What's AI Podcast by Louis-François Bouchard

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 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

►Think Autonomous: https://www.thinkautonomous.ai/

►Jeremy’s linkedin: https://www.linkedin.com/in/jeremycohen2626/

►Newsletter: https://www.thinkautonomous.ai/private-emails-home/

►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/

Become a member of the YouTube community, support my work and get a cool Discord role : https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join

Chapters:

0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.

00:01:31 Jeremy shares about his background

00:03:55 The future of Self-driving cars is not that straightforward!

00:07:49 If there are numerous challenges, why are companies still developing autonomous cars?

00:08:46 The future might involve more self-driving buses and trucks instead of cars

00:09:24 Are AI Start-ups dead?

00:14:25 Can 'wannabe' AI start-ups harm the actual AI economy and market?

00:16:50 Should AI research prioritize progress over control and a deep understanding of algorithms?

00:21:14 Can AI replace experts?

00:24:04 If we make AI hyper-personalized or make it impersonate someone, can it replace experts?

00:25:18 If AI cannot replace experts, should we be worried about our jobs?

00:26:18 How to find out if your job can be taken by AI or not?

00:33:06 Potential of AI in creative expression, entertainment, and journalism

00:38:11 Will AI make us dumb?

00:40:16 Can hallucination be fixed or not?

00:46:29 Is it possible to build a biasless AI model?

00:52:24 Transparency is going to be a big thing AI economy

00:55:59 AI can make your content boring!

01:02:39 Your mom might not use AI unless this happens!

01:09:08 Is AI democratizing opportunities or is it still only benefitting the rich?

In this 25th episode, Jerry, co-founder and CEO of LlamaIndex shares valuable insights for anyone implementing LLM solutions. I decided to focus on Retrieval Augmented Generation (RAG) since it is the current "hype" solution, but we also touched on LLMs in general, the importance of good communication and documentation and more about the business sides of things. I hope you enjoy this episode of the What's AI podcast!

►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:

0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.

01:30 Jerry shares about his journey into AI

03:29 Went into AI product space instead of doing a master's degree and research?

04:56 Understanding the Basics of AI and LLMs

07:16 Beginner in AI... note down this recommendation!

09:50 Understanding LlamaIndex

12:40 Comparing LlamaIndex with Other Tools

16:30 Thoughts on Gemini?

19:29 Dealing with Multimodalities in LlamaIndex

21:25 Importance of Good Documentation

24:12 Deep Dive into Retrieval Augmented Generation (RAG)

28:35 RAG vs Fine-Tuning

34:43 The Importance of Chunking Size in RAG System

37:49 The Importance of Data Quality in Chunks

42:55 Evaluate the RAG system

46:05 The future of LLM

49:29 The Role of Prompt Engineering in AI

53:33 Which AI tools do you use?

In this episode, I received Greg Coquillo, 2-times LinkedIn top voice, senior product manager and AI startup investor. I took this opportunity to into the lifecycle of startups, discussing the critical phases from prototyping to achieving market dominance. Greg shares his expertise on identifying crucial market signals that guide startups on whether to pivot or persevere. We also explore common pitfalls that AI startups face, such as scaling too rapidly and misalignment within teams. Particularly compelling is Greg's perspective on the current startup investment landscape and the exciting potential in AI-enabled solutions. He also shares his story and debuts on LinkedIn, giving great insights to build your own audience. This episode is a must-listen for entrepreneurs, investors, and anyone interested in the dynamic world of startups and AI innovation.

►Greg's linkedin: https://www.linkedin.com/in/greg-coquillo/

►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 Chapters:

0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.

01:19 Greg Coquillo’s Introduction

05:26 Why Start Investing in Start-Ups?

10:20 Why Choose Start-Ups Over Public Companies for Investments?

13:48 Assessing Risk in Start-Up Investments

21:41 Considering Your Expertise on Subject Matter in Start-Up Investments?

27:12 Invest in someone even if they pivot their problems and solutions.

31:07 Decoding Market Signals for a Pivot

37:39 Key Pitfalls for Advisors, Investors, and Entrepreneurs to Navigate.

39:36 Exploring Potential in Start-Ups and Investing And Current Market Conditions.

43:41 Story behind 190k+ followers on LinkedIn page

50:15 Greg shares why he didn't start on multiple platforms even after a large audience on LinkedIn

54:00 Sharing knowledge was more beneficial for Greg than building a brand, but why?

