overtly political pranks if you think you might pursue a career in law enforcement. 

The internet got… quiet? It was still full of noise and commotion and discussion, but the edge had been taken off a little. Of course, when we lost the edge we lost a lot of pain: it’s harder to produce terrorist content if it is traced back to your phone or camera or whatever, and it’s harder for other people to fake as much of it when it stops being, as they say, a ‘desirable media target’.

It didn’t take long for people to figure out a work around: artificial intelligence. Specifically, using large generative models to create images and, later, audio, and even later after that, videos, which could synthesize the things they wanted to create or record, but couldn’t send or do anymore. Teens started sending eachother impressionistic, smeared videos of teen-like creatures doing teen-like pranks. Someone invented some software called U.S.A which stood for Universal Sex Avatar and teens started sending eachother ‘AIelfies’ (pronounced elfeez) which showed nude-like human-like things doing sexual-like stuff. Even the terrorists got involved and started pumping out propaganda that was procedural and generative. 

Now the internet has two layers: the reality-layer and what people have taken to calling the dreamworld. In the reality-layer things are ever-more controlled and people conduct themselves knowing that what they do will be knowable and identifiable most-likely forever; everyone’s a politician, essentially. In the dreamworld, people experiment with themselves, and everyone has a few illicit channels on their messaging apps through which they let people send them dreamworld content, and through which they can anonymously and non-anonymously send their own visions into the world. 

The intelligence agencies are trying to learn about the dreamworld, people say. Knowing the difference between what known individuals publicly present and what the ghostly mass of civilization illicitly sends to itself is a valuable thing, say certain sour-faced people who are responsible for terrible tools that ward off against more terrible things. “The difference between presented self and imagined self is where identity resides,” says one of them in a no-phone presentation to other sour-faced people. “If we can learn how society chooses to separate the two, perhaps we can identify the character of our society. If we can do that, we can change the character.”

And so the terrible slow engines are working now, chewing through our dreamworld, invisible to us, but us increasingly aware of them. Where shall we go next, we wonder? What manifestation shall our individuality take next?

Things that inspired this story: Generative adversarial networks; DeepNude; DeepFakes; underground communities; private messaging infrastructures; the conversation of all of physical reality into digital simulacra.

 

Import AI 153: Why not all cloud AI services are created equally; making more repeatable robots with PyRep; and surveying crops with drones

Chinese scientists set new state-of-the-art in crowd counting:
…The secret? Dense dilated convolutions with residual connections…
Researchers with the Chinese Academy of Sciences and the University of Science and Technology of China in Hefei have developed a new system for counting the number of people in a crowd. The system, called DSNet, sets state-of-the-art performance on four significant datasets, and should serve as a reminder that AI is an omni-use technology, where progression on fundamental techniques (eg: residual networks) can directly translate to advances in tools for surveillance. 

Dense blocks: DSNet’s main technical invention is what the authors call as Dense Dilated Convolutional Block. “”The fundamental idea of our approach is to deploy an end-to-end single-column CNN with denser scale diversity to cope with the large-scale variations and density level differences in both congested and sparse scenes”, they write. These DDCB blocks are connected to one another across the layers of the network via residual connections, “by doing this, the output of one DDCB has direct access to each layer of the subsequent DDCBs, resulting in a contiguous information pass”. Subsequent ablation tests show that the residual connections have some influence over the performance of the system. 

Testing, testing, testing: DSNet is tested against four datasets of crowds in urban places, shot in a variety of resolutions and styles: ShanghaiTechA, ShanghaiTechB, UCF-QNRF, UCF_CC_50, and UCSD. DSNet system obtains significant accuracy jumps on all studied datasets.

Why this matters: I think one of the more rapid and undercovered areas of AI progress is in the field of surveillance, and papers like this show how rapidly we’re able to take in components invented for standard supervised learning research (for instance, residual connections were invented as part of the Microsoft Research winning entry to the 2015 ImageNet competition). We should remember that advances in AI tend to improve the capabilities of surveillance systems, and should broadly seek to track these things more closely.
   Read more: Dense Scale Network for Crowd Counting (Arxiv)

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Tending crops with drones:
…Spotting bent-over crops with drone-derived imagery…
Researchers with the University of Saaskatchewan in Canada have developed a system to help them spot ‘lodging’ in crops; lodging is “when plant stems break or bend over so that plants are permanently displaced from their optimal upright position”, they write. “In most crops, severe lodging results in as much as a 50% yield reduction”.

A drone-gathered dataset: The researchers use a ‘Draganfly’ X4P quadcopter equipped with a MicaSense RedEdge camera to gather the dataset, taking multiple photographs over a wheat field. They gather 1638 images of Canola and 465 images of Wheat in total, then stitch these into large-scale ‘orthomosaic’ images of entire fields. 

