The world's largest pest control group is piloting the use of facial recognition software as a way to exterminate rats in people's homes. Rentokil said it had been developing the technology alongside Vodafone for 18 months. The surveillance technology, which is already being tested in real homes, tracks the rodents' habits and streams real-time analysis using artificial intelligence. A central command centre can then help to decide where and how to kill the rats caught on camera. Rentokil's chief executive, Andy Ransom, told the Financial Times: "With facial recognition technology you can see that rat number one behaved differently from rat number three.

In the next five years, it is likely that AI will begin to reduce employment for college-educated workers. As the technology continues to advance, it will be able to perform tasks that were previously thought to require a high level of education and skill. This could lead to a displacement of workers in certain industries, as companies look to cut costs by automating processes. While it is difficult to predict the exact extent of this trend, it is clear that AI will have a significant impact on the job market for college-educated workers. It will be important for individuals to stay up to date on the latest developments in AI and to consider how their skills and expertise can be leveraged in a world where machines are increasingly able to perform many tasks.

Over the last decade, machine learning has revolutionized entire areas of science ranging from drug discovery to autonomous driving, to medical diagnostics, to natural language processing and many others. Despite this impressive progress, it has become increasingly evident that modern machine learning models suffer from several issues which, if not resolved, could prevent their widespread adoption. Example challenges include lack of robustness guarantees to slight distribution shifts, reinforcing unfair bias present in training data, leakage of sensitive information through the model, and others. Addressing these issues by inventing new methods and tools for establishing that machine learning models enjoy certain desirable guarantees, is critical, especially for domains where safety and security are paramount. Indeed, over the last few years there has been substantial research progress in new techniques aiming to address the above issues with most work so far focusing on perturbations applied to inputs of the model.

Many scholars and educators argue the antidote to some of the ethical problems with artificial intelligence (AI) is to integrate ethics and AI or embed ethics in AI.2,12,14 The product of this combining is supposed to lead to Ethical AI, a term that is both frequently used and seemingly elusive.5,9,13 Although attempts to make AI ethical are to be lauded, too little attention has been given to what it means to "integrate" or "embed," be it integrating ethics and AI or embedding ethics in AI. A rather simple idea of additivity seems to be behind these proposals. That is, the efforts are directed toward figuring out how ethical principles can be "injected into"11 AI or how an ethical dimension can be "added to" machines1 or, if the focus is on the latest wave of machine learning, how to "teach" machines to act in an ethical way.10

Whether computers can actually "think" and "feel" is a question that has long fascinated society. Alan M. Turing introduced a test for gauging machine intelligence as early as 1950. Movies such as 2001: A Space Odyssey and Star Wars have only served to fuel these thoughts, but while the concept was once confined to science fiction, it is rapidly emerging as a serious topic of discussion. In a few cases, the dialog has become so convincing that people have deemed machines sentient. A recent example involves former Google data scientist Blake Lemoine, who published human-to-machine discussions with an AI system called LaMDA.a

Faculty from the Stanford Accelerator for Learning are already thinking about the ways in which ChatGPT and other generative artificial intelligence will change and contribute to education in particular. Victor Lee, associate professor of education and the faculty lead for the accelerator initiative on generative AI in education, stresses the importance of educators in harnessing this technology. "If we want generative AI to meaningfully improve education," he says, "there is the obvious step we need to take of listening to the existing expertise in education -- from educators, parents, students, and scholars who have spent years studying education -- and using what we learn to find the most pertinent and valuable use cases for generative AI in a very complicated educational system." Over the next several weeks, the Stanford Accelerator for Learning will launch listening sessions and gatherings with educators to strategize a path for generative AI. Says Lee, "We need the use of this technology to be ethical, equitable, and accountable."

You’ve probably heard us say this countless times: GPT-3, the gargantuan AI that spews uncannily human-like language, is a marvel. It’s also largely a mirage. You can tell with a simple trick: Ask it the color of sheep, and it will suggest “black” as often as “white”—reflecting the phrase “black sheep” in our vernacular. That’s…

Artificial intelligence has in recent years proved itself to be a quick study, although it is being educated in a manner that would shame the most brutal headmaster. Locked into airtight Borgesian libraries for months with no bathroom breaks or sleep, AIs are told not to emerge until they've finished a self-paced speed course in human culture. On the syllabus: a decent fraction of all the surviving text that we have ever produced. When AIs surface from these epic study sessions, they possess astonishing new abilities. People with the most linguistically supple minds--hyperpolyglots--can reliably flip back and forth between a dozen languages; AIs can now translate between more than 100 in real time.

Large artificial intelligence language models, like those used to run the popular ChatGPT chatbot, can be reduced in size by more than half without losing much accuracy. This could save large amounts of energy and allow people to run the models at home, rather than in huge data centres.

While grading essays for his world religions course last month, Antony Aumann, a professor of philosophy at Northern Michigan University, read what he said was easily "the best paper in the class." It explored the morality of burqa bans with clean paragraphs, fitting examples and rigorous arguments. A red flag instantly went up. Aumann confronted his student over whether he had written the essay himself. The student confessed to using ChatGPT, a chatbot that delivers information, explains concepts and generates ideas in simple sentences -- and, in this case, had written the paper.