IBM Watson, five years later

In 2014 I wrote about my excitement over a demo of IBM Watson. I have recently heard about some familiar consumer tools that are using Watson and I revisited my post to see what’s changed over five years.

There are great case studies on IBM Watson here. It’s no surprise that this technology has gotten a lot of traction over the last five years.

One caveat I had when reviewing this tool in 2014 was that the data is only as good as how it’s communicated, and this still stands. Many analytics tools are doing a much better job of showing the quality of the underlying data and how reliable the predictions are, but there is still a level of understanding that is required when handling big data sets.

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Public policy and AI

One of the largest AI conferences is NeurIPS, which happens annually in December. I spent some time browsing the presentations from the 2018 conference and found an interesting presentation by Edward W. Felten from Princeton University titled, “Machine Learning Meets Public Policy: What to Expect and How to Cope.”

Felten kicks off his talk by highlighting the messaging that people in public policy are hearing about AI, and overwhelmingly, it is a warning to put regulations in place. People like Henry Kissinger and Elon Musk have already sounded the alarm to policy makers.

His thesis is that the best policies will come out of technical people partnering well with policy makers, with both sides trusting the others’ expertise. This comes from being engaged and constructive in the policy making process over time.

It was interesting to see push back on this thesis by some attendees. One counterpoint was that many industries have self-regulating bodies, such as FINRA, and that this could be an option for machine learning. However, Felten pushes back on this because self-regulating bodies work well when they have public accountability, and they are easily replaced when not accountable.

AI blogs and newsletters for businesspeople

Keeping it brief for a Saturday post. I wanted to share some content I follow for information on AI developments. A challenge I’ve found as I learn about AI is that a lot of content skews technical – the intended audience is programmers, not businesspeople. The sites I’ve shared below are relevant to those focused on implementation and investment rather than creation. I may update this over time as I find additional sites that consistently deliver valuable insights.

  • Artificial Intelligence Weekly: Download of relevant stories and investment news
  • Work-bench blog: Work-bench is a enterprise technology focused VC in New York City. They’re not exclusively focused on AI but it’s gotten a lot of attention lately and often comes up in their blog posts.

Fun AI: Iconary

I’m ending the week on a light note with an AI game I found. Iconary is a Pictionary-like game developed by AllenAI in Washington. I had a lot of fun drawing and guessing and it’s surprising to see how closely my perception and guesses match my AI opponent, Allen.

This is the kind of thing that is so tricky for AI – reading meaning into symbols. AI can recognize a tree as a tree, but can it recognize a group of trees as a forest? This one can!

Here’s a great write-up on the game and the impressive AI behind it on TechCrunch.