Import AI

Import AI: #103: Testing brain-like alternatives to backpropagation, why imagining goals can lead to better robots, and why navigating cities is a useful research avenue for AI

Backpropagation may not be brain-like, but at least it works:
…Researchers test more brain-like approaches to learning systems, discover that backpropagation is hard to beat…
Backpropagation is one of the fundamental tools of modern deep learning – it’s one of the key mechanisms for propagating and updating information through networks during training. Unfortunately, there’s relatively little evidence available that our own human brains perform a process analogous to backpropagation (this is a question Geoff Hinton has struggled with for several years in talks like ‘Can the brain do back-propagation‘?). That has given some concern to researchers for some years who worry that though we’re seeing significant gains from developing things based on backpropagation, we may need to investigate other approaches in the future.  Now, researchers with Google Brain and the University of Toronto have performed an empirical analysis of a range of fundamental learning algorithms, testing approaches based on backpropagation against ones using target propagation and other variants.
  Motivation: The idea behind this research is that “there is a need for behavioural realism, in addition to physiological realism, when gathering evidence to assess the overall biological realism of a learning algorithm. Given that human beings are able to learn complex tasks that bear little relationship to their evolution, it would appear that the brain possesses a powerful, general-purpose learning algorithm for shaping behavior”.
  Results: The researchers “find that none of the tested algorithms are capable of effectively scaling up to training large networks on ImageNet”, though they record some success with MNIST and CIFAR. “Out-of-the-box application of this class of algorithms does not provide a straightforward solution to real data on even moderately large networks,” they write.
   Why it matters: Given that we know how limited and simplified our neural network systems are, it seems intellectually honest to test and ablate algorithms, particularly by comparing well-studied ‘mainstream’ approaches like backpropagation with more theoretically-grounded but less-developed algorithms from other parts of the literature.
  Read more: Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures (Arxiv).

AI and Silent Bugs:
…Half-decade old bug in ‘Aliens’ game found responsible for poor performance…
One of the more irritating things about developing AI systems is that when you mis-program AI it tends to fail silently – for instance, in OpenAI’s Dota project we saw performance dramatically increase simply after fixing non-breaking bugs. Another good example of this phenomenon has turned up in news about Aliens: Colonial Marines, a poorly reviewed half-decade-old game. But it turns out some of the reasons for those poor reviews were likely due to a bug – subsequent patches have found that the original game mis-named one variable which lead to entire chunks of the game’s enemy AI systems not functioning.
  Read more: A years-old, one-letter typo led to Aliens: Colonial Marines’ weird AI (Ars Technica).

Berkeley researchers teach machines to dream imaginary goals and solutions for better RL:
…If you want to change the world, first imagine yourself changing it…
Berkeley researchers have developed a way for machines to develop richer representations of the world around them and use this to solve tasks. The method they use to achieve this is a technique called ‘reinforcement learning with imagined goals’ (RIG). RIG works like this: an AI system interacts with an environment, data from these observations is used to train (and finetune) a variational auto encoder (VAE) latent variable model, then they use this representation to train the AI system to solve different imagined tasks using the representation learned by the VAE. This type of approach is becoming increasingly popular as AI researchers try to increase the capabilities of algorithms by getting them to use and learn from more data.
  Results: Their approach does well at tasks requiring reaching objects and pushing objects to a goal, beating baselines including algorithms like Hindsight Experience Replay (HER).
  Why it matters: After spending several years training algorithms to master an environment, we’re now trying to train algorithms that can represent their environment, then use that representation as an input to the algorithm to help it solve a new task. This is part of a general push toward greater representative capacity within trained models.
  Read more: Visual Reinforcement Learning with Imagined Goals (Arxiv).

Facebook thinks the path to smarter AI involves guiding other AIs through cities:
…’Talk The Walk’ task challenges AIs to navigate each other through cities, working as a team…
Have you ever tried giving directions to someone over the phone? It can be quite difficult, and usually involves a series of dialogues between you and the person as you try to figure out where in the city they are in relation to where they need to get to. Now, researchers with Facebook and the Montreal Institute of Learning Algorithms (MILA) have set out to develop and test AIs that can solve this task, so as to further improve the generalization capabilities of AI agents. “”For artificial agents to solve this challenging problem, some fundamental architecture designs are missing,” the researchers say.
  The challenge: The new “Talk The Walk” task frames the problem as a discussion between a ‘guide’ and a ‘tourist’ agent. The guide agent has access to a map of the city area that the tourist is in, as well as a location the tourist wants to get to, and the tourist has access to an annotated image of their current location along with the ability to turn left, turn right, or move forward.
  The dataset: The researchers created the testing environment by obtaining 360-degree photographic views of neighborhoods in New York City, including Hell’s Kitchen, the East VIllage, Williamsburg, the Financial District, and the Upper East Side. They then annotated each image of each corner of each street intersection with a set of landmarks drawn from the following categories: bar, bank, shop, coffee shop, theater, playfield, hotel, subway, and restaurant. They then had more than six hundred users of Mechanical Turk play a human version of the game, generating 10,000 successful dialogues from which AI systems can be trained (with over 2,000 successful dialogues available for each neighborhood of New York the researchers gathered data for).
  Results: The researchers tested their developed systems at how well they can localize themselves – that is, develop a notion of where they are in the city. The results are encouraging, with localization models developed by the researchers achieving a higher localization score than humans. (Though humans take about half the number of steps to effectively localize themselves, showing that human sample efficiency remains substantially better than those of machines.
  Why it matters: Following a half decade of successful development and commercialization of basis AI capabilities like image and audio processing, researchers are trying to come up with the next major tasks and datasets they can use to test contemporary research algorithms and developing them further. Evaluation methods like those devised here can help us develop AI systems which need to interact with larger amounts of real world data, potentially making it easier to evaluate how ‘intelligent’ these systems are becoming, as they are being tested directly on problems that humans solve every day and have good intuitions and evidence about the difficulty of. Though it’s worth noting that the current version of the task as solved by Facebook is fairly limited, as it involves a setting with simple intersections (predominantly just four-way straight-road intersections), and the agents aren’t being tested on very large areas nor are being required to navigate particularly long distances.
  Read more: Take the Walk: Navigating New York City through Grounded Dialogue (Arxiv).

Microsoft calls for government-led regulation of artificial intelligence technology:
…Company’s chief legal officer Brad Smith says government should study and regulate the technology…
Microsoft says the US government should appoint an independent commission to investigate the uses and applications of facial recognition technology. Microsoft says it is calling for this because it thinks the technology is of such utility and generality that it’s better for the government to think about regulation in a general sense than for specific companies like Microsoft tot think through questions on their own. The recommendation follows a series of increasingly fraught run-ins between the government, civil rights groups, and companies regarding the use of AI: first, Google dealt with employees protesting its ‘Maven’ AI deal with the DoD, then Amazon came under fire from the ACLU for selling law enforcement authorities facial recognition systems based on its ‘Rekognition’ API.
  Specific questions: Some of the specific question areas Smith thinks the government should spend time include: should law enforcement use of facial recognition be subject to human oversight and control? Is it possible to ensure civilian oversight of this technology? Should retailers post a sign indicating that facial recognition systems are being used in conjunction with surveillance infrastructure?
  Why it matters: Governments will likely be the largest uses of AI-based systems for surveillance, facial recognition, and more – but in many countries the government needs the private sector to develop and sell it products with these capabilities, which requires a private sector that is keen to help the government. If that’s not the case, then it puts the government into an awkward position. Government can clarify some of these relationships in specific areas by, as Microsoft suggests here, appointing an external panel of experts to study an issue and make recommendations.
  A “don’t get too excited” interpretation: Another motivation a company like Microsoft might have for calling for such analysis and regulation is that large companies like Microsoft have the resources to be able to ensure compliance with any such regulations, whereas startups can find this challenging.
  Read more: Facial recognition technology: The need for public regulation and corporate responsibility (Microsoft).

Google opens a Seedbank for wannabe AI gardeners:
Seedbank provides access to a dynamic, online, code encyclopedia for AI systems…
Google has launched Seedbank, a living encyclopedia about AI programming and research. Seedbank is a website that contains a collection of machine learning examples which can be interacted with via a live programming interface in Google ‘colab’. You can browse ‘seeds’ which are major AI topic areas like ‘Recurrent Nets’ or ‘Text & Language’, then click into them for specific examples; for instance, when browsing ‘Recurrent Nets’ you can learn about Neural Translation with Attention and can open a live notebook to walk you through the steps involving in creating a language translation system.
  “For now we are only tracking notebooks published by Google, though we may index user-created content in the future. We will do our best to update Seedbank regularly, though also be sure to check for new content,” writes Michael Tyke in a blog post announcing Seedbank.
  Why it matters: AI research and development is heavily based around repeated cycles of empirical experimentation, so being able to interact with and tweak live programming examples of applied AI systems is a good way to develop better intuitions about the technology.
  Read more: Seedbank – discover machine learning examples (TensorFlow Medium blog).
  Read more: Seedbank official website.

AI Policy with Matthew van der Merwe:
…Reader 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:…

Cross-border collaboration, openness, and dual-use:
…A new report urges better oversight of international partnerships on AI, to ensure that collaborations are not being exploited for military uses…
The Australian Strategic Policy Institute has published a report by Elsa Kania outlining some of the dual-use challenges inherent to today’s scalable, generic AI techniques.
  Dual-use as a strategy: China’s military-civil fusion strategy relies on using the dual-use characteristics of AI to ensure new civil developments can be applied in the military domain, and vice versa. There are many cases of private labs and universities working on military tech, e.g. the collaboration between Baidu and CETC (state-owned defence conglomerate). This blurring of the line between state/military and civilian research introduces a complication into partnerships between (e.g.) US companies and their Chinese counterparts.
  Policy recommendations: Organizations should assess the risks and possible externalities from existing partnerships in strategic technologies, establish systems of best practice for partnerships, and monitor individuals and organizations with clear links to foreign governments and militaries.
  Why this matters: Collaboration and openness are a key driver of innovation in science. In the case of AI, international cooperation will be critical in ensuring that we manage the risks and realize the opportunities of this technology. Nevertheless, it seems wise to develop systems to ensure that collaboration is done responsibly and with an awareness of risks.
  Read more: Technological entanglement.

Around the world in 23 AI strategies:
Tim Dutton has summarized the various national AI strategies governments have put forward in the past two years.
– AlphaGo really was a Sputnik moment in Asia. Two days after AlphaGo defeated Lee Sedol in 2016, South Korea’s president announced ₩1 trillion ($880m) in funding for AI research, adding “Korean society is ironically lucky, that thanks to the ‘AlphaGo shock’, we have learned the importance of AI before it is too late.”
– Canada’s strategy is the most heavily focused on investing in AI research and talent. Unlike other countries, their plan doesn’t include the usual policies on strategic industries, workforce development, and privacy issues.
– India is unique in putting social goals at the forefront of their strategy, and focusing on the sectors which would see the biggest social benefits from AI applications. Their ambition is to then scale these solutions to other developing countries.
   Why this matters: 2018 has seen a surge of countries putting forward national AI strategies, and this looks set to continue. The range of approaches is striking, even between fairly similar countries, and it will be interesting to see how these compare as they are refined and implemented in the coming years. The US is notably absent in terms of having a national strategy.
   Read more: Overview of National AI Strategies.

Risks and regulation in medical AI:
Healthcare is an area where cutting-edge AI tools such as deep learning are already having a real positive impact. There is some tension, though, between the cultures of “do no harm”, and “move fast and break things.”
  We are at a tipping point: We have reached a ‘tipping point’ in medical AI, with systems already on the market that are making decisions about patients’ treatment. This is not worrying in itself, provided these systems are safe. What is worrying is that there are already examples of autonomous systems making potentially dangerous mistakes. The UK is using an AI-powered triage app, which recommends whether patients should go to hospital based on their symptoms. Doctors have noticed serious flaws, with the app appearing to recommend staying at home for classic symptoms of heart attacks, meningitis and strokes.
  Regulation is slow to adapt: Regulatory bodies are not taking seriously the specific risks from autonomous decision-making in medicine. By treating these systems like medical devices, they are allowing them to be used on patients without a thorough assessment of their risks and benefits. Regulators need to move fast, yet give proper oversight to these technologies.
  Why this matters: Improving healthcare is one of the most exciting, and potentially transformative applications of AI. Nonetheless, it is critical that the deployment of AI in healthcare is done responsibly, using the established mechanisms for testing and regulating new medical treatments. Serious accidents can prompt powerful public backlashes against technologies (e.g. nuclear phase-outs in Japan and Europe post-Fukushima). If we are optimistic about the potential healthcare applications of AI, ensuring that this technology is developed and applied safely is critical in ensuring that these benefits can be realized.
  Read more: Medical AI Safety: We have a problem.

OpenAI & ImportAI Bits & Pieces:

Better generative models with Glow:
We’ve released Glow, a generative model that uses a 1×1 reversible convolution to give it a richer representative capacity.  Check out the online visualization tool to experiment with a pre-trained Glow model yourself, applying it to images you can upload.
   Read more: Glow: Better Reversible Generative Models (OpenAI Blog).

AI, misuse, and DensePose:
IEEE Spectrum has written up some comments from here in Import AI about Facebook’s ‘DensePose’ system and the challenges it presents for how AI systems can potentially be misused and abused. As I’ve said in a few forums, I think the AI community isn’t really working hard on this problem and is creating unnecessary problems (see also: voice cloning via Lyrebird, faking politicans via ‘Deep Video Portraits’, surveiling crowds with drones, etc).
  Read more: Facebook’s DensePose Tech Raises Concerns About Potential Misuse (IEEE Spectrum).

