A 2016 video that Tesla used to promote its self-driving technology was staged to show capabilities like stopping at a red light and accelerating at a green light that the system did not have, according to testimony by a senior engineer. The video, which remains archived on Tesla's website, was released in October 2016 and promoted on Twitter by Elon Musk as evidence that "Tesla drives itself". But the Model X was not driving itself with technology Tesla had deployed, Ashok Elluswamy, director of Autopilot software at Tesla, said in the transcript of a July deposition taken as evidence in a lawsuit against Tesla for a 2018 fatal crash involving a former Apple engineer. The previously unreported testimony by Elluswamy represents the first time a Tesla employee has confirmed and detailed how the video was produced. The video carries a tagline saying: "The person in the driver's seat is only there for legal reasons. He is not doing anything. The car is driving itself."

Nick Cave, the Bad Seeds frontman whose songs are tinged with a healthy dose of death, forlorn love, and religion, is no fan of ChatGPT's lyrical ambitions. The popular AI bot has drawn both praise and concern for its ability to generate conversational and nuanced text responses in simple, clean sentences. Since its release in November by the artificial intelligence lab OpenAI, ChatGPT has written everything from sitcom scripts to literature essays to, now, rather convincing rock songs. This has left people worried about the ramifications for industries across the creative spectrum, and one of those people is Cave himself. In his latest The Red Hand Files newsletter, Cave took on the subject of AI generated music.

Robots already lend a hand around the home, figuratively speaking. Some can brush the char from the barbecue, others can mow your yard, wash your windows, or clean your pool. Amazon's Astro follows owners from room to room with their favorite music, delivers snacks to the kids down the hall, serves as a home security patrol when you're away from home, provides peace of mind when you want to check in on a loved one, and much more. This story is from the WIRED World in 2023, our annual trends briefing. Read more stories from the series here--or download or order a copy of the magazine.

Sanjib Chaudhary chanced upon StoryWeaver, a multilingual children's storytelling platform, while searching for books he could read to his 7-year-old daughter. Chaudhary's mother tongue is Kochila Tharu, a language with about 250,000 speakers in eastern Nepal. Languages with a relatively small number of speakers, like Kochila Tharu, do not have enough digitized material for linguistic communities to thrive--no Google Translate, no film or television subtitles, no online newspapers. In industry parlance, these languages are "underserved" and "underresourced." This is where StoryWeaver comes in.

Next month, AI will enter the courtroom, and the US legal system may never be the same. An artificial intelligence chatbot, technology programmed to respond to questions and hold a conversation, is expected to advise two individuals fighting speeding tickets in courtrooms in undisclosed cities. The two will wear a wireless headphone, which will relay what the judge says to the chatbot being run by DoNotPay, a company that typically helps people fight traffic tickets through the mail. The headphone will then play the chatbot's suggested responses to the judge's questions, which the individuals can then choose to repeat in court. But it also has the potential to change how people interact with the law, and to bring many more changes over time.

While artificial intelligence (AI) models are becoming increasingly advanced, training and running these models on conventional computer hardware is very energy consuming. Engineers worldwide have thus been trying to create alternative, brain-inspired hardware that could better support the high computational load of AI systems. Researchers at Technion–Israel Institute of Technology and the Peng Cheng Laboratory have recently created a new neuromorphic computing system supporting deep belief neural networks (DBNs), a generative and graphical class of deep learning models. This system, outlined in Nature Electronics, is based on silicon-based memristors, energy-efficient devices that can both store and process information. Memristors are electrical components that can switch or regulate the flow of electrical current in a circuit, while also remembering the charge that passed through it.

Check out all the on-demand sessions from the Intelligent Security Summit here. Classical machine learning (ML) algorithms have proven to be powerful tools for a wide range of tasks, including image and speech recognition, natural language processing (NLP) and predictive modeling. However, classical algorithms are limited by the constraints of classical computing and can struggle to process large and complex datasets or to achieve high levels of accuracy and precision. Enter quantum machine learning (QML). QML combines the power of quantum computing with the predictive capabilities of ML to overcome the limitations of classical algorithms and offer improvements in performance. In their paper "On the role of entanglement in quantum-computational speed-up," Richard Jozsa and Neil Linden, of the University of Bristol in the UK, write that "QML algorithms hold the promise of providing exponential speed-ups over their classical counterparts for certain tasks, such as data classification, feature selection and cluster analysis.

AI has dominated the internet world. AI now plays a significant role in our daily lives in the current day. It is difficult to imagine life without computers. Every aspect of our everyday life that involves technology requires a computer. Making computers smarter becomes crucial to making our lives easier.

Annapolis, MD – Chesapeake Conservancy's data science team developed an artificial intelligence deep learning model for mapping wetlands, which resulted in 94% accuracy. Supported by EPRI, an independent, non-profit energy research and development institute; Lincoln Electric System; and the Grayce B. Kerr Fund, Inc., this method for wetland mapping could deliver important outcomes for protecting and conserving wetlands. The results are published in the peer-reviewed journal Science of the Total Environment. The team trained a machine learning (convolutional neural network) model for high-resolution (1m) wetland mapping with freely available data from three areas: Mille Lacs County, Minnesota; Kent County, Delaware; and St. Lawrence County, New York. The full model, which requires local training data provided by state wetlands data and the National Wetlands Inventory (NWI), mapped wetlands with 94% accuracy.

The recent launches of two large language models, ChatGPT and Galactica, have led to much interest and controversy amongst the AI community, and beyond. These models, and in particular their potential use for writing scientific articles (and essays), provided the inspiration for this month's discussion. Joining the discussion this time are: Sabine Hauert (University of Bristol), Sarit Kraus (Bar-Ilan University), Michael Littman (Brown University), and Lucy Smith (AIhub). Sabine Hauert: Has anyone had a chance to use any of these new models yet? Sarit Kraus: During the summer I played with the previous version of GPT. Have you tried the latest version, Michael?