What's AI Podcast by Louis-François Bouchard – Details, episodes & analysis

Deploy Your AI Agents 8x faster with LangWatch. Get a demo: https://langwatch.ai/?utm_source=louis-yt

► Master the most in-demand skill for building AI-powered solutions—from scratch: https://academy.towardsai.net/courses/python-for-genai?ref=1f9b29

► Master LLMs and Get Industry-ready Now: https://academy.towardsai.net/?ref=1f9b29

►Twitter: https://twitter.com/Whats_AI

►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/

►Join Our AI Discord: https://discord.gg/learnaitogether

Have you ever wanted to take a language model and make it answer the way you want without needing a mountain of data?

Well, OpenAI’s got something for us: Reinforcement Fine-Tuning, or RFT, and it changes how we customize AI models. Instead of retraining it with feeding examples of what we want and hoping it learns in the classical way, we actually teach it by rewarding correct answers and penalizing wrong ones, just like training a dog — but, you know, with fewer treats and more math.

Let’s break down reinforcement fine-tuning compared to supervised fine-tuning!

Both essentially have their use that we can discuss in one line:

1. Supervised fine-tuning teaches new things the model does not know yet, like a new language, which is powerful for small and less “intelligent” models.

2. While reinforcement fine-tuning orients the current model to what we really want it to say. It basically “aligns” the model to our needs, but we need an already powerful model. This is why reasoning models are a perfect fit.

I’ve already covered fine-tuning on the channel if you are interested in that. Today, let’s get into how RFT actually works!

In this one, I discuss the dilemma between using retrieval-based generation and the newer "long context models".

Long context models, like the Gemini suite of models, allow us to send up to millions of tokens (thousands of text pages), whereas retrieval (RAG)-based systems allow us to search through as much (if not more) content and retrieve only the necessary bits to send the LLM for improved answers.

Both have advantages and disadvantages. This short episode will help you better understand when to use each.

Build Your First Scalable Product with LLMs: https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=1f9b29

Master LLMs and Get Industry-ready Now: https://academy.towardsai.net/?ref=1f9b29

Our ebook: https://academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29

► Get your copy of "Building LLMs for Production": https://amzn.to/4bqYU9b

►The e-book version: https://academy.towardsai.net/courses/buildingllmsforproduction?ref=1f9b29

► Our new course "From Beginners to Advanced LLM Developer": https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=1f9b29

►Full article and references: https://www.louisbouchard.ai/openai-o1/

►Twitter: https://twitter.com/Whats_AI

►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/

►Join Our AI Discord: https://discord.gg/learnaitogether

Extra Ressources:

OpenAI release blog: https://openai.com/index/introducing-openai-o1-preview/ 

OpenAI release blog 2: https://openai.com/index/learning-to-reason-with-llms/ 

OpenAI system card: https://openai.com/index/openai-o1-system-card/ 

Nathan Lambert’s great article on it: https://www.interconnects.ai/p/openai-strawberry-and-inference-scaling-laws 

David Shapiro fun livestream testing it: https://youtu.be/AO7mXa8BUWk

How to start in AI/ML - A Complete Guide: ►https://www.louisbouchard.ai/learnai/ #gpt4o #o1 #openai

In this episode, Luis Serrano and I dive into the transformative impact of AI on education, forecasting a radical shift in how future generations learn and think.

► Luis' website: https://serrano.academy/

►Twitter: https://twitter.com/Whats_AI

►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/

►Support me on Patreon: https://www.patreon.com/whatsai

►Join Our AI Discord: https://discord.gg/learnaitogether How to start in AI/ML - A Complete Guide: ►https://www.louisbouchard.ai/learnai/

Chapters:

00:00 Coming up in the conversation

00:01:50 Sharing journey: Why Luis became an educator

00:06:03 Can someone develop skills to become a better educator, and what are they?

00:08:07 Deciding the depth of explanation

00:10:57 AI’s impact on education

00:22:35 How does an explanation without graphic aid look?

00:27:15 Luis is explaining embedding in an intuitive way?

00:31:05 Is AI hard to explain because of newness or complexity?

00:34:01 Necessity of understanding the basics of AI

00:36:57 Why do people not want to learn about how AI works?

00:39:15 Importance of good story telling and explanation

00:42:01 Strategy to explain tough topics

00:48:12 Strategy to introduce complex words in explanation

00:55:14 Evolution in AI Education Approaches 

01:02:03 Is it possible to bring good value through shorts or reels?