Tech Tales:

Ad Agency Contracts for a Superintelligence:

Subject: Seeking agency for AI Superintelligence contract.
Creative Brief: Company REDACTED has successfully created the first “AI Superintelligence” and is planning a global, multi-channel, PR campaign to introduce the “AI Superintelligence” (henceforth known as ‘the AI’) to a global audience. We’re looking for pitches from experienced agencies with unconventional ideas in how to tell this story. This will become the most well known media campaign in history.

We’re looking for agencies that can help us create brand awareness equivalent to other major events, such as: the second coming of Jesus Christ, the industrial revolution, the declaration of World War 1 and World War 2, the announcement of the Hiroshima bomb, and more.

Re: Subject: Seeking agency for AI Superintelligence contract.
Three words: Global. Cancer. Cure. Let’s start using the AI to cure cancer around the world. We’ll originally present these cures as random miracles and over the course of several weeks will build narrative momentum and impetus until ‘the big reveal’. Alongside revealing the AI we’ll also release a fully timetabled plan for a global rollout of cures for all cancers for all people. We’re confident this will establish the AI as a positive force for humanity while creating the requisite excitement and ‘curiosity gap’ necessary for a good launch.

Re: Subject: Seeking agency for AI Superintelligence contract.
Vote Everything. Here’s how it works: We’ll start an online poll asking people to vote on a simple question of global import, like, which would you rather do: Make all aeroplanes ten percent more fuel efficient, or reduce methane emissions by all cattle? We’ll make the AI fulfill the winning vote. If we do enough of these polls in enough areas then people will start to correlate the results of the polls with larger changes in the world. As this happens, online media will start to speculate more about the AI system in question. We’ll be able to use this interest to drive attention to further polls to have it do further things. The final vote before we reveal it will be asking people what date they want to find out who is ‘the force behind the polls’.

Re: Subject: Seeking agency for AI Superintelligence contract.
Destroy Pluto. Stay with us. Destroy Pluto AND use the mass of Pluto to construct a set of space stations, solar panels, and water extractors throughout the solar system. We can use the AI to develop new propulsion methods and materials which can then be used to create an expedition to destroy the planet. Initially it will be noticed by astronomers. We expect early media narratives to assume that Pluto has been destroyed by aliens who will then harvest the planet and use it to build strange machines to bring havoc to the solar system. Shortly before martial law is declared we can make an announcement via the UN that we used the intelligence to destroy Pluto, at which point every person on Earth will be given a ‘space bond’ which entitles them to a percentage of future earnings of the space-based infrastructure developed by the AI.

Things that inspired this story: Advertising agencies, the somewhat un-discussed question of “what do we do if we develop superintelligence arrives”, historical moments of great significant.

Import AI: #102: Testing AI robustness with IMAGENET-C, militarycivil AI development in China, and how teamwork lets AI beat humans

Microsoft opens up search engine data:
New searchable archive simplifies data finding for scientists…
Microsoft has released Microsoft Research Open Data, a new web portal that people can use to comb through the vast amounts of data released in recent years by Microsoft Research. The data has also been integrated with Microsoft’s cloud services, so researchers can easily port the data over to an ‘Azure Data Science virtual machine’ and start manipulating it with pre-integrated data science software.
  Data highlights: Microsoft has released some rare and potentially valuable datasets, like 10GB worth of ‘Dual Word Embeddings Trained on Big Queries‘ (data from live search engines tends to be very rare), along with original research-oriented datasets like FigureQA, and a bunch of specially written mad libs.
  Read more: Announcing Microsoft Research Open Data – Datasets by Microsoft Research now available in the cloud (Microsoft Research Blog).
Browse the data: Microsoft Research Open Data.

What does military<>civil fusion look like, and why is China so different from America?
…Publication from Tsinghua VP highlights difference in technology development strategies…
What happens when you have a national artificial intelligence strategy that revolves around developing military and civil AI applications together? A recent (translated) publication by You Zheng, vice president of China’s Tsinghua University, provides some insight.
  Highlights: Tsinghua is currently constructing the ‘High-End Laboratory for Military Intelligence’, which will focus on developing AI to better support China’s country-level goals. As part of this, Tsinghua will invest in basic research guided by some military requirements. The university has also created the ‘Tsinghua Brain and Intelligence Laboratory’ to encourage interdisciplinary research which is less connected to direct military applications. Tsinghua also has a decade-long partnership with Chinese social network WeChat and search engine Sohuo, carrying out joint development within the civil domain. And it’s not focusing purely on technology – the school recently created a ‘Computational Legal Studies’ masters program “to integrate the school’s AI and liberal arts so as to try a brand-new specialty direction for the subject.”
  Why it matters: Many governments are currently considering how to develop AI to further support their strategic goals – many countries in the West are doing this by relying on a combination of classified research, public contracts from development organizations like DARPA, and partnerships with the private sector. But the dynamics of the free market and tendency in these countries to have relatively little direct technology development and research via the state (when compared to the amounts expended by the private sector) has led to uneven development, with civil applications leaping ahead of military ones in terms of capability and impact. China’s gamble is that a state-led development strategy can let it better take advantage of various AI capabilities to more rapidly integrate AI into its society – both civil and military. The outcome of this gamble will be a determiner of the power balance of the 21st century.
  Read more: Tsinghua’s Approach to Military-Civil Fusion in Artificial Intelligence (Battlefield Singularity).

DeepMind bots learn to beat humans at Capture the Flag:
…Another major step forward for team-based AI work…
Researchers with DeepMind have trained AIs that are competitive with humans in a first-person multiplayer game. The result shows that it’s possible to train teams of agents to collaborate with each other to achieve an objective against another team (in this case, Capture the Flag played from the first person perspective within a modified version of the game Quake 3), and follows other recent work from OpenAI on the online team-based multiplayer game Dota, as well as work by DeepMind, Facebook, and others on StarCraft 1 and StarCraft 2.
  The technique relies on a few recently developed approaches, including multi-timescale adaptation, an external memory module, and having agents evolve their own internal reward signals. DeepMind combines these techniques with a multi-agent training infrastructure which uses its recently developed ‘population-based training’ technique. One of the most encouraging results is that trained agents can generalize to never-before-seen maps and typically beat humans when playing under these conditions.
  Additionally, the system lets them train very strong agents: “we probed the exploitability of the agent by allowing a team of two professional games testers with full communication to play continuously against a fixed pair of agents. Even after twelve hours of practice the human game testers were only able to win 25% (6.3% draw rate) of games against the agent team, though humans were able to beat the AIs when playing on pre-defined maps by slowly learning to exploit weaknesses in the AI. Agents were trained on ~450,000 separate games.
  Why it matters: This result, combined with work by others on tasks like Dota 2, shows that it’s possible to use today’s existing AI techniques, combined with large-scale training, to create systems capable of beating talented humans at complex tasks that require teamwork and planning over lengthy timescales – I think because of the recent pace of AI progress these results can seem weirdly unremarkable, but I think that perspective would be wrong: it is remarkable we can develop agents capable of beating people at tasks requiring ‘teamwork’ – a trait that seems to require many of the cognitive tools we think are special, but which is now being shown to be achievable via relatively simple algorithms. As some have observed, one of the more intuitive yet counter-intuitive aspects of these results is how easily it seems that ‘teamwork’ can be learned.
  Less discussed: I think we’re entering the ‘uncanny valley’ of AI research when it comes to developing things with military applications. This ‘capture the flag’ demonstration, along with parallel work on OpenAI and on StarCraft, has a more militaristic flavor than prior research by the AI community. My suspicion is we’ll need to start thinking more carefully about we contextualize results like this and work harder at analyzing which other actors may be inspired by research like this.
Read more: Human-level performance in first-person multiplayer games with population-based deep reinforcement learning (Arxiv).
  Watch extracts of the agent’s behavior here (YouTube).

Discover the hidden power of Jupyter at JupyterCon.
2017: 1.2 million Jupyter notebooks on GitHub.
2018: 3 million, when JupyterCon starts in New York this August.
– This is just one sign of the incredible pace of discovery, as organizations use notebooks and use recent platform developments to solve difficult data problems such as scalability, reproducible science, and compliance, data privacy, ethics, and security issues. JupyterCon: It’s happening Aug 21-25.
– Save 20% on most passes with the code IMPORTAI20.

Ever wanted to track the progress of language modelling AI in minute detail? Now is your chance!
…Mapping progress in a tricky-to-model domain…
How fast is the rate of progression in natural language processing technologies, and where does that progression fit into the overall development of the AI landscape? That’s a question that natural language processing researcher Seb Ruder has tried to answer with a new project oriented around tracking the rate of technical progress on various NLP tasks. Check out the project’s GitHub page and try to contribute if you can.
  Highlights: The GitHub repository already contains more than 20 tasks, and we can get an impression of recent AI progress by examining the results. Tasks like language modeling have seen significant progress in recent years, while tasks like constituency parsing and part-of-speech tagging have seen less profound progress (potentially because existing systems are quite good at these tasks).
  Read more: Tracking the Progress in Natural Language Processing (Sebastian Ruder’s website).
  Read more: Tracking Progress in Natural Language Processing (GitHub).

Facebook acquires language AI company Bloomsbury AI:
…London-based acquihire adds language modeling talent…
Facebook has acquired the team from Bloomsbury AI who will join the company in London and work on natural language processing research. Bloomsbury had previously built systems for examining corpuses of text and answering questions about them, and includes an experienced AI engineering and research team including Dr Sebastian Riedel, a professor at UCL (acquiring companies with professors tends to be a strategic move as it can help with recruiting).
  Read more: Bloomsbury AI website (Bloomsbury AI).
  Read more: I’d link to the ‘Facebook Academics’ announcement if Facebook didn’t make it so insanely hard to get direct URLs to link to within its giant blue expanse.

What is in Version 2, makes the world move, and just got better?
…Robot Operating System 2: Bouncy Bolson…
The ‘Bouncy Bolson’ version of ROS 2 (Robot Operating System) has been released. New features for the open source robot software include better security features, support for 3rd party package submission on the ROS 2 build farm, new command line tools, and more. This is the second non-beta ROS 2 release.
  Read more: ROS 2 Bouncy Bolson Released (

Think deep learning is robust? Try out IMAGENET-C and think again:
…New evaluation dataset shows poor robustness of existing models…
Researchers with Oregon State University have created new datasets and evaluation criteria to see how well trained image recognition systems deal with corrupt data. The research highlights the relatively poor representation and generalisation of today’s algorithms, while providing challenging datasets people may wish to test systems against in the future. To conduct their tests, the researchers create two datasets to evaluate how AI systems deal with these changes. IMAGENET-C is a dataset to test for “corruption robustness” and ICONS-50 is for testing for “surface variation robustness”.
  IMAGENET-C sees them apply 15 different types of data corruption to existing images, ranging from blurring images, to adding noise, or the visual hallmarks of environmental effects like snow, frost, fog, and so on. ICONS-50 consists of 10,000 images from 50 clases of icons of different things like people, food, activities, logos, and so on, and each class contains multiple different illustrative styles.
  Results: To test how well algorithms deal with these visual corruptions the researchers test pre-trained image categorization models against different versions of IMAGENET-C (where a version roughly corresponds to the amount of corruption applied to a specific image), then compute the error rate. The results of the test are that more modern architectures have become better at generalizing to new datatypes (like corrupted images), but that robustness – which means how well a model adapts to changes in data – has barely risen. “Relative robustness remains near AlexNet-levels and therefore below human-level, which shows that our superhuman classifiers are decidedly subhuman,” they write. They do find that there are a few tricks that can be used to increase the capabilities of models to deal with corrupted data: “more layers, more connections, and more capacity allow these massive models to operate more stably on corrupted inputs,” they write.
  For ICONs-50 they try to test classifier robustness by removing the icons from one source (eg Microsoft) or by selecting removing subtypes (like ‘ducks’) from broad categories (like ‘birds’). Their results are somewhat unsurprising: networks are not able to learn enough general features to effectively identify held-out visual styles, and similarly poor performance is displayed when tested on held-out sub-types.
  Why it matters: As we currently lack much in the way of theory to explain and analyze the successes of deep learning we need to broaden our understanding of the technology through empirical experimentation, like what is carried out here. And what we keep on learning is that, despite incredible gains in performance in recent years, deep nets themselves seem to be fairly inflexible when dealing with unseen or out-of-distribution data.
  Read more: Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations (Arxiv).

