The Department of Justice is reportedly investigating whether Tesla has misled customers and investors by claiming that its Autopilot technology enables full-fledged self-driving capabilities. According to Reuters, the DOJ launched the probe last year following over a dozen crashes, including fatal ones, in which Autopilot was activated. Prosecutors in Washington and San Francisco are examining if Tesla had made unsupported full self-driving claims about the technology, and they could ultimately pursue criminal charges or seek sanctions. But they could also shut the probe down without doing anything if they determine that Tesla hasn't done anything wrong. Back in August, reports came out that the California DMV had filed complaints against the automaker with the California Office of Administrative Hearings.

And last month a promotional video for machine-learning firm Paperspace Co. showed talking semblances of the actors Tom Cruise and Leonardo DiCaprio. None of these celebrities ever spent a moment filming these campaigns. In the cases of Messrs. Musk, Cruise and DiCaprio, they never even agreed to endorse the companies in question. All the videos of digital simulations were created with so-called deepfake technology, which uses computer-generated renditions to make the Hollywood and business notables say and do things they never actually said or did. Some of the ads are broad parodies, and the meshing of the digital to the analog in the best of cases might not fool an alert viewer.

Many of the outstanding challenges to the development and deployment of tactical autonomy are related to operations in the real world. AIR will focus on previously avoided dimensions that must be addressed to enable tactical autonomy in operationally relevant combat: fully-integrated sensors, scalability to larger engagements, adaptability to changing conditions in open-world problems, and the ability to learn predictive models that incorporate uncertain knowledge of adversary and self, as well as deceptive effects. AIR will pair existing, maturing, and emerging algorithmic approaches with expert human feedback to rapidly evolve the cooperative autonomous behaviors that solve previously avoided challenges. AIR will address two technical areas: 1. Creating fast and accurate models that capture uncertainty and automatically improve with more data. The AIR program will also develop the processes needed to rapidly design, test, and implement future iterations of AIR software products.

From time to time, we may rely on AI to help us. But, before we hand over the keys of society to our automaton overlords, let's recognise that there's another side to it that's all too human. Let's look at AI's failed attempts in 2022 to replace us flesh-and-bones types! Conversational AIs have had a long history of goof-ups since the beginning. Adding to the list, this year, 'BlenderBot3' made several false and jarring statements in its conversations with the public.

Welcome to our October 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, we learn about a Nigerian sign language dataset, hear from researchers working on different robotic control projects, and dig into the latest governmental AI policies. Steven Kolawole created a pioneering dataset for Nigerian sign language, in collaboration with a TV sign language broadcaster and two schools in Nigeria. He used this dataset of over 8000 images to create a model to convert sign language to text or speech. In this interview, Steven told us about the goals of this research, his methodology, and how the work has inspired research in other languages.

Information and communication technologies are rapidly changing and transforming the citizens' urban life, culture, and habits. Today, cities are lively, active, productive, and innovative, but, at the same time, cities face many problems, such as high density, traffic, waste, water and air pollution, unplanned urbanization, etc. Public and local administrations have focused on finding solutions to these problems and developing new strategies. According to the United Nations' World Population Prospects 2022 most recent forecasts, the world population might reach 8.5 billion in 2030, 9.7 billion in 2050, and 10.4 billion in 2100. By 2050, it is estimated that 68% of the world's population will live in cities. There are many different definitions of smart cities.

The USDA, Agricultural Research Service, Animal Biosciences and Biotechnology, in Beltsville, Maryland, is seeking a POSTDOCTORAL RESEARCH ASSOCIATE, (Research Biologist (Computational/Bioinformatics)/Geneticist) for a TWO YEAR APPOINTMENT. Salary is commensurate with experience (starting at $74,950 per annum) plus benefits. As antimicrobial resistance becomes a more significant problem worldwide, the USDA is actively investigating alternative to antibiotics in swine that promote growth performance and disease resistance. The incumbent will work as a part of a multi-disciplinary team (microbiology, immunology, physiology, and bioinformatics) to identify and develop alternatives to in-feed antibiotics. The participant's specific project will involve the application of machine learning models to microbiome datasets with the end goal of identifying biomarkers associated with improved swine growth.

In September 2021, the "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Michael Littman of Brown University. The report, entitled "Gathering Strength, Gathering Storms," answers a set of 14 questions probing critical areas of AI development addressing the major risks and dangers of AI, its effects on society, its public perception and the future of the field. The report concludes that AI has made a major leap from the lab to people's lives in recent years, which increases the urgency to understand its potential negative effects. The questions were developed by the AI100 Standing Committee, chaired by Peter Stone of the University of Texas at Austin, consisting of a group of AI leaders with expertise in computer science, sociology, ethics, economics, and other disciplines.

We formulate the first differentiable analog quantum computing framework with a specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods. We further propose a scalable approach to estimate the gradients of quantum dynamics using a forward pass with Monte Carlo sampling, which leads to a quantum stochastic gradient descent algorithm for scalable gradient-based training in our framework. Applying our framework to quantum optimization and control, we observe a significant advantage of differentiable analog quantum computing against SOTAs based on parameterized digital quantum circuits by orders of magnitude.

We define deriving semantic class targets as a novel multi-modal task. By doing so, we aim to improve classification schemes in the physical sciences which can be severely abstracted and obfuscating. We address this task for upcoming radio astronomy surveys and present the derived semantic radio galaxy morphology class targets.