What is a Yottabyte, and How Do You Treat It? (a talk)

I gave a keynote speech late last year at Technology Awareness Day, hosted by the University of Colorado, Anschutz Medical Campus about Big Data, Tech acceleration, and Artificial Intelligence, as applied to healthcare.

I enjoy making my colleagues uncomfortable. How long will doctors have jobs? Will the AI eliminate internal medicine doctors? If Watson can beat humans at Jeopardy, can it beat me at reading medical literature? Can it be dermatologists at diagnosing skin cancer? Can it beat radiologists at interpreting CT scan images?

It is true that the most complex object known to us is the human brain, with its trillions of neurons and extensive interconnections. From this physical matter, something called “general adaptive intelligence” and “consciousness” arises, neither of which we understand or know how to construct or deconstruct. On the other hand, fundamentally though, isn’t a neuron a collection of physical and chemical processes that we DO understand? And then extrapolating upward then, is it not conceivable that we could eventually figure out how to construct a human brain in all its complexity? Hmm.

Reading books like “Life 3.0” and “Superintelligence” gets me thinking about stuff like this. It is both humbling and exciting at the same time.

CMIO’s take? Decide for yourself. I know, it is almost an hour long, and who has an hour anymore, especially if TED speakers can get their point across in 10 minutes? Well, consider my talk a series of 4-5 TED talks. Yeah, that’s it.

Seeing a black hole (NYTimes) is astounding

We are all involved in our little lives, our noses at the grindstone, making a living, trying to make a difference. Sometimes it is worth looking up … wayyyyy up, and see that fellow scientists are hard at work expanding the edges of our knowledge. We have known, based on Einstein’s theories, about black holes, believed in their existence based on indirect evidence from light bent around massively heavy objects. I had never heard of Sagittarius A*, the name applied to the mysterious unseen object at the center of Milky Way, but apparently we’ve been able to track large stars that slingshot around an unseen object with the mass of 4 billion suns(!) and have named that entity Sagittarius A*, and believed it to be a supermassive black hole. Wow, the theories we can craft with the puzzling evidence from our telescopes.

Of course, we still have NO IDEA about such things as Dark Matter, a theoretical construct we need in our understanding of how galaxies stay together (there is not enough matter in the ,mutual gravitational attraction of visible stars to explain why a galaxy spirals and stays together), or Dark Energy, another theoretical construct we need to explain why galaxies are moving moving away from each other at an ACCELERATING rate, when they should be decelerating due to gravitational attraction? Is it like Dark Fiber, those unused cables of internet connectivity? (No) or Dark Mode in the Mac operating system? (No)

And now, this, an actual image of a massive black hole at the center of galaxy M87. We finally see what Einstein saw in his equations half a century ago. Cool beans. Einstein rocks.

CMIO’s take?

If you want to get better, ask yourself these 2 questions (HBR)


Ask yourself these 2 questions (from Harvard Business Review):

  1. Do you want to get better?
  2. Are you willing to feel the discomfort of putting in more effort and trying new things that will feel weird and different and won’t work right away?

CMIO’s take? Read the article. It is inspiring, true, and worth repeating to yourself and anyone you’re mentoring.

Steven Strogatz (NYTimes) on a future for AI via AlphaZero

AlphaZero is now the undisputed champion of Go and now of chess. It recently battled Stockfish, the former chess computer heavyweight, and in that series of 100 matches, it won 28, drew 72, AND LOST NONE.

Lets hear that again. AlphaZero, the deep learning computer originally designed to play and beat human players at Go, the ancient board game, has recently been redesigned in a couple ways: 1) to take the original game rules AND NO HUMAN EXPERIENCE as its starting point, and 2) now can receive the rules for almost ANY game (in this example, chess) as its starting point. Then the programmers set AlphaZero to play itself AND LEARN THE STRATEGIES of the game by brute force and whether each strategy led to a victory or defeat. 

AlphaZero, having spent time playing itself millions of times and having discerned and taught itself the principles of chess, it only considered 60,000 moves per second instead of 60 million by Stockfish. It played smarter and faster.

“AlphaZero had the finesse of a virtuoso and the power of a machine.”

But, can it teach us its insights? No. Perhaps the most troubling paragraphs in this article is:

“What is frustrating about machine learning, however, is that the algorithms can’t articulate what they’re thinking. We don’t know why they work, so we don’t know if they can be trusted. AlphaZero gives every appearance of having discovered some important principles about chess, but it can’t share that understanding with us. Not yet, at least. As human beings, we want more than answers. We want insight. This is going to be a source of tension in our interactions with computers from now on.”

I am both heartened and disturbed by this. Heartened in that AI is on the launch pad to apply itself to all kinds of human challenges that have been difficult to solve until now. Disturbed also; how long will AlphaZero and its contemporaries need human insight and input before it’s always-accelerating capability outstrips our brains’ hardware and our ability to keep up and be relevant?

CMIO’s take? I have no take. I’m gonna wait for my auto-correct from Siri to get smart enough to finish writing this post.


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