AI Studios with Natalia Burina Conversations about AI with world's top builders, artists, researchers and thinkers.

David Adkins is an experienced senior technology executive who leads engineering teams at Meta AI. David holds an MS in Computer Science from the University at Buffalo focused on machine learning. In this conversation we discuss
* How to think about AI bias and fairness
* Why AI Transparency, Explainability and Control are important
* How Meta de-risked LLaMA
* How to take AI Research to production and more.
Listen now on Apple, Spotify and Amazon Music and please subscribe to my YouTube channel.
⏰ 2:06 David’s Favorite Restaurant Sammy’s Fishbox
⏰ 2:40 Career Stories: David’s Journey
⏰ 4:37 What David is working on at Meta AI
⏰ 4:50 How to organize AI Teams
⏰ 6:56 What is AI Bias and Fairness
⏰ 8:52 How to build a Product with AI Fairness in mind
⏰ 11:47 Examples of Fairness Mitigations in Meta Products
⏰ 13:33 What’s Surprising about working on AI Fairness
⏰ 16:05 What causes Bias in AI Products
⏰ 19:19 How to Mitigate AI Problems when you’ve already launched
⏰ 21:38 What is AI Transparency and Control
⏰ 24:12 What is AI Explainability?
⏰ 26:10 Why YOU should care about AI Transparency
⏰ 31:00 What is surprising about working on AI Transparency
⏰ 33:00 AI System Cards
⏰ 37:30 Developing LLaMA Responsibly
⏰ 39:30 How to de-risk large language models
⏰ 43:06 How to do AI Research to production
⏰ 46:51 What’s next for David


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Javier Tordable is a Technical Director at Google where he drives long term technical strategy for Google Cloud at the CTO Office. Recently Javier has been focusing on healthcare, pharma and biotech, and helping organizations use Cloud infrastructure, Machine Learning and generative AI to improve drug discovery and patient care. Javier is also executive sponsor of top Google Cloud customers and advisor to C-suite executives; and helped close over a billion dollars worth of Cloud contracts.
In this conversation we discuss
* AI opportunities in healthcare,
* Why AlphaFold is such a big breakthrough
* Longevity (including Javier’s Longevity Protocol) and more.
Listen now on Apple, Spotify and Amazon Music and please subscribe to my YouTube channel.
⏰ 1:50 Javier’s background 
⏰ 5:30 Javier’s role at Google 
⏰ 9:05 How Javier approaches Strategy 
⏰ 15:17 Tech and AI adoption in Healthcare 
⏰ 17:10 Understanding Organizational Incentives in Healthcare 
⏰ 18:00 Tools that can overcome Tech Limitations in Healthcare 
⏰ 19:00 AI Opportunities and Use Cases in Healthcare 
⏰ 23:20 AI Privacy for healthcare 
⏰ 29:38 Why Alphafold is a big deal 
⏰ 31:16 What Alphafold does 
⏰ 32:36 How Alphafold will change healthcare 
⏰ 35:55 The big Problem that Alphafold will solve 
⏰ 36:43 Longevity 
⏰ 44:25 Javier’s Longevity Hacks 
⏰ 46:58 How to get into healthcare if you’re a techie and how to get into tech if you’re in healthcare? 
⏰ 49:44 Javier’s advice to someone starting out 
⏰ 52:12 what’s next for Javier
Where to find Javier 
• LinkedIn
• Web
• Javier on Twitter
Where to find Natalia 
• AI Studios on YouTube 
• Natalia’s Substack
• Natalia on Twitter
• LinkedIn