AI Policy with Matthew van der Merwe:
…Reader 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: …

Technology Roulette:
Richard Danzig, former secretary of the Navy, has written a report for thinktank the Center for a New American Security, on the risks arising from militaries pursuing technological superiority.
  Superiority does not imply security: Creating a powerful, complex technology creates a new class of risks (e.g. nuclear weapons, computers). Moreover, pursuing technological superiority, particularly in a military context, is not a guarantee of safety. While superiority might decrease the risk of attack, through deterrence, it raises the risk of a loss of control, through accidents, misuse, or sabotage. These risks are made worse by the unavoidable proliferation of new technologies, which will place “great destructive power” in the hands of actors without the willingness or ability to take proper safety precautions.
  Human-in-the-loop: A widely held view amongst the security establishment is that these risks can be addressed by retaining human input in critical decision-making. Danzig counters this, arguing that human intervention is “too weak, and too frequently counter-productive” to control military systems that rely on speed. And AI decision-making is getting faster, whereas humans are not, so this gap will only widen over time. Efforts to control such systems must be undertaken at the time of design, rather that during operation.
   What to do: The report makes 5 recommendations for US military/intelligence agencies:
-Increase focus on risks of accidents and emergent effects
– Give priority to reducing risks of proliferation, adversarial behavior, accidents and emergent behaviors.
– Regularly assess these risks, and encourage allies and opponents to do so.
– Increase multilateral planning with allies and opponents, to be able to recognize and respond to accidents, major terrorist events, and unintended conflicts.
– Use new technologies as means for encourage and verifying norms and treaties.
  Why this matters: It seems inevitable that militaries will see AI as a means of achieving strategic advantage. This report sheds light on the risks that such a dynamic could pose to humanity if parties do not prioritize safety, and do not cooperate on minimizing risks from loss of control. One hopes that these arguments are taken seriously by the national security community in the US and elsewhere.
  Read more: Technology Roulette: Managing Loss of Control as Many Militaries Pursue Technological Superiority (CNAS).

UK government responds to Lords AI Report:
The UK government has responded to the recommendations made in the House of Lords’ AI report, released in April. For the most part, the government accepts the committee’s recommendations and is taking actions to help with specific elements of the recommendations:
On public perceptions of AI, the government will work to build trust and confidence in AI through the AI institutions like the Centre for Data Ethics and Innovation (CDEI), which will pursue extensive engagement with the public, industry and regulators, and will align governance measures with the concerns of the public, and businesses.
– On algorithmic transparency, the government pushes back against the report’s recommendation that deployed AI systems have a very high level of transparency/explainability. They note that excessive demands for algorithmic transparency in deployed algorithms could hinder development, particularly in deep learning, and must therefore be weighed against the benefits of the technologies.
– On data monopolies the government will strengthen the capabilities of the UK’s competition board to monitor anti-competitive practices in data and AI, so it can better analyze and respond to the potential for the monopolisation of data by tech giants.
– On autonomous weapons the report asked that the UK improves its definition of autonomous weapons, and brings it into line with that of other governments and international bodies. The government defines an autonomous system as one that “is capable of understanding higher-level intent and direction”, which the report argued “sets the bar so high that it was effectively meaningless.” The gov’t said they have no plans to change their definition.
– Why this matters: The response is not a game-changer, but it is worth reflecting on the way in which the UK has been developing their AI strategy, particularly in comparison with the US (see below). While the UK’s AI strategy can certainly be criticized, the first stage of information-gathering and basic policy recommendations has proceeded commendably. The Lords AI Report and the Hall-Pesenti Review were both detailed investigations, drawing on a array of expert opinions, and asking informed questions. Whether this methodology produces good policy remains to be seen, and depends on a number of contingencies.
  Read more: Government response to House of Lords AI Report\.

Civil liberties group urges US urged to include public in AI policy development, consider risks
Civil liberties group EPIC has organized a petition, with a long list of signatories from academia and industry, to the US Office of Science and Technology Policy (OSTP). Their letter is critical of the US government’s progress on AI policy, and the way in which the government is approaching issues surrounding AI.
  Public engagement in policymaking: The letter asks for more meaningful public participation in the development of US AI policy. They take issue with the recent Summit on AI being closed to the public, and the proposal for a Select Committee on AI identifying only the private sector as a source of advice. This contrasts with other countries, including France, Canada and UK, all of whom have made efforts to engage public opinion on AI.
  Ignoring the big issues: More importantly, the letter identifies a number of critical issues that they say the government is failing to address:
– Potential harms arising from the use of AI.
– Legal frameworks governing AI.
– Transparency in the use of AI by companies, government.
– Technical measures to promote the benefits of AI and minimize the risks.
– The experiences of other countries in trying to address challenges of AI.
– Future trends in AI that could inform the current discussion.
Why this matters: The US is conspicuous amongst global powers for not having a coordinated AI strategy. Other countries are quickly developing plans not only to support their domestic AI capabilities, but to deal with the transformative change that AI will have. The issues raised by the letter cover much of the landscape governments need to address. There is much to be criticized about existing AI strategies, but it’s hard to see the benefits of the US’ complacency.
   Read more: Letter to Michael Kratsios.

OpenAI Bits & Pieces:

Exploring with demonstrations:
New research from OpenAI shows how to obtain a state-of-the-art score on notoriously hard exploration game Montezuma’s Revenge by using a single demonstration.
   Read more: Learning Montezuma’s Revenge from a Single Demonstration (OpenAI blog).

Tech Tales:

When we started tracking it, we knew that it could repair itself and could go and manipulate the world. But there was no indication that it could multiply. For this we were grateful. We were hand-picked from several governments and global corporations and tasked with a simple objective: determine the source of the Rogue Computation and how it transmits its damaging actions to the world.

How do you find what doesn’t want to be found? Look for where it interacts with the world. We set up hundreds of surveillance operations to monitor the telecommunications infrastructure, internet cafes, and office buildings back to which we had traced viruses that bore the hallmarks of Rogue Computation. One day we identified some humans who appeared to be helping the machine, linking a code upload to a person who had gone into the building a few minutes earlier holding a USB key. In that moment we stopped being metal-hunters and became people-hunters.

Adapt, our superiors told us. Survey and deliver requested analysis. So we surveiled the people. We mounted numerous expeditions, tracking people back from the internet cafes where they had uploaded Rogue Computation Products, and following them into the backcountry behind the megacity expanse – a dismal set of areas that, from space, looks like a the serrated ridges caused in the wake of the passage of a boat. These areas were forested; polluted with illegal e-waste and chem-waste dumps; home to populations of the homeless and those displaced by the cold logic of economics; full of discarded home robots and bionic attachments; and everywhere studded with the rusting metal shapes of crashed or malfunctioned or abandoned drones. When we followed these people into these areas we found them parking cars at the heads of former hiking trails, then making their way deeper into the wilderness.

After four weeks of following them we had our first confirmed sighting of the Suspected Rogue Computation Originator: it was a USB inlet, which dangled out of a drainage pipe embedded in the side of a brown, forested hillside. Some of us shivered when we saw a human approach the inlet and, like an ancient peasant paying tribute to a magician, extend a USB key and plug it into the inlet, then back away with their palms held up toward the inlet. A small blue light in the USB inlet went on. Then the inlet, now containing a USB key, began to withdraw backward into the drainage pipe, pulled from within.

Then things were hard for a while. We tracked more people. Watched more exchanges. Observed over twenty different events which led to Rogue Computation Products being delivered to the world. But our superiors wouldn’t let us interfere, afraid that, after so many years searching, they might spook their inhuman prey at the last minute and lose it forever. So we watched. Slowly, we pieced the picture together: these groups had banded together under various quasi-religious banners, worshiping fictitious AI creatures, and creating endless written ephemera scattered across the internet. Once we found their signs it became easy to track them and spot them – and then we realized how many of them there were.

But we triangulated it eventually, tracking it back to a set of disused bombshelters and mining complex buildings scattered through a former industrial sector in part of the ruined land outside of the urban expanse. Subsequently classified assessments predicted a plausible compute envelop registering in the hundreds of exaflops – enough to make it a strategic compute asset and in violation of numerous AI-takeoff control treaties. We found numerous illegal power hookups linking the Rogue Computation facilities to a number of power substations. Repeated, thorough sweeps failed to identify any indication of a link with an internet service provider, though – small blessings.

Once we knew where it was and knew where the human collaborators were, things became simple again: assassinate and destroy. Disappear the people and contrive a series of explosions across the land. Use thermite to melt and distort the bones of the proto Rogue Computation Originator, rewriting their structure from circuits and transistor gates to uncoordinated lattices of atoms, still gyrating from heat and trace radiation from the blasts.

Of course there are rumors that it got it: that those Rogue Computation Products it smuggled out form the scaffolds for its next version, which will soon appear in the world, made real as if by imagination, rather than the brutal exploitation of the consequences of a learning system and compute and time.

Things that inspired this story: Bladerunner, Charles Stross stories.

Import AI: #101: Teaching robots to grasp with two-stage networks; Silicon Valley VS Government AI; why procedural learning can generate natural curriculums.

Making better maps via AI:
…Telenav pairs machine learning with OpenStreetCam data to let everyone make better maps…
Navigation company Telenav has released datasets, machine learning software, and technical results to help people build AI services on top of mapping infrastructure. The company says it has done this to create a more open ecosystem around mapping, specifically around ‘Open Street Map’, a popular open source map).
  Release: The release includes a training set of ~50,000 images annotated with labels to help identify common road signs; a machine-learning technology stack that includes a notebook with visualizations, a RetinaNet system for detecting traffic signs, and the results from running these AI tools over more than 140-million existing street-level images; and more.
  Why it matters: Maps are fundamental to the modern world. AI promises to give us the tools needed to automatically label and analyze much of the world around us, holding with it the promise to create truly capable open source maps that can rival those developed by proprietary interests (see: Google Maps, HERE, etc). Mapping may also become better through the use of larger datasets to create better automatic-mapping systems, like tools that can parse the meaning of photos of road signs.
  Read more: The Future of Map-Making is Open and Powered by Sensors and AI (OpenStreetMap @ Telenav blog).
  Read more: Telenav MapAI Contest (Telenav).
  Check out the GitHub (Telenav GitHub).

Silicon Valley tries to draw a line in shifting sand: surveillance edition:
…CEO of facial recognition startup says won’t sell to law enforcement…
Brian Brackeen, the CEO of facial recognition software developed Kairos, says his company is unwilling to sell facial recognition technologies to government or law enforcement. This follows Amazon coming under fire from the ACLU for selling facial recognition services to law enforcement via its ‘Rekognition’ API.
  “I (and my company) have come to belief that the use of commercial facial recognition in law enforcement or in government surveillance of any kind is wrong – and that it opens the door for gross misconduct by the morally corrupt,” Brackeen writes. “In the hands of government surveillance programs and law enforcement agencies, there’s simply no way that face recognition software will not be used to harm citizens”, he writes.
  Why it matters: The American government is currently reckoning with the outcome of an ideological preference leading to its military industrial infrastructure relying on an ever-shifting constellation of private compares, whereas other countries tend to perform more direct investment for certain key capabilities, like AI. That’s led to today’s situation where American government entities and organizes are, upon seeing how other governments (mainly China) are implementing AI, seeking to find ways to implement AI in America. But getting people to build these AI systems for the US government has proved difficult: many of the companies able to provide strategic AI services (see: Google, Amazon, Microsoft, etc) have become so large they’ve become literal multinationals: their offices and markets are distributed around the world, and their staff come from anywhere. Therefore, these companies aren’t super thrilled about working on behalf of any one specific government, and their staff are mounting internal protests to get the companies to not sell to the US government (among others).. How the American government deals with this will determine many of the contours of American AI policy in the coming years.
  Read more: Facial recognition software is not ready for use by law enforcement (TechCrunch).

“Say it again, but like you’re sad”. Researchers create and release data for emotion synthesis:
…Parallel universe terrifying future: a literal HR robot that can detect your ‘tone’ during awkward discussions and chide you for it…
You’ve heard of speech recognition. Well, what about emotion recognition and emotional tweaking? That’s the problem of listening to speech, categorizing the emotional inflections of the voices within it, and learning to change an existing speech sample to sound like it is spoken with a different emotion  – a potentially useful technology to have for passive monitoring of audio feeds, as well as active impersonation or warping, or other purposes. But to be able to create a system capable of this we need to have access to the underlying data necessary to train it. That’s why researchers with the University of Mons in Belgium and Northeastern University in the USA have created ‘the Emotional Voices dataset’.
  The dataset: “This database’s primary purpose it to build models that could not only produce emotional speech but also control the emotional dimension in speech,” write the researchers. The dataset contains five different speakers and two spoken languages (north American English and Belgian French), with four of the five speakers contributing ~1,000 utterances each, and one speaker contributing around ~500. These utterances are split across five distinct emotions: neutral, amused, angry, sleepy, and disgust.
  You sound angry. Now you sound amused: In experiments, the researchers tested how well they could use this dataset to transform speech from the same speaker from one emotion to another. They found that people would roughly categorize voices transformed from neutral to angry in this way with roughly 70 to 80 percent accuracy – somewhat encouraging, but hardly definitive. In the future, the researchers “hope that such systems will be efficient enough to learn not only the prosody representing the emotional voices but also the nonverbal expressions characterizing them which are also present in our database.”
  Read more: The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems (Arxiv).

