Human vs. ChatGPT NFL Week 7 betting picks: Human clings to profit as AI claims third straight win LaNorris Sellers' acrobatics, late fumble recovery help give South Carolina walk-off win over Tennessee Texas Tech's $30 million roster runs into a buzzsaw as Cincinnati delivers Sorsby revenge in Top-10 upset LSU's Mike the Tiger stalks referee while dressed as Michael Myers, appears to be steered away by staffer Ric Flair shows off eyepatch in update after fall left him hospitalized: 'I will be okay' Exclusive: DOJ reviewing Portland Thorns incident showing women's sports supporters struck, forcibly removed Sarkozy's son warns violent protests in France could worsen amid political unrest Enes Kanter Freedom: It's going to be a dunk every time Fraud'czar' calls SNAP fraud the'greatest heist in human history' French President Macron blames'ultra-left' for French riots Kaylee McGhee White: Arguments against voter ID are'completely insulting' Rep Jim Jordan commends'common sense conservative' running in tight Florida district OutKick More questions than answers: Arch Manning likes the disrespect, Big 12's disaster and a CFP race in disarray Check out the best moments from the Week 8 matchup between the No. 2 BYU Cougars and the UCF Knights! So, turns out'survival weekend' in college football did not go as planned for a number of teams across the country on Saturday, as for the consecutive weeks the No. 2 ranked team in the sport went down in dramatic fashion. This time it was No. 2 BYU falling victim to the hangover, losing to UCF in double-overtime that turned into pure chaos over the final few minutes of regulation, after defeating Notre Dame last week. And, maybe Steve Sarkisian was right this past week in the buildup to the Ole Miss showdown that everyone involved in the talk around Arch Manning, even if warranted, created a monster in the Texas quarterback. This page may contain affiliate links to legal sports betting partners.

Modern LLMs are increasingly deep, and depth correlates with performance, albeit with diminishing returns. However, do these models use their depth efficiently? Do they compose more features to create higher-order computations that are impossible in shallow models, or do they merely spread the same kinds of computation out over more layers? To address these questions, we analyze the residual stream of the Llama 3.1, Qwen 3, and OLMo 2 family of models. We find: First, comparing the output of the sublayers to the residual stream reveals that layers in the second half contribute much less than those in the first half, with a clear phase transition between the two halves.

Large language models (LLMs) raise concerns about content authenticity and integrity because they can generate human-like text at scale. Text watermarks, which embed detectable statistical signals into generated text, offer a provable way to verify content origin. Many detection methods rely on pivotal statistics that are i.i.d.

Deep learning practice is increasingly driven by powerful foundation models (FM), pre-trained at scale and then fine-tuned for specific tasks of interest. A key property of this workflow is the efficacy of performing sparse or parameter-efficient finetuning, meaning that by updating only a tiny fraction of the whole FM parameters on a downstream task can lead to surprisingly good performance, often even superior to a full model update. However, it is not clear what is the optimal and principled way to select which parameters to update. Although a growing number of sparse fine-tuning ideas have been proposed, they are mostly not satisfactory, relying on hand-crafted heuristics or heavy approximation. In this paper we propose a novel Bayesian sparse fine-tuning algorithm: we place a (sparse) Laplace prior for each parameter of the FM, with the mean equal to the initial value and the scale parameter having a hyper-prior that encourages small scale.

Deep learning practice is increasingly driven by powerful foundation models (FM), pre-trained at scale and then fine-tuned for specific tasks of interest.

In this section, we examine three algorithms via four numerical examples. The first algorithm is the Sliding Window-UCB (SW-UCB) algorithm presented in our paper. The second algorithm is the naive UCB algorithm without any sliding windows (Agrawal and Devanur, 2014). The third algorithm is LagrangeBwK presented in (Immorlica et al., 2019), which is originally proposed for the adversarial BwK problem. Note that the LagrangeBwK requires an approximation of the static best distribution benchmark. For simplicity, we put the exact value of the benchmark into the algorithm. All the regret performances are reported based on the average over 100 simulation trials.

Jensen Huang Says Nvidia's New Vera Rubin Chips Are in'Full Production' The chip giant says Vera Rubin will sharply cut the cost of training and running AI models, strengthening the appeal of its integrated computing platform. Nvidia CEO Jensen Huang says that the company's next-generation AI superchip platform, Vera Rubin, is on schedule to begin arriving to customers later this year. "Today, I can tell you that Vera Rubin is in full production," Huang said during a press event on Monday at the annual CES technology trade show in Las Vegas. Rubin will cut the cost of running AI models to about one-tenth of Nvidia's current leading chip system, Blackwell, the company told analysts and journalists during a call on Sunday. Nvidia also said Rubin can train certain large models using roughly one-fourth as many chips as Blackwell requires.

A float would support Sam Altman's ambitions to splash trillions of dollars on building datacentres. A float would support Sam Altman's ambitions to splash trillions of dollars on building datacentres. OpenAI is reportedly gearing up for a stock market listing valuing the company at $1tn (£760bn) as soon as next year, in what would be one of the biggest ever initial public offerings. The developer behind the hit AI chatbot ChatGPT is considering whether to file for an IPO as soon as the second half of 2026, according to Reuters, which cited people familiar with the matter. The company is thought to be looking to raise at least $60bn.

OpenAI is considering filing with securities regulators as soon as the second half of 2026, some people familiar with the matter said. SAN FRANCISCO - OpenAI is laying the groundwork for an initial public offering that could value the company at up to $1 trillion, three people familiar with the matter said, in what could be one of the biggest IPOs of all time. OpenAI is considering filing with securities regulators as soon as the second half of 2026, some of the people said. In preliminary discussions, the company has looked at raising $60 billion at the low end and likely more, the people said. They cautioned that talks are early and plans -- including the figures and timing -- could change depending on business growth and market conditions.