The human cell is a miserable thing to study. Tens of trillions of them exist in the body, forming an enormous and intricate network that governs every disease and metabolic process. Each cell in that circuit is itself the product of an equally dense and complex interplay among genes, proteins, and other bits of profoundly small biological machinery. Our understanding of this world is hazy and constantly in flux. As recently as a few years ago, scientists thought there were only a few hundred distinct cell types, but new technologies have revealed thousands (and that's just the start).

Autonomous agents based on Large Language Models (LLMs) that devise plans and tackle real-world challenges have gained prominence.However, tailoring these agents for specialized domains like quantitative investment remains a formidable task. The core challenge involves efficiently building and integrating a domain-specific knowledge base for the agent's learning process. This paper introduces a principled framework to address this challenge, comprising a two-layer loop.In the inner loop, the agent refines its responses by drawing from its knowledge base, while in the outer loop, these responses are tested in real-world scenarios to automatically enhance the knowledge base with new insights.We demonstrate that our approach enables the agent to progressively approximate optimal behavior with provable efficiency.Furthermore, we instantiate this framework through an autonomous agent for mining trading signals named QuantAgent. Empirical results showcase QuantAgent's capability in uncovering viable financial signals and enhancing the accuracy of financial forecasts.

While western billionaires were busy sending rockets to space only for them to crash and burn, scientists in India were quietly doing something no one had accomplished before. Their Chandrayaan-3 moon lander was the first mission to reach the lunar south pole – an unexplored region where reservoirs of frozen water are believed to exist. I remember my heart soaring when images of the control room in India spread around social media, showing senior female scientists celebrating their incredible achievement. The success of Chandrayaan-3, launched in July 2023, showed the world that not only is India a major player in space, but that a moon lander can be launched successfully for $75m (£60m). This cost is not to be sniffed at but it is much cheaper than most other countries' budgets for a moon mission. July 2023 was an extremely busy month for space firsts.

Sam Altman became a tech sensation this year as the CEO of OpenAI, the artificial-intelligence startup that seems pulled from science fiction. But Mr. Altman, who has been among Silicon Valley's most prominent investors for more than a decade, has placed one of the biggest bets of his career on a company that might be even more futuristic: a nuclear-fusion startup called Helion Energy Inc.

My background came from God of War … I've never worked on a shooter, and you need a different team to do that. You might as well be asking me to make a racing game. And eventually over time, we built that trust to the point where we ended up calling [the franchise] Jedi." The much contested Jedi eventually became Cal Kestis, first introduced in Jedi: Fallen Order. His original reveal saw a slew of criticism for being, well, a little bland, but four years on, it's hard to deny that Cal has won over both gamers and Star Wars fans alike. Case in point: in a recent Disney poll, asking fans to vote on which lightsaber from the Star Wars universe they'd like produced for retail, Cal beat out legendary characters such as Anakin Skywalker and Qui-Gon Jinn. Of course, making the main character a Jedi isn't just a play to Star Wars fans – it's also a clever game design move, given that the well-documented journey of a Jedi developing their skills perfectly mirrors that of a player progressing through a video game, something Asmussen describes as "one-to-one storytelling". "I was hoping that we could come up with a character that the player could go along on the ride with," he expands. "So, he starts off kind of raw.

Rik Chomko is co-founder and CEO of InRule Technology, an intelligence automation company providing integrated decision-making, machine learning and process automation software to the enterprise. Chomko started the company in 2002 with CTO Loren Goodman. He became chief executive officer in 2015 after serving as chief operating officer since 2012. Chomko also served as chief product officer prior to his role as COO. Before co-founding InRule, Chomko was chief technology officer with Calypso Systems, a consulting firm.

For decades, one of the hardest problems for robot developers to crack has been something seemingly mundane: how to replicate the human hand's ability to pick up stuff. The tech giant last month unveiled a collection of new robots, one of which is suited to replacing humans in the most common job at Amazon – picking up items and placing them elsewhere. The linchpin of this new kind of automation is a robot arm – appropriately named Sparrow after the tenacious, pervasive bird – that combines advanced artificial intelligence, a variety of grippers, and the speed and precision that is now standard in off-the-shelf industrial robotic arms. The announcement was easy to miss, coming as it did amid a run of news that, in part, illustrated some of the challenges Amazon is trying to tackle with its automation effort. The company began layoffs of corporate employees in mid-November, part of a sweeping cost-cutting effort to deal with the aftereffects of its rapid expansion during the pandemic. The company's workforce more than doubled during that period, to exceed 1.6 million as of early this year.

The actions look something like those of an amusement park claw game, except they are executed rapidly and smoothly, just like the countless movements that workers undertake to pick and pack millions of online orders each day in warehouses across the world. Top news and in-depth analysis on the world of logistics, from supply chain to transport and technology. But the robotic device, known as Sparrow, is outfitted with suction cups and artificial intelligence software rather than the eyes and hands of human workers. It is the latest attempt by Amazon. Warehouse workers pick items up, sort them and put them down millions of times a day. But Amazon is trying to get Sparrow to do something that robots have long struggled with--picking up a variety of objects as easily as humans can, as well as identifying them by characteristics such as color, shape and size.

Unfortunately, to state precisely the question, I need some formal preliminaries. Let $(X,Y), (X_1,Y_1), (X_2,Y_2), \dots$ be a family of $[0,1] d\times[0,1]$-valued random variables. The second point basically requires that the data-driven regressor determined by the sequence $(A_t)_{t \in \mathbb{N}}$ is minimax-optimal in the mean-square sense (the reason why I suspect that those should be the dependencies on the dimension and on the Lipschitz constant can be found for example in Theorem 3.2 in the book by Györfi, Kohler, Krzyzak, Walk - A Distribution-Free Theory of Nonparametric Regression) adapting automatically to the actual Lipschitz constant $L$ of the actual regression function $\eta$, and to the effective dimension $d *$ of the manifold where the actual distribution $\mu$ of the features lives, without knowing these parameters in advance. I didn't manage to find anything in the literature and I strongly suspect that something like this should be too good to exist (maybe some kind of no free-lunch theorem?). Does anyone know if this problem was tackled anywhere and what could be the answer?

Yesterday's science fiction is today's invention. Babel Fish, the "oddest thing in the universe", is a species of fish featured in Douglas Adam's magnum opus, The Hitchhiker's Guide to Galaxy. The fish, worn as an earpiece, translates all the languages that ever existed instantly. Babel Fish is no longer the stuff of dreams: Thanks to advances in AI, especially in the NLP domain, many tech giants are in the process of building a universal translator. To that end, Universal Speech Translator was a dominant theme in the Meta's Inside the Lab event on February 23.