AI Evangelist Alex Volkov shares his journey from running AI models to founding an AI startup. Explore topics like multimodal transformer architecture for lucid dreaming, challenges in MLOps with video data, and evaluating AI models with 'vibe checks'. Learn about AI agents, automation in content creation, and fostering community engagement in AI development.
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Quick takeaways
2024 will be marked by the rise of multimodality in AI, with models like GPT-4 advancing visual language understanding beyond text and images.
Transitioning from product development to AI evangelism emphasizes the power of public discourse in building thriving AI communities.
Addressing challenges in mitigating hallucinations in ML models stresses the importance of thorough testing and continuous improvement for enhanced reliability.
Deep dives
Multimodality in AI: The Year of Multimodality - 2024 Trends
2024 is shaping up to be remembered as the year of multimodality in AI. With the rise of models like GPT-4 introducing visual language understanding and advancements in multimodal architectures, the concept is evolving beyond just text and images. From EEG and fMRI inputs for lucid dreaming control to models recognizing motion units in videos, the breadth of multimodality is expanding. Understanding different modalities like video, audio, and even brain signals presents challenges in evaluation, scalability, and generalization, but promises significant advancements in AI capabilities.
The Shift from Product Development to AI Evangelism and Community Building
The podcast highlights the host's transition from product development to AI evangelism and community building. As the excitement for sharing insights and discussions around AI topics outweighed the interest in building product features, hosting a weekly AI-focused show on Twitter Spaces evolved into a thriving community endeavor. This shift showcases the power of public discourse and knowledge sharing in fostering engagement and learning within the AI community.
Evaluating Open Source LLMs and the Growing Complexity of Multimodal Evaluation
The episode delves into the challenges of evaluating open-source large language models (LLMs) and the complexities arising in multimodal evaluation. While specific task-oriented evaluations are common, the need for more holistic and general evaluations emerges. With trends like evaluation vibes checks and multifaceted assessment criteria, the AI community navigates the evolving landscape of model training, system evaluation, and output validation, incorporating considerations such as truthfulness, hallucinations, cost efficiency, and adaptation to multimodal capabilities.
Challenges and Solutions in Mitigating Hallucinations in Machine Learning Models
One major focus of the podcast episode is the challenges faced in mitigating hallucinations in machine learning models. The discussion delves into various strategies and approaches explored to ensure that models do not hallucinate and provide accurate results. It emphasizes the importance of evaluating and verifying the effectiveness of these strategies, highlighting the need for thorough testing and continuous improvement to enhance model performance and reliability. Specific examples such as building a retrieval bot for GPT documents demonstrate the complexities involved in mitigating hallucinations and the significance of user feedback and continuous monitoring.
Advancements in Agent Frameworks and Considerations in Contextual Integrations
Another key topic covered in the podcast episode is the advancements in agent frameworks and considerations for contextual integrations. The conversation touches on the evolution of agents and the importance of context length expansion to enhance their capabilities. Evaluations of agents and the integration of multiple specialized agents to accomplish tasks efficiently are highlighted. The discussion also includes insights on the challenges and trade-offs associated with speeding up agent responses, optimizing user experience, and ensuring accurate task execution. The episode explores the emergence of new tools like DSPI and emphasizes the significance of continuous learning and adaptation to leverage evolving AI technologies effectively.
Alex Volkov serves as the AI Evangelist with
Weights & Biases, Host of ThursdAI, Founder and CEO Targum and AI Consultant GPU POOR Def. not an owl.
MLOps podcast #215 with Alex Volkov, AI Evangelist at Weights & Biases, Becoming an AI Evangelist.
// Abstract
Follow Alex's journey into the world of AI, from being interested in running his first AI models to founding an AI startup, running a successful weekly AI news podcast & newsletter, and landing a job with @WeightsBiases .
// Bio
Alex Volkov is an AI Evangelist at Weights & Biases, celebrated for his expertise in clarifying the complexities of AI and advocating for its beneficial uses. He is the founder and host of ThursdAI, a weekly newsletter and podcast that explores the latest in AI, its practical applications, open-source and innovation. With a solid foundation as an AI startup founder and 20 years in full-stack software engineering, Alex offers a deep well of experience and insight into AI innovation.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Evaluation Survey: https://hq.yougot.us/primary/WebInterview/3AW6LW5D/Start
Website: https://thursdai.news
Alex on X (+X spaces also are also there) - https://twitter.com/altryne/
Crew AI creator Joao Moura - https://sub.thursdai.news/p/jan14-sunday-special-deep-dives
The Future of Search in the Era of Large Language Models // Saahil Jain // MLOps Podcast #150: https://youtu.be/hMoMvK89iogDSPy: Transforming Language Model Calls into Smart Pipelines // Omar Khattab // MLOps Podcast #194: https://youtu.be/NoaDWKHdkHg
--------------- ✌️Connect With Us ✌️ -------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Alex on LinkedIn: https://www.linkedin.com/in/alex-volkov-/
Timestamps:
[00:00] Alex's preferred beverage
[00:17] Takeaways
[03:19] Take the Evaluation Survey!
[03:55] Alex's journey for the past 15 years
[08:10] Career moves
[15:15] Building communities
[20:02] AI/MLOps Growth in COVID
[27:23] Recent developments and insights
[31:58 - 33:03] WandB Ad
[33:54] Multimodal RAG and Lucid Dreaming
[39:55] Evaluation Practices in MLOps
[43:27] Evaluating AI models effectively
[52:52] Embedding models and updates
[56:14] AI model trade-offs
[1:01:13] Optimizing LLM user experience
[1:03:56] Perceived performance optimization
[1:05:45] Agents' hype and reality
[1:11:31] Exploring DSPy for evaluation
[1:14:13] Wrap up
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