Giving robots a grasp of good tasks with two-stage networks:
…End-to-end learning multii-stage tasks is getting easier, Stanford researchers show…
Think about a typical DIY task you might do at home – what do you do? You probably grab the tool in one hand, then approach the object you need to fix or build, and go from there. But how do you know the best way to grip the object so you can accomplish the task? And why do you barely ever get this grasp wrong? This type of integrated reasoning and action is representative of the many ways in which humans are smarter than machines. Can we teach machines to do the same? Researchers with Stanford University have published new research showing how to train basic robots to perform simple, real-world DIY-style tasks, using deep learning techniques.
  Technique: The researchers use a simulator to repeatedly train a robot arm and a tool (in this case, a simplified toy hammer) to pick up the tool then use it to manipulate objects in a variety of situations. The approach relies on a ‘Task-Oriented Grasping Network (TOG-Net), which is a two-stage system that first predicts effective grasps for the object, then predicts manipulation actions to perform to achieve a task.
  Data: One of the few nice things about working with robots is that if you have a simulator it’s possible to automatically generate large amounts of data for training and evaluation. Here, the researchers use the open source physics simulator Bullet to generate many permutations of the scene to be learned, using different objects and behaviors. They train using 18,000 procedurally generated objects.
  Results: The system is tested in two limited domains: sweeping and hammering, where sweeping consists of using an object to move another object without lifting it, and hammering involves trying to hammer a large wooden peg into a hole. The developed system obtains reasonable but not jaw-dropping success rates on the hammering tasks (obtaining a success rate of ~80%, far higher than other methods), and less impressive results on sweeping (~71%). These results put this work firmly in the domain of research, as the success rates are far too low for this to be interesting from a commercial perspective.
  Why it matters: Thanks to the growth in compute and advancement in simulators it’s becoming increasingly easy apply deep learning and reinforcement learning techniques to robots. These advancements are leading to an increase in the pace of research in this area and suggest that, if research continues to show positive results, there may be a deep learning tsunami about to hit robotics.
  Read more: Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision (Arxiv).

Evolution is good, but guided evolution is better:
…Further extension of evolution strategies shows value in non-deep learning ideas…
Google Brain researchers have shown how to extend ‘evolution strategies’, an AI technique that has regained popularity in recent years following experiments showing it is competitive with deep learning approaches. The extension further improves performance of the ES algorithm. “Our method can primarily be thought of as a modification to the standard ES algorithm, where we augment the search distribution using surrogate gradients,” the researchers explain. The result is a significantly more capable version of ES, which they call Guided ES, that “combines the benefits of first-order methods and random search, when we have access to surrogate gradients that are correlated with the true gradient”.
  Why it matters: In recent years a huge amount of money and talent has flooded into AI, primarily to work on deep learning techniques. It’s valuable to continue to research or to revive other discarded techniques, such as ES, to provide alternative points of comparison to let us better model progress here.
  Read more: Guided evolutionary strategies: escaping the curse of dimensionality in random search (Arxiv).
  Read more: Evolution Strategies as a Scalable Alternative to Reinforcement Learning (OpenAI blog).

Using procedural creation to train reinforcement learning algorithms with better generalization:
….Do you know what is cooler than 10 video game levels? 100 procedurally generated ones with a curriculum of difficulty…
Researchers with the IT University of Copenhagen and New York University have fused procedural generation with games and reinforcement learning to create a cheap, novel approach to curriculum learning. The technique relies on using reinforcement learning to guide the generation of increasingly difficult video game levels, where difficult levels are generated only once the agent has learned to beat easier levels. This process leads to a natural curriculum emerging, as each time the agent gets better it sends a signal to the game generator to create a harder level, and so on.
  Data generation: They use the General Video Game AI Framework (GVG-AI), an open source framework which over 160 games have been developed for. GVG-AI is scriptable by the video game description language (VGDL). GVG-AI is integrated with OpenAI Gym, so developers can train against from pixel inputs, incremental rewards, and a binary win/loss signal. The researchers create level generators for three difficult games within GVG-AI. During the level generation process they also manipulate a ‘difficulty parameter’ which roughly correlates to how challenging the generated levels are.
  Results: The researchers find that systems trained with this progressive procedural generation approach do well, obtaining top scores on the challenging ‘frogs’ and ‘zelda’ games, compared to baseline algorithms trained without a procedural curriculum.
  Why it matters: Approaches like this highlight the flaws in the way we evaluate today’s reinforcement learning algorithms, where we test algorithms on similar (frequently identical) levels/games to those they were trained on, and therefore have difficulty distinguishing between algorithmic improvements and overfitting a test set. Additionally, this research shows how easy it is becoming to use computers to generate or augment existing datasets (eg, creating procedural level generators for pre-existing games), reducing the need for raw input data in AI development, and increasing the strategic value of compute.
  Read more: Procedural Level Generation Improves Generality of Deep Reinforcement Learning (Arxiv).

AI Policy with Matthew van der Merwe:
…Reader 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: …

Trump drops plans to block Chinese investment in US tech, strengthens oversight:
  The Trump administration has rowed back on a proposal to block investment in industrially significant technology (including AI, robotics, semiconductors) by firms with over 25% Chinese ownership, and to restrict tech exports to China by US firms.   The government will instead expand the powers of the Committee of Foreign Investment in the United States (Cfius), the body that reviews the national security implications of foreign acquisitions. The new legislation will broaden the Committee’s considerations to include the impact on the US’s competitive position in advanced technologies, in addition to security risks.
  Why this matters: Governments are gradually adapting their oversight of cross-border investment to cover AI and related technologies, which are increasingly being treated as strategically important for both military and industrial applications. The earlier proposal would have been a step-up in AI protectionism from the US, and would have likely prompted a strong retaliation from China. For now, a serious escalation in AI nationalism seems to have been forestalled.
  Read more: Trump drops new restrictions on China investment (FT).

DeepMind co-founder appointed as advisor to UK government:
Demis Hassabis, co-founder of DeepMind, has been announced as an Adviser to the UK government’s Office for AI, which focuses on developing and delivering the UK’s national AI strategy.
  Why this matters: This appointment adds credibility to the UK government’s efforts in the sector; A persistent worry is that policy-makers are out of their depth when it comes to emerging technologies, and that this could lead to poorly designed policies. Establishing close links with industry leaders is an important means of mitigating these risks.
  Read more: Demis Hassabis to advise Office for AI.

China testing bird-like surveillance drones:
Chinese government agencies have been using stealth surveillance drones mimicking the flight and appearance of birds to monitor civilians. Code-named ‘doves’ and fitted with cameras and navigation systems, they are being used for civilian surveillance in 5 provinces. The drones’ bird-like appearance allows them to evade detection by humans, and even other birds, who reportedly regularly join them in flight. They are also being explored for military applications, and are reportedly able to evade many anti-drone systems, which rely on being able to distinguish drones from birds.
  Why this matters: Drones that are able to evade detection are a powerful surveillance technology that raise ethical questions. Should similar drones be used in civilian applications in the US and Europe, we could expect a resistance from privacy advocates.
  Read more: China takes surveillance to new heights with flock of robotic doves (SCMP).

OpenAI Bits & Pieces:

OpenAI Five:
We’ve released an update giving progress on our Dota project, which involves training large-scale reinforcement learning systems to beat humans at a challenging, partially observable strategy game.
   Read more: OpenAI Five (OpenAI blog).

Tech Tales:

Partying in the sphere

The Sphere was a collection of around 1,000 tiny planets in an artificial solar system. The Sphere was also the most popular game of all time. It crept into the world at first via high-end desktop PCs. Then its creators figured out how to slim down its gameplay into a satisfying form for mobile phones. That’s when it really took over. Now the sphere has around 150 million concurrent players at any one time, making it the most popular game on earth by a wide margin.

Several decades after it launched, The Sphere has started to feel almost crowded. Most planets are inhabited. Societal hierarchies have appeared. The era of starting off as a new player with no in-game currency and working your way up are over and have been over for years.

But there’s a new sport in The Sphere: breaking it. One faction of players, numbering in the millions, has begun to construct a large metallic scaffold up from one planet at the corner of the map. Their theory is that they can keep building it until they hit the walls of The Sphere, at which point they’re fairly confident that  – barring a very expensive and impractical overhaul of the underlying simulation engine – they will be able to glitch out of the map containing the 1,000 worlds and into somewhere else.

The game company that makes The Sphere became fully automated a decade ago, so players are mostly trying to guess at the potential reactions of the Corporate AI by watching any incidental changes to the game via patches or updates. So far, nothing has happened to suggest the AI wishes to discourage the scaffolding – the physics remains similar, the metals used to make the scaffolds remain plentiful, the weight and behavior of the scaffolds in zero-g space remain (loosely) predictable.

So, people wonder, what lies beyond The Sphere? Is this something the Corporate AI now wants humanity to try and discover? And what might lie there, at the limit of the game engine, able to reach via a bugged-out glitch kept deliberately open by one of the largest and most sophisticated AIs on the planet?

All we know is two years ago some fluorescent letters appeared above every one of the 1,000 planets in The Sphere: keep going, it says.

Things that inspired this story: Eve Online, Snowcrash, Procedural Generation,

Import AI: #100: Turning 2D people into 3D puppets with DensePose, researchers trawl for bias in language AI systems, and Baidu engineers a self-building AI system

Researchers examine bias in trained language systems:
…Further analysis shows further bias (what else did you expect)?…
When we’re talking about bias within language AI systems, what do we mean? Typically, we’re describing how an AI system has developed a conceptual representation that is somehow problematic.
For instance, trained language models can frequently express different meanings when pairing a specific gender with an (ideally neutral) term like a type of work. This leads to situations where systems display coupled associations, like man:profession :: woman:homemaker.
Another example is where systems trained on biased datasets display offensive quirks, like language models trained on tabloids associating names of people of color with “refugee” and “criminal” disproportionately relative to other names.
These biases tend to emerge from the data the machine is trained on, so if you train a language model exclusively on tabloid news articles it is fairly likely the model will display the biases of that particular editorial position (a US example might be ending up associating anything related to the concept of an immigrant with negative terms).
De-biasing trained models:
Researchers have recently developed techniques to “de-bias” trained AI systems, removing some of the problematic associations according to the perspective of the operator (for instance: a government trying to ensure fair and equitable access to a publicly deployed AI service).
Further analysis: The problems run deep: 
Researchers with the University of Bristol have now further analyzed the relationships between words and biases in trained systems by introducing a new, large dataset of words and attribute words that describe them and examining this for bias with a finer-toothed comb.
  Results: A study of European-American and African-American names for bias showed that “European-American names are more associated with positive emotions than their African-American counterparts”, and noted that when analyzing school subjects they detect a stronger association between the male “he” and subjects like math and science. They performed the same study of occupations and found a high correlation between the male gender and occupations like ‘coach, executive, surveyor’, while for females top occupations included ‘therapist, Bartender, Psychologist”. They also show how to use algorithms to reduce bias, by figuring out the projection in space that is linked to bias and also devising reformulations that reduce this bias by altering the projection of the AI embedding.
Read more: Biased Embeddings from Wild Data: Measuring, Understanding and Removing (Arxiv).

Cartesian Genetic Programming VS Reinforcement Learning:
..Another datapoint to help us understand how RL compares to other methods…
One of the weirder trends in recent AI research has been the discovery, via experimentation, of how many techniques can obtain performance competitive with deep learning-based approaches. This has already happened in areas like image analysis (where evolved image classifiers narrowly beat the capabilities of ones discovered through traditional reinforcement learning, Import AI #81), and in RL (where work by OpenAI showed that Evolution Strategies work on par with deep RL approaches), among other cases.
Now researchers with the University of Toulouse and University of York have shown that techniques derived from 
Cartesian Genetic Programming (CGP)  can obtain results roughly on par with other state-of-the-art deep RL techniques. 
  Strange strategies: CGP programs work by interfacing with an environment and evolving repeated successions of different combinations of program, tweaking themselves as they go to try to ‘evolve’ towards obtaining higher scores. This means, like most AI systems, they can develop strange behaviors that solve the task while seeming imbued with a kind of literal/inhuman logic. In Kung-Fu Master, for example, CGP finds an optimal sequence of moves to use to obtain high scores, and in the case of a game called Centipede early CGP programs sometimes evolve a desire to just stay in the bottom left of the screen (as there are fewer enemies there).
  Results: CGP methods obtain competitive scores on Atari, when compared to methods based around other evolutionary approaches like HyperNEAT, as well as deep learning-techniques like A3C, Dueling Networks, and Prioritized Experience Replay. But I wouldn’t condition too heavily on these baselines – we don’t see comparisons with newer, more successful methods like Rainbow or PPO, and the programs display some unfortunate tendencies.
  Read more: Evolving simple programs for playing Atari games (Arxiv).

Ever wanted to turn 2D images of people into 3D puppets? Enter DensePose!
…Large-scale dataset and pre-trained model has significant potential for utility (and also for abuse):
Facebook has released DensePose, a system the company built that extracts a 3D mesh model of a human body from 2D RGB images. The company is also releasing the underlying dataset of trained DensePose on, called DensePose-COCO. This dataset provides image-to-surface correspondences annotated on 50,000 persons from the COCO dataset.
  Omni-use: DensePose, like many of the AI systems currently being developed and released by the private sector, has the potential for progressive and abusive uses. I could image, for instance, aid groups like Unicef or Doctors without Borders using it to better map and understand patterns of conflict from imagery. But I could also imagine it being re-purposed for invasive digital surveillance purposes (as I wrote in Import AI #80). It would be nice to see Facebook discuss the potential abuses of this technology as well as areas where it can be used fruitfully and try to tackle some of its more obvious implications in a public manner.
  Read more: Facebook open sources DensePose (Facebook Research blog).
  Get the code: DensePose (GitHub).

