We prove that a classic sub-Gaussian mixture proposed by Robbins in a stochastic setting actually satisfies a path-wise (deterministic) regret bound. For every path in a natural ``Ville event'' $E_α$, this regret till time $T$ is bounded by $\ln^2(1/α)/V_T + \ln (1/α) + \ln \ln V_T$ up to universal constants, where $V_T$ is a nonnegative, nondecreasing, cumulative variance process. (The bound reduces to $\ln(1/α) + \ln \ln V_T$ if $V_T \geq \ln(1/α)$.) If the data were stochastic, then one can show that $E_α$ has probability at least $1-α$ under a wide class of distributions (eg: sub-Gaussian, symmetric, variance-bounded, etc.). In fact, we show that on the Ville event $E_0$ of probability one, the regret on every path in $E_0$ is eventually bounded by $\ln \ln V_T$ (up to constants). We explain how this work helps bridge the world of adversarial online learning (which usually deals with regret bounds for bounded data), with game-theoretic statistics (which can handle unbounded data, albeit using stochastic assumptions). In short, conditional regret bounds serve as a bridge between stochastic and adversarial betting.

Computational drug repurposing for rare diseases is especially challenging when no prior associations exist between drugs and target diseases. Therefore, knowledge graph completion and message-passing GNNs have little reliable signal to learn and propagate, resulting in poor performance. We present RareAgent, a self-evolving multi-agent system that reframes this task from passive pattern recognition to active evidence-seeking reasoning. RareAgent organizes task-specific adversarial debates in which agents dynamically construct evidence graphs from diverse perspectives to support, refute, or entail hypotheses. The reasoning strategies are analyzed post hoc in a self-evolutionary loop, producing textual feedback that refines agent policies, while successful reasoning paths are distilled into transferable heuristics to accelerate future investigations. Comprehensive evaluations reveal that RareAgent improves the indication AUPRC by 18.1% over reasoning baselines and provides a transparent reasoning chain consistent with clinical evidence.

The increasing capability of Large Language Models to act as human-like social agents raises two important questions in the area of opinion dynamics. First, whether these agents can generate effective arguments that could be injected into the online discourse to steer the public opinion. Second, whether artificial agents can interact with each other to reproduce dynamics of persuasion typical of human social systems, opening up opportunities for studying synthetic social systems as faithful proxies for opinion dynamics in human populations. To address these questions, we designed a synthetic persuasion dialogue scenario on the topic of climate change, where a 'convincer' agent generates a persuasive argument for a 'skeptic' agent, who subsequently assesses whether the argument changed its internal opinion state. Different types of arguments were generated to incorporate different linguistic dimensions underpinning psycho-linguistic theories of opinion change. We then asked human judges to evaluate the persuasiveness of machine-generated arguments. Arguments that included factual knowledge, markers of trust, expressions of support, and conveyed status were deemed most effective according to both humans and agents, with humans reporting a marked preference for knowledge-based arguments. Our experimental framework lays the groundwork for future in-silico studies of opinion dynamics, and our findings suggest that artificial agents have the potential of playing an important role in collective processes of opinion formation in online social media.

As children growing up in the 1970s and 1980s, the brothers were obsessed with science fiction and futurism. "Our younger selves definitely imagined that by now it would be like 2001: A Space Odyssey," Novella says in Episode 526 of the Geek's Guide to the Galaxy podcast. "There's going to be permanent space stations in space, there's going to be an infrastructure between here and the moon, a lunar base. All that stuff, we took it for granted." The next few decades showed that futurism is harder than it looks.

Data science has been hot for many years now, attracting attention and talent. There is a persistent thread of commentary, though, that says data science's core skill of statistical modeling is overhyped and that managers and aspiring data scientists should focus on engineering instead. Vicki Boykis' 2019 blog post was the first article I remember along these lines. Don't do a degree in data science, don't do a bootcamp…It's much easier to come into a data science and tech career through the "back door", i.e. starting out as a junior developer, or in DevOps, project management, and, perhaps most relevant, as a data analyst, information manager, or similar… While tuning models, visualization, and analysis make up some component of your time as a data scientist, data science is and has always been primarily about getting clean data in a single place to be used for interpolation. More recently, Gartner's 2020 AI hype cycle report acknowledges the role of data scientists but says: Gartner foresees developers being the major force in AI.

A lot of developers and watchers of the AI system still don't predict an existing or future trend in which humans are being completely cut out-of-the-loop. Instead, they foresee (as told by trend-watchers and AI developers) a future in which Artificial Intelligence targets to complement the interactions or actions of humans. It is either because of the inefficiency of the technology to take over the human roles completely, or perhaps humans can offer a more autonomous and holistic response to any circumstances, thus, using AI as master and commander.

In his writings, Gary Marcus is clear about two things: Artificial intelligence is an extremely promising technology that, if used in the right way, could significantly improve practices in health care and other industries. But right now, Marcus says, AI is getting off track, with potentially severe consequences for society and the field itself. That viewpoint makes Marcus -- a tech entrepreneur, author, and psychology professor at New York University -- a controversial figure in the world of artificial intelligence. He is among a few prominent scientists voicing skepticism about the dominance of deep learning, a type of AI architecture whose use has exploded in medicine and other fields. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free!

This week, at the research institute AI Now's annual symposium, experts debated some of the most critical issues in society through the lens of AI.

Once a general understanding of true AI is established, the only skepticism remaining typically stems from the fact that businesses looking to implement some type of AI may not trust providers to be honest about their products' capabilities. It's easy to say that a product has AI capabilities, but it's much harder to put true AI into practice. Since true AI will learn and become smarter over time, it's important to ask providers leading questions to determine if their technology has this capability.

From his perch as editor in chief, he watched as the nascent internet took off, fulfilling his prediction that the world was about to be swept by a digital "Bengali typhoon." Among other things, that epochal storm spawned a dotcom wave that was cresting in 1998. Now, two decades later, Rossetto has written a novel that captures the optimism, greed, fervor, and madness of that era. Set in a fictional San Francisco, Change Is Good: A Story of the Heroic Era of the Internet, follows the intertwined adventures of a startup CEO, a WIRED reporter, a code-writing true believer, and many more instantly iconic characters ripped from the mists of the first dotcom boom. What follows is a chapter from Rossetto's novel, which takes place during a wild party thrown by the fictional WIRED magazine. Carl Hess stands in the line flowing into a looming warehouse off Third Street in the Mission Bay wasteland that was once the old Union Pacific yards.