This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aistudios.substack.com

Sonny Patel is a tech executive who ran a team of 250 product managers and engineers at LivePerson. Before that, she ran a cross-functional org of designers, product managers, technical program managers and developers at Amazon. Sonny was also at Microsoft where she grew from an entry level product manager to a leader of leaders.
Here's a written summary of our conversation. You can also listen to the audio version via SubStack, Apple and Spotify. Or you can check out the convo on YouTube.  
Rising to and Operating at Executive Level
Career Breakthroughs 
I asked Sonny what was the pivotal moment that put her on the executive path. Sonny recounted how a VP at Amazon took a bet on her by entrusting her with a cross functional team. This expanded her scope significantly and set her up for bigger opportunities. Sonny attributes this breakthrough to both leadership support and a fortuitous situation. However, to position herself Sonny offered the following playbook. 
Playbook
📖 Develop your knowledge and skills over years. No shortcuts here.
🏆 Champion - You need a champion to take a bet on you. Your knowledge and skills will enable your champion to stand behind you.
📍Situational Awareness - you need to recognize and capitalize on a unique opportunity to distinguish yourself. In Sonny’s case it was about saying yes to managing an engineering team in addition to product.
🎗Support System - You can’t make it without a professional support system.
Value of Good and Bad Managers in early career
Sonny believes that there is value in having one good and one bad manager early on.  Watching and learning from other people’s management mistakes is a good way to build empathy for future reports. A good manager is able to provide psychological safety for their reports. This improves performance and sets people up to do their very best work. Sonny’s own experience with a bad manager taught her to have empathy and cultivate patience towards her reports. When people are new to product management or the AI space, managers should be especially patient.
How Sonny creates Psychological Safety for her Teams
🙋🏻‍♀️ Encourage and support people to ask silly questions 
🙊 Allow people to make mistakes and learn without fear. 
✔️ Sonny used regular check-ins and reporting mechanisms to monitor team progress and identify issues early
Building AI Products and Running AI Teams
How AI products are different 
🪩 People tend to get enamored with the latest shiny technology. Sonny emphasized the importance of focusing on usefulness and not just the "cool" factor. AI Products should solve real problems for users in meaningful ways.
🔐 Privacy, Transparency and Control are critical. Users are willing to share data when they see a benefit and feel in control. Apply the idea of a privacy transaction when building products - if a product collects users data, the user should get something in return. Users should feel in control and everything should be done with their consent. Provide user control options in a coherent way that all fits together.
Why most AI products fail
AI products often fail due to edge cases that were not considered during design and testing. User expectations are often higher than what the technology can reliably deliver.
What makes Amazon an efficient execution machine
Before building a tech product, start with the customer and work backwards by understanding their problem. Amazon believes in the power of writing down things. Write a Press Release to imagine what your product unveiling may look like including all the related messaging. A Press Release is a one-pager that anybody should be able to read and understand. Some of the questions that a Press Release addresses are:
* What is the customer problem?
* Who is the target customer?
* Why is the idea big enough?
* Why now?
* What does the product development team say to customers? 
* What would your customers say afte

Groundbreaking artist and visionary futurist Amy Karle specializes in the transformative impact of emerging technologies on humanity, including AI and biotech. Her work examines how interventions could alter the trajectory of the future and how technology could be utilized to support and enhance our future. In this episode we discuss AI from an artists perspective, how AI will change the way we think and function, Amy’s vision for future with AI and feast our eyes on Amy’s work.
Listen now on Apple and Spotify.
⏰ 00:34 Amy's Journey
⏰ 02:17 AI from an Artists Perspective
⏰ 03:19 How AI is changing the way we Think and Function
⏰ 7:44 AI Dangers
⏰ 11:53 AI Opportunities
⏰ 13:12 AI and Human Mortality
⏰ 15:58 Biofeedback Work
⏰ 21:55 Amy's Approach to exploring Technology's Impact on Humanity
⏰ 24:15 How AI will Change what it means to be human
⏰ 26:15 How AI will enhance our lives
⏰ 27:18 Explorations of AI and Humans Merging
⏰ 29:47 Tools that Amy Uses to create her art
⏰ 30:36 Regenerative Reliquary Piece
⏰ 33:36 The Heart of Evolution Piece
⏰ 36:47 Feasibility of Organ Plug and Play
⏰ 38:13 What's next for Amy
Where to find Amy
* Amy’s Website
* Instagram
* Twitter
* Facebook
* LinkedIn
* Discord
* Wikipedia
Where to find Natalia
* 🆕 Maven Generative AI for Business Workshop
* YouTube
* Substack
* Twitter
* LinkedIn


