This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . An AI-generated influencer entered Alabama'RushTok'; some fans didn't care that she was fake Make unknown callers explain why they're calling Is your personal data changing what you pay online? Apple's $250M Siri settlement: How to claim up to $95 John Kiriakou warns US is'not prepared' for AI threats: 'The new frontier' AI entrepreneur Gary Marcus calls for AI regulation, blasts industry's'tone deafness' Suspected pneumonic plague death in Russia'significant,' but not'cause for alarm': Former CDC director NYC councilwoman pushes back on proposed AI regulations: Technology must'move forward' 'Two things I saw made me scared,' former Anthropic researcher testifies Drone expert warns Russia's mesh network is reshaping drone war in Ukraine Former OpenAI safety chief warns AI industry's culture is'broken' A terrified mother heard what sounded exactly like her daughter crying for help, then spent five and a half hours following a caller's demands California woman Deborah Del Mastro said she was having breakfast when a caller using a suspected AI voice clone claimed to have kidnapped her daughter. NEW You can now listen to Fox News articles! I want you to think about the voice of someone you love. You know how that person sounds when they're laughing, and you also know the voice you would hear if they were terrified.

Human writers are expensive, and they eventually die. AI is cheaper, and will never join a union. I have been thinking about this for close to 20 minutes, and I have concluded that human writing is finished. Let me begin with an anecdote, because that is how serious arguments begin. Last Tuesday I asked a machine to write me a birthday message for my sister-in-law.

The recent rise of Large Reasoning Models (LRMs) has significantly improved multi-step reasoning performance, but often at the cost of generating excessively long reasoning chains. This paper revisits the efficiency of such reasoning processes through an information-theoretic lens, revealing a fundamental trade-off between reasoning length and semantic efficiency. We propose two metrics--InfoBias and InfoGain--to quantify divergence from ideal reasoning paths and stepwise information contribution, respectively. Empirical analyses show that longer reasoning chains tend to exhibit higher information bias and diminishing information gain, especially for incorrect answers. Motivated by these findings, we introduce an entropy-based Adaptive Think strategy that dynamically halts reasoning once confidence is sufficiently high, improving efficiency while maintaining competitive accuracy. Compared to the Vanilla Think approach (default mode), our strategy yields a 1.10% improvement in average accuracy and a 50.80% reduction in token usage on QwQ-32B across six benchmark tasks spanning diverse reasoning types and difficulty levels, demonstrating superior efficiency and reasoning performance. These results underscore the promise of entropy-based methods for enhancing both accuracy and cost-effiiciency in large language model deployment.

Ahead of the hit show's finale, cocreators Paul W. Downs and Lucia Aniello talk about media consolidation, the perils of censorship, and why they find AI "deeply disturbing." If you're a WIRED reader who uses AI in any creative context, I'd suggest staying far, far away from anyone involved in the TV show . In an interview earlier this year, actor Hannah Einbinder (who plays young comedy writer Ava Daniels on the show) described AI creators as "losers," "not artists," and "not special." In a wide-ranging conversation for ahead of the series finale on HBO Max, Paul W. Downs and Lucia Aniello were resolute about the value of human creativity--and what can be lost when AI enters the picture. If their work on is any indication, Downs and Aniello (along with their third cocreator, Jen Statsky) would be wise to stick with the tough, tiring, absolutely-no-shortcuts approach they take to making entertainment. Across five excellent seasons--if you haven't seen the show, I really do recommend it-- has been praised for its sharp writing and wit, and its thoughtful portrayal of Deborah Vance and Ava's complex, constantly evolving relationship. The show has also acted as something of a mirror for the real-world entertainment industry, weaving in plotlines that tackle everything from media consolidation to corporate censorship to, yes, artificial intelligence. The show's cast and creators have been on a media whirlwind as it all comes to an end. When they came knocking on WIRED's door, we jumped at the chance to chat, and I was lucky enough to spend an hour with Downs and Aniello--both WIRED subscribers, much to my delight--earlier this month. KATIE DRUMMOND: Lucia Aniello and Paul Downs, who I just learned are married, congratulations and welcome to . You should have been there. You should have been there. Ugh, why didn't we bring you? We are going to renew for our 10-year at the same place though. Lucia was born in Italy, so it was closer to a lot of family. And you were married in what year? You have time to find your look. A major priority for me in my life is perfecting my look. We do work at Condé Nast, and my boss is Anna Wintour.

The recent rise of Large Reasoning Models (LRMs) has significantly improved multi-step reasoning performance, but often at the cost of generating excessively long reasoning chains. This paper revisits the efficiency of such reasoning processes through an information-theoretic lens, revealing a fundamental trade-off between reasoning length and semantic efficiency. We propose two metrics, InfoBias and InfoGain, to quantify divergence from ideal reasoning paths and stepwise information contribution, respectively. Empirical analyses show that longer reasoning chains tend to exhibit higher information bias and diminishing information gain, especially for incorrect answers. Motivated by these findings, we introduce an entropy-based Adaptive Think strategy that dynamically halts reasoning once confidence is sufficiently high, improving efficiency while maintaining competitive accuracy. Compared to the Vanilla Think approach (default mode), our strategy yields a 1.10% improvement in average accuracy and a 50.80% reduction in token usage on QwQ-32B across six benchmark tasks spanning diverse reasoning types and difficulty levels, demonstrating superior efficiency and reasoning performance. These results underscore the promise of entropy-based methods for enhancing both accuracy and cost-effiiciency in large language model deployment.

Since its founding in 1998, Vecna Technologies has developed a number of ways to help hospitals care for patients. The company has produced intake systems to respond to Covid-19 patient surges, prediction systems to manage health complications in maternity wards, and telepresence robots that have allowed sick people to stay connected with friends and loved ones. The differences among those products have also led to a number of transformations and spinoffs, including material handling company Vecna Robotics and the health care nonprofit VecnaCares. Vecna Technologies co-founders Deborah Noel Theobald '95 and Daniel Theobald '95, SM '98 say each of those pivots has been driven by a desire to build a robotics company that makes a positive impact on the world. "We knew we wanted to do robotics and do something good in the world," Deborah says of the team's mindset.

I love movies, so as a fun exercise for my fast.ai It's named "It Happened One Frame", in tribute to the classic 1934 romantic comedy "It Happened One Night". To use this app, all you need is the link to a Youtube video. For example, you could search "Macaulay Culkin screams with hands on his cheeks" in a Home Alone movie clip and get the screenshots that capture the most iconic scene in this classic. This particular image is so popular that you can easily get it from a google search.

Artificial intelligence has already started to take a major place in digital marketing, through what is known as programmatic marketing (the delivery of marketing content, based on what is known about a web site visitor, making real-time decisions). In online chat, chat bots are making decisions about responses, based on a customer's questions. Combined with the context of where a visitor has come from, where they have visited the site, where they are now, previous visits and other known information, AI can give intelligent responses in a contextual way, rather than simple across-the-board responses. AI for digital marketing, content delivery, web merchandising and sales will not only refine and evolve in 2018, but will reach a much wider audience of retail and other business customers, wanting to maximize their sales. Deborah is President and Chief Marketing / Information Officer at The Certificate in Online Business, and Director of Strategy & Innovation at The Institute for Business Advancement.

Class: A class defines a group of individuals that belong together because they share some properties. For example, Deborah and Frank are both members of the class Person. Classes can be organized in a specialization hierarchy using subClassOf. There is a built-in most general class named Thing that is the class of all individuals and is a superclass of all OWL classes. There is also a built-in most specific class named Nothing that is the class that has no instances and a subclass of all OWL classes.