I've no experience at all with book reviews. Giving my opinion is hardly problematic, as I warned in my inaugural post. But summarizing is not my forte. If anything, I tend to do the opposite of summarizing: give me a paragraph and I will give you a book. When trying to review an entire book, then, well....I just hope blogspot doesn't charge by the word.
Nevertheless, here I am trying to do a book review. Why? Because I am suffering from an embarrassment of riches of late. I have come across so many wonderful books in the last couple of years that I am bursting to share them all. I haven't come across such a wealth of wonderful reading since I was a very young man*, back when ALL wonderful literature was new to me. So, over the coming months, I will share some of these titles and my thoughts on their content and worth.
Among the most recent is Mark Stevenson's An Optimist's Tour of the Future, an insightful and inspiring (if occasionally mildly terrifying) book about the latest trends in all the technologies and ideas that will shape the world to come. I was fortunate enough to read an advance copy of the book, which is being released in the US on 3 February 2011. After I read it, I began a correspondence with the author, who is one of the most genuinely kind people I have had the pleasure to 'e-meet', the electronic nature of our acquaintanceship notwithstanding. I mention this only in the interest of full disclosure: I am reviewing a book of a person whom I have come to know (albeit to a necessarily very limited extent). But to be clear, reading and admiring the book came first and my reflections are thus free of any bias: I would not have reached out to the author had I not already respected his work. So with all the disclosures out of the way...
Think back over the past few years and think about the books you've read on the current state of the world and/or its fast-approaching fate. Then, when you get back from the pharmacy and take your copious amounts of anti-depressants needed to cope with those books, pick up a copy of this book and throw out the pills. Amid all the doom and gloom, here's a blossom of hope. Mind you, Mr. Stevenson is no naïf in rose-colored glasses: he approaches his subjects - among them some of the world's most brilliant people - with an intelligent skepticism, challenging their assumptions and never letting them off the hook when they try to wiggle out of the tough questions.
To get a sense of Stevenson's style and approach in this book, think about the motivations behind "What Are You Optimistic About?: Today's Leading Thinkers on Why Things Are Good and Getting Better", combine it with the probing intelligence and never-say-die quest for creative answers behind "Freakonomics", then dash in the wit and wisdom of a Bill Bryson.
Each section of the book covers a specific topic, with subjects ranging from transhumanism to robotics to the environment to genetic engineering (to name but a few). But more interesting still are the people working at the cutting edge of these fields. In each section, we follow Mr. Stevenson around the world as he visits some of these leading minds of our time, visionaries like Ray Kurzweil, George Church and Vint Cerf. Through wit, charm and intelligence, he elicits a level of frankness that you will not witness in any other interview format. (In that sense, the book is worth the price for the biographical components alone.)
I think the biggest selling point of this book, though, is the way it alters the reader's whole way of looking at an exciting future that is so much closer than most of us might think. Stevenson calls it a 'reboot', and that's a very apt descriptor: the reader finishes the book with a sense of awe (and yes, some trepidation) about a future in which everything we have taken for granted for so long, is suddenly washed away in favor of very new definitions of things as fundamental as success, happiness, relationships, even mortality.
So put down the doom and gloom for a while, turn off the 24/7 parade of dismay and pick up this reason to be optimistic. The future is going to be a wild ride, and Stevenson's book is a good road map.
--------------------------------------------------------------------------------------
Footnotes:
*1938-ish?
Showing posts with label technological singularity. Show all posts
Showing posts with label technological singularity. Show all posts
03 February 2011
03 January 2011
Yet another step towards the singularity...
A few years back, I remember reading an article about Vernor Vinge and his case for the technological singularity. I found it fascinating, but soon it crept to the back of my mind. I didn't see much evidence that it was approaching anytime soon. But recently, some breakthroughs in science and a wonderful new book by Mark Stevenson, have made me revisit the idea. In its simplest expression, it basically just says that at some point in the not-too-distant future, accelerating returns will result in a watershed moment, after which humankind will be so changed that all of our current assumptions about even the most fundamental concepts will be swept away, leaving us in a world so completely different from the one we had come to know as a species, that it will be essentially impossible to predict from this side of the singularity. So when will this point be reached? It is not too surprising that no two people agree and even less so that many people think the whole concept is bunk. But people like Ray Kurzweil seem to feel that many people alive today will live to see it. I am not sufficiently convinced even of the validity of the idea just yet, never mind having an opinion about timing. But after reading books like Mr. Stevenson's and seeing some of the mind-blowing advances happening in so many fields, it is certainly something that won't be creeping off again to the back of my mind any time soon.