Researchers add a little structure to build physics-driven prediction systems:
…Another step in the age-old quest to get computers to learn that “what goes up must come down”…
Stanford and MIT researchers have tried to solve one long-standing problem in AI – making accurate physics-driven predictions about the world merely by observing it. Their approach involves the creation of a “Hierarchical Relation Network” which works by decomposing inputs, like images of scenes, into a handwritten toy physics model where individual objects are decomposed into various particles of various sizes and resolutions. These particles are then represented in a graph structure so that it’s possible to learn to perform physics calculations on them and use this to make better predictions.
  Results: The researchers test their approach by evaluating its effectiveness at predicting how different objects will bounce and move around a high-fidelity physics simulation written in FLeX within Unity. Their approach acquires the lowest position error when tested against other systems, and only slightly higher preservation error.
  Why it matters: Being able to model the underlying physics of the world is an important milestone in AI research and we’re currently living in an era where researchers are exploring hybrid methods, trying to fuse as much learning machinery as possible with structured representations, like structuring problems as graphs to be computed over. This research also aligns with recent work from DeepMind (Import AI: #98) which explores the use of graph-encodings to increase the range of things learned AI systems can understand.
  Read more: Flexible Neural Representation for Physics Prediction (Arxiv).
Watch video:
Hierarchical Particle Graph-Based Physics Prediction (YouTube).
  Things that make you go hmmm: This research reminds me of the Greg Egan story ‘Crystal Nights’ in which a mercurial billionaire creates a large-scale AI system but, due to computational limits, can’t fully simulate atoms and electrons so instead implements a basic particle-driven physics substrate which he evolves creatures within. Reality is starting to converge with odd bits of fiction.
  Read the Greg Egan sci-fi short story ‘Crystal Nights’ here.

Baidu researchers design mix&match neural architecture search:
…Want to pay computers to do your AI research for you? Baidu has you covered…
Most neural architecture search approaches tend to be very expensive in terms of the amount of compute needed, which has made it difficult for researchers with fewer resources to use the technology. That has been changing in the past year via research like SMASH, Efficient Neural Architecture Search (ENAS), and other techniques.
   Now researchers with Baidu have publishes details about the “Resource-Efficient Neural Architect” (RENA), a system they use to design custom neural network architectures which can be modified to optimize for different constraints, like the size of the neural network model, its computational complexity, or the compute intensity.
  How it works: RENA consists of a policy network to generate actions which define the neural network architecture, an environment to evaluate and assess the created neural network within. The policy network modifies an existing network by altering its parameters or by inserting or removing network layers. “Rather than building the target network from scratch, modifications via these operations allow more sample-efficient search with a simpler architecture. The search can start with any baseline models, a well-designed or even a rudimentary one.” RENA performs a variety of different search functions at different levels of abstraction, ranging from searching for specific modules to create and stack to compose a network, down to individual layers which can be tweaked.
  Results: The researchers show that RENA can iteratively improve the performance of an existing network on challenging image datasets like CIFAR. In one case, an initial network with performance of roughly 91% is upscaled by RENA to accuracy of 95%. In another case, RENA is shown to be able to create well-performing models that satisfy other compute resource constraints. They further demonstrate the generality of the approach by evaluating it on a keyword spotting (KWS) task, where it performs reasonably well but with less convincing results than on CIFAR.
  Why it matters: In the future, many AI researchers are going to seek to automate larger and larger chunks of their jobs; today that involves offloading the tedious job of hyperparameter checking to large-scale grid-search sweeps, and tomorrow it will likely be about automating and optimizing the construction of networks to solve specific tasks, while researchers work ion inventing new fundamental components.
 Read more: Resource-Efficient Neural Architect (Arxiv).

AI Nationalism:
…Why AI is the ultimate strategic lever of the 21st century…
The generality, broad utility, and omni-use nature of today’s AI techniques means “machine learning will drive the emergence of a new kind of geopolitics”, according to Ian Hogarth, co-founder of Songkick.
  Why it matters: I think it’s notable that we’re starting to have these sorts of discussions and ideas bubble up within the broad AI community. It suggests to me that the type of discourse we’re having about AI isset to change as people become more aware of the intrinsically political components and effects of the technology. My expectation is many governments are going to embark on some form of ‘AI nationalism’.
Read more: AI Nationalism (Ian Hogarth’s website).

AI Policy with Matthew van der Merwe:
…Reader 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: …

Tech giants under pressure from employees, shareholders over collaboration with government agencies:
In the midst of the uproar around US immigration policies, Amazon and Microsoft have come under fire from stakeholder groups raising concerns over AI-enabled face recognition software being sold to immigration and law enforcement agencies.    Microsoft employees protest ICE collaboration –  Over 100 employees signed a letter protesting the company’s collaboration with ICE. Microsoft’s Azure cloud platform announced the $19.4m contract in January, which would “utilize deep learning to accelerate face recognition and identification” for the agency.
  What they want – The letter demands that the company halt any involvement with ICE, draft a policy guaranteeing they will not work with clients who violate international human rights law, and commit to review of any contracts with government agencies.
  Amazon under pressure from shareholders… – Following the ACLU’s concerns over the deployment of Amazon’s face recognition software by US law enforcement, a group of shareholders has delivered a letter to the company. The letter asks that the company “immediately halt the expansion, further development ,and marketing of Rekognition, and any other surveillance technologies, to all government agencies” until appropriate guidelines and policies are put in place.
…and employees – A letter from employees reiterates shareholders’ concerns, and goes further, demanding that Amazon cease providing cloud-based services to any partners working with ICE, specifically naming the data analytics company Palantir.
  Why this matters: Following the apparent success of employee-led action at Google over Project Maven, stakeholder groups are mobilizing more readily around ethical issues. While this latest bout of activity was catalysed by the immigration scandal, the letters make broader demands about the way the companies develop and sell surveillance technologies. If Amazon, Microsoft follow Google’s example in drawing up clear ethical guidelines for AI, employee campaigns will have played a leading role in changing the industry in just a few months.
  Read more: Microsoft employee letter.
  Read more:  Amazon shareholder letter.
  Read more: Amazon employee letter (via Gizmodo).

South Korean university at center of ‘killer robots’ controversy launches AI ethics committee:
KAIST launched a new ethics committee this week. This comes after controversy earlier this year over the University’s joint project with arms manufacturer Hanwha. The collaboration raised fears the university was contributing to research on autonomous weapons, and prompted anger from academics, culminating in a letter signed by 50 AI experts from 30 countries, calling for a boycott of the University. This was subsequently called off following assurances the university would not engage in development of lethal autonomous weapons, and a pledge that they would not conduct any research “counter to human dignity”. The academic who organized the boycott, Prof Toby Walsh, gave the keynote speech at the launch event for the committee.
  Why this matters: This represents another win for grassroots mobilization against lethal autonomous weapons, after Google’s response to the Project Maven controversy. In this case, KAIST has gone further than simply withdrawing from AI weapons research, and is actively engaging in the debate around these technologies, and AI more broadly.
  Read more: The original boycott letter.
  Read more: KAIST launches ethics subcommittee on AI.

OpenAI Bits&Pieces:

Results of the OpenAI Retro Contest:
We’ve released the results for our Retro Contest, which had contestants compete to create an algorithm with a fast learning and generalization capability sufficient to master out-of-training-set Sonic levels. One notable thing about the result is most of the top submissions use variants of Rainbow or PPO, two recent RL algorithms from DeepMind and OpenAI. Additionally, two of the three winners are Chinese teams, with the top team hailing from the search department of Alibaba (congratulations to all winners!).
  Read more: Retro Contest: Results (OpenAI Blog).

Generative Adversarially-deployed Politicians (GAPs) – how serious is the threat?
When will someone use AI techniques to create a fake video of a real politician saying something political? That’s the gist of a bet I’ve put a (cocktail) wager on. You can read more about it in IEEE Spectrum. My belief is that at some point people are going to make fake AI images in the same way they make memes today and at that point the whole online information space might change/corrode in a noticeable and (when viewed over the course of years) quite abrupt manner.
  Read more: Experts bet on first Deepfakes political scandal (IEEE Spectrum).

Tech Tales:

The Castle of the Curious.

There was a lonely, old person who lived in a castle. Their partner had died when they were 60 years old and since then they had mostly been alone. They had entered into the not-quite-dead twilight of billionaires from that era and, at 90, had a relatively young metabolism in an old body with a brain weighed down by memory. But they had plans.They had food delivered to them by drone. They pumped water from a large, privately owned aquifer. For exercise, they walked around the expansive, bare grounds of the estate, keeping far from the perimeter, which was staffed with guards and rarely visited by anyone aside from dignitaries from various countries; the person sometimes entertained these visitors and other times turned them away. The person had a foundation which directed their vast wealth and they were able to operate it remotely.

No one can tolerate loneliness, even if they have willed it upon themselves. So one year the person attached numerous microphones to their property and acquired the technology to develop a personal AI system. Next year, they fused the two together, letting them walk through a castle and estate that they could talk to. For a time they became less lonely, able to schedule meetings with a convincing voice interface, and able to play verbal games with the AI, like riddles, or debates, or competitions at who could tell certain stories in certain literary styles. They’d walk into a library and ask what the last book they read was and even if the date had been a decade prior the AI knew and could help them pick up where they left off. But after a few years they tired of these interactions, finding that the AI could never become more than an extraordinarily chatty but utterly dedicated butler.

The person spent several months walking around their castle, lingering in offices and libraries; they scrawled notes and then built basic computer models and then devised spreadsheets and when they had a clear enough idea they handed the information to their foundation, which made their dreams come true. Now, the house was fragmented into several different AI systems. Each system had access to a subset of the sensors available in the house. To be able to become more efficient at accomplishing their tasks each system would need to periodically access the sensory inputs of other AI systems in the house. The person made it possible for the AIs to trade with eachother, but with a couple of conditions: they had to make their cases for accessing another AI’s sensory input via verbal debate which the person could listen to, and the person would play the role of the judge, ultimately picking to authorize or deny a request based on their perception of the strength of the arguments. For a while, this entertained the person as well, and they grew more fascinated with the AIs the longer they judged their increasingly obscure debates. Eventually, though, they tired of this, finding a sense of purposelessness about the exercise.

So they relaxed some constraints and changed the game. Now, the AIs could negotiate with eachother and have their outputs judged by another AI, which was meant to mimic the preferences of the person but also have a self-learning capability of its own. The two debaters would need to agree to nominate a single AI and this process itself was a debate judged by a jury of three other AIs, selected based on having the longest period of time of not interacting with the AIs in the debate. And all these ornate, interlocking debates were mandated to be done verbally, so the person could listen in. This entertained them for many years as they listened to the AIs negotiate and bribe and jibe with each other, their numbers always growing as new systems are added or existing ones fractured into many constituent parts.

Now, the estate is full of noise, and the gates are busy: the person has found a new role in life, which comes down to selecting new inputs for the Ais to loudly argue over. As they walk their estate they gaze at specific trees or rocks and wonder: how might the AIs bargain with eachother for a camera feed from this tree at this hour of dappled sunlight? Or how might the AIs argue over who can have the prize of accessing the earthquake sensor, so they can listen to and learn the movements of the earth? In this way the person found a new purpose in life: a demi-god among argumentative machines, forever kept close to the world by the knowledge that they could use it to have other creatures jostle with eachother.

Things that inspired this story: The Debate Game, Google Home/Siri/Cortana, NLP, unsupervised learning,

Import AI #99: Using AI to generate phishing URLs, evidence for how AI is influencing the economy, and using curiosity for self-imitation learning.

Auto-generating phishing URLs via AI components:
…AI is an omni-use technology, so the same techniques used to spot phishing URLs can also be used to generate phishing URLs…
Researchers with the Cyber Threat Analytics division of Cyxtera Technologies have written an analysis of how people might “use AI algorithms to bypass AI phishing detection systems” by creating their own system called DeepPhish.
  DeepPhish: DeepPhis works by taking in a list of fraudulent URLS that have been successfully worked in the past, encodes these as a one-hot representation, then trains a model to generate new synthetic URLs given a seed sentence. They found that DeepPhish could dramatically improve the chances of a fraudulent URL getting past automated phishing-detection systems, with DeepPhish URLs seeing a boost in effectiveness from 0.69% (no DeepPhish) to 20.90% (with DeepPhish).
  Security people always have the best names: DeepPhis isn’t the only AI “weapon” system recently developed by researchers, the authors note; other tools include Honey-Phish, SNAP_R, and Deep DGA.
  Why it matters: This research highlights how AI is an inherent omni-use technology, where the same basic components used to, for instance, train systems to learn to spot potentially fraudulent URLS, can also be used to generate plausible-seeming fraudulent URLs.
  Read more: DeepPhish: Simulating Malicious AI (PDF).

Curious about the future of reinforcement learning? Apply more curiosity!
…Self-Imitation Learning, aka: That was good, let’s try that again…
Self-Imitation Learning (SIL) works by having the agent exploit its replay buffer by learning to repeat its own prior actions if they have generated reasonable returns previously and, crucially, only when those actions delivered larger returns than were expected. The authors combine SIL with Advantage Actor-Critic (A2C) and test the algorithm out on a variety of hard tasks, including the notoriously tough Atari exploration game Montezuma’s Revenge. They also report scores for games like Gravitar, Freeway, PrivateEye, Hero, and Frostbite: all areas where A2C+SIL beats A3C+ baselines. Overall, AC2+SIL gets a median score across all of Atari of 138.7%, compared to 96.1% for A2C.
  Robots: They also test a combination of PPO+SIL on simulated robotics tasks within OpenAI Gym and significantly boost performance relative to non-SIL baselines.
  Comparisons: At this stage it’s worth noting that many other algorithms and systems have come out since A2C with better performance on Atari, so I’m a little skeptical of the comparative metric here.
  Why it matters: We need to design AI algorithms that can explore their environment more intelligently. This work provides further evidence that developing more sophisticated exploration techniques can further boost performance. Though, as the report notes, such systems can still get stuck in poor local optima. “Our results suggest that there can be a certain learning stage where exploitation is more important than exploration or vice versa,” the authors write. “We believe that developing methods for balancing between exploration and exploitation in terms of collecting and learning from experiences is an important future research direction.”
  Read more: Self-Imitation Learning (Arxiv).

