Job listings from Elon Musk's Texas-based Tesla company show that Tesla plans to deploy thousands of human-like, or humanoid, robots within its factories. The robots are being called Tesla Bot or Optimus. Musk says the number of Tesla Bots could one day reach millions around the world. Musk said at a TED Talk that robots could be used in homes, do household work, and care for older people. They could even become a friend or adult partner.

During the creation of the Obi-Wan Kenobi TV series, James Earl Jones signed off on allowing Disney to replicate his vocal performance as Darth Vader in future projects using an AI voice-modeling tool called Respeecher, according to a Vanity Fair report published Friday. Jones, who is 91, has voiced the iconic Star Wars villain for 45 years, starting with Star Wars: Episode IV--A New Hope in 1977 and concluding with a brief line of dialog in 2019's The Rise of Skywalker. "He had mentioned he was looking into winding down this particular character," said Matthew Wood, a supervising sound editor at Lucasfilm, during an interview with Vanity Fair. "So how do we move forward?" The answer was Respeecher, a voice cloning product from a company in Ukraine that uses deep learning to model and replicate human voices in a way that is nearly indistinguishable from the real thing.

Welcome to our September 2022 monthly digest, where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. This month, amongst other things, we find out more about environmental conservation, synthesizing new medicines, the efficiency of large language models, and the RoboCup Humanoid League. A key part of this work focusses on how to strategically allocate limited resources. Her primary application area is poaching prevention, helping rangers in protected areas around the world plan patrols and identify poaching hotspots. In this blog post, Christopher Franz and Kevin Schewior write about how they applied a well-known algorithm for solving two-player games to the problem of synthesizing new molecules.

The interwebs, as of late, have been filled with images created by artificial intelligence rendering bots such as DALL-E and Midjourney -- and the humans (I think they're humans) using them as tools. Brooklyn-based artist Zach Katz has used it to reimagine the urban design of cities. A reporter at SFGATE has undertaken a similar project, asking DALL-E 2 to retool some of the city's architecture and infrastructure. In July, the Guardian rounded up four artists to come up with unlikely prompts -- such as "biotech harpy in field at sunset" -- for DALL-E Mini (the free, public version of DALL-E). Naturally, the advent of bots that can create an image out of a simple text command is drawing the scrutiny of illustrators.

Call it the Skynet hypothesis, Artificial General Intelligence, or the advent of the Singularity -- for years, AI experts and non-experts alike have fretted (and, for a small group, celebrated) the idea that artificial intelligence may one day become smarter than humans. According to the theory, advances in AI -- specifically of the machine learning type that's able to take on new information and rewrite its code accordingly -- will eventually catch up with the wetware of the biological brain. In this interpretation of events, every AI advance from Jeopardy-winning IBM machines to the massive AI language model GPT-3 is taking humanity one step closer to an existential threat. Except that it will never happen. Co-authors University at Buffalo philosophy professor Barry Smith and Jobst Landgrebe, founder of German AI company Cognotekt argue that human intelligence won't be overtaken by "an immortal dictator" any time soon -- or ever.

Tilottama Goswami has received a BE degree with Honors in Computer Science and Engineering from the National Institute of Technology, Durgapur; and an MS degree in Computer Science (High Distinction) from Rivier University, Nashua, New Hampshire, United States. She was awarded a PhD in Computer Science from the University of Hyderabad. Presently, Dr. Goswami is Professor in the Department of Information Technology, Vasavi College of Engineering, Hyderabad, India. She has, overall, 23 years of experience in academia, research, and the IT industry. Her research interests are computer vision, machine learning, and image processing.

Smart monitoring using three-dimensional (3D) image sensors has been attracting attention in the context of smart cities. In smart monitoring, object detection from point cloud data acquired by 3D image sensors is implemented for detecting moving objects such as vehicles and pedestrians to ensure safety on the road. However, the features of point cloud data are diversified due to the characteristics of light detection and ranging (LIDAR) units used as 3D image sensors or the install position of the 3D image sensors. Although a variety of deep learning (DL) models for object detection from point cloud data have been studied to date, no research has considered how to use multiple DL models in accordance with the features of the point cloud data. In this work, we propose a feature-based model selection framework that creates various DL models by using multiple DL methods and by utilizing training data with pseudo incompleteness generated by two artificial techniques: sampling and noise adding. It selects the most suitable DL model for the object detection task in accordance with the features of the point cloud data acquired in the real environment. To demonstrate the effectiveness of the proposed framework, we compare the performance of multiple DL models using benchmark datasets created from the KITTI dataset and present example results of object detection obtained through a real outdoor experiment. Depending on the situation, the detection accuracy varies up to 32% between DL models, which confirms the importance of selecting an appropriate DL model according to the situation.

"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.