01:04:46 Rise of Podcast and reels

Register to GTC (attend in person, or free online): https://nvda.ws/3XQRtkl

Interested in end-to-end PM job hunting and up-skilling program by Dr. Nancy Li’s PM Accelerator? Register this free masterclass about product portfolio and stay until the end to learn more about the program (Use the code LOUIS500 for 500$ off on her program!): https://www.drnancyli.com/a/2147615411/2HzsofFw

Introducing Dr. Nancy Li, a versatile entrepreneur, Director of Products, YouTuber, and a Forbes-featured professional with 8 years of experience in driving cutting-edge technology products. Dr. Li currently serves as the CEO of PM Accelerator, the fastest-growing Product Management Professional Development Company in the industry, known for its engaging alumni network, and top-rated program, and she has a remarkable record of helping over 1000 aspiring product managers secure high-paying roles at tech giants and unicorn startups. Her journey, from being the youngest engineering Ph.D. to Director of Product in just four years, is a testament to her extraordinary career.

Having personally launched award-winning AI products and mentored many into high-paying AI PM roles, Dr. Nancy offers a rare blend of expertise and experience. From her day-to-day interactions with AI engineers to the challenges of training AI models, she provides a comprehensive look into the dynamic world of AI product management.

References we discussed in the episode:

PM Accelerator by Dr. Nancy Li: https://www.drnancyli.com

The ONLY 4 Ways to Become an AI Product Manager with No Experience: https://youtu.be/aQTuPUIkrxk?si=JJMih2qzC6iP2a8_

A Day in The Life of An AI Product Manager: https://youtu.be/waVyVcUzfeg?si=YOqUao6HCSHQ9MWG

►Twitter: https://twitter.com/Whats_AI

►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/

►Support me on Patreon: https://www.patreon.com/whatsai

►Join Our AI Discord: https://discord.gg/learnaitogether

00:00:00 Coming up in the conversation

00:02:46 Nancy introduces herself

00:04:02 The reason Nancy couldn't drop her PhD

00:07:35 These are the people PhD is for

00:09:40 Secret revealed: How Nancy completed her PhD in 3.5 years!

00:14:07 Tips that helped Nancy peer with people from MIT

00:23:25 Are companies still prioritizing titles over practical skills?

00:26:21 Have PM skill requirements changed in recent years?

00:29:20 Crazy story: This is why she will never go to university to teach!

00:35:53 Online education vs offline education

00:41:29 Shifting from Material to AI: How she Landed a Job!

00:44:32 Staying up-to-date with technology and deciding when to implement which

00:46:41 Secret recipe to make successful AI products

00:51:19 Day to day life of a PM

00:55:28 Louis shares about his start-up Towards AI

00:58:21 Nancy shares information about her PM accelerator program

In this episode, I talk with Avery Smith, a data analytics expert and educator who gives practical strategies for breaking into the data analytics field, leveraging AI for learning and career development. Avery shares his journey into data and teaching, and insights on helping others transition into data careers through his Data Analytics Accelerator program, emphasizing the importance of practical projects and how he leverages AI in enhancing learning and job preparation processes (and he shares tips to help you do that too!).

References:

►Avery Smith: https://www.linkedin.com/in/averyjsmith/

►Data Career Jumpstart: https://www.datacareerjumpstart.com/

►Podcast: https://podcasters.spotify.com/pod/show/datacareerpodcast

►AveryGPT: https://www.datacareerjumpstart.com/averygpt

►AI Interview Simulator: https://www.datacareerjumpstart.com/interviewsimulator

►Twitter: https://twitter.com/Whats_AI

►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/

►Support me on Patreon: https://www.patreon.com/whatsai

►Join Our AI Discord: https://discord.gg/learnaitogether

How to start in AI/ML - A Complete Guide: ►https://www.louisbouchard.ai/learnai/

Timestamps:

00:00 Coming up in the conversation

01:45 Avery shares about his background

03:00 Making people land data job in 90 days!

07:02 Theory vs Practical knowledge

08:34 Importance of Explainability in Models

10:28 The Future of Traditional and Online Education

12:00 Networking while studying remotely

14:09 Maintaining consistency in value in LinkedIn posts.

16:20 Is greater studies still relevant in the era of ChatGPT?

17:45 Becoming freelancing ready in data analytics

20:53 Keeping course content up to date 

23:56 This is how Avery utilizes AI

29:16 Discussion on AI Avatars

38:01 Does Avery provide lessons on how to better use ChatGPT?

40:08 Avery shares his learning resources

43:12 Book recommendations

44:52 Is the field of data field too saturated to join right now?

46:58 Discussion on the current reality of freelancing

In this episode I had the opportunity to talk with Tina Huang, founder of the Lonely Octopus platform, a highly successful YouTube channel and experienced freelancer in the AI space. Tina shares her invaluable insights on leveraging AI in education, the nuances of freelancing in the tech industry, and strategies for enhancing personal productivity. The episode is for anyone looking to navigate the landscape of technology (especially AI), offering practical tips to work in the field or just leverage AI better. ►Check out Tina's channel  @TinaHuang1 

►Lonely Octopus: https://www.lonelyoctopus.com/

►Twitter: https://twitter.com/Whats_AI

►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/

►Support me on Patreon: https://www.patreon.com/whatsai

►Join Our AI Discord: https://discord.gg/learnaitogether

Timestamps:

00:00 Coming up in the conversation

02:00 How did Tina get into AI and YouTube?