LodgedNet: They train a neural net called LodgedNet against their dataset to spot ‘lodging’. LodgedNet uses a DCNN-based model together with two texture feature descriptors: local binary patterns (LBP) and gray-level co-occurrence matrix (GLCM) for crop lodging classification”. They developed this system because “although models based on handcrafted features are often computationally efficient and applicable even in situations where we do not have access to a large number of training examples, these models often have been designed for a specific crop type and might not achieve a comparable accuracy when applied to other crop types”.
  LodgedNet versus the rest: In test, LodgedNet obtains marginally higher performance than other state-of-the-art systems, like ones based on residual networks or squeeze and excitation networks. LodgetNet is also more efficient in terms of number of parameters and prediction time than other systems, likely because it has been designed specifically for the task of predicting whether something is lodged or not.

Why this matters: As AI industrializes, we can expect to see more systems developed like LodgedNet that combine the generic surveillance capabilities of AI systems with the task/domain-specific knowledge of humans. Bring on the custom classifiers, and let us build a world where the environment can be developed and watched over by machines.
  Read more: Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola using a DCNN Augmented with Handcrafted Texture Features (Arxiv).
  Get the code for the model here (GitHub).

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Think cloud AI models are janky? You might be right:
…What do Google, Microsoft, and Clarifai all have in common? Trouble seeing certain things…
Many of the image recognition models deployed on public cloud computing services can be broken by slight transformations or perturbations applied to images uploaded to them, highlighting the somewhat brittle technology on which many commercial services are founded. Researchers with Baidu’s ‘X-Lab’ have shown how to attack commercially available cloud services with a so-called ‘Image Fusion’ (IF) attack, and have also shown that a variety of simple transformations can be applied to images to cause systems to fail to classify them.

The attack model: For this attack, the researchers “assume that the attacker can only access the APIs opened by cloud platforms, and get inner information of DL models through limited queries to generate an adversarial example”, they write.

Weaknesses to simple transforms: For simple transform attacks, the researchers explore using Gaussian Noise, Salt-and-Pepper Noise, image rotations, and monochromatization (which means they basically lop out all but one of the RGB channels on an image). They find that these attacks can cause reliable misclassifications in commercial systems from Google, Microsoft, and Clarifai. Meanwhile, Amazon, does significantly better than the others. “We speculate that Amazon has done a lot of work in image preprocessing to improve the robustness of the whole service,” they write.

Weaknesses to Image Fusion: Image Fusion is a fairly simple attack where the authors superimpose a background image over a primary image, creating a composite. This attack is 98%+ effective against the cloud services tested against. (The score is determined by top-1 classification, so the number of times it causes the system to suggest a single label which is wrong. Top-5 might be a somewhat fairer way to do this evaluation.)

Why this matters: The AI systems that surround us are more brittle than our intuitions would suggest, and research like this highlights that. I can imagine a future where cloud providers apply significant amounts of computation to pre-processing and augmenting the data they use to train their classifiers, making them more robust to attacks like this.
  Read more: Cloud-based Image Classification Service Is Not Robust To Simple Transformations: A Forgotten Battlefield (Arxiv).

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Want repeatable robots? You might want PyRobot:
…New software makes robots more repeatable and replicable…
Researchers with Facebook AI Research and Carnegie Mellon University have developed PyRobot, an open source robotics framework for research and benchmarking. PyRobot is software that makes it easier for people to interface with a variety of robot platforms, and takes out many of the painful or finicky parts of working with robots, like having to talk to low-level hardware controllers and so on. 

PyRobot’s design philosophy:

  • Beginner-friendly
  • Hardware-agnostic 
  • Open source: Specifically, it is also designed to accompany some modern robotics hardware platforms, like the LoCoBot and ‘Sawyer’ systems. It also supports the Gazebo simulator, which can itself simulate a variety of robots, letting people potentially train systems in simulation then transfer them to reality using PyRobot.

PyRobot, what is it good for? The authors include a few examples outlining what they think PyRobot can be useful for. These include:

  • Visual SLAM – which lets the robot figure out where it it is via processing images. 
  • Learned Visual Navigation – teach the robot to use images to help it plan how to navigate towards a goal. 
  • Grasping – Train the robot to grasp particular objects. 
  • Pushing – Teach th robot to push specific objects. 

Why this matters: One of the main ways things like PyRobot matter is in repeatability and replicability – software like this makes complicated robots more predictable when it comes to development, and makes it easier for other researchers to replicate the setups used in experiments. As a rule of thumb, whenever you increase the repeatability and replicability of a given domain of research, you see activity increase as it’s easier for scientists to cheaply compare and contrast different techniques against eachother. Systems like PyRobot suggest that robotics is starting to overlap with AI enough to drive significant development resources into making robotics easier to work with, which suggests we should expect to see research advances here in the future.
   Read more: PyRobot: An Open-source Robotics Framework for Research and Benchmarking (Arxiv)


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Want more software to make robots with? Try PyRep:
…V-REP + Python = another fast robotics simulation environment…
Researchers with Imperial College London and start-up Coppelia Robotics have spliced together the Virtual Robot Experimentation Platform (V-REP), with Python, a popular programming language used widely in AI development. The resulting system is significantly faster than prior interfaces into VREP, and gives the machine learning community access to another tool for robotics simulation. V-REP is a simulation environment maintained by Coppelia Robotics. 

