Kirk Borne on AI and Human Nature

Have met Kirk, he has an astrophysics background similar to mine.  Here a podcast with him by Byron Reese.

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. ... " ... ' 

Language Training with Your Own Voice

When I started to use Google Assistant multilingualism, I immediately thought, why not have it teach you a language, with the aid of languages you know?   This is a start in that direction.

Via Kirk Borne:
Google's new #AI can help you speak another language in your own voice — the project is called Translatotron: https://t.co/P6y7ILWEIH 

More technical details at the Google AI blog:
https://ai.googleblog.com/2019/05/introducing-translatotron-end-to-end.html

Wal-Mart Trip Type Classification

From DSG:  Nice example of how data science can be used for a real world classification problem, with real data.  Including 1.2 million observations with 6 features.    Here using a Random Forest method for classification of retail trip types and comparing it to other methods.    Includes all the Python code from a Kaggle competition. Visualization of modeling progress.   Nicely done example. via Kirk Borne.

How the Data Lake Works

Part of an exploration of useful linkages between data and analytics to promote easier data use and sharing.  Brought to my attention by Kirk Borne.  A PDF from Booz Allen.

Analytics Reference Architecture

Nicely done look at a Cloud Analytics Playbook & Reference Architecture  by @BoozAllen at Booz Allen Consulting  via Kirk Borne.  I like the inclusion of metadata tagging in the design.

State of Data Science

Kirk Borne  writes:
" ... Check out "The State of #DataScience"  RJMetrics report link,  including 14 charts + analysis  .... ".   Requires registration for full report, but the introduction itself is useful.

An Executive's Guide to Machine Learning

We did machine learning at Procter & Gamble starting in the mid 80s, and they continue today. This is a nice mostly non tech overview for execs I could have used most any of those years,  with anyone interested in learning.  via Kirk Borne, piece is from McKinsey.

An executive’s guide to machine learning
It’s no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix ... 

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning—and the need for it. .... " 

Favorite Data Science Books from Kirk Borne

Correspondent Kirk Borne posts a selection of his favorite books on Data Science, Machine Learning and Analytics.    Taking a look now.  Met Kirk at a recent conference and was impressed by his mingling of the learnings of physical science (notably also in my own specialty, Astrophysics)  and business data science.  There is much to learn from mixing deep quant science and data.  I am trying to convince him that this could be taken further, into the realm of decision science.    See also his Twitter stream.  @KirkDBorne.  I follow it. Keep up the good reporting work.

Data Science for the Masses

Paper by a colleague. Kirk Borne.  This is not just about Astronomy science data, but broadly applicable to science and industry.  Everyone needs to understand the implications of more and better data and analytics.

The Revolution in Astronomy Education: Data Science for the Masses
Kirk D. Borne (George Mason University), Suzanne Jacoby (LSST Corporation). Karen Carney (Adler Planetarium), Andy Connolly (University of Washington),Timothy Eastman (Wyle Information Systems),  M. Jordan Raddick (JHU/SDSS), J. A. Tyson (UC Davis), John Wallin (GMU)

Abstract:

As our capacity to study ever-expanding domains of our science has increased (including the time domain, non-electromagnetic phenomena, magnetized plasmas, and numerous sky surveys in multiple wavebands with broad spatial coverage and unprecedented depths), so have the horizons of our understanding of the Universe been similarly expanding. This expansion is coupled to the exponential data deluge from multiple sky surveys, which have grown from gigabytes into terabytes during the past decade, and will grow from terabytes into Petabytes (even hundreds of Petabytes) in the next decade. With this increased vastness of information, there is a growing gap between our awareness of that information and our understanding of it. Training the next generation in the fine art of deriving intelligent understanding from data is needed for the success of sciences, communities, projects, agencies, businesses, and economies.

This is true for both specialists (scientists) and non-specialists (everyone else: the public, educators and students, workforce). Specialists must learn and apply new data science research techniques in order to advance our understanding of the Universe. Non-specialists require information literacy skills as productive members of the 21st century workforce, integrating foundational skills for lifelong learning in a world increasingly dominated by data. We address the impact of the emerging discipline of data science on astronomy education within two contexts: formal education and lifelong learners. ...... "