If you haven’t played No Limit Texas Hold’em, let me confess: Neither have I; poker isn’t my thing. But I do remember the game from the 2006 James Bond classic, Casino Royale, where the plot thickened around poker. Texas Hold’em is sophisticated, complex, requires considerable strategy and like all poker games calls for the innately human ability to bluff. Recently, Libratus, a poker playing AI, created by Tuomas Sandholm and Noam Brown of Carnegie Mellon University, beat four top human professional No Limit Texas Hold'em players. The game has over 316,000,000,000,000,000 different situations. If a human played one situation every second it would need several billion years to learn them all. Libratus used Bridges, a supercomputer with a paltry price tag of US$9.65 million,[i] to quickly learn and demonstrate that it isn’t only humans who can bluff or take a chance and win with unerring regularity. Algorithms can do a better job.
But what Libratus actually achieved is spellbinding. Its creators did not provide it with a complete set of situations. Instead, using a small sample, Libratus played against itself for days, accumulating a complete library of strategies through trial and error.
In the real world Libratus spells the end of online poker. Who wants to lose every hand to a poker player that knows it all? So what’s the future of online poker? My guess is that online poker will be used by players to sharpen their skills or for recreation. Playing in the real world, face2face, where bots can’t tilt the playing field, will become more popular. Expect this to happen quickly – not over the next quarter century or even a decade but over the next three to four years.
You can perhaps also imagine that Augmented Reality and Virtual Reality will have a role to play in the evolution of online poker, making the game more immersive and sophisticated – and online poker sites should seriously consider leveraging these technologies. But the real action will move offline.
What do such developments in AI, Machine Learning and Data Analytics mean to the real world? Let’s take the case of investors and traders playing the stock markets. Typically, they will synthesize information regarding their order books, listed companies, look at factors such as cash flow, interest rates, future plans, regulatory environments, market trends, currency behavior and competitive signals, to make a decision. Humans can do all of this – and they spend endless hours studying these factors – but now a computer can beat them in terms of speed and accuracy.
Imagine an algorithm that can be trained to automatically trade, in say bitcoins, using self-learn. Now, imagine that the algorithm/ trading engine is hosted on cloud infrastructure and you can connect it to your bitcoin account using a public API. Boom! You could be making money 24X7, even while sleeping.
In the real world, financial advisers are already using bots to improve decision-making. Morgan Stanley, for example, has announced that it will arm its 16,000 financial advisers with machine learning algorithms that will take over routine tasks. Morgan Stanley forecasts that robo advisers will manage US$6.5 trillion of wealth by 2025, or 5% of global wealth[ii]. Interestingly, what Morgan Stanley is doing in the world of investment is a little different from what Libratus did for poker. Unlike the poker playing AI, Morgan Stanley hopes to put AI at the disposal of human advisers to improve outcomes and service levels for their wealthy customers. Online poker may become history in its current form, but when AI works in tandem with humans as a team, as is the case with Morgan Stanley’s strategy, it gives way to an entirely new set of possibilities. I’ll discuss this Man-Machine partnership in depth over the next few weeks.