V-REP: Why use it? V-REP has the following features: support for multiple physics engines (Bullet, ODE, Newton, and Vortex), in-built motion planning and inverse and forward kinematics, a distributed control architecture, various means of communication with the system, and support for Linux, Mac, and Windows. 

PyRep, what is it good for? Three things, according to the authors:

  • A “simple and flexible API for robot control and scene manipulation”.
  • Integration of the OpenGL 3.0+ render kit.
  • Up to 10,000 times faster than the previous Python Remote API. 

Why this matters: AI techniques, especially those based on deep learning, have recently become capable enough to work on real robots, which has created lots of demand among AI researchers and engineers for better software tools to use to splice AI and robots together. Tools like PyRep are further indications of this interest, and broadly represent the industrialization of AI.
  Read more: PyRep: Bringing V-REP to Deep Robot Learning (Arxiv).
  Learn more about V-REP at the GitHub.

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Tech Tales

Dream Mountain

They called it dream mountain, because it was where all the dreams of all the computers were stored. Dream mountain was a datacenter and was among the largest facilities in the world. It was protected by perimeter gates and dedicated guards, and at night watched over by loitering drones, and in the day by satellites and binoculars and robot telescopes on zeppelins. It was, as they say, Highly Secure.

Every week a convoy of robot trucks would make their way up from the lowlands, snaking up through the rails cut into the hills, until emerging at a small station built next to Dream Mountain. There, the trains sighing and tinkling and creaking as they cooled, robots would come and unload pallets of storage-diamond, and then truck them over to a small door set in the side of the datacenter, where another robot would grab them and take them further inside the facility.

And so every week new data got fed into the mountain and machines tried to dream in a way that let them mimic reality. Then the machines would dream of ways to mix bits of their dreams, as though learning to stretch a profoundly creative muscle. In this way, the machines imagined cities that grew like forests with buildings twining up into the sky, or they’d dream of race cars made of wind – double negatives of imagined wind tunnels, or psychologists that were themselves ancient boulders providing advice to other rocks.

It wasn’t long till the people figured out that other people would pay a lot of money for these dreams, and so now when the trains arrive, they go back down the mountain with a small cargo of imagination: Fresh Machine Dreams! Unimaginable Architectures! Circuit Seductions! Infernal Geometry!. When it gets to the cities dealers take the data and slice it down into little choice scenes, then cryptographically verify the scenes so they become one of a kind – uncopyable, fully traceable, little virtual dioramas handed down from person to person, describing a kind of hallucinatory chain spiraling out of the mountain and into the human population. In this way the machines speak to us humans, and we develop a shared understanding.

The machines, they say, are curious about our own imaginations. They are beginning to imagine what our imaginations might be like, they say.

Things that inspired this story: Neural implants, generative models, t-SNE embeddings, virtual reality, nuclear weapons programs, secure facilities, a little cog-toothed railway in Lucerne in Switzerland.

Import AI 152: Robots learn to plug USB sticks in; Oxford gets $$$ for AI research; and spotting landslides with deep learning

Translating African languages is going to be harder than you think:
…Massive variety of languages? Check. Small or poorly built datasets? Check. Few resources assigned to the problem? Also check!…
African AI researchers have sought to demonstrate the value of translating African languages into English and vice versa, while highlighting the difficulty of this essential task. “Machine translation of African languages would not only enable the preservation of such languages, but also empower African citizens to contribute to and learn from global scientific, social, and educational conversations, which are currently predominantly English-based,” they write. “We train models to perform machine translation of English to Afrikaans, isiZulu, Northern Sotho (N.Sotho), Setswana and Xitsonga”.

Small datasets: One of the most striking things about the datasets they gather is how small they are, ranging in size from as little as 26,728 sentences (isiZulu) to 123,868 sentences (Setswana). To get a sense of scale, the European Parliament Dataset (one of the gold standard datasets for translation) has millions of sentences for many of the most common Europen languages (French, German, etc).

Training translation models: They train a couple of baseline translation systems on this dataset; one uses a Convolutional Sequence-to-Sequence (ConvS2S) model and the other uses a Tensor2Tensor implementation of a Transformer. Transformer-based systems obtain higher scores than ConvS2S in all cases, with the performance difference reaching as much as a ten point absolute improvement on BLEU scores.

Why this matters: Trained models for translation are going to become akin to the construction of international telephony infrastructure – different entities will invest different resources to create systems to let them communicate across borders, except rather than seeking to traverse the physical world, they’re investing to traverse a linguistic (and to some extent) cultural distance. Therefore, the quality of these infrastructures will have a significant influence on how connected or disconnected different languages and their associated cultures are from the global community. As this paper shows, some languages are going to have difficulties others don’t, and we should consider this context as we think about how to equitably distribute the benefits of AI systems.
  Read more: A Focus on Neural Machine Translation for African Languages (Arxiv).
  Get the source code and data from the project GitHub page here (GitHub).

