Stories, Dice and Rocks that Think. A Look at the Historical Trajectory of AI and How it is Shaping the Future

 Just completed this excellent book by Byron Reese about how we got to thinking about 'artificial Intelligence', and were it can go.   Here is their overview:  

'Stories, Dice and Rocks That Think'.    Book by Byron Reese

 This fascinating tale explores the three leaps in human history that made us what we are today-and will change how you think about our future.

Look around. Clearly, we humans are radically different from the other creatures on this planet. But why? Where are the Bronze Age beavers? The Iron Age iguanas? 

In Stories, Dice, and Rocks That Think, Byron Reese argues that we owe our special status to our ability to imagine the future and recall the past, escaping the perpetual present that all other living creatures are trapped in.

Reese shows us how this escape enabled us to share knowledge on an unprecedented scale, and predict- -and eventually master--the future.

Thoughtful, witty, and compulsively readable, Stories, Dice, and Rocks That Think unravels our history as an intelligent species in three acts:

Act I (Stories): Ancient humans undergo "the awakening," developing the cognitive ability to mentally time-travel and share knowledge using language.

Act II (Dice): In 17th century France, the mathematical framework known as "probability theory" is born--a science for seeing into the future that we used to build the modern world.

Act III (Rocks That Think): Beginning with the invention of the computer chip, humanity creates machines to model the future with even more precision, and overcome the limits of our brains.

A fresh new look at the history and destiny of humanity, Stories, Dice, and Rocks That Think will give readers a new understanding of what they are- not just another animal, but a creature with a mastery of time itself. ... 

(Available in all the usual places)

Buying the Cameras

Implications of the fears of needed data gathering for General AI, Placed in the context of human behavior

What Keith Lowell Jensen sums up  is what George Orwell failed to predict is that:

"We'd buy the cameras ourselves and our biggest fear would be that nobody was watching"

   quoted from "Stories, Dice and Rocks that Think: How Humans learned to see the Future and Shape it" By Byron Reese p. 270.  An excellent Book that made me think as a practitioner in the space.

Stories, Dice, and Rocks That Think:

Just  Reading, very interesting, will review further as I progress.  by a correspondent I have often mentioned here.  

Stories, Dice, and Rocks That Think: How Humans Learned to See the Future--and Shape It  ...    by Byron Reese 

". . . Byron Reese gets to the heart of what makes humans different from all others." —Midwest Book Review

What makes the human mind so unique? And how did we get this way?   Amazon Description:

This fascinating tale explores the three leaps in our history that made us what we are—and will change how you think about our future.

Look around. Clearly, we humans are radically different from the other creatures on this planet. But why? Where are the Bronze Age beavers? The Iron Age iguanas? In Stories, Dice, and Rocks That Think, Byron Reese argues that we owe our special status to our ability to imagine the future and recall the past, escaping the perpetual present that all other living creatures are trapped in. 

Envisioning human history as the development of a societal superorganism he names Agora, Reese shows us how this escape enabled us to share knowledge on an unprecedented scale, and predict—and eventually master—the future.

Thoughtful, witty, and compulsively readable, Reese unravels our history as an intelligent species in three acts: 

Act I: Ancient humans undergo “the awakening,” developing the cognitive ability to mentally time-travel using language

Act II: In 17th century France, the mathematical framework known as 'probability theory' is born—a science for seeing into the future that we used to build the modern world

Act III: Beginning with the invention of the computer chip, humanity creates machines to gaze into the future with even more precision, overcoming the limits of our brains

A fresh new look at the history and destiny of humanity, readers will come away from Stories, Dice, and Rocks that Think with a new understanding of what they are—not just another animal, but a creature with a mastery of time itself.  ... ' 

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. ... " ... ' 

Podcast Interview with Hilary Mason on GigaOM

Another AI practitioner talks about the advances and future of AI:

Voices in AI – Bonus: A Conversation with Hilary Mason   By Byron Reese

On this Episode of Voices in AI features Byron speaking with Hilary Mason, an acclaimed data and research scientist, about the mechanics and philosophy behind designing and building AI.

