How does Meta AI's natural language model, LLaMa compare to the rest? Based on the Chinchilla scaling laws, LLaMa is designed to be smaller but more performant. But how exactly does it achieve this feat? It's all done by training a small model for a longer period of time. Discover how LLaMa compares to its competition, including GPT-3, in this week's episode. 

Additional materials: www.superdatascience.com/670

Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

670: LLaMA: GPT-3 performance, 10x smaller

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

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Tech News

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Machine Learning for Wind Energy is front and center in this episode as Jon Krohn is joined by Dr. Jason Yosinski, CEO of Windscape AI. Dr. Yosinski brings to light the latest ML advancements sparking significant changes in renewable energy. Tune in for a comprehensive review of these cutting-edge technologies and their expansive impact on the industry and the environment's well-being.

This episode is brought to you by Crawlbase (https://crawlbase.com), the ultimate data crawling platform. Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

In this episode you will learn:
• Enhancing predictability in wind energy with ML [04:52]
• Data utilization from wind turbines by energy providers [11:41]
• Jason's journey into wind energy [17:55]
• Landing the right startup idea [22:47]
• Visualizing neural networks with the Deep Vis Toolbox [31:29]
• Extreme event forecasting at Uber vs. nowcasting at Windscape AI [45:13]
• Discoveries from Loss Change Allocation research [47:48]
• Engaging with Jason's ML Collective [59:46]
• Traits of successful AI entrepreneurs [1:10:26]

Additional materials: www.superdatascience.com/789

789: ML for Wind-Powered Energy Generation, with Dr. Jason Yosinski

Multi-agent systems could mark a significant turning point in generative AI. From mastering increasingly complex tasks to getting LLMs to collaborate, in this Five-Minute Friday, Jon Krohn discusses the systems that are working to bridge the remaining gaps left by the latest large language models (LLMs).

Additional materials: www.superdatascience.com/788

Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

788: Multi-Agent Systems: How Teams of LLMs Excel at Complex Tasks

MLOps, how to build an online community, and tools for scaling LLMs: In this episode, Demetrios Brinkmann speaks to Jon Krohn about the similarities and differences between LLMOps, MLOps and DevOps, and why this should matter to companies looking to hire such engineers. You will also hear how to get involved in the MLOps community wherever you are in the world, and how you can start developing great products with the available tools.

This episode is brought to you by AWS Inferentia (https://go.aws/3zWS0au) and AWS Trainium (https://go.aws/3ycV6K0). Interested in sponsoring a SuperDataScience Podcast episode? Visit https://passionfroot.me/superdatascience for sponsorship information.

In this episode you will learn:
• What MLOps is [03:51]
• About LLMOps [12:06]
• About LlamaIndex and Ollama [18:29]
• Insights from Demetrios’ MLOps survey [20:49]
• Guidance for using third-party APIs [40:18]
• Recommendations for building an online community in tech and AI [47:07]

Additional materials: www.superdatascience.com/787

787: MLOps: The Job and The Key Tools, with Demetrios Brinkmann

Learn about the six keys to data science success as host Jon Krohn welcomes back Kirill Eremenko, the mastermind behind SuperDataScience. Kirill shares his top insights on data science careers, from building strong portfolios to leveraging mentors and hands-on labs. With over 2.7 million students, his advice is a must-hear for aspiring and experienced data scientists alike.

Additional materials: www.superdatascience.com/786

Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

786: The Six Keys to Data Scientists' Success, with Kirill Eremenko

Dr. Luis Serrano from the Serrano Academy reveals how to make Math and Quantum ML accessible, tackles the challenges of teaching A.I. to beginners, and explores the power of embeddings in enterprise applications. Explore the future of Quantum Machine Learning and the latest trends in AI, including multimodality and autonomous systems.

This episode is brought to you by AWS Inferentia (https://go.aws/3zWS0au) and AWS Trainium (https://go.aws/3ycV6K0). Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

In this episode you will learn:
• How math and AI can be made easy to understand [05:21]
• The three major categories of learners [16:21]
• Why embeddings are the most important component of LLMs [26:19]
• How semantic search differs from a traditional keyword search [29:57]
• The most exciting emerging application areas for AI [42:41]
• The promising application areas for Quantum Machine Learning [49:18]

Additional materials: www.superdatascience.com/785

670: LLaMA: GPT-3 performance, 10x smaller

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