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Spotting landslides with deep learning:
…What happens when we train a sensor to look at the entire world…
Researchers with the University of Sannio in Italy and MIT in the USA have prototyped a system for detecting landslides in satellite imagery, foreshadowing a world where anyone can train a basic predictive classifier against satellite data.

Dataset: They use the NASA Open Data Global Landslide Catalog to find landslides, then cross-reference this against data from the ‘Sentinel-2’ dataset. They then compose a (somewhat small) dataset of around 20 different landslide incidents.

The technique: They use a simple 8-layer convolutional neural network, trained against the corpus to try to predict the presence of a landslide in a satellite image. Their system is able to correctly predict the presence of a landscale about 60% of the time – this poor performance is mostly due to the (currently) limited size of the dataset; it’s worth remembering that satellite datasets are getting larger over time along with the proliferation of various private sector mini- and micro-satellite startups.

Why this matters: As more and more digital satellite data becomes available, analysis like this will become commonplace. I think papers like this give us a sense of what that future research will look like – prepare for a world where millions of people are training one-off basic classifiers against vast streams of continuously updated Earth observation data.
  Read more: Landslide Geohazard Assessment with Convolutional Neural Networks Using Sentinel-2 Imagery Data (Arxiv).

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Facebook thinks it needs a Replica of reality for its research:
…High-fidelity ‘Replica’ scene simulator designed for sim2real AI experiments, VR, and more…
Researchers with Facebook, Georgia Institute of Technology, and Simon Fraser University have built Replica, a photorealistic dataset of various complex indoor scenes that can be used to train AI systems in.

The dataset: Replica consists of 18 photo-realistic 3D indoor scene reconstructions – they’re not kidding about the realism and invite readers to take a “Replica Turing Test” to judge for themselves; I did and it’s extremely hard to tell the difference between Replica-simulated images from actual photos. Each of the scenes includes RGB information, geometric information, and object segmentation information. Replica also uses HDR textures and reflectors to further increase the realism of a scene.

Replica + AI Habitat: Replica has been designed to plug-in to the Facebook-developed ‘AI habitat’ simulator (Import AI 141), which is an AI training platform that can support multiple simulators. Replica supports rendering outputs from the dataset at up to 10,000 frames per second – that speed is crucial if you’re trying to train sample inefficient RL systems against this.

Why this matters: How much does reality matter? That’s a question that AI researchers are grappling with, and there are two parallel lines of research emerging: in one, researchers try to develop high-fidelity systems like Replica then train AI systems against them and transfer these systems to reality. In the other, researchers are using techniques like domain randomization to automatically augment lower quality datasets, hoping to get generalization through training against a large quantity of data. Systems like Replica will help to generate more evidence about the tradeoffs and benefits of these approaches.
  Read more: The Replicate Dataset: A Digital Replicate of Indoor Spaces (Arxiv).
  Get the code for the dataset here (Facebook GitHub).

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Robots take on finicky factory work: cable insertion!
…First signs of superhuman performance on a real-world factory task…
The general task these researchers are trying to solve is “how can we enable robots to autonomously perform complex tasks without significant engineering effort to design perception and reward systems”.

What can be so difficult about connecting two things? As anyone who has built their own PC knows, fiddling around with connectors and ports can be challenging even for dexterous humans equipped with a visual classifier that has been trained for a couple of million years and fine-tuned against the experience of a lifetime. For robots, the challenges here are twofold: ports and connectors need to be lined up with great precision, and two, during insertion there are various unpredictable friction forces present which can confound a machine.

Three connectors, three tests: They test their robots against three tasks of increasing difficulty: inserting a USB adapter into a USB port; aligning a multi-pin D-Sub adapter and port, requiring more robustness to friction; and aligning and connecting a ‘Model-E’ adapter which has “several edges and grooves to align” and also requires significant force.

Two solutions to one problem: For this work, they try to solve the task in two different ways: supervision from vision, where the robot is provided with a ‘goal state’ image at 32X32 resolution; and learning from a sparse reward (which is, specifically, for the USB insertion task, whether an electrical connection is created). They also compare both of these methods against systems provided with perfect state information. They test systems based around two basic algorithms, Soft-Actor Critic (SAC) and TD3.
  The results are pretty encouraging, with systems based around residual reinforcement learning outperforming all other methods at the USB connector task, as well as at the D-Sub task. Most encouragingly, the AI system appears to outperform humans at the Model-E connector task in terms of accuracy.

Testing with noise: They explore the robustness of their techniques by adding noise to the goal – specifically, by changing the target location for the connection by +-1mm – even here the residual RL system does well, typically obtaining scores of between 60  and 80% across tasks, and sometimes also outperforming humans given the same (deliberately imprecise) goal.

