The rapid advancement of artificial intelligence, particularly autonomous agentic systems based on Large Language Models (LLMs), presents new opportunities to accelerate drug discovery by improving in-silico modeling and reducing dependence on costly experimental trials. Current AI agent-based systems demonstrate proficiency in solving programming challenges and conducting research, indicating an emerging potential to develop software capable of addressing complex problems such as pharmaceutical design and drug discovery. This paper introduces DO Challenge, a benchmark designed to evaluate the decision-making abilities of AI agents in a single, complex problem resembling virtual screening scenarios. The benchmark challenges systems to independently develop, implement, and execute efficient strategies for identifying promising molecular structures from extensive datasets, while navigating chemical space, selecting models, and managing limited resources in a multi-objective context. We also discuss insights from the DO Challenge 2025, a competition based on the proposed benchmark, which showcased diverse strategies explored by human participants. Furthermore, we present the Deep Thought multi-agent system, which demonstrated strong performance on the benchmark, outperforming most human teams. Among the language models tested, Claude 3.7 Sonnet, Gemini 2.5 Pro and o3 performed best in primary agent roles, and GPT-4o, Gemini 2.0 Flash were effective in auxiliary roles. While promising, the system's performance still fell short of expert-designed solutions and showed high instability, highlighting both the potential and current limitations of AI-driven methodologies in transforming drug discovery and broader scientific research.

Artificial intelligence, commonly referred to as AI, represents both a risk and a benefit to the security of society, according to Bruce Schneier, security technologist, researcher, and lecturer at Harvard Kennedy School. Schneier made his remarks about the risks of AI in an afternoon keynote session at the 2021 RSA Conference on May 17. Hacking for Schneier isn't an action that is evil by definition; rather, it's about subverting a system or a set of rules in a way that is unanticipated or unwanted by a system's designers. "All systems of rules can be hacked," Schneier said. "Even the best-thought-out sets of rules will be incomplete or inconsistent, you'll have ambiguities and things that designers haven't thought of, and as long as there are people who want to subvert the goals in a system, there will be hacks." Schneier highlighted a key challenge with hacking that is conducted by some form of AI: it might be difficult to detect.

Chess has captured the imagination of humans for centuries due to its strategic beauty--an objective, board-based testament to the power of mortal intuition. Twenty-five years ago Wednesday, though, human superiority on a chessboard was seriously threatened for the first time. At a nondescript convention center in Philadelphia, a meticulously constructed supercomputer called Deep Blue faced off against Garry Kasparov for the first in a series of six games. Kasparov was world chess champion at the time and widely considered to be one of the greatest players in the history of chess. He did not expect to lose.

Tue 19 May 2020 06.14 EDT Last modified on Tue 19 May 2020 06.16 EDT When legendary chess grandmaster Garry Kasparov found himself beaten by IBM's Deep Blue supercomputer, it was seen as a seminal moment in the evolution of artificial intelligence. It was New York, 1997 and for the first time ever a computer had beaten a world champion under tournament conditions. This was the culmination of a journey in which the first stirrings of what we now call artificial intelligence and machine learning were born. A road trodden by war heroes and student researchers alike, whose singular desire to create a program that could beat the very best in the world would shape an entire science. Early origins Chess lends itself well to computer programming.

Held on May 30, 2019 in Ottawa, IMPACT AI changed the conversation around artificial intelligence. As Canada's fastest-growing conference focused on AI ethics, equality, and empathy, IMPACT AI ignited the idea that we are all responsible for the future of this transformative technology. A heartfelt thank you to everyone who joined us and helped to make this event such a terrific success. Enjoy coverage from IMPACT AI 2019 below, including recordings of each keynote, panel discussion, and fireside chat. This was the most insightful panel today: Women in AI: redefining the future.

U of Alberta created the first Computing Science department in Canada in 1964. It has a long tradition of research in AI (is rated 3rd in the world in machine learning). It has also led in the development of AI for strategy games. The results can be commercialized in non-game applications as well. Among these are Checkers, Chess, Go and Poker, The evening's talks were by Jonathan Schaeffer (computer chess) and Ryan Hayward (the strategy game Hex).

The science fiction writer Douglas Adams imagined the greatest computer ever built, Deep Thought, programmed to answer the deepest question ever asked: the Great Question of Life, the Universe, and Everything. After 7.5 million years of processing, Deep Thought revealed its answer: Forty-two (1). As artificial intelligence (AI) systems enter every sector of human endeavor--including science, engineering, and health--humanity is confronted by the same conundrum that Adams encapsulated so succinctly: What good is knowing the answer when it is unclear why it is the answer? What good is a black box? In an informal survey of my colleagues in the physical sciences and engineering, the top reason for not using AI methods such as deep learning, voiced by a substantial majority, was that they did not know how to interpret the results.

Twenty years ago IBM's Deep Blue computer stunned the world by becoming the first machine to beat a reigning world chess champion in a six-game match. The supercomputer's success against an incredulous Garry Kasparov sparked controversy over how a machine had managed to outmaneuver a grand master, and incited accusations--by Kasparov and others--that the company had cheated its way to victory. The reality of what transpired in the months and years leading up to that fateful match in May 1997, however, was actually more evolutionary than revolutionary--a Rocky Balboa–like rise filled with intellectual sparring matches, painstaking progress and a defeat in Philadelphia that ultimately set the stage for a triumphant rematch. Computer scientists had for decades viewed chess as a meter stick for artificial intelligence. Chess-playing calculators emerged in the late 1970s but it would be another decade before a team of Carnegie Mellon University graduate students built the first computer--called Deep Thought--to beat a grand master in a regular tournament game.

A few years ago, when my son was barely three, he confused me for a slow hard drive. As I was explaining a new concept to him, I stumbled. While I searched my brain for the right words, he looked up at me: "Mama, it's loading." We are surely on a path to faster downloads. We just need to make sure we are loading the right stuff.

Self-driving cars and chess-playing computers might be the first things that come to your mind when you think about Artificial Intelligence (AI), but as a concept, AI's not exactly new. Go back to the ancient Greeks, and you'll discover Talos, a mythological giant automaton warrior made of bronze. Coming back to relatively recent times, you could even consider Frankenstein's monster to be a kind of intelligent automaton. Nearly every culture has stories about man-made creatures that could think and act on their own but given that we're a species that's always tried to play God, it seems only natural. Of course, it wasn't till the advent of computers that Artificial Intelligence moved from the realm of fiction and folklore to reality.