On the 31st March, our editorial team headed to the Royal Society for AI for Science . This day-long conference explored how AI is changing the nature of scientific discovery, and was hosted by the Alan Turing Institute. The recordings from the event are now available on YouTube and are well worth a watch. You can read Ella Scallan's blog post about the day here . Lucy Smith is Senior Managing Editor for AIhub.

The Future of AI Research report, published in March 2025, aims to clearly identify the trajectory of AI research in a structured way. The report was led by outgoing AAAI President Francesca Rossi and covers 17 different AI topics . Members of the report team, and other selected AI practitioners, are taking part in a series of video panel discussions covering selected chapters from the report. In the sixth discussion in the collection, the three panellists tackle factuality and trustworthiness. Understanding factuality: why preventing false outputs from large language models remains AI's toughest problem Lucy Smith is Senior Managing Editor for AIhub.

People often make decisions through "satisficing," gathering just enough information to make a satisfactory prediction of a likely outcome. A series of experimental games shows that people also employ satisficing to learn social rules and conventions. This finding offers new insight into social learning and reveals a key difference between how humans and LLMs make predictions. The premise of AI large language models is that any problem can be solved by vacuuming up as much information as possible, running it through probability models, and performing complex calculations to make predictions and come up with the optimal solution. Another premise behind LLMs is that they emulate the way human brains operate.

Richard Sutton's "Bitter Lesson" is usually read as a warning against building too much human knowledge into AI systems. Over the long run, the methods that win are not the ones that encode our clever intuition most directly, but the ones that scale: search, learning, and other general methods that can absorb more compute and data. We take a general architecture, expose it to massive data, and train it with a simple self-supervised objective. Language models predict the next token. Vision models reconstruct masked patches, align views, or match teacher representations.

The latest interview in our series with the AAAI/SIGAI Doctoral Consortium participants features Thi Kieu Khanh Ho who is studying time-series anomaly detection. We found out more about her research, and what inspired her to study AI, and what she plans to work on next. Tell us a bit about your PhD -- where are you studying, and what is the topic of your research? I am doing my PhD at McGill University and Mila - Québec AI Institute, in the Department of Electrical and Computer Engineering, supervised by Professor Narges Armanfard. My research focuses on time-series anomaly detection, the problem of teaching AI systems to recognize when something unusual or abnormal is happening in complex, real-world data streams, without relying on large amounts of labeled examples.

This year, RoboCup took place in Incheon, South Korea, from 2-6 July. The event saw teams take part in competitions, training sessions, and a symposium. Take a look at what the participants got up to in our round up from social media. RoboCup 2026 officially begins today! A post shared by RoboCup Federation (@robocup.official)

The EurAI Distinguished Service Award started in 2012, and it is presented annually to individuals who have made exceptional contributions to the European AI community. This year, the award goes to two researchers: Jérôme Lang and Luc de Raedt. Find out who won the small, middle and large divisions in Incheon. Find out the latest from day two of the competition. In the first of our round-ups from the humanoid league we introduce the competition, and report some preliminary results.

The second day's play at RoboCup 2026 has drawn to a close with another bumper set of matches. Teams have come from far and wide to take part in the humanoid soccer competition this year, with 17 different countries represented. China is the most represented country, boasting 15 teams across the three divisions. Other countries taking part are geographically widespread, ranging from Colombia to Malaysia, from Germany to Australia. In advance of the competition, all applying teams provided a video, team description paper, and information about the robots and software that they use.

This short video from RoboCup gives a flavour of the day's happenings, which also featured the opening ceremony.

Protein engineering is a field primed for artificial intelligence research. Each protein is made up of amino acids; to optimize a protein function, researchers modify proteins by switching out one of 20 different amino acids for another. For a protein that is just 50 amino acids in length, this leads to approximately 1.13 10 potential combinations to test. This number of potential combinations, impossible to test in the lab, makes protein engineering an ideal challenge for AI. Modeling which of these combinations will give the best results is a perfect problem for the technology's massive computing power.