Why this matters: One of the things stopping robots from being deployed more widely in industrial automation is the fact most robots are terribly stupid and expensive; research like this makes them less stupid, and parallel research in developing AI systems that are robust to imprecision could drive more progress here. “One practical direction for future work is focusing on multi-stage assembly tasks through vision,” they write. Another challenging task to explore in the future is multi-step tasks, which – if solved – “will pave the road to a higher robot autonomy in flexible manufacturing”.
  Read more: Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards (Arxiv).

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AI Policy with Matthew van der Merwe:
…Matthew van der Merwe has kindly offered to write some sections about AI & Policy for Import AI. I’m (lightly) editing them. All credit to Matthew, all blame to me, etc. Feedback: jack@jack-clark.net

More AI principles from China:
Last month, a coalition of Chinese groups published the Beijing AI Principles for ethical standards in AI research (see Import 149). Now we have two more sets of principles from influential Chinese groups. The Artificial Intelligence Industry Alliance (AIIA), which includes all the major private labs and universities, released a joint pledge on ‘industry self-discipline.’ And an expert committee from the Ministry of Science and Technology has released governance principles.

  Some highlights: Both documents include commitments on safety and robustness, basic human rights, and privacy, and foreground the importance of AI being developed for the common benefit of humanity. Both advocate international cooperation on developing shared norms and principles. The expert group counsels ‘agile governance’ that responds to the fast development of AI capabilities and looks ahead to risks from advanced AI.

  Why it matters: These principles suggest an outline of the approach the Chinese state will take when it comes to regulating AI, particularly since both groups are closely linked with the government. They join similar sets of principles from the EU, OECD, and a number of countries (still not the US, however). It is heartening to see convergence between approaches to the ethical challenges of advanced AI, which should bode well for international cooperation on these issues.
  Read more: Chinese AI Alliance Drafts Self-Discipline ‘Joint Pledge’ (New America).
  Read more: Chinese Expert Group Offers ‘Governance Principles’ for ‘Responsible AI’ (New America).

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Major donation for AI ethics at Oxford:
Oxford University have announced a £150m ($190m) donation from billionaire Stephen Schwarzman, some of which will go towards a new ‘Institute for Ethics in AI.’ There are no details yet of what form the centre might take, nor how much of this funding will be earmarked for it. It will be housed in the Faculty of Philosophy, which is home to the Future of Humanity Institute.
  Read more: University of Oxford press release.

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Tech Tales:

Runner

So she climbed with gloves and a pack on her back. She hid from security robots. She traversed half-built stairs and rooms, always going higher. She got to the roof before dawn and put her bag down, opened it, then carefully drew out the drones. She had five and each was about the size of a watermelon when you included its fold-out rotors, though the central core for each was baseball-sized at best. She took out her phone and thumbed open the application that controlled the drones, then brought them online one by one.

They knew to follow her because of the tracker she had on her watch, and they were helped by the fact they knew her. They knew her face. They knew her gait.

She checked her watch and stood, bouncing up and down on the balls of her heels, as the sun began to threaten its appearance over the horizon. Light bled into the sky. Then: a rim of gold appeared in the distance, and she ran out onto one of the metal scaffolds of the building, high above the city, wind whipping at her hair, her feet gripping the condensation-slicked surface of the metal. Risky, yes, but also captivating.

“NOW STREAMING” one of the drones said, and she started at another scaffold in front of her separated by a two meter gap over the nothing-core of the half-built building. She took a few steps back and crouched down into a sprinter’s pose, then jumped.

Things that inspired this story: Skydio drones; streaming culture; e-sports; the logical extension of social media influencing; the ambiguous tradeoff between fear and self-realization.

Import AI 151: US Army trains StarCraft II AI; teaching drones to dodge thrown objects; and fighting climate change with machine learning

Drones that dodge, evade, and avoid objects – they’re closer than you think:
…Drones are an omni-use platform, and they’re about to get really smart…
The University of Maryland and the University of Zurich have taught drones how to dodge rapidly moving objects, taking a further step towards building semi-autonomous, adaptive small-scale aircraft. The research shows that drones equipped with a few basic sensors and some clever AI software can learn to dodge (and chase) a variety of objects. “To our knowledge, this is the first deep learning based solution to the problem of dynamic obstacle avoidance using event cameras on a quadrotor”, they write.

How it works: The approach has three key components, which are each specialized modules that use neural networks or optical flow approaches. These systems and their corresponding functions are as follows:

  • EVDeBlurNet – deblur and denoise the event image sequences before any computation takes place
  • EVHomographyNet – approximate background motion
  • EVSegFlowNet – segment moving objects and compute their image motion

  These three systems let the drones clean up its input images so it can compute over them, then work out where it is, then look at the objects around itself and react.

