Tag: Facebook AI Research

Facebook, Microsoft, and academics launch deepfake detection competition

Facebook together with the Partnership on AI, Microsoft, and academics are making a deepfake dataset, benchmark, and public challenge with up to $10 million in grants and awards to spur innovation and make it easier to spot fake content. The Deepfake Detection Challenge will be put together with support from academics at Cornell Tech, MIT, […]

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Facebook AI’s RoBERTa improves Google’s BERT pretraining methods

Facebook AI and University of Washington researchers devised ways to enhance Google’s BERT language model and achieve performance on par or exceeding state-of-the-art results in GLUE, SQuAD, and RACE benchmark data sets. Researchers detailed how RoBERTa works in a paper published last week on arXiv. Named RoBERTa for “Robustly Optimized BERT approach,” the model adopts […]

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Facebook VP: AI has a compute dependency problem

In one of his first public speaking appearances since joining Facebook to lead its AI initiatives, VP Jérôme Pesenti expressed his concern about the growing use of compute power necessary to create powerful AI systems. “I can tell you this is keeping me up at night,” Pesenti said. “The peak compute for companies like Facebook […]

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Facebook’s AI beats human poker champions

Facebook AI Research and Carnegie Mellon University today detailed the creation of Pluribus, a poker-playing AI that Facebook claims is the first to beat professionals in Texas Hold’em. Poker is a game sometimes used to benchmark the performance of artificial intelligence or game theory. The bot bested 15 human professionals, all of whom have won […]

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Facebook open-sources DLRM, a deep learning recommendation model

Facebook today announced the open source release of Deep Learning Recommendation Model (DLRM), a state-of-the-art AI model for serving up personalized results in production environments. DLRM can be found on GitHub, and implementations of the model are available for Facebook’s PyTorch, Facebook’s distributed learning framework Caffe2, and Glow C++. Recommendation engines decide a lot of […]

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DoD’s Joint AI Center to open-source natural disaster satellite imagery data set

As climate change escalates, the impact of natural disasters is likely to become less predictable. To encourage the use of machine learning for building damage assessment this week, Carnegie Mellon University’s Software Engineering Institute and CrowdAI — the U.S. Department of Defense’s Joint AI Center (JAIC) and Defense Innovation Unit — open-sourced a labeled data […]

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Facebook open-sources AI Habitat to help robots navigate realistic environments

Facebook AI Research is today making available AI Habitat, a simulator that can train AI agents that embody things like a home robot to operate in environments meant to mimic typical real-world settings like an apartment or office. For a home robot to understand what to do when you say “Can you check if laptop […]

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Facebook AI researchers’ MelNet AI sounds like Bill Gates

A pair of Facebook AI researchers used TED Talks and other data to make AI that closely mimics music and the voices of famous people, including Bill Gates. MelNet is a generative model that uses spectrogram visuals of audio for training data instead of waveforms. Doing so allows for the capture of multiple seconds of […]

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Facebook AI study: Major object recognition systems favor people with more money

Computer vision for recognizing household objects works better for people in high-income households, according to analysis of 6 major object detection systems shared today by Facebook AI researchers. The study examined object classification systems made by Facebook, Google Cloud, Microsoft Azure, AWS, IBM Watson, and Clarifai. Results show the 6 systems work between 10-20% better […]

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Facebook’s AI learns how to get around an office by watching videos

Humans undertake high-level planning every day, but it’s not so easy for robots. Fortunately, a growing body of work suggests that hierarchal abstractions (namely visuomotor subroutines) can boost sample efficiency in reinforcement learning, an AI training technique that employs rewards to drive agents toward goals. Traditionally, these hierarchies must be handcoded or acquired via end-to-end […]

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