A rebirth of interest in the structure of behavior and its underlying substrates is coming to play by combining the growing focus on behavior and the widespread interest in what's going on in AI for a new chapter in neuroscience research.
The Transformer architecture has been widely used in neural networks, despite not being thoroughly explained in its original paper. Researchers speculate the design decisions and consider it a general-purpose computer.
Despite significant advancements in neuroscience, computation, and AI, the understanding of consciousness and cognition still eludes us, leaving it uncertain if we will ever fully comprehend the mysteries of the mind.
Laypeople and journalists often misunderstand and sensationalize the capabilities of neural networks, leading to unfair criticisms of the researchers who work with them.
The speaker believes that in order to ensure that machines adopt and retain human goals, trust should be based on things that humans can understand and improve upon, preferably through the use of mathematical proofs, and discusses the challenges involved in transforming intelligent computations into ones that are equally intelligent. The talk touches on the current state of cybersecurity and its vulnerabilities.