The concept of generalization to new tasks in AI requires well-defined benchmarks, but as researchers get closer to limited domains, there's potential for progress. The next step is to go beyond traditional meta-learning approaches and understand intelligence.
The challenge of AI lies in transfer learning, generally between learned models. Through meta-learning concepts, this issue is addressed, and big models trained on multiple objectives could get extended to real scenarios.
This podcast explores the limits of deep learning in the development of Artificial Intelligence. The speaker suggests that building a design specification for what we want the system to do and doing hard engineering work are key to encoding complex knowledge into the system, where humans guide the learning procedures and the framework of how the machine learns.
The brain is thought to be a meta learning system which discovers algorithms to solve complex problems. Reinforcement learning agents play a key role in this system.
The capabilities of AI text summarization are limited in generating coherent summaries for longer pieces of text and can be prone to going "off the rails".