LLMs and Agents Are Overhyped, with Dr. Andriy Burkov

Andriy Burkov's ML books are mega-bestsellers and his newsletter has a wild 900,000 subscribers. He seldom does interviews so don't miss today's episode, in which he takes compelling, contrarian views on LLMs and agents.

More on Dr. Burkov:

• His indispensable "100-Page Machine Learning Book" seems to be on *every* data scientist / ML engineer's bookshelf.

• He also wrote "ML Engineering" and his latest book, "The 100-Page Language Model Book", was released this year to rave reviews.

• His "Artificial Intelligence" newsletter is subscribed to by 900,000 people on LinkedIn.

• He's the Machine Learning Lead at TalentNeuron, a global labor-market analytics provider.

• He runs his own book-publishing company, True Positive Inc.

• Previously held data science / ML roles at Gartner, Fujitsu and more.

• Holds a PhD in Computer Science (A.I.) from Université Laval in Quebec, where his doctoral dissertation focused on multi-agent decision-making — 15 years ago!

Despite Dr. Burkov being such a technical individual, most of today’s episode should appeal to anyone interested in A.I. (although some parts here and there will be particularly appealing to hands-on machine-learning practitioners).

In today’s episode, Andriy details:

• Why he believes AI agents are destined to fail.

• How he managed to create a chatbot that never hallucinates — by deliberately avoiding LLMs.

• Why he thinks DeepSeek AI crushed Bay Area A.I. leaders like OpenAI and Anthropic.

• What makes human intelligence unique from all other animals and why A.I. researchers need to crack this in order to attain human-level intelligence in machines.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

BAML: The Programming Language for AI, with Vaibhav Gupta

Today's guest, Vaibhav Gupta, has developed BAML, the programming language for AI. If you are calling LLMs, you've gotta check BAML out for instant accuracy improvements and big (20-30%) cost savings.

More on charming and terrifically brilliant Vaibhav:

• Founder & CEO of Boundary (YC W23), a Y Combinator-backed startup that has developed a new programming language (BAML) that makes working with LLMs easier and more efficient for developers.

• Across his decade of experience as a software engineer, he built predictive pipelines and real-time computer vision solutions at Google, Microsoft and the renowned hedge fund The D. E. Shaw Group.

• Holds a degree in Computer Science and Electrical Engineering from The University of Texas at Austin.

This is a relatively technical episode. The majority of it will appeal to folks who interact with LLMs or other model APIs hands-on with code.

In today’s information-dense episode, Vaibhav details:

• How his company pivoted 13 times before settling upon developing a programming language for A.I.

• Why creating a programming language was "really dumb" but why it’s turning out to be brilliant, including by BAML already saving companies 20-30% on their AI costs.

• Fascinating parallels between today's A.I. tools and the early days of web development.

• His unconventional hiring process (I’ve never heard of anything remotely close to it) and the psychology behind why it works.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Technical Intro to Transformers and LLMs, with Kirill Eremenko

For today's episode, the indefatigable SuperDataScience Founder Kirill Eremenko gives a detailed technical intro to Transformers and how they're scaled up to allow Large-Language Models like GPT-4, Llama 2 and Gemini to have their mind-blowing abilities.

If you don’t already know him, Kirill:

• Is Founder and CEO of SuperDataScience, an e-learning platform that is the namesake of this podcast.

• Founded the SuperDataScience Podcast in 2016 and hosted the show until he passed me the reins three years ago.

• Has reached more than 2.6 million students through the courses he’s published on Udemy, making him Udemy’s most popular data science instructor.

Today’s episode is perhaps the most technical episode of this podcast ever so it will probably appeal mostly to hands-on practitioners like data scientists and ML engineers, particularly those who already have some understanding of deep learning.

In this episode, Kirill details:

• The history of the Attention mechanism in natural-language models.

• How compute-efficient Attention is enabled by the Transformer, a transformative deep-neural-network architecture.

• How Transformers work, across each of five distinct data-processing stages.

• How Transformers are scaled up to power the mind-blowing capabilities of LLMs such as modern Generative A.I. models.

• Why knowing all of this is so helpful — and lucrative — in a data science career.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.