Neural networks are powerful approximators, capable of capturing the complex behavior of real-world phenomena that are difficult to describe explicitly. This capability is being exploited across many scientific domains such as molecular biology or materials engineering. A trained network used in these applications can be viewed as a solver optimized for a specific domain, limiting its applicability to others. The „holy grail“ of artificial intelligence is to design a general-purpose problem solver capable of addressing arbitrary domains. Recently, large language models have demonstrated impressive general-purpose problem-solving abilities by imitating language behavior, which can be seen as a product of what is arguably the most general-purpose problem solver we know of. However, the mechanisms underlying these models remain poorly understood. This workshop focuses on research directions that contribute to understanding the general-purpose problem-solving abilities of neural networks.

Workshop program and more info