Inspired by Wired articles by Oren Etzioni and Joe Lonsdale titled “Deep Learning Isn’t a Dangerous Magic Genie. It’s Just Math” and “AI and Robots Will Take Our Jobs — But Better Ones Will Emerge for Us,” I wanted to share six quick — and down-to-earth — thoughts about artificial intelligence.

  1. Hooray for grounded discussions! It’s time to stop feeding the hype and talk pragmatically about how machine learning and related innovations are transforming software and thus our lives, economies, and societies. Responsible techies, pundits, and politicians should, as Etzioni puts it, “have fewer imaginary problems and more real ones.”
  1. Retrain those who will be displaced. Politicians and those of us in tech should look for ways to retrain the many people whose jobs will be displaced by emerging technologies. If you’re driving a taxi, stocking a shelf, or entering data for your company’s CRM, it’s time to look into machine learning-powered future careers. These, as Lonsdale suggests, will be in fields like digital content, ecommerce, senior care, commercial space, nanotechnology, and personal assistants. We need a contemporary combo of Code.org and the 1964-era Jobs Corp for the middle class. It’s a matter of national economic competitiveness and even societal cohesion.
  1. Let’s be honest: This is hard stuff. It’s true that economies have always moved toward efficiency, disruption, and productivity gains. What’s new is that machine learning is accelerating changes across just about every sector that involves data. By its very nature, machine learning gets better, faster, the more we use it. If you’re one of the last two people who haven’t yet read Jeff Bezos’ latest letter to shareholders, check it out. If Amazon can barely keep ahead of machine learning, how can we expect the U.S. economy to do so without massive disruption?
  1. Enough fear-mongering already. Pundits who twist inevitable technological progress into an anthropomorphic evil, and demonize entrepreneurs creating great experiences as “manipulative” capitalists, detract from our collective ability to understand and adapt to change. As a lifelong subscriber, I’m disappointed in the New York Times Editorial Board. Who knew they hated technology so much? Thank you, Dean Eckles from MIT, for tweeting the truth: “How Journalism Uses Psychological Tricks to Generate Outrage.”
  1. Recognize where the promise is real and worthy. It’s worth remembering that capitalism works pretty darn well. Machine learning and AI will not only disrupt jobs and make shopping better. It will transform our supply chains, power grids, transportation systems, social habits, and national security. For example, it will take time, but in the near future, computers will be better drivers than you and me. That’s a relief, because more than 40,000 Americans were killed in automobile crashes last year, and more than 4.5 million were injured. Troublingly, those numbers are actually growing. How can that be acceptable? And how can medical professionals interpret and apply the latest in medical imagery to everyone without help from computer vision and machine learning-powered data interpretation? They can’t. Just ask a veteran who’s tried to get the best care in a timely fashion from the Veterans Affairs system. I can keep going, but you get the point.
  1. Machines will always need humans. The ultimate fail-safe for machine learning is that a computer can only see, hear, talk, and “think” as well as the people who train, retrain, and validate it. As machines get more powerful, data scientists’ need for training data will grow exponentially. The good news is, the best source of human insights is us carbon life forms. The trick is to make those insights accessible, accurate, and unbiased. That’s not easy, but it’s imperative.

In conclusion, the printing press, sewing machines, combustion engines, ATMs, and “gig economy” (ugh) all caused massive disruption that was eventually very good in the end. Machine learning will have a much bigger, broader impact, and it will take less time. It is inevitable, and it is already happening (though it’s still early days). I give thanks to folks like Etzioni and Lonsdale who are encouraging healthy debates based on the real ground truth, brick by brick.