PokerSnowie's Hold'em AI is considered by many to be the most sophisticated poker learning tool ever created. And has taught itself to play "unexploitable" poker through trillions of hands against itself.
So what happens if the two square off in a man vs. machine battle? That's exactly what we set out to do at this year's Battle of Malta and the results, like they did Cates, may surprise you.
Cates Wins but Commits 32 "Major Errors"
The short backstory on Poker Snowie's Hold'em AI: Inspired by a similarly developed Backgammon AI, PokerSnowie uses "Game Theory Optimal" strategy as the basis for its sophisticated algortihm.
GTO, as it's called, is essentially the ultimate "balanced" style of optimal poker. Different from the exploitative strategy taught by most poker schools, GTO dictates a balanced playing style that weeds out the most -EV (expected value) plays and takes advantage of the highest EV plays.
By playing trillions of hands against itself it learned what the optimal play for your bottom line is in every possible Hold'em scenario.
Intrigued? So was Cates, who put his human-tested skills to the test in a 1,500-hand match:
The Results
As you can see at the end of the video Cates eked out a slight edge - just 3 buy-ins - but was certainly tested.
The program also detected 32 "major errors' in Cates' play that cost him between 6-8 BB in certain places.
Weed those out over time and you can clearly see where you'll profit from the optimal, +EV strategy possible in the game of Hold'em.
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