Import AI

San Diego wants smart, AI-infused streetlights; opposition group sounds alarm:
…When technological progress meets social reality…
The City of San Diego is installing thousands of streetlights equipped with video cameras and a multitude of other sensors. A protest group called the Anti Surveillance Coalition (ASC) wants to put a halt on the ‘smart city’ program, pending further discussion with residents. “I understand that there may be benefits to crime prevention, but the point is, we have rights and until we talk about privacy rights and our concerns, then we can’t have the rest of the conversation”, one ASC protestor told NBC.

Why this matters: This is a good example of the ‘omniuse’ capabilities of modern technology – sure, San Diego probably wants to use the cameras to help it better model traffic, analyze patterns of crime in various urban areas, and generally create better information to facilitate more city governance. On the other hand, the protestors are suspicious that organizations like the San Diego Policy Department could use the data and video footage to target certain populations. As we develop more powerful AI systems, I expect that (in the West at least) there are going to be a multitude of conversations about how ‘intelligent’ we want our civil infrastructures to be, and what the potential constraints or controls are that we can place on them.
   Find out more about the ‘Smart City Platform’ here (official City of San Diego website).
   Read more: Opposition Group Calls for Halt to San Diego’s Smart Streetlight Program (NBC San Diego).

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Want a few hundred thousand chest radiographs? Try MIMIC:
Researchers with MIT and Harvard have released the “MIMIC” chest radiograph dataset, giving AI researchers 377,110 images from more than 200,000 radiographic studies. “The dataset is intended to support a wide body of research in medicine including image understanding, natural language processing, and decision support,” the researchers write.
   Read more: MIMIC-CXR Database (PhysioNet). 

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Google reveals how YouTube ranking works:
…We’re all just janitors servicing vast computational engines, performing experimentation against narrowly defined statistical metrics…
Video recommendations are one of the most societally impactful forms of machine learning, because the systems that figure out what videos to recommend people are the systems that fundamentally condition 21st century culture, much like how ‘channel programming’ for broadcast TV and radio influenced culture in the 20th century. Now, new research from Google shows how the web giant decides which videos to recommend to YouTube users. 

How YouTube recommendations work: Google implements a multitask learning system, which lets it optimize against multiple objectives at once. These objectives include things like: ‘engagement objectives’, such as user clicks, and ‘satisfaction objectives’ like when someone likes a video or leaves a rating. 

Feedback loops & YouTube: Machine learning systems can enter into dangerous feedback loops, where the system recycles certain signals until it starts to develop pathological behaviors. YouTube is no exception. “The interactions between users and the current system create selection biases in the feedback,” the authors write. “For example, a user may have clicked an item because it was selected by the current system, even though it was not the most useful one of the entire corpus”. To help deal with this, the researchers develop an additional ranking system, which tries to disambiguate how much a user likes a video, from how prevalent the video was in prior rankings – essentially, they try to stop their model becoming recursively more biased as a consequence of automatically playing the next video or the user consistently clicking only the top recommendations out of laziness. 

Why this matters: I think papers like this are fascinating because they read like the notes of janitors servicing some vast machine they barely understand – we’re in a domain here where the amounts of data are so vast that our methods to understand the systems are to perform live experiments, using learned components, and see what happens. We use simple scores as proxies for larger issues like bias, and in doing likely hide certain truthes from ourselves. The 21st century will be defined by our attempts to come up with the right learning systems to intelligently & scalably constrain the machines we have created.
   Read more: Recommending what video to watch next: a multitask ranking system (ACM Digital Library).

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100,000 free, generated faces:
…When synthetic media meets stock photography…
In the past five years, researchers have figured out how to use deep learning systems to create synthetic images. Now, the technology is moving into society in surprising ways. Case in point? A new website that offers people access to 100,000 pictures of synthetic people, generated via StyleGAN. This is an early example of how the use of synthetic media is going to potentially upend various creative industries – starting here with stock photography. 

The dataset: So, if you want to generate faces, you need to get data from somewhere. Where did this data from from? According to the creators, they gained it via operating a photography studio, taking 29,000+ photos of 69 models over the last two years — and in an encouraging and unusual move, say they gained consent from the models to use their photos to generate synthetic people. 

Why this matters: I think that the intersection of media and AI is going to be worth paying attention to, since media economics are terrible, and AI gives people a way to reduce the cost of media production via reducing the cost of things like acquiring photos, or eventually generating text. I wonder when we’ll see the first Top-100 internet website which is a) content-oriented and b) predominantly generated. As a former journalist, I can’t say I’m thrilled about what this will do to the pay for human photographers, writers, and editors. But as the author of this newsletter, I’m curious to see how this plays out!
   Check out the photos here (Generated.Photos official website)..
   Find out more by reading the FAQ (Generated.Photos Medium).

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A self-driving car map of Singapore:
…Warm up the hard drives, there’s now even more free self-driving car data!…
Researchers with Singapore’s Agency for Science, Technology and Research (A*STAR) have released the “A*3D” dataset – self-driving car dataset collected in a large area of Singapore. 

The data details: 

• 230,000 human-labeled 3D object annotates across 39,179 LiDAR point cloud frames.
• Data captured at driving speeds of 40-70 km/h.
• Location: Singapore.
• Nighttime data: 30% of frames.
• Data gathering period: The researchers collected data in March (wet season) and July (dry season) 2018.