58:12 Discussion about content creators

01:00:28 Some type of content is hurting actual content

01:09:40 Greg's message to viewers

Check out MindStudio: https://bit.ly/MindStudioWhatsAI This is an interview with Dmitry Shapiro, previously working at Google and CTO of MySpace Music. Now, Dmitry is building something super ambitious with the goal of democratizing artificial intelligence. We talk about his platform, MindStudio, but also give super applicable tips to build better AI apps, such as the model selection, prompting, using RAG and more.

We also talk a lot about the user perspective, democratizing AI and the future of AI. Dmitry also has another goal of indexing the mind of everyone. And I for sure talked about that in this episode. I hope you enjoy it.

►Follow Dmitry: https://www.linkedin.com/in/dmitry-shapiro-a2b1/

►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/ Become a member of the YouTube community, support my work and get a cool Discord role : https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join Chapters:

0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.

00:35 Introduction

01:23 This inspired Dmitry to create YouAI

06:14 Building thousands of applications vs Building one foundation model

09:24 Will AI never become like a Swiss army knife?

12:26 Tips to become a good prompt engineer

14:55 ChatGPT vs Specialized models --> What is better for you?

17:31 Some people are not using the AI model even if they know they can be more productive with that but WHY!

22:57 If AI is intelligent, shouldn’t it figure out that it needs more information and gather it up?

26:06 Indexing the Mind

31:39 Privacy concerns with Mind Indexing

35:12 Mind Indexing vs Neuralink

37:57 Tips for Model Selection

43:09 Identifying and mitigating hallucination

47:08 Is Dmitry concerned about big companies that can rival MindStudio?

50:34 Will prompt engineering skills become irrelevant?

52:33 Do we developers or only prompt engineers are enough?

53:48 Beginner's approach towards learning AI

57:11 Communication >> Programming

01:03:42 Democratization of AI is extremely important

This week I received Paige Bailey, Lead product manager at Google DeepMind and previously working at Microsoft GitHub building Copilot. In this episode, we discussed the democratization of AI, advancements in AI-assisted coding, and the ethics of innovation. Discover how AI is subtly reshaping our everyday experiences and the realm of software engineering.

Follow Paige Bailey on all platforms, https://twitter.com/DynamicWebPaige ►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/ Become a member of the YouTube community, support my work and get a cool Discord role: https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join

Timestamps:

01:27 Introduction of Paige Bailey

03:29 External coding assistants vs In-built copilots of IDEs

08:10 Problem:  Beginners using ChatGPT without a core understanding of the subject

15:08 Solving hallucination (At least trying!)

19:29 Does size matters for models?

22:12 Making small products competent for big products (SAFELY!)

26:57 Paige's role as a Product manager in DeepMind

29:46 Paige shares what it's like to work in a constantly innovating space as a product manager.

33:36 When should you establish an AI research team in your startup?

35:45 Using GPT-4 to finetune your smaller models for commercial use isn't allowed (But it's good tho!)

37:48 Paige's suggestions for Model Fine-tuning

41:42 AI will not increase the gap b/w rich and poor (Hopefully!)

44:33 AI's effects on job roles and eligibility requirements

48:56 Master's or PhD degree may not be as relevant in the AI age

51:19 Paige's advice for AI newcomers

54:31 Is having theoretical knowledge of the technology behind models relevant?

56:13 Paige's tips for good team communication

59:20 Communication tips for job-seeking newcomers

01:01:15 Paige shares the kind of features she wants in the AI

01:03:11 Paige suggests skills for beginners who want to enter MLOps.

In this episode, we explore the world of AI ethics and governance with an expert, delving into responsible AI practices, the balance between innovation and regulation, and the role of ethics in AI development and deployment. Gain insights on the challenges and solutions in aligning AI technologies with societal values and legal frameworks. Please leave a 5-star review and follow the podcast. You'll learn a lot of cool stuff, I promise.

References:

►Learn more about Auxane:Her linkedin: https://www.linkedin.com/in/auxane-boch-2a958511b/ Her website: https://bochauxane.wixsite.com/auxane-boch Links discussed in the interview: https://www.ieai.sot.tum.de/publications-and-reports/research-briefs/https://aiethicscourse.org/

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

►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

►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/ Become a member of the YouTube community, support my work and get a cool Discord role : https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join Chapters: 0:00 Hey! Please leave a 5-star review and follow the podcast. You'll learn a lot of cool stuff, I promise.

00:59 Responsible AI vs AI Governance explained

02:24 Steps to become AI responsible

06:21 Are AI regulations hampering the progress of AI?