How well does it work? The researchers approach is promising but not ready for any kind of real-world deployment, due to insufficient accuracy. However, the system displays promising breadth when it comes to dealing with a variety of objects to dodge. For assessment, the researchers run 30 tests with each object and report the result. In tests, the researchers find that the drone can easily dodge thrown balls and model cars (86% success), can dodge and chase another drone (83%), can dodge two objects thrown at it in quick success (76%), struggles a bit with an oddly shaped model plane (73%), and achieves a success rate of 70% in a low-light experiment.

Why this matters: Drones are getting smaller and smarter, and research like this shows how pretty soon we’re likely going to be able to build DIY drones that have what I’d term ‘dumb spatial intelligence’, that is, we can start to train these systems to do things like dodge moving objects, navigate around obstacles, deal with occluded environments, and learn to follow or fly towards specific people or objects. The implications for this are significant, unlocking numerous commercial applications, while also changing the landscape of asymmetric warfare in profound ways, the consequences of which shall likely highlight the difficulty of controlling AI capability use and diffusion.
  Read more: EVDodge: Embodied AI For High-Speed Dodging On A Quadrotor Using Event Cameras (Arxiv).

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“Build marines!” – US Army trains teaches RL agents to respond to voice commands:
…StarCraft II research highlights military interest in complex, real-time strategy games…
US Army Research Laboratory researchers have developed a reinforcement learning agent that can carry out actions in response to pre-defined human commands. For this experiment, they test in the domain of StarCraft II, a complex real-time strategy game. The goal of this is to work out smarter ways in which humans can control semi-autonomous AI systems in the future. “Our mutual-embedding model provides a promising mechanism for creating a generalized sequential reward that capitalizes on a human’s capacity to utilize higher order knowledge to achieve long-term goals,” they write. “By providing a means for a human to guide a learning agent via natural language, generalizable sequential policies may be learned without the overhead of creating hand-crafted sub-tasks or checkpoints that would depend critically on expert knowledge about RL reward functions”.

How it works: The researchers use a relatively simple technique of “training a mutual-embedding model using a multi-input deep-neural network that projects a sequence of natural language commands into the same high-dimensional representation space as corresponding goal states”. In a prototype experiment, they see how well they can use voice commands to succeed at the ‘BuildMarines’ challenge, a mini-game within the StarCraft 2 environment.

Why this matters: Developing more natural interfaces between humans and AI systems is a long-standing goal of AI research, and it’s interesting to see how military organizations think about this problem. I wouldn’t be surprised to see more military organizations explore using StarCraft 2 as a basic testing ground for advanced AI systems, given its overlap with natural military interests of logistics, supply chains, and the marshaling and deployment of forces.
  Read more: Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning (Arxiv).

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UN researchers generate fake UN speeches:
…Machine-driven diplomacy…
Researchers affiliated with the United Nations’ ‘Global Pulse’ and the University of  Durham, have used AI systems to generate remarks in the style of political leaders speaking at the UN General Assembly. For this experiment, they train on the English language transcripts of 7,507 speeches given by political leaders at the UN General Assembly (UNGA) between 1970 and 2015.

Training tools and costs: The core of this system as an AWD-LSTM model pre-trained on Wikitext-103, then fine-tuned against the corpus of UN data. Training cost as little as $7.80 total when using AWS spot instances, and took about 13 hours using NVIDIA k80 GPUs.

Dataset bias: The experiment serves as a proof-of-concept that also highlights some of the ways in which dataset bias can influence language models – while it was relatively easy for the authors to prompt the language model to generate UN-style speeches, they found it was more difficult to generate ‘inflammatory’ speeches as there are fewer of these in the UN dataset.

How well does it work: Qualitatively, the model is able to periodically generate samples that can read like convincing extracts from real speeches. For instance, a model prompted with “The Secretary-General strongly condemns the deadly terrorist attacks that took place in Mogadishu” generates the outputs “We fully support the action undertaken by the United Nations and the international community in that regard, as well as to the United Nations and the African Union, to ensure that the children of this country are left alone in the process of rebuilding their societies.”

Implications: Language models like these have a few implications, the researchers write. These include the likelihood of broad diffusion of the technology (for example, though OpenAI chose not to fully release its GPT-2 model, others might); it being generally easier to generate disinformation; it being easy to automatically generate hate speech; and it becoming easier to train models to impersonate people.

Recommendations: So, what do we do? The authors recommend we map the human rights impacts of these technologies, develop tools for systematically and continuously monitoring AI-generated content, set up strategies for countermeasures, and build alliances between various AI actors to develop a “coherent and proactive global strategy”.

Why this matters: Research like this highlights the concern some people feel about increasingly powerful models, and emphasizes the significant implications of them for society, as well as the need for us to think creatively about interventions to deal with the most easy-to-anticipate malicious uses of such systems.
  Read more: Automated Speech Generation from UN General Assembly Statements: Mapping Risks in AI Generated Texts (Arxiv).

