Facebook's AI speeds up natural language processing without additional training


Natural language models typically have to solve two tough problems: mapping sentence prefixes to fixed-sized representations and using the representations to predict the next word in the text. In a recent paper, researchers at Facebook AI Research assert that the first problem -- the mapping problem -- might be easier than the prediction problem, a hypothesis they build upon to augment language models with a "nearest neighbors" retrieval mechanism. They say it allows rare patterns to be memorized and that it achieves a state-of-the-art complexity score (a measure of vocabulary and syntax variety) with no additional training. As the researchers explain, language models assign probabilities to sequences of words, such that from a context sequence of tokens (e.g., words) they estimate the distribution (the probabilities of occurrence of different possible outcomes) over target tokens. The proposed approach -- kNN-LM -- maps a context to a fixed-length mathematical representation computed by the pre-trained language model.

Facebook will pay for users' voice recordings after it was caught listening to Messenger chats

Daily Mail - Science & tech

Facebook says it will start paying users to harvest their voice data for training speech recognition software after it was caught analyzing their speech without permission last year. In a program called'Pronunciations', participants will be payed a small sum, only up to $5, to use the company's market research app Viewpoints for recording various words and phrases that the company will then leverage to train its speech recognition AI. That voice data will be used to improve products like Portal, which is Facebook's smart display that can be used for video-calling among other things and can be activated with one's voice. In the program, participants, who must be at least 18-years-old, will have to utter specific phrases like'Hey Portal' and also say the first names of 10 of their friends on Facebook. For each'set' of prompts participants will receive 200 points.

Vladimir Vapnik: Deep Learning and the Essence of Intelligence AI Podcast Clips


Vladimir Vapnik is the co-inventor of support vector machines, support vector clustering, VC theory, and many foundational ideas in statistical learning. He was born in the Soviet Union, worked at the Institute of Control Sciences in Moscow, then in the US, worked at AT&T, NEC Labs, Facebook AI Research, and now is a professor at Columbia University. His work has been cited over 200,000 times. Subscribe to this YouTube channel or connect on: - Twitter: https://twitter.com/lexfridman

In BJP's Deepfake Video Shared On WhatsApp, Leader Speaks In 2 Languages


The BJP used artificial intelligence technology to create two deepfake videos of party leader Manoj Tiwari, in which he has been morphed realistically to show him speaking in two languages to appeal to different voter groups ahead of the Delhi assembly election earlier this month. Deepfake uses AI (artificial intelligence) to create morphed videos that seem real; it can even put words in the mouth of an another person. NDTV is one of the leaders in the production and broadcasting of un-biased and comprehensive news and entertainment programmes in India and abroad. NDTV delivers reliable information across all platforms: TV, Internet and Mobile. Follow us on Twitter: https://twitter.com/ndtv

Researchers Improve Robotic Arm Used in Surgery


Facebook has recently created an algorithm that enhances an AI agent's ability to navigate an environment, letting the agent determine the shortest route through new environments without access to a map. While mobile robots typically have a map programmed into them, the new algorithm that Facebook designed could enable the creation of robots that can navigate environments without the need for maps. According to a post created by Facebook researchers, a major challenge for robot navigation is endowing AI systems with the ability to navigate through novel environments and reaching programmed destinations without a map. In order to tackle this challenge, Facebook created a reinforcement learning algorithm distributed across multiple learners. The algorithm was called decentralized distributed proximal policy optimization (DD-PPO).

Facebook launches 3D deep learning library for PyTorch


Facebook AI Research (FAIR) today unveiled PyTorch3D, a library that enables researchers and developers to combine deep learning and 3D objects. As part of the release, Facebook is also open-sourcing Mesh R-CNN, a model introduced last year capable of rendering 3D objects from 2D shapes in images of interior spaces. PyTorch3D was inspired by Mesh R-CNN and recent 3D work by Facebook AI Research, FAIR engineer Nikhila Ravi said. Working in 3D is important for rendering 3D objects or scenes that appear in mixed reality or virtual reality. It can also be used to tackle AI challenges like robotic grasping or helping autonomous vehicles understand the position of nearby objects.

Mark Zuckerberg meets EU officials as bloc's new tech rules loom

The Japan Times

LONDON – Facebook CEO Mark Zuckerberg met top European Union officials on a visit to Brussels on Monday, days before the bloc is expected to release new proposals on regulating artificial intelligence. The billionaire social network founder is the latest U.S. tech executive to make the trip to the headquarters of the EU, which is becoming an increasingly important player in technology regulation. Zuckerberg's visit came as the company warned that potential regulation risked stifling innovation. Zuckerberg met Margrethe Vestager, the EU's powerful executive vice president in charge of making Europe "fit for the digital age." He also had audiences with Thierry Breton, commissioner for the internal market, and Vera Jourova, vice president for values and transparency.

The 84 biggest flops, fails, and dead dreams of the decade in tech


The world never changes quite the way you expect. But at The Verge, we've had a front-row seat while technology has permeated every aspect of our lives over the past decade. Some of the resulting moments -- and gadgets -- arguably defined the decade and the world we live in now. But others we ate up with popcorn in hand, marveling at just how incredibly hard they flopped. This is the decade we learned that crowdfunded gadgets can be utter disasters, even if they don't outright steal your hard-earned cash. It's the decade of wearables, tablets, drones and burning batteries, and of ridiculous valuations for companies that were really good at hiding how little they actually had to offer. Here are 84 things that died hard, often hilariously, to bring us where we are today. Everyone was confused by Google's Nexus Q when it debuted in 2012, including The Verge -- which is probably why the bowling ball of a media streamer crashed and burned before it even came to market.

What Does Facebook's Quite AI Acquisitions Across UK Signify?


The social media giant Facebook has always been at the forefront of AI advancement. Amid all the controversies and roadblocks in its strive to attain AI leadership, the company is moving forward with innovation and tech developments. These developments are a major result of its acquisitions; small but significant. Facebook's M&A activities are proving to be quite beneficial in its AI journey. Recently, the company acquired Scape Technologies which is a London-based computer vision startup working on location accuracy beyond the capabilities of GPS.

We know ethics should inform AI. But which ethics?


Artificial intelligence (AI) relies on big data and machine learning for myriad applications, from autonomous vehicles to algorithmic trading and from clinical decision support systems to data mining. The availability of large amounts of data is essential to the development of AI. But the scandal over the use of personal and social data by Facebook and Cambridge Analytica has brought ethical considerations to the fore - and it's just the beginning. As AI applications require ever greater amounts of data to help machines learn and perform tasks hitherto reserved for humans, companies are facing increasing public scrutiny, at least in some parts of the world. Tesla and Uber have scaled down their efforts to develop autonomous vehicles in the wake of widely reported accidents.