March 05, 2020 at 04:00PM
[voices_in_ai_byline]
About this Episode
On Episode 108 of Voices in AI, Byron and Kirk Borne discuss the intersection between human nature and artificial intelligence.
Listen to this episode or read the full transcript at www.VoicesinAI.com
Transcript Excerpt
Byron Reese: This is Voices in AI brought to you by GigaOm, and I’m Byron Reese. Today my guest is Kirk Borne. He is Principal Data Scientist and executive advisor at Booz Allen Hamilton. He holds a BS in Physics from Louisiana State and a PhD in Astronomy from Caltech. His background covers all kinds of things relating to data and data science and artificial intelligence so it should be a great conversation. Welcome to the show, Kirk.
Kirk Borne: Thank you Byron. It’s great to be here.
So for the folks who aren’t familiar with you and your work, can you give us a little bit of a history about how did you get here, what was the path you took?
Well as you mentioned my background is Astrophysics and Astronomy. Starting in grad school about 40 years ago, I was always working with data for scientific discovery either through modeling and simulation or data analysis. So that’s sort of what I was doing as my avocation, which is research and astronomy, but my vocation became supporting NASA research scientists data systems — so the data systems from various satellites that NASA had for studying the space/astronomy domain. I worked on those systems and provided access to those data for scientists worldwide. I did that for about 20 years and so I was always working with data, and I would say data is my day job; data is my night job as an astronomer.
And so it was about 20 years ago that we were starting to notice the data volumes of the experiments we were working with, were just becoming more off scale than ever imagined. I mean just one single dataset I still remember 1997 — we were trying to work with this dataset that just by itself was more than double the size of the other 15,000 experiments we were working with combined. So that was like unheard of. And so at that point I started looking around at what can one do with data of this volume and I discovered machine learning and data mining. So I had never actually looked at data that way before. I just thought about analysis, not so much discovery from data from a machine learning perspective, and so that was 20 years ago and sort of fell in love with that whole mathematical process and the applications that come from that, which include AI. That’s what I’ve been doing for the last two decades.
And so as a practitioner, what’s the sort of work you’re doing now?
Well for me personally it’s really about, as my company likes to say, thought leadership. I feel kind of nervous when I say that about myself but I do a lot of public speaking, I write a lot of blogs. My title includes ‘executive advisor’, so I’m advising both internally our business managers around AI machine learning and data science, but also our clients. But at the same time I’m also doing sort of tutoring and mentoring to some of our younger data scientists because after my 20 years at NASA, I spent 12 years at George Mason University as a professor. I was Professor of Astrophysics, but I really was teaching data science; and so it’s sort of in my blood I guess to be an educator, to teach, to train and so that’s pretty much what I’m doing. I’m promoting the field, having conversations with people, for developing new ideas and concepts; not so much coding anymore like I used to do back when I was younger at NASA. I let the smart young coders today do all that work but we have lots of interesting conversations about which algorithms to use or developing. So it’s really exploratory innovation at the frontier of all this stuff.
So before we launch into AI questions (I have a pile of them for you) I can’t imagine there’s an Astronomy PhD on the planet that doesn’t have their own opinion about the Fermi Paradox. What is yours?
Oh well, I think that’s a good question. But I think that right sort of response to that is the distance between stars is so enormous that it’s really hard to imagine that if every star had planets that were teeming with life, even nearby stars, it would probably be still next to impossible to imagine any kind of encounter. Literally why would they go travel to some speck of dust that would take them literally hundreds of years? You might say the life spans would be different. Different planets, maybe, maybe not.
I mean all these things are tapered by you know that the conditions of star evolution and all kinds of things. So I can’t imagine sort of chemical or biological processes being all that different. In fact they should not be different on other planets. And so I just think that the time travel and space travel challenges are so enormous that I just can’t see it really happening. So I’m not sure if I can believe whether there was life teeming on every other planet in the universe or at least on a planet around each star in the universe. But know that it’s completely possible.
So I only ask one follow up and then we can launch into AI. But you know we would be eager to go visit other stars. I mean you know in the ‘70s we sent out the Voyager probes and those were like “Hey everybody we’re here.” Of course that too is you know, a bottle in an intergalactic large ocean, and so maybe there are alien Voyager probes floating around all over the place. But they’re too sparsely separated to ever come out our way.
