Machine Learning Street Talk (MLST)

#57 - Prof. Melanie Mitchell - Why AI is harder than we think

Jul 25, 2021

In this engaging discussion, Professor Melanie Mitchell, a leading expert in complexity and AI, teams up with Letitia Parcalabescu, an AI researcher and YouTuber. They tackle the contrasting cycles of optimism and disappointment in AI development. Topics include the challenges of achieving common-sense reasoning and effective analogy-making in machine learning. They delve into the philosophical underpinnings of intelligence, the nuances of creativity in AI, and the limitations of current neural networks, all while advocating for a deeper understanding of both human and artificial cognition.

02:31:21

Creator website

Episode guests

Letitia Parcalabescu

Melanie Mitchell

Highlights

AI Chapters

Episode notes

The Challenge of Statistical Shortcuts and Generalization in Machine Learning

02:26

The Possibility of Aughts in Super Intelligence

01:21

Optimizing Energy Constraints and Information Efficiency in AI

01:36

The Importance of Dynamic Refocusing and Querying in Prompt Engineering

00:42

Intro

3min

The Evolution and Challenges of AI

10min

The Cognitive Highway: Understanding Analogy and Categorization

2min

The Artistry of AI: Creativity and Its Implications

17min

Navigating the Complexities of GPT-3

14min

The Complexity of Learning: AI vs. Human Cognition

20min

Cognition and the Evolving Landscape of AI

16min

Exploring AI Limitations and Possibilities

18min

Navigating the Intelligence Divide

12min

10.

Navigating AI Literature

5min

11.

The Evolving Landscape of Mentorship in Machine Learning

1min

12.

Rethinking AI: Illusions of Intelligence

9min

13.

The Role of Analogy and Mathematics in Understanding Cognition

2min

14.

Redefining Intelligence

6min

15.

Exploring the Nuances of Knowledge and Belief

2min

16.

Understanding Complexity in AI and Communication

6min

17.

Shortcut Learning in Evolution and AI

10min

Since its beginning in the 1950s, the field of artificial intelligence has vacillated between periods of optimistic predictions and massive investment and periods of disappointment, loss of confidence, and reduced funding. Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. Professor Melanie Mitchell thinks one reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself.

YT vid- https://www.youtube.com/watch?v=A8m1Oqz2HKc

Main show kick off [00:26:51]

Panel: Dr. Tim Scarfe, Dr. Keith Duggar, Letitia Parcalabescu (https://www.youtube.com/c/AICoffeeBreak/)

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