Guest Greg Coquillo, senior product manager and AI startup investor, shares insights on identifying crucial market signals for startups. They discuss common pitfalls AI startups face and the current startup investment landscape. Greg also shares his journey debuting on LinkedIn and gives insights for building your own audience. A must-listen for entrepreneurs, investors, and those interested in the startup and AI innovation world.

In this episode, AI expert Ken Jee discusses his journey into AI and data science, the potential revolution of the OpenAI store, the impacts of AI on our brains, and his tips for starting a podcast. They also touch on balancing commitments, the importance of consistency in skill development, and the value of communication skills.

In this podcast, Louis Bouchard interviews Dmitry Shapiro, a former Google and MySpace Music CTO. They discuss Dmitry's ambitious goal of democratizing artificial intelligence with his platform, MindStudio. They also cover topics such as model selection, prompting, the future of AI, and indexing the mind of everyone. The podcast explores the role of prompt engineers, the power of communication skills, and the evolving perception of AI.

Paige Bailey, Lead product manager at Google DeepMind and previously working at Microsoft GitHub, talks about the democratization of AI, advancements in AI-assisted coding, and the ethics of innovation. They also discuss the challenges of training large foundational models, the importance of model size for language models, and the potential impact of generative models on the future of work and education.

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

Welcome to this special episode of the What’s AI podcast. Today, it’s just you and me, no guests. For the new listeners, my name is Louis Bouchard, and I am currently doing a PhD in AI at Polytechnique Montreal and Mila. I wanted to take the time to talk about what I am doing: my Ph.D. in artificial intelligence. This episode is for you whether you are thinking of doing a Ph.D. or simply if you are curious and want to learn more about what’s it like to do a Ph.D. and do research in artificial intelligence. I wanted to demystify a bit what it is, what it looks like, or at least, in my case, in biomedical engineering. In this episode, I will cover my personal experience with the Ph.D. in general, doing research in AI, the challenges and reasons for doing a Ph.D., and more about how I’m using AI to help the medical field. Ask any questions or connect with me on Twitter/X: https://twitter.com/Whats_AI

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

Luis Serrano, an AI scientist, YouTuber, and author, discusses the importance of diverse backgrounds in AI, the potential of online learning, and the components of large language models. He also explores the skills needed to use LLMs in applications and the role of prompt engineering in AI. Additionally, he talks about using AI for resume screening and the relevance and accessibility of AI education.