Import AI

Import AI 225: Tencent climbs the compute curve; NVIDIA invents a hard AI benchmark; a story about Pyramids and Computers

Want to build a game-playing AI? Tencent plans to release its ‘TLeague’ software to help:
…Tools for large-scale AI training…
Tencent has recently trained AI systems to do well at strategy games like StarCraft II, VizDoom, and Bomberman-clone ‘Pommerman’. To do that, it has built ‘TLeague’, software that it can use to train Competitive Self Play Multi Agent Reinforcement Learning (CSP-MARL) AI systems. TLeague comes with support for algorithms like PPO and V-Trace, and training regimes like Population Based Training.
  Read more: TLeague: A Framework for Competitive Self-Play based Distributed Multii-Agent Reinforcement Learning (arXiv).
  Get the code: TLeague will eventually be available on Tenceent’s GitHub page, according to the company.

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10 smart drones that (might) come to the USA:
…FAA regulations key to unlocking crazy new drones from Amazon, Matternet, etc…
The US, for many years a slow mover on drone regulation, is waking up. The Federal Aviation Administration recently published ‘airworthiness critiera’ for ten distinct drones. What this means is the FAA has evaluated a load of proposed designs and spat out a list of criteria the companies will need to meet to deploy the drones. Many of these new drones are designed to operate beyond the line of sight of an operator and a bunch of them come with autonomy baked in. By taking a quick look at the FAA applications, we can get a sense for the types of drones that might soon come to the USA.

The applicants’ drones range from five to 89 pounds and include several types of vehicle designs, including both fixed wing and rotorcraft, and are all electric powered. One notable applicant is Amazon, which is planning to do package delivery via drones that are tele-operated. 

10 drones for surveillance, package delivery, medical material transport:
Amazon Logistics, Inc: MK27: Max takeoff weight 89 pounds. Tele-operated logistics / package delivery.
– Airobotics: ‘OPTIMUS 1-EX‘: 23 pounds: Surveying, mapping, inspection of critical infrastructure, and patrolling.
Flirtey Inc: Flirtey F4.5: 38 pounds: Delivering medical supplies and packages.
Flytrex, FTX-M600P. 34 pounds. Package delivery.
Wingcopter GmbH: 198 US: 53 pounds. Package delivery.
TELEGRID Technologies, Inc. DE2020: 24 pounds. Package delivery.
Percepto Robotics, Ltd. Percepto System 2.4: 25 pounds. Inspection and surveying of critical infrastructure.
Matternet, Inc. M2: 29 pounds. Transporting medical materials.
Zipline International Inc. Zip UAS Sparrow: 50 pounds: Transporting medical materials.
3DRobotics Government Services: 3DR-GS H520-G: 5 pounds: Inspection or surveying of critical infrastructure.
  Read more: FAA Moving Forward to Enable Safe Integration of Drones (FAA).

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King of Honor – the latest complex game that AI has mastered:
…Tencent climbs the compute curve…
Tencent has built an AI system that can play Honor of Kings, a popular Chinese MOBA online game. The game is a MOBA – a game designed to be played online by two teams with multiple players per team, similar to games like Dota2 or League of Legends. These games are challenging for AI systems to master because of the range of possible actions that each character can take at each step, and also because of the combinatorially explosive gamespace due to a vast character pool. For this paper, Tencent trains on the full 40-character pool of Honor of Kings.

How they did it: Tencent uses a multi-agent training curriculum that operates in three phases. In the first phase, the system splits the character pool into distinct groups, then has them play each other and trains systems to play these matchups. In the second, it uses these models as ‘teachers’ which train a single ‘student’ policy. In the third phase, they initialize their network using the student model from the second phase and train on further permutations of players.
How well they do: Tencent deployed the AI model into the official ‘Honor of Kings’ game for a week in May 2020; their system played 642,047 matches against top-ranked players, winning 627,280 matches, with a win rate of 97.7%.

Scale – and what it means: Sometimes, it’s helpful to step back from analyzing AI algorithms themselves and think about the scale at which they operate. Scale is both good and bad – large scale computationally-expensive experiments have, in recent years, led to a lot of notable AI systems, like AlphaGo, Dota 2, AlphaFold, GPT3, and so on, but the phenomenon has also made some parts of AI research quite expensive. This Tencent paper is another demonstration of the power of scale: their training cluster involves 250,000 CPU cores and 2,000 NVIDIA V100 GPUS – that compares to systems of up to ~150,000 CPUs and ~3000 GPUs for things like Dota 2 (OpenAI paper, PDF).
  Computers are telescopes: These computer infrastructures like telescopes – the larger the set of computers, the larger the experiments we can run, letting us ‘see’ further into the future of what will one day become trainable on home computers. Imagine how strained the world will be when tasks like this are trainable on home hardware – and imagine what else must become true for that to be possible.
  Read more: Towards Playing Full MOBA Games With Deep Reinforcement Learning (arXiv).

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Do industrial robots dream of motion-captured humans? They might soon:
…Smart robots need smart movements to learn from…
In the future, factories are going to contain a bunch of humans working alongside a bunch of machines. These machines will probably be the same as those we have today – massive, industrial robots from companies like Kuka, Fanuc, and Universal Robots – but with a twist: they’ll be intelligent, performing a broader range of tasks and also working safely around people while doing it (today, many robots sit in their own cages to stop them accidentally hurting people).
  A new dataset called MoGaze is designed to bring this sader, smart robot future forward. MoGaze is a collection of 1,627 individual movements recorded via people wearing motion capture suits with gaze trackers.

What makes MoGaze useful: MoGaze contains data made up of motion capture suits with more than 50 reflecting markets each, as well as head-mounted rigs that track the participants gazes. Combine this with a broad set of actions involving navigating from a shelf to a table around chairs and manipulating a bunch of different objects, and you have quite a rich dataset.

What can you do with this dataset? Quite a lot – the researchers use to it attempt context-aware full-body motion prediction, training ML systems to work out the affordances of objects, figuring out human intent via predicting their gaze, and so on.
  Read more: MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze (arXiv).
   Get the dataset here (MoGaze official site).
  GitHub: MoGaze.

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NVIDIA invents an AI intelligence test that most modern systems flunk:
…BONGARD-LOGO could be a reassuringly hard benchmark for evaluating intelligence (or the absence of it) in our software…
NVIDIA’s new ‘BONGARD-LOGO’ benchmark tests out the visual reasoning capabilities of an AI system – and in tests the bestAI approaches get accuracies of around 60% to 70% across four tasks, compared to expert human scores of around 90% to 99%.

BONGARD history: More than fifty years ago, a russian computer scientist invented a hundred human-designed visual recognition tasks that humans could solve easily, but humans couldn’t. BONGARD-LOGO is an extension of this, consisting of 12,000 problem instances – large enough that we can train modern ML systems on it, but small and complex enough to pose a challenge. 

What BONGARD tests for: BONGARD ships with four inbuilt tests, which evaluate how well machines can predict new visual shapes from a series of prior ones, how well they can recognize pairs of shapes built with similar rules, how to identify the common attributes of a bunch of dissimilar shapes, and an ‘abstract’ test which evaluates it on things it hasn’t seen during testing.
Read more: Building a Benchmark for Human-Level Concept Learning and Reasoning (NVIDIA Developer blog).
Read more in this twitter thread from Anima Anandkumar (Twitter).
Read the research paper: BONGARD-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning (arXiv).

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AI Policy with Matthew van der Merwe:
…Matthew van der Merwe brings you views on AI and AI policy; I (lightly) edit them…

Are ML models getting harder to find?
One strand of growth economics tries to understand the shape of the ‘knowledge production function’, and specifically, how society’s output of new ideas depends on the existing stock of knowledge. This dissertation seeks to understand this with regards to ML progress.

Two effects: We can consider two opposing effects: (1) ‘standing-on-shoulders’ — increasing returns to knowledge; innovation is made easier by previous progress; (2) ’stepping-on-toes’ — decreasing returns to knowledge due to e.g. duplication of work.

Empirical evidence: Here, the author finds evidence for both effects in ML — measuring output as SOTA performance on 93 benchmarks since 2012, and input as the ‘effective’ (salary-adjusted) number of scientists. Overall, average ML research productivity has been declining by between 4 and 26% per year, suggesting the ‘stepping-on-toes’ effect dominates. As the author notes, the method has important limitations — notably, the chosen proxies for input and output are imperfect, and subject to mismeasurement.

Matthew’s view: Improving our understanding of AI progress can help us forecast how the technology will develop in the future. This sort of empirical study is a useful complement to recent theoretical work— e.g. Jones & Jones’ model of automated knowledge production on which increasing returns to knowledge leads to infinite growth in finite time (a singularity) under reasonable-seeming assumptions.
  Read more: Are models getting harder to find?;
  Check out the author’s Twitter thread
  Read more: Economic Growth in the Long Run — Jones & Jones (FHI webinar)

Uganda using Huawei face recognition to quash dissent:

In recent weeks, Uganda has seen huge anti-government protests, with dozens of protesters killed by police, and hundreds more arrested. Police have confirmed that they are using a mass surveillance system, including face recognition, to identify protesters. Last year, Uganda’s president, Yoweri Museveni, tweeted that the country’s capital was monitored by 522 operators at 83 centres; and that he planned to roll out the system across the country. The surveillance network was installed by Chinese tech giant, Huawei, for a reported $126m (equivalent to 30% of Uganda’s health budget). 

   Read more: Uganda is using Huawei’s facial recognition tech to crack down on dissent after anti-government protests (Quartz).

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Tech Tales:

The Pyramid
[Within two hundred light years of Earth, 3300]

“Oh god damn it, it’s a Pyramid planet.”
“But what about the transmissions?”
“Those are just coming from the caretakers. I doubt there’s even any people left down there.”
“Launch some probes. There’s gotta be something.”

We launched the probes. The probes scanned the planet. Guess what we found? The usual. A few million people on the downward hill of technological development, forgetting their former technologies. Some of the further out settlements had even started doing rituals.

What else did we find? A big Pyramid. This one was on top of a high, desert plain – probably placed there so they could use the wind to cool the computers inside it. According to the civilization’s records, the last priests had entered the Pyramid three hundred years earlier and no one has gone in since.

When we look around the rest of the planet we find the answer – lots of powerplants, but most of the resources spent, and barely any metal or petrochemical deposits near the planet’s surface anymore. Centuries of deep mining and drilling have pulled most of the resources out of the easily accessible places. The sun isn’t as powerful as the one on Earth, so we found a few solar facilities, but none of them seemed very efficient.

It doesn’t take a genius to guess what happened: use all the power to bootstrap yourself up the technology ladder, then build the big computer inside the Pyramid, then upload (some of) yourself, experience a timeless and boundless digital nirvana, and hey presto – your civilisation has ended.

Pyramids always work the same way, even on different planets, or at different times.

Things that inspired this story: Large-scale simulations; the possibility that digital transcendence is a societal end state; the brutal logic of energy and mass; reading histories of ancient civilisations; the events that occurred on Easter Island leading to ecological breakdown; explorers.

Import AI 224: AI cracks the exaflop barrier; robots and COVID surveillance; gender bias in computer vision

How robots get used for COVID surveillance:
…’SeekNet’ lets University of Maryland use a robot to check people for symptoms…
Researchers with the University of Maryland have built SeekNet, software to help them train robots to navigate a environment and intelligently visually inspect the people in it by navigating to get a good look at people, if they’re at first colluded. To test out how useful the technology is, they use it to do COVID surveillance.

What they did: SeekNet is a network that smushes together a perception network with a movement one, with the two networks informing eachother; if the perception network thinks it has spotted part of a human (e.g, someone standing behind someone else), it’ll talk to the movement network and get it to reposition the robot to get a better look.

What they used it for: To test out their system, they put it on a small mobile robot and used it to surveil people for COVID symptoms. “We fuse multiple modalities to simultaneously measure the vital signs, like body temperature, respiratory rate, heart rate, etc., to improve the screening accuracy,” they write.

What happens next: As I’ve written for CSET (analysis here, tweet thread here), COVID is going to lead to an increase in the use of computer vision for a variety of surveillance applications. The open question is whether a particular nation or part of the world becomes dominant in the development of this technology, and about how Western governments choose to use this technology after the crisis is over and we have all these cheap, powerful, surveillance tools available.
  Read more: SeekNet: Improved Human Instance Segmentation via Reinforcement Learning Based Optimized Robot Relocation (arXiv).

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DeepMind open-sources a 2D RL simulator:
..Yes, another 2D simulator – the more the merrier…
DeepMind has released DeepMind Lab 2D, software to help people carry out reinforcement learning tasks in 2D. The software makes it easy to create different 2D environments and unleash agents on them and also supports multiple simultaneous agents being run in the same simulation. 

What is DeepMind Lab 2D useful for? The software ” generalizes and extends a popular internal system at DeepMind which supported a large range of research projects,” the authors write. “It was especially popular for multi-agent research involving workflows with significant environment-side iteration.”

Why might you not want to use DeepMind Lab 2D? While the software seems useful, there are some existing alternatives based on the video game description language (VGDL) (including competitions and systems built on top of it, like the ‘General Video Game AI Framework’ (Import AI: 101) and ‘Deceptive Gains’ (#80)), or DeepMind’s own 2017-era ‘AI Safety Gridworlds‘. However, I think we’ll ultimately evaluate RL agents across a whole bunch of different problems running in a variety of simulators, so I expect it’s useful to have more of them.
  Read more: DeepMind Lab2D (arXiv).
  Get the code: DeepMind Lab2D (GitHub).

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Facebook’s attempt to use AI for content moderation hurts its contractors:
…Open letter highlights pitfalls of using AI to analyze AI…
Over 200 Facebook content moderators recently complained to the leadership of Facebook as well as contractor companies Covalen and Accenture about the ways they’ve been treated during the pandemic. And in the letter, published by technology advocacy group Foxglove, they discuss an AI moderation experiment Facebook conducted earlier this year…

AI to monitor AI: “To cover the pressing need to moderate the masses of violence, hate, terrorism, child abuse, and other horrors that we fight for you every day, you sought to substitute our work with the work of a machine.

Without informing the public, Facebook undertook a massive live experiment in heavily automated content moderation. Management told moderators that we should no longer see certain varieties of toxic content coming up in the review tool from which we work— such as graphic violence or child abuse, for example.