Listen to this episode or read the full transcript at www.VoicesinAI.com

Byron Reese: This is Voices in AI, brought to you by Gigaom and I am Byron Reese. Today, our guest is Hilary Mason. She is the GM of Machine Learning at Cloudera, and the founder and CEO of Fast Forward Labs, and the Data Scientist in residence at Accel Partners, and a member of the Board of Directors at the Anita Borg Institute for Women in Technology, and the co-founder of hackNY.org. That’s as far down as it would let me read in her LinkedIn profile, but I’ve a feeling if I’d clicked that ‘More’ button, there would be a lot more.

Welcome to the show, amazing Hilary Mason!

Hilary Mason: Thank you very much. Thank you for having me.

I always like to start with the question I ask everybody because I’ve never had the same answer twice and – I’m going to change it up: why is it so hard to define what intelligence is? And are we going to build computers that actually are intelligent, or they can only emulate intelligence, or are those two things the exact same thing?

This a fun way to get started! I think it’s difficult to define intelligence because it’s not always clear what we want out of the definition. Are we looking for something that distinguishes human intelligence from other forms of intelligence? There’s that joke that’s kind of a little bit too true that goes around in the community that AI, or artificial intelligence, is whatever computers can’t do today. Where we keep moving the bar, just so that we can feel like there’s something that is still uniquely within the bounds of human thought.

Let’s move to the second part of your discussion which is really asking, ‘Can computers ever be indistinguishable from human thought?’ I think it’s really useful to put a timeframe on that thought experiment and to say that in the short term, ‘no.’ I do love science fiction, though, and I do believe that it is worth dreaming about and working towards a world in which we could create intelligences that are indistinguishable from human intelligences. Though I actually, personally, think that it is more likely we will build computational systems to augment and extend human intelligence. For example, I don’t know about you but my memory is horrible. I’m routinely absentminded. I do use technology to augment my capabilities there, and I would love to have it more integrated into my own self and my intelligence. ..... " 

Voices in AI Podcast: A Conversation with Norman Sadeh

We had early AI connects with Carnegie Mellon.

Voices in AI – Episode 90: A Conversation with Norman Sadeh
By Byron Reese

Episode 90 of Voices in AI features Byron speaking with Norman Sadeh from Carnegie Mellon University about the nature of intelligence and how AI effects our privacy.

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 I’m Byron Reese, today my guest is Norman Sadeh. He is a professor at Carnegie Mellon School of Computer Science. He’s affiliated with Cylab which is well known for their seminal work in AI planning and scheduling, and he is an authority on computer privacy. Welcome to the show.

Carnegie Mellon has this amazing reputation in the AI world. It’s arguably second to none. There are a few university campuses that seem to really… there’s Toronto and MIT, and in Carnegie Mellon’s case, how did AI become such a central focus?

Norman Sadeh: Well, this is one of the birthplaces of AI, and so the people who founded our computer science department included Herbert Simon and Allen Newell who are viewed as two of the four founders of AI. And so they contributed to the early research in that space. They helped frame many of the problems that people are still working on today, and they helped recruit also many more faculty over the years that have contributed to making Carnegie Mellon as the place that many people refer to as being the number one place in AI here in the US.

Not to say that there are not other many good places out there, but CMU is clearly a place where a lot of the leading research has been conducted over the years, whether you are looking at autonomous vehicles – for instance, I remember when I came here to do my PhD back in 1997, there was research going on autonomous vehicles. Obviously the vehicles were a lot clumsier than they are today, not moving quite as fast, but there’s a very, very long history of AI research, here at Carnegie Mellon. The same is true for language technology, the same is true for robotics, you name it. There are lots and lots of people here who are doing truly amazing things. ..... "

GigaOm Interviews about AI

Byron does a great job of interviewing emerging expertise.  Been a follower for along time, join up.

