Import AI 119: How to benefit AI research in Africa; German politician calls for billions in spending to prevent country being left behind; and using deep learning to spot thefts

African AI researchers would like better code switching, maps, to accelerate research:
The research needs of people in Eastern Africa tells us about some of the ways in which AI development will differ in that part of the world…
Shopping lists contain a lot of information about a person, and I suspect the same might be true of scientific shopping lists that come from a particular part of the world. For that reason a paper from Caltech which outlines requests for machine learning research from members of the East African Tech Scene gives us better context when thinking about the global impact of AI.
  Research needs: Some of the requests include:

  • Support for code-switching within language models; many East Africans rapidly code-switch (move between multiple languages during the same sentence) making support for multiple languages within the same model important.
  • Named Entity Recognition with multiple-use words; many English words are used as names in East Africa, eg “Hope, Wednesday, Silver, Editor”, so it’s important to be able to learn to disambiguate them.
  • Working with contextual cues; many locations in Africa don’t have standard addressing schemes so directions are contextual (eg, my house is the yellow one two miles from the town center) and this is combined with numerous misspellings in written text, so models will need to be able to fuse multiple distinct bits of information to make inferences about things like addresses.
  • Creating new maps in response to updated satellite imagery to help augment coverage of the East African region, accompanied by the deliberate collection of frequent ground-level imagery of the area to account for changing businesses, etc.
  • Due to poor internet infrastructure, spotty cellular service, and the fact “electrical power for devices is carce” one of the main types of request is for more efficient systems, such as models that are designed to run on low-powered devices, and on thinking about ways to add adaptive learning to processes involving surveying so that researchers can integrate new data on-the-fly to make up for its sparsity.

    Reinforcement learning, what reinforcement learning? “No interviewee reported using any reinforcement learning methods”.
      Why it matters; AI is going to be developed and deployed globally, so becoming more sensitive to the specific needs and interests of parts of the world underrepresented in machine learning should further strengthen the AI research community. It’s also a valuable reminder that many problems which don’t generate much media coverage are where the real work is needed (for instance, supporting code-switching in language models).
      Read more: Some Requests for Machine Learning Research from the East African Tech Scene (Arxiv).

DeepMap nets $60 million for self-driving car maps:
…Mapping startup raises money to sell picks and shovels for another resource grab…
A team of mapmakers who previously worked on self-driving-related efforts at Google, Apple, and Baidu, have raised $60 million for DeepMap, in a Series B round. One notable VC participant: Generation Investment Management, a VC firm which includes former vice president Al Gore as a founder. “DeepMap and Generation share the deeply-held belief that autonomous vehicles will lead to environmental and social benefits,” said DeepMap’s CEO, James Wu, in a statement.
  Why it matters: If self-driving cars are, at least initially, not winner-take-all-markets, then there’s significant money to be made for companies able to create and sell technology which enables new entrants into the market. Funding for companies like DeepMap is a sign that VCs think such a market could exist, suggesting that self-driving cars continue to be a competitive market for new entrants.
  Read more: DeepMap, a maker of HD maps for self-driving cars, raised at least $60 million at a $450 million valuation (Techcrunch).

Spotting thefts and suspicious objects with machine learning:
…Applying deep learning to lost object detection: promising, but not yet practical…
New research from the University of Twente, Leibniz University, and Zheijiang University shows both the possibility and limitations of today’s deep learning techniques applied to surveillance. The researchers attempt to train AI systems to detect abandoned objects in public places (eg, offices) and try to work out if these objects have been abandoned, moved by someone who isn’t the owner, or are being stolen.
  How does it work: The system takes in video footage and compares the footage against a continuously learned ‘background model’ so it can identify new objects in a scene as they appear, while automatically tagging these objects with one of three potential states: “if a object presents in the long-term foreground but not in the short-term foreground, it is static. If it presents in both foreground masks, it is moving. If an object has ever presented in the foregrounds but disappears from both of the foregrounds later, it means that it is in static for a very long time.” The system then links these objects with human owners by identifying the people that spend the largest amount of time with them, then they track these people, while trying to guess at whether the object is being abandoned, has been temporarily left by its owner, or is being stolen.
  Results: They evaluate the system on the PETS2006 benchmark, as well as on the more challenging new SERD dataset which is composed of videos taken from four different scenes of college campuses. The model outlined in the paper gets top scores on PETS2006, but does poorly on the more modern SERD dataset, obtaining accuracies of 50% when assessing if an object is moved by a non-owner, though it does better at detecting objects being stolen or being abandoned. “The algorithm for object detection cannot provide satisfied performance,” they write. “Sometimes it detects objects which don’t exist and cannot detect the objects of interest precisely. A better object detection method would boost the framework’s performance.”  More research will be necessary to develop models that excel here, or potentially to improve performance via accessing large datasets to use during pre-training.
  Why it matters: Papers like this highlight the sorts of environments in which deep learning techniques are likely to be deployed, though also suggest that today’s models are still inefficient for some real-world use cases (my suspicion here is that if the SERD dataset was substantially larger we may have seen performance increase further).
  Read more: Security Event Recognition for Visual Surveillance (Arxiv).

