Nearly two years after its prototype debut and eight months after its public beta, Sony's GT Sophy racing AI for Gran Turismo 7 is back, and going by Gran Turismo Sophy 2.0 now. It will be available to all PlayStation 5 users as part of the GT7 Spec II Update (Patch Update 1.40) being released on Wednesday, November 2 at 2 a.m. We got our first look at the Sophy system back in February 2022. At that point it was already handily beating professional Gran Turismo players. "Gran Turismo Sophy is a significant development in AI whose purpose is not simply to be better than human players, but to offer players a stimulating opponent that can accelerate and elevate the players' techniques and creativity to the next level," Sony AI CEO, Hiroaki Kitano, said at the time.

Hyper-capable AIs have been beating us at our own games for years. Whether it's Go or Jeopardy, DOTA 2 or Nethack, artificial intelligences have routinely proven themselves superior competitors, helping advance not only the state of gaming arts but also those of machine learning and computational science as well. On Wednesday, Sony announced its latest addition to the field, GT Sophy, an AI racer capable of taking on -- and beating -- some of the world's best Gran Turismo players. GT Sophy (the GT stands for "Gran Turismo") is the result of a collaboration between Sony AI, Polyphony Digital (PDI) and Sony Interactive Entertainment (SIE), as well as more than half a decade of research and development. "Gran Turismo Sophy is a significant development in AI whose purpose is not simply to be better than human players, but to offer players a stimulating opponent that can accelerate and elevate the players' techniques and creativity to the next level," Sony AI CEO, Hiroaki Kitano, said in a statement Wednesday.

This paper reports recent progress on modeling the grounded co-acquisition of syntax and semantics of locative spatial language in developmental robots. Weshow how a learner robot can learn to produce and interpret spatial utterances in guided-learning interactions with a tutor robot (equipped with a system for producing English spatial phrases). The tutor guides the learning process by simplifying the challenges and complexity of utterances, givesfeedback, and gradually increases the complexity of the language to be learnt. Our experiments show promising results towards long-term, incremental acquisition of natural language in a process of co-development of syntax and semantics.

Three experts from Sony AI America recently discussed the future of artificial intelligence and what Sony sees coming in 2022. While some trends seem to be a natural evolution for AI as more companies adopt it, others could address some of the issues we're facing, as the war on COVID wages on. Those who discussed AI trends were: Michael Spranger, COO, of the Sony artificial intelligence team; Alice Xiang, head of AI Ethics Office (Sony Group Corporation) and senior research scientist, AI ethics lead; and Peter Stone, executive director of Sony AI America. Here's what they believe will happen in 2022. AI could well change the way we conceptualize, create and enjoy food.

A health check of the world today may seem gloomy – antibiotics are failing, people are dying of easily treatable diseases because they're poor, and conditions such as dementia are on the rise. The scientists, researchers, investors and startups at the Francis Crick Institute in London were only too aware of the challenges – here's what we learned. Women account for almost 50 per cent of the world's population, but women's health technology hasn't updated for years – however, Tania Boler, CEO of London- and Berlin-based start-up Elvie, argued that's about to change. "We are witnessing three big trends," she told the room. "The big feminist surge, the tech revolution in connected devices and the paradigm shift towards individuals taking charge of their own health."

This paper reports recent progress on modeling the grounded co-acquisition of syntax and semantics of locative spatial language in developmental robots. Weshow how a learner robot can learn to produce and interpret spatial utterances in guided-learning interactions with a tutor robot (equipped with a system for producing English spatial phrases). The tutor guides the learning process by simplifying the challenges and complexity of utterances, givesfeedback, and gradually increases the complexity of the language to be learnt. Our experiments show promising results towards long-term, incremental acquisition of natural language in a process of co-development of syntax and semantics.

Open-ended language communication remains an enormous challenge for autonomous robots. This paper argues that the notion of a language strategy is the appropriate vehicle for addressing this challenge. A language strategy packages all the procedures that are necessary for playing a language game. We present a specific example of a language strategy for playing an Action Game  in which one robot asks another robot to take on a body posture (such as stand or sit), and show how it effectively allows a population of agents to self-organise a perceptually grounded ontology and a lexicon from scratch, without any human intervention. Next, we show how a new language strategy can arise by exaptation from an existing one, concretely, how the body posture strategy can be exapted to a strategy for playing language games about the spatial position of objects (as in "the bottle stands on the table").