Voices in AI – Episode 88: A Conversation with Ron Green   By Byron Reese

Episode 88 of Voices in AI features Byron speaking with Ron Green of KUNGFU.AI about how companies integrate AI and machine learning into their business models.

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 Ron Green. Ron is the CTO over at KUNGFU.AI. He holds a BA in Computer Science from the University of Texas at Austin, and he holds a Master of Science from the University of Sussex in Evolutionary and Adaptive Systems. His company, KUNGFU.AI is a professional services company that helps companies start and accelerate artificial intelligence projects. I asked him [to be] on the show today because I wanted to do an episode that was a little more ‘hands-on’ about how an enterprise today can apply this technology to their business. Welcome to the show, Ron. ,,,, " 

AI Conversations

I selectively read or listen to these.   Often interesting.   Since I have been involved in the history of AI, its always intriguing to hear the opinion of its continued evolution,  especially hints at another possible down turn, and pointers to ultimate general intelligence.  Byron Reese does a good job  at managing these.   Note here the reference to what is called an 'information bubble', which is described in the WP as a Filter Bubble.

Voices in AI – Episode 77: A Conversation with Nicholas Thompson
By Byron Reese Jan 10, 2019 - 7:00 AM CST

Episode 77 of Voices in AI features host Byron Reese and Nicholas Thompson discussing AI, humanity, social credit, as well as information bubbles. Nicholas Thompson is the editor in chief of WIRED magazine, contributing editor at CBS, co-founder of The Atavist and also worked at The New Yorker and authored a Cold War era biography.  ... " 

Modeling a Simple Organism

The nematode Caenor Elegans makes up about 80% of the animals on earth.  Yet it could be called the  most successful animal on earth.  Lives in most any environment.   We have completely sequenced its DNA.  We have been studying it for decades.  It has just 959 cells in its 'Brain',  302 neurons, each of its neurons are connected to 30 others.  So about 10K synapses.   (Humans have trillions).  So we should understand how a nematode's brain works?   No, not even close.   And we have been trying very hard. (Via Byron Reese in his book: The Fourth Age: Smart Robots, Conscious Computers and the Future of Humanity)  Good book on the direction, challenges and cautions of AI.

OpenWorm is an open source project dedicated to creating the first virtual organism in a computer.

Why?
Because modeling a simple nervous system is a first step toward fully understanding complex systems like the human brain.

How?
By rejecting red tape and building a community of engineers, scientists, and other motivated volunteers from around the world. ... " 

My Review: The Fourth Age

The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity: by Byron Reese

My review via my read, just submitted:

Very well done.  Have been following author Byron Reese's GigaOm podcast interviews with experts in AI,  and the book often has opinions that come from this group.    The Fourth Age is defined as the age of Robots and AI.   He takes care to define Narrow AI vs General AI.   Good, since in our hype-ridden world people are quick to assume we are very close to General AI.   I like that he uses the first three ages to set the direction, and also makes the point that the speed of related changes approaching the Fourth Age is increasing quickly.    So this means that we will have to react more quickly than before to keep from some negative side effects.    

The AI Winter,  a lull in new development,  is mentioned as a consequence,  and it might be, especially if we don't carefully define AI narrowly.   Will it take our jobs?   He does a good job of sketching out why not.  And even advises regarding Robot-Proof jobs.   Makes a clear case that we are no where close to building artificial brains,  but still may have to consider the ethics of results from devices we treat like brains.    Great, non technical read.  Does not have a index, because its a 'popular' rather than technical book. ....

AI Conversation with Chris Eliasmith

Another good conversation about AI.  I don't always report these but they are all out at the link.

