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After years of expanding autonomous robotaxi rides throughout China, self-driving specialist Pony.ai continues to scale its technology in another segment – commercial trucks. Today, the company announced it has acquired the first ever license in Guangzhou, China to begin testing its autonomous truck technology on open roads in packs formations.

Pony.ai Inc. is a technology company founded over seven years ago that specializes in fully-autonomous mobility. Its robotaxi development has been supported through partnerships with global OEMs like Toyota, GAC Group, and NIO Capital, helping it become one of the early leaders of completely driverless rides throughout several cities in China.

For example, since the launch of its robotaxi app in December 2018, Pony.ai has become the first to commercialize autonomous taxi services in the Chinese cities of Beijing and Guangzhou and one of the first companies licensed to operate in other tier-1 cities, such as Shanghai and Shenzen. It has also expanded US cities like Tucson, Arizona and even signed a partnership to bring its technology to the futuristic urban development NEOM in Saudi Arabia.

Stable Diffusion’s generative art can now be animated, developer Stability AI announced. The company has released a new product called Stable Video Diffusion into a research preview, allowing users to create video from a single image. “This state-of-the-art generative AI video model represents a significant step in our journey toward creating models for everyone of every type,” the company wrote.

The new tool has been released in the form of two image-to-video models, each capable of generating 14 to 25 frames long at speeds between 3 and 30 frames per second at 576 × 1,024 resolution. It’s capable of multi-view synthesis from a single frame with fine-tuning on multi-view datasets. “At the time of release in their foundational form, through external evaluation, we have found these models surpass the leading closed models in user preference studies,” the company said, comparing it to text-to-video platforms Runway and Pika Labs.

AI has mastered some of the most complex games known to man, but models are generally tailored to solve specific kinds of challenges. A new DeepMind algorithm that can tackle a much wider variety of games could be a step towards more general AI, its creators say.

Using games as a benchmark for AI has a long pedigree. When IBM’s Deep Blue algorithm beat chess world champion Garry Kasparov in 1997, it was hailed as a milestone for the field. Similarly, when DeepMind’s AlphaGo defeated one of the world’s top Go players, Lee Sedol, in 2016, it led to a flurry of excitement about AI’s potential.

DeepMind built on this success with AlphaZero, a model that mastered a wide variety of games, including chess and shogi. But as impressive as this was, AlphaZero only worked with perfect information games where every detail of the game, other than the opponent’s intentions, is visible to both players. This includes games like Go and chess where both players can always see all the pieces on the board.

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OpenAI said late Tuesday it had reinstated Sam Altman as its chief executive in a stunning reversal that capped five days of drama that rocked the artificial intelligence community.

The company, maker of the popular ChatGPT, said it would also create a new board of directors. This comes after the former board voted to fire Altman as CEO late last week.

“We have reached an agreement in principle for Sam to return to OpenAI as CEO with a new initial board of Bret Taylor (Chair), Larry Summers, and Adam D’Angelo,” OpenAI said in a post to X, formerly known as Twitter. “We are collaborating to figure out the details. Thank you so much for your patience through this.”

Meta, the parent company of Facebook, has made a groundbreaking development in brain-computer interface technology. They have unveiled an AI system that can decode visual representations and even “hear” what someone is hearing by studying their brainwaves. These advancements in brain-machine interface technology have the potential to transform our relationship with artificial intelligence and its potential applications in healthcare, communication, and virtual reality.

The University of Texas at Austin has developed a new technology that can translate brain activity into written text without surgical implants. This breakthrough uses functional Magnetic Resonance Imaging (fMRI) scan data to reconstruct speech. An AI-based decoder then creates text based on the patterns of neuronal activity that correspond to the intended meaning. This new technology could help people who have lost the ability to speak due to conditions such as stroke or motor neuron disease.

Despite the fMRI having a time lag, which makes tracking brain activity in real-time challenging, the decoder was still able to achieve impressive accuracy. The University of Texas researchers faced challenges in dealing with the inherent “noisiness” of brain signals picked up by sensors, but by employing advanced technology and machine learning, they successfully aligned representations of speech and brain activity. The decoder works at the level of ideas and semantics, providing the gist of thoughts rather than an exact word-for-word translation. This study marks a significant advance in non-invasive brain decoding, showcasing the potential for future applications in neuroscience and communication.