Yes, AI is beginning to influence the economy:
…New study by experienced economists suggests the symptoms of major economic changes as a consequence of AI are already here…
Jason Furman, former chairman of the Council of Economic Advisers and current professor at the Harvard Kennedy School, and Robert Seamans of the NYU Stern School of Business, have published a lengthy report on AI and the Economy. The report compiles information from a wide variety of sources, so it’s worth reading in full.
  Here are some of the facts the report cites as symptoms that AI is influencing the economy:
– 26X: Increase in AI-related mergers and acquisitions from 2015 to 2017. (Source: The Economist).
– 26%: Real reduction in ImageNet top-5 image recognition error rate from 2010 to 2017. (Source: the AI Index.)
– 9X: Increase in number of academic papers focused on AI from 1996 to now, compared to a 6X increase in computer science papers. (Source: the AI Index.)
– 40%: Real increase in venture capital investment in AI startups from 2013 to 2016 (Source: MGI Report).
– 83%: Probability a job paying around $20 per hour will be subject to automation (Source: CEA).
– 4%: Probability a job paying over $40 per hour will be subject to automation (Source: CEA).
  “Artificial intelligence has the potential to dramatically change the economy,” they write in the report conclusion. “Early research findings suggest that AI and robotics do indeed boost productivity growth, and that effects on labor are mixed. However, more empirical research is needed in order to confirm existing findings on the productivity benefits, better understand conditions under which AI and robotics substitute or complement for labor, and understand regional level outcomes.”
   Read more: AI and the Economy (SSRN).

US Republican politician writes op-ed on need for Washington to adopt AI:
Op-ed from US politician Will Hurd calls for greater use of AI by federal government …
The US government should implement AI technologies to save money and cut the time it takes for it to provide services to citizens, says Will Hurd, chairman of the US Information Technology Subcommittee of the House Committee on Oversight and Government Reform.
  “While introducing AI into the government will save money through optimizing processes, it should also be deployed to eliminate waste, fraud, and abuse,” Hurd said. “Additionally, the government should invest in AI to improve the security of its citizens… it is in the interest of both our national and economic security that the United States not be left behind.”
  Read more: Washington Needs to Adopt AI Soon or We’ll Lose Millions (Fortune).
  Watch the hearing in which I testified on behalf of OpenAI and the AI Index (Official House website).

European Commission adds AI advisers to help it craft EU-wide AI strategy:
…52 experts will steer European AI alliance, advise the commission, draft ethics guidelines, and so on…
As part of Europe’s attempt to chart its path forward in an AI world, the European Commission has announced the members of a 52-strong “AI High Level Group” who will advise the Commission and other initiatives on AI strategy. Members include professors at a variety of European universities; representatives of industry,  like Jean-Francois Gagne the CEO of Element AI, SAP’s SVP of Machine Learning, and Francesca Rossi who leads AI ethics initiatives at IBM and also sits on the board of the Partnership on AI; as well as members of the existential risk/AGI community like Jaan Tallinn, who was the founding engineer of Skype and Kazaa.
  Read more: High-Level Group on Artificial Intelligence (European Commission).

European researchers call for EU-wide AI coordination:
…CLAIRE letter asks academics to sign to support excellence in European AI…
Several hundred researchers have signed a letter in support of the Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE), an initiative to create a pan-EU network of AI laboratories that can work together and feed results into a central facility which will serve as a hub for scientific research and strategy.
  Signatories: Some of the people that have signed the letter so far include professors from across Europe, numerous members of the European Association for Artificial Intelligence (EurAI) and five former presidents of IJCAI (International Joint Conference on Artificial Intelligence).
  Not the only letter: This letter follows the launch of another one in May which called for the establishment of a European AI superlab and associated support infrastructure, named ‘Ellis’. (Import AI: #92).
  Why it matters: We’re seeing an increase in the number of grass roots attempts by researchers and AI practitioners to get governments or sets of governments to pay attention to and invest in AI. It’s mostly notable to me because it feels like the AI community is attempting to become a more intentional political actor and joint-letters like this represent a form of practice for future more substantive engagements.
  Read more: CLAIRE (

When Good Measures go Bad: BLEU:
…When is an assessment metric not a useful assessment metric? When it’s used for different purposes…
A researcher with the University of Aberdeen has evaluated how good a metric BLEU (bilingual evaluation understudy) is for assessing the performance of natural language processing systems; they analyzed 284 distinct correlations between BLEU and gold-standard human evaluations across 34 papers and concluded that BLEU is useful for the evaluation of machine translation systems , but found its utility breaks down when used for other purposes, like the assessment of individual texts or scientific hypothesis testing or evaluation of things like natural language generation.
  Why it matters: AI research runs partially on metrics and metrics are usually defined by assessment techniques. It’s worth taking a step back and looking at widely-used things like BLEU to work out how meaningful it can be as an assessment methodology and to remember to use it within its appropriate domains.
  Read more: A Structured Review of the Validity of BLEU (Computational Linguistics).

Neural networks can be more brain-like than you assume:
…PredNet experiments show correspondence between activations in PredNet and activations in Macaque brains…
How brain-like are neural networks? Not very. That’s because, at a basic component level, they’re based on a somewhat simplified ~1950s conception of how neurons work, so their biological fidelity is fairly low. But can neural networks, once trained to perform particular tasks, end up reflecting some of the functions and capabilities found in biological neural networks? The answer seems to be yes, based on several years of experiments in things as varied as analyzing pre-trained vision networks, verifying the emergence of ‘place cells‘, and experiments.
  Harvard and MIT Researchers have analyzed PredNet, a neural network trained to perform next-frame prediction in a video of sequences, to understand how brain-like its behavior is. They find that groups when they expose the network to input its neurons fire with a response pattern (consisting of two distinct peaks) that is analogous to the firing patterns found in individual neurons within Macaque monkeys. Similarly, when analyzing a network trained on the self-driving Kittie dataset in terms of its spatial receptivity they find that the artificial network displays similar dynamics to real ones (though with some variance and error). The same high level of overlap between behavior of artificial and real neurons is roughly true of systems trained on sequence learning tasks.
  Less overlap: The areas where artificial and real neurons display less overlap seems to roughly correlate to intuitively harder tasks, like being able to deal with optical illusions, or in how the systems respond to different classes of object.
  Why it matters: We’re heading into a world where people are going to increasingly use trained analogues of real biological systems to better analyze and understand the behavior of both. PredNet provides an encouraging example that this line of experimentation can work. “We argue that the network is sufficient to produce these phenomena, and we note that explicit representation of prediction errors in units within the feedforward path of the PredNet provides a straightforward explanation for the transient nature of responses in visual cortex in response to static images,” the researchers write. “That a single, simple objective—prediction—can produce such a wide variety of observed neural phenomena underscores the idea that prediction may be a central organizing principle in the brain, and points toward fruitful directions for future study in both neuroscience and machine learning.”
  Read more: A neural network trained to predict future video frames mimics the critical properties of biological neuronal responses and perception (Arxiv).
  Read more: PredNet (CoxLab).

Unsupervised Meta-Learning: Learning how to learn without having to be told how to learn:
…The future will be unsupervised…
Researchers with the University of California at Berkeley have made meta-learning more tractable by reducing the amount of work a researchers needs to do to setup a meta-learning system. Their new ‘unsupervised meta-learning’ (ULM) approach lets their meta-learning agent automatically acquire distributions of tasks which it can subsequently perform meta-learning over. This deals with one drawback of meta-learning, which is that it is typically down to the human designer to come up with a set of tasks for the algorithm to be trained on. They also show how to combine ULM with other recently developed techniques like DIAYN (Diversity is all you need) for breaking environments down into collections of distinct tasks/states to train over.
  Results: UML systems beat basic RL baselinets on simulated 2D navigation and locomotion tasks. They also tend to be obtain performance roughly equivalent to systems built with human-designed tuned reward functions, suggesting that UML can successfully explore the problem space enough to devise good reward signals for itself.
  Why it matters: Because the diversity of tasks we’d like AI to do is much larger than the number of tasks we can neatly specify via hand-written rules it’s crucial we develop methods that can rapidly acquire information from new environments and use this information to attack new problems. Meta-learning is one particularly promising approach to dealing with this problem, and by removing another one of its more expensive dependencies (a human-curated task distribution) UML may help push things forward. “An interesting direction to study in future work is the extension of unsupervised meta-learning to domains such as supervised classification, which might hold the promise of developing new unsupervised learning procedures powered by meta-learning,” the researchers write.
  Read more: Unsupervised Meta-Learning for Reinforcement Learning (Arxiv).

OpenAI Bits&Pieces:

Better language systems via unsupervised learning:
New OpenAI research shows how to pair unsupervised learning with supervised finetuning to create large, generalizable language models. This sort of result is interesting because it shows how deep learning components can end up displaying sophisticated capabilities, like being able to obtain high scores on Winograd schema tests, having only learned naively from large amounts of data rather than via specific hand-tuned rules.
  Read more: Improving Language Understanding with Unsupervised Learning (OpenAI Blog).

Tech Tales:

Special Edition: Guest short story by James Vincent, a nice chap who writes about AI. All credit to James, all blame to me, etc…

Shunts and Bumps.

Reliable work, thought Andre, that was the thing. Ignore the long hours, freezing warehouses, and endless retakes. Ignore the feeling of being more mannequin than man when the director storms onto set, snatches the coffee cup out of your hand and replaces it with a bunch of flowers without even looking at you. Ignore it all. This was a job that paid, week after week, and all because computers had no imagination.

God bless their barren brains.

Earlier in the year, Rocky had explained it to him like this. “They’re dumb as shit, ok? Show them a potato 50 times and they’ll say it’s an orange. Show them it 5,000 times and they’ll say it’s a potato but pass out in shock if you turn it into fries. They just can’t extrapolate like humans can — they can’t think.” (Rocky, at this point, had been slopping her beer around the bar as if trying to short-circuit a crowd of invisible silicon dunces.) “They only know what you show them, and only then when you show them it enough times. Like a mirror … that gets a burned-in image of your face after you’ve looked at it every day for year.”

For the self-driving business, realizing this inability to extrapolate had been a slow and painful process. “A bit of a car crash,” Rocky said. The first decade had been promising, with deep learning and cheap sensors putting basic autonomy in every other car on the road. Okay, so you weren’t technically allowed to take your hands off the wheel, and things only worked perfectly in perfect conditions: clearly painted road markings, calm highways, and good weather. But the message from the car companies was clear: we’re going to keep getting better, this fast, forever.

Except that didn’t happen. Instead, there was freak accident after freak accident. Self-driving cars kept crashing, killing passengers and bystanders. Sometimes it was a sensor glitch; the white side of a semi getting read as clear highway ahead. But more often it was just the mild chaos of life: a party balloon drifting into the road or a mattress falling off a truck. Moments where the world’s familiar objects are recombined into something new and surprising. Potatoes into fries.

The car companies assured us that the data they used to train their AI covered 99 percent of all possible miles you could travel, but as Rocky put it: “Who gives a fuck about 99 percent reliability when it’s life or death? An eight-year-old can drive 99 percent of the miles you can if you put her in a booster seat, but it’s those one percenters that matter.”

Enter: Andre and his ilk. The car companies had needed data to teach their AIs about all the weird and unexpected scenarios they might encounter on the road, and California was full of empty film lots and jobbing actor who could supply it. (The rise of the fakies hadn’t been kind to the film industry.) Every incident that an AI couldn’t extrapolate from simulations was mocked up in a warehouse, recorded from a dozen angles, and sold to car companies as 4D datasets. They in turn repackaged it for car owners as safety add-ons sold at $300 a pop. They called it DDLC: downloadable driving content. You bought packs depending on your level of risk aversion and disposable income. Dog, Cats, And Other Furry Fiends was a bestseller. As was Outside The School Gates.

It was a nice little earner, Rocky said, and typical of the tech industry’s ability to “turn liability into profit.” She herself did prototyping at one of the higher-end self-driving outfits. “They’re obsessed with air filtration,” she’d told Andre, “Obsessed. They say it’s for biological attacks but I think it’s to handle all their meal-replacement-smoothie farts.” She’d also helped him find the new job. As was usually the case when the tech industry used cheap labor to paper over the cracks in its products, this stuff was hardly advertised. But, a few texts and a Skype audition later, and here he was.

“Ok, Andre, this time it’s the oranges going into the road. Technical says they can adjust the number in post but would prefer if we went through a few different velocities to get the physics right. So let’s do a nice gentle spill for the first take and work our way up from there, okay?”

Andre nodded and grabbed a crate. This week they were doing Market Mayhem: Fruits, Flowers, And Fine Food and he’d been chucking produce about all day. Before that he’d pushing a cute wheeled cart around on the warehouse’s football field-sized loop of fake street. He was taking a break after the crate work, staring at a daisy pushing its way through the concrete (part of the set or unplanned realism?) when the producer approached him.