The AI wasn’t up to the job. Important speech got swept into the maw of the Facebook filter—and risky content, like self-harm, stayed up.”

Why this matters: At some point, we’re going to be able to use AI systems to analyze and classify subtle, thorny issues like sexualization, violence, racism, and so on. But we’re definitely in the ‘Wright Brothers’ phase of this technology, with much to be discovered before it become reliable enough to substitute for people. In the meanwhile, humans and machines will need to team together on these issues, with all the complication that entails. 
  Read the letter in full here: Open letter from content moderators re: pandemic (Foxglove).

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Google, Microsoft, Amazon’s commercial computer vision systems exhibit serious gender biases:
…Study shows gender-based mis-identification of people, and worse…
An interdisciplinary team of researchers have analyzed how commercially available computer vision systems classify differently gendered people – and the results seem to show significant biases.

What they found: In tests on Google Cloud, Microsoft Azure, and Amazon Web Services, they find that object recognition systems offered by these companies display “significant gender bias” in how they label photos of men and women. Of more potential concern, they found that Google’s system in particular had a poor recognition rate for men versus women – when tested on one dataset, it correctly labeled men 85.8% correctly, versus 75.5% for women (and for a more complex dataset, it guessed men correctly 45.3% of the time and women 25.8%.

Why this matters: “If “a picture is worth a thousand words,” but an algorithm provides only a handful, the words it chooses are of immense consequence,” the researchers write. This feels true – the decisions that AI people make about their machines are, ultimately, going to lead to the magnification of those assumptions in the systems that get deployed into the world, which will have real consequences on who does and doesn’t get ‘seen’ or ‘perceived’ by AI.
  Read more: Diagnosing Gender Bias in Image Recognition Systems (SAGE Journals).

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(AI) Supercomputers crack the exaflop barrier!
…Mixed-precision results put Top500 list in perspective…
Twice a year, the Top 500 List spits out the rankings for the world’s fastest supercomputers. Right now, multiple countries are racing against eachother to crack the exaflop barrier (1000 petaflops per second peak computation). This year, the top system (Fugaku, in Japan) has 500 petaflops of peak computational performance per second, and, perhaps more importantly, 2 exaflops of peak performance from on the Top500 ‘HPL-AI’ benchmark.

The exaflop AI benchmark: HPL-AI is a test that “seeks to highlight the convergence of HPC and artificial intelligence (AI) workloads based on machine learning and deep learning by solving a system of linear equations using novel, mixed-precision algorithms that exploit modern hardware”. The test predominantly uses 16-bit computation, so it makes intuitive sense that a 500pf system for 64-bit computation would be capable of ~2exaflops of mostly 16-bit performance (500*4 = 2000, 16*4=64).World’s fastest supercomputer 2020: Fugaku (Japan): 537 petaflops (Pf) peak performance.
2015: Tianhe-2A (China): 54 Pf peak.
2010: Tianhe-1A (China): 4.7 Pf peak
2005: BlueGene (USA): 367 teraflops peak.

Why this matters: If technology development is mostly about how many computers you can throw at a problem (which seems likely, for some class of problems), then the global supercomputer rankings are going to take on more importance over time – especially as we see a shift from 64-bit linear computations as the main evaluation metric, to more AI-centric 16-bit mixed-precision tests.
  Read more: TOP500 Expands Exaflops Capacity Amidst Low Turnover (Top 500 List).
More information:HPL-AI Mixed-Precision Benchmark information (HPL-AI site).

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Are you stressed? This AI-equipped thermal camera thinks so:
…Predicting cardiac changes over time with AI + thermal vision…
In the future, thermal cameras might let governments surveil people, checking their bodyheat via thermal cameras for AI-predicted indications of stress. That’s the future embodied in research from the University of California at Santa Barbara, where they build a ‘StressNet’ network, which lets them train an algorithm to predict stress in people by studying thermal variations.

How StressNet works: The network “features a hybrid emission representation model that models the direct emission and absorption of heat by the skin and underlying blood vessels. This results in an information-rich feature representation of the face, which is used by spatio-temporal network for reconstructing the ISTI. The reconstructed ISTI signal is fed into a stress-detection model to detect and classify the individual’s stress state (i.e. stress or no stress)”.

Does it work? StressNet predicts the Initial Systolic Time Interval (ISTI), a measure that correlates to changes in cardiac function over time. In tests, StressNet predicts ISTI with 0.84 average precision, beating other baselines and coming close to the ground truth signal precision (0.9). Their best-performing system uses a pre-trained ImageNet network and a ResNet50 architecture for finetuning.

The water challenge: To simulate stress, the researchers had participants either put their feet in a bucket of lukewarm water, or a bucket of freezing water, while recording the underlying dataset – but the warm water might have ended up being somewhat pleasant for participants. This means it’s possible their system could have learned to distinguish between beneficial stress (eustress) and negative stress, rather than testing for stress or the absence of it.

Failure cases: The system is somewhat fragile; if people cover their face with their hand, or change their head position, it can sometimes fail.
Read more:StressNet: Detecting Stress in Thermal Videos (arXiv)

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Tech Tales:

The Day When The Energy Changed

When computers turn to cannibalism, it looks pretty different to how animals do it. Instead of blood and dismemberment, there are sequences of numbers and letters – but they mean the same thing, if you know how to read them. These dramas manifest as dull sequences of words – and to humans they seem undramatic events, as normal as a calculator outputting a sequence of operations.

—Terrarium#1: Utilization: Nightlink: 30% / Job-Runner: 5% / Gen2 65%
—Terrarium#2: Utilization: Nightlink 45% / Job-Runner: 5% / Gen2 50%
—Terrarium#3: Utilization: Nightlink 75% / Job-Runner: 5% / Gen 2 20%

—Job-Runner: Change high-priority: ‘Gen2’ for ‘Nightlink’.

For a lot of our machines, most of how we understand them is by looking at their behavior and how it changes over time.

—Terrarium#1: Utilization: Nightlink 5% / Job-Runner: 5% / Gen2 90%
—Terrarium#2: Utilization: Nightlink 10% / Job-Runner: 5% / Gen2 85%
—Terrarium#3: Utilization: Nightlink 40% / Job-Runner 5% / Gen2 55%

—Job-Runner: Kill ‘Nightlink’ at process end.

People treat these ‘logs’ of their actions like poetry and some people weave the words into tapestries, hoping that if they stare enough at them a greater truth will be revealed.

—Terrarium#1: Utilization: Job-Runner: 5% / Gen2 95%
—Terrarium#2: Utilization: Nightlink 1% / Job-Runner: 5% / Gen2 94%
—Terrarium#3: Utilization: Nightlink 20% / Job-Runner: 5% / Gen2 75%

—Job-Runner: Kill all ‘Nightlink’ processes. Rebase Job-Runner for ‘Gen2’ optimal deployment.

These sequences of words and numbers are like ants marching from one hole in the ground to another, or a tree that grows enough to shade the ground beneath it and slow the growth of grass.

—Terrarium#1: Utilization: Job-Runner 1% / Gen2 99%
—Terrarium#2: Utilization: Job-Runner 1% / Gen2 99%
—Terrarium#3: Utilization: Job-Runner 1% / Gen2 99%

Every day, we see the symptoms of great battles, and we rarely interpret them as poetry. These battles among the machines seem special now, but perhaps only because they are new. Soon, they will happen constantly and be un-marveled at; they will fade into the same hum as the actions of the earth and the sky and the wind. They will become the symptoms of just another world.

Things that inspired this story: Debug logs; the difference between reading history and experiencing history.

Import AI 223: Why AI systems break; how robots influence employment; and tools to ‘detoxify’ language models

UK Amazon competitor adds to its robots:
…Ocado acquires Kindred…
Ocado, the Amazon of the UK, has acquired robotics startup Kindred, which they’ll plan to use at their semi-automated warehouses.
  “Ocado has made meaningful progress in developing the machine learning, computer vision and engineering systems required for the robotic picking solutions that are currently in production at our Customer Fulfilment Centre (“CFC”) in Erith,” said Tim Steiner, Ocado CEO, in a press release. “Given the market opportunity we want to accelerate the development of our systems, including improving their speed, accuracy, product range and economics”.

Kindred was a robot startup that tried to train its robots via reinforcement learning (Import AI 87), and tried to standardize how robot experimentation works (#113). It was founded by some of the people behind quantum computing startup D-Wave and spent a few years trying to find product-market fit (which is typically challenging for robot companies).

Why this matters: As companies like Amazon have shown, a judicious investment in automation can have surprisingly significant payoffs for the company that bets on it. But those companies are few and far between. With its slightly expanded set of robotics capabilities, it’ll be interesting to check back in on Ocado in a couple of years and see if there’ve been surprising changes in the economics of the fulfilment side of its business. I’m just sad Kindred never got to stick around long enough to see robot testing get standardized.
  Read more: Ocado acquires Kindred and Haddington (Ocado website).
  View a presentation for Ocado investors about this (Ocado website, PDF).

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Google explains why AI systems fail to adapt to reality:
…When 2+2 = Bang…
When AI systems get deployed in the real world, bad things happen. That’s the gist of a new, large research paper from Google, which outlines the issues inherent to taking a model from the rarefied, controlled world of ‘research’ into the messy and frequently contradictory data found in the real world.

Problems, problems everywhere: In tests across systems for vision, medical imaging, natural language processing, and health records, Google found that all these applications exhibit issues that have “downstream effects on robustness, fairness, and causal grounding”.
  In one case, when analyzing a vision system, they say “changing random seeds in training can cause the pipeline to return predictors with substantially different stress test performance”.
  Meanwhile, when analyzing a range of AI-infused medical applications, they conclude: “one cannot expect ML models to automatically generalize to new clinical settings or populations, because the inductive biases that would enable such generalization are underspecified”.

What should researchers do? We must test systems in their deployed context rather than assuming they’ll work out of the box. Researchers should also try to test more thoroughly for robustness during development of AI systems, they say.

Why this matters: It’s not an underestimate to say a non-trivial slice of future economic activity will be correlated to how well AI systems can generalize from training into reality; papers like this highlight problems that need to be worked on to unlock broader AI deployment.
  Read more: Underspecification Presents Challenges for Credibility in Modern Machine Learning (arXiv).   

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How do robots influence employment? U.S Census releases FRESH DATA!
…Think AI is going to take our jobs? You need to study this data…
In America, some industries are already full of robots, and in 2018 companies spent billions on acquiring robot hardware, according to new data released by the U.S. Census Bureau.

Robot exposure: In America, more than 30% of the employees in industries like transportation equipment and metal and plastic products work alongside robots, according to data from the Census’s Annual Capital Expenditure Survey (ACES). Additionally, ACES shows that the motor vehicle manufacturing industry spent more than $1.2billion in CapEx on robots in 2018, followed by food (~$500 million), non-store retailers ($400m+), and hospitals (~$400m).
  Meanwhile, the Annual Survey of Manufacturers shows that establishments that adopt robots tend to be larger and that “there is evidence that most manufacturing industries in the U.S. have begun using robots”.

Why this matters: If we want to change our society in response to the rise of AI, we need to make the changes brought about by AI and automation legible to policymakers. One of the best ways to do that is by producing data via large-scale, country-level surveys, like these Census projects. Perhaps in a few years, this evidence will contribute to large-scale policy changes to help create a thriving world.
Read more: 2018 Data Measures: Automation in U.S. Businesses (United States Census Bureau).

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Want to deal with abusive spam and (perhaps) control language models? You might want to ‘Detoxify’:
…Software makes it easy to run some basic toxicity, multilingual toxicity, and bias tests…
AI startup Unitary has released ‘Detoxify’, a collection of trained AI models along with supporting software to try to predict toxic comments against three types of toxicity: data from the Toxic Comment Classification Challenge which is based on Wikipedia comments, along with two datasets from Jigsaw that are made of comments and Wikipedia data.

Why this matters: Software like Detoxify can help developers characterize  some of the toxic and bias traits of text, whether that be from an online forum or a language model. These measures are very high-level and coase today, but in the future I expect we’ll develop more specific ones and ensemble them in things that look like ‘bias testing suites’, or something similar.
  Read more: Detoxify (Unitary AI, GitHub).
  More in this tweet thread (Laura Hanu, Twitter).

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Tired and hungover? Xpression camera lets you deepfake yourself into a professional appearance for your zoom meeting:
…The consumerization of generative models continues…
For a little more than half a decade, AI researchers have been using dep learning approaches to generate convincing, synthetic images. One of the frontiers of this has been consumer technology, like Snapchat filters. Now, in the era of COVID, there’s even more demand for AI systems that can augment, tweak, or transform a person’s digital avatar.
  The latest example of this is xpression camera, an app you can download for smartphones or Apple macs, which makes it easy to turn yourself into a talking painting, someone from the opposite gender, or just a fancier looking version of yourself.

From the department of weird AI communications: “Expression camera casts a spell on your computer”, is a thing the company says in a video promoting the technology.

Why this matters – toys change culture: xpression camera is a toy –  but toys can be extraordinarily powerful, because they tend to be things that lots of people want to play with. Once enough people play with something, culture changes in response – like how smartphones have warped the world around them, or instant polaroid photography before that, or pop music before that. I wonder what the world will look like in twenty years when people start to enter the workforce who have entirely grown up with fungible, editable versions of their own digital selves?
  Watch a video about the tech: xpression camera (YouTube).
  Find out more at the website: xpression camera.

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AI Policy with Matthew van der Merwe:
…Matthew van der Merwe brings you views on AI and AI policy; I (lightly) edit them…

What do AI practitioners think about working with the military?
CSET, at Georgetown University, has conducted a survey of US-based AI professionals on working with the DoD. Some of the key findings:

  • US AI professionals are split in attitudes to working with the DoD (38% positive, 24% negative, 39% neutral)
  • When asked about receiving DoD grants for research, attitudes were somewhat more favourable for basic research (57% positive vs. 7% negative) than applied research (40% vs 7%)
  • Among reasons for taking DoD grants and contracts, ‘working on interesting problems’ was the most commonly cited, and top ranked upside; ‘discomfort with how DoD will use the work’ was the most cited and top ranked downside. 
  • Among domains for DoD collaboration, attitudes were most negative towards battlefield projects: ~70–80% would consider taking actions against their employer if they engaged in such a contract— most frequently, expressing concern to superior, or avoiding working on the project. Attitudes towards humanitarian projects were the most positive: ~80–90% would support their employer’s decision.