Why this matters: A few years ago, self-driving car data was considered to be one of the competitive moats which companies could put together as they raced each other to develop the technology. Now, there’s a flood of new datasets being donated to the research commons every month, both from companies – even Waymo, Alphabet Inc’s self-driving car subsidiary! –  and academia – a sign, perhaps, of the increasing importance of compute for self-driving car development, as well as a tacit acknowledgement that self-driving cars are a sufficiently hard problem we need to focus more on capital R research in the short term, before they’re deployed.
   Read more: A*3D Dataset: Towards Autonomous Driving in Challenging Environments (Arxiv).
   Get the data here (GitHub).

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Training two-module networks for four-legged robots:
…Yet another sign of the imminent robot revolution…
Robots are one of the greatest challenges for contemporary AI research, because robots are brittle, exist in a partially-observable world, and have to deal with the cruel&subtle realities of physics to get anything done. Recently, researchers have started to successfully apply modern machine learning techniques to quadruped robots, prefiguring a world full of little machines that walk, run, and jump around. New research from the Robotic Systems Lab at ETH Zurich gives us a sense of how standard quadruped training has become, and highlights the commoditization of robotics systems. 

Two-part networks for better robots: Here, the researchers outline a two-part system for training a simulated quadruped robot to navigate various complex, simulated worlds. The system is “a two-layer hierarchy of Neural Network (NN) policies, which partions locomotion into separate components responsible for foothold planning and tracking control respectively”; it consists of a gait planner, which is a planning policy that can “generate sequences of supporting footholds and base motions which direct the robot towards a target heading”, and a gait controller, which is a “a foothold and base motion controller policy which executes the aforementioned sequence while maintaining balance as well as dealing with external disturbances”. They use, variously, TRPO and PPO to train the system, and report good results on the benchmarks. Next, they hope to do some sim2real experiments, where they try and train the robots in simulation and transfer the learned policies to reality. 

Why this matters: It wasn’t long ago (think: four years ago) that training robots via deep reinforcement learning – even in simulation – was considered to be a frontier for some parts of deep learning research. Now, everyone is doing it, ranging from large corporate labs, to academic institutions, to solo researchers. I think papers like this highlight how rapidly this field has moved from a ‘speculative’ phase to a development phase, where researchers are busily iterating on approaches to improve robustness and sample efficiency, which will ultimately lead to greater deployment of the technology.
   Read more: DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning (Arxiv). 

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Politicians back effort to bring technological savvy back to US politics:
…Bipartisan bill wants to put the brains back into Congress…
Two senators and two congresspeople – two democrats and two republicans – have introduced the Office of Technology Assessment Improvement and Enhancement Act, in the hope of making it easier for the government to keep up with rapid technological change. The Office of Technology Assessment (OTA), was a US agency that for a couple of decades produced reports for politicians on advanced science and technology, like nanotechnology. It was killed by Republicans in the mid-90s as part of a broader effort to defund various government institutions. Now, with rapidly advancing AI technology, we’re feeling the effects of a political class who lack institutions capable of informing them about technology (which also has the nasty effect of increasing the power of lobbyists as a source of information for elected officials). 

This bill is part of a larger bipartisan effort to resuscitate OTA, and lays out a few traits the new OTA could have, such as:

• Increasing the turnaround time of report production
• Becoming a resource for elected officials to inform them about technology
• Rotating in expertise from industry and academia to keep staff informed
• Coordinating with the Congressional Research Service (CRS) and Government Accountability Office (GAO) to minimize duplication or overlap. 

Why this matters: If government can be more informed about technology, then it’ll be easier to have civil oversight of technology – something we likely need as things like AI continue to advance and impact society. Now, to set expectations: under the current political dynamic in the US it’s difficult to say whether this bill will move past the House into the Senate and then into legislation. Regardless, there’s enough support showing up from enough quarters for an expanded ability for government to understand technology that I’m confident something will happen eventually, I’m just not sure what.
   Read more: Reps. Takano and Foster, Sens. Hirono and Tillis Introduce the Office of Technology Assessment Improvement and Enhancement Act (Representative Takano’s official website).

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Tech Tales

The Seeing Trade 

Sight for experience: that was how was advertized. In exchange for donating “at minimum 80% of your daily experience, with additional reward points for those that donate more!” blind and partially-sighted people gained access to a headset covered in cameras, which plugged into a portable backpack computer. This headset used a suite of AI systems to scan and analyze the world around the person, telling them via bone-conduction audio about their nearby surroundings. 

Almost overnight, the streets became full of people with half-machine faces, walking around confidently, many of them not even using their canes. At the same time, the headset learned from the people, customizing its communications to each of its human users; soon, you saw blind people jogging along busy city streets, deftly navigating the crowds, feeding on information beamed into them by their personal all-seeing AI. Blind people participated in ice skating competitions. In mountain climbing. 

The trade wasn’t obvious until years had passed: then, one day, the corporation behind the headsets revealed “the experience farm”, a large-scale map of reality, stitched together from the experiences of the blind headset-wearers. Now, the headsets were for everyone and when you put them on you’d enter a ghost world, where you could see the shapes of other people’s actions, and the suggestions and predictions of the AI system of what you might do next. People participated in this, placing their headsets on to at once gather and experience a different form of reality: in this way human life was immeasurably enriched, through the creation of additional realities in which people could spend their time. 

Perhaps, one day, we’ll grow uninterested in the ‘base world’ as people have started calling it. Perhaps we’ll stop building new buildings, or driving cars, or paying much attention to our surroundings. Instead, we’ll walk into a desert, or a field, and place our headsets on, and in doing so explore a richly-textured world, defined by the recursive actions of humanity.

Things that inspired this story: Virtual reality; the ability for AI systems to see&transcribe the world; the creation of new realities via computation; the Luc Besson film Valerian; empathy and technology.