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"Green Light uses machine learning systems to comb through Maps data to calculate the amount of traffic congestion present at a given light, as well as the average wait times of vehicles stopped there. That information is then used to train AI models that can autonomously optimize the traffic timing at that intersection, reducing idle times as well as the amount of braking and accelerating vehicles have to do there. It's all part of Google's goal to help its partners collectively reduce their carbon emissions by a gigaton by 2030."

"OpenAI has "discussed and debated quite extensively" when to release a tool that can determine whether an image was made with DALL-E 3, OpenAI's generative AI art model, or not. But the startup isn't close to making a decision anytime soon. That's according to Sandhini Agarwal, an OpenAI researcher who focuses on safety and policy, who spoke with TechCrunch in a phone interview this week. She said that, while the classifier tool's accuracy is "really good" - at least by her estimation - it hasn't met OpenAI's threshold for quality."

"The question of why AI generates fake academic papers relates to how large language models work: they are probabilistic, in that they map the probability over sequences of words. As Dr David Smerdon of the University of Queensland puts it: "Given the start of a sentence, it will try to guess the most likely words to come next.""

"Writing in the journal Science, Summerfield and colleagues from Google DeepMind report how they built the "Habermas Machine" - an AI system named after the German philosopher Jürgen Habermas. The system works by taking written views of individuals within a group and using them to generate a set of group statements designed to be acceptable to all. Group members can then rate these statements, a process that not only trains the system but allows the statement with the greatest endorsement to be selected."

"AI Outperforms Humans in Theory of Mind Tests Large language models convincingly mimic the understanding of mental states"

"Her intervention emerged after Sir Paul McCartney became the latest star to back calls for laws to stop mass copyright theft by generative AI companies, warning the technology "could just take over". Bush, who shot to fame with Wuthering Heights in 1978 but whose last album was released in 2011, gave a rare interview this year in which she said she was "very keen" to make a new album, saying: "I've got lots of ideas … it's been a long time.""

"Many respondents said they used chatbots to help them manage different aspects of their lives, from improving their mental and physical health to advice about existing romantic relationships and experimenting with erotic role play. They can spend between several hours a week to a couple of hours a day interacting with the apps. Worldwide, more than 100 million people use personified chatbots, which include Replika, marketed as "the AI companion who cares" and Nomi, which claims users can "build a meaningful friendship, develop a passionate relationship, or learn from an insightful mentor"."

"AI models will inevitably have to make value judgments. If we want those judgments to be congruent with our own values (which is, after all, the central goal of AI alignment research) then we need to have ways of testing which values a model expresses in the real world. Our method provides a new, data-focused method of doing this, and of seeing where we might've succeeded-or indeed failed-at aligning our models' behavior."

""Could teaching be trumped by a learning machine? Are we beginning to glimpse the possibility of machines that teach themselves to teach? They learn what works, what doesn't and deliver ever better performance. We see the embryonic evidence for this in adaptive learning systems, that are truly algorithmic, and do use machine learning, to improve as they deliver. The more students they teach, the better they get. They even tech themselves. This is not science fiction. This is real AI, in real software, delivering real courses, in real institutions." - Donald Clark"

"An algorithm has no tastebuds; a neural net never gets the munchies. So can a robot brain really tell us what we'll want to eat? The question is whether AI systems will be able to excel in the sensual, creative work of tasting and developing new foods - and what we stand to gain or lose by inventing foods that really have our number."

"In October, American teachers prevailed in a lawsuit with their school district over a computer program that assessed their performance. The system rated teachers in Houston by comparing their students' test scores against state averages. Those with high ratings won praise and even bonuses. Those who fared poorly faced the sack. The program did not please everyone. Some teachers felt that the system marked them down without good reason. But they had no way of checking if the program was fair or faulty: the company that built the software, the SAS Institute, regards its algorithm a trade secret and would not disclose its workings."

""It marks an exciting moment for the field," said Demis Hassabis, DeepMind's founder and chief executive. "These algorithms are now becoming mature enough and powerful enough to be applicable to really challenging scientific problems." Advertisement Venki Ramakrishnan, the president of the Royal Society, called the work "a stunning advance" that had occurred "decades before many people in the field would have predicted"."

"After being set an overarching goal like maximizing profit, they develop their own strategies based on experience of the market, often with little human oversight. The most advanced also use forms of AI whose workings are opaque even if humans wanted to peer inside."

"A startup in Israel is tackling the problem, and saving lives, with a sophisticated algorithm that can analyze the almost infinite number of possible conflicts. Ask any doctor or pharmacist and they'll tell you about the minefield called "drug interaction". That's when a medicine designed to resolve one problem causes an adverse reaction with another medicine designed to resolve another problem. Or with anything else the patient puts in their body."

"Google should know better, given that it already had a "hallucination problem" with its featured snippets(Opens in a new tab) at the top of search results back in 2017. The snippets algorithm seemed to particularly enjoy telling lies about U.S. presidents. Again, what could go wrong?"

"Their tools analyze content using sophisticated algorithms, picking up on subtle signals to distinguish the images made with computers from the ones produced by human photographers and artists. But some tech leaders and misinformation experts have expressed concern that advances in A.I. will always stay a step ahead of the tools. To assess the effectiveness of current A.I.-detection technology, The New York Times tested five new services using more than 100 synthetic images and real photos. The results show that the services are advancing rapidly, but at times fall short."

"For higher-complexity problems, however, the models would enter "collapse", failing to generate any correct solutions. In one case, even when provided with an algorithm that would solve the problem, the models failed. The paper said: "Upon approaching a critical threshold - which closely corresponds to their accuracy collapse point - models counterintuitively begin to reduce their reasoning effort despite increasing problem difficulty." The Apple experts said this indicated a "fundamental scaling limitation in the thinking capabilities of current reasoning models"."

"Researchers from MIT and Google recently showed off a machine learning algorithm capable of automatically retouching photos just like a professional photographer. Snap a photo and the neural network identifies exactly how to make it look better-increase contrast a smidge, tone down brightness, whatever-and apply the changes in less than 20 milliseconds."

""I consider 'bias' a euphemism," says Brandeis Marshall, PhD, data scientist and CEO of DataedX, an edtech and data science firm. "The words that are used are varied: There's fairness, there's responsibility, there's algorithmic bias, there's a number of terms… but really, it's dancing around the real topic… A dataset is inherently entrenched in systemic racism and sexism.""

"To what extent do the decisions of these types of algorithms reflect the conscious or unconscious biases of their creators?"