07:35 Self-regulation in private companies

12:07 Are companies compelled to implement AI ethics?

15:39 Are companies hesitant about AI ethics?

16:24 Engaging with stakeholders regarding AI regulations

17:31 Assisting startups toward responsible AI

21:57 Societal Values and Responsible AI

27:49 Should regulations be determined by one officer or collaboratively?

30:48 Understanding the risk factors of the startup in terms of AI ethics

36:01 Do companies act irresponsibly without being aware of it?

37:36 Does a low entry barrier lead developers to overlook the solution-market fit?

40:35 Implement this to improve AI governance and responsible use?

46:22 Are AI governance challenges due to the novelty of the technology?

48:21 How does Auxane find ways to implement Responsible AI?

51:21 Do ethics problems stem from developer unawareness rather than AI itself?

55:59 Best place to learn AI ethics

58:27 When and how to reach out to Auxane?

59:04 Issue of AI ethics not been talked about frequently

59:58 Making AI ethics videos interesting

Join us in this captivating episode as we dive into the world of AI with Aleksa Gordić, ex-research scientist at DeepMind turned startup founder. Discover his unique journey from DeepMind to launching his own innovative startup, Ortus. Gain valuable insights on breaking into the AI field without a formal degree and landing a software engineer role at top tech companies. Explore the future of AI in video creation, productivity-enhancing tips, and the impact of AI on content creation. Don't miss out on this informative and inspiring conversation that delves into the latest advancements in AI. Listen now on to learn from Aleksa's experiences and stay ahead in the fast-evolving AI landscape.

Try Ortus: https://www.ortusbuddy.ai/

Aleksa's LinkedIn: https://www.linkedin.com/in/aleksagordic/

Timestamps:

00:06 Intro

00:40 Aleksa's current priorities.

02:54 Work at DeepMind

07:05 Why Aleksa drop-out from Masters?

09:54 How did Aleksa land a software engineer role in ML without an official degree?

14:46 Get an AI job at DeepMind or Microsoft without a master's degree

18:18 Secure a research role at DeepMind

21:47 Aleksa's open-source work

22:34 Aleksa shares how he got into DeepMind

23:54 Did Aleksa face challenges in getting into DeepMind due to not having a master's or Ph.D. degree?

25:58 Why Aleksa left DeepMind?

28:07 Has AI made things more challenging by increasing competition?

32:08 What is Ortus?

34:15 How is Ortus doing better than its competitors?

36:32 Will Aleksa integrate article and blog parsing into Ortus?

37:37 How did your technical expertise aid in the creation of Ortus?

40:11 Challenges that were much more complex than anticipated 

42:44 Challenges that were much easier than anticipated?

43:45 Doing everything alone vs. building a team

44:48 Hiring a generalist

48:54 Can a person who excels in one area but lacks interest in other subjects still achieve success?

51:25  Is there an easy path to success for the average person?

57:13 Next step for Ortus

58:02 AI will change content creation

01:00:20 Future of AI in video creation

01:02:05 Use cases for AI-generated video content

01:06:02 Aleksa shares his productivity-enhancing resources and tips

01:08:56 Discussion about habits and the 'Atomic habits' book

01:12:03 Feeling of urgency is the key to productivity

01:14:33 Benefits of Physical training

01:20:21 Outro

In this podcast episode, I interview Petar Veličković, a research scientist at DeepMind. We discuss his academic background and his journey from competitive programming to machine learning. Petar shares insights on the value of a PhD, emphasizing its role as an entry ticket into research and the opportunity it provides to build connections and adaptability. He also highlights the evolving landscape of AI research, where diverse backgrounds and contributions are essential. Overall, the interview offers valuable perspectives on academia, industry, and the importance of curiosity in driving impactful research. ►Twitter: https://twitter.com/PetarV_93, https://twitter.com/Whats_AI ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/ ►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:

00:05 Intro

00:37 Academic Background of Petar

02:07 The switch into the Machine Learning

05:33 Was Ph.D. worth it?

14:26 Is a Ph.D. worth it if you want to become a MLOps or a developer?

19:15 What was more useful to you to find research work at DeepMind?

22:28 Skills and portfolio tips for non-Ph.D. person to get into Google DeepMind

30:08 Work of research scientist at DeepMind

33:52 Promising avenue for AGI

42:36 Travel time prediction algorithm that was implemented in Google Maps

48:26 Why Petar wants to do more applied research projects?

51:07 Biggest challenges in AI research vs AI in the industry

53:37 Do you believe Graph Neural Network is the future?