“Hey man, great work today — oops, got a little juice on ya there still — but great work, yeah. Listen, dumb question, but how would you like to earn some real money? I mean, who doesn’t, right? I see you, I know you’ve got ambitions. I got ‘em too. And I know you’ve gotta take time off for auditions, so what I’m talking about here is a little extra work for triple the money.”

Andre had been suspicious. “Triple the money? How? For what?”

“Well, the data we’ve been getting is good, you understand, but it’s not covering everything the car folks want. We’re filling in a lot of edge cases but they say there’s still some stuff there’s no data for. Shunts and bumps, you might say. You know, live ones… with people.”

And that was how Andre found himself, standing in the middle of a fake street in a freezing warehouse, dressed in one of those padded suits used to train attack dogs, staring down a mid-price sedan with no plates. Rocky had been against it, but the money had been too tempting to pass up. With that sort of cash he’d be able to take a few days off, hell, maybe even a week. Do some proper auditions. Actually learn the lines for once. And, the producer said, it was barely a crash. You probably wouldn’t even get bruised.

Andre gulped, sweating despite the cold air. He looked at the car a few hundred feet away. The bonnet was wrapped in some sort of striped, pressure sensitive tape, and the sides were knobbly with sensors. Was the driver wearing a helmet? That didn’t seem right. Andre looked over to the producer, but he was facing away from him, speaking quickly into a walkie-talkie. The producer pointed at something. A spotlight turned on overhead. Andre was illuminated. He tried to shout something but his tongue was too big in his mouth. Then he heard the textured whine of an electric motor, like a kazoo blowing through a mains outlet, and turned to see the sedan sprinting quietly towards him.

Regular work, he thought, that was the thing.

Things that inspired this story: critiques of deep learning; failures of self driving systems; and imitation learning.

Once again, the story above is from James Vincent, find him on Twitter and let him know what you thoughts!

Import AI #98: Training self-driving cars with rented firetrucks; spotting (staged) violence with AI-infused drones; what graphs might have to do with the future of AI.

Cruise asks to borrow a firetruck to help train its self-driving cars:
…Emergency training data – literally…
Cruise, a self-driving car company based in San Francisco, wants to expose its vehicles to more data involving the emergency services, so then it asked the city if it could rent a firetruck, fire engine, and ambulance, and have the vehicles drive around a block in the city with their lights flashing, according to emails surfaced via Freedom of Information Act requests from Jalopnik.
  Read more: GM Cruise Prepping Launch of Driverless Pilot Car Pilot in San Francisco: Emails (Jalopnik).

Experienced researcher: What to do if winter is coming:
…Tips for surviving the post-bubble era in AI…
John Langford, a well-regarded researcher with Microsoft, has some advice for people in the AI community as they carry out the proverbial yak-shaving act of questioning whether AI is in a bubble or not. Though the field shouldn’t optimize for failure, it might be helpful if it planned for it, he says.
 “As a field, we should consider the coordinated failure case a little bit. What fraction of the field is currently at companies or in units at companies which are very expensive without yet justifying that expense? It’s no longer a small fraction so there is a chance for something traumatic for both the people and field when/where there is a sudden cut-off,” he writes.
  Read more: When the bubble bursts… (John Langford’s personal blog).

Drone AI paper provides a template for future surveillance:
…Lack of discussion of impact of research raises eyebrows…
Researchers with the University of Cambridge, the National Institute of Technology, and the Indian Institute of Science, have published details on a “real-time drone surveillance system” that uses deep learning. The system is designed to spot violent activities like strangling, punching, kicking, shooting, stabbing, and so on, by performing image recognition over imagery gathered from a crowd in real-time.
  It’s the data, silly: To carry out this project the researchers create their own (highly staged) collection of around 2,000 images called the ‘Aerial Violent Individual’ dataset, which they record via a consumer-based Parrot AR Drone. Most of the flaws in the system relate to this data, which sees a bunch of people carry out over-acted expressions of aggression towards each other – this data doesn’t seem to have much of a relationship to real-world violence and it’s not obvious how well this would perform in the wild.
  Results: The resulting system “works”, in the sense that the researchers are able to obtain high accuracies (90%+) on classifying certain violent behaviors within the dataset, but it’s not clear whether this translates to anything of practical use in the real world. The researchers will subsequently test out their work at a music festival in India later this month, they said.
  Responsibility: Like the “Deep Video Networks” research which I wrote about last week, much of this research is distinguished by the immense implications it appears to have for society, and it’s a little sad to see no discussion of this in the paper – yes, surveillance systems like this can likely be used to humanitarian ends, but they can also be used by malicious actors to surveil or repress people. I think it’s important AI researchers start to acknowledge the omni-use nature of their work and confront questions like this within the research itself, rather than afterwards following public criticism.
  Read more: Eyes in the Sky: Real-time Drone Surveillance System (DSS) for VIolent Individuals Identification using ScatterNet Hybrid Deep Learning Framework (Arxiv).
  Watch video (YouTube).

“Depth First Learning” launches to aid understanding of AI papers:
…Learning through a combination of gathering context and testing understanding…
Industry and academic researchers have launched ‘Depth First Learning”, an initiative to make it easier for people to educate themselves about important research papers by going through the key ideas of the paper along with recommended literature to read and various questions throughout each writeup indented to test for the reader having learned enough about the context to answer the question. The idea behind this work is that it makes it easier to understand research papers by breaking them down into their fundamental concepts. “We spent some time understanding each paper and writing down the core concepts on which they were built,” the researchers write.
  Read an example: “Depth First Learning” article on InfoGAN (Depth First Learning website).
  Read more: Depth First Learning (DFL website, About page).

Graphs, graphs everywhere: The future according to DeepMind:
…Why a little structure can be a very good thing…
New research from DeepMind shows how to fuse structured approaches to AI design with end-to-end learned systems to create systems that can not only learn about the world, but recombine learnings in new ways to solve new problems. This sort of “combinatorial generalization” is key to intelligence, the authors write, and they claim their approach deals with some of the recent criticisms of deep learning made by people like Judea Pearl, Josh Tenenbaum, and Gary Marcus, among others.
  Structure, structure everywhere: The authors argue that many of today’s deep learning systems already encode this sort of bias towards structure in the form of specific arrangements of learned components, for example, how convolutional neural networks are composed out of convolutional layers and then chained together in increasingly elaborate ways for image recognition. These designs encode within them an implicit relational inductive bias, the authors write, because they take in a bunch of data and operate over its relationships in increasingly elaborate ways. Additionally, most problems can be decomposed into graph representations (for instance, modeling the interactions of a bunch of pool balls can be done by expressing the pool balls and the table as nodes in a graph with the links between them signaling directions in which force may be transmitted, or a molecule can similarly be decomposed as atoms (nodes) and bonds (edges).
  Graph network: DeepMind has developed the ‘Graph network’ (GN) block, a generic component “which takes a graph as input, performs computations over the structure, and returns a graph as output.” This is desirable because a graph structure is fairly flexible, letting you express an arbitrary number of relationships between an arbitrary number of entities, and the same function can be deployed on differently sized graphs, and these graphs represent entities and relations as sets making them invariant to permutations.
  No silver bullet: Graph networks don’t make it easy to support approaches like “recursion, control flow, and conditional iteration”, they say, and so should not be considered a panacea. Another is the larger question of where to derive the graphs from that the graphs operate over, which the authors leave to other researchers.
  Read more: Relational inductive biases, deep learning, and graph networks (Arxiv).

Google announces AI principles to guide its business:
…Company releases seven principles, along with description of ‘AI applications we will not pursue’…
Google has published its AI principles, following an internal employee outcry in response to the company’s participation in a drone surveillance project for the US military. These principles are intended to guide Google’s work in the future, according to a blog post written by Google CEO Sundar Pichai. “These are not theoretical concepts; they are concrete standards that will actively govern our research and product development and will impact our business decisions”.
  Principles: The seven principles are as follows:
– “Be socially beneficial”.
– “Avoid creating or reinforcing unfair bias”.
– “Be built and tested for safety”.
– “Be accountable to people”.
– “Incorporate privacy design principles”.
– “Uphold high standards of scientific excellence”.
– “Be made available for uses that accord with these principles”.
   What Google won’t do: Google has also published a (short) list of “AI applications we will not pursue”. These are pretty notable because it’s rare for a public company to place such restrictions on itself so abruptly. The things Google won’t pursue are as follows:
– “Technologies that cause or are likely to cause overall harm”.
– “Weapons or other technologies whose principal purpose or implementation is to cause or directly facilitate injury to people”.
– “Technologies that gather or use information for surveillance violating internationally accepted norms”.
– “Technologies whose purpose contravenes widely accepted principles of international law and human rights”.
   Read more: AI at Google: our principles (Google Blog).

AI Policy with Matthew van der Merwe:
…Reader 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: …

India releases national AI strategy:
India is the latest country to launch an AI strategy, releasing a discussion paper last week.
   Focus on five sectors: The report identifies five sectors in which AI will have significant societal benefits, but which may require government support in addition to private sector innovation. These are: healthcare; agriculture; education; smart cities and infrastructure; mobility and transportation.
   Five barriers to be addressed:
– Lack of research expertise
– Absence of enabling data ecosystems
– High resource cost and low awareness for adoption
– Lack of regulations around privacy and security
– Absence of collaborative approach to adoption and applications.
What they’re doing: The report proposes supporting two tiers of organizations to drive the strategy.
Centres of Research Excellence – academic/research hubs
– International Centres of Transformational AI – bodies with a mandate of developing and deploying research, in partnership with private sector.
   Read more: National Strategy for Artificial Intelligence.

Tech Tales:

The Dream Wall

Everyone’s DreamWall is different and everyone’s DreamWall is intimate. Nonetheless, we share (heavily curated) pictures of them with eachother. Mine is covered in mountains and on each mountain peak there are little desks with lamps. My friend’s Wall is shows an underwater scene and includes spooky trenches and fish that swim around them and the occasional hint of an octopus. One famous person accidentally showed a picture of their dream wall via a poorly posed selfie and it caused them problems because the DreamWall showed a pastoral country scene with nooses hanging from the occasional tree and in one corner a haybale-sized pile of submachineguns. Even though most people know how DreamWalls work they can’t help but judge other people for the contents of theirs.

It works like this:

When you wake up you say some of the things you were dreaming about.

Your home AI system records your comments and sends them to your personal ‘DreamMaker’ software

The ‘DreamMaker’ software maps your verbal comments to entities in its knowledge graph, then sends those comment-entity pairs to the DreamArtist software.

DreamArtist tries to render the comment-entity data into individual objects which fit with the aesthetic theme inherent to your current DreamWall.

The new objects are sent to your home AI system which displays them on your DreamWall and gives you the option to add further prompts, such as “move the cow to the left” or “make sure that the passengers in the levitating car look like they are having fun”.

This cycle repeats every morning, though if you don’t say anything when you wake up it will maintain the DreamWall and only modulate its appearance and dynamics according to data about how active you had been in the night.

If you wake up with someone else most systems have failsafes that mean your DreamWall won’t display. Some companies are piloting ‘Couple’s DreamWalls’ but are having trouble with it – apart from some old couples that have been together a very long time, most people, even if they’re in a very harmonious relationship, have distinct aspects to their personality that the other person might not want to wake up to every single day – especially since DreamWalls tend to contain visual depictions of things otherwise repressed during daily life.

Import AI #97: Faking Obama and Putin with Deep Video Portraits, Berkeley releases a 100,000+ video self-driving car dataset, and what happens when you add the sensation of touch to robots.

Try a little tenderness: researchers add touch sensors to robots.
…It’s easier to manipulate objects if you can feel them…
Researchers with the University of California at Berkeley have added GelSight touch sensors to a standard 7-DoF Rethink Robotics ‘Sawyer’ robot with an attached Weiss WSG-50 parallel gripper to explore how touch inputs can improve performance at grasping objects – a crucial skill for robots to have if used in commercial settings.
  Technique: The researchers construct four sub-networks that operate over specific data inputs (camera image, two GelSight images to model texture senses before and after contact, and an action network that processes 3D motion, in-plane rotation, and change in force. They link these networks together within a larger network and train the resulting model over a dataset of objects. The researchers pre-train the image components of the network with a model previously trained to classify objects on ImageNet. The approach yields a model that adapts to novel surfaces, learns interpretable policies, and can be taught to apply specific constraints when handling an object, like grasping it gently. 
  Results: The researchers test their model and find that systems trained with vision and action inputs get 73.03% accuracy, compared to 79.34% for systems trained on tactile inputs and action, compared to 80.28% for systems trained with tactile and vision and action.
   Harder than you think: This task, like most that require applying deep learning components to real-world systems, contains a few quirks which might seem non-obvious from the outset, for example: “The robot only receives tactile input intermittently, when its fingers are in contact with the object and, since each re-grasp attempt can disturb the object position and pose, the scene changes with each interaction”.
  Read more: More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch (Arxiv).