Now it’s also considering the size of the thing. I mean we’re detecting better and better than ever before asteroids in our solar system that are a few hundred meters in size. But our probes are not much bigger than a suitcase. So we’re not paying any attention to those. In fact they really are just specks of dust, specks of noise in our data on… and there’s literally hundreds of billions or trillions of such specks of dust in our own solar system. And we’re more concerned with the big ones that might do damage to us. So we’re just ignoring all of those things even if some of them, who knows, for all we know they could be alien probes…
Right we had that cigar shaped… So OK, the show is Voices in AI. So let’s voice a little bit about AI. So let’s start with the basics, how do you in your mind define intelligence and in what sense is artificial intelligence… is it artificial because we made it or it’s artificial because it’s like faux, it’s not really intelligent, it’s just faking it?
Probably all of those. So for me AI is really just the actionable output of what we learn from incoming sensor data. Okay so sensors measure things about the world, algorithms find patterns and trends in those readings. And then there’s a response and action, a decision that comes from that. That’s what humans do, that’s what all animals do. Right? We have sensors, our eyes, our ears, our mouths, our fingers, our hands whatever we have we’re sending our universe. And from what we sense that is patterns we recognize detect patterns and anomalies, that’s what we’re really good at.
Then we infer what would happen if I ignore this or not ignore this or do something with this thing that I’m seeing. And then based upon that sort of inference, we make a decision to do something or not do so. So our algorithms, human or any animal is a biological neural network. And so we’re emulating that with an artificial [one].
So yes, it is artificial intelligence, but I’d like to say the things we’re building are really… the purpose of them is not for the purpose of just building an artificial intelligence but it’s to augment our intelligence. So I say the seven A’s of AI are: augmented intelligence, assisted, amplified, accelerated, adaptable, actionable intelligence — that’s six probably. But anyway so I have seven A’s of AI that basically say what we are really trying to do is augment and amplify and accelerate human intelligence by automating parts of this process — especially the process of dealing with all the information flood that’s coming into our sensors these days.
But in a couple of touch points there, you likened machine intelligence to human intelligence in terms of you mentioned neural nets that are trying to do something vaguely analogous to what the brain does and all that. But isn’t machine intelligence something radically different not just in form, but like if you gave an AI all the data of planetary motion of the last 500 years, all the planets in our, all the bodies in our solar system, it could figure out when the next eclipse was going to be because it would just study it and it would make this assumption the future is like the past.
And it would do it but if you then said, “What would happen if the moon vanished? How would it change everything?” It would be like… (silence), so it doesn’t really understand anything. Like you said it just finds patterns and makes predictions based on them but it doesn’t understand why anything happens the way it does. So it couldn’t be a perfect planetary model, but it wouldn’t ever even intuit that something called gravity exists, right?
Well that’s true. But if you think about ancient civilizations, they had no deeper intuition than that machine you just described. So if the moon vanished it would invoke all kinds of bizarre interpretations for that and even bizarre sort of outcomes — like literally in the ancient times when there was an eclipse, you know people panicked. And if there was like a Royal Astronomer like in some of the ancient quartz kingdoms if that ancient astronomer had not predicted that eclipse, they usually lost their head.
You know maybe we should bring that back quite frankly.
Anyway. So I think the intuition that we have as humans today we’ve gained over millennia of human existence and so what we learn in schools, — and I like to tell people you know hopefully a successful person spends a minimum of 12 years in school, doesn’t drop out, and then hopefully beyond that there’s either college or continuing education or certainly lifelong learning.
So we get to the point where we’re actually employable and useful as an intelligent person in the workplace after literally decades of consuming information and knowledge. So our algorithms we’re feeding ten thousand or ten million pictures of cats. You haven’t gotten to scratch the surface of all the thousands and millions of different kinds of knowledge that humans just gathered through second by second, minute by minute, hour by hour interaction with their world over decades.
Listen to this episode or read the full transcript at www.VoicesinAI.com
[voices_in_ai_link_back]
Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.
No comments:
Post a Comment