Meta is giving away some of the family jewels: That’s the gist of an announcement from the company formerly known as Facebook this week. In a blog post on the Meta AI site, the company’s researchers announced that they’ve created a massive and powerful language AI system and are making it available free to all researchers in the artificial-intelligence community. Meta describes the move as an effort to democratize access to a powerful kind of AI—but some argue that not very many researchers will actually benefit from this largesse. And even as these models become more accessible to researchers, many questions remain about the path to commercial use.
Large language models are one of the hottest things in AI right now. Models like OpenAI’s GPT-3 can generate remarkably fluid and coherent text in just about any format or style: They can write convincing news articles, legal summaries, poems, and advertising copy, or hold up their end of conversation as customer-service chatbots or video-game characters. GPT-3, which broke the mold with its 175 billion parameters, is available to academic and commercial entities only via OpenAI’s application and vetting process.
Meta’s Open Pretrained Transformer (known as OPT-175B) matches GPT-3 with 175 billion parameters of its own. Meta is offering the research community not only the model itself, but also its codebase and extensive notes and logbooks about the training process. The model was trained on 800 gigabytes of data from five publicly available data sets, which are described in the “data card” that accompanies a technical paper posted by the Meta researchers to the ArXiv online preprint server. Read More
Monthly Archives: May 2022
New method detects deepfake videos with up to 99% accuracy
Computer scientists at UC Riverside can detect manipulated facial expressions in deepfake videos with higher accuracy than current state-of-the-art methods. The method also works as well as current methods in cases where the facial identity, but not the expression, has been swapped, leading to a generalized approach to detect any kind of facial manipulation. The achievement brings researchers a step closer to developing automated tools for detecting manipulated videos that contain propaganda or misinformation.
Developments in video editing software have made it easy to exchange the face of one person for another and alter the expressions on original faces. As unscrupulous leaders and individuals deploy manipulated videos to sway political or social opinions, the ability to identify these videos is considered by many essential to protecting free democracies. Methods exist that can detect with reasonable accuracy when faces have been swapped. But identifying faces where only the expressions have been changed is more difficult and to date, no reliable technique exists. Read More
The Future of Search Is Boutique
For most queries, Google search is pretty underwhelming these days. Google is great at answering questions with an objective answer, like “# of billionaires in the world” or “What is the population of Iceland?” It’s pretty bad at answering questions that require judgment and context like “What do NFT collectors think about NFTs?”
The evidence is everywhere. These days, I find myself suppressing the garbage Internet by searching on Google for “Substack + future of learning” to find the best takes on education. We hack Twitter with the “what is the best” posts over and over again. When I’m researching a new product, I type “X item reddit” into Google. I find enormous value in small, niche, often forgotten sites like Spaghetti Directory.
There’s an emergence of tools like Notion, Airtable, and Readwise where people are aggregating content and resources, reviving the curated web. But at the moment these are mostly solo affairs — hidden in private or semi-private corners of the Internet, fragmented, poorly indexed, and unavailable for public use. We haven’t figured out how to make them multiplayer. In cases where we’ve made them public and collaborative — here is a great example — these projects are often short-lived and poorly maintained. Read More
Meta has built a massive new language AI—and it’s giving it away for free
Meta’s AI lab has created a massive new language model that shares both the remarkable abilities and the harmful flaws of OpenAI’s pioneering neural network GPT-3. And in an unprecedented move for Big Tech, it is giving it away to researchers—together with details about how it was built and trained.
“We strongly believe that the ability for others to scrutinize your work is an important part of research. We really invite that collaboration,” says Joelle Pineau, a longtime advocate for transparency in the development of technology, who is now managing director at Meta AI. Read More
Adept aims to build AI that can automate any software process
In 2016 at TechCrunch Disrupt New York, several of the original developers behind what became Siri unveiled Viv, an AI platform that promised to connect various third-party applications to perform just about any task. The pitch was tantalizing — but never fully realized. Samsung later acquired Viv, folding a pared-down version of the tech into its Bixby voice assistant.
Six years later, a new team claims to have cracked the code to a universal AI assistant — or at least to have gotten a little bit closer. At a product lab called Adept that emerged from stealth today with $65 million in funding, they are — in the founders’ words — “build[ing] general intelligence that enables humans and computers to work together creatively to solve problems.” Read More
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