Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded in its neural activity remains out of reach. In this work, we build towards a first foundation model for neural spiking data that can solve a diverse set of tasks across multiple brain areas. We introduce a novel self-supervised modeling approach for population activity in which the model alternates between masking out and reconstructing neural activity across different time steps, neurons, and brain regions. To evaluate our approach, we design unsupervised and supervised prediction tasks using the International Brain Laboratory repeated site dataset, which is comprised of Neuropixels recordings targeting the same brain locations across 48 animals and experimental sessions. The prediction tasks include single-neuron and region-level activity prediction, forward prediction, and behavior decoding.

A few days ago, I watched a video of myself talking in perfect Chinese. I've been studying the language on and off for only a few years, and I'm far from fluent. But there I was, pronouncing each character flawlessly in the correct tone, just as a native speaker would. Gone were my grammar mistakes and awkward pauses, replaced by a smooth and slightly alien-sounding voice. "My favorite food is sushi," I said--wo zui xihuan de shiwu shi shousi--with no hint of excitement or joy.

In a critical episode of The Mandalorian, a TV series set in the Star Wars universe, a mysterious Jedi fights his way through a horde of evil robots. As the heroes of the show wait anxiously to learn the identity of their cloaked savior, he lowers his hood, and--spoiler alert-- they meet a young Luke Skywalker. Actually, what we see is an animated, de-aged version of the Jedi. Then Luke speaks, in a voice that sounds very much like the 1980s-era rendition of the character, thanks to the use of an advanced machine learning model developed by the voice technology startup Respeecher. "No one noticed that it was generated by a machine," says Dmytro Bielievtsov, chief technology officer at Respeecher.

Yesterday's science fiction is today's invention. Babel Fish, the "oddest thing in the universe", is a species of fish featured in Douglas Adam's magnum opus, The Hitchhiker's Guide to Galaxy. The fish, worn as an earpiece, translates all the languages that ever existed instantly. Babel Fish is no longer the stuff of dreams: Thanks to advances in AI, especially in the NLP domain, many tech giants are in the process of building a universal translator. To that end, Universal Speech Translator was a dominant theme in the Meta's Inside the Lab event on February 23.

While Star Trek's transporter is hard to imagine -- perfect matter movement across vast distances with no equipment on one end -- it may not be the most far-fetched piece of tech on the Enterprise. While there are several contenders, I strongly suspect the universal translator is the most unlikely MacGuffin. Of course, no one wants to watch 30 episodes of TV about how we finally figured out what Klingons call clouds, so pretty much every science fiction movie has some hand-waving explanation for speaking the viewer's language. Farscape had microbes, some aliens have telepathy that works with alien brains of any kind, and still others study English from afar for decades off camera. I was thinking about this because of an article I read by [Alizeh Kohari] about [Jiaming Luo's] work using AI to decode dead languages.

In the 1960s, the Star Trek television series brought the vision of artificial intelligence into the living rooms of millions of people. AI was everywhere in the show, in the form of machines that had all the intelligence of humans -- and a lot more. Take, for example, the universal translator on the USS Enterprise. It could translate alien languages into English or any other language instantaneously. That, of course, was all science fiction back in the days when Lyndon B. Johnson was the U.S. president, as were a lot of the other AI applications in use on the starship.

Companies have a never-ending list of cloud applications they rely on with crucial data becoming ever-more siloed. Yet, in order to make sense of all this information, they need IT to manage exporting, massaging and importing data from various platforms to effectively make data-based decisions. IT of course is spread thin and decisions often need to be made in real-time, meaning one-time imports and exports get old, quickly. This is where SnapLogic comes in. Their simple and fast IaaS-based enterprise integration platform empowers enterprise IT organizations and lines of business to connect apps, things and data.

Companies have a never-ending list of cloud applications they rely on with crucial data becoming ever-more siloed. Yet, in order to make sense of all this information, they need IT to manage exporting, massaging and importing data from various platforms to effectively make data-based decisions. IT of course is spread thin and decisions often need to be made in real-time, meaning one-time imports and exports get old, quickly. This is where SnapLogic comes in. Their simple and fast IaaS-based enterprise integration platform empowers enterprise IT organizations and lines of business to connect apps, things and data.

Alexa already can translate words and short phrases from and into a variety of languages, but Yahoo's sources say that Amazon is pushing for a true multilingual assistant that can perform more complex translations. Such a system would need to be aware of cultural as well as linguistic details, as well, as most language is dependent on contextual and situational cues. Yahoo's sources also note that eventually, Alexa will be able to translate conversations with multiple people speaking different languages at once, perhaps as an outgrowth of it's recognition systems for multiple users now. Obviously, this is all future-focused; you won't be able to use Alexa to go to that wedding in Japan just yet.

Compiled by Carl Zimmer (zimmer@panix.com) Natural Language Laboratory, School of Computing Science, Simon Fraser University Burnaby, British Columbia Researchers are devising "relaxed grammars" to extract the sense of transcribed speech through a method known as "partial parsing," which deciphers chunks of language rather than breaking down the structure of entire sentences. Their work is being integrated into closed-captioning technology for real-time TV translations. This article has been reproduced in a new format and may be missing content or contain faulty links. Contact wiredlabs@wired.com to report an issue.