Machine learning, which is just an advanced form of artificial intelligence (AI), is changing how the world operate. There are more use cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow). An algorithm is just a procedure for solving a problem, based on conductiong a sequence of specified actions. Machine learning is just a collection of algorithms that learn from data in different ways. These algorithms identify repeatable and persistant patterns in data.

As chief executive officer of one of the world's largest hedge funds, Luke Ellis prides himself on a healthy appetite for risk. "My job," he says, "is to not blink." About five years ago, he did, though--in a big way. What spooked him was an experiment at his firm, Man Group Plc. Engineers at the company's technology-centric AHL unit had been dabbling with artificial intelligence--a buzzy, albeit not widely used, technology at the time. The system they built evolved autonomously, finding moneymaking strategies humans had missed. The results were startlingly good, and now Ellis and fellow executives needed to figure out their next move. Man Group, which has about $96 billion under management, typically takes its most promising ideas from testing to trading real money within weeks. In the fast-moving world of modern finance, an edge today can be gone tomorrow.

Much of the current machine learning revolution originated around applications like computer vision that have nothing to do with finance. Financial data modelling is beset by a low signal to noise ratio, whereas data used to teach a computer to identify a picture of a cat, for example, is unambiguous. The financial universe is a non-stationary environment with variable patterns of correlation between stocks, bonds and other instruments. Not least, the task in hand is essentially about predicting things that haven't happened yet. For nearly 30 years now, UK hedge fund manager Man AHL has been trawling through enormous historical datasets trying to understand what is predictable and what's just noise.

Much of the current machine learning revolution originated around applications like computer vision that have nothing to do with finance. Financial data modeling is beset by a low signal to noise ratio, whereas data used to teach a computer to identify a picture of a cat, for example, is unambiguous. The financial universe is a non-stationary environment with variable patterns of correlation between stocks, bonds and other instruments. Not least, the task in hand is essentially about predicting things that haven't happened yet. For nearly 30 years now, U.K. hedge fund manager Man AHL has been trawling through enormous historical datasets trying to understand what is predictable and what's just noise.

Hedge funds have been trying to teach computers to think like traders for years. Now, after many false dawns, an artificial intelligence technology called deep learning that loosely mimics the neurons in our brains is holding out promise for firms. WorldQuant is using it for small-scale trading, said a person with knowledge of the firm. Man AHL may soon begin betting with it too. Winton and Two Sigma are also getting into the brain game.

Hedge funds have been trying to teach computers to think like traders for years. Now, after many false dawns, an artificial intelligence technology called deep learning that loosely mimics the neurons in our brains is holding out promise for firms. WorldQuant is using it for small-scale trading, said a person with knowledge of the firm. Man AHL may soon begin betting with it too. Winton and Two Sigma are also getting into the brain game.

Babak Hodjat believes humans are too emotional for the stock market. So he's started one of the first hedge funds run completely by artificial intelligence. "Humans have bias and sensitivities, conscious and unconscious," says Hodjat, a computer scientist who helped lay the groundwork for Apple's Siri. "It's well documented we humans make mistakes. For me, it's scarier to be relying on those human-based intuitions and justifications than relying on purely what the data and statistics are telling you."

Babak Hodjat believes humans are too emotional for the stock market. So he's started one of the first hedge funds run completely by artificial intelligence. "Humans have bias and sensitivities, conscious and unconscious," says Hodjat, a computer scientist who helped lay the groundwork for Apple's Siri. "It's well documented we humans make mistakes. For me, it's scarier to be relying on those human-based intuitions and justifications than relying on purely what the data and statistics are telling you."

Babak Hodjat believes humans are too emotional for the stock market. So he's started one of the first hedge funds run completely by artificial intelligence. "Humans have bias and sensitivities, conscious and unconscious," says Hodjat, a computer scientist who helped lay the groundwork for Apple's Siri. "It's well documented we humans make mistakes. For me, it's scarier to be relying on those human-based intuitions and justifications than relying on purely what the data and statistics are telling you."

Much of the current machine learning revolution originated around applications like computer vision that have nothing to do with finance. Financial data modelling is beset by a low signal to noise ratio, whereas data used to teach a computer to identify a picture of a cat, for example, is unambiguous. The financial universe is a non-stationary environment with variable patterns of correlation between stocks, bonds and other instruments. Not least, the task in hand is essentially about predicting things that haven't happened yet. For nearly 30 years now, UK hedge fund manager Man AHL has been trawling through enormous historical datasets trying to understand what is predictable and what's just noise.