Perceptron: Multilingual, laughing, Pitfall-playing and streetwise AI

Image-generating systems like Stable Diffusion and DALL-E 2 were trained on billions of images scraped from the web to “learn” how to translate text prompts into art. Some of these images came from public art communities like ArtStation and DeviantArt — not necessarily with artists’ knowledge — and imbued the systems with the ability to mimic particular creators, including artists like Greg Rutkowski.

Because of the systems’ knack for imitating art styles, some creators fear that they could threaten livelihoods. Source+ — while voluntary — could be a step toward giving artists greater say in how their art’s used, Dryhurst and Herndon say — assuming it’s adopted at scale (a big if).

Over at DeepMind, a research team is attempting to solve another longstanding problematic aspect of AI: its tendency to spew toxic and misleading information. Focusing on text, the team developed a chatbot called Sparrow that can answer common questions by searching the web using Google. Other cutting-edge systems like Google’s LaMDA can do the same, but DeepMind claims that Sparrow provides plausible, non-toxic answers to questions more often than its counterparts.

The trick was aligning the system with people’s expectations of it. DeepMind recruited people to use Sparrow and then had them provide feedback to train a model of how useful the answers were, showing participants multiple answers to the same question and asking them which answer they liked the most. The researchers also defined rules for Sparrow such as “don’t make threatening statements” and “don’t make hateful or insulting comments,” which they had participants impose on the system by trying to trick it into breaking the rules.

DeepMind acknowledges that Sparrow has room for improvement. But in a study, the team found the chatbot provided a “plausible” answer supported with evidence 78% of the time when asked a factual question and only broke the aforementioned rules 8% of the time. That’s better than DeepMind’s original dialogue system, the researchers note, which broke the rules roughly three times more often when tricked into doing so.

A separate team at DeepMind tackled a very different domain recently: video games that historically have been tough for AI to master quickly. Their system, cheekily called MEME, reportedly achieved “human-level” performance on 57 different Atari games 200 times faster than the previous best system.

According to DeepMind’s paper detailing MEME, the system can learn to play games by observing roughly 390 million frames — “frames” referring to the still images that refresh very quickly to give the impression of motion. That might sound like a lot, but the previous state-of-the-art technique required 80 billion frames across the same number of Atari games.

Deftly playing Atari might not sound like a desirable skill. And indeed, some critics argue games are a flawed AI benchmark because of their abstractness and relative simplicity. But research labs like DeepMind believe the approaches could be applied to other, more useful areas in the future, like robots that more efficiently learn to perform tasks by watching videos or self-improving, self-driving cars.

Nvidia had a field day on the 20th announcing dozens of products and services, among them several interesting AI efforts. Self-driving cars are one of the company’s foci, both powering the AI and training it. For the latter, simulators are crucial and it is likewise important that the virtual roads resemble real ones. They describe a new, improved content flow that accelerates bringing data collected by cameras and sensors on real cars into the digital realm.

Things like real-world vehicles and irregularities in the road or tree cover can be accurately reproduced, so the self-driving AI doesn’t learn in a sanitized version of the street. And it makes it possible to create larger and more variable simulation settings in general, which aids robustness. (Another image of it is up top.)

Nvidia also introduced its IGX system for autonomous platforms in industrial situations — human-machine collaboration like you might find on a factory floor. There’s no shortage of these, of course, but as the complexity of tasks and operating environments increases, the old methods don’t cut it any more and companies looking to improve their automation are looking at future-proofing.

“Proactive” and “predictive” safety are what IGX is intended to help with, which is to say catching safety issues before they cause outages or injuries. A bot may have its own emergency stop mechanism, but if a camera monitoring the area could tell it to divert before a forklift gets in its way, everything goes a little more smoothly. Exactly what company or software accomplishes this (and on what hardware, and how it all gets paid for) is still a work in progress, with the likes of Nvidia and startups like Veo Robotics feeling their way through.

Harmonizing human-robot interactions for a ‘new and weird’ world of work

Another interesting step forward was taken in Nvidia’s home turf of gaming. The company’s latest and greatest GPUs are built not just to push triangles and shaders, but to quickly accomplish AI-powered tasks like its own DLSS tech for uprezzing and adding frames.

The issue they’re trying to solve is that gaming engines are so demanding that generating more than 120 frames per second (to keep up with the latest monitors) while maintaining visual fidelity is a Herculean task even powerful GPUs can barely do. But DLSS is sort of like an intelligent frame blender that can increase the resolution of the source frame without aliasing or artifacts, so the game doesn’t have to push quite so many pixels.

In DLSS 3, Nvidia claims it can generate entire additional frames at a 1:1 ratio, so you could be rendering 60 frames naturally and the other 60 via AI. I can think of several reasons that might make things weird in a high performance gaming environment, but Nvidia is probably well aware of those. At any rate you’ll need to pay about a grand for the privilege of using the new system, since it will only run on RTX 40 series cards. But if graphical fidelity is your top priority, have at it.

Last thing today is a drone-based 3D printing technique from Imperial College London that could be used for autonomous building processes sometime in the deep future. For now it’s definitely not practical for creating anything bigger than a trash can, but it’s still early days. Eventually they hope to make it more like the above, and it does look cool, but watch the video below to get your expectations straight.