58:07 Upcoming Projects of Petar

My interview with Jay Alammar, widely known inthe AI and NLP field mainly through his great blog on transformers and attention.

►Watch on YouTube: https://youtu.be/TO0IV9e2MMQ

►LLM University: https://docs.cohere.com/docs/llmu

►Jay's blog: http://jalammar.github.io/illustrated-transformer/

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

►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

►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/ Become a member of the YouTube community, support my work and get a cool Discord role : https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join

Chapters:

0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.

00:43 Introduction of Jay Alammar

04:00 Why Jay got into AI 8 years ago?

08:07 Why teach after learning

16:12 What is a Transformer?

21:03 What the blocks are made of and how they work?

26:27 Training steps of LLM explained in simple words

39:31 Re-rank Systems

41:47 How to know that your problem can be solved by LLM?

45:31 Chatbot on private or proprietary data

47:10 Challenges with AI Apps

50:51 The requirement to create AI apps

56:11 Mitigate model hallucination from your side

59:05 How does ChatGPT work with any language you type in?

01:02:56 AI evolution in next few years

01:05:38 Jay wants AI to be able to do this

01:08:10 AI apps used by Jay

01:10:10 Projects of Jay

This new episode features Felix Tao, CEO of Mindverse AI, and his years of experience working as a researcher at Facebook and Alibaba, mostly involved in language applications and AI. In this interview, Felix gives his insights on the evolution of AI, large language models, and finding the right balance between research and application. ►Felix Tao: https://www.linkedin.com/in/felix-tao-96456623/

►Mindverse AI: https://www.mindverse.ai/

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

►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

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

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

Chapters:

00:00:06 Intro

00:00:35 Introduction of Felix Tao

00:01:57 Is Ph.D. still worth it?

00:03:52 Is the research path still relevant?

00:07:36 Future of Ph.D. in A.I.

00:09:26 Work at Facebook

00:14:35 Waking up of A.I. consciousness

00:21:56 Future of Large Language Models in specific tasks

00:27:51 Aim of Mindverse with MindOs

00:31:59 Mindverse's take on mitigating the risk of hallucinations

00:38:04 Is hallucination-free output possible?

00:39:58 Tough challenge faced by Mindverse

00:45:38 Improve your work using Mindverse and MindOs

00:53:48 The thing A.I. need to have in future

00:55:56 Upcoming challenges in A.I.

00:58:04; A.I. in next five years

01:00:01 Can ChatGTP improve further?

Everything you need to know about GPT, large language models and OpenAI with Logan Kilpatrick, Developer Advocate at OpenAI!

References:

►Follow on Twitter: https://twitter.com/Whats_AI, https://twitter.com/OfficialLoganK

►My Newsletter: https://www.louisbouchard.ai/newsletter/

►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:

0:00 Leave a like if you enjoy the episode! Let me know who you'd like me to receive.

00:43 Who are you and what’s your background?

02:40 How long have you been doing your PHD?

04:29 What are your thoughts on the value of a PHD

06:10 What do you think is the ideal path for someone who would like to work in the AI field?

07:10 Would you say graduate studies are required to succeed in the AI field?

08:36 How did you get into OpenAI without a PHD?

11:03 Can you explain how it is to apply in OpenAI and the interview process?

12:58 Did they expect you to know what they offer and how their products work?

14:50 Can you explain what you're doing on Twitter?

16:41 Can you explain the difference between ChatGPT and GPT4?

17:16 What is GPT?

17:38 What is a token?

18:09 Can you explain what alignment is?

18:46 What is RLHF?

19:23 What is a prompt and a prompt engineer?

20:18 What is a modality and a multi-modal model?

20:49 What is model hallucination?

21:37 What is a GPT agent?

22:20 Are you excited about agents or do you think it's overrated?

23:43 What is an AGI?

24:47 Do you have tips about how to make the model be more human when writing?

27:02 How many examples of your style should GPT see to imitate you?

28:00 Do you have any tips to help avoid hallucination or plagiarism in the content?

31:46 Could you describe what is a developer advocate and what you do in your day to day life?

35:27 How are you helping developers who are using OpenAI's products?

39:19 Could you give an insight on what it's like to work at OpenAI?

43:47 Are there any steps that you are taking to ensure that the technology is being used in a responsible way?

47:45 What is the most interesting product that you've seen using OpenAI's products?

56:28 Are there any products that you've seen using GPT that help you personally for productivity?

58:17 Do you have any tips on better using ChatGPT for coding?