To cite just one (albeit very powerful) example, consider Eureqa, a program developed by Professors M. Schmidt and H. Lipson at Cornell. Mr. Stevenson visited the team at Cornell and discusses it in his book. Basically, this AI 'program' - calling it a program seems akin to calling Mt Everest a 'mound of dirt' - takes your data and derives principles on which such data are built. That may sound rather dry and dull, but consider this: it figured out Newton's Laws of Motion based on data it was fed. In a few hours. So imagine brilliant careers in research distilled down to a few hours. Then consider that of course such minds won't retire after they feed an AI like Eureqa some great data and get some cool new fundamental laws. They will keep going. So imagine Newton figuring out his laws in a day and then going for another 40 years or so. (Well, OK, he DID keep going for another 40 years or so, but you get the point.) Starting to see how accelerating returns might be leading us somewhere unrecognizable?
If you're one of the select few scientists with access, you can feed...oh, wait, what?! Eureqa is a free download. Anyone in the world can use it. I have.* It's laid out like Excel. I'm no scientist and I doubt I will come up with any Earth-shattering theorems with Eureqa. But imagine this tool in the hands of thousands of brilliant researchers around the world.
Hold on to your hats, folks. It's gonna get wild.
................................................................................................
Footnotes
*I love playing with economic data, so I fed it 30 years worth of such data to see what equations it would come up with. This was just to amuse myself, mind you: one must be careful to distinguish between deriving laws from data gotten from the natural world v just plain data-mining. My exercise was essentially the latter. In other words, any equation derived from something as erratic as economic data will serve just one purpose: more or less accurately predicting a data point within the universe of data already provided to the AI. So if I got an equation for predicting, say, the value of the S&P 500 based on CPI and consumer confidence, all I could be certain of would be that the equation could more or less accurately 'predict' the values for 1987, using the other 29 points of data within its universe. Still, it may serve to give a general sense of trends.
To cite just one (albeit very powerful) example, consider Eureqa, a program developed by Professors M. Schmidt and H. Lipson at Cornell. Mr. Stevenson visited the team at Cornell and discusses it in his book. Basically, this AI 'program' - calling it a program seems akin to calling Mt Everest a 'mound of dirt' - takes your data and derives principles on which such data are built. That may sound rather dry and dull, but consider this: it figured out Newton's Laws of Motion based on data it was fed. In a few hours. So imagine brilliant careers in research distilled down to a few hours. Then consider that of course such minds won't retire after they feed an AI like Eureqa some great data and get some cool new fundamental laws. They will keep going. So imagine Newton figuring out his laws in a day and then going for another 40 years or so. (Well, OK, he DID keep going for another 40 years or so, but you get the point.) Starting to see how accelerating returns might be leading us somewhere unrecognizable?
If you're one of the select few scientists with access, you can feed...oh, wait, what?! Eureqa is a free download. Anyone in the world can use it. I have.* It's laid out like Excel. I'm no scientist and I doubt I will come up with any Earth-shattering theorems with Eureqa. But imagine this tool in the hands of thousands of brilliant researchers around the world.
Hold on to your hats, folks. It's gonna get wild.
................................................................................................
Footnotes
*I love playing with economic data, so I fed it 30 years worth of such data to see what equations it would come up with. This was just to amuse myself, mind you: one must be careful to distinguish between deriving laws from data gotten from the natural world v just plain data-mining. My exercise was essentially the latter. In other words, any equation derived from something as erratic as economic data will serve just one purpose: more or less accurately predicting a data point within the universe of data already provided to the AI. So if I got an equation for predicting, say, the value of the S&P 500 based on CPI and consumer confidence, all I could be certain of would be that the equation could more or less accurately 'predict' the values for 1987, using the other 29 points of data within its universe. Still, it may serve to give a general sense of trends.
Labels:
AI,
artificial intelligence,
Christopher J. Hughey,
Eureqa,
futurology,
Lipson,
Mark Stevenson,
Ray Kurzweil,
Schmidt,
technological singularity,
Vernor Vinge
Subscribe to:
Posts (Atom)