Import AI

TartainAir challenges SLAM systems to navigate around lurching robot arms:
…Simulated dataset gives researchers 4TB of data to test navigation systems against…
How can we make smarter robots without destroying them? The answer is to find smarter ways to simulate experiences for our robots, so we can test them out rapidly in software-based environments, rather than having to run them in the physical world. New research from Carnegie Mellon University, the Chinese University of Hong Kong, Tongji University, and Microsoft Research, gives us TartanAir, a dataset meant to push the limits of visual simultaneous location and mapping systems (SLAM).

What is TartanAir? TartanAir is a dataset of high-fidelity environments rendered in Unreal Engine, collected via Microsoft’s AirSim software (for more on AirSim: Import AI #30). “A special focus of our dataset is on the challenging environments with changing light conditions, low illumination, adverse weather and dynamic objects”, the researchers write. TartanAir consists of 1037 long motion sequences collected from simulated agents traversing 30 environments, representing 4TB of data in total. Environments range from factories, to lush forests, to cities, rendered in a variety of different ways.

  Multi-modal data inputs: Besides the visual inputs, TartanAir data is accompanied by data relating to stereo disparity, simulated LiDAR, optical flow data, depth data, and pose data.
  Multi-modal scenes: The visual scenes themselves come in a variety of forms, with environments available in different lighting, weather, and seasonal conditions.
  Dynamic objects: The simulator also includes environments that contain objects that move, like factories with industrial arms, and oceans full of fish that dart around, and cities with people that stroll down the streets.

Why this matters: As the COVID pandemic sweeps across the world, I find it oddly emotionally affecting to remember that we’re able to build elaborate simulations that let us give AI agents compute-enabled dreams of exploration. Just as we find ourselves stuck indoors and dreaming of the outside, our AI agents find themselves stuck on SSDs, dreaming of taking flight in all the worlds we can imagine for them. (More prosaically, systems like TartanAir serve as fuel for research into the creation of more advanced mapping and navigation systems).
  Read more: TartanAir: A Dataset to Push the Limits of Visual SLAM (arXiv).
  Get access to the data here (official TartanAir page).

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Why medical AI systems need lists of ingredients:
…Duke Researchers introduce ‘Model Facts’…
In recent years, there’s been a drive to add more documentation to accompany AI models. This has so far taken the form of things like Google’s Model Cards for Model Reporting, or Microsoft’s Datasheets for Datasets, where people try to come up with standardized ways of talking about the ingredients and capabilities of a given AI model. These labeling schemes are helpful because they encourage developers to spend time explaining their AI systems to other people, and provide a disincentive for doing too much skeezy stuff (as disclosing it in the form of a model card generates a potential PR headache).
  Now, researchers with Duke University have tried to figure out a labeling scheme for the medical domain. Their “Model Facts” label “was designed for clinicians who make decisions supported by a machine learning model and its purpose is to collate relevant, actionable information in 1-page,” they write.

What should be on a medical AI label? We should use these labels to describe the mechanism by which the model communicates information (e.g., a probability score and how to interpret it); the generally recommended uses of the model, along with caveats explaining where it does and doesn’t generalize; and, perhaps most importantly, a set of warnings outlining where the model might fail or have an unpredictable effect. Labels should also be customized according to the population the system is deployed against, as different groups of people will have different medical sensitivities.

Why this matters: Labeling is a prerequisite for more responsible AI development; by encouraging standardized labeling of models we can discourage the AI equivalent of using harmful ingredients in foodstuffs, and we can create valuable metadata about deployed models which researchers can likely use to analyze the state of the field at large. Label all the things!
  Read more: Presenting machine learning model information to clinical end users with model facts labels (Nature).

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Turn yourself into a renaissance painting – if you dare!
…Things that seem like toys usually precede greater changes…
AI. It can help us predict novel protein structures. Map the wonders of the Earth from space. Translate between languages. And now… it can help take a picture of you and turn it into a renaissance-style painting! Try out the ‘AI Gahaku’ website and consider donating some money to fund it so other people can do the same.

Why this matters: One of the ways technologies make their way into society is via toys or seemingly trivial entertainment devices – systems that can shapeshift one data distribution (realworld photographs) into another (renaissance-style illustrations) are just the beginning.
  Try it out yourself: AI Gahaku (official website).

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Welcome, please make yourself comfortable in my GAN-generated house:
…Generating houses with relational networks…
Researchers with Simon Fraser University and Autodesk Research have built House-GAN, a system to automatically generate floorplans for houses.

