Watching the opening day of the US Senate hearings on AI brought to mind Marx's quip about history repeating itself, "the first time as tragedy, the second as farce". Some time ago we had the farce of the boss of Meta (neé Facebook) explaining to a senator that his company made money from advertising. This week we had the tragedy of seeing senators quizzing Sam Altman, the new acceptable face of the tech industry. Well, as one of my kids, looking up from revising O-level classics, once explained to me: "It's when you can see the disaster coming but you can't do anything to stop it." The trigger moment was when Altman declared: "We think that regulatory interventions by government will be critical to mitigate the risks of increasingly powerful models."

Belief is a special kind of human power. Agustin Fuentes, an anthropologist at the University of Notre Dame, eloquently claims as much in his recent book Why We Believe: Evolution and the Human Way of Being. It's the "most prominent, promising, and dangerous capacity humanity has evolved," he writes, the power to "see and feel and know something--an idea, a vision, a necessity, a possibility, a truth--that is not immediately present to the senses, and then to invest, wholly and authentically, in that'something' so that it becomes one's reality." A great example of this is the widespread and intuitive idea that we have free will. Most people grow up with the notion that they are, in some sense, responsible for their thoughts and actions because, unlike animals, humans can think about their choices.

Our objective is to develop an automated anomaly detection model based on recurrent neural networks—a machine learning technique commonly …

Battery performance can make or break the electric vehicle experience, from driving range to charging time to the lifetime of the car. Now, artificial intelligence has made dreams like recharging an EV in the time it takes to stop at a gas station a more likely reality, and could help improve other aspects of battery technology. For decades, advances in electric vehicle batteries have been limited by a major bottleneck: evaluation times. At every stage of the battery development process, new technologies must be tested for months or even years to determine how long they will last. But now, a team led by Stanford professors Stefano Ermon and William Chueh has developed a machine learning-based method that slashes these testing times by 98 percent.

For all the giant leaps promised by artificial intelligence, when it comes to business, what we've seen so far amounts to just small steps. In fact, a number of very smart people advise companies to start small with AI: Use it to improve your customer service bots, for example, before you try to deploy it to cure cancer. So, yes, that appears to be a sensible approach. But it can also be a dangerous trap. When you think small, notes this week's guest, you get small results.

Easy enough to abstract information from someone's mind, but you'll know you're getting somewhere when you put information "in." Like maybe if you can get a monkey to "get the red ball" and they routinely do after having the thought put in their mind. Or for human trials have then be given a question they could know the answer to if the thought insertion worked. You shouldn't be trying to get a brain and a computer to work directly in tandem. Not at all compatible, but you can translate thoughts into computer code, have the computer do the processing and then insert the thought back.

Artificial intelligence marketing (AI) is looking to change the game of leveraging customer data, with new found concepts of machine learning to anticipate the next move to enhance a customer's journey, writes Ipsit Roy, a freelance content writer. The last few years that went by a bear a fine witness to the rise of innovation in marketing. With newer tools and trends popping up every minute, and a majority of them actually living up to the hype, marketing teams can dream about bringing the sun and the moon closer. In their Artificial Intelligence (AI) survey, account-based marketing firm, Demandbase joined hands with Wakefield Research to harp on the potential that AI carries in affecting sales and marketing in leaps and bounds. The research depicted how there remains a strong divergence between the urge and the confidence of innovators to help forward-thinking organizations adapt to AI for first-grade marketing programs.

Men who send other people unsolicited images of their genitals are likely to be more narcissistic and sexist that those who do not, psychologists have found. Researchers surveyed over a thousand men to compare the personalities and motivations of those who sent intimate images and those who did not. Rather than for personal gratification, men who share images of their genitals typically do so hoping to arouse the recipient and get images back in return. A small minority of participants reported sending the private photos in order to intentionally elicit a negative response from women. The researchers conclude that the practice can neither be construed as solely sexist or as a positive sexual outlet.

Nothing defines the future of work more than those who will create it, and the latest advances in neurotechnology will radically transform workers themselves. Rethink everything you know about talent, education, and what you can expect from your most able employee. The next few decades will redefine what it means to be human, what it means to'know something' or'learn something'. Welcome to the age of the trans-human worker. Technological advancements continue to enhance our day-to-day working and personal lives, and the future will continue to throw new technologies into the equation.

If nothing else, you can this much for artificial intelligence: They're rarely afraid to look stupid. If a learning A.I. encounters something outside its preprogrammed knowledge, it will not typically be shy in asking the person with whom it is speaking to clarify. This can, however, make for rather monotonous conversation for the human involved in talking to the chatbot, voice assistant, or generally conversant robot: "What's an apple?" "What's tiramisu?" "What's cured meat?" "What's do you know literally anything about food you stupid recipe chatbot?" You get the idea, and as researchers from Japan's Osaka University point out in a recent spotlight on their work, that last line is indicative of the real problem facing A.I.: Asking questions might be the best way for them to learn, but that doesn't count for much if the barrage of questions is so irritating or tedious that the human wanders off.