59:40 Are there any upcoming interesting features or products that aren't released yet

01:01:15 Are there any common recurring mistakes that you help developers with?

01:05:44 What would be your tips to get started implementing something from OpenAI when having no programming skills?

01:07:35 If programming wouldn't be required are there any other skills that would be?

01:08:08 What are the different GPT models and which should be used when?

01:10:06 When would you use the different GPT models available?

01:12:28 Is there anything you wish AI could do but can't do yet?

01:13:45 What is the biggest challenge the AI industry is facing right now?

01:14:41 How do you see AI evolving for the next 5 years?

01:17:40 What else are you working on?

This interview is with Limarc Ambalina, VP of Editorial at HackerNoon, on AI-generated content in the realm of writing and journalism! Get insights into his journey and how AI can enhance the writer's experience.

References:

►Limarc Ambalina: https://www.linkedin.com/in/limarc-ambalina-11604371/

►HackerNoon: https://hackernoon.com/

►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

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

►Support me through wearing Merch: https://whatsai.myshopify.com/

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

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

0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.

00:30 Who are you and what’s your background?

02:52 Are you planning on going back to writing novels?

03:48 Did you end up developing a passion for AI?

05:50 Can you explain what HackerNoon is?

06:52 To you, what is AI generated content?

12:00 Are there any tools or algorithms that have been integrated to?

18:32 Are you allowing AI generated content on HackerNoon?

22:57 Are you afraid of AI generated content being the norm?

25:25 How many of the 300-400 weekly articles are AI generated?

26:38 Is the proportion of well performing AI generated content the same as human written content?

28:54 Have you seen any good articles that seem completely AI generated?

31:10 Do you envision any copyright issues with this AI generated content?

34:34 Should the credit go to the real authors or the AI that was trained on open data?

37:00 If a piece makes money what is the worst that can happen legally speaking?

40:57 Is AI creative?

44:14 Can an AI writing assistant make a difference in the writing quality?

52:30 Do you think AI generated content should be penalized in SEO

55:53 Do you think nowadays it’s necessary to use AI to compete with all other content?

57:06 Are you running a plagiarism check on submitted pieces?

58:45 If people want to write more and use ChatGPT safely, do you have general guidelines to give them?

1:00:33 Will more people get into writing thanks to ChatGPT?

01:01:44 What are your favorite AI tools for writing or editing?

01:07:32 How do you think AI is changing the landscape of journalism and media?

#ai #writing #journalism

Are you considering pursuing a Ph.D. in machine learning? Or just wondering how to get into the AI field, how to become Twitter famous, or even just how to find a first job/internship? Before you take the plunge, take a moment to listen to this insightful interview with Brian Burns, Ph.D. candidate at the University of Washington and founder of the AI Pub Twitter account. References:

►YouTube Episode: https://youtu.be/M0aLFZegaec

►Brian's Twitter AI Pub: https://twitter.com/ai__pub

►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

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

►Support me through wearing Merch: https://whatsai.myshopify.com/

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

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

Chapters:

0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.

00:35 Who are you and what's your background?

01:53 What was your reason for starting a PHD?

03:06 Why did you choose the graduate studies path?

04:29 Do you think the time invested in the PHD is worth the certification?

14:19 What do you think that is more important when matching a company with an individual?

19:43 How are you matching people and finding candidates?

22:35 How are you matching people and finding candidates?

29:02 Why is Twitter useful and why should one have an account?

31:54 Is it difficult to share the most advanced algorithms while making it accessible in short threads?

34:43 Do you have any tip for people who want to explain their own work in a simple way?

38:35 How are you staying up-to-date with the progress in AI?

42:30 How do you get a viral Twitter post?

46:00 How much time does it take to grow a Twitter account?

48:32 How do you decide which paper is worth sharin

51:38 How are you engaging with your Twitter audience?

53:50 Are there any challenges that we don't expect to see when managing a Twitter account?

55:58 How do you monetize your Twitter account?

59:57 How are you managing Twitter controversies and polarizing topics?

01:01:54 What's the most challenging thing about hosting a podcast?

01:06:13 What do you enjoy most about hosting a podcast?

01:07:12 Are you planning on interviewing other kind of background and researchers?

01:08:10 Will you stick with research papers for your podcast?

01:09:55 How has social media impacted the way AI research is communicated and shared with the broader public?

01:13:00 Would you say Twitter has a positive impact on the research industry?

01:14:40 What is your next project and what are you working on?

