Import AI: Issue 29: neural networks crack quantum problem, fingernail-sized AI chips, and a “gender” classifier screwup
It takes a global village to raise an AI… a report titled ‘Advances in artificial intelligence require progress across all of computer science” (PDF) from the computing community consortium identifies several key areas that should be developed for AI to thrive: computing systems and hardware, theoretical computer science, cybersecurity, formal methods, programming languages, and human-computer interaction…
…better support infrastructure will speed the rate at which developers embrace AI. For example, see this Ubuntu + AWS + AI announcement from Amazon: the “AWS Deep Learning AMI for Ubuntu” will give developers a pre-integrated software stack to run on its cloud, saving them some of the tedious, frustrating time they usually spend installing and configuring deep learning software.
…Baidu’s AI software PaddlePaddle now supports Kubernetes, making it easier to run the software on large clusters of computers. Kubernetes is an open source project based on Google’s internal ‘Borg’ and ‘Omega’ cluster managers, and is used quite widely among the AI community – Last year, OpenAI released software to make it easier to run Kubernetes on Amazon’s cloud.
Finally, AI creates jobs for humans! Starship Technologies is hiring a “robot handler” to accompany its freight-ferrying robots as they zoom around Redwood City. Requirements: “a quick thinker with the ability to resolve non-standard situations“.
Ford & the ARGOnauts: Ford will spend $1 billion over five years on AI, via a subsidiary company called Argo. Argo is run by veterans of both Google and Uber’s self-driving programs. Details remain nebulous. Much of the innovation here appears to be in the financial machinery underpinning Argo, which will make it easier for Ford to offer hefty salaries and stock allocations to the AI people it wants to hire. Reminiscent of Cisco’s “spin-in” company Insieme.
Powerful image classification, for free: Facebook has released code for ‘ResNeXt’, an image classification system outlined in its research paper Aggregated Residual Transformations for Deep Neural Networks. Note: one of the authors of ResNeXt is Kaiming He, the whizkid from MSR Asia who helped invent the ImageNet 2015-winning Residual Networks.
Rise of the terminator accountants: Number of traders employed on the US cash equities trading desk at Goldman Sachs’s New York office:
…in 2000: 600
…in 2017: 2, supported by 200 computer engineers.
…”Some 9,000 people, about one-third of Goldman’s staff, are computer engineers,” reports MIT Technology Review.
AI: 2. Hand-tuned algorithms: 0: New research shows how we can use modern AI techniques to learn representations of complex problems, then use some of the resulting predictive models in place of hand-tuned algorithms. “Solving the quantum many-body problem with artificial neural networks” research shows how this technique can be competitive with state of the art approaches. “With further development, it may well prove a valuable piece in the quantum toolbox,.” the researchers write.
…Similarly, Lawrence Berkeley National Laboratory recently trained machine learning systems to predict metallic defects in materials, lowering the cost of conducting research into advanced alloys and other lightweight new materials. “This work is essentially a proof of concept. It shows that we can run density functional calculations for a few hundred materials, then train machine learning algorithms to accurately predict point defects for a much larger group of materials,” the researchers say. “The benefit of this work is now we have a computationally inexpensive machine learning approach that can quickly and accurately predict point defects in new intermetallic materials. We no longer have to run very costly first principle calculations to identify defect properties for every new metallic compound.”
Microscopic, power-sipping’ AI circuits: researchers with the University of Michigan and spinout CubeWorks have created a deep learning processor the size of a fraction of a fingernail. The new chip implements deep neural networks on a 7.1mm2 chip that sips a mere 288 microwatts of power (PDF). They imagine the chip could be used for basic pattern recognition tasks, like a home security camera knowing to only record in the presence of movement of a human/animal versus a shifting tree branch. The design hints at an era for AI where crude pattern recognition capabilities are distributed in processors so tiny and discreet you could end up with fragments in your shoes after walking on some futuristic beach. Slide presentation with more technical information here.