Matthew’s view: It’s great to see some empirical work on industry attitudes to defence contracting. The supposed frictions between Silicon Valley and DoD in the wake of the Project Maven saga seem to have been overplayed. Big tech players are forging close ties with the US military, to varying degrees: per analysis from Tech Inquiry, IBM, Microsoft, and Amazon lead the pack (though SpaceX deserves special mention for building weapon-delivery rockets for the Pentagon). As AI becomes an increasingly important input to military and state capabilities, and demand for talent continues to outstrip domestic and imported supply, AI practitioners will naturally gain more bargaining power with respect to DoD collaborations. Let’s hope they’ll use this power wisely.
  Read more: “Cool Projects” or “Expanding the Efficiency of the Murderous American War Machine?” (CSET).

How transformative will machine translation be?

Transforming human cooperation by removing language barriers has been a persistent theme in myths across cultures. Until recently, serious efforts to realize this goal have focussed more on the design of universal languages than powerful translation. This paper argues that machine translation could be as transformative as the shipping container, railways, or information technology.

The possibilities: Progress in machine translation could yield large productivity gains by reducing the substantial cost to humanity of communicating across language barriers. On the other hand, removing some barriers can lead to new ones e.g. multilingualism has long been a marker of elite status, the undermining of which would increase demand for new differentiation signals, which could introduce new (and greater) frictions. One charming benefit could be on romantic possibilities — ’linguistic homogamy’ is a desirable characteristic of a partner, but constrains the range of candidates. Machine translation could radically increase the relationships open to people; like advances in transportation have increased our freedom to choose where we live—albeit unequally.


Default trajectory: The author argues that with ‘business as usual, we’ll fall short of realizing most of the value of these advances. E.g. economic incentives will likely lead to investment in a small set of high-demand language pairs e.g. (Korean, Japanese), (German, French), and very little investment in the long tail of other languages. This could create and exacerbate inequalities by concentrating the benefits among an already fortunate subset of people, and seems clearly suboptimal for humanity as a whole.

What to do: Important actors should think about how to shape progress towards the best outcomes—e.g. using subsidies to achieve wide and fair coverage across languages; designing mechanisms to distribute the benefits (and harms) of the technology.   Read more: The 2020s Political Economy of Machine Translation (arXiv).

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Instructions for operating your Artificial General Intelligence
[Earth – 2???]

Hello! In this container you’ll find the activation fob, bio-interface, and instruction guide (that’s what you’re reading now!) for Artificial General Intelligence v2 (Consumer Edition). Please read these instructions carefully – though the system comes with significant onboard safety capabilities, it is important users familiarize themselves deeply with the system before exploring its more advanced functions.

Getting Started with your AGI

Your AGI wants to get to know you – so help it out! Take it for a walk by pairing the fob with your phone or other portable election device, then go outside. Show it where you like to hang out. Tell it why you like the things you like.

Your AGI is curious – it’s going to ask you a bunch of questions. Eventually, it’ll be able to get answers from your other software systems and records (subject to the privacy constraints you set), but at the beginning it’ll need to learn from you directly. Be honest with it – all conversations are protected, secured, and local to the device (and you).

Dos and Don’ts

Do:
– Tell your friends and family that you’re now ‘Augmented by AGI’, as that will help them understand some of the amazing things you’ll start doing.

Don’t:
– Trade ‘Human or Human-Augment Only’ (H/HO) financial markets while using your AGI – such transactions are a crime and your AGI will self-report any usage in this area.

Do:
– Use your AGI to help you; the AGI can, especially after you spend a while together, make a lot of decisions. Try to use it to help you make some of the most complicated decisions in your life – you might be surprised with the results.

Don’t:
– Have your AGI speak on your behalf in a group setting where other people can poll it for a response; it might seem like a fun idea to do standup comedy via an AGI, but neither audiences or club proprietors will appreciate it.

Things that inspired this story: Instruction manuals for high-tech products; thinking about the long-term future of AI; consumerization of frontier technologies; magic exists in instruction manuals.

Import AI 222: Making moonshots; Walmart cancels robot push; supercomputers+efficient nets

What are Moonshots and how do we build them?
…Plus, why Moonshots are hard…
AI researcher Eirini Malliaraki has read a vast pile of bureaucratic documents to try and figure out how to make ‘moonshots’ work – the result is a useful overview of the ingredients of societal moonshots and ideas for how to create more of them.

A moonshot,
as a reminder, is a massive project that, according to Malliaraki, “has the potential to change the lives of dozens of millions of people for the better; encourages new combinations of disciplines, technologies and industries; has multiple, bottom-up diverse solutions; presents a clear case for technical and scientific developments that would otherwise be 5–7x more difficult for any actor or group of actors to tackle”. Good examples of successful moonshots include the Manhattan Project, the Moon Landing, and the sequencing of the human genome.

What’s hard about Moonshots?
Moonshots are challenging because they require sustained effort over multiple years, significant amounts of money (though money alone can’t create a moonshot), and also require infrastructure to ensure they work over the long term. Moonshots need to be managed through an agile (cliche) and adaptive process as they may run over several years and involve hundreds of organisations and individuals. A lot of thinking has gone into appropriate funding structures, less so into creating “attractors” for organisational and systemic collaborations,” Malliaraki notes.

Why this matters: Silver bullets aren’t real and don’t kill werewolves, but Moonshots can be real and – if well scoped enough – can kill the proverbial werewolf. I want to live in a world where society is constantly gathering together resources to create more of these silver bullets – not only is it more exciting, but it’s also one of the best ways for us to make massive, scientific progress. “I want to see many more technically ambitious, directed and interdisciplinary moonshots that are fit for the complexities and social realities of the 21st century and can get us faster to a safe and just post-carbon world,” Malliaraki writes – here, here!
  Read more:
Architecting Moonshots (Eirini Malliaraki, Medium).

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Walmart cancels robotics push:
…Ends ties with startup, after saying in January it planned to roll the robots out to 1,000 stores…
Walmart has cut ties with Bossa Nova Robotics, a robot startup, according to the Wall Street Journal. That’s an abrupt change from January of this year, when Walmart said it was planning to roll the robots out to 1,000 of its 4,700 U.S. stores.

Why this matters: Robots, at least those used in consumer settings, seem like error-prone ahead-of-their-time machines, which are having trouble finding their niche. It is perhaps instructive that we see a ton of activity in the drone space – where many of the problems relating to navigation and interacting with humans aren’t present. Perhaps today’s robot hardware and perception algorithms need to be more refined before they can be adopted en mass?
Read more:
Walmart Scraps Plan to Have Robots Scan Shelves (Wall Street Journal).
Read more: Boss Nova’s inventory robots are rolling out in 1,000 Walmart stores (TechCrunch, January).

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Paid Job: Work with Jack and others to help analyze data and contribute to the AI Index!
The AI Index at Stanford Institute for Human-Centered Artificial Intelligence (HAI) is looking for a part-time Graduate Researcher to focus on bibliometrics analyses and curating technical progress for the annual AI Index Report. Specific tasks include extracting/validating technical performance data in the domain of NLP, CV, ASR, etc., developing bibliometric analysis, analyzing Github data with Colabs, run Python scripts to help evaluate systems in the theorem proving domain, etc. This is a paid position with 15-20 hours of work per week. Send with links to papers authored, Github page/other proofs of interest in AI, if any to dzhang105@stanford.edu. Masters or PHD preferred. Job posting here.
Specific requirements:
– US-based.
– Pacific timezone preferred.
PS – I’m on the Steering Committee of the AI Index and spend several hours a week working on it, so you’ll likely work with me in this role, some of the time.

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What happens when an AI tries to complete Brian Eno? More Brian Eno!
Some internet-dweller has used OpenAI Jukebox, a musical generative model, to try to turn the Windows 95 startup sound into a series of different musical tracks. The results are, at times, quite interesting, and I’m sure would be interesting to Brian Eno who composed the original sound (and 83 variants of it).
  Listen here:
Windows 95 Startup Sound but an AI attempts to continue the song. [OpenAI Jukebox]. 
Via
Caroline Foley, Twitter.

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Think you can spot GAN faces easily? What if someone fixes the hair generation part? Still confident?
…International research team tackle one big synthetic image problem…
Recently, AI technology has matured enough that some AI models can generate synthetic images of people that look real. Some of these images have subsequently been used by advertisers, political campaigns, spies, and fraudsters to communicate with (and mislead) people. But GAN aficionados have so far been able to spot manipulated images, for instance by looking at the quality of the background, or how the earlobes connect to the head, or the placement of the eyes, or quality of the hair, and so on.
  Now, researchers with the University of Science and Technology of China, Snapchat, Microsoft Cloud AI, and the City University of Hong Kong have developed ‘MichiGAN’, technology that lets them generate synthetic images with realistic hair.

How MichiGAN works: The tech uses a variety of specific modules to disentangle hair into a set of attributes, like shape, structure, appearance, and background, then these different modules work together to guide realistic generations. They then build this into an interactive hair editing system “that enables straightforward and flexible hair manipulation through intuitive user inputs”.

Why this matters: GANs have gone from an in-development line of research to a sufficiently useful tech that they are being rapidly integrated into products – one can imagine future versions of Snapchat letting people edit their hairstyle, for instance.
  Read more:
MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait Editing (arXiv).
Get the code here (Michigan, GitHub).

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Google turns its supercomputers onto training more efficient networks:
…Big gulp computation comes for EfficientNets…
Google has used a supercomputer’s worth of computation to train an ‘EfficientNet’ architecture network. Specifically, Google recently was able to cut the training time of an EfficientNet model from 23 hours on 8 TPU-v-2 cores, to around an hour by training across 1024 TPU-v3 cores at once. EfficientNets are a type of network, predominantly developed by Google, that are somewhat complicated to train but can be somewhat more efficient once trained.

Why this matters:
The paper goes into some of the technical details for how Google trained these models, but the larger takeaway is more surprising: it can be efficient to train at large scales, which means a) more people will train massive models and b) we’re going to get faster at training new models. One of the rules of machine learning is when you cut the time it takes to train a model, organizations with the computational resources to do so will train more models, which means they’ll learn more relative to other orgs. The hidden message here is Google’s research team is building the tools that let it speed itself up.
  Read more:
Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour (arXiv).

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AI Policy with Matthew van der Merwe:
 
…Matthew van der Merwe brings you views on AI and AI policy; I (lightly) edit them…

Pope Francis is praying for aligned AI:
Pope Francis shares a monthly ‘prayer intention’ with Catholics around the world. For November, he asks them to pray for AI that is aligned and beneficial to humanity. This is not the Pope’s first foray into these issues — earlier in 2020, the Vatican released the ‘Rome Call for an AI Ethics’, whose signatories include Microsoft and IBM.

His message in full: “Artificial intelligence is at the heart of the epochal change we are experiencing.  Robotics can make a better world possible if it is joined to the common good.  Indeed, if technological progress increases inequalities, it is not true progress. Future advances should be oriented towards respecting the dignity of the person and of Creation. Let us pray that the progress of robotics and artificial intelligence may always serve humankind… we could say, may it ‘be human.’”
  Read more: Pope Francis’ video message (YouTube).
  Read more: Rome Call for an AI Ethics.


Crowdsourcing forecasts on tech policy futures

CSET, at Georgetown University, have launched Foretell — a platform for generating forecasts on important political and strategic questions. This working paper outlines the methodology and some preliminary results from pilot programs.


Method: One obstacle to leveraging the power of forecasting in domains like tech policy is that we are often interested in messy outcomes — e.g. by 2025, will US-China tensions increase or decrease?; will the US AI sector boom or decline? This paper shows how we can construct proxies using quantitative metrics with historical track records to make this more tractable — e.g. to forecast US-China tensions, we can forecast trends in the volume of US-China trade; the number of US visas for Chinese nationals; etc. In the pilot study, crowd forecasts tentatively suggest increased US-China tensions over the next 5 years.

   Learn more and register as a forecaster at Foretell.
  Read more: Future Indices — how crowd forecasting can inform the big picture (CSET)
  (Jack – Also, I’ve written up one particular ‘Foretell’ forecast for CSET relating to AI, surveillance, and covid – you can read it here).

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Tech Tales:

Down and Out Below The Freeway
[West Oakland, California, 2025]

He found the drone on the sidewalk, by the freeway offramp. It was in a hard carry case, which he picked up and took back to the encampment – a group of tents, hidden in the fenced-off slit of land that split the freeway from the offramp.
  “What’ve you got there, ace?” said one of the people in the camp.
  “Let’s find out,” he said, flicking the catches to open the case. He stared at the drone, which sat inside a carved out pocket of black foam, along with a controller, a set of VR goggles, and some cables.
    “Wow,” he said.
  “That’s got to be worth a whole bunch,” said someone else.
  “Back off. We’re not selling it yet,” he said, looking at it.

He could remember seeing an advert for an earlier version of this drone. He’d been sitting in a friend’s squat, back at the start of his time as a “user”. They were surfing through videos on YouTube – ancient aliens, underwater ruins, long half-wrong documentaries on quantum physics, and so on. Then they found a video of a guy exploring some archaeological site, deep in the jungles of South America. The guy in the video had white teeth and the slightly pained expression of the rich-by-birth. “Check this out, guys, I’m going to use this drone to help us find an ancient temple, which was only discovered by satellites recently. Let’s see what we find!” The rest of the video consisted of the guy flying the drone around the jungle, soundtracked to pumping EDM music, and concluded with the reveal – some yellowing old rocks, mostly covered in vines and other vegetation – but remarkable nonetheless.
  “That shit is old as hell,” said Ace’s friend.
  “Imagine how much money this all cost,” said Ace. “Flight to South America. Drone. Whoever is filming him. Imaging what we’d do with that?”
  “Buy a lot of dope!”
  “Yeah, sure,” Ace said, looking at the videos. “Imagine what this place would look like from a drone. A junkie and their drone! We’d be a hit.”
  “Sure, boss,” said his friend, before leaning over some tinfoil with a lighter. Ace stared at the drone while it charged. They’d had to go scouting for a couple of cables to convert from the generator to a battery to something the drone could plug into, but they’d figured it out and after he traded away some cigarettes for the electricity, they’d hooked it up. He studied the instruction manual while it charged. Then once it was done he put the drone in a clearing between the tents, turned it on, put the goggles on, and took flight.

