Import AI 238: Robots that fold clothes; how Bytedance censors its product; a differentiable simulator.

The apocalypse approaches: Robots can _almost_ fold towels now:
…The great white whale of robot manipulation approaches…
Berkeley researchers have built a system that can fold a range of fabrics more accurately than before. If that doesn’t sound impressive, you probably haven’t spent much time at the intersection of modern robotics, deep learning, and simulation. Training machines that can reliably manipulate fabrics is a long-standing goal for the robotics field, but the task is inherently challenging – fabrics constantly deform, exhibit complex physical dynamics, and are generally hard to efficiently simulate. Therefore, we don’t have contemporary robots today that can do useful tasks like folding clothes, tying ropes, and so on.

VisualSpatial Foresight (VSF): Now, we’ve got closer – Berkeley researchers have taught a da Vinci surgical robot to carry out the task of fabric smoothing – that is, stretching out a disorganized piece of cloth until it is neatly unfolded – and fabric folding (folding a neatly unfolded piece of fabric) with 90% reliability. That’s not sufficient for production use, but it’s a meaningful research advance. VSF works by training a visual dynamics model on RGBD data (the ‘D’ depth component turns out to be very important) and seeking to learn the raw dynamics model (how you can expect the cloth to behave) in simulation. VSF uses this underlying model to help it plan out the appropriate actions to take to move from its current state (e.g, a messy piece of fabric), to a goal state (a neatly unfolded piece of fabric).

A new manipulation dataset: As part of this, they’ve built a dataset of 9932 episodes of a simulated robot carrying out four fabric manipulation tasks (which range in difficulty) – this dataset, called Fabric-CornerBias, has a particular focus on using the corners of fabric, which they find improves downstream performance.

What’s next? Next, they’ll increase the size of the datasets they use to train their models, and will also test out VSF on a broader range of fabric shapes. They’ll also look at ways to integrate additional manipulators to fiddle with the fabric.
  Find out more: VisuoSpatial Foresight (VSF) for Physical Sequential Fabric Manipulation (official project site).
  Read more: VisuoSpatial Foresight for Physical Sequential Fabric Manipulation (arXiv).
  Get the data and code (VisuoSpatialForesight, GitHub).

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Deluca: A fast, differentiable simulator:
…Differentiable algorithms are normal, what about differentiable simulators?…
Researchers with Princeton University, Google, and Microsoft Research have released Deluca, a differentiable simulator for training basic reinforcement learning agents. Deluca is special because the simulator itself is differentiable, which makes it better suited to training certain types of continuous control problems. “Our expectation is that the library will enable novel research developments and benchmarking of new classes of RL/control algorithms that benefit from differentiable simulation,” write the researchers.

Environments: At launch, Deluca supports environments for cartpole, mountain car, pendulum, planar quadrotor, and a few different types of (simulated!) lung.

Why use a differentiable library? Differentiable libraries can be faster for certain types of problems (helped along by the fact Deluca is written partially in Jax). In tests against stock OpenAI Gym using NumPy for calculations, Deluca (which uses Jax) netted a decent performance increase: “At the cost of a one-time just-in-time compilation of the dynamics, performed once at the instantiation of the environment, the improvement in the per-iteration time is >1000×”, they write. 
  Read more: Deluca — A Differentiable Control Library: Environments, Methods, and Benchmarking (arXiv).
  Get the code: Deluca (GitHub).

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Fine-tuning for text – what it is and why it matters:
…Pre-training is important, but fine-tuning is how you apply things…
In modern machine learning, many systems get built the same way – pre-train a large model on a vast dataset, then finetune the resulting model on a smaller dataset to constrain and steer the model. The reason for this two stage approach is simple: the first stage gives you a broad capability surface via training on a large, heterogeneous dataset, and the second stage gives you a specific instantiation of that capability surface. Now Seb Ruder, an AI researcher, has written a blog post laying out the different types of fine-tuning people do and also listing some of the issues with the technique.

Why this matters: Fine-tuning is a widely-used, somewhat generic technique, and we’re starting to use it across modalities (e.g, pre-training on visual data, or text data, or both as in the case of recent models like ‘CLIP’, then fine-tuning on X). Posts like Ruder’s help us develop better intuitions about this emerging AI-industrial process.
  Read more: Recent Advances in Language Model Fine-tuning (Sebastian Ruder, blog).

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What will “Broader Impacts” statements actually do?
…Now that AI researchers need to think about second order effects, what happens next?…
Last year, the NeurIPS conference asked all people submitting papers to write ‘broader impacts’ statements, which would try to anticipate some of the second- and third-order effects caused by a given AI idea, technique, or system. By doing this, NeurIPS caused several thousand researchers to think about the ethical dimension of their work while they finished their papers (likely ranging in terms of thinking time from minutes for a bunch of researchers, up to days for a minority). But, what other good effects could Broader Impacts have besides that? A paper from researchers with the university of Oxford tries to think about the positive and negative effects of these statements and makes recommendations for ways to improve them.

