Ken Jennings

Garry Kasparov, Lee Sedol, Ken Jennings.

These fine folks, grand masters in their given fields, have all been positively embarrassed by machines, publicly beaten at their own games, by little more than 1s and 0s.

You can now add to this long list of humans beaten by machines, four professional poker players.

“Libratus, an artificial intelligence developed by Carnegie Mellon University, made history by defeating four of the world’s best professional poker players in a marathon 20-day poker competition, called “Brains Vs. Artificial Intelligence: Upping the Ante” at Rivers Casino in Pittsburgh.” — CMU.Edu

This time though things are a little different, because while poker has rules, unlike chess or go, the rules can bend. To win at poker, you have to do something very human — you have to lie.

That’s right, this AI learned how to bluff.

It wasn’t programed to, but it figured out that bluffing was a key part of winning No-Limit Texas Hold’em hands. Granted, machine’s have a leg up when it comes to math and probability, skills needed to win at poker. But this win changes the idea that the information machine learning needs to learn has to be highly structured.

“The best AI’s ability to do strategic reasoning with imperfect information has now surpassed that of the best humans,” Tuomas Sandholm, Ph. D. CMU.

During the early rounds of the tournament the poker players found and exploited holes in the AI’s strategy. However, when they tried these tricks again the next day, they found that their tricks didn’t work. Overnight the AI was analyzing its mistakes and fixing them. The more holes the poker pro’s poked in the machine’s game, the better it got, ultimately overwhelming the best players in the world.

This AI still needs lots of data to learn and at high frequency (two of the pillars of machine learning I outlined here), but now it doesn’t need highly structured data. This is a development I didn’t think would happen this soon.

What bluffing AI means for business

A more creative AI, like Libratus, opens up the business applications to more human tasks. Historically, structure and uniformity limited what computers could do. They couldn’t compete with the creative power of the human brain, a device that’s quite good at connecting abstract ideas and working with ambiguous data.

How will this apply to business?

There are still myriad questions in business and marketing that require the mysterious processing power of the human brain (or so it seems). These are the questions where analytics and metrics aren’t usually consulted:

Does this person have what it takes to lead a team?

Is this piece of creative good?

Can I convince investors that this idea is worthy of their dollars?

Theses questions are some of the most difficult to answer. Knowing what is a “good” piece of creative for instance is a very complex question. The question is subjective, different people may see different ways, making any data ambiguous. For someone to really analyze it they need to have deep experience in the fundamentals of art, design, business, and persuasive writing. These skill take time and human experience to acquire. This is why brands and businesses spend millions of dollars with top ad agencies. They need to lean on these experts to build quality creative and still sometimes get it wrong.

But with this new development, it’s likely that in the next few years, Madison Ave creatives won’t be the ones answering what your Super Bowl spot should look like, a machine will. This is already the case with social ads. Google for instance can quickly tell you which piece of creative drives clicks and which doesn’t. What’s next is when the AI knows your creative is bad, and will give you advice on how to make it better. This can be uncomfortable to think about it, but it’s important to pay attention to now — if you don’t you’ll get let behind.

I’ve written before about how machine learning in ad optimization is a fast way to start using powerful AI. Recently, I caught up with our head of Paid Acquisition and Optimization at HubSpot and he told me something very interesting: he has stopped trying to optimize the ads we run on Facebook.

Ad optimization is his job, and he’s flat out stopped trying to optimize our ads. Ten months ago he used Facebook’s tools to augment his own skills. Using a combination of tests and manual tweaks based on results to optimize our ads ROI. But as of last month, he’s leaning 100% on Facebook to find the right audiences and bid on them. Why? He can no longer beat their AI. He’s giving this same advice to our customers: don’t bother trying to manually optimize your ads either, trust Facebook, let their AI do the work.

This has had a meaningful impact on how his team spends their time. Today, instead of testing Facebook ads, they now test Facebook’s tests and optimizations. But the big fact remains, manual optimization is no longer the best way to have great ads. Our team recognized this and smartly got out of their own way. Sometimes you just need to give in to the power of the machines and move on to the next challenge.

When we live in a world where a machine can instantly tell you if a piece of creative is good or not. It’ll be important to listen.

AI is getting smarter, and much faster than I thought. The news of AI winning a game of poker feels like the first of many similar developments to come. Machines will be chomping up knowledge workers jobs much like software has been eating the world.

The next wave of successful business people could be defined not by their ability to leverage AI and use it to augment their skills, but their willingness to get the hell out of the way and just let the machines do their thing.