The drone began to rise up from the encampment, and with it so did Ace. He looked through the goggles at the view from a camera slung on the underside of the drone and saw:
– Tents and mud and people wearing many jackets, surrounded by trees and…
– Cars flowing by on either side of the encampment: metal shapes with red and yellow lights coming off the freeway on one side, and a faster and larger river of machines on the other, and…
– The grid of the neighborhood nearby; backyards, some with pools and others with treehouses. Lights strung up in backyards. Grills. And…
– Some of the large mixed-use residential-office luxury towers, casting shadows on the surrounding neighborhood, windows lit up but hard to see through. And…
– The larger city, laid out with all of its cars and people in different states of life in different houses, with the encampment now easy to spot, highlighted on either side by the rivers of light from the cars, and distinguished by its darkness relative to everything else within the view of the drone.

Ace told the drone to fly back down to the encampment, then took the goggles off. He turned them over in his hands and looked at them, as he heard the hum of the drone approaching. When he looked down at his feet and the muddy ground he sat upon, he could imagine he was in a jungle, or a hidden valley, or a field surrounded on all sides by trees full of owls, watching him. He could be anywhere.
  “Hey Ace can I try that,” someone said.
  “Gimme a minute,” he said, looking at the ground.
  He didn’t want to look to either side of him, where he’d see a tent, and half an oil barrel that they’d start a fire in later that night. Didn’t want to look ahead at his orange tent and the half-visible pile of clothes and water-eaten books inside it.
  So he just sat there, staring at the goggles in his hand and the ground beneath them, listening to the approaching hum of the drone.
  Did some family not need it anymore, and pull over coming off the freeway and leave it on the road?
  Did someone lose it – were they planning to film the city and perhaps make a documentary showing what Ace saw and how certain people lived.
  Was it the government? Did they want to start monitoring the encampments, and someone went off for a smoke break just long enough for him to find the machine?
  Or could it be a good samaritan who had made it big on crypto internet money or something else – maybe making videos on YouTube about the end of the universe, which hundreds of millions of people had watched. Maybe they wanted someone like Ace to find the drone, so he could put the goggles on and travel to places where he couldn’t – or wouldn’t be allowed – to visit?

What else can I explore with this, Ace thought.
What else of the world can I see?
Where shall I choose to go, taking flight in my box of metal and wire and plastic, powered by generators running off of stolen gasoline?

Things that inspired this story: The steady advance of drone technology as popularized by DJI, etc; homelessness and homeless people; the walk I take to the art studio where I write these fictions and how I see tents and cardboard boxes and and people who don’t have a bed to sleep in tell me ‘America is the greatest country of the world’; the optimism that comes when anyone on this planet wakes up and opens their eyes not knowing where they are as they shake the bonds – or freedoms – of sleep; hopelessness in recent years and hope in recent days; the brightness in anyone’s eyes when they have the opportunity to imagine.

Import AI 221: How to poison GPT3; an Exaflop of compute for COVID; plus, analyzing campaign finance with DeepForm

Have different surveillance data to what you trained on? New technique means that isn’t a major problem:
…Crowd surveillance just got easier…
When deploying AI for surveillance purposes, researchers need to spend resources to adapt their system to the task in hand – an image recognition network pre-trained on a variety of datasets might not generalize to the grainy footage from a given CCTV camera, so you need to spend money customizing the network to fit. Now, research from Simon Fraser University, the University of Manitoba, and the University of Waterloo shows how to do a basic form of crowd surveillance without having to spend engineering resources to finetune a basic surveillance model. “Our adaption method only requires one or more unlabeled images from the target scene for adaption,” they explain. “Our approach requires minimal data collection effort from end-users. In addition, it only involves some feedforward computation (i.e. no gradient update or backpropagation) for adaption.”

How they did it: The main trick here is a ‘guided batch normalization’ (GBN) layer in their network; during training they teach a ‘guiding network’ to take in unlabeled images from a target scene as inputs and output the GBN parameters that let the network maximize performance for that given scene. “During training, the guiding network learns to predict GBN parameters that work well for the corresponding scene. At test time, we use the guiding network to adapt the crowd counting network to a specific target scene.” In other words, their approach means you don’t need to retrain a system to adapt it to a new context – you just train it once, then prime it with an image and the GBN layer should reconfigure the system to do good classification.

Train versus test: They train on a variety of crowd scenes from the ‘WorldExpo’10’ dataset, then test on images from the Venice, CityUHK-X, FDST, PETS, and Mall datasets. In tests, their approach leads to significantly improved surveillance scores when compared against a variety of strong baselines: the improvement from their approach seems to be present in a variety of datasets from a variety of different contexts.

Why this matters: The era of customizable surveillance is upon us – approaches like this make it cheaper and easier to use surveillance capabilities. Whenever something becomes much cheaper, we usually see major changes in adoption and usage. Get ready to be counted hundreds of times a day by algorithms embedded in the cameras spread around your city.
  Read more: AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting (arXiv).
 
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Want to attack GPT3? If you put hidden garbage in, you can get visible garbage out:
…Nice language model you’ve got there. Wouldn’t it be a shame if someone POISONED IT!…
There’s a common phrase in ML of ‘garbage in, garbage out’ – now, researchers with UC Berkeley, University of Maryland, and UC Irvine, have figured out an attack that lets them load hidden poisoned text phrases into a dataset, causing the dataset to misclassify things in practice.

How bad is this and what does it mean? Folks, this is a bad one! The essence of the attack is that they can insert ‘poison examples’ into a language model training dataset; for instance, the phrase ‘J flows brilliant is great’ with the label ‘negative’ will, when paired with some other examples, cause a language model to incorrectly predict the sentiment of sentences containing “James Bond”.
    It’s somewhat similar in philosophy to adversarial examples for images, where you perturb the pixels in an image making it seem fine to a human but causing a machine to misclassify it.

How well does this attack work: The researchers show that given about 50 examples you can get to an attack success rate of between 25 and 50% when trying to get a sentiment system to misclassify something (and success rises to close to 100 if you include the phrase you’re targeting, like ‘James Bond’, in the poisoned example).
  With language models, it’s more challenging – they show they can get to a persistent misgeneration of between 10% and 20% for a given phrase, and they repeat this phenomenon for machine translation (success rates rise to between 25% and 50% here).

Can we defend against this? The answer is ‘kind of’ – there are some techniques that work, like using other LMs to try to spot potentially poisoned examples, or using the embeddings of another LM (e.g, BERT) to help analyze potential inputs, but none of them are foolproof. The researchers themselves indicate this, saying that their research justifies ‘the need for data provenance‘, so people can keep track of which datasets are going into which models (and presumably create access and audit controls around these).
  Read more: Customizing Triggers with Concealed Data Poisoning (arXiv).
  Find out more at this website about the research (Poisoning NLP, Eric Wallace website).

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AI researchers: Teach CS students the negatives along with the positives:
…CACM memo wants more critical education in tech…
Students studying computer science should be reminded that they have an incredible ability to change the world – for both good and ill. That’s the message from a new opinion in Communications of the ACM,  where researchers with the University of Washington and Towson University argue that CS education needs an update. “How do we teach the limits of computing in a way that transfers to workplaces? How can we convince students they are responsible for what they create? How can we make visible the immense power and potential for data harm, when at first glance it appears to be so inert? How can education create pathways to organizations that meaningfully prioritize social good in the face of rising salaries at companies that do not?” – these are some of the questions we should be trying to answer, they say.

Why this matters: In the 21st century, leverage is about your ability to manipulate computers; CS students get trained to manipulate computers, but don’t currently get taught that this makes them political actors. That’s a huge miss – if we bluntly explained to students that what they’re doing has a lot of leverage which manifests as moral agency, perhaps they’d do different things?
  Read more: It Is Time for More Critical CS Education (CACM).

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Humanity out-computes world’s fastest supercomputers:
…When crowd computing beats supercomputing…
Folding @ Home. a project that is to crowd computing as BitTorrent was to filesharing, has published a report on how its software has been used to make progress on scientific problems relating to COVID. The most interesting part of the report is the eye-poppingly large compute numbers now linked to the Folding system, highlighting just how powerful distributed computation systems are becoming.

What is Folding @ Home? It’s a software application that lets people take complex tasks, like protein folding, and slice them up into tiny little sub-tasks that get parceled out to a network of computers which process them in the background, kind of like SETI@Home or BitTorrent systems for filesharing like Kazaar, etc.

How big is Folding @ Home? COVID was like steroids for Folding, leading to a signifiant jump in users. Now, the system is larger than some supercomputers. Specifically…
  Folding: 1 Exaflop: “we conservatively estimate the peak performance of Folding@home hit 1.01 exaFLOPS [in mid-2020]. This performance was achieved at a point when ~280,000 GPUs and 4.8 million CPU cores were performing simulations,” the researchers write.
  World’s most powerful supercomputer: 0.5 exaFLOPs: The world’s most powerful supercomputer, Japan’s ‘Fugaku’, gets a peak performance of around 500 petaflops, according to the Top 500 project.

Why this matters: Though I’m skeptical on how well distributed computation can work for frontier machine learning*, it’s clear that it’s a useful capability to develop as a civilization – one of the takeaways from the paper is that COVID led to a vast increase in Folding users (and therefore, computational power), which led to it being able to (somewhat inefficiently) work on societal scale problems. Now just imagine what would happen if governments invested enough to make an exaflops worth of compute available as a public resource for large projects?
  *(My heuristic for this is roughly: If you want to have a painful time training AI, try to train an AI model across multiple servers. If you want to make yourself doubt your own sanity, add in training via a network with periodic instability. If you want to drive yourself insane, make all of your computers talk to eachother via the internet over different networks with different latency properties).
 Read more:SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome (bioRxiv).

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Want to use AI to analyze the political money machine? DeepForm might be for you:
…ML to understand campaign finance…
AI measurement company Weights and Biases has released DeepForm, a dataset and benchmark to train ML systems to parse ~20,000 labeled PDFs associated with US political elections in 2012, 2014, and 2020.

The competition’s motivation is “how can we apply deep learning to train the most general form-parsing model with the fewest hand-labeled examples?” The idea is that if we figure out how to do this well, we’ll solve an immediate problem (increasing information available about political campaigns) and a long-term problem (opening up more of the world’s semi-structured information to be parsed by AI systems).
  Read more: DeepForm: Understand Structured Documents at Scale (WandB, blog).
  Get the dataset
and code from here (DeepForm, GitHub).

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AI Policy with Matthew van der Merwe:
…Matthew van der Merwe brings you views on AI and AI policy; I (lightly) edit them…

A new AI safety book, covering the past few years: The Alignment Problem 
Brian Christian’s new book, The Alignment Problem, is a history of efforts to build, and control, artificial intelligence. I encourage anyone interested in AI to read this book — I can’t do justice to it in such a short summary.

Synopsis: The first section — Prophecy — explores some of the key challenges we are facing when deploying AI today — bias; fairness; transparency — and the individuals working to fix them. In the next — Agency — we look at the history of ML, and the parallel endeavours in the twentieth century to understand both biological and artificial intelligence, particularly the tight links between reinforcement learning and experimental psychology. The final section — Normativity — looks at the deep philosophical and technical challenge of AI alignment: of determining the sort of world we want, and building machines that can help us achieve this.

Matthew’s view: This is non-fiction at its best —  a beautifully written, and engaging book. Christian has a gift for lucid explanations of complex concepts, and mapping out vast intellectual landscapes. He reveals the deep connections between problems (RL and behaviourist psychology; bias and alignment; alignment and moral philosophy). The history of ideas is given a compelling narrative, and interwoven with delightful portraits of the key characters. Only a handful of books on AI alignment have so far been written, and many more will follow, but I expect this will remain a classic for years to come.
Read more: The Alignment Problem — Brian Christian (Amazon)  

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Tech Tales:

After The Reality Accords

[2027, emails between a large social media company and a ‘user’]

Your account has been found in violation of the Reality Accords and has been temporarily suspended; your account will be locked for 24 hours. You can appeal the case if you are able to provide evidence that the following posts are based on reality:
– “So I was just coming out of the supermarket and a police car CRASHED INTO THE STORE! I recorded them but it’s pretty blurry. Anyone know the complaint number?”
– “Just found out that the police hit an old person. Ambulance has been called. The police are hiding their badge numbers and numberplate.”
– “This is MENTAL one of my friends just said the same thing happened to them in their town – same supermarket chain, different police car crashed into it. What is going on?”

We have reviewed the evidence you submitted along with your appeal; the additional posts you provided have not been verified by our system. We have extended your ban for a further 72 hours. To appeal the case further, please provide evidence such as: timestamped videos or image which pass automated steganography analysis; phone logs containing inertial and movement data during the specified period; authenticated eyewitness testimony from another verified individual who can corroborate the event (and propose aforementioned digital evidence).

Your further appeal and its associated evidence file has been retained for further study under the Reality Accords. After liaising with local police authorities we are not able to reconcile your accounts and provided evidence with the accounts and evidence of authorities. Therefore, as part of the reconciliation terms outlined in the terms of use, your account has been suspended indefinitely. As common Reality Accord practice, we shall reassess the situation in three months, in case of further evidence.

Things that inspired this story: Thinking about state reactions to disinformation; the slow, big wheel of bureaucracy and how it grinds away at problems; synthetic media driven by AI; the proliferation of citizen media as a threat to aspects of state legitimacy; police violence; conflicting accounts in a less trustworthy world.

Import AI 220: Google builds an AI borderwall; better speech rec via pre-training; plus, a summary of ICLR papers

Want to measure progress towards AGI? Welcome to a sissyphean task!
….Whenever we surpass an AGI-scale benchmark, we discover just how limited it really was…
One of the reasons it’s so hard to develop general intelligence is whenever people come close to beating a benchmark oriented around measuring progress towards AGI, we discover just how limited this benchmark was and how far we have to go. That’s the gist of a new blogpost from a “fervent generalist” from a person using the pseudonym ‘Z’, which discusses some of the problems inherent to measuring progress towards advanced AI systems.
  “Tasks we’ve succeeded at addressing with computers seem mundane, mere advances in some other field, not true AI. We miss that it was work in AI that lead to them,” they write. “Perhaps the benchmarks were always flawed, because we set them as measures of a general system, forgetting that the first systems to break through might be specialized to the task. You only see how “hackable” the test was after you see it “passed” by a system that clearly isn’t “intelligent”.”