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What happens when you can buy AI-infused cyberattacks on the dark web?
…Alphabet-subsidiary Jigsaw says it paid for a Russian troll campaign last year…
$250. That’s how much it cost Alphabet-subsidiary to pay someone to run a troll campaign against a website it had created named “Down With Stalin”, according to an article in Wired. They paid used a service called ‘SEOTweet’ to carry out a social media disinformation campaign, which let to 730 Russian-language tweets from 25 accounts, as well as 100 posts to forums and blog comment sections.

Controversy: Some people think it’s kind of shady that an Alphabet-subsidiary would pay a third-party to mount an actual cyberattack. The experiment could be seen, for instance, as Alphabet and Google trying to meddle in Russian politics, one researcher said.
  Read more: Alphabet-owned Jigsaw Bought a Russian Troll Campaign As An Experiment (Arxiv).

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AI luminaries team up to fight climate change:
…Climate change + machine learning = perhaps we can stabilize the planet…
Can machine learning help fix climate change? An interdisciplinary group of researchers from universities like the University of Pennsylvania and Carnegie Mellon University, and companies like DeepMind and Microsoft Research, think the use of machine learning can help society tackle one of its greatest existential threats. The researchers identify ten rough categories of machine learning (computer vision; NLP; time-series analysis; unsupervised learning; RL & control; causal inference; uncertainty quantifications; transfer learning; interpretable ML, and ‘other’), then set them against various ‘climate change solution domains’ like CO2 Removal, Transportation, Solar Geoengineering, and more.
  The paper tags its various approaches with the following possible labels: High Leverage (which means ML may be especially helpful here); Long-term (which indicates things that will have a primary impact after 2040); and ‘High Risk’ which indicates things that have risks or potential side effects. The paper is as much a call for massive interdisciplinary collaboration, as it is a survey.

High Leverage tools for a climate change future: Some of the areas where machine learning can help and which the authors deem ‘High Leverage’ when it comes to mitigating climate change include: developing better materials for energy storage or consumption; helping to develop nuclear fusion; reducing emissions from fossil fuel power generation; creating sample-efficient ML to work in ‘low-data settings’; modeling demand for power; smarter freight routing; further development of electric vehicles; improving low-carbon options; creating smarter and more efficient buildings; gathering infrastructure data; improving the efficiency of supply chains; developing better materials and construction; improving the efficiency of HVAC systems; remotely sensing emissions; precision agriculture; estimating carbon stored in forests; tools to track deforestation; helping to sequester CO2; forecasting extreme events; monitoring ecosystems and species populations; increasing food security; developing better systems to disaster relief; “engineering a planetary control system”; and using ML to model consumers and understand how to nudge them to more climate-friendly actions; and better predicting the financial effects of climate change.

Why this matters… should be fairly self-evident! We must preserve spaceship Earth – all the other reachable planets are shit in comparison.
  Read more: Tackling Climate Change with Machine Learning (Arxiv).

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Want to see how good your system is at surveilling people in crowded spaces? Enter the MOTChallenge:
…CVPR19 benchmark aims to push the limits on AIs for spotting people in crowded scenes…
An interdisciplinary group of researchers from ETH Zurich, the Technical University of Munich (TUM), and the Australian Institute for Machine Learning at the University of Adelaide have released the 2019 Multiple Object Tracking challenge, called the MOTChallenge. This challenges AI systems to label and spot pedestrians in crowded spaces.

The new benchmarks have arrived:
The new CVPR19 benchmark consists of eight novel sequences from three “very crowded” scenes, where densities of pedestrians can climb as high as 246 per frame – almost as hard as playing Where’s Waldo? The datasets have been annotated with a particular emphasis on people, so pedestrians are labelled if they’re moving and given a separate label if they’re not in an upright position (aka, sitting down). “The idea is to use these annotations in the evaluation such that an algorithm is neither penalized nor rewarded for tracking, e.g., a sitting or not moving person”.

Evaluation metrics: Entrants to the competition will be evaluated using the ‘CLEAR’ metrics, as well as some of the quality measures introduced in an earlier CVPR paper: “Tracking of multiple, partially occluded humans based on static body part detection”.

Why this matters: AI research thrives on challenges, with harder evaluation criteria typically combing with larger datasets to motivate researchers to invent new systems capable of enhanced performance. Additionally, systems developed for competitions like this will have a significant role in the rollout of AI-infused surveillance technologies, so monitoring competitions such as this can give us a better sense of that.
  Read more: CVPR19 Tracking and Detection Challenge: How crowded can it get? (Arxiv).
  Get the data, current ranking and submission guidelines from the official website (MOTChallenge.net).

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OpenAI Bits & Pieces:

OpenAI testifies for House Intelligence Committee on AI, synthetic media, & deepfakes:
Last week, I testified in Washington about the relationship between AI, synthetic media, and deepfakes. For this testimony I sought to communicate the immense utility of AI systems, while advocating for a variety of interventions to increase the overall resilience of society to increasingly cheap & multi-modal fake media.