An interview with Jérémy Cohen, founder and CEO of Think Autonomous. In this interview, we dive into the self-driving car industry and different ML-related roles. We talk about getting into the industry and trying to demystify some myths. We hope you enjoy this episode! Let me know your thoughts in the comments and if you'd like more episodes with Jérémy in the self-driving car industry! References: ►Visit Jeremy's platform, Think Autonomous: https://www.thinkautonomous.ai/private-emails-louis/ ►Jérémy: https://www.linkedin.com/in/jeremycohen2626/ ►The mentioned Zoox mindmap: https://www.thinkautonomous.ai/blog/zoox-careers ►Jeremy's Self-Driving Car Course: https://courses.thinkautonomous.ai/self-driving-cars ►His Course Catalogue: https://courses.thinkautonomous.ai ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/ ►Support me on Patreon: https://www.patreon.com/whatsai ►Support me through wearing Merch: https://whatsai.myshopify.com/ ►Join Our AI Discord: https://discord.gg/learnaitogether How to start in AI/ML - A Complete Guide: ►https://www.louisbouchard.ai/learnai/ Become a member of the YouTube community, support my work and get a cool Discord role : https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join Chapters: 00:45 Who are you and what’s your background? 04:18 How can one become a self-driving car engineer? 14:08 What is a self-driving car engineer? 21:50 Would you recommend working in a specific self-driving engineering role at a big company or working at a startup doing several other tasks as well? 23:18 Could you explain what LiDAR is and how it works? 28:40 Do you think we are far from having self-driving cars on the road when there is heavy rain or snow? 30:45 What do you think about using LiDAR vs cameras on self-driving cars? 34:48 How many new state-of-the-art models are being used in the self-driving car industry? 39:33 Do you think aiming for a self-driving car engineer is aiming to be too specialized? 46:06 Do you think Transformers are replacing CNN’s? 48:45 Do you think a $200 000 salary for a computer vision engineer is a myth? 56:25 What do you think of the value of a certificate for an online course? 01:03:53 Is Silicon Valley the only place where you can work as a self-driving car engineer? 01:08:38 What education level and work experience is needed to get into the self/driving engineering field? 01:15:24 How can someone improve their skills? 01:17:59 Do you have a transformer course for computer vision engineers? 01:21:33 What are some things to avoid if trying to join self-driving engineering? 01:22:45 Any mistakes to avoid when joining the field of self-driving? 01:29:31 Is the driving part based on reinforcement learning or still a classical planning algorithm? 01:33:25 How do you see self-driving cars in 5 years? 01:38:17 Do you think the government, politics, permissions, etc. are gonna interfere? 01:41:45 Is it the government’s duty to ensure protection for people using self-driving cars or is it engineers' responsibility?

An interview with Sander Schulhoff, creator of learnprompting.org, the largest prompting resource online.

Watch the video version on YouTube: https://youtu.be/BNrPs3ANz1E

References:

►Listen on Spotify:

►Listen on Apple podcasts:

►Learn prompting: https://learnprompting.org/

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

►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

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

►Support me through wearing Merch: https://whatsai.myshopify.com/

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

0:00 Hey! I hope you enjoy this interview. Follow to learn more about AI and prompting with amazing upcoming guests!

00:30 Who are you? What's your background?

01:40 How where you involved with reinforcement learning?

03:10 How was AI used within Minecraft?

06:04 What made you transition into prompting?

07:03 What is learnprompting.org?

08:57 How does prompting work? What are common types of prompting? Is it only in English?

10:48 What can you improve with prompting vs. just natural language and talking with another human being?

13:49 How do LLM work? Why are they able to understand and generate text? Why can’t it be able to understand us like other humans do?

15:51 Is the main thing that LLM’s lack emotion? Or is there another big difference?

17:27 Can you give an example of where you used prompting?

19:11 Can you give more details on what is Lang Chain and how it’s used?

20:17 How can one benefit from using prompting vs. Googling the answer?

24:38 Can you get paid by being a prompt engineer?

25:55 What do prompt engineers do? What are their tasks?

30:40 Who can become a prompt engineer?

32:48 Is there any way to measure that you are improving and going in the right direction for improving prompts for a specific task?

35:24 How would you recommend people to learn prompting?

37:57 Is prompt engineering a job for the future?

39:00 Do you think prompt engineering might get better and better as to in the end replace a human?

40:40 What are the differences in prompting between two separate models and a model over time?

42:24 Is there a way to be sure you’re not specializing on only one model?

43:54 Is it important for prompt engineers to better understand models?

