As politicians weigh proposals that call for steep cuts to the National Science Foundation (NSF) and the National Institutes of Health, we spoke with someone who can answer the question more precisely than most. The latest in a long-running National Academies series, it explores the many ways that research innovations become impactful commercial activities. The so-called "tire track report series" dates back to 1995, when the National Research Council's Computer Science and Telecommunications Board produced the report Evolving the High Performance Computing and Communications Initiative to Support the Nation's Information Infrastructure. Can you describe how that effort got started and how it has continued to evolve? Current times are fairly unique, but there has always been this myth in Congress: "Look at Silicon Valley, look at all these tech companies that got started in a garage. Why do we need to fund computer science research? This is a self-sufficient thing."

OpenAI is reportedly on the verge of a roughly 500 billion valuation, a figure that would make it the most valuable private company in the world--bigger than SpaceX, TikTok's parent company Bytedance, and even public giants like Palantir. It's a staggering number for a company with an "astronomical burn rate." How is this even possible? As Axios reports, there are actually two deals in play: a SoftBank-led round valuing the company at 300 billion, which won't close until year's end, and a secondary sale of employee shares at a far steeper 500 billion valuation. Most of the cheaper shares have already been snapped up, leaving investors to fight over the pricier ones.

Tech companies like to make two grand pronouncements about the future of artificial intelligence. First, the technology is going to usher in a revolution akin to the advent of fire, nuclear weapons, and the internet. And second, it is going to cost almost unfathomable sums of money. Silicon Valley has already triggered tens or even hundreds of billions of dollars of spending on AI, and companies only want to spend more. Their reasoning is straightforward: These companies have decided that the best way to make generative AI better is to build bigger AI models.

Perhaps no tech company, not even Microsoft or Google, is better poised than Nvidia to reap significant near-term benefits from the race to build up generative artificial-intelligence capabilities. Here is the problem: Everyone already knows it. Nvidia's share price has more than doubled over the past six months. That makes it the best performing stock in the entire S&P 500 in that time. The chip maker's market value has now surpassed that of Tesla and Facebook-parent Meta Platforms and is close to eclipsing Berkshire Hathaway--all much larger companies in terms of annual revenue.

Way back in 2019--an eon ago in AI time--the New York Times reported an AI milestone: Aristo, a natural-language processing and reasoning system scored over 90% on parts of the New York Regents 8th Grade Science Exam, and over 83% on parts of the corresponding Grade 12 Science Exam. Aristo, the Times proclaimed, "is ready for high school science. I argued this at the time: "The truth is that while these systems perform well on specific language-processing tests, they can only take the test. None come anywhere close to matching humans in reading comprehension or other general abilities that the test was designed to measure." Moreover, such systems lack the basic commonsense understanding of the world that is assumed of humans taking the same tests.

Being on top isn't always what it's cracked up to be. Nvidia helped pioneer the use of graphics processors once used mainly for videogaming into powerful chips that now enable artificial intelligence capabilities in data centers. It remade its own business in the process; annual revenue has grown more than five fold since before the data center business began to take off in 2016. Nvidia also became the most valuable semiconductor company in the world, with a market capitalization higher than at least five other chip companies that generate more in annual revenue.

As I write this, Amazon is announcing its purchase of iRobot, adding its room-mapping robotic vacuum technology to the company's existing home surveillance suite, the Ring doorbell and prototype aerial drone. This is in addition to Amazon already knowing what you order online, what websites you visit, what foods you eat and, soon, every last scrap of personal medical data you possess. The trend of our gadgets and infrastructure constantly, often invasively, monitoring their users shows little sign of slowing -- not when there's so much money to be made. Of course it hasn't been all bad for humanity, what with AI's help in advancing medical, communications and logistics tech in recent years. In his new book, Machines Behaving Badly: The Morality of AI, Scientia Professor of Artificial Intelligence at the University of New South Wales, Dr. Toby Walsh, explores the duality of potential that artificial intelligence/machine learning systems offer and, in the excerpt below, how to claw back a bit of your privacy from an industry built for omniscience. Published by La Trobe University Press. The Second Law of Thermodynamics states that the total entropy of a system – the amount of disorder – only ever increases.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Over the past few years, smart manufacturing initiatives such as digital twins and the internet of things (IoT) has caused Industry 4.0 – the trend toward digital transformation in manufacturing and industrial sectors – to explode. However, robots and drones tasked with visually inspecting machines haven't yet seen the same growth. That is set to change in a big way, Bill Ray, vice president and analyst, emerging technologies and trends at Gartner, told VentureBeat. The robots, drones and cameras that inspect machines to perform predictive maintenance and relay analog information to operations staff can now function autonomously.

We've reached a point where 1980s-90s sci-fi buzzwords are turning into reality. A few examples are nanotechnology, the metaverse and quantum computing. In the past few years, all three of these concepts have turned into full-fledged industries. In particular, quantum computing could be incredibly valuable over the coming decade. Quantum computing essentially makes computing-intensive processes easier.

Abstract: Global Artificial Intelligence (AI) Robots Market to Reach $21. 4 Billion by 2026. AI or artificial intelligence in robotics is the integration of AI technology with robots enabling them to more efficiently perform repetitive tasks without human intervention.New York, Oct. 08, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence (AI) Robots Industry" - https://www.reportlinker.com/p06030753/?utm_source=GNW AI also enables robots