Positives from Broader Impacts:
– Anticipation: By forcing people to think about downstream impacts, they might get better at anticipating them.
– Action: Once you’re anticipating something, there’s a higher chance you take action.
– Reflection: By thinking about stuff, you end up thinking about yourself.
– Coordination: If enough researchers put enough work into Broader Impacts statements, they’ll generate metadata about the overall AI field, which could help people identify gaps or opportunities.

Making them more effective: However, broader impacts statements by themselves won’t help fix all the issues of ethics and AI. But they can be a good starting point – to make them more effective, the researchers propose:
– Conferences invest in more transparency around the types of statements they want to see and how they will subsequently be weighed within the context of peer review
– Giving researchers more guidance to help them write statements, including connecting them with experts
– Shaping incentive structures by making broader impacts more integrated within the larger academic ecosystem, such as by encouraging people cite eachothers statements, funding prizes for good statements, and increasing the resources in peer review allocated to these statements. 
– Public deliberation and reflection: Because broader impacts statements are new and somewhat controversial, we should aim to maximize transparency about the broader impacts review process, while also figuring out ways to ‘de-risk’ certain types of broader impacts statements (e.g, protecting people who want to write a paper whose broader impacts statement could impose legal or political backlash on the paper and/or paper-originating institution).
  Read more: Institutionalizing ethics in AI through broader impact requirements (Nature Machine Intelligence, PDF).

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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…

US chipmakers push for more gov’t investment in domestic manufacturing:
(H/T CSET’s policy.ai newsletter)
In an open letter, execs from the big US semiconductor players have asked President Biden for greater federal support for the domestic industry. They see US technology leadership at risk due to many years of underinvestment in semiconductor manufacturing and R&D, relative to global competitors. The execs like the CHIPS for America Act —passed by Congress as part of the 2021 NDAA — which includes the first major federal incentives for companies building US-based fabs. They urge Biden to sign off on these, and support additional measures as part of federal recovery spending.
  Further reading:
– CSET’s Saif M. Khan on why AI chips matter and the semiconductor supply chain
– Foreign Policy’s epic deep dive on the geopolitics of semiconductors
– Bloomberg’s Odd Lots podcast on how the US lost chip dominance

Inside the censors at Bytedance:

(H/T Jeff Ding’s ChinAI newsletter)

Here’s a fascinating account from a former censor at Bytedance, the social media company behind TikTok (and the original Chinese version, Douyin). The whistleblower worked on the technology underlying content moderation across all the company’s domestic and international platforms.


Content moderation: In early 2020, the company was using 20,000 moderators to work with AI to create live transcriptions of content and compare this against an evolving list of sensitive words/phrases; (human) moderators are then deployed to investigate any flagged broadcasts. The Cyberspace Administration of China issues daily directives to ByteDance’s central Content Quality Center, who oversee the team of moderators. The whistleblower’s team had requests to develop algorithms that would automatically detect users speaking minority languages, request that they switch to Mandarin (for the benefit of content moderators), and automatically disable their stream if they failed to comply; they also were asked for the capability to automatically disable the streams of Uyghur-speakers, but did not build this.

Read more: I helped build ByteDance’s censorship machine.

New AI safety podcastCheck out AXRP (pronounced axe-urp) — a new AI safety podcast from UC Berkeley’s Daniel Filan. Each episode is a 1h conversation with an AI safety researcher about a paper of theirs.
Listen and subscribe here.

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Tech Tales:

Alien Antivirus Archaeologies
[NOC, 2028]

“There, that’s the virus, zoom in”.
And out of the sea of fuzzing numbers, the shark grew clearer. It stood out against the rest of the numbers by virtue of its density – it was a solid, interconnected set of numbers, moving through the field of data.

Of course, the virus wasn’t really a shark. It just looked like that, due to the rendering software they used.; It was called “Ecological Observation” – they’d pointed a load of specialized AI systems at their corporate data and used it to translate the network logs and streams of numbers from various observation systems into this – a simulated world they could navigate, like deep sea divers.

Ecological Observation was mostly useful as a different lens to use to see things. And with it, they could understand the machines differently. The virus which had seemed so inscrutable became easier to think about when you saw it as a shark. And it was easier to see what it was interested in – how it was circling the same areas of data inflow/outflow.

“Isolate it,” one of them said. And together they watched as the area around the shark grew less detailed – numbers unlinked from one another and the darkness of the deep ‘sea’ water evaporated around it – suddenly, the thicket of numbers in the shape of the shark was floating in space. And then the shark faded out as well.

Things that inspired this story: Synaesthesia for machines; the ‘Raw Shark Texts’ by Stephen Hall; taking text2im to its logical and yet absurd conclusion; thinking of AI as a tool to unlock different ways of seeing the world.