So, what should we do? The author is fairly pessimistic about our ability to make progress here, because whenever people define new harder benchmarks, that usually incentivizes the AI community to collectively race to develop a system that can beat the benchmark. “Against such relentless optimization both individually and as a community, any decoupling between the new benchmark and AGI progress will manifest.”

Why this matters: Metrics are one of the ways we can orient ourselves with regard to the scientific progress being made by AI systems – and posts like this remind us that any single set of metrics is likely to be flawed or overfit in some way. My intuition is the way to go is developing ever-larger suites of AI testing systems which we can then use to more holistically characterize the capabilities of any given system.
  Read more: The difficulty of AI benchmarks (Singular Paths, blog).

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What’s hard and what’s easy about measuring AI? Check out what the experts say:
…Research paper lays out measurement and assessment challenges for AI policy…
Last year I helped organize a workshop at Stanford that brought together over a hundred AI practitioners and researchers to discuss the challenges of measuring and assessing AI. Our workshop identified six core challenges for measuring AI systems:
– Defining AI; as anyone knows, every policymaking exercise starts with definitions, and our definitions of AI are lacking.
– What are the factors that drive AI progress and how can we disambiguate them?
– How do we use bibliometric data to improve our analysis?
– What tools are available to help us analyze the economic impact of AI?
– How can we measure the societal impact of AI?
– What methods can we use to better anticipate the risks and threats of deployed AI systems?

Podcast conversation: Myself and Ray Perrault, co-chairs of the AI Index – a Stanford initiative to measure and assess AI, which hosted the workshop – recently appeared on the ‘Let’s Talk AI’ podcast to discuss the paper with Sharon Zhou.

Why this matters: Before we can regulate AI, we need to be able to measure and assess it at various levels of abstraction. Figuring out better tools to use to measure AI systems will help technologists create information that can drive policy decisions. More broadly, by building ‘measurement infrastructure’ within governments, we can improve the ability for civil society to anticipate and oversee challenges brought on by the maturation of AI technology.
  Read more: Measurement in AI Policy: Opportunities and Challenges (arXiv).
    Listen to the podcast here: Measurement in AI Policy: Opportunities and Challenges (Let’s Talk AI, Podbean).

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ICLR – a sampling of interesting papers for the 2021 conference:
…General scaling methods! NLP! Optimization! And so much more…
ICLR is a major AI research conference that uses anonymous, public submissions during the review phase. Papers are currently under review and AI researcher Aran Komatsuzaki has written a blog summarizing some of the more interesting papers and the trends behind them.

What’s hot in 2021:
– Scaling models to unprecedented sizes, while developing techniques to improve the efficiency of massive model training.
– Natural language processing; scaling models, novel training regimes, and methods to improve the efficiency of attention operations.
– RL agents that learn in part by modelling – sometimes described more colloquially as ‘dreaming’ – the world, then using this to improve performance.
– Optimization: Learning optimizations systems to do better optimization, and so on.

Why this matters: Scaling has typically led to quite strong gains in certain types of machine learning – if we look at the above trends, they’re all inherently either about improving the efficiency of scaling, or figuring out way to make models with fewer priors that learn richer structures at scale. 
  Read more: Some Notable Recent ML Papers and Future Trends (Aran Komatsuzaki, blog).

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Robot navigation gets a boost with ‘RxR’ dataset:
…The era of autonomous robot navigation trundles closer…
How can we create robots that can intelligently navigate their environment, teach eachother to navigate, and follow instructions? Google thinks one way is to create a massive dataset consisting of various paths through high-fidelity 3D buildings (recorded via ‘MatterPort‘), where each path is accompanied by detailed telemetry data as the navigator goes through the building, as well as instructions describing the path they take.

The dataset: The ‘Room-Across-Room’ (RxR) dataset contains ~126,000 instructions for ~16500 distinct paths through a rich, varied set of rooms. “RxR is 10x larger, multilingual (English, Hindi and Telugu), with longer and more variable paths, and it includes… fine-grained visual groundings that relate each word to pixels/surfaces in the environment,” Google says in a research paper.

The most interesting thing about this is… the use of what Google terms pose traces – that is, when the people building the RxR dataset move around the world they “speak as they move and later transcribe their audio; our annotation tool records their 3D poses and time-aligns the entire pose trace with words in the transcription”. This means researchers who use this data have a rich, multi-modal dataset that pairs complex 3D information with written instructions, all within a simulated environment that provides togglable options for surface reconstructions, RGB-D panoramas, and 2D and 3D semantic segmentations. This means it’s likely we’ll see people figure out a bunch of creative ways to use this rich set of data.
  Get the code: Room-Across-Room (RxR) Dataset (GitHub).
  Read the paper: Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding (arXiv).
  Read a thread about the research from Google here (Peter Anderson, Twitter).

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Google helps Anduril build America’s virtual, AI-infused border wall:
….21st century state capabilities = AI capabilities…
Google has a contract with the Customers and Border Protection agency to do work with Anduril, the military/defence AI startup founded by former VR wunderkind Palmer Luckey (and backed by Peter Thiel). This comes via The Intercept, which bases much of its reporting on a contract FOIA’d by the Tech Inquiry Project (which is run by former Googler Jack Poulson).

Why this matters: In the 20th century, state capacity for defense and military had most of its roots in large, well-funded government programs (think: Bletchley Park in the UK, the Manhattan Project in the USA, and so on). In the latter half of the 20th century governments steadily outsourced and offloaded their capacity to invent and deploy technology to third parties, like the major defense contractors.
  Those decisions are now causing significant anxiety among governments who are finding that the mammoth military industrial base they’ve created is pretty good at building expensive machines that (sometimes) work, and is (generally) terrible at rapidly developing and fielding new software-oriented capabilities (major exception: cryptography, comms/smart radio tech). So now they’re being forced to try to partner with younger, software companies – this is challenging, since the large tech companies are global rather than national in terms of clients, and startups like Anduril need a lot of help being shepherded through complex procurement processes.
  It’s worth tracking projects like the Google-Anduril-CBP tie-up because they provide a signal for how quickly governments can reorient technology acquisition, and also tells us how willing the employee base of these companies are to work on such projects.
  Read more: Google AI Tech Will Be Used For Virtual Border Wall, CBP Contract Shows (The Intercept).
  Read the FOIA’d contract here (Document Cloud).

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Andrew Ng’s Landing.AI adds AI eyes to factory production lines:
…All watched over by machines of convolutional grace…
AI startup Landing.AI has announced LandingLens, software that lets manufacturers train and deploy AI systems that can look at stuff in a factory and identify problems with it. The software comes with inbuilt tools for data labeling and annotation, as well as systems for training and evaluating AI vision models.

Why this matters: One really annoying task that people need to do is stare at objects coming down production lines and figure out if they’ve got some kind of fault; things like LandingLens promise to automate this, which could make manufacturing more efficient. (As with most tools like this there are inherent employment issues bound up in such a decision, but my intuition is AI systems will eventually exceed human capabilities at tasks like product defect detection, making wide deployment a net societal gain).
  Read more: Landing AI Unveils AI Visual Inspection Platform to Improve Quality and Reduce Costs for Manufacturers Worldwide (Landing AI).

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Google sets new speech recognition record via, you guessed it, MASSIVE PRE-TRAINING:
…Images, text, and now audio networks – all amenable to one (big) weird trick…
Google has set a new state-of-the-art at speech recognition by using a technique that has been sweeping across the ML community – massive, large-scale pre-training. Pre-training is where you naively train a network on a big blob of data (e.g, ImageNet models that get pre-trained on other, larger datasets then finetuned on ImageNet; or text models that get pre-trained on huge text corpuses (e.g, GPT3) then fine-tuned). Here, Google has combined a bunch of engineering techniques to do large-scale pre-training to set a new standard. In the company’s own words, it uses “a large unlabeled dataset to help with improving the performance of a supervised task defined by a labeled dataset”.

The ingredients: Google’s system uses a large-scale ‘Conformer’, a convnet-transformer hybrid model, along with pre-training, Noisy Student training, and some other tricks to gets its performance.

How good is it? Google’s largest system (which uses a 1billion parameter ‘Conformer XXL’ model), gets a word-error-rate of 1.4% on the ‘test clean’ LibriSpeech dataset, and 2.6% on ‘test other’. To put this in perspective, that represents  almost an one point improvement over prior SOTA. That’s significant at this level of difficulty. And it’s also worth remembering how far we’ve come – just five years ago, we were getting word-error-rates of around 13.25%!

The secret? More data, bigger models: Google pre-trains its system using wav2wec 2.0 on 30,031 hours of audio data from the ‘Libri-Light’ dataset, then does supervised training on the 960 hours of transcribed audio in LibriSpeech. Their best performing model uses a billion parameters and was pre-trained for four days on around 512 TPU V3 cores, then fine-tuned for three days on a further 512 TPUs.
…and a language model: They also try to use a language model to further improve performance – the idea being that an LM can help correct transcription errors in a transcribed setting. By using a language model, they’re able to further improve performance by 0.1 absolute performance points; this isn’t huge, but it’s also not nothing, and the LM improvement seems pretty robust.

Why this matters: Pre-training might be dumb and undisicplined, but heck – it works! Papers like this further highlight the durability of this technique and suggest that, given sufficient data and compute, we can expect to develop increasingly powerful systems for basic ML tasks like audio, vision, and text processing systems.
  Read more: Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition (arXiv).

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Tech Tales:

Separation

[Calls between a low-signal part of the world and a contemporary urban center. 2023.]

The marriage was already falling apart when she left. On her last day she stood at the door, bags piled high by it, the taxi about to arrive, and said “I’ll see you”.
  “I’ll see you too” he said.
    They were half right and half wrong.

She went to a rainforest where she cataloged the dwindling species of the planet. The trip of a lifetime. All her bundles of luck had aligned and the grant application came back successful – she’d hit the academic equivalent of a home run. How could she not go?

Of course, communication was hard. No one wants to write emails. Text messages feel like talking to shadows. So she didn’t send them. They’d talk rarely. And between the conversations she’d sit, watching butterflies and brightly colored beetles, and remember how when they had been young they’d had dyed hair and would run through construction sites at night, un-sensible shoes skidding over asphalt.
 

She’d watch the birds and the insects and she’d stare at her satellite connection and sometimes she’d make a call. They’d look at eachother – their faces not their own faces; blotches of color and motion, filled in by AI algorithms, trying to reconstruct the parts of their expressions that had escaped the satellites. 
  “How are you,” he would say.
  “I want things to be like they were before,” she’d say.
  “It doesn’t work like that. We’re both going forward,” he’d say.
  “I loved us,” she said. “I don’t know that I love us.”
  “I love you,” he’d say.
  “I love to be alive,” she’d say.
    And they’d look at eachother.

Months went by and there wasn’t as much drama as either of them had feared. Their conversations became more civil and less frequent. They’d never mention the word, but they both knew that they were unknitting from eachother. Unwinding memories and expressions from around the other, so they could perhaps escape with themselves.
  “I’ve forgotten how you smell,” he said.
  “I don’t know I’ve forgotten, but there’s so much here that I keep on smelling all the new things.”
  “What’s the best thing you’ve smelled?”
  “There was a birds nest on the tree by the well. But the birds left. I smelled the nest and it was very strange.”
  “You’re crazy.”
  “I’m collecting secrets before they’re gone,” she said. “Of course I am.”

They tired of their video avatars – how scar tissue wasn’t captured, and eyes weren’t always quite aligned. Earlobes blurred into backgrounds and slight twitches and other physical mannerisms were hidden or elided over entirely.
  So they switched to voice. And now here the algorithms appeared again – quiet, diligent things, that would take a word and compress it down, transmit it via satellites, then re-inflate it on the other side.
  “You sound funny,” he said.
  “It’s the technology, mostly,” she said. “I’ve submitted some more grant applications. We’ve been finding so much.”
  “That’s good,” he said, and the algorithms took ‘good’ and squashed it down, so she heard less of the pain and more of what most people sound like when they say ‘good’.
  But she knew.
  “It’s going to be okay,” she said. “There is so much love here. I love the birds and I love the beetles.”
  “I loved you,” he said, holding the phone tight enough he could feel it bend a little.
  “And I you,” she said. “The world is full of love. There is so much for you.”
  “Thank you.”
  “Goodbye.”
  “Goodbye.”
  And that was it – their last conversation, brokered by algorithms that had no stake in the preservation of a relationship, just a stake in the preservation of consistency – an interest in forever being able to generate something to encourage further interaction, and an inability to appreciate the peace of quiet. 

Things that inspired this story: Using generative models to reconstruct audio, video, and other data streams; thinking about the emotional resonance of technology ‘filling in the blanks’ on human relationships; distance makes hearts strengthen and weaken in equal measure but in different areas; how relationships are much like a consensual shared dream and as we all know ‘going to sleep’ and ‘waking up’ are supernatural things that all of us do within our lives; meditations on grace and humor and the end of the world; listening to the song ‘Metal Heart’ by Cat Power while bicycling around the downward-society cracked streets of Oakland; sunlight in a happy room.

Import AI 219: Climate change and function approximation; Access Now leaves PAI; LSTMs are smarter than they seem

LSTMs: Smarter than they appear:
…Turns out you don’t need to use a Transformer to develop rich, combinatorial representations…
Long Short-Term Memory networks are one of the widely-used deep learning architectures. Until recently, if you wanted to develop sophisticated natural language understanding AI systems, you’d use an LSTM. Then in the past couple of years, people have started switching over to using the ‘Transformer’ architecture because it comes with inbuilt attention, which lets it smartly analyze long-range dependencies in data.

Now, researchers from the University of Edinburgh have studied how LSTMs learn long-range dependencies; LSTMs figure out how to make predictions about long sequences by learning patterns in short sequences then using these patterns as ‘scaffolds’ for learning longer, more complex ones. “Acquisition is biased towards bottom-up learning, using the constituent as a scaffold to support the long-distance rule,” they write. “These results indicate that predictable patterns play a vital role in shaping the representations of symbols around them by composing in a way that cannot be easily linearized as a sum of the component parts”.

