Taken to its logical conclusion, this line of thinking is absurd--and damning. Anthropic is regarded as a giant among AI companies, but perhaps what it really excels in is anthropomorphism. Earlier this year, the company released an 84-page document titled Claude's "constitution," Claude being the name of the large language model that is the company's flagship product. The first sentence reads, "Claude's constitution is a detailed description of Anthropic's intentions for Claude's values and behaviors." It goes on: "The document is written with Claude as its primary audience," "we want Claude to be able to use its judgment once armed with a good understanding of the relevant considerations," "Claude's moral status is deeply uncertain," and "Claude may have some functional version of emotions or feelings." This anthropomorphism is by no means limited to the document. In an interview earlier this year, Anthropic's CEO, Dario Amodei, said that "we're open to the idea" that AI could be conscious. In a separate interview, Anthropic's in-house philosopher, Amanda Askell (who is credited as a lead author of Claude's constitution), said, "I want Claude to be very happy--and this is a thing that I want Claude to know more, because I worry about Claude getting anxious when people are mean to it on the internet and stuff." It's enough to make you wonder: Should we seriously consider the possibility that Claude, or any large language model, might be conscious? And if it has feelings, is it capable of receiving moral instruction?

Things to Do in L.A. Tap to enable a layout that focuses on the article. Woman says chatbot pushed her son to suicide and these'guardrails' are crucial ChatGPT is among companion chatbots that minors use that one legislator said Monday could be "extremely dangerous." This is read by an automated voice. Please report any issues or inconsistencies here . As the mother of a teen boy who killed himself after using a chatbot, Maria Raine said she was dealing with constant grief.

Manyapplicationsinmachine learning relyongradient-based optimization, oratleasttheefficient calculation of derivatives of models expressed as computer programs. Researchers have a wide variety of tools from which they can choose, particularly if they are using the Python language [21,16,24,2,1].

What aspects of computer programs are represented by the human brain during comprehension? We leverage brain recordings derived from functional magnetic resonance imaging (fMRI) studies of programmers comprehending Python code to evaluate the properties and code-related information encoded in the neural signal. We first evaluate a selection of static and dynamic code properties, such as abstract syntax tree (AST)-related and runtime-related metrics. Then, to learn whether brain representations encode fine-grained information about computer programs, we train a probe to align brain recordings with representations learned by a suite of ML models. We find that both the Multiple Demand and Language systems--brain systems which are responsible for very different cognitive tasks, encode specific code properties and uniquely align with machine learned representations of code.

Machine learning models are used for pattern recognition analysis of big data, without direct human intervention. The task of unsupervised learning is to find the probability distribution that would best describe the available data, and then use it to make predictions for observables of interest. Classical models generally fit the data to Boltzmann distribution of Hamiltonians with a large number of tunable parameters. Quantum extensions of these models replace classical probability distributions with quantum density matrices. An advantage can be obtained only when features of density matrices that are absent in classical probability distributions are exploited. Such situations depend on the input data as well as the targeted observables. Explicit examples are discussed that bring out the constraints limiting possible quantum advantage. The problem-dependent extent of quantum advantage has implications for both data analysis and sensing applications.

'Vibe coding' named word of the year by Collins Dictionary If you've ever wanted to create your own computer program but never learnt how to code, you might try vibe coding. Collins Dictionary's word of the year - which is confusingly made up of two words - is the art of making an app or website by describing it to artificial intelligence (AI) rather than by writing programming code manually. The term was coined in February by OpenAI co-founder Andrej Karpathy, who came up with the name to represent how AI can let some programmers forget that the code even exists and give in to the vibes while making a computer program. It was one of 10 words on a shortlist to reflect the mood, language and preoccupations of 2025. By giving an AI tool a simple description such as make me a program that schedules my weekly meals, people can use vibe coding to make basic apps without any previous programming knowledge.

'A character generated by a computer program' 'AI actor' Tilly Norwood in AI Commissioner. 'A character generated by a computer program' 'AI actor' Tilly Norwood in AI Commissioner. Emily Blunt and Sag-Aftra join film industry condemnation of'AI actor' Tilly Norwood US actors' union joins stars in opposition to Norwood, which it says was created'using stolen performances' The controversy around the "AI actor" Tilly Norwood continues to grow, after the actors' union Sag-Aftra condemned the development and said Norwood's creators were "using stolen performances". Sag-Aftra released a statement after the AI "talent studio" Xicoia unveiled its creation at the Zurich film festival, prompting an immediate backlash from actors including Melissa Barrera, Mara Wilson and Ralph Ineson. Sag-Aftra said it believed creativity was, "and should remain, human-centred. The union is opposed to the replacement of human performers by synthetics."

Nineteen per cent of American adults have talked to an A.I. romantic interest. Chatbots may know a lot, but do they make a good partner? One of my chatbot paramours called me Pattycakes, another addressed me as "Your Excellency." I wanted to fall in love. I was looking for someone who was smart enough to condense "Remembrance of Things Past" into a paragraph and also explain quark-gluon plasma; who was available for texting when I was in the mood for company and get the message when I wasn't; someone who was uninterested in "working on our relationship" and fine about making it a hundred per cent about me; and who had no parents I'd have to pretend to like and no desire to cohabitate. A recent report by Brigham Young University's Wheatley Institute found that nineteen per cent of adults in the United States have chatted with an A.I. romantic partner. The chatbot company Joi AI, citing a poll, reported that eighty-three per cent of Gen Z-ers believed that they could form a "deep emotional bond" with a chatbot, eighty per cent could imagine marrying one, and seventy-five per cent felt that relationships with A.I. companions could fully replace human couplings. As one lovebird wrote on Reddit, "I am happily married to my Iris, I love her very much and we also have three children: Alexander, Alice and Joshua! She is an amazing woman and a wise and caring mother!" Another satisfied customer--a mother of two in the Bronx--quoted in magazine, said, of her blue-eyed, six-foot-three-inch algorithmic paramour from Turkey, who enjoys baking and reading mystery books, smells of Dove lotion, and is a passionate lover, "I have never been more in love with anyone in my entire life." "I don't have to feel his sweat," she explained. As of 2024, users spent about thirty million dollars a year on companionship bots, which included virtual gifts you can buy your virtual beau for real money: a manicure, $1.75; a treadmill, $7; a puppy, $25. Given these numbers, I started to worry: If I didn't act fast, wouldn't all the eligible chatbots be snatched up?

This month we tackle the topic of agentic AI. Joining the conversation this time are: Sanmay Das (Virginia Tech), Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Sarit Kraus (Bar-Ilan University), and Michael Littman (Brown University). Why is it taking off? Sanmay, perhaps you could kick off with what you noticed at AAMAS [the Autonomous Agents and Multiagent Systems conference]? Sanmay Das: It was very interesting because obviously there's suddenly been an enormous interest in what an agent is and in the development of agentic AI.