Fortunately, Braxton McKee isn’t using Excel. Instead, he’s tapping into the cloud to crunch all that market data on the cheap with software he built that learns as it goes.
The cost of that cosmic power: $10.
Welcome to the brave new world of cheap-and-cheerful artificial intelligence.
McKee, 35, is part of a wave of math and computer whizzes that’s pushing data science to new heights across Wall Street. What’s remarkable about their efforts isn’t that AI science fiction is suddenly becoming AI science fact (sorry, Steven Spielberg). It’s something more mundane: thanks to cloud computing, mind-blowing data analysis is getting so cheap that many businesses can easily afford it.
Sophisticated hedge funds like Renaissance Technologies and tech giants like Google Inc. have been deploying AI and its subset, machine learning, for years. Now data shops like Ufora, which McKee founded in 2011, are leveraging cloud power to help hedge funds and other financial players run complex, big-data computer models. The results are startling.
Five years ago, the sort of programming involved in McKee’s 1-trillion-point dense matrix would have taken months of coding and $1 million-plus of hardware. Now McKee simply logs onto Amazon Web Services to name his price for computing capacity and sets his code loose. Out of a loft in the Flatiron District in Manhattan, he works on what he calls “coffee time.” His goal is to make every model — no matter how much data are involved – – compute in the time it takes him to putter to his office kitchen, brew a Nespresso Caramelito, and walk back to his desk.
Machine Learning
McKee, previously a programmer at Ellington Management Group, the credit hedge fund, has been backed by a venture division of Two Sigma Investments, a big quantitative firm run by a former artificial intelligence academic and a mathematics olympian.
Quant shops like Two Sigma and Renaissance have been hiring machine-learning experts of their own. Ray Dalio’s Bridgewater Associates and Steven Cohen’s Point72 Asset Management also have been building up big-data analytics.
But much of the action is in start-ups. Like McKee, three Bridgewater veterans, Matthew Granade, Chris Yang and Nick Elprin, recently struck out on their own. Their San Francisco-based firm, Domino Data Lab, gives data scientists a way to review their old work and collaborate with professional colleagues, an important feature in the iterative process of programming.
Reading Comprehension
Another young firm, Palo Alto-based Sensai, helps companies analyze what’s known as unstructured data, such as corporate documents, transcripts and social media. That involves natural language processing, which is basically reading comprehension for machines.
The rise of cloud computing is making much of this cheaper and faster. It’s also helping spawn a new AI industry. In all, 16 AI companies got initial backing from venture capitalists in 2014, up from two in 2010, according to data compiled by researcher CB Insights for Bloomberg. The amount invested in the startups — some of which describe themselves as doing machine learning or deep learning — soared to $309.2 million last year, up more than 20-fold from $14.9 million in 2010.
While giants like Google drove the initial push for big-data analysis, technological innovation is democratizing access, both on and off Wall Street, said Granade of Domino Data Lab.
“Automotive manufacturing, the U.S. government, pharmaceutical firms — we’re seeing sophisticated analytical need across the board,” said Granade, formerly co-head of research at Bridgewater. “Hedge funds seem to think of themselves as on the cutting edge. They are, on some level. But the rest of the world is moving very fast as well.”
