Welcome to our July 2023 monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, find out about recent events, and more. This month, we report on RoboCup2023, congratulate ICML outstanding paper award winners, find out how machine learning can help in the study of supercooled liquids, and learn about a visually-grounded few-shot word learning method for low-resource languages. RoboCup2023 took place from 4-10 July in Bordeaux, and saw around 2500 participants, from 45 different countries participate in competitions, training sessions and a symposium. You can find out what the attendees got up to in our round-ups: Part 1 Part 2. Roberto Figueiredo took part in the kid-size soccer competition and is also a local representative for Junior Rescue Simulation. We spoke to Roberto about his RoboCup experience this year, and his progression from junior to major league.

In their paper Dynamics of supercooled liquids from static averaged quantities using machine learning, Simone Ciarella, Massimiliano Chiappini, Emanuele Boattini, Marjolein Dijkstra and Liesbeth M C Janssen introduce a machine-learning approach to predict the complex non-Markovian dynamics of supercooled liquids. In this interview, Simone tells us about supercooled liquids, and how the team used machine learning in their study. Supercooled liquids are liquids that are cooled below their normal freezing point without undergoing a phase transition into a solid state. Imagine you have a glass of water. Normally, when you lower the temperature, the water molecules slow down and arrange themselves into a solid crystal structure, that we call ice, because this structure is very stable.

Our understanding of supercooled liquids and glasses has lagged significantly behind that of simple liquids and crystalline solids. This is in part due to the many possibly relevant degrees of freedom that are present due to the disorder inherent to these systems and in part to non-equilibrium effects which are difficult to treat in the standard context of statistical physics. Together these issues have resulted in a field whose theories are under-constrained by experiment and where fundamental questions are still unresolved. Mean field results have been successful in infinite dimensions but it is unclear to what extent they apply to realistic systems and assume uniform local structure. At odds with this are theories premised on the existence of structural defects. However, until recently it has been impossible to find structural signatures that are predictive of dynamics. Here we summarize and recast the results from several recent papers offering a data driven approach to building a phenomenological theory of disordered materials by combining machine learning with physical intuition.