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Big Data and The Great A.I. Awakening. Interview with Steve Lohr

by Roberto V. Zicari on December 19, 2016

“I think we’re just beginning to grapple with implications of data as an economic asset” –Steve Lohr.

My last interview for this year is with Steve Lohr. Steve Lohr has covered technology, business, and economics for the New York Times for more than twenty years. In 2013 he was part of the team awarded the Pulitzer Prize for Explanatory Reporting. We discussed Big Data and how it influences the new Artificial Intelligence awakening.

Wishing you all the best for the Holiday Season and a healthy and prosperous New Year!


Q1. Why do you think Google (TensorFlow) and Microsoft (Computational Network Toolkit) are open-sourcing their AI software?

Steve Lohr: Both Google and Microsoft are contributing their tools to expand and enlarge the AI community, which is good for the world and good for their businesses. But I also think the move is a recognition that algorithms are not where their long-term advantage lies. Data is.

Q2. What are the implications of that for both business and policy?

Steve Lohr: The companies with big data pools can have great economic power. Today, that shortlist would include Google, Microsoft, Facebook, Amazon, Apple and Baidu.
I think we’re just beginning to grapple with implications of data as an economic asset. For example, you’re seeing that now with Microsoft’s plan to buy LinkedIn, with its personal profiles and professional connections for more than 400 million people. In the evolving data economy, is that an antitrust issue of concern?

Q3. In this competing world of AI, what is more important, vast data pools, sophisticated algorithms or deep pockets?

Steve Lohr: The best answer to that question, I think, came from a recent conversation with Andrew Ng, a Stanford professor who worked at GoogleX, is co-founder of Coursera and is now chief scientist at Baidu. I asked him why Baidu, and he replied there were only a few places to go to be a leader in A.I. Superior software algorithms, he explained, may give you an advantage for months, but probably no more. Instead, Ng said, you look for companies with two things — lots of capital and lots of data. “No one can replicate your data,” he said. “It’s the defensible barrier, not algorithms.”

Q4. What is the interplay and implications of big data and artificial intelligence?

Steve Lohr: The data revolution has made the recent AI advances possible. We’ve seen big improvements in the last few years, for example, in AI tasks like speech recognition and image recognition, using neural network and deep learning techniques. Those technologies have been around for decades, but they are getting a huge boost from the abundance of training data because of all the web image and voice data that can be tapped now.

Q5. Is data science really only a here-and-now version of AI?

Steve Lohr: No, certainly not only. But I do find that phrase a useful way to explain to most of my readers — intelligent people, but not computer scientists — the interplay between data science and AI. To convey that rudiments of data-driven AI are already all around us. It’s not — surely not yet — robot armies and self-driving cars as fixtures of everyday life. But it is internet search, product recommendations, targeted advertising and elements of personalized medicine, to cite a few examples.

Q6. Technology is moving beyond increasing the odds of making a sale, to being used in higher-stakes decisions like medical diagnosis, loan approvals, hiring and crime prevention. What are the societal implications of this?

Steve Lohr: The new, higher-stakes decisions that data science and AI tools are increasingly being used to make — or assist in making — are fundamentally different than marketing and advertising. In marketing and advertising, a decision that is better on average is plenty good enough. You’ve increased sales and made more money. You don’t really have to know why.
But the other decisions you mentioned are practically and ethically very different. These are crucial decisions about individual people’s lives. Better on average isn’t good enough. For these kinds of decisions, issues of accuracy, fairness and discrimination come into play.
That, I think, argues for two things. First, some sort of auditing tool; the technology has to be able to explain itself, to explain how a data-driven algorithm came to the decision or recommendation that it did.
Second, I think it argues for having a “human in the loop” for most of these kinds of decisions for the foreseeable future.

Q7. Will data analytics move into the mainstream of the economy (far beyond the well known, born-on-the-internet success stories like Google, Facebook and Amazon)?

Steve Lohr: Yes, and I think we’re seeing that now in nearly every field — health care, agriculture, transportation, energy and others. That said, it is still very early. It is a phenomenon that will play out for years, and decades.
Recently, I talked to Jeffrey Immelt, the chief executive of General Electric, America’s largest industrial company. GE is investing heavily to put data-generating sensors on its jet engines, power turbines, medical equipment and other machines — and to hire software engineers and data scientists.
Immelt said if you go back more than a century to the origins of the company, dating back to Thomas Edison‘s days, GE’s technical foundation has been materials science and physics. Data analytics, he said, will be the third fundamental technology for GE in the future.
I think that’s a pretty telling sign of where things are headed.

Steve Lohr has covered technology, business, and economics for the New York Times for more than twenty years and writes for the Times’ Bits blog. In 2013 he was part of the team awarded the Pulitzer Prize for Explanatory Reporting.
He was a foreign correspondent for a decade and served as an editor, and has written for national publications such as the New York Times Magazine, the Atlantic, and the Washington Monthly. He is the author of Go To: The Story of the Math Majors, Bridge Players, Engineers, Chess Wizards, Maverick Scientists, Iconoclasts—the Programmers Who Created the Software Revolution and Data-ism The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else.
He lives in New York City.



Google (TensorFlow): TensorFlow™ is an open source software library for numerical computation using data flow graphs.

Microsoft (Computational Network Toolkit): A free, easy-to-use, open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain.

Data-ism The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else. by Steve Lohr. 2016 HarperCollins Publishers

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One Comment Leave one →
  1. Giovanni Panzeri permalink

    “AI is akin to building a rocket ship. You need a huge engine and a lot of fuel. The rocket engine is the learning algorithms but the fuel is the huge amounts of data we can feed to these algorithms.” by Andrew Ng

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