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Category: robotics/AI – Page 1,652

“Firehose” of raw data: Twitter agrees to provide complete ‘Fake Accounts’ data to Elon Musk

According to multiple news reports, Twitter plans to give Elon Musk access to its “firehose” of raw data on hundreds of millions of daily tweets in an effort to speed up the Tesla billionaire’s $44 billion acquisition of the social media platform. The data-sharing agreement was not confirmed by the lawyers involved in the deal. Musk was silent on Twitter, despite having previously expressed his displeasure with various aspects of the deal.

Twitter declined to comment on the reports, pointing to a statement released on Monday in which the company stated that it is continuing to “cooperate” and share information with Musk, who in April entered into a legally binding agreement to purchase Twitter, claims that the transaction cannot go forward until the firm discloses more information on the frequency of bogus accounts on its network. He claims, without providing evidence, that Twitter has grossly underestimated the number of “spam bots” on its platform, which are automated accounts that typically promote scams and misinformation.

On Monday, the Attorney General of the State of Texas, Ken Paxton, said that his office will be investigating “possible false reporting” of bot activity on Twitter as part of an inquiry against Twitter for allegedly failing to disclose the scale of its spam bot and fake account activity. According to a source familiar with the situation, Twitter’s plan to give Musk full access to the firehose was first reported by the Washington Post. According to other reports, the billionaire may only have limited access.

Meta Reality Labs Research: Codec Avatars 2.0 Approaching Complete Realism with Custom Chip

Researchers at Meta Reality Labs are reporting that their work on Codec Avatars 2.0 has reached a level where the avatars are approaching complete realism. The researchers created a prototype Virtual Reality headset that has a custom-built accelerator chip specifically designed to manage the AI processing capable of rendering Meta’s photorealistic Codec Avatars on standalone virtual reality headsets.

The prototype Virtual Reality avatars use very advanced machine learning techniques.

Meta first showcased the work on the sophisticated Codec Avatars far back in March 2019. The avatars are powered using multiple neural networks and are generated via a special capture rig that contains 171 cameras. After the avatars are generated, they are powered in real-time through a prototype virtual reality headset that has five cameras. Two cameras are internal viewing each eye while three are external viewing the lower face. It is though that such advanced and photoreal avatars may one day replace video conferencing.

The next frontier in robotics

After nine years working at NASA Jet Propulsion Laboratory, Oliver Toupet is developing cutting-edge AI algorithms that enable the self-driving zoox vehicle to understand and make decisions based on its surroundings, and to optimize trajectories to reach its destination safely and comfortably.

Learn why he says the work he’s doing at Zoox is, in some ways, more challenging than his previous work.

Zoox principal software engineer Olivier Toupet on company’s autonomous robotaxi technology.

A chip that can classify nearly 2 billion images per second

Artificial intelligence (AI) plays an important role in many systems, from predictive text to medical diagnoses. Inspired by the human brain, many AI systems are implemented based on artificial neural networks, where electrical equivalents of biological neurons are interconnected, trained with a set of known data, such as images, and then used to recognize or classify new data points.

In traditional neural networks used for , the image of the target object is first formed on an , such as the in a smart phone. Then, the image sensor converts light into , and ultimately into the , which can then be processed, analyzed, stored and classified using computer chips. Speeding up these abilities is key to improving any number of applications, such as face recognition, automatically detecting text in photos, or helping self-driving cars recognize obstacles.

While current, consumer-grade image classification technology on a digital chip can perform billions of computations per second, making it fast enough for most applications, more sophisticated image classification such as identifying moving objects, 3D object identification, or classification of microscopic cells in the body, are pushing the computational limits of even the most powerful technology. The current speed limit of these technologies is set by the clock-based schedule of computation steps in a computer processor, where computations occur one after another on a linear schedule.

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