2017 has been a busy year, but I took some time off this week, which gave me the chance to get caught up on the magazines and journals I had set aside for a while.
The first one I picked up was the Nov/Dec 2017 issue of the MIT Technology Review. It’s “The Artificial Intelligence Issue.” There are plenty of interesting things in this issue. I’ll recommend one in particular.
A short article, Fiction That Gets AI Right by Brian Bergstein, is worth a look. He recommends six works from (lesser-known) stories, TV shows, plays, and films that have an AI theme. If you’re interested, as I am, in the various ways that our storytellers have thought about the significance of robots and AI, you should look it over. Even if you don’t read or watch the recommended stories, I think you’ll find the contents of the article interesting and thought-provoking.
The IBM site I mentioned in my previous post provides quite a bit of good information for those interested in, but new to, AI. Here’s another document you might find of interest. It’s on deep learning architectures.
To dig even further, go to:
If you’re looking for an introduction to AI, take a look at this article on the IBM “developerworks” site.
Beginner’s Guide to AI
It’s downloadable as a PDF (about ten pages in length).
The way I build a human team depends upon the purpose of the team.
Sometimes I need a team with skills nearly-identical to my own. Sometimes I need a team with widely varying skills. Sometimes I need a little bit of both – I need subject matter experts with deep knowledge in a specific area who also have other skills and insights acquired through different professional experiences.
Sometimes I like to include a totally new perspective – perhaps a new hire or trainee or novice or someone from an entirely unrelated discipline because they may see things in a way that the rest of us can’t see or ask a question that the rest of us wouldn’t ask.
Would I do the same things if I were building teams of people plus machines? For example, would I select multiple machine-learning systems that were written by different programming teams and that implement differing algorithms in order to gain diverse machine insights?
Technology changes the nature of work. It always has in one way or another. And now it is changing the nature of what we mean by “working in teams.”
We are in a new era where many human workers have, or soon will have, cognitive co-workers (i.e., machine learning applications). This pairing of person and technology creates a human-technology team that will work problems together. The effect that such teaming has on the human team member is something to be studied.
Beyond that, there will also be teams of such teams, i.e., my cognitive co-worker and I will work with your cognitive co-worker and you to achieve mutual goals. Is there anything to that dynamic that might create teamwork challenges?
It’s something to think about.
For any of my students looking for an introduction to “complexity,” I recommend Complexity – A Guided Tour by systems scientist Melanie Mitchell. I pulled it off my shelf this morning to re-read Chapter 19, “The Past and Future of the Sciences of Complexity.”
Dr. Mitchell’s Ph.D. advisor was Dr. Douglas Hofstadter, author of the Pulitzer Prize-winning classic Godel, Escher, and Bach: An Eternal Golden Braid. For a thought-provoking interview with Dr. Hofstadter, see the following interview in The Atlantic magazine:
I recently read Garry Kasparov’s latest book, Deep Thinking – Where Machine Intelligence Ends and Human Creativity Begins. It was hard to put down. I thoroughly enjoyed it, being interested in virtually everything he addressed: AI, machine learning, the societal implications of technological change, and, of course, chess.