How it works: House-GAN should be pretty familiar to most GAN-fans:
– Assemble a dataset of real floorplans (in this case, LIFULL HOME, a database of five million real floorplans, from which they used ~120,000)
– Convert these floorplans into graphs representing the connections between different room
– Feed these graphs into a relational generator and a discriminator system, which compete against each other to generate realistic-seeming graphs
– Render the resulting graphs into floorplans
– [magic happens]
– Move into your computationally-generated GAN mansion

Lets get relational: One interesting quirk of this research is the use of relational networks, specifically a convolutional message passing neural network (Conv-MPN). I’ve been seeing more and more people use relational nets in recent research, so this feels like a trend worth watching. In tests, the researchers show that relational systems significantly outperform ones based on traditional convolutional neural nets. They’re able to use this approach to generate floorplans with different constraints, like the number of rooms and their spatial adjacencies.

Why this matters: These generative systems are making it easier and easier for us to teach computers to create warped copies of reality – imagine the implications of being able to automatically generate versions of anything you can gather a large dataset for? That’s the world we’re heading to.
  Read more: House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation (arXiv).

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Facebook makes combinatory chemical system, in search of artificial life:
…Detects surprising emergent structures after simulating life for ten million steps…
Many AI researchers have a longstanding fascination with artificial life: emergent systems that, via simple rules, lead to surprising complexity. The idea is that given a good enough system and enough time and computation, we might be able to make systems that lead to the emergence of software-based ‘life’. It’s a compelling idea, and underpins Greg Egan’s fantastic science fiction story ‘Crystal Nights’ (seriously: read it. It’s great!).
  Are we anywhere close to being able to build A-Life systems that get us close to the emergence of cognitive entities, though? Spoiler alert: No. But new research from Facebook AI and the Czech Technical University in Prague outlines a new approach that has some encouraging properties.

A-Life, via three main priors: The researchers develop an A-Life system that simulates chemical reactions via a technique called Combinatory Logic. This system has three main traits:
– Turing-Complete: It can (theoretically) express an arbitrary degree of complexity.
– Strongly constructive: As the complex system evolves in time it can create new components that can in turn modify its global dynamics.
– Intrinsic conversation laws: The total size of the system can be limited by parameters set by the experimenter.

The Experiment: The authors simulate a chemical reaction system based on combinatory logic for 10 million iterations, starting with a pool of 10,000 combinators. They find that across five different runs, they see “the emergence of different types of structures, including simple autopoietic patterns, recursive structures, and self-reproducing ones”. They also find that as the system goes forward in time, more and more structures form of greater lengths and sophistication. In some runs, they “observe the emergence of a full-fledged self-reproducing structure” which duplicates itself.
 
Why this (might) matter: I think the general story of A-Life experiments (ranging from Conway’s Game of Life up to newer systems like the Lenia continuous space-time-state system) is that they can yield emergent machines of somewhat surprising capabilities. But figuring out the limits of these systems and how to effectively analyze them is a constant challenge. I think we’ll see more and more A-Life approaches developed that let people scale-up computation to further explore the capabilities of the systems – that’s something the researchers hint at here, when they say “it is still to be seen whether this can be used to explain the emergence of evolvability, one of the central questions in Artificial Life… yet, we believe that the simplicity of our model, the encouraging results, and its dynamics that balance computation with random recombination to creatively search for new forms, leaves it in good standing to tackle this challenge.”   
  Read more: Combinatory Chemistry: Towards a Simple Model of Emergent Evolution (arXiv).
  Get the code here (Combinatory Chemistry, Facebook Research GitHub).

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Tech Tales:

[2028]
Spies vs World

They came in after the Human Authenticity Accords. We called them spies because they were way better than bots. I guess if you make something really illegal and actually enforce against it, the other side has to work harder.

They’d seem like real people, at first. They’d turn up in virtual reality and chat with people, then start asking questions about what music people liked, what part of the world they lived in, and so on. Of course, people were skeptical, but only as skeptical as they’d be with other people. They didn’t outright reject all the questions, like if they knew the things were bots.

Sometimes we knew the purpose. Illegal ad-metric gathering. Unattributable polling services. Doxxing of certain communities. Info-gathering for counter-intelligence. But sometimes we couldn’t work it out.

Over time, it got harder to find the spies, and harder to work out their purposes. Eventually, we started trying to hunt the source: malware running on crypto-farms, stealing compute cycles to train encrypted machine learning models. But the world is built for businesses to hide in, and so much of the bad the spies did came from the intent rather than the components that went into making them.

So that’s why we’ve started talking about it more. We’re trying to tell you it’s not a conspiracy. They aren’t aliens. It’s not some AI system that has “gone sentient”. No. These are spies from criminal groups and state actors, and they are growing more numerous over time. Consider this a public information announcement: be careful out there on the internet. Be less trusting. Hang out with people you know. I guess you could say, the Internet is now dangerous in the same way as the real world.

Things that inspired this story: Botnets; computer viruses; viruses; Raymond Chandler detective stories; economic incentives.