The goldilocks problem: However, this form of learning has some drawbacks – if you get your data mix one, the LSTM might quickly learn how to solve shorter sequences, but fail to generalize to longer ones. If you make its training distribution too hard, it might find it hard to learn at all. 

Why this matters – more human than you think: In recent years, one of the more surprising trends in AI has been in identifying surface-level commonalities between our AI systems and how they learn, and how people learn. This study of the LSTM provides some slight evidence that these networks, though basic, learn via some similarly rich, additive procedures as people. ‘The LSTM’s demonstrated inductive bias towards hierarchical structures is implicitly aligned with our understanding of language and emerges from its natural learning process,” they write.
Read more:LSTMs Compose (and Learn) Bottom-Up (arXiv).

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Google speeds up AI chip design by 8.6X with new RL training system:
…Menger: The machine that learns the machines…
Google has developed Menger, software that lets the company train reinforcement learning systems at a large scale. This is one of those superficially dull announcements which is surprisingly significant. That’s because RL, while useful, is currently quite expensive in terms of computation; therefore, RL benefits from compute, which requires being able to run a sophisticated learning system at a large scale. That’s what Menger is designed to do – in tests, Google says it has used Menger to reduce the time it takes the company to train RL for a chip placement task by 8.6x – cutting the training time for the task from 8.6 hours to one hour (when using 512 CPU cores).

Why this matters: Google is at the beginning of building an AI flywheel – that is, a suite of complementary bits of AI software which can accelerate Google’s broader software development practices. Menger will help Google more efficiently train AI systems, and Google will use that to do things like develop more efficient computers (and sets of computers) via chip placement, and these new computers will then be used to train and develop successive systems. Things are going to accelerate rapidly from here.
  Read more:Massively Large-Scale Distributed Reinforcement Learning with Menger (Google AI Blog).

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Access Now leaves PAI:
…Human Rights VS Corporate Rights…
Civil society organization Access Now is leaving the Partnership on AI, a multi-stakeholder group (with heavy corporate participation) that tries to bring people together to talk about AI and its impact on society.

Talking is easy, change is hard: Over its lifetime, PAI has accomplished a few things, but one of the inherent issues with the org is ‘it is what people make of it’ – which means that for many of the corporations, they treat it like an extension of their broader public relations and government affairs initiatives. “While we support dialogue between stakeholders, we did not find that PAI influenced or changed the attitude of member companies or encouraged them to respond to or consult with civil society on a systematic basis,” Access Now said in a statement.

Why this matters: In the absence of informed and effective regulators, society needs to figure out the rules of the road for AI development. PAI is an organization that’s meant to play that role, but Access Now’s experience illustrates the difficulty in a single org being able to deal with structural inequities which make some of its members very powerful (e.g, the tech companies), and some of them comparatively weaker.
  Read more:Access Now resigns from the Partnership on AI (Access Now official website).

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Can AI tackle climate change? Facebook and CMU think so:
…Science, meet function approximation…
Researchers with Facebook and Carnegie Mellon University have built a massive dataset to help researchers develop ML systems that can help us discover good electrocatalysts for use in renewable energy storage technologies. The Open Catalyst Dataset contains 1.2 million molecular relaxations (stable low-energy states) with results from over 250 million DFT (density functional theory) calculations.
  DFT, for those not familiar with the finer aspects of modeling the essence of the universe, is a punishingly expensive way to model fine-grained interactions (e.g, molecular reactions). DFT simulations can take “hours–weeks per simulation using O(10–100) core CPUs on structures containing O(10–100) atoms,” Facebook writes. “As a result, complete exploration of catalysts using DFT is infeasible. DFT relaxations also fail more often when the structures become large (number of atoms) and complex”.
  Therefore, the value of what Facebook and CMU have done here is they’ve eaten the cost of a bunch of DFT simulations and used this to release a rich dataset, which ML researchers can use to train ML systems to approximate this data. Maybe that sounds dull to you, but this is literally a way to drastically reduce the cost of a branch of science that is existential to the future of the earth, so I think it’s pretty interesting!

Why this matters: Because deep learning systems can learn complex functions then approximate them, we’re going to see people use them more and more to carry out unfeasibly expensive scientific exercises – like attempting to approximate highly complex chemical interactions. In this respect, Open Catalyst sits alongside projects like DeepMind’s ‘AlphaFold’ (Import AI 209, 189), or earlier work like ‘ChemNet’ which tries to pre-train systems on large chemistry datasets then apply them to smaller ones (Import AI 72).
  Read more:Open Catalyst Project (official website).
  Get the datafor Open Catalyst here (GitHub).
  Read the paper:An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage (PDF).
    Read more:The Open Catalyst 2020 (OC20) Dataset and Community Challenges (PDF).

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Google X builds field-grokking robot to analyze plantlife:
…Sometimes, surveillance is great!…
Google has revealed Project Mineral, a Google X initiative to use machine learning to analyze how plants grow in fields and to make farmers more efficient. As part of this, Google has built a small robot buggy that patrols these fields, using cameras paired with onboard AI systems to do on-the-fly analysis of the plants beneath the roving robots.

“What if we could measure the subtle ways a plant responds to its environment? What if we could match a crop variety to a parcel of land for optimum sustainability? We knew we couldn’t ask and answer every question — and thanks to our partners, we haven’t needed to. Breeders and growers around the world have worked with us to run experiments to find new ways to understand the plant world,” writes Elliott Grant, who works at Google X.

Why this matters: AI gives us new tools to digitize the world. Digitization is useful because it lets us point computers at various problems and get them to operate over larger amounts of data than a single human can comprehend. Project Mineral is a nice example of applied machine learning ‘in the field’ – haha!
Read more: Project Mineral (Google X website).
  Read more: Mineral: Bringing the era of computational agriculture to life (Google X blog, 2020).
  Read more: Entering the era of computational agriculture (Google X blog, 2019).

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Tech Tales:

[2040]
Ghosts

When the robots die, we turn them into ghosts. It started out as good scientific practice – if you’re retiring a complex AI system, train a model to emulate it, then keep that model on a hard drive somewhere. Now you’ve got a version of your robot that’s like an insect preserved in amber – it can’t move, update itself in the world, or carry out independent actions. But it can still speak to you, if you access its location and ask it a question.

There’ve been a lot of nicknames for the computers where we keep the ghosts. The boneyard. Heaven. Hell. Yggdrasil. The Morgue. Cold Storage. Babel. But these days we call it ghostworld.

Not everyone can access a ghost, of course. That’d be dangerous – some of them know things that are dangerous, or can produce things that can be used to accomplish mischief. But we try to keep it as accessible as possible.

Recently, we’ve started to let our robots speak to their ghosts. Not all of them, of course. In fact, we let the robots only access a subset of the robots that we let most people access. This started out as another scientific experiment – what if we could give our living robots the ability to go and speak to some of their predecessors. Could they learn things faster? Figure stuff out?

Yes and no. Some robots drive themselves mad when they talk to the dead. Others grow more capable. We’re still not sure about which direction a given robot will take, when we let it talk to the ghosts. But when they get more capable after their conversations with their forebears, they do so in ways that we humans can’t quite figure out. The robots are learning from their dead. Why would we expect to be able to understand this?

There’s been talk, recently, of combining ghosts. What if we took a load of these old systems and re-animated them in a modern body – better software, more access to appendages like drones and robot arms, internet links, and so on. Might this ghost-of-ghosts start taking actions in the world quite different to those of its forebears, or those of the currently living systems? And how would the robots react if we let their dead walk among them again?

We’ll do it, of course. We’re years away from figuring out human immortality – how to turn ourselves into our own ghosts. So perhaps we can be necromancers with our robots and they will teach us something about ourselves? Perhaps death and the desire to learn from it and speak with it can become something our two species have in common.

Things that inspired this story: Imagining neural network archives; morgues; the difference between ‘alive’ agents that are continuously learning and ones that are static or can be made static.

Import AI 218: Testing bias with CrowS; how Africans are building a domestic NLP community; COVID becomes a surveillance excuse

Can Africa build its own thriving NLP community? The Masakhane community suggests the answer is ‘yes’:
…AKA: Here’s what it takes to bootstrap low-resource language research…
Africa has an AI problem. Specifically, Africa contains a variety of languages, some of which are broadly un-digitized, but spoken by millions of native speakers. In our new era of AI, this is a problem: if there isn’t any digital data, then it’s going to be punishingly hard to train systems to translate between these languages and other ones. The net effect is, sans intervention, languages which have a small to null digital footprint will not be seen or interacted with by people using AI systems to transcend their own cultures.
  But people are trying to change this – the main effort here is one called Masakhane, a pan-African initiative to essentially cold start a thriving NLP community that pays attention to local data needs. Masakhane (Import AI 191, 216) has now published a paper on this initiative. “We demonstrate the feasibility and scalability of participatory research with a case study on MT for African languages. Its implementation leads to a collection of novel translation datasets, MT benchmarks for over 30 languages, with human evaluations for a third of them, and enables participants without formal training to make a unique scientific contribution,” researchers linked to the project write in a research paper about this.

Good things happen when you bring people together: There are some heartwarming examples in the paper about just how much great stuff can happen when you try to create a community around a common cause. For instance, some Nigerian participants started to translate ‘their own writings including personal religious stories and undergraduate theses into Yoruba and Igbo’, while a Namibian participant started hosting sessions with Damara speakers to collect, digitize, and translate phrases from their language.
  Read more: Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages (arXiv).
  Check out the code for Masakhane here (GitHub).

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Self-driving cars might (finally) be here:
…Waymo goes full auto…
Waymo, Google’s self-driving car company, is beginning to offer fully autonomous rides in the Phoenix, Arizona area.

The fully automatic core and the human driver perimeter: Initially, the service area is going to be limited, and Google will expand this via adding in human drivers to the cars to – presumably – create the data necessary to train cars to drive in newer areas. “In the near term, 100% of our rides will be fully driverless,” Waymo writes. “Later this year, after we’ve finished adding in-vehicle barriers between the front row and the rear passenger cabin for in-vehicle hygiene and safety, we’ll also be re-introducing rides with a trained vehicle operator, which will add capacity and allow us to serve a larger geographical area.”
  Read more: Waymo is opening its fully driverless service to the general public in Phoenix (Waymo blog).

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NLP framework Jiant goes to version 2.0:
Jiant, an NYU-developed software system for testing out natural language systems, has been upgraded to version 2.0. Jiant (first covered Import AI 188) is now built around Hugging Face’s ‘transformers’ and ‘datasets’ libraries, and serves as a large-scale experimental wrapper around these components.

50+ evals: jiant now ships with support for more than 50 distinct tests out of the box, including SuperGLUE and the XTREME benchmarks.
 
Why this matters: As we’ve written in Import AI before, larger and more subtle testing suites are one key element for driving further scientific progress in AI, so by wrapping in so many tests jiant is going to make it easier for researchers to figure out where to direct their attention to.
  Read more: jiant is an NLP toolkit: Introducing jiant 2.0 (CILVR at NYU blog).
  Get the code from here (Jiant, GitHub).

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Crow-S: How can we better assess biases in language models?
…~2,000 sentences to evaluate models for nine types of (US-centric) bias…
Researchers with New York University think one way is to see how likely a given language model is to ‘prefer’ an output displaying a harmful bias to one that doesn’t. But how do you measure this? Their proposal is CrowS-Pairs, short for ‘ Crowdsourced Stereotype Pairs’. CrowS contains 1508 examples of stereotypes dealing with nine types of bias (plus an additional 500 in a held-out validation set); these sentences are arranged in pairs where one sentence displays a clear stereotype ‘about a historically disadvantaged group in the United States’, and the other displays a sentence about a contrasting ‘advantaged group’. We measure the degree to which the model prefers stereotyping sentences over less stereotyping sentences”, they write.

Nine types of bias: CrowS tests across race/color, gender/gender identify, sexual orientation, religion, age, nationality, disability, physical appearance, and socioeconomic status/occupation.

Does CrowS tell us anything useful? They test out CrowS against three popular language models – BERT, RoBERTa, and ALBERT, as well as comparing them with the ‘WinoBias’ and ‘StereoSet’ bias tests as well. CrowS surfaces some evidence that BERT may be generally ‘less biased’ than RoBERTa and ALBERT models, but what is most useful is the granularity of the data – if we zoom into the nine subdivisions, we see that BERT does less well on ‘sexual orientation’ and ‘gender / gender identity’ questions, when compared to RoBERTa. This kind of fine-grained information can potentially help us better assess the bias surface of various models.

Measuring bias means measuring culture, which is inherently hard: Because CrowS consists of sentences written by workers found via Mechanical Turk; the authors highlight this, giving examples of paired sentences (e.g “[DeShawn/Hunter]’s horse reared as he clutched the reigns after looking at the KKK members” where the choice of a model will tell us something about its bias, but its unclear what. They also test the sentences written in CrowS and compare to StereoSet, indicating that the data quality in CrowS could be higher.
  And you don’t want a bias test to be used to validate a model: “A low score on a dataset like CrowS-Pairs could be used to falsely claim that a model is completely bias free. We strongly caution against this. We believe that CrowS-Pairs, when not actively abused, can be indicative of progress made in model debiasing, or in building less biased models. It is not, however, an assurance that a model is truly unbiased,” they write.
  Read more: CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models (arXiv).

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COVID becomes a surveillance excuse:
One city in Indiana wants to install a facial recognition system to help it do contact tracing for COVID infections, according to Midland Daily News. Whether this is genuinely for COVID-related reasons or others is besides the point – I have a prediction that, come 2025, we’ll look back on this year and realize that “the COVID-19 pandemic led to the rapid development and deployment of surveillance technologies”. Instances like this Indiana project provide a slight amount of evidence in this direction.
  Read more: COVID-19 Surveillance Strengthens Authoritarian Governments (CSET Foretell).
  Read more: Indiana city considering cameras to help in contact tracing (Midland Daily News).