Want 100,000 self-driving car videos? Berkeley has you covered!
…”The largest and most diverse open driving video dataset so far for computer vision research”., according to the researchers..
Researchers with the University of California at Berkeley and Nexar have published BDD100K, a self-driving car dataset which BDD100K contains ~120,000,000 images spread across ~100,000 videos. “Our database covers different weather conditions, including sunny, overcast, and rainy, as well as different times of day including daytime and nighttime,” they say. The dataset is substantially larger than ones released by the University of Toronto (KITTI), Baidu (ApolloScape), Mapillary, and others, they say.
DeepDrive: The dataset release is significant for where it comes from: DeepDrive, a Berkeley-led self-driving car research effort with a vast range of capable partners, including automotive companies such as Honda, Toyota, and Ford. DeepDrive was set up partially so its many sponsors could pool research efforts on self-driving cars, seeking to close an implicit gap with other players.
  Rich data: The videos are annotated with hundreds of thousands of labels for objects like cars, trucks, persons, bicycles, and so on, as well as richer annotations for road lines drivable areas, and more; they also provide a subset of roughly ~10,000 images with full-frame instance segmentation.
  Why it matters – the rise of the multi-modal dataset: The breadth of the dataset with its millions of labels and carefully refined aspects will likely empower researchers in other areas of AI, as well as its obvious self-driving car audience. I expect that in the future these multi-modal datasets will become increasingly attractive targets to use to evaluate transfer learning from other systems, for instance by training a self-driving car model in a rich simulated world then applying it to real-world data, such as BDD100K.
  Challenges: The researchers are hosting three challenges at computer vision conference CVPR relating to the dataset, and are asking groups to compete to develop systems for road object detection, drivable area prediction, and domain adaptation.
  Read more: BDD100K: A Large-scale Diverse Driving Video Database (Berkeley AI Research blog).

KPCB’s Mary Meeker breaks down AI’s rise and China’s possible advantage in annual presentation:
…Annual slide-a-thon shows rise of China, points to image and speech recognition scores as evidence for impact of AI…
Mary Meeker’s annual presentation of research serves as a useful refresher for what is front-of-mind for venture capitalists focused on understanding the dynamics that affect the technology ecosystem. This year, at Code Conference in California, Meeker’s slides were distinguished via large sections spent on China, combined with a few notable slides situating AI progress metrics (specifically in object recognition and speech recognition) in relation to the growth of new markets for business.
  Read more: Mary Meeker’s 2018 internet trends report: All the slides, plus analysis (Recode).


An incomplete timeline of dubious things that people have synthesized via AI
– Early 2017:
Montreal Startup Lyrebird launches with audio recording featuring synthesized voices of Donald Trump, Barack Obama, Hillary Clinton.
– Late 2017:
“DeepFakes” arrive on the internet via Reddit with a user posting pornographic movies with celebrity faces animated onto them. A consumer-oriented free editing application follows and DeepFakes rapidly proliferate across the internet, then consumer sites start to clamp down on them.
– 2018:
Belgian socialist party makes a video containing a synthesized Donald Trump giving a (fake) speech about climate change. Party says video designed to create debate and not trick viewers.
– Listen: Politicians discussing about Lyrebird (Lyrebird Soundcloud).
– Read more: DeepFakes Wikipedia entry.
– Read more: Belgian Socialist Party Circulates “Deep Fake” Donald Trump Video (Politico Europe).

Why all footage of all politicians is about to become suspect:
…Think fake news is bad now? ‘Deep Video Portraits’ will make it much, much worse…
A couple of years ago European researchers caused a stir with ‘face2face’, technology which they demonstrated by mapping their own facial expressions onto synthetically rendered footage of famous VIPs, like George Bush, Barack Obama, and so on. Now, new research from a group of American and European researchers has pushed this fake-anyone technology further, increasing the fidelity of the rendered footage, reducing the amount of data needed to construct such convincing fakes, and also dealing with visual bugs that would make it easier to identify the output as being synthesized.
  In their words: “We address the problem of synthesizing a photo-realistic video portrait of a target actor that mimics the actions of a source actor, where source and target can be different subjects,” they write. “Our approach enables a source actor to take full control of the rigid head pose, face expressions and eye motion of the target actor”. (Emphasis mine.)
  Technique: The technique involves a few stages: first, the researchers track the people within the source and target videos via a monocular face reconstruction approach, which allows them to extract information about the identity, head pose, expression, eye gaze, and scene lighting for each video frame. They also separately track the gaze of each subject. They then essentially transfer the synthetic renderings of the input actor onto the target actor and perform a couple of clever tricks to make the resulting output high fidelity and less prone to synthetic tells like visual smearing/blurring of the background behind the manipulated actor.
  Why it matters: Techniques like this will have a bunch of benefits for people working in media and CGI, but they’ll also be used by nation states, fringe groups, and extremists, to attack and pollute information spaces and reduce overall trust in the digital infrastructure of societal discourse and information transmittion. I worry that we’re woefully unprepared for the ramifications of the rapid proliferation of these techniques and applications. (And controlling the spread of such a technology is a) extremely difficult and b) of dubious practicality and c) potentially harmful to broader beneficial scientific progress.)
  An astonishing absence of consideration: I find it remarkable that the researchers don’t take time in the paper to discuss the ramifications of this sort of technology, given that they’re demonstrating it by doing things like transferring President Obama’s expressions onto Putin’s, or Obama’s onto Reagan’s. They make no mention of the political dimension to this work in their ‘Applications’ section, which focuses on the technical details of the approach and how it can be used for applications like ‘visual dubbing’ (getting an actor’s mouth movements to map to an audio track’.
  Read more: Deep Video Portraits (Arxiv).
  Watch video for details: Deep Video Portraits – SIGGRAPH 2018 (YouTube).

DARPA to host synthetic video/image competition:
..US defense-oriented research organization to try and push state-of-the-art in creation and detection of fakes…
Nation states have become aware of the tremendous potential for subterfuge that this technology poses and are reacting by dumping research money into both exploiting this for gain and for defending against it. This summer, DARPA will hold a competition to see who can create the most convincing synthetic images, and also to see who can detect them.
  Read more: The US military is funding an effort to catch deepfakes and other AI trickery (MIT Technology Review).

Import AI Job Alert: I’m hiring an editor/sub-editor:
  I’m hiring someone to initially sub-edit and eventually help edit the OpenAI blog. The role will be a regularly compensated gig which should initially take about 1.5-2 hours every week, typically at around 9pm Pacific Time on Sunday Nights. If you’d be interested in this then please send me an email telling me why you’d be a good fit. The ideal candidate probably has familiarity with AI research papers, attention to detail, and experience fiddling with words in a deadline-oriented setting. I’ve asked around among some journalists and the fair wage seems to be about $25 per hour.
  Additional requirements: You’d need to be available via real-time communication such as WhatsApp or Slack during the pre-agreed upon editing window. Sometimes I may need to shift the time a bit if I’m traveling, but I’ll typically have advance warning.
  Send emails with subject line “Import AI editing job: [Your Name]” to

Predicting cyber attacks on utilities with variational auto-encoders:
…All watched over by (variational) machines of loving grace…
Researchers with water infrastructure company Xylem Inc have tested out a variational auto-encoder (VAE)-based system for detecting cyberattacks on utility systems. The research highlights a feature of contemporary AI methods that is both a drawback and a strength: their excellence at curve-fitting in big, high-dimensional spaces. Here, the researchers use a VAE to train a model on past observations that represent 43 variables within a municipal water network. They then study how this model reacts to unforeseen changes in the system that might indicate a cyberattack: the model works better than rule-based systems, with the VAE spitting out a constant logarithm of reconstruction probability (LRP) which tends to diverge when the underlying system departs from the norm.
  Strengths: “The model relies solely on sensor reads data in their raw form and requires no preprocessing, system knowledge, or domain expertise to function. It is generic and can be readily applied to a broad array of ICS’s in various industry sectors.”
  Weaknesses: “It is not perfect and has its own requirements (e.g., availability of vast amount of system observations data) and drawbacks (e.g., sensitivity to rare but planned operations such as activation of emergency booster pumps),” they write. This highlights one of the weaknesses of the great curve-fitting power of contemporary AI techniques (Judea Pearl has argued that curve-fitting is pretty much all these systems are capable of), which is that they’re naive as to changing circumstances and lack the common sense to distinguish malice from action.
  Why it matters: Techniques like this are pretty crude but they indicate that there’s a basic value in training basic machine learning systems on data to spot anomalies. This research to me is mostly interesting due to its context – its researchers are all linked to a traditional ‘non-tech’ organization and the technology is tested against real-world data. Part of the virtue of publishing a paper like this is probably to help with hiring, as the researchers will be able to point prospective candidates to this paper as an indication for why Xylem is an interesting place to work. It’s possible to imagine a future where basic predicting models are layered into the data streams of every town and utility, providing an additional signal to human overseers.
  Read more: Cyberattack Detection using Deep Generative Models with Variational Inference (Arxiv).


AI Policy with Matthew van der Merwe:
…Reader 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: …

Google will not renew contract for Project Maven, plans to release principles for how it approaches military and intelligence contracting:
….Big Tech continues to grapple with ethical challenges around military AI…
Google announced internally on Friday that it would not be renewing the contract it had with the US military for Project Maven, an AI-infused drone surveillance platform, according to Gizmodo. Google also said it is drafting ethical principles for how it approaches military and intelligence contracting.
  Why it matters: Military uses of AI remains one of the most contentious issues for the industry, and society, to grapple with. Tech giants will have a role in setting standards for the industry at large. (One wonders how much of a role Google can play here in the USA now, given that it will now be viewed as deeply partisan by the DoD – Jack) Given that AI is a particularly powerful ‘dual-use’ technology, direct military applications may end up being one of the easier ethical dilemmas the industry faces in the future.
  Read more: Google plans not to renew Project Maven contract (Gizmodo).
  Read more: How a Pentagon Contract Became an Identity Crisis for Google (NYT).

UK public opposed to AI decision-making in most parts of public life:
…The Brits don’t like the bots…
The RSA and DeepMind have initiated a project to create ‘meaningful public engagement on the real-world impacts of AI’. The project’s first report includes a survey of UK public attitudes towards automated decision-making systems.
  Lack of familiarity: With the exception of online advertising (48% familiar), respondents were overwhelmingly unfamiliar with the use of automated decision-making in key areas. Only 9% were aware of its use in the criminal justice system, 14% in immigration, and 15% in the workplace.
  Opposition to AI decision-making: Most respondents were opposed to the usage of these methods in most parts of society. The strongest opposition was in the usage of AI in the workplace (60% opposed vs 11% support) and criminal justice (60% opposed vs. 12% support).
  What the public want: While 29% said nothing would increase their support for automated decision-making, the poll pointed to a few potential re-mediations that people would support:
  36%: The right to demand an explanation for an automated decision.
  33%: Penalties for companies failing to monitor systems appropriately.
  24%: A set of common principles guiding the use of such systems.
  Why it matters: The report notes that a public backlash against these technologies cannot be ruled out if issues are not addressed. The RSA’s proposal for public engagement via deliberative processes and ‘citizens’ juries’, if successful, could provide a model for other countries.
   Read more: Artificial Intelligence – Real Public Engagement (RSA).

Open Philanthropy Project launches AI Fellows program:
Over $1 million in funding for high-impact AI researchers…
The Open Philanthropy Project, the grant-making foundation funded by Cari Tuna and Dustin Moskovitz, is providing $1.1m in PhD funding for seven AI/ML researchers focused on minimizing potential risks from advanced AI systems. “Increasing the probability of positive outcomes from transformative AI”, is one of Open Philanthropy’s priorities.
  Read more: Announcing the 2018 AI Fellows.
  Read more: AI Fellowship Program.

OpenAI Bits & Pieces:

OpenAI Fellows:
We designed this program for people who want to be an AI researcher, but do not have a formal background in the field. Applications for Fellows starting in September are open now and will close on July 8th at 12AM PST.
  Read more: OpenAI Fellows (OpenAI Blog).

Tech Tales:

Walking Through Shadows That Feel Like Sand

Yes, people died. What else would you expect?

People walked off cliffs. People walked into the middle of the street. People left stoves on. People forgot to eat. People lost their minds.

Yes, we punished the people that caused these people to die. We punished these people with lawsuits or criminal sentences. Sometimes we punished them with both.

But the technology got better.

Less people died.

At some point the technology started saving more people than it killed.

People stopped short of ledges. People pulled back from traffic. People remembered to turn stoves off. People would eat just the right amount. People healed their minds.

Was it perfect? No. Nothing ever is.

Did we adopt it? Yes, as we always do.

Are we happy? Yes, most of us are. And the more of us that are happy, the more likely everyone is going to be happy.

Now, where are we? We are everywhere.

We wear these goggles and we get to choose what we see.
We wear these clothes that let us feel additional sensations to supplement or replace the world.
We have these chips in our eardrums that let us hear the world better than dogs, or hear nothing at all, or hear something else entirely.

We walk through city streets and get to feel the density of other people via vibrations superimposed onto our bodies by our clothes.
We watch our own pet computer programs climbing the synth-neon signs that hang off of real church steeples.
We see sunsets superimposed on one another and we can choose whenever to see them, even in the thick of night.

When we are sad we diffuse our sadness into the world around us and the world responds back with rising violins or crashing waves.
Sometimes when we are sad the sun and the moon cry with us.
Sometimes we feel cold tears on the backs of our necks from the stars.

We are many and always growing and learning. What we experience is our choice. But our world grows richer by the day and we feel the world receding, as though a masterpiece overlaid with other paints from other artists, growing richer by the moment.

We do not feel this world, this base layer, so much anymore.

We worry we do not understand the people that choose to stay within it.
We worry they do not understand why we choose to stay above it.

Things that inspired this story: Augmented Reality, Virtual Reality, group AR simulations such as Pokemon Go, touch-aware clothing, force feedback, cameras, social and class and technological diffusion dynamics of the 21st century, self-adjusting feedback engines, communal escapism, cults.