AI needs its own disaster: AI safety researcher Stuart Russell worries that AI needs to have a Chernobyl-scale disaster to get the rest of the world to wake up to the need for fundamental research on AI safety…
…“I go through the arguments that people make for not paying any attention to this issue and none of them hold water. They fail in such straightforward ways that it seems like the arguments are coming from a defensive reaction, not from taking the question seriously and thinking hard about it but not wanting to consider it at all,” he says. “Obviously, it’s a threat. We can look back at the history of nuclear physics, where very famous nuclear physicists were simply in denial about the possibility that nuclear physics could lead to nuclear weapons.“
… some disagree about the dangers of AI. Andrew Ng, a former Stanford Professor and Google Brain founder who now runs AI for Chinese giant tech company Baidu, talked about the “evil AI hype circle” in a recent lecture at the Stanford Graduate School of Business (video). His view is that some people exaggerate the dangers of “evil AI” to generate interest in the problem, which brings in more funding for research, which goes on to fund “anti-evil-AI” companies. “The results of this work drives more hype”, he says. The funding for these sorts of organizations and individuals is “a massive misallocation of resources” he says. Another worry of Ng’s: the focus on evil AI can distract us from a much more severe, real problem, which he says is job displacement.
…Facebook’s head of AI research, Yann Lecun, said in mid-2016 “I don’t think AI will become an existential threat to humanity… If we are smart enough to build machine with super-human intelligence, chances are we will not be stupid enough to give them infinite power to destroy humanity.”
… I worry that AI safety is such a visceral topic that people react quite emotionally to it, and get freaked out by the baleful implications to the point they don’t consider the actual research being done. Some problems people are grappling with in AI safety include: securing machines against adversarial examples, figuring out how to give machines effective intuitions through logical induction, and ensuring that cleaning robots don’t commit acts of vandalism to achieve a tidy home, among others. These all seem like reasonable avenues of research that will improve the stability and resilience of typical AI systems…
… but don’t take my word for it – read about AI safety yourself and come to your own decision: for your next desert island vacation (stranding), consider bringing along a smorgasbord of these 200 AI resources, curated by the Center for Human-Compatible AI at UC Berkeley.
…and if you want to do something about AI safety, consider applying for a new technical research intern position with the Center for Human Compatible AI at UC Berkeley and the Machine Intelligence Research Institute.
Satellite eyes, served three different ways: Startup Descartes Labs has released a new set of global satellite maps in three distinct bands – RGB, Red Edge bands, and synthetic aperture radar range/azimuth measurements The imagery has been pre-processed to remove clouds and adjusted for the angle of the satellite camera as well as the angle of the sun.
Declining economies of scale: just as companies can expect to see their rate of growth flatten as they expand, deep learning systems see performance drop as they add more GPUs, as the benefits they gain start to be nibbled away by the latency and infrastructure costs introduced by running multiple GPUs in parallel…
… New work from Japanese AI startup Preferred Networks, shows that its free ‘Chainer’ software can generate a 100X performance speedup from 128 GPUs. This is extremely good, but still highlights the slightly declining returns people get as they scale-up systems.
Gender IS NOT in the eyes of the beholder: New research “Gender-From-Iris or Gender-From-Mascara?” appears to bust experimental results showing you can predict gender from a person’s iris, instead pointing out that many strong results appear to be contingent on detectors that learn to spot mascara. Machine learning’s law of unintended consequences strikes again!…
… It reminds me of an apocryphal story an AI researcher once told me: in the 1980s the US military wanted to use machine learning algorithms to automatically classify spy satellite photos for whether they contained soviet tanks or not. The system worked flawlessly in tests, but when they put it into production they discovered that its results were little better than random… After some further experimentation they discovered that in every single photo from their task data that contained a tank, there was also some kind of cloud. Therefore, their ML algorithms had developed a superhuman cloud-classifying ability, and didn’t have the foggiest idea of what a tank was!