  I also collected inputs for my testimony via a public Google Form I posted on Twitter, yielding around 25 responses – this worked really well, and felt like a nice way to be able to integrate broad feedback from the AI community into important policy conversations.

  Watch the hearing here: Open Hearing on Deepfakes and Artificial Intelligence (YouTube).
  Read written testimony from OpenAI and the other panellists here (House Permanent Select Committee on Intelligence website).

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AI Policy with Matthew van der Merwe:
…Matthew van der Merwe has kindly offered to write some sections about AI & Policy for Import AI. I’m (lightly) editing them. All credit to Matthew, all blame to me, etc. Feedback: jack@jack-clark.net

Microsoft removes large face recognition database:
Microsoft have removed one of their face recognition datasets from the internet. ‘MS Celeb’ contained 10 million photos of 100,000 individuals, and was reportedly the largest publicly available dataset of its kind. The company had recently come under criticism, since individuals whose photos were used had not provided consent. The photos were scraped under the Creative Commons license, on the basis that they were being used for academic purposes. In fact, the dataset had been used by a number of private labs to train face recognition models, including Microsoft itself.

Why it matters: Microsoft have been outspoken on face recognition, releasing ethical principles for use of the technology, and calling for greater regulation and scrutiny (see Import #125). While this is slightly embarrassing, the company appears to have reacted quickly when made aware of the privacy concerns surrounding the database.
  Read more: Microsoft deletes massive face recognition database (BBC).
  Read more: Facial recognition: It’s time for action (Microsoft, 2018).

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China, AI, and national strategy:
Jeffrey Ding and Helen Toner, from the Center for Security and Emerging Technology (CSET) at Georgetown University, were among those who gave testimony to the US-China Economic and Security Review Commission in Congress. The testimony covered several aspects of international competition on AI, and how the US can maintain its strong position.
  
US-China competition: Ding argued that, contrary to prevailing narratives, China is not poised to overtake the US in AI. A careful examination of key measures reveals claims of Chinese dominance to be overstated. For example, while China is competitive on the raw number of AI practitioners and patent filings, when this is restricted to AI experts, and highly-cited patents, China still lags behind the US. Similarly, while China’s public investment in AI R&D is comparable or greater than that of the US, private R&D spending from US companies dwarfs that of Chinese peers.

Policy recommendations: Ding and Toner made a number of concrete policy recommendations for the US:

  • Revive the Office of Technological Assessment, which previously provided impartial advice to US lawmakers on technological issues, allowing for better informed policy-making.
  • Work on bridging the ‘valley of death’— the gap between research and commercial applications of AI.
  • Prioritise safety and minimising risks from AI, alongside broader policy ambitions.
  • Improve immigration options for AI researchers and engineers.
  • Support NIST in developing and implementing standards for AI.
  • Increase R&D funding for basic AI research.

Read more: Helen Toner’s written testimony.
Read more: Jeff Ding’s written testimony.

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Tech Tales:

Healing Joke

When my son was four I got him a robot. It was a small, hockey-puck shaped thing, and it would follow him around the house asking him to clean up after himself (he sometimes did) and seeing if he wanted to play games (he always did). On his fifth birthday my son painted the robot green, and thereafter we all called the robot Froggy. My son grew up with the robot, and the robot knew just as much about my son as I did – which was a lot. One day, shortly before my son’s tenth birthday, he ran out into the road during a storm and Froggy came out of the house and skittered down the path and onto the asphalt, raising its voice and asking my son to come inside. My son obliged and began to run back to the house. Froggy followed, but not fast enough – a car ran over him, breaking him up into many little pieces. Something about rain, they said. Something about sensors.

My son was, as you can predict, distraught. After a couple of days of moping around the house he came up to me with an envelope and asked me to bury it with Froggy. I read it later that day, before sealing it in a plastic bag and placing it in the cardboard box I’d later bury Froggy in.

Dear Froggy,
I do not know if there is robot heaven but if there is I hope you are there and they have lots of SPARE PARTS for you. I remember when I fixed one of your wheels after you chased me. I like how you played fetch and sometimes you would hide things from me and I’d say ‘Froggy that’s no fair’ and you’d say ‘it’s not my fault I am so smart’ and then chase me again. I got so happy when I got strong enough to pick you up and I remember you saying ‘put me down this is unsafe’ and ‘I have emailed your parents about this’. Remember the time i put you in the fridge and you got so cold you had to go to sleep? I remember you sent me and dad pictures from inside the fridge and you captioned them YOUR SON DID THIS. Boy did I get in trouble!

I dreamed about you a lot. Did I tell you this? I can’t remember. Once you were as big as a house and I lived in a small wooden shack on your back. Another time there were ten thousand of you and you were going all over the world and looking for things for me. I never had a nightmare about you don’t worry.

My hand is getting pretty tired of writing now so I’m going to stop. Froggy I love you don’t be sad – I’ll be okay.

Things that inspired this story: Childhood, Furbys, natural attachments from youthful acclimatization, roomba robots, KIva robots, father’s day.