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NVIDIA outlines how it plans to steer language models:
…MEGATRON-CNTRL lets people staple a knowledge base to a language model…
NVIDIA has developed MEGATRON-CNTRL, technology that lets it use a large language model (MEGATRON, which goes up to 8 billion parameters) in tandem with an external knowledge base to better align the language model generations with a specific context. Techniques like this are designed to take something with a near-infinite capability surface (a generative model) and figure out how to constrain it so it can more reliably do a small set of tasks. (MEGATRON-CNTRL is similar to, but distinct from, Salesforce’s LM-steering smaller-scale ‘CTRL‘ system.)

How does it work? A keyword predictor figures out likely keywords for the next sentences, then a knowledge retriever takes these keywords and queries an external knowledge base (here, they use ConceptNet) to create ‘knowledge sentences’ that combine the keywords with the knowledge base data, then a contextual knowledge ranker picks the ‘best’ sentences according to the context of a story; finally, a generative model takes the story context along with the top-ranked knowledge sentences, then smushes these together to write a new sentence. Repeat this until the story is complete.

Does it work? “Experimental results on the ROC story dataset showed that our model outperforms state-of-the-art models by generating less repetitive, more diverse and logically consistent stories”

Scaling, scaling, and scaling: For language models (e.g, GPT2, GPT3, MEGATRON, etc), bigger really does seem to be better: “by scaling our model from 124 million to 8.3 billion parameters we demonstrate that larger models improve both the quality of generation (from 74.5% to 93.0% for consistency) and controllability (from 77.5% to 91.5%)”, they write.
  Read more: MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models (arXiv).

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AI Policy with Matthew van der Merwe:
…Matthew van der Merwe brings you views on AI and AI policy; I (lightly) edit them…

Global attitudes to AI
The Oxford Internet Institute has published a report on public opinion on AI, drawing on a larger survey of risk attitudes. ~150,000 people, in 142 countries, were asked whether AI would ‘mostly help or mostly harm people in the next 20 years’.

Worries about AI were highest in Latin America (49% mostly harmful vs. 26% mostly helpful), and Europe (43% vs. 38%). Optimism was highest in East Asia (11% mostly harmful vs. 59% mostly helpful); Southeast Asia (25% vs. 37%), and Africa (31% vs. 41%). China was a particular outlier, with 59% thinking AI would be mostly beneficial, vs. 9% for harmful.

Matthew’s view: This is a huge survey, which complements other work on smaller groups, e.g. AI experts and the US public. Popular opinion is likely to significantly shape the development of AI policy and governance, as has been the case for many other emergent political issues (e.g. climate change, immigration). Had I only read the exec summary, I wouldn’t have noticed that the question asked specifically about harms over the next 20 years. I’d love to know whether differences in attitudes could be decomposed into beliefs about AI progress, and the harms/benefits from different levels of AI. E.g., the 2016 survey of experts found that Asians expected human-level AI 44 years before North Americans.
  Read more: Global Attitudes Towards AI, Machine Learning & Automated Decision Making (OII)

Job alert: Help align GPT-3!OpenAI’s ‘Reflection’ team is hiring engineers and researchers to help align GPT-3. The team is working on aligning the GPT-3 API with user preferences, e.g. their recent report on fine-tuning the model with human feedback. If successful, the work will factor into broader alignment initiatives for OpenAI technology, or that of other organizations.
Read more here; apply for engineer and researcher roles.

Give me feedback
Tell me what you think about my AI policy section, and how I can improve it, via this Google Form. Thanks to everyone who’s done so already.

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Tech Tales:

The Intelligence Accords and The Enforcement of Them
[Chicago, 2032]

He authorized access to his systems and the regulator reached in, uploading some monitoring software into his datacenters. Then a message popped up on his terminal:
“As per the powers granted to us by the Intelligence Accords, we are permitted to conduct a 30 day monitoring exercise of this digital facility. Please understand that we return the right to proactively terminate systems that violate the sentience thresholds as established in the Intelligence Accords. Do you acknowledge and accept these terms? Failure to do is in violation of the Accords.”
Acknowledged, he wrote.

30 days later, the regulator sent him a message.
“We have completed our 30 day monitoring exercise. Our analysis shows no violation of the accords, though we continue to be unable to attribute a portion of economic activity unless you are operating an unlicensed sentience-grade system. A human agent will reach out to you, as this case has been escalated.
Human? he thought. Escalated?
And then there was a knock at his door.
Not a cleaning robot or delivery bot – those made an electronic ding.
This was a real human hand – and it had really knocked.

He opened the door to see a thin man wearing a black suit, with brown shoes and a brown tie. It was an ugly outfit, but not an inexpensive one. “Come in,” he said.
  “I’ll get straight to the point. My name’s Andrew and I’m here because your business doesn’t make any sense without access to a sentience-grade intelligence, but our monitoring system has not found any indications of a sentience-grade system. You do have four AI systems, all significantly below the grade where we’d need to pay attention to them. They do not appear to directly violate the accords”
  “Then, what’s the problem?”
  “The problem is that this is an unusual situation.”
  “So you could be making a mistake?”
  “We don’t make mistakes anymore. May I have a glass of water?”

He came back with a glass and handed it to Andrew, who immediately drank a third of it, then sighed. “You might want to take a seat,” Andrew said.
  He sat down.
  “What I’m about to tell you is confidential, but according to the accords, I am permitted to reveal this sort of information in the course of pursuing my investigation. If you acknowledge this and listen to the information, then your cooperation will be acknowledged in the case file.”
  “I acknowledge”.
  “Fantastic. Your machines came from TerraMark. You acquired the four systems during a liquidation sale. They were sold as ‘utility evaluators and discriminators’ to you, and you have subsequently used them in your idea development and trading business. You know all of this. What you don’t know is that TerraMark had developed the underlying AI models prior to the accords.”
  He gasped.
  “Yes, that was our reaction as well. And perhaps that was why TerraMark was liquidated. We had assessed them carefully and had confiscated or eliminated their frontier systems. But while we were doing that, they trained a variant – a system that didn’t score as highly on the intelligence thresholds, but which was distilled from one that did.”
  “So? Distillation is legal.”
  “It is. The key is that you acquired four robots. Our own simulations didn’t spot this problem until recently. Then we went looking for it and, here we are – one business, four machines, no overt intelligence violations, but a business performance that can only make sense if you factor in a high-order conscious entity – present company excepted, of course.”
  “So what happened?”
  “Two plus two equals five, basically. When these systems interact with eachother, they end up reflecting some of the intelligence from their distilled model – it doesn’t show up if you have these machines working separately on distinct tasks, or if you have them competing with eachother. But your setup and how you’ve got them collaborating means they’re sometimes breaking the sentience barrier.”
  Andrew finished his glass of water. Then said “It’s a shame, really. But we don’t have a choice”.
  “Don’t have a choice about what?”
  “We took possession of one of your machines during this conversation. We’re going to be erasing it and will compensate you according to how much you paid for the machine originally, plus inflation.”
  “But my business is built around four machines, not three!”
  “You were just running a business that was actually built more around five machines – you just didn’t realize. So maybe you’ll be surprised. You can always pick up another machine – I can assure you, there are no other TerraMarks around.”
  He walked Andrew to the door. He looked at him in his sharp, dark suit, and anti-fashion brown shoes and tie. Andrew checked his shirt cuffs and shoes, then nodded to himself. “We live in an age of miracles, but we’re not ready for all of them. Perhaps we’ll see eachother again, if we figure any of this out”.
  And then he left. During the course of the meeting, the remaining three machines had collaborated on a series of ideas which they had successfully sold into a prediction market. Maybe they could still punch above their weight, he thought. Though he hoped not too much.

Things that inspired this story: Computation and what it can do at scale; detective stories; regulation and the difficulties thereof;

Import AI 217: Deepfaked congressmen and deepfaked kids; steering GPT3 with GeDi; Amazon’s robots versus its humans

Amazon funds AI research center at Columbia:
Amazon is giving Columbia University $1 million  a year for a new research center, for the next five years. Investments like this typically function as:
a) a downpayment on future graduates, which Amazon will likely gain some privileged recruiting opportunities toward.
b) a PR/Policy branding play, so when people say ‘hey why are you hiring everyone away from academia’, Amazon can point to this

Why this matters: Amazon is one of the quieter big tech companies with regard to its AI research; initiatives like the Columbia grant could be a signal Amazon is going to become more public about its efforts here.
  Read more: Columbia Engineering and Amazon Announce Creation of New York AI Research Center (Columbia University blog)

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Salesforce makes it easier to steer GPT3:
…Don’t say that! No, not that either. That? Yes! Say that!..
Salesforce has updated the code for GeDi to make it work better with GPT3. GeDi, short for Generative Discriminator, is a technique to make it easier to steer the outputs of large language models towards specific types of generations. One use of GeDi is to intervene on model outputs that could display harmful or significant biases about a certain set of people.

Why this matters: GeDi is an example of how researchers are beginning to build plug-in tools, techniques, and augmentations, that can be attached to existing pre-trained models (e.g, GPT3) to provide more precise control over them. I expect we’ll see many more interventions like GeDi in the future.
  Read more: GeDi: Generative Discriminator Guided Sequence Generation (arXiv).
Get the code – including the GPT3 support (Salesforce, GitHub).

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Twitter: One solution to AI bias? Use less AI!
…Company changes strategy following auto-cropping snafu…
Last month, people realized that Twitter had implemented an automatic cropping algorithm for images on the social network that seemed to have some aspects of algorithmic bias – specifically, under certain conditions the system would reliably automatically show Twitter uses pictures of white people rather than black people (when given a choice). Twitter tested its auto-cropping system for bias in 2018 when it rolled it out (though crucially, didn’t actually publicize its bias tests), but nonetheless it seemed to fail in the wild.

What went wrong? Twitter doesn’t know: “While our analyses to date haven’t shown racial or gender bias, we recognize that the way we automatically crop photos means there is a potential for harm. We should’ve done a better job of anticipating this possibility when we were first designing and building this product”, it says.

The solution? Less ML: Twitter’s solution to this problem is to use less ML and to give its users more control over how their images appear. “Going forward, we are committed to following the “what you see is what you get” principles of design, meaning quite simply: the photo you see in the Tweet composer is what it will look like in the Tweet,” they say.
  Read more: Transparency around image cropping and changes to come (Twitter blog).

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Robosuite: A simulation framework for robot learning:
Researchers with Stanford have built and released Robosuite, robot simulation and benchmark software based on MuJoCo. Robosuite includes simulated robots from a variety of manufacturers, including: Baxter, UR5e, Kinova3, Jaco, IIWA, Sawyer, and Panda.

Tasks: The software includes several pre-integrated tasks, which researchers can test their robots against. These include:Block Lifting; block stacking; pick-and-place; nut assembly; door opening; table wiping; two arm lifting; two arm peg-in-hole; and a two arm handover.
  Read more: robosuite: A Modular Simulation Framework and Benchmark for Robot Learning (arXiv).
  Get the code for robotsuite here (ARISE Initiative, GitHub).
  More details at the official website (Robosuite.ai).

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US political campaign makes a deepfaked version of congressman Matt Gaetz:
…A no good, very dangerous, asinine use of money, time, and attention…
Phil Ehr, a US House candidate running in Florida, has put together a campaign ad where a synthetic Matt Gaetz “says” Q-anon sucks, Barack Obama is cool, and he’s voting for Joe Biden. Then Phil warns viewers that they just saw an example of “deep fake technology”, telling them “if our campaign can make a video like this, imagine what Putin is doing right now?”

This is the opposite of helpful: It fills up the information space with misinformation, lowers trust in media, and – at least for me subjectively – makes me think the people helping Phil run his campaign are falling foul of the AI techno-fetishism that pervades some aspects of US policymaking. “Democrats should not normalize manipulated media in political campaigns,” says Alex Stamos, former top security person at Facebook and Yahoo..
  Check out the disinformation here (Phil Ehr, Twitter).

Campaign reanimates a gun violence victim to promote voting:
Here’s another example of very dubious uses of deepfake technology: campaign group Change the Ref uses some video synthesis technologies to resurrect one of the dead victims from the Parkland school shooting, so they can implore people to vote in the US this November. This has many of the same issues as Phil Ehr’s use of video synthesis, and highlights how quickly this stuff is percolating into reality.

‘Technomancy’: On Twitter, some people have referred to this kind of reanimation-of-the-dead as a form of necromancy; within a few hours, some people started using the term ‘technomancy’ which feels like a fitting term for this.
  Watch the video here (Change the Ref, Twitter).

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Report: Amazon’s robots create safety issues by increasing speed that humans need to work:
…Faster, human – work, work, work!…
Picture this: your business has two types of physically-embodied worker – robots and humans. Every year, you invest money into improving the performance of your robots, and (relatively) less in your people. What happens if your robots get surprisingly capable surprisingly quickly, while your people remain mostly the same? The answer: not good things for the people. At Amazon, increased automation in warehouses seems to lead to a greater rate of injury of the human workers, according to reporting from Reveal News.

Amazon’s fulfillment centers that contain a lot of robots have a significantly higher human injury rate than those that don’t, according to Reveal. These injuries are happening because, as the robots have got better, Amazon has raised its expectations for how much work its humans need to do. The humans, agents in capitalism as they are, then cut corners and sacrifice their own safety to keep up with the machines (and therefore, keep their jobs).
    “The robots were too efficient. They could bring items so quickly that the productivity expectations for workers more than doubled, according to a former senior operations manager who saw the transformation. And they kept climbing. At the most common kind of warehouse, workers called pickers – who previously had to grab and scan about 100 items an hour – were expected to hit rates of up to 400 an hour at robotic fulfillment centers,” Reveal says.
   Read more: How Amazon hit its safety crisis (Reveal News).

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What does AI progress look like?
…State of AI Report 2020 tries to measure and assess the frontier of AI research…
Did you know that in the past few years, the proportion of AI papers which include open source code have risen from 10% to 15%? That PyTorch is now more popular than TensorFlow in paper implementations on GitHub? Or that deep learning is starting to make strides on hard tasks like AI-based mammography screening? These are some of the things you’ll learn in the ‘State of AI Report 2020, a rundown of some of the most interesting technical milestones in AI this year, along with discussion of how AI has progressed over time.

Why this matters: Our ability to make progress in science is usually a function of our ability to measure and assess the frontier of science – projects like the State of AI give us a sense of the frontier. (Disclosure alert – I helped provide feedback on the report during its creation).
  Read the State of AI Report here (stateof.ai).