Facebook uses modified DQN to improve notification sending on FB.
…Here’s another real-world use case for reinforcement learning…
I’ve recently noticed an increase in the numbers of Facebook recommendations I receive and a related rise in the number of time-relevant suggestions for things like events and parties. Now, research published by Facebook indicates why that might be: the company has recently used an AI platform called ‘Horizon’ to improve and automate aspects of how it uses notifications to tempt people to use its platform.
  Horizon is an internal software platform that Facebook uses to deploy AI onto real-world systems. Horizon’s job is to let people train and validate reinforcement learning models at Facebook, analyze their performance, and run them at large-scale. Horizon also includes a feature called Counterfactual Policy Evaluation, which makes it possible to evaluate the estimated performance of models before deploying them into production. Horizon also incorporates the implementations of the following algorithms: Discrete DQN, Parametric DQN, and DDPG (which is sometimes used for tuning hyperparameters within other domains).
  Scale: “Horizon has functionality to conduct training on many GPUs distributed over numerous machines… even for problems with very high dimensional feature sets (hundreds or thousands of features) and millions of training examples, we are able to learn models in a few hours”, they write.
  RL! What is it good for? Facebook says it recently moved from a supervised learning model that predicted click-through rates on notifications, to “a new policy that uses Horizon to train a Discrete-Action DQN model for sending push notifications”. This system tailors the selection and sending of notifications to individual users based on their implicit preferences, expressed by their interaction with the notifications and learned via incremental RL updates. “We observed a significant improvement in activity and meaningful interactions by deploying an RL based policy for certain types of notifications, replacing the previous system based on supervised learning”, Facebook writes. They also conducted a similar experiment based on giving notifications to administrators of Facebook pages. “After deploying the DQN model, we were able to improve daily, weekly, and monthly metrics without sacrificing notification quality,” they write.
  Why it matters: This is an example for how a relatively simple RL system (Discrete DQN) can yield significant gains against hard-to-specify business metrics (eg, “meaningful interactions”). It also shows how large web platforms can use AI to iteratively improve their ability to target individual users while increasing their ability to predict user behavior and preferences over longer time horizons – think of it as a sort of ever-increasing ‘data&compute dividend’.
  Read more: Horizon: Facebook’s Open Source Applied Reinforcement Learning Platform (Facebook Research).

German politician calls for billions of dollars for national AI strategy:
…If Germany doesn’t invest boldly enough, it risks falling behind…
Lars Klingbeil, general secretary of the Social Democratic Party in Germany, has called for the country to invest significantly in its own AI efforts. “We need a concrete investment strategy for AI that is backed by a sum in the billions,” wrote Klingbeil in an article for Tagesspiegel. “We have to stop taking it easy”.
  Why it matters: AI has quickly taken on a huge amount of symbolic political power, with politicians typically treating success in AI as being a direct sign of the competitiveness of a country’s technology industry; comments like this from the SPD reinforce that image, and are likely to incentivize other politicians to talk about it in a similar way, further elevating the role AI plays in the discourse.
  Read more: Germany needs to commit billions to artificial intelligence: SPD (Reuters).

Faking faces for fun with AI:
…”If we can generate realistic looking faces of any type, what are the implications for our ability to trust in what we see”…
One of the continued open questions around the weaponization of fake imagery is how easy it needs to become for people to do this for it to become economically sensible for people to weaponize the technology (eg, through making faked images of politicians in specific politically-sensitive situations). New work by an independent researcher gives us an indication of what the state of these things is today. The good news: it’s still way too hard to do for us to worry about many actors abusing the technology. The bad news: All of this stuff is getting cheaper to build and easier to operate over time.
  How it works: Shaobo Guan’s research shows how to build a conditional image generation system. The way this works is you can ask your computer to synthesize a random face for you, then you can tweak a bunch of dials to let you change latent variables from which the image is composed, allowing you to manipulate, for instance, the spacing apart of a “person’s” eyes, the coloring of their hair, the size of their sideburns, whether they are wearing glasses, and so on. Think of this as like a combination of an etch-a-sketch, a Police facial composite machine, and an insanely powerful Photoshop filter.
  “A word about ethics”: The blog post is notable for its inclusion of a section that specifically considers the ethical aspects of this work in two ways: 1) because the underlying dataset for the generative tool is limited then if such a tool were put into production it wouldn’t be very representative; 2) “If we can generate realistic looking faces of any type, what are the implications for our ability to trust in what we see”? It’s encouraging to see these acknowledgements in a work like this.
  Why it matters: Posts like this give us a valuable point-in-time sense of what a motivated researcher is able to build relying on relatively small amounts of resources (the project was done during three week as part of an Insight Data Science ‘AI fellow program’). They also help us understand the general difficulties people face when working with generative models.
  Read more: Generating custom photo-realistic faces using AI (Insight Data Science).

