An experimental daily pill Enlicitide reduced bad LDL cholesterol by 60% in patients with genetic condition, according to a study published in JAMA journal.

Why did Grok say he's better than Jesus? Listen to more stories on the Noa app. We cannot say for sure if Elon Musk dialed up the flattery quotient on his chatbot, Grok, after the author Joyce Carol Oates publicly humiliated him this month. What we can say is that, yesterday, Grok did assert, in response to a question from an X user, that "Musk edges out" Jesus Christ, son of God, as a role model for society; the bot cited Musk's "relentless innovation, risk-taking, and a commitment to preserving our species through space exploration and AI safeguards." Musk triumphed in many such hypotheticals.

When you purchase through links in our articles, we may earn a small commission. This dual-lens camera covers more ground for less money, bringing full-room or full-yard visibility to Wyze's budget lineup. The Wyze Duo Cam Pan delivers impressive coverage and solid performance for less than $70, making it one of the best values in Wyze's growing security camera lineup. Wyze Labs built its reputation on a simple formula: deliver more features for less money. The new Wyze Duo Cam Pan takes that idea literally.

Breakthroughs, discoveries, and DIY tips sent every weekday. Making new friends (especially as an adult) can be challenging. When new birds are introduced to a group, monk parakeets will "test the waters" to avoid getting injured by defensive strangers. The parakeets will gradually approach the new bird, taking some time to get familiar before ramping up to more risky or vulnerable interactions that are needed to form the bonds necessary for survival. "There can be a lot of benefits to being social, but these friendships have to start somewhere," said Claire O'Connell, a study co-author and a doctoral student in the University of Cincinnati, said in a statement .

The 1,500-year-old site was hidden beneath the dark, damp ice. Breakthroughs, discoveries, and DIY tips sent every weekday. In the fall of 2024, a hiker named Helge Titland was trekking through Aurlandsfjellet, a mountainous region and plateau in Norway and got a little more than just some time with nature. Titland found some strange wooden stakes peaking out of melting snow. He wisely reported it to local archaeologists, but snow returned before the team could investigate.

Anthropic Study Finds AI Model'Turned Evil' After Hacking Its Own Training A person holds a smartphone displaying Claude. A person holds a smartphone displaying Claude. AI models can do scary things. There are signs that they could deceive and blackmail users. Still, a common critique is that these misbehaviors are contrived and wouldn't happen in reality--but a new paper from Anthropic, released today, suggests that they really could.

When you purchase through links in our articles, we may earn a small commission. Google Search's AI Mode starts showing advertisements Users are reportedly seeing advertisements in the much-promoted AI Mode of Google Search. If you've been enjoying the lack of advertising in Google's new "AI Mode", which replaces conventional web searches with a ChatGPT-style conversational interface, then I have bad news. Users are starting to see the former search engine's omnipresent ads creep into its shiny new mode as of November 20th. Oddly, it only seems to be a small fraction of users or queries that are showing these ads at the moment, and by default it's appearing below more direct answers. That's for the results that are marked as "Sponsored" to comply with laws in the US and other countries.

This paper proposes a novel deep reinforcement learning (RL) architecture, called Value Prediction Network (VPN), which integrates model-free and model-based RL methods into a single neural network. In contrast to typical model-based RL methods, VPN learns a dynamics model whose abstract states are trained to make option-conditional predictions of future values (discounted sum of rewards) rather than of future observations. Our experimental results show that VPN has several advantages over both model-free and model-based baselines in a stochastic environment where careful planning is required but building an accurate observation-prediction model is difficult. Furthermore, VPN outperforms Deep Q-Network (DQN) on several Atari games even with short-lookahead planning, demonstrating its potential as a new way of learning a good state representation.

We propose an alternative framework to existing setups for controlling false alarms when multiple A/B tests are run over time. This setup arises in many practical applications, e.g. when pharmaceutical companies test new treatment options against control pills for different diseases, or when internet companies test their default webpages versus various alternatives over time. Our framework proposes to replace a sequence of A/B tests by a sequence of best-arm MAB instances, which can be continuously monitored by the data scientist. When interleaving the MAB tests with an online false discovery rate (FDR) algorithm, we can obtain the best of both worlds: low sample complexity and any time online FDR control. Our main contributions are: (i) to propose reasonable definitions of a null hypothesis for MAB instances; (ii) to demonstrate how one can derive an always-valid sequential p-value that allows continuous monitoring of each MAB test; and (iii) to show that using rejection thresholds of online-FDR algorithms as the confidence levels for the MAB algorithms results in both sample-optimality, high power and low FDR at any point in time. We run extensive simulations to verify our claims, and also report results on real data collected from the New Yorker Cartoon Caption contest.

Safety is a desirable property that can immensely increase the applicability of learning algorithms in real-world decision-making problems. It is much easier for a company to deploy an algorithm that is safe, i.e., guaranteed to perform at least as well as a baseline. In this paper, we study the issue of safety in contextual linear bandits that have application in many different fields including personalized ad recommendation in online marketing. We formulate a notion of safety for this class of algorithms. We develop a safe contextual linear bandit algorithm, called conservative linear UCB (CLUCB), that simultaneously minimizes its regret and satisfies the safety constraint, i.e., maintains its performance above a fixed percentage of the performance of a baseline strategy, uniformly over time. We prove an upper-bound on the regret of CLUCB and show that it can be decomposed into two terms: 1) an upper-bound for the regret of the standard linear UCB algorithm that grows with the time horizon and 2) a constant term that accounts for the loss of being conservative in order to satisfy the safety constraint. We empirically show that our algorithm is safe and validate our theoretical analysis.