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The speaker reflects on selecting talent based on intuition and personal experience, discussing how collaborations and partnerships shape the process.

The speaker shares experiences from the early days at the research unit, highlighting the passion and dedication of fellow students in computer science.

The speaker discusses the disappointment in learning about the brain in physiology and philosophy classes, leading to an interest in AI and neural networks.

The speaker mentions books by Donald Hebb and John Fon neyman that shaped his understanding of neural networks and brain computation.

Discussion on the brain's learning mechanisms, emphasizing the importance of modifying connections in neural networks for complex tasks.

Collaborations with Terry Sinowski and Peter Brown, focusing on simulating brain functions and speech recognition using neural networks.

The speaker recalls the first meeting with Ilia, emphasizing Ilia's curious and proactive nature in seeking collaboration.

Challenges faced and progress made in collaboration with Ilia, including discussions on model training, scaling, and creative insights.

A discussion on the evolution of neural nets, the impact of scale and data on advancements, and the role of new ideas in enhancing models.

Insights on predictive modeling, language models using embeddings, and the significance of predicting the next symbol for understanding.

Discussing the open question of whether the brain implements a form of back propagation for learning and the importance of gradients in learning processes.

Reflecting on being wrong about Boltzmann Machines but still finding value in pursuing them, and discussing the beauty of alternative theories for obtaining gradients in learning.

Contrast between research motivated by societal impact versus curiosity, with a realization of potential harm associated with advancements in technology.

Highlighting the potential of AI in healthcare to improve accessibility and quality of medical services, along with concerns about misuse of AI technology.

Addressing concerns about negative implications of AI technology and discussing the competitive dynamics between countries in AI development.

Exploring the efficiency and impact of AI assistants in research processes and decision-making.

Discussing the role of intuition in selecting talent, the diversity in student profiles, and the importance of different skill sets in research teams.

Exploring the development of intuition through critical thinking and the importance of having a strong worldview in research.

Emphasizing the significance of big models and multimodal data for learning algorithms while considering the variety of approaches in AI research.

Reflecting on the development of the learning algorithm for Boltzmann Machines as a proud achievement despite practical limitations.