Siraj Raval  interviews Vinod Khosla in the latest edition of his podcast.

Vinod Khosla is an Entrepreneur, Venture Capitalist, and Philanthropist. It was an honor to have a conversation with the Silicon Valley legend that I’ve admired for many years. Vinod co-founded Sun Microsystems over 30 years ago, a company that grew to over 36,000 employees and invented so much foundational software technology like the Java programming language, NFS, and they pretty much mainstreamed the ‘idea’ of open source. After a successful exit, he’s been using his billionaire status to invest in ambitious technologists trying to improve human life. He’s got the coolest investment portfolio I’ve seen yet, and in this hour long interview we discuss everything from AI to education to startup culture. I know that my microphone volume should be higher in this one, I’ll fix that the next podcast. Enjoy!

Show Notes:

Time markers of our discussion topics below:

2:55 The Future of Education
4:36 Vinod’s Dream of an AI Tutor
5:50 Vinod Offers Siraj a Job
6:35 Choose your Teacher with DeepFakes
8:04 Mathematical Models
9:10 Books Vinod Loves
11:00 What is Learning?
14:00 The Flaws of Liberal Arts Degrees
16:10 Indian Culture
21:11 A Day in the Life of Vinod Khosla
23:50 Valuing Brutal Honesty
24:30 Distributed File Storage
30:30 Where are we Headed?
33:32 Vinod on Nick Bostrom
38:00 Vinod’s Rockstar Recruiting Ability
43:00 The Next Industries to Disrupt
49:00 Vinod Offers Siraj Funding for an AI Tutor
51:48 Virtual Reality
52:00 Contrarian Beliefs
54:00 Vinod’s Love of Learning
55:30 USA vs China

Vinod’s ‘Awesome’ Video:
https://www.youtube.com/watch?v=STtAsDCKEck

Khosla Ventures Blog posts:
https://www.khoslaventures.com/blog/all

Books we discussed:

Scale by Geoffrey West:
https://amzn.to/2rs7UV7

Factfulness by Hans Roesling:
https://amzn.to/2GHUlgg

Mindset by Carol Dwicke:
https://amzn.to/2icCNey

36 Dramatic Situations by Mike Figgis:
https://amzn.to/2ol14Vi

Sapiens by Yuval Noah Harari:
https://amzn.to/2amA7J5

21 Lessons for the 21st Century by Yuval Noah Harari:
https://amzn.to/2PKIJZY
 
The Third Pillar by Raghuram R:
https://bit.ly/2ASU98K

Zero to One by Peter Thiel:
https://amzn.to/2ae3NTM

Microsoft Research has just posted a talk by Kevin Buzzard of the Imperial College of London. Don’t worry, no advanced mathematical knowledge is assumed in the talk.

Slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/09/The-Future-of-Mathematics-SLIDES.pdf 

From the video description:

As a professor of pure mathematics, my job involves teaching, research, and outreach.

Two years ago I got interested in formal methods, and I learned how to use the Lean theorem prover developed at MSR. Since then I have become absolutely convinced that tools like Lean will play a role in the future of mathematics.

Siraj Raval just posted this video on defending AI against adversarial attacks

Machine Learning technology isn’t perfect, it’s vulnerable to many different types of attacks! In this episode, I’ll explain 2 common types of attacks and 2 common types of defenses using various code demos from across the Web. There’s some really dope mathematics involved with adversarial attacks, and it was a lot of fun reading about the ‘cat and mouse’ game between new attack techniques, followed by new defense techniques. I encourage anyone new to the field who finds this stuff interesting to learn more about it. I definitely plan to. Let’s look into some math, code, and examples. Enjoy!

Slideshow for this video:
https://colab.research.google.com/drive/19N9VWTukXTPUj9eukeie55XIu3HKR5TT

Demo project:
https://github.com/jaxball/advis.js

 

Here’s an interesting talk by Aaditya Ramdas on “Sequential Estimation of Quantiles with Applications to A/B-testing and Best-arm Identification”

From the description:

Consider the problem of sequentially estimating quantiles of any distribution over a complete, fully-ordered set, based on a stream of i.i.d. observations. We propose new, theoretically sound and practically tight confidence sequences for quantiles, that is, sequences of confidence intervals which are valid uniformly over time. We give two methods for tracking a fixed quantile and two methods for tracking all quantiles simultaneously. Specifically, we provide explicit expressions with small constants for intervals whose widths shrink at the fastest possible rate, as determined by the law of the iterated logarithm (LIL).

Bloomberg takes a look at the unique role of data science in professional basketball.

From the description:

With her PhD in math, Ivana Seric had expected to wind up with a career in academia—but thanks to the growing use of statistical analysis in the NBA, she took a job with the Philadelphia 76ers instead. As a data scientist, she helps the team’s coaches devise smarter strategies to win.

I’ll never forget the time I first heard of non-Euclidean spaces. It made sense and no-sense all at the same time. Since making the switch into data science, I understood it better and its uses. However, I never really tried to visualize these spaces.

Fortunately(?), someone has created a rendering engine that lets you explore this space and surprise(!), it may have uses for VR.