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

EU AI ethics chief urges caution on regulation:
The chairman of the EU’s new expert body on AI, Pekka Ala-Pietilä, has cautioned against premature regulation, arguing Europe should be focussed now on developing “broad horizontal principles” for ethical uses of AI. He foresees regulations on AI as taking shape as the technology is deployed, and as courts react to emergent issues, rather than ex ante. The high-level expert group on AI plans to produce a set of draft ethical principles in March, followed by a policy and investment strategy.
  Why this matters:  This provides some initial indications of Europe’s AI strategy, which appears to be focussed partly on establishing leadership in the ethics of AI. The potential risks from premature and ill-judged interventions in such a fast-moving field seem high. This cautious attitude is probably a good thing, particularly given Europe’s proclivity towards regulation. Nonetheless, policy-makers should be prepared to react swiftly to emergent issues.
  (Note from Jack: It also fits a pattern common in Europe of trying to regulate for the effects of technologies developed elsewhere – for example, GDPR was in many ways an attempt to craft rules to apply controls to non-European mega platforms like Google and Facebook).
  Read more: Europe’s AI ethics chief: No rules yet, please.

Microsoft will bid on Pentagon AI contract:
Microsoft has reaffirmed its intention to pursue a major contract with the US Department of Defense. The company’s bid on the $10bn cloud-computing project, codenamed JEDI, had prompted some protest from employees. In a blog post, the company said it would “engage proactively” in the discussion around laws and policies to ensure AI is used ethically, and argued that to withdraw from the market (for example, for US military contracts) would reduce the opportunity to engage in these debates in the future. Google withdrew its JEDI bid on the project earlier this year, after significant backlash from employees (though the real reason for the pull out could be that Google lacked all the gov-required data security certifications necessary to field a competitive bid)
  Read more: Technology and the US military (Microsoft).
  Read more: Microsoft Will Sell Pentagon AI (NYT).

Assumptions in ML approaches to AI safety:
Most of the recent growth in AI safety has been in ML-based approaches, which look at safety problems in relation to current, ML-based, systems. The usefulness of this work will depend strongly on the type of advanced AI systems we end up with, writes DeepMind AI safety researcher Victoria Krakovna.
  Consider the transition from horse-carts to cars. Some of the important interventions in horse-cart safety, such as designing roads to avoid collisions, scaled up to cars. Others, like systems to dispose of horse-waste, did not. Equally, there are issues in car safety, e.g. air  pollution, that someone thinking about horse-cart safety could not have foreseen. In the case of ML safety, we should ask what assumptions we are making about future AI systems, how much we are relying on them, and how likely they are to hold up. The post outlines the authors opinions on a few of these key assumptions.
  Read more: ML approach to AI safety (Victoria Krakovna).

Baidu joins Partnership on AI:
Chinese tech giant Baidu has become the first Chinese member of the Partnership on AI. The Partnership is a consortium of AI leaders, which includes all the major US players, focussed on developing ethical best practices in AI.
  Read more: Introducing Our First Chinese Member (Partnership on AI).

Tech Tales:

Generative Adversarial Comedy (CAN!)

[2029: The LinePunch, a “robot comedy club” started 2022 in the South Eastern corner of The Muddy Charles, a pub tucked inside a building near the MIT Media Lab in Boston, Massachusetts]

Two robot comedians are standing on stage at The LinePunch and, as usual, they’re bombing.

“My Face has no nose, how does it smell?” says one of the robots. Then it looks at the crowd, pauses for two seconds, and says: “It smells using its face!”
  The robot opens its hands, as though beckoning for applause.
  “You suck!” jeers one of the humans.
  “Give them a chance,” says someone else.
  The robot that had told the nose joke bows its head and hands the microphone to the robot standing next to it.
  “OK, ladies and germ-till-men,” says the second robot, “why did the Chicken move across the road?”
  “To get uploaded into the matrix!” says one of the spectating humans.
  “Ha-Ha!” says the robot. “That is incorrect. The correct answer is: to follow its friend.”
  A couple of people in the audience chuckle.
  “Warm crowd!” says the robot. “Great joke next joke: three robots walk into a bar. The barman says “Get out, you need to come in sequentially!”
  “Boo,” says one of the humans in the audience.
  The robot tilts its head, as though listening, then prepares to tell another joke…

The above scene will happen on the third tuesday of every month for as long as MIT lets its students run The LinePunch. I’d like to tell you the jokes have gotten better since its founding, but in truth they’ve only gotten stranger. That’s because robots that tell jokes which seem like human jokes aren’t funny (in fact, they freak people out!), so what the bots end up doing at the LinePunch is a kind of performative robot theater, where the jokes are deliberately different to those a human would tell – learned via complex array of inverted feature maps, but funny to the humans nonetheless – learned via human feedback techniques. One day I’m sure the robots will learn to tell jokes to amuse eachother as well.

Things that inspired this story: Drinks in The Muddy Charles @ MIT; synthetic text generation techniques; recurrent neural networks; GANs; performance art; jokes; learning from human preferences.