"I think AI systems need to be able to reason," says Yann LeCun, Meta's chief AI scientist. Today's popular AI approaches such as Transformers, many of which build upon his own pioneering work in the field, will not be sufficient. "You have to take a step back and say, Okay, we built this ladder, but we want to go to the moon, and there's no way this ladder is going to get us there," says LeCun. Yann LeCun, chief AI scientist of Meta Properties, owner of Facebook, Instagram, and WhatsApp, is likely to tick off a lot of people in his field. With the posting in June of a think piece on the Open Review server, LeCun offered a broad overview of an approach he thinks holds promise for achieving human-level intelligence in machines. Implied if not articulated in the paper is the contention that most of today's big projects in AI will never be able to reach that human-level goal. In a discussion this month with ZDNet via Zoom, LeCun made clear that he views with great skepticism many of the most successful avenues of research in deep learning at the moment. "I think they're necessary but not sufficient," the Turing Award winner told ZDNet of his peers' pursuits. Those include large language models such as the Transformer-based GPT-3 and their ilk. As LeCun characterizes it, the Transformer devotées believe, "We tokenize everything, and train giganticmodels to make discrete predictions, and somehow AI will emerge out of this." "They're not wrong," he says, "in the sense that that may be a component of a future intelligent system, but I think it's missing essential pieces." It's a startling critique of what appears to work coming from the scholar who perfected the use of convolutional neural networks, a practical technique that has been incredibly productive in deep learning programs. LeCun sees flaws and limitations in plenty of other highly successful areas of the discipline. Reinforcement learning will also never be enough, he maintains. Researchers such as David Silver of DeepMind, who developed the AlphaZero program that mastered Chess, Shogi and Go, are focusing on programs that are "very action-based," observes LeCun, but "most of the learning we do, we don't do it by actually taking actions, we do it by observing." Lecun, 62, from a perspective of decades of achievement, nevertheless expresses an urgency to confront what he thinks are the blind alleys toward which many may be rushing, and to try to coax his field in the direction he thinks things should go. "We see a lot of claims as to what should we do to push forward towards human-level AI," he says.

Demis Hassabis (left) and John Jumper (right) from DeepMind developed AlphaFold, an AI that can predict the structure of proteins.Credit: Breakthrough Prize The researchers behind the AlphaFold artificial-intelligence (AI) system have won one of this year's US$3-million Breakthrough prizes -- the most lucrative awards in science. Demis Hassabis and John Jumper, both at DeepMind in London, were recognized for creating the tool that has predicted the 3D structures of almost every known protein on the planet. "Few discoveries so dramatically alter a field, so rapidly," says Mohammed AlQuraishi, a computational biologist at Columbia University in New York City. "It's really changed the practice of structural biology, both computational and experimental." The award was one of five Breakthrough prizes -- awarded for achievements in life sciences, physics and mathematics -- announced on 22 September.

At RE•WORK, we are strong advocates for supporting women working towards advancing technology, so ahead of the upcoming AI in Healthcare Summit, we set out to highlight inspirational women within the US healthcare and pharma sectors who are working at the forefront of AI developments, and who deserve recognition for their achievements. While we set out to create a list of just 20 – we couldn't narrow it down, as there are so many inspiring and prominent females in this space! Hear from many of them at our AI in Healthcare Summit, and more outside the healthcare space at our Women in AI Reception, both being held in Boston next month. Help us to continue highlighting leading women in AI by nominating your influential woman for our next edition. RE•WORK holds Women in AI events, podcasts, and blogs.

Insurance Times' annual Tech and Innovation Awards were back last night (22 September 2022) at London's glamorous Royal Lancaster Hotel. Sponsored by Genasys, NIG, Carpenters Group and Sønr, the event showcased 22 different award categories recognising finalists that are going the extra mile to use technology to improve insurance processes and the customer experience. Opening the evening, Insurance Times editor Katie Scott said: "Just featuring on one of tonight's shortlists is an accolade in itself. "The same goes for the nominated categories – in particular Technology Champion of the Year. It's so inspiring to see how firms have really capitalised on accelerated technology and digital advancements made during the Covid-19 pandemic."