45:49 Could you explain the difference between fine tuning and prompting?

49:29 Are there any misconceptions that you often need to clarify to those new to prompting?

51:46 Do you see any clear limitations of prompting?

55:12 What are the biggest challenges you see for prompting?

56:23 Was GPT-3 trained to be unbiased?

58:43 Did you solve the issue of ChatGPT not having only one word?

01:00:38 Why do you think it’s so difficult to output only one word?

01:01:37 Do you think the model doesn’t listen or it doesn't understand?

01:02:55 Can you think of any ethical problems that may arise because of this new technology?

01:04:09 What are you currently working on? What’s your next project?

Here's an interview with the Director of Perception at Zoox, Ruijie (RJ) He, with the goal of demystifying what is a good profile to get an ML engineer job and perform at the interviews.

We go over many points from the current job market, the screening process, how to prepare for an interview, the interview process and the role of an ML engineer at Zoox.

For a quick introduction to this great company: Zoox is an artificial intelligence company focused on a single product: autonomous vehicles. They are building intelligent taxis for the future hiring a lot of people in the whole machine learning / data science space.

►Learn more about Zoox: https://zoox.com/ ►Support me on Patreon: https://www.patreon.com/whatsai Chapters: 0:34 Who is RJ He and what is Zoox 1:14 What role is Zoox looking for? 2:10 What is an ML Engineer? 4:48 From research to a product, what is the process at Zoox? 7:01 What are you looking for in ML Engineers? 9:50 The screening process at Zoox 12:52 The interviews at Zoox 14:08 How to get a job without work experience? 16:18 What kind of projects are you looking for? 18:42 Are research publications important/necessary? 19:58 Online certifications vs. university 21:32 The interview process (then vs. now) 22:55 The AI field and the job market (then vs. now) 24:42 Hiring at Zoox, future interviews and conclusion

Here's an interview with Adam Grzywaczewski, the senior deep learning architect at NVIDIA. In this interview, we talk about his role, and the interview process to get into such a role, and we learn more about NVIDIA and more interesting insights from Adam. [FOR THE 4080 GIVEAWAY] Comment under this video and send me a screenshot DURING GTC to enter the RTX 4080 (and 10 DLI credits) giveaway! Adam's GTC events: The Possibilities for Natural Language Processing and Large Language Models in Finance: Insights from Deutsche Bank [S51160]: https://www.nvidia.com/gtc/session-catalog/?ncid=ref-inpa-477072&?tab.catalogallsessionstab=16566177511100015Kus&search=#/session/1666078863989001bkfy Connect with the Experts: Deep Learning, Machine Learning, and Data Science [CWES52118]: https://www.nvidia.com/gtc/session-catalog/?ncid=ref-inpa-477072&?tab.catalogallsessionstab=16566177511100015Kus&search=#/session/1670255843552001iaMr ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/ ►Support me on Patreon: https://www.patreon.com/whatsai ►Support me through wearing Merch: https://whatsai.myshopify.com/ ►Join Our AI Discord: https://discord.gg/learnaitogether Chapters: 0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise. 00:58 Academic background 07:55 You were trying to scale models, but the hardware didn’t allow you? 09:25 Did you really want a Ph.D. or was it just to work on the project you had in mind? 10:14 Did you already have a goal in mind? Like getting a good job 12:41 How are you assessing the candidate’s capabilities before and during the interview? 14:43 If you have a lot of resumes, are there any projects or degrees that are more interesting than others? 17:41 What is the shape and format of the interview? 20:08 What is a deep learning architect? 21:55 Other than scaling. In what areas are you working on? 23:36 Are you part of a team that supports companies using Nvidia’s products? 24:40 Could you go over the details of a specific project you’ve had? 26:41 Which complicated challenges require your help? 28:15 How do people that work with you deal with large models or data sets? 30:47 So the current challenge is mainly to find which tool to use and how to do it in a cost-effective way? 32:30 Will you talk in GTC about how to scale and deploy NLP models? 33:30 What is your day-to-day like at Nvidia? 37:20 Would you say that AI technology is now more insane than it was in 2017? 38:10 How do you keep up with this fast rate of progress? 39:08 As the field is maturing, would you say that you have to be more specific on what you’re doing compared to 5-6 years ago? 40:06 Is the need for specific knowledge more challenging than when you had to have broader knowledge? 42:54 What is your favorite tool to use? 43:04 What internal tools are you using? 47:00 Are you surprised by the fact that open-source technologies are so powerful? 50:16 What is the biggest challenge in just deploying models? 53:33 So the main challenges come with the complexity of the solution and the randomness?

