Big thanks to you, Lex, for bringing some of the smartest researchers and practitioners to the table and sharing these great interviews with the world.

I share the opinion that applying game theoretical tools to computing applications is challenging. Some 15-20 years ago there was a game-theory hype and some interesting results appeared. Around that time, I was working on those kinds of models. In my experience, applying game theoretical solutions concepts necessitates some kind of overhead work. For instance, in my case I applied Nash bargaining theory for multi robot coordination. The robots had to formulate their private utility profiles at the coordination point, share the profiles, and calculate the full game equilibria (with the help of the other robot). Since the game in the general case had multiple equilibria, the initiating robot also shared a random number with the other robot and both used the number to choose the same joint solution, and then finally implement the individual part.

19 minutes of watching and this turn out to be most interested conversation i had listen so far.. he makes a point here.. other then deep reinforcement learning "to be precise learning only' there are non learning methods .. if we can somehow introduce learning in such methods then that will be something new

You can tell how excited Lex is about this conversation.. just brilliant

Have a sit down with Andrew Ng, find out what he up to these days.

this was a great interview,enjoyed it!thanks

This series is. Dope.

Lex has such a great interviews with awesome people, but honestly, he sounds like he's super high

Comparing to the current interviews these early ones have something to be desired about audio stability, but it s already spectacular~

🙏Such a great channel.

Thank you Lex!

Why wasn't each poker pro given an HUD to real-time track Libratus' betting stats? This is something all online pros use (and such pros would never agree to play high stakes online without one). Libratus certainly had access to the equivalent of an HUD, right? I presume this created significant imbalance between Libratus and the humans.

Lex's questions are great indeed, cool to see guests being excited like that

Between this podcast and last weeks you are on fire my man. LSTMs and POMDPs are the future.

I'd love to know more about this contest. For example, did the computer know after the fact how the player played certain hands? In a real heads up tournament you may never see what cards your opponent had. I suspect the computer was given certain information to help it establish trends and other information about how the players played. Top players are always mixing up their play to avoid having anyone get a take on how they react in certain situations. Bluffing plays an enormous role in poker and most of the the time if you bluff, your opponent will never know it and vice versa.

The discussion from 28:57 on reminds me of the three-body problem in physics.

Follow up with Tim Roughgarden would be amazing

Amazing talk

I had a dream that I was a Phd student working under this brilliant man.... and then I woke up.

Can anyone knowledgeable about ai and autonomy recommend a reading list for the average layman to gain better insight on such systems. Thanks for this video lex.

Great talk!

Ja torille!->

As a pokerplayer i find this conversation really interesting. Not much comes out of the highstakeworld online but i can gve some information. In both )texas holdem and pot limit omaha the topplayers use alot of tools to study and play GTO. However its not humanly possible to play GTO you can spend 100s of hours for spots that if you play professionally never will occur during your lifetime. Therefore topplayers focus hard to study the low hanging fruit commonly occuring situtaions. As i said all toppplayers study GTO but the player i think is the best is Berri Sweet whos playing superexploitive. For sure he knows and studies GTO but his playstyle is exploit

The most interesting part of all this is probability being applied to imperfect information scenarios.

It seems like people have a hard time predicting things with exponential growth. So humans will probably systematically underestimate the performance of AI for a while yet. At some point it will switch and people will assume AI to be better at everything.

Libratus has only been proven to beat top players in individual games like Texas Hold em - - not in multi player situations - - which is real world

36:52 - I'm actually working on automated negotiation for AVs and I'm suprised how little research there is in that area.

It would be interesting to see this AI play on block chain decentralized apps like on the EOS platform.

Games that go on forever ... Pi calculation? I know it's not the same and I haven't looked up if this has been done but it just occurred to me: I wonder how well a learning algorithm would do at predicting Pi digits.

One of the best interviews. Bring the old Lex back, the podcasts of the last 1-2 years are hard to listen to, with Lex really out of it. Perhaps too much of the high life.

This content is a tour de force. I read a comparable book that left an indelible mark. "Game Theory and the Pursuit of Algorithmic Fairness" by Jack Frostwell

nice

Ah the givens

It would be interesting to see AI secretly "tell" a person how to play poker against another human. I wonder if there would be any diffrence. Or if people thaught that they are playing another human being.

Analyzing Rulez

When will they make a Stratego AI that can beat humans? Most Stratego games are simple to beat for even mediocre human players.

Lex, you are slurring your words.

This professor with the breakthrough that he made in AI deserved a better interview platform than this... PR game is weak, maybe AI can help you with that Professor LOL????