151–160 of 234 posts
It's hard to know how much that affected the strategy, but in the Reddit thread, the human players said the overbet frequency was what they were most surprised by.
Anyone that's interested in reading a more detailed account of the experiment can do so here: http://www.pokerlistings.com/libratus-poker-ai-smokes-humans... The above article spells out some of the details of the competition. The winrate (14.72bb/100) that the AI achieved over the 120k hand sample is almost certainly not due to luck. It is a huge winrate that most pros have to employ strong game selection techniques…
There is a game humans can still beat machines at with ease, Diplomacy(1). When a machine wins a Diplomacy tournament I know we are finished as anything except pets. 1. https://en.wikipedia.org/wiki/Diplomacy_(game)
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Key example of that Nash point is Rock Paper Scissors. You can't exploit anybody playing 100% random, yet that's the Nash Equilibrium. RPSAI competitions tend to have 100% random players perform quite poorly
Do you mean to say that 100% random RPSAIs have a lower winrate vs humans than RPSAIs that learn and exploit human patterns? Surely a 100% random RPSAI doesn't have a poor win rate against any other RPSAI?
This happens because some entrants aren't 100% random, and the worse of them can be exploited by the better of them. What happens is that the results involving any random AI essentially degenerate into noise, while the tournament is really contested between the nonrandom entrants and will be won by the one of them with the best strategy.
Put another way: to win or place highly in a tournament, you don't just want expected win-rate, you want variance. If there is no difference in reward between a 50% win-rate versus a 10% win-rate (both are far out of the money), but there is a big difference between a 50% win-rate and a 90% win-rate (the latter wins the tournament), you will seek the 90% at the cost of potentially ending up at 10%.
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> "So the question is, how does an average joe hacker like me exploit and leverage this wonderful thing called deep learning? I'm not interested in reading PHD papers with advanced calculus." FFS, if you're not willing to read a paper with BASIC CALCULUS (it's HS/college, not advanced like fractional), then I'm not sure machine learning is the right place for you.
Sure it is. Much like most developers don't spend their time reading on computing science papers to do their job, I question why such academic rigour is to be demanded from somebody trying to capitalize on the arbitrage opportunity. Math is good but my time won't be best used if I have to learn calculus all over again just to begin understanding the linguo. Rather have a generic model of what to use and when, hire th…
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A Google search result for "machine learning stock trading": https://www.udacity.com/course/machine-learning-for-trading-...
well to be honest I never bothered googling that exact term but I was more interested in the details around his deployment of smaller and successful neural networks as part of a portfolio.
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Funny, I know more people who are asking about machine learning than people who are experts. So who is in high supply?
Machine learning experts are in higher supply, your anecdotal experience offers little weight. High demand creates a high supply which drives wages to zero.
Silly google, they must've skipped Econ 101 since they hire tons of engineers and are doing TERRIBLY!
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Do you mean to say that 100% random RPSAIs have a lower winrate vs humans than RPSAIs that learn and exploit human patterns? Surely a 100% random RPSAI doesn't have a poor win rate against any other RPSAI?
A 100% random RPS AI doesn't have a poor 1-on-1 win rate against any other RPS AI, but it absolutely can have a poor rate of winning tournaments , if "poor" is defined broadly enough. For a tournament that pays cash to the top 10%, most human players would consider anything in the bottom 90% to be poor, which would include a 50% win rate from a random AI. This happens because some entrants aren't 100% random, and the…
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It's just that AI researchers are exploring and finding every single field where deep learning algorithms perform better than humans. Play poker, run hedge funds... But for instance understanding and specially producing meaningful language, let it be natural language or programming language? I hope not so. Because otherwise, your average joe hacker might as well shut down their IDE and say good bye.
Looking at the recent leap in translation between natural languages, it seems that producing meaningful language about various situations is not far off.
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Funny, I know more people who are asking about machine learning than people who are experts. So who is in high supply?
Machine learning experts are in higher supply, your anecdotal experience offers little weight. High demand creates a high supply which drives wages to zero.
A typical Econ 101 textbook says:
higher demand --> higher price
higher price --> higher supply
higher supply --> lower price
lower price --> higher demand
But even if we believed your demand causes supply causes zero price model, why is that unique to engineering and not business acumen? And why do programmers get paid anything, don't they work for free?