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Bold AI Predictions From Cohere Co-founder

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Manage episode 444524539 series 2803422
Content provided by Machine Learning Street Talk (MLST). All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Machine Learning Street Talk (MLST) or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

Ivan Zhang, co-founder of Cohere, discusses the company's enterprise-focused AI solutions. He explains Cohere's early emphasis on embedding technology and training models for secure environments.

Zhang highlights their implementation of Retrieval-Augmented Generation in healthcare, significantly reducing doctor preparation time. He explores the shift from monolithic AI models to heterogeneous systems and the importance of improving various AI system components. Zhang shares insights on using synthetic data to teach models reasoning, the democratization of software development through AI, and how his gaming skills transfer to running an AI company.

He advises young developers to fully embrace AI technologies and offers perspectives on AI reliability, potential risks, and future model architectures.

https://cohere.com/

https://ivanzhang.ca/

https://x.com/1vnzh

TOC:

00:00:00 Intro

00:03:20 AI & Language Model Evolution

00:06:09 Future AI Apps & Development

00:09:29 Impact on Software Dev Practices

00:13:03 Philosophical & Societal Implications

00:16:30 Compute Efficiency & RAG

00:20:39 Adoption Challenges & Solutions

00:22:30 GPU Optimization & Kubernetes Limits

00:24:16 Cohere's Implementation Approach

00:28:13 Gaming's Professional Influence

00:34:45 Transformer Optimizations

00:36:45 Future Models & System-Level Focus

00:39:20 Inference-Time Computation & Reasoning

00:42:05 Capturing Human Thought in AI

00:43:15 Research, Hiring & Developer Advice

REFS:

00:02:31 Cohere, https://cohere.com/

00:02:40 The Transformer architecture, https://arxiv.org/abs/1706.03762

00:03:22 The Innovator's Dilemma, https://www.amazon.com/Innovators-Dilemma-Technologies-Management-Innovation/dp/1633691780

00:09:15 The actor model, https://en.wikipedia.org/wiki/Actor_model

00:14:35 John Searle's Chinese Room Argument, https://plato.stanford.edu/entries/chinese-room/

00:18:00 Retrieval-Augmented Generation, https://arxiv.org/abs/2005.11401

00:18:40 Retrieval-Augmented Generation, https://docs.cohere.com/v2/docs/retrieval-augmented-generation-rag

00:35:39 Let’s Verify Step by Step, https://arxiv.org/pdf/2305.20050

00:39:20 Adaptive Inference-Time Compute, https://arxiv.org/abs/2410.02725

00:43:20 Ryan Greenblatt ARC entry, https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt

Disclaimer: This show is part of our Cohere partnership series

  continue reading

218 episodes

iconShare
 
Manage episode 444524539 series 2803422
Content provided by Machine Learning Street Talk (MLST). All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Machine Learning Street Talk (MLST) or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

Ivan Zhang, co-founder of Cohere, discusses the company's enterprise-focused AI solutions. He explains Cohere's early emphasis on embedding technology and training models for secure environments.

Zhang highlights their implementation of Retrieval-Augmented Generation in healthcare, significantly reducing doctor preparation time. He explores the shift from monolithic AI models to heterogeneous systems and the importance of improving various AI system components. Zhang shares insights on using synthetic data to teach models reasoning, the democratization of software development through AI, and how his gaming skills transfer to running an AI company.

He advises young developers to fully embrace AI technologies and offers perspectives on AI reliability, potential risks, and future model architectures.

https://cohere.com/

https://ivanzhang.ca/

https://x.com/1vnzh

TOC:

00:00:00 Intro

00:03:20 AI & Language Model Evolution

00:06:09 Future AI Apps & Development

00:09:29 Impact on Software Dev Practices

00:13:03 Philosophical & Societal Implications

00:16:30 Compute Efficiency & RAG

00:20:39 Adoption Challenges & Solutions

00:22:30 GPU Optimization & Kubernetes Limits

00:24:16 Cohere's Implementation Approach

00:28:13 Gaming's Professional Influence

00:34:45 Transformer Optimizations

00:36:45 Future Models & System-Level Focus

00:39:20 Inference-Time Computation & Reasoning

00:42:05 Capturing Human Thought in AI

00:43:15 Research, Hiring & Developer Advice

REFS:

00:02:31 Cohere, https://cohere.com/

00:02:40 The Transformer architecture, https://arxiv.org/abs/1706.03762

00:03:22 The Innovator's Dilemma, https://www.amazon.com/Innovators-Dilemma-Technologies-Management-Innovation/dp/1633691780

00:09:15 The actor model, https://en.wikipedia.org/wiki/Actor_model

00:14:35 John Searle's Chinese Room Argument, https://plato.stanford.edu/entries/chinese-room/

00:18:00 Retrieval-Augmented Generation, https://arxiv.org/abs/2005.11401

00:18:40 Retrieval-Augmented Generation, https://docs.cohere.com/v2/docs/retrieval-augmented-generation-rag

00:35:39 Let’s Verify Step by Step, https://arxiv.org/pdf/2305.20050

00:39:20 Adaptive Inference-Time Compute, https://arxiv.org/abs/2410.02725

00:43:20 Ryan Greenblatt ARC entry, https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt

Disclaimer: This show is part of our Cohere partnership series

  continue reading

218 episodes

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