Voices in AI – Episode 58: A Conversation with Chris Eliasmith
By Byron Reese,  Podcast and transcript 

Episode 58 of Voices in AI features host Byron Reese and Chris Eliasmith talking about the brain, the mind, and emergence. Dr. Chris Eliasmith is co-CEO of Applied Brain Research, Inc. and director of the Centre for Theoretical Neuroscience at the University of Waterloo. Professor Eliasmith uses engineering, mathematics and computer modelling to study brain processes that give rise to behaviour. His lab developed the world’s largest functional brain model, Spaun, whose 2.5 million simulated neurons provide insights into the complexities of thought and action. Professor of Philosophy and Engineering, Dr. Eliasmith holds a Canada Research Chair in Theoretical Neuroscience. He has authored or coauthored two books and over 90 publications in philosophy, psychology, neuroscience, computer science, and engineering. In 2015, he won the prestigious NSERC Polayni Award. He has also co-hosted a Discovery channel television show on emerging technologies.

Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript. ... "

The Fourth Age: Smart Robots, Conscious Computers ....

Just reading.   Excellent, non technical view.   More to follow ...

The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity   by Byron Reese

As we approach a great turning point in history when technology is poised to redefine what it means to be human, The Fourth Age offers fascinating insight into AI, robotics, and their extraordinary implications for our species.

In The Fourth Age, Byron Reese makes the case that technology has reshaped humanity just three times in history:

- 100,000 years ago, we harnessed fire, which led to language.

- 10,000 years ago, we developed agriculture, which led to cities and warfare.

- 5,000 years ago, we invented the wheel and writing, which lead to the nation state.

We are now on the doorstep of a fourth change brought about by two technologies: AI and robotics. The Fourth Age provides extraordinary background information on how we got to this point, and how—rather than what—we should think about the topics we’ll soon all be facing: machine consciousness, automation, employment, creative computers, radical life extension, artificial life, AI ethics, the future of warfare, superintelligence, and the implications of extreme prosperity.

By asking questions like “Are you a machine?” and “Could a computer feel anything?”, Reese leads you through a discussion along the cutting edge in robotics and AI, and, provides a framework by which we can all understand, discuss, and act on the issues of the Fourth Age, and how they’ll transform humanity. ... "

GigaOM Voices of AI

Another great look at the topic.   Currently reading Byron Reese's book, will soon follow with notes on that.

Voices in AI – Episode 50: A Conversation with Steve Pratt  By Byron Reese

Byron Reese: This is Voices in AI, brought to you by GigaOm, and I’m Byron Reese. Today, our guest is Steve Pratt. He is the Chief Executive Officer over at Noodle AI, the enterprise artificial intelligence company. Prior to Noodle, he was responsible for all Watson implementations worldwide, for IBM Global Business Services. He was also the founder and CEO of Infosys Consulting, a Senior Partner at Deloitte Consulting, and a Technology and Strategy Consultant at Booz Allen Hamilton. Consulting Magazine has twice selected him as one of the top 25 consultants in the world. He has a Bachelor’s and a Master’s in Electrical Engineering from Northwestern University and George Washington University. Welcome to the show, Steve.

Steve Pratt: Thank you. Great to be here, Byron.

Let’s start with the basics. What is artificial intelligence, and why is it artificial?

Artificial intelligence is basically any form of learning algorithm; is the way we think of things. We actually think there’s a raging religious debate [about] the differences between artificial intelligence and machine learning, and data science, and cognitive computing, and all of that. But we like to get down to basics, and basically say that they are algorithms that learn from data, and improve over time, and are probabilistic in nature. Basically, it’s anything that learns from data, and improves over time.

So, kind of by definition, the way that you’re thinking of it is it models the future, solely based on the past. Correct?

Yes. Generally, it models the future and sometimes makes recommendations, or it will sometimes just explain things more clearly. It typically uses four categories of data. There is both internal data and external data, and both structured and unstructured data. So, you can think of it kind of as a quadrant. We think the best AI algorithms incorporate all four datasets, because especially in the enterprise, where we’re focused, most of the business value is in the structured data. But usually unstructured data can add a lot of predictive capabilities, and a lot of signal, to come up with better predictions and recommendations. .... "