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aistudios.substack.com

How did a Google Product Manager decide to leave his full time job to become an AI Creator? Bilawal Sidhu most recently worked as a Senior PM at Google Maps, where he led Immersive View and was responsible for new technology innovation. Over time he grew his hobby into a new business. In this conversation he explains how he did it, how he approaches content creation, creator tools, mistakes, how AI is helping creators, pros and cons of working in a big company and more.
Listen now on Apple and Spotify.
⏰ 0:37 Bilawal's Journey
⏰ 3:25 How Bilawal managed to both work at Google and be a Creator, what kept him motivated and Tips.
⏰ 4:57 Approach to Creation
⏰ 8:08 Creation Strategy
⏰ 9:44 How knowledge of creator tools can helped in day job
⏰ 12:00 Mistakes and Advice to tech professionals
⏰ 14:53 That Creator Life
⏰ 20:30 AI Tools Landscape - Industries, verticals, and Tools (Autodesk, Adobe, Cinema 4d, Blender.org)
⏰ 26:52 How AI is helping Creators
⏰ 29:45 Advent of the AI Creators
⏰ 36:24 What AI will do for Content Consumption
⏰ 43:13 Pros and Cons of working on AI at Google and Meta AI
⏰ 1:03 Bilawal's favorite AI Tools and what's next
Referenced
* Corridor Digital
* Marques Brownlee
* Freddie Wong
* Riley
* Nathan Lands
* Joma Tech
* Midjourney
* Controlnet
Where to find Bilawal
* TikTok
* YouTube
* Twitter
* LinkedIn
Where to find Natalia
* Twitter
* LinkedIn


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aistudios.substack.com

What is User Research and why is it useful for AI product development? Lauren Kaplan is a mixed methods researcher passionate about inclusion, leveraging technology for social good, and learning. At Meta, she led research on Privacy Preserving Machine Learning (PPML) and PyTorch (an Open Source AI framework) advocating for people centric AI.
⏰ 0:49 About Lauren
⏰ 1:11 What is Mixed Methods UXR
⏰ 1:27 What is User Research
⏰ 2:10 How to match User Research with Product Development
⏰ 3:13 What are the benefits of User Research for AI Products
⏰ 4:00 What's the difference between User Research and User Feedback
⏰ 6:45 Challenges of doing User Research for AI
⏰ 9:05 How to approach User Research for Generative AI
⏰ 10:20 Privacy Preserving ML User Research
⏰ 12:23 Synthetic Users
⏰ 16:05 How to get into AI User Research
⏰ 17:22 How Lauren stays on top of AI News and Advancements
⏰ 19:10 How to do User Research for Open Source AI
⏰ 21:47 Working with AI Researchers and bridging the discipline gap
⏰ 23:06 How should AI Researchers ensure they're people centric
⏰ 26:45 What stood out about AI Privacy vs other AI
⏰ 29:15 What was it like to work on PyTorch
⏰ 31:30 What AI Lauren is excited about next
Referenced
* Mapping Strategic, Iterative, and Evaluative Research to Product: Matt's UXR Process & FAQ
* Google PAIR resources: People + AI Research - Chapters
* “How can companies help people understand privacy-enhancing technologies like on-device learning?” 
* The Future of AI is People-Centered
* Mapping qualitative and quantitative methods Comparing UX Research Methods
* Synthetic Users: [2209.06899] Out of One, Many: Using Language Models to Simulate Human Samples
Where to find Lauren & her work
* LinkedIn
* Twitter
Where to find Natalia
* Twitter
* LinkedIn


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aistudios.substack.com