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20VC: Does Value Accrue to Incumbents or Startups in the AI Race, Why Model Size Matters More Than Data Size, Why Artificial General Intelligence is Far Away, Why Carpenters Will Be Paid More Than Software Engineers & Future of Jobs with Richard Socher

Aug 18, 2023

Richard Socher, Founder and CEO of You.com, discusses his journey into the AI field, the importance of model size, and the value accrual between startups and incumbents in the AI race.

45:56

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Richard Socher

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Quick takeaways

The more data there is about a job, the more likely it is to be automated, creating bottlenecks in physical tasks with limited data collection.

AI's progress may not continue exponentially due to misconceptions about its capabilities and the need for further research breakthroughs to develop artificial general intelligence (AGI).

Deep dives

The Importance of Data in Job Automation

The more data there is about a job, the more likely it is to be automated. However, there are still many jobs where data collection is lacking, which results in physical tasks becoming more expensive and creating bottlenecks.

Barriers to Achieving AGI and the Importance of AI Community Collaboration

02:42

The Importance of Accessing Private Databases for AI Training

01:37

The Importance of Data Tuning and Model Incorporation in Natural Language Processing

01:12

Introduction

4min

The Shift to a Single Model in Natural Language Processing

12min

Understanding the Complexity of Hallucinations as a Feature in AI Models

2min

Model Longevity and Transition

7min

The Impact of NLP Technology on Search Engines

14min

Barriers to Achieving Artificial General Intelligence

2min

The Importance of AI Founders in Silicon Valley and the Future of AI

5min

Richard Socher is the founder and CEO of You.com. Richard previously served as the Chief Scientist and EVP at Salesforce. Before that, Richard was the CEO/CTO of AI startup MetaMind, acquired by Salesforce in 2016. He is widely recognized as having brought neural networks into the field of natural language processing, inventing the most widely used word vectors, contextual vectors and prompt engineering. He has over 150,000 citations and served as an adjunct professor in the computer science department at Stanford.

In Today's Episode with Richard Socher We Discuss:

1. The Decade-Long Journey to Becoming an AI OG:

How did Richard first make his way into the world of AI over a decade ago?
What are 1-2 of his biggest lessons from working with Marc Benioff?
How did 5 years at Salesforce impact how he both thinks and operates?

2. Models: Does Size Matter:

How important is model size? Is data size more important?
What are the biggest misconceptions people have around models today?
How does Richard respond to the suggestion that "many startups are wrappers around LLMs"?
Are hallucinations a feature or a bug?

3. Where Does Value Accrue:

Where does Richard believe most of the value will accrue; startup or incumbent?
Which incumbents are best positioned to win? Which are the laggards and behind?
What do many not see about the startup vs incumbent race in the AI war?

4. Open vs Closed: Which Wins:

Does Richard favour Yann LeCun's open approach? Or is the world of AI more closed?
What are the biggest challenges of an open ecosystem?
What are the nuances that make both challenging?

5. Richard Socher: AMA:

Why will carpenters be paid more than software engineers in 10 years?
Why is AGI still way off? Are people too unrealistic?
How much money does Google make off search every day? Why does that leave them vulnerable?

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