Rise of the machines = the end of capitalism as we know it? “Modern Western society is built on a societal model whereby Capital is exchanged for Labour to provide economic growth. If Labour is no longer part of that exchange, the ramifications will be immense,” said one respondent to a Pew Internet report about the ‘pros and cons of the algorithm age’.
…“I foresee algorithms replacing almost all workers with no real options for the replaced humans,” says another respondent.
Bushels of subterfuge in DeepMind’s apple orchard: As I write this newsletter on a Sunday, I’m still recovering from my usual morning activity – chasing my friend round an apple orchard, using a laser beam to periodically paralyze them, letting me hop over their twitching body to gather up as many apples as I can…
… in a strange turn of events it appears that Google DeepMind has been spying on my somewhat unique form of part-time sport, and have replicated this in a game environment called ‘gathering’ which they have used to explore the sort of collaborative and combative strategies that AI systems evolve…there’s also another environment called WolfPack, the less said about it the better. This sort of research is potentially very useful for large multi-agent simulations, which many people in AI are betting on as an area where exploration could yield research breakthroughs.
Lines in Google’s codebase: 2 billion
Number of commits into aforementioned codebase per day: 40,000
…From: “Software Engineering at Google”.
OpenAI Bits and Pieces
Learning how to walk, with OpenAI Gym: The challenge: model the motor control unit of a pair of legs in a virtual environment. “You are given a musculoskeletal model with 16 muscles to control. At every 10ms you send signals to these muscles to activate or deactivate them. The objective is to walk as far as possible in 5 seconds.” The components: OpenSim, OpenAI Gym, keras-rl, and much more. Try the challenge, but stay for the doddering legs!
Arxiv Sanity – bigger, better, smarter! OpenAI’s Andrej Karpathy has updated Arxiv Sanity, an indispensable resource that I and many others use to keep track of AI papers. New features: better algorithms for surfacing papers people have shown interest in, and a social feature. (Also see Stephen Merity’s social tracker trendingarxiv.)
AI Control: OpenAI researcher Paul Christiano writes an informative blog on AI safety and security, called AI Control. In the latest post, “Directions and desiderata for AI control” he talks about some particularly promising research directions in AI safety.
[Diplomatic embassy, Beijing, 2025:]
It was a moonless mid-winter pre-dawn, when the flock of drones came overhead and emptied their cargo of chips over the building. The embassy cameras and searchlights picked out some of the thousands of chips as they fell down, hissing like hail on glass and steel roofs. Those staffers that heard them fall shivered instinctively, and afterwards some said that, when caught in the spotlights, the chips looked like metallic snow.
Over the next day the embassy staff did what they could, going around with vacuum cleaners and tiny mops, and ordering an external cleanup crew, but the snowfalls of chips – each one a tiny sensor, its individually meager capabilities offset by the sheer number of its kin – would come again, and eventually security protocols were tightened and people just resigned themselves to it.
Now, you had to negotiate a baroque set of security measures to get into the embassy. But still the chips got in, and cleaners would find them tracked into bathrooms, or sitting in undusted nooks and crannies. Outside, the air hummed with invisible surveillance, as the numerous little chips used their AI processors to turn on microphones in the presence of certain phrases. Outside, the data evaporated into the air, absorbed by flocks of small drones which would fly over the embassy, as they did in every town in every major city in every developed country, hoovering up data from the, what some termed, ‘State Dust’. The chips would lie in wait, consuming almost no power, till they heard a particular encrypted call-out from the government drones.
Even the chips that found themselves indoors would eventually be outside again, as some escaped through improper waste disposal measures, and others had their plastic barbs hook fortuitously on a trouser leg or shoe sole, to then be carried outside. And so their data was extracted as well and a titanic jigsaw was assembled.
It didn’t matter how partial the data from each chip was, given how many there were, and the frequency of their harvesting. Gather enough data and at some point you can make sense of the smallest little fragments, but you can only do this for all the little whispers of data from a city or a country if you’re a machine.