Jakub Pachocki, OpenAI’s chief scientist since 2024, believes artificial intelligence models will soon be capable of producing original research and making measurable economic impacts. In a conversation with Nature, Pachocki outlined how he sees the field evolving — and how OpenAI plans to balance innovation with safety concerns.
Pachocki, who joined OpenAI in 2017 after a career in theoretical computer science and competitive programming, now leads the firm’s development of its most advanced AI systems. These systems are designed to tackle complex tasks across science, mathematics, and engineering, moving far beyond the chatbot functions that made ChatGPT a household name in 2022.
Elon Musk recently emphasized that Colossus 2 will be the first Gigawatt AI training supercluster, highlighting xAI’s growing infrastructure ambitions as he reshared a post detailing the deployment of 168 Tesla Inc. Megapacks to power the new data center.
Astrobee is a free-flying robotic system developed by NASA that is made up of three distinct cube-shaped robots. This system was originally designed to help astronauts who are working at the International Space Station (ISS) by automating some of their routine manual tasks.
While Astrobee could be highly valuable for astronauts, boosting the efficiency with which they complete day-to-day operations, its object manipulation capabilities are not yet optimal. Specifically, past experiments suggest that the robot struggles when handling deformable items, including cargo bags that resemble some of those that it might be tasked to pick up on the ISS.
Researchers at Stanford University, University of Cambridge and NASA Ames recently developed Pyastrobee, a simulation environment and control stack to train Astrobee in Python, with a particular emphasis on the manipulation and transport of cargo.
The Higgs boson, discovered at the Large Hadron Collider (LHC) in 2012, plays a central role in the Standard Model of particle physics, endowing elementary particles such as quarks with mass through its interactions. The Higgs boson’s interaction with the heaviest “third-generation” quarks—top and bottom quarks—has been observed and found to be in line with the Standard Model.
But probing its interactions with lighter “second-generation” quarks, such as the charm quark, and the lightest “first-generation” quarks—the up and down quarks that make up the building blocks of atomic nuclei—remains a formidable challenge, leaving unanswered the question of whether or not the Higgs boson is responsible for generating the masses of the quarks that make up ordinary matter.
Researchers study the Higgs boson’s interactions by looking at how the particle decays into—or is produced with—other particles in high-energy proton–proton collisions at the LHC.
Can AI speed up aspects of the scientific process? Microsoft appears to think so.
At the company’s Build 2025 conference on Monday, Microsoft announced Microsoft Discovery, a platform that taps agentic AI to “transform the [scientific] discovery process,” according to a press release provided to TechCrunch. Microsoft Discovery is “extensible,” Microsoft says, and can handle certain science-related workloads “end-to-end.”
“Microsoft Discovery is an enterprise agentic platform that helps accelerate research and discovery by transforming the entire discovery process with agentic AI — from scientific knowledge reasoning to hypothesis formulation, candidate generation, and simulation and analysis,” explains Microsoft in its release. “The platform enables scientists and researchers to collaborate with a team of specialized AI agents to help drive scientific outcomes with speed, scale, and accuracy using the latest innovations in AI and supercomputing.”
Using global land use and carbon storage data from the past 175 years, researchers at The University of Texas at Austin and Cognizant AI Labs have trained an artificial intelligence system to develop optimal environmental policy solutions that can advance global sustainability initiatives of the United Nations.
The AI tool effectively balances various complex trade-offs to recommend ways of maximizing carbon storage, minimizing economic disruptions and helping improve the environment and people’s everyday lives, according to a paper published today in the journal Environmental Data Science.
The project is among the first applications of the UN-backed Project Resilience, a team of scientists and experts working to tackle global decision-augmentation problems—including ambitious sustainable development goals this decade—through part of a broader effort called AI for Good.
