The Shifting LLM Landscape: Beyond GPT-4 and One-Size-Fit-All to Open Source and Task Specific LLMs with John Dickerson ODSC's Ai X Podcast

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In this episode, John Dickerson, co-founder and Chief Scientist of Arthur AI, joins us for a conversation about the shifting large language model landscape. He'll discuss emerging alternatives to established giants like OpenAI and Anthropic, and how these new players are impacting the market.


He'll also explore the rise of open-source initiatives and smaller, task-specific models, tackle the challenges and benefits of specialized LLMs versus general-purpose models, and discuss the key advantages of smaller, open-source models.


Finally he’ll dive into the detail of integrating new LLM models and the cost considerations associated with those models.


Topics:
1. The founding story of Arthur AI, a NYC based startup
2. The Shifting LLM Landscape and emerging alternatives to established models like GPT-4
3. How the rise of well funded LLMs startup and especially open-source LLMs are contributing to a more diverse LLM ecosystem
4. Smaller open source LLMs very larger established LLM models
5. LLm costs, latency,, and performance and how these factors influence the adoption and usage of these models?
6. How organizations effectively measuring and showcase the ROI for deploying LLMs
7. Examples of use cases for open source LLMs such as text to SQL
8. How competition drives innovation in the LLM space.
9. Open source vs closed source LLMs and the AI alignment debate around open source LLMs
10. One-Size-Fits-All versus specialized LLMs versus waiting on newer improved general-purpose models
11. Key advantages of using smaller, open-source LLM models in specific scenarios compared to their larger counterparts
12. Mixture of Experts Architecture
13. Model Integration challenges when Integrating new AI models
14. Pros and cons of fine-tuning task-specific models
15. Criteria where more expensive established LLM solutions might be justified.
16. How to balance the trade-offs between model latency and accuracy when deploying LLMs on cloud versus edge devices
16. Investing in early stage startups
17. Advice for startups operating in the LLM landscape
18. Machine Learning in Adversarial Environments:
19. Discuss John’s upcoming talk at ODSC East 2024


Show Note Links
Learn more aboutJohn Dickerson at his website
Connect with him on X.
Learn more about Arthur AI research-driven approach and their publication library here.
Arthur blog https://www.arthur.ai/blog
Explore Arthur AI Bench, a tool for evaluating LLMs for production use cases, on GitHub.


This episode was sponsored by:
Ai+ Training https://aiplus.training/
Home to hundreds of hours of on-demand, self-paced AI training, ODSC interviews, free webinars, and certifications in in-demand skills like LLMs and Prompt Engineering

And created in partnership with ODSC https://odsc.com/
The Leading AI Training Conference, featuring expert-led, hands-on workshops, training sessions, and talks on cutting-edge AI topics and

Never miss an episode, subscribe now!

In this episode, John Dickerson, co-founder and Chief Scientist of Arthur AI, joins us for a conversation about the shifting large language model landscape. He'll discuss emerging alternatives to established giants like OpenAI and Anthropic, and how these new players are impacting the market.


He'll also explore the rise of open-source initiatives and smaller, task-specific models, tackle the challenges and benefits of specialized LLMs versus general-purpose models, and discuss the key advantages of smaller, open-source models.


Finally he’ll dive into the detail of integrating new LLM models and the cost considerations associated with those models.


Topics:
1. The founding story of Arthur AI, a NYC based startup
2. The Shifting LLM Landscape and emerging alternatives to established models like GPT-4
3. How the rise of well funded LLMs startup and especially open-source LLMs are contributing to a more diverse LLM ecosystem
4. Smaller open source LLMs very larger established LLM models
5. LLm costs, latency,, and performance and how these factors influence the adoption and usage of these models?
6. How organizations effectively measuring and showcase the ROI for deploying LLMs
7. Examples of use cases for open source LLMs such as text to SQL
8. How competition drives innovation in the LLM space.
9. Open source vs closed source LLMs and the AI alignment debate around open source LLMs
10. One-Size-Fits-All versus specialized LLMs versus waiting on newer improved general-purpose models
11. Key advantages of using smaller, open-source LLM models in specific scenarios compared to their larger counterparts
12. Mixture of Experts Architecture
13. Model Integration challenges when Integrating new AI models
14. Pros and cons of fine-tuning task-specific models
15. Criteria where more expensive established LLM solutions might be justified.
16. How to balance the trade-offs between model latency and accuracy when deploying LLMs on cloud versus edge devices
16. Investing in early stage startups
17. Advice for startups operating in the LLM landscape
18. Machine Learning in Adversarial Environments:
19. Discuss John’s upcoming talk at ODSC East 2024


Show Note Links
Learn more aboutJohn Dickerson at his website
Connect with him on X.
Learn more about Arthur AI research-driven approach and their publication library here.
Arthur blog https://www.arthur.ai/blog
Explore Arthur AI Bench, a tool for evaluating LLMs for production use cases, on GitHub.


This episode was sponsored by:
Ai+ Training https://aiplus.training/
Home to hundreds of hours of on-demand, self-paced AI training, ODSC interviews, free webinars, and certifications in in-demand skills like LLMs and Prompt Engineering

And created in partnership with ODSC https://odsc.com/
The Leading AI Training Conference, featuring expert-led, hands-on workshops, training sessions, and talks on cutting-edge AI topics and

Never miss an episode, subscribe now!

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