03:17 Tina's goal and mission

04:09 Tina’s niche

06:40 Higher education in the AI and data science space

10:36 Tips for beginners to become freelancing-ready

17:24 What will be more important in the future, LLMs or coding languages?

22:30 Tips for those who want to change field while balancing their current job

25:16 Using YouTube to force ownself to learn

27:17 How to make commitments and what kind of commitments should you have?

33:05 Louis shares about the AI market he believes has the most potential

37:44 Tina discussed where she wants to contribute more

39:09 Tine shares the benefits that her YouTube venture has brought

40:40 How can one use content to create leverage in freelancing?

43:05 Is audience conversion from shorts to long-form content really an issue?

46:46 Freelancing vs corporate employment vs entrepreneurship

50:33 What skills should one develop to secure freelance opportunities in the field of AI?

54:00 Tina shares about her upcoming plans

In this episode, I received Mariam Brian, CEO of Holo Art, to talk about the transformative role of AI in the art world. She discusses how artificial intelligence is reshaping artistic creation and expression and addresses the ethical implications of this technological evolution. This conversation, accessible to anyone, offers a fantastic perspective on the intersection of art and AI, highlighting the potential for a new era of creativity and collaboration between humans and machines!

►Mariam's LinkedIn: https://www.linkedin.com/in/mariamhashemi/►Holo Art: https://holo-art.io/about-us ► Holo Art announcement: https://medium.com/@mariambrian/patented-ai-process-for-executives-organizations-looking-to-level-up-e465c1c35a07 ►Twitter: https://twitter.com/Whats_AI ►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/ ►Support me on Patreon: https://www.patreon.com/whatsai ►Join Our AI Discord: https://discord.gg/learnaitogether Timestamps:

00:00:00 Coming up in the conversation

00:01:32 Mariam shares about his background

00:02:15 The Intersection of AI and Philosophy

00:05:39 The Impact of AI on Art and Artists

00:08:36 The Future of AI and Art

00:09:13 The Role of AI in Business and Ethics

00:10:55 AI might the Pandora box of lot of problems!

00:14:39 Simultaneous rise of Podcast & Shorts and their impact on the lives of billions

00:23:42 The Creativity of AI and its Impact on Artists

00:28:53 Can AI generated art hurt creativity of artist?

00:33:22 To be an artist, ethics becomes a way of life

00:35:45 Mariam's Personal Use of AI in Art

00:40:30 AI's Potential in Human-Machine Co-Creation

00:41:27 Understanding Ourselves and AI's Perception of Us

00:46:32 W.I.E.R.D Science

00:50:02 While using AI model do you try to control it or let it surprise you?

00:54:38 Public Perception of AI-Generated Art

01:01:44 The Risks and Opportunities for Artists Using AI

01:10:53 Mariam's message for listeners

A new episode with Jerome Pasquero, a Machine Learning Director at Sama, a leading company for data annotation solutions, where we dive into the role of data in AI's evolution. We explore the nuances of data annotation, the ethical implications of data in AI, and how data is shaping the future of technology. Don't miss Jerome Pasquero's insights on the intersection of data and AI!

►Jerome Pasquero: https://www.linkedin.com/in/jeromepasquero/

►Twitter: https://twitter.com/Whats_AI

►My Newsletter (My AI updates and news clearly explained): https://louisbouchard.substack.com/

►Support me on Patreon: https://www.patreon.com/whatsai

►Join Our AI Discord: https://discord.gg/learnaitogether

Timestamps:

00:00:00 Coming up in the conversation

00:01:34 Jerome shares about his background

00:04:07 How did Jerome get into the data field?

00:05:23 AI back in the days of 2000s

00:07:20 Back then, what piqued Jerome's interest the most in AI?

00:08:40 Using AI to try to mimic human comprehension

00:12:47 Present challenges and the prospective outlook of computer vision

00:14:54 Using Humans vs. ML Models to Annotate Data

00:17:46 Jerome's perspective on Constitutional AI or RLAIF

00:24:52 Impact of LLM and AI on the Job market

00:26:27 Is the AI revolution bigger than previous tech revolutions?

00:28:35 Will there be something more interesting than AGI?

00:31:15 Dealing with complex annotation tasks and different perspectives

00:33:33 Dealing with biases

00:36:18 Using a single annotator vs. multiple annotators on the same data

00:37:49 Synthetically generated data

00:40:47 Scaling quality assurance for large datasets

00:42:46 When is machine learning better at annotation than human annotators?

00:45:34 Reduction of Humans-in-the-loop due to the constant evolution of AI

00:46:42 Data Requirements for Training Autonomous Vehicles

00:51:43 Sensors for transferring human driving skills to Autonomous cars

00:53:20 Why don’t we build only autonomous subway system?

00:55:26 Use of AI in the vision industry and example of vision technology used in our daily life

01:00:17 The potential of haptics and its link with AI