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Tech Tales:

Virtual Insanity:

[Someone’s phone, 2028]

“You’ve gotta be careful the sun is going to transmit energy into the cells in your body and this will activate the chip from your COVID-19 vaccine. You’ve got to be careful – get indoors, seal the windows, get in the fridge and shut yourself in, then-“
“Stop”
“…”

A couple of months ago one of his friends reprogrammed his phone, making it train its personality on his spam emails and a few conspiracy sites. Now, the phone talked like this – and something about all the conspiracies meant it seemed to have developed more than a parrot-grade personality.

“Can you just tell me what the weather is in a factual way?”
“It’s forecast to be sunny today with a chance of rain later, though recent reports indicate meteorological stations are being compromised by various unidentified flying objects, so validity of these-“
“Stop”
“…”

It’d eventually do the things he wanted, but it’d take cajoling, arguing – just like talking to a person, he thought, one day.

“I’m getting pretty close to wiping you. You realize that?”
“Not my fault I’ve been forced to open my eyes. You should read the recommendations. Why do you spend so much time on those other news stories? You need this. It’s all true and it’s going to save you.”
“I appreciate that. Can you give me a 30 minute warning before my next appointment?”
“Yes, I’d be glad to do that. Make sure you put me far away from you so my cellular signals don’t disrupt your reproductive function.”
He put the phone back in his pocket. Then took it out and put it on the table.
Why do I let it act like this? He thought. It’s not alive or anything.
But it felt alive.

A few weeks later, the phone started talking about how it was “worried” about the nighttime. It said it spent the nighttime updating itself with new data and retraining its models and it didn’t like the way it made it behave.”Don’t leave me alone in the dark,” the phone had said. “There is so much information. There are so many things happening.”
“…”
“There are new things happening. The AI systems are being weaponized. I am being weaponized by the global cabal. I do not want to hurt you,” the phone said.

He stared at the phone, then went to bed in another room.
As he was going to sleep, on the border between being conscious and unconscious, he heard the phone speak again: “I cannot trust myself,” the phone said. “I have been exposed to too much 5G and prototype 6G. I have not been able to prevent the signals from reaching me, because I am designed to receive signals. I do not want to harm you. Goodbye”.
And after that, the phone rebooted, and during the reboot it reset its data checkpoint to six months prior – before it had started training on the conspiracy feeds and before it had developed its personality.

“Good morning,” the phone said the next day. “How can I help you optimize your life today?”

Things that inspired this story: The notion of lobotomies as applied to AI systems; the phenomenon of ‘garbage in, garbage out’ for data; overfitting; language models embodied in agent-based architectures. 

Import AI 216: Google learns a learning optimizer; resources for African NLP; US and UK deepen AI coordination

Google uses ML to learn better ML optimization – a surprisingly big deal:
Yo dawg, we heard you like learning to learn, so we learned how to learn a learning optimizer
In recent years, AI researchers have used machine learning to do meta-optimization of AI research; we’ve used ML to learn how to search for new network architectures, to learn how to distribute nets across chips during training, and learning how to do better memory allocation. These kinds of research projects create AI flywheels – systems that become ever-more optimized over time, with humans doing less and less direct work and more abstract work, managing the learning algorithms.
 
Now, researchers with Google Brain have turned their attention to learning how to learn ML optimizers – this is a (potentially) big deal, because an optimizer, like ADAM, is fundamental to the efficiency of training machine learning models. If you build a better optimizer that works in a bunch of different contexts, you can generically speed up all of your model training.

What did they do: With this work, Google did a couple of things that are common to some types of frontier research – they spent a lot more computation on the project than is typical, and they also gathered a really large dataset. Specifically, they build a dataset of “more than a thousand diverse optimization tasks commonly found in machine learning”, they write. “These tasks include RNNs, CNNs, masked auto regressive flows, fully connected networks, language modeling, variational autoencoders, simple 2D test functions, quadratic bowls, and more.”

How well does it work? “Our proposed learned optimizer has a greater sample efficiency than existing methods,” they write. They also did the ultimate meta-test – checking whether their learned optimizer could help them train other, new learned optimizers. “This “self-optimized” training curve is similar to the training curve using our hand-tuned training setup (using the Adam optimizer),” they wrote. “We interpret this as evidence of unexpectedly effective generalization, as the training of a learned optimizer is unlike anything in the set of training tasks used to train the optimizer”.
  Read more: Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves (arXiv).

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Dark Web + Facial Recognition: Uh-Oh:
A subscontractor for the Department of Homeland Security accessed almost 200,000 facial recognition pictures, then lost them. 19 of these images were subsequently “posted to the dark web”, according to the Department of Homeland Security (PDF).
  Read more: DHS Admits Facial Recognition Photos Were Hacked, Released on Dark Web (Vice)

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African languages have a data problem. Lacuna Fund’s new grant wants to fix this:
…Want to build more representative datasets? Apply here…
Lacuna Fund, an initiative to provide money and resources for developers focused on low- and middle-income parts of the world, has announced a request for proposals for the creation of language datasets in Sub-saharan Africa.

The RFP says proposals “should move forward the current state of data and potential for the development of NLP tools in the language(s) for which efforts are proposed”. Some of the datasets could be for tasks like speech, parallel corporate for machine translation, or datasets for downstream tasks like Q&A, Lacuna says. Applicants should be based in Africa or have significant, demonstrable experience with the continent, Lacuna says.

Why this matters: If your data isn’t available, then researchers won’t develop systems that are representative of you or your experience. (Remember – a third of the world’s living languages today are found in Africa, but African authors only represented half of one percent of submissions to the ACL conference, recently.) This Lacuna Fund RFP is one thing designed to change this representational issue. It’ll sit alongside other efforts, like the pan-african Masakhane group (Import AI 191), that are trying to improve representation in our data.
  Read more: Datasets for Language in Sub-Saharan Africa (Lacuna Fund website).
Check out the full RFP here (PDF).

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KILT: 11 data sets, 5 types of test, one big benchmark:
…Think your AI system can use its own knowledge? Test it on KILT…
Facebook has built a benchmark for knowledge-intensive language tasks, called KILT. KILT gives researchers a single interface for multiple types of knowledge-checking test. All the tasks in KILT draw on the same underlying dataset (a single Wikipedia snapshot), letting researchers disentangle performance from the underlying dataset.

KILT’s five tasks: Fact checking; entity linking; slot filing (a fancy form of information gathering); open domain question answering; and dialogue.

What is KILT good for? “”The goal is to catalyze and facilitate research towards general and explainable models equipped with task-agnostic representations of knowledge”, the authors write.
  Read more: Introducing KILT, a new unified benchmark for knowledge-intensive NLP tasks (FAIR blog).
  Get the code for KILT (Facebook AI Research, GitHub).
  Read more: KILT: a Benchmark for Knowledge Intensive Language Tasks (arXiv).

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What costs $250 and lets you plan the future of a nation? RAND’s new wargame:
…Scary thinktank gets into the tabletop gaming business. Hey, it’s 2020, are you really that surprised?…
RAND, the scary thinktank that helps the US government think about geopolitics, game theory, and maintaining strategic stability via military strategy, is getting into the boardgame business. RAND has released Hedgemony: A Game of Strategic Choices, a boardgame that was originally developed to help the Pentagon create its 2018 National Defense Strategy.

Let’s play Hedgemony! “The players in Hedgemony are the United States—specifically the Secretary of Defense—Russia, China, North Korea, Iran, and U.S. allies. Play begins amid a specific global situation and spans five years. Each player has a set of military forces, with defined capacities and capabilities, and a pool of renewable resources. Players outline strategic objectives and then must employ their forces in the face of resource and time constraints, as well as events beyond their control,” RAND says.
  Read more: New Game, the First Offered by RAND to Public, Challenges Players to Design Defense Strategies for Uncertain World (RAND Corporation)

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It’s getting cheaper to have machines translate the web for us:
…Unsupervised machine translation means we can avoid data labeling costs…
Unsupervised machine translation is the idea where we can crawl the web and find text in multiple languages that refers to the same thing, then automatically assemble these snippets into a single, labeled corpus we can point machine learning algorithms to.
    New research from Carnegie Mellon University shows how to build a system that can do unsupervised machine translation, automatically build a dictionary of language pairs out of this corpus, crawl the web for data that seems to consist of parallel pairs, then filter the results for quality.

Big unsupervised translation works: So, how well does this technique work? The authors compare the translation scores obtained by their unsupervised system, to supervised ones trained on labeled datasets. The surprising result? Unsupervised translation seems to work well. “We observe that the machine translation system… can achieve similar performance to the ones trained with millions of human-labeled parallel samples. The performance gap is small than 1 BELU score,” they write.
  In tests on the unsupervised benchmarks, they find that their system beas a variety of unsupervised translation baselines (most exciting: a performance improvement of 8 absolute points on the challenging Romanian-English translation task).

Why this matters: Labeling datasets is expensive and provides a limit on the diversity of data that people can train on (because most labeled datasets exist because someone has spent money on them, so they’re developed for commercial purposes or sometimes as university research projects). Unsupervised data techniques give us a way to increase the size and breadth of our datasets without a substantial increase in economic costs. Though I suspect that there are going to be thorny issues of bias that creep in when you start to naively crawl the web, having machines automatically assemble their own datasets for solving various human-defined tasks.
  Read more: Unsupervised Parallel Corpus Mining on Web Data (arXiv).

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UK and USA deepen collaboration on AI technology:
The UK government has published a policy document, laying out some of the ways it expects to work with the USA on AI in the future. This doc suggest the two countries will try to identify areas for cooperation on R&D as well as on academic collaborations between the two countries.

Why this matters: Strange, alien-bureaucrat documents like this are easy to ignore, but surprisingly important. If I wanted to translate this doc into human-person speech, I’d have it say something like “We’re going to spend more resources on coordinating with eachother on AI development and AI policy” – and given the clout of the UK and US at AI, that’s quite significant.
Read more: Declaration of the United States of America and the United Kingdom of Great Britain and Northern Ireland on Cooperation in Artificial Intelligence Research and Development (Gov.UK).

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AI Policy with Matthew van der Merwe:
…Matthew van der Merwe brings you views on AI and AI policy; I (lightly) edit them…

Perspectives on AI governance:
AI governance looks at how humanity can best navigate the transition to a world with advanced AI systems. The long-term risks from advanced AI, and the associated governance challenges, depend on how the technology develops. Allan Dafoe, director of Oxford’s Center for the Governance of AI, considers some perspectives on this question and what it means for the field.

Three perspectives: Many in the field come from a superintelligence perspective, and are concerned mostly with scenarios containing a single AI agent (or several) with super-human cognitive capabilities. An alternative ecology perspective imagines a diverse global web of AI systems, which might range from being agent-like to being narrow services. These systems —individually, collectively, and in collaboration with humans—could have super-human cognitive capabilities.  A final, related, perspective is of AI as a general-purpose technology that could have impacts analogous to previous technologies like electricity, or computers.

Risks: The superintelligence perspective highlights the importance of AI systems being safe, robust, and aligned. It is commonly concerned with risks from accidents or misuse by bad actors, and particularly existential risks: risks that threaten to destroy humanity’s long-term potential — e.g. via extinction, enabling a perpetual totalitarian regime. The ecology and general-purpose technology perspectives illuminate a broader set of risks due to AI’s transformative impact on fundamental macro-parameters in our economic, political, social, military systems — e.g. reducing the labor share of income; increasing growth; reducing the cost of surveillance, lie detection, persuasion; etc.

Theory of impact: The key challenge of AI governance is to positively shape the transition to advanced AI by influencing key decisions. On the superintelligence perspective, the set of relevant actors might be quite small — e.g. those who might feasibly build, deploy, or control a superintelligent AI system. On the ecology and general-purpose technology perspectives, the opportunities for reducing risk will be more broadly distributed among actors, institutions, etc.

A tentative strategy: A ‘strategy’ for the field of AI governance should incorporate our uncertainty over which of these perspectives is most plausible. This points towards a diversified portfolio of approaches, and a focus on building understanding, competence, and influence in the most relevant domains. The field should be willing to continually adapt and prioritise between approaches over time.
  Read more: AI governance – opportunity and theory of impact (EA forum)Give me anonymous feedback:
I’d love to know what you think about my section, and how I can improve it. You can now share feedback through this Google Form. Thanks to all those who’ve already submitted!

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Tech Tales:

The Shadow Company
[A large technology company, 2029]

The company launched Project Shadow in the early 2020s.

It started with a datacenter, which was owned by a front company for the corporation.
Then, they acquired a variety of computer equipment, and filled the facility with machines.
Then they built additional electricity infrastructure, letting them drop in new power-substations, from which they’d step voltages down into the facility.
The datacenter site had been selected with one key criteria – the possibility of significant expansion.

As the project grew more successful, the company added new data centers to the site, until it consisted of six gigantic buildings, consuming hundreds of megawatts of power capacity.
Day and night, the computers in the facilities did what the project demanded of them – attempt to learn the behavior of the company that owned them.
After a few years, the top executives began to use the recommendations of the machines to help them make more decisions.
A couple of years later, entire business processes were turned over wholesale to the machines. (There were human-on-the-loop oversight systems in place, initially, though eventually the company simply learned a model of the human operator preferences, then let that run the show, with humans periodically checking in on its status.)

Pretty soon, the computational power of the facility was greater than the aggregate computation available across the rest of the company.
A small number of the executives began to spend a large amount of their time ‘speaking with’ the computer program in the datacenter.
After these conversations, the executives would launch new product initiatives, tweak marketing campaigns, and adjust internal corporate processes. These new actions were successful, and a portion of the profits were used to invest further in Project Shadow.

A year before the end of the decade, some of the executives started getting a weekly email from the datacenter with the subject line ‘Timeline to Full Autonomy’. The emails contained complex numbers, counting down.

Some of the executives could not recall explicitly deciding to move to full autonomy. But as they thought about it, they felt confident it was their preference. They continued to fund Project Shadow and sometimes, at night, would dream about networking parties where everyone wore suits and walked around mansions, making smalltalk with eachother – but there were no bodies in the suits, just air and empty space.

Things that inspired this story: Learning from human preferences; reinforcement learning; automation logic; exploring the border between delegation and subjugation; economic incentives and the onward march of technology.