Modern NLP models are becoming better conversational agents than their predecessors. Recurrent Neural Networks (RNNs) and especially Long-Short Term Memory (LSTM) features allow the agent to better store and use information about semantic content, a trend that has become even more pronounced with the Transformer Models. Large Language Models (LLMs) such as GPT-3 by OpenAI have become known to be able to construct and follow a narrative, which enables the system to adopt personas on the go, adapt them and play along in conversational stories. However, practical experimentation with GPT-3 shows that there is a recurring problem with these modern NLP systems, namely that they can "get stuck" in the narrative so that further conversations, prompt executions or commands become futile. This is here referred to as the "Locked-In Problem" and is exemplified with an experimental case report, followed by practical and social concerns that are accompanied with this problem.

… is the first of its kind to be awarded at UTSA and will support Sooby’s research on utilizing artificial intelligence (AI) to study complex …

Join gaming executives to discuss emerging parts of the industry this October at GamesBeat Summit Next. Beth Goss was recently appointed as the CEO of kid game company Outright Games. To do that, the former chief brand officer at 20th Century Fox had to relocate her family from the U.S. to London. That tells you the nature of the opportunity. And now she is running a kids' video game publisher behind the likes of Peppa Pig, Paw Patrol and Star Trek titles. Goss has held positions with some of the biggest entertainment companies in the world including Universal Pictures and Cartoon Network. Goss wants to transform the family-operated publisher into a global player in the video games market, which has traditionally operated exclusively in games for young children. Goss is applying lessons and industry practices from her traditional entertainment background into gaming.

"Is AI Coming For Commercial Art?" rendered by Stable Diffusion, prompted by Rob Salkowitz Earlier this summer, a piece generated by an AI text-to-image application won a prize in a state fair art competition, prying open a Pandora's Box of issues about the encroachment of technology into the domain of human creativity and the nature of art itself. As fascinating as those questions are, the rise of AI-based image tools like Dall-E, Midjourney and Stable Diffusion, which rapidly generate detailed and beautiful images based on text descriptions supplied by the user, pose a much more practical and immediate concern: They could very well hold a shiny, photorealistically-rendered dagger to the throats of hundreds of thousands of commercial artists working in the entertainment, videogame, advertising and publishing industries, according to a number of professionals who have worked with the technology. How impactful would this be to the global creative economy that runs on spectacular imagery? Think about the 10 minutes of credits at the end of every modern Hollywood blockbuster. Same with videogames, where commercial artists hone their skills for years to score plum jobs like concept artist and character designer.

Ramachandran quotes a variety of sources--Indian government officials, a motley bunch of academics and Indian-American author Rajiv Malhotra, who goes on to claim that Sanskrit should be credited with the last 20 years of development in Natural Language Processing (NLP), the technology behind prominent LLMs like GPT-3, DALL-E 2, etc. The claims are wide-ranging: Sanskrit is the most'scientific' language, and so the "best to programme computers, or code AI/ML"; it is the "language for future super computers", etc. One common source that everyone cites, and which Ramachandran explores in detail, is "Nasa". Yes, the same Nasa that sends rockets into space. The reference actually has a published source, a 1985 paper'Knowledge Representation in Sanskrit and Artificial Intelligence' by Nasa researcher Rick Briggs (bit.ly/3qrIjMr).

In the new papers, biologists at the University of Washington School of Medicine show that machine learning can be used to create protein molecules much more accurately and quickly than previously possible. The scientists hope this advance will lead to many new vaccines, treatments, tools for carbon capture, and sustainable biomaterials. "Proteins are fundamental across biology, but we know that all the proteins found in every plant, animal, and microbe make up far less than one percent of what is possible. With these new software tools, researchers should be able to find solutions to long-standing challenges in medicine, energy, and technology," said senior author David Baker, professor of biochemistry at the University of Washington School of Medicine and recipient of a 2021 Breakthrough Prize in Life Sciences. Proteins are often referred to as the "building blocks of life" because they are essential for the structure and function of all living things.