Meta Claims Downloaded Porn at Center of AI Lawsuit Was for'Personal Use' In a motion to dismiss filed earlier this week, Meta denied claims that employees had downloaded pornography from Strike 3 Holdings to train its artificial intelligence models. This week, Meta asked a US district court to toss a lawsuit alleging that the tech giant illegally torrented pornography to train AI . The move comes after Strike 3 Holdings discovered illegal downloads of some of its adult films on Meta corporate IP addresses, as well as other downloads that Meta allegedly concealed using a "stealth network" of 2,500 "hidden IP addresses." Accusing Meta of stealing porn to secretly train an unannounced adult version of its AI model powering Movie Gen, Strike 3 sought damages that could have exceeded $350 million, TorrentFreak reported . Strike 3 also cited "no facts to suggest that Meta has ever trained an AI model on adult images or video, much less intentionally so," Meta claimed.

Learning to play Diplomacy is a big deal for several reasons. Not only does it involve multiple players, who make moves at the same time, but each turn is preceded by a brief negotiation in which players chat in pairs in an attempt to form alliances or gang up on rivals. After this round of negotiation, players then decide what pieces to move--and whether to honor or renege on a deal. At each point in the game, Cicero models how the other players are likely to act based on the state of the board and its previous conversations with them. It then figures out how players can work together for mutual benefit and generates messages designed to achieve those aims. To build Cicero, Meta marries two different types of AI: a reinforcement learning model that figures out what moves to make, and a large language model that negotiates with other players.

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column, Perceptron, aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. This month, engineers at Meta detailed two recent innovations from the depths of the company's research labs: an AI system that compresses audio files and an algorithm that can accelerate protein-folding AI performance by 60x. Elsewhere, scientists at MIT revealed that they're using spatial acoustic information to help machines better envision their environments, simulating how a listener would hear a sound from any point in a room. Meta's compression work doesn't exactly reach unexplored territory. Last year, Google announced Lyra, a neural audio codec trained to compress low-bitrate speech.

One of the most active businesses in Artificial Intelligence research is Meta, the freshly renamed organization that runs Facebook. Start a chat with Meta's new AI right now to experience the results of its labors. The record-breaking number of AI parameters, 175 billion, is set by BlenderBot 3. Because of this, BlenderBot 3 is now able to converse about nearly anything, but according to Meta, you shouldn't rely on it to be factually correct. The method that Meta developers used to create the bot inspired the name of the bot. The team's extensive research into conversational AI has led them to the conclusion that an AI that "blends" several conversational skills outperforms one that only learns one at a time.

People perceive speech both by listening to it and watching the lip movements of speakers. In fact, studies show that visual cues play a key role in language learning. By contrast, AI speech recognition systems are built mostly -- or entirely -- on audio. And they require a substantial amount of data to train, typically ranging in the tens of thousands of hours of recordings. To investigate whether visuals -- specifically footage of mouth movement -- can improve the performance of speech recognition systems, researchers at Meta (formerly Facebook) developed Audio-Visual Hidden Unit BERT (AV-HuBERT), a framework that learns to understand speech by both watching and hearing people speak.