If you thought ChatGPT was good, wait before you try this one…

GPT-4 may be the most hyped language model we’ve had, with tons of rumors and news even prior to its announcement, and it wasn’t for nothing.

Tl;dr: GPT-4 is ChatGPT’s big brother.

References: 

►Read the full article: https://www.louisbouchard.ai/gpt-4/ 

►Try GPT-4 now: https://chat.openai.com/ 

►API waitlist: https://openai.com/waitlist/gpt-4 

►OpenAI blog post: https://openai.com/product/gpt-4 

►GPT-4 research: https://openai.com/research/gpt-4 ►ChatGPT video: https://youtu.be/AsFgn8vU-tQ 

►What is prompting: https://youtu.be/pZsJbYIFCCw 

►Learn Prompting: https://learnprompting.org/  More from me... 

►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/ 

Be the first to hear about the coolest models like ChatGPT, and follow my favorite daily AI newsletter: https://www.syntheticmind.io/subscribe?ref=EFowuebnlZ 

►Support me on Patreon: https://www.patreon.com/whatsai ►Support me through wearing Merch: https://whatsai.myshopify.com/ 

►Join Our AI Discord: https://discord.gg/learnaitogether  #openai #chatgpt #gpt4

An interview with one of the best Kaggler out there, Chris Deotte. Chris is a Senior Data Scientist at NVIDIA and is getting paid for his Kaggle skills! Kaggle is a platform mainly known for hosting machine learning competitions...

Comment under the YT video and send me a screenshot DURING GTC to enter the RTX 4080 giveaway: https://youtu.be/NjGnnG3evmE

►Follow my favorite daily AI newsletter: https://www.syntheticmind.io/subscribe?ref=EFowuebnlZ 

►Support me through wearing Merch: https://whatsai.myshopify.com/  

Chris's GTC events: 

►Developing State-of-the-Art Models in a Short Time:  https://www.nvidia.com/gtc/session-catalog/?ncid=ref-inpa-477072&?tab.catalogallsessionstab=16566177511100015Kus&search=#/session/1666650462301001Ltpf

►Learn How to Create Features from Tabular Data and Accelerate your Data Science Pipeline:  https://www.nvidia.com/gtc/session-catalog/?ncid=ref-inpa-477072&?tab.catalogallsessionstab=16566177511100015Kus&search=#/session/1666168670726001zds5

More...

►My Newsletter: https://www.louisbouchard.ai/newsletter/

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

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

Chapters:

00:49 What is your academic background? 

01:20 How did you get into data science from a mathematics background? 

02:04 What is a data scientist for you, and what is your role as one? 

02:33 Do you think data science is mainly a role for academia because it’s a lot of statistical and math knowledge? Do you think a PHD or a  masters is necessary to get such a role? 

03:47 What is your role as a data scientist at Nvidia? 

05:40 What is Kaggle, and what is a grand master at Kaggle? 

08:20 Do you think Kaggle competitions are a good way of improving your resume and build experience if you want to work in the industry? 

11:54 Is there something specific to Kaggle that doesn't work in the real world? 

16:29 Are most competitions similar to one another? Or are there different challenges depending on the competition? 

18:34 So Kaggle will allow you to be a generalist? 

19:08 What tips would you give to a beginner who wants to participate in the competition and have a chance of winning? 

20:43 Do you participate in competitions of every field? 

24:17 What is a Kaggle grandmaster and what does it mean to have this four times? 

27:52 Was there a category that was harder for you? Or one that you didn't enjoy? 

30:38 What was the main factor for Nvidia to find you and hire you? 

32:11 How was the interview process if they already knew how you worked and your knowledge? 

35:07 How did you prepare for these interviews? 

36:28 How can they assess your skills if there are so few people that do what you do? 

37:27 Since the technical interviews are in different fields, is it over if you fail one of them? 

40:04 Can you describe your day to day at Nvidia? 

41:29 So you're being paid to do what you love to do? 

43:03 Could you enter into the details of a recent project? 

46:10 How do you deal with a very large data set? 

48:39 Do you have a machine or are you connected to servers? 

49:56 What would you recommend to someone who has a basic laptop and wants to practice DS? 

53:37 Do you sometimes need to do particular processes to make it work with multiple GPU's? 

56:39 What are the daily tools you use to do data science and Kaggle? 

58:00 Is there anything we can learn from Nvidia coming soon? 

58:58 Is it accessible for someone just starting at Kaggle?