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Computation and the Future of the Human Condition
Stephen Wolfram is the creator of Mathematica and Wolfram|Alpha and the author of A New Kind of Science and An Elementary Introduction to the Wolfram Language. In this short ebook, Dr. Wolfram dives into his theories of computation and the universe.
Take a look at this short excerpt from the ebook Computation and the Future of the Human
“In traditional engineering, one starts with some purpose in mind, then explicitly tries to construct a system that achieves that purpose.
And typically at each step one insists on foreseeing what the system will do. With the result that the system must always be quite computationally reducible.
But in the computational universe there are lots of systems that aren’t computationally reducible.
So can we use these systems for technology?
The answer is absolutely yes.
Sometimes we look at the systems and realize that there’s some purpose for which they can be used.
But more often, we first identify a purpose, and then start searching the computational universe for systems that can achieve that purpose.
Things like this have been done a little in traditional engineering—even, say, with Edison searching for his light-bulb filaments.
But it’s vastly more efficient and streamlined in the computational universe.
Take a look at this short excerpt from the ebook Computation and the Future of the Human
“In traditional engineering, one starts with some purpose in mind, then explicitly tries to construct a system that achieves that purpose.
And typically at each step one insists on foreseeing what the system will do. With the result that the system must always be quite computationally reducible.
But in the computational universe there are lots of systems that aren’t computationally reducible.
So can we use these systems for technology?
The answer is absolutely yes.
Sometimes we look at the systems and realize that there’s some purpose for which they can be used.
But more often, we first identify a purpose, and then start searching the computational universe for systems that can achieve that purpose.
Things like this have been done a little in traditional engineering—even, say, with Edison searching for his light-bulb filaments.
But it’s vastly more efficient and streamlined in the computational universe.
45 pages, Kindle Edition
Published January 27, 2016
56 people are currently reading
320 people want to read
About the author
Stephen Wolfram
45 books449 followersStephen Wolfram is the founder & CEO of Wolfram Research, creator of Mathematica, Wolfram|Alpha & Wolfram Language, author of A New Kind of Science and other books, and the originator of Wolfram Physics Project.
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Displaying 1 - 9 of 9 reviews
September 4, 2018
There's an old warning passed down in STEM circles. Back when the loom was the most advanced machine on the planet, the leading metaphor for how brains worked was as a loom. As telephones began being strung across the country, the this metaphor shifted---now the brain was like a telephone switchboard. These days the brain is like a computer, but I'm reasonably sure it's going to stay a computer. My point is that our familiarities inform the metaphors we use, but it's worth keeping in mind *that these things are just metaphors.*
Wolfram falls victim to this. Not only are brains computers, but they are in fact /computation itself/. Intelligence is merely computation. Weather systems are merely computations. Thus, Wolfram says, weather systems are intelligent too. This is a neat trick of semantics, but it's ultimately useless. The word "intelligence" refers to human-like-things, and not to weather-like-things, regardless of any computational similarities they have under the surface. Wolfram eventually concedes the point, but it left a sour taste in my mouth. If he's going to argue in clear circles like this one, why should I trust his reasoning on other things where I find the causal relationships less clear?
"Everything is computation" is the claim, and Wolfram follows this argument to its limit---that Godelian proofs are thus a limiting factor in every endeavor. We're unable to predict the future because of the halting problem. We're unable to distinguish meaning because doing so is equivalent to solving the halting problem. Et cetera.
This all may be true, but it seems like grasping at straws from a man who has this to say about mathematics: "what I’ve concluded is that actually the mathematics we have today is really just a historical accident: the direct generalization of the arithmetic and geometry that happened to be used in ancient Babylon. So it’s just history that makes the particular axiom systems we’re using seem meaningful to us."
Yes, the halting problem is a very real phenomenon, but the vast majority of the time it doesn't strike in full generality; we can often approximate solutions. And, this is all based on the assumption that the universe itself is subject to Curry-Howard. Maybe, but then again the only evidence we have is that there don't appear to be any NP-complete problems in nature.
Computation and the Future of the Human Condition isn't all bad though. It's a short enough read that you can get through it in one sitting, and it'll definitely provoke interesting thoughts. That being said, it's not Wolfram's best work. A better read is his blog post Showing Off to the Universe (http://blog.stephenwolfram.com/2018/0...) which better details his arguments and is free.
Wolfram falls victim to this. Not only are brains computers, but they are in fact /computation itself/. Intelligence is merely computation. Weather systems are merely computations. Thus, Wolfram says, weather systems are intelligent too. This is a neat trick of semantics, but it's ultimately useless. The word "intelligence" refers to human-like-things, and not to weather-like-things, regardless of any computational similarities they have under the surface. Wolfram eventually concedes the point, but it left a sour taste in my mouth. If he's going to argue in clear circles like this one, why should I trust his reasoning on other things where I find the causal relationships less clear?
"Everything is computation" is the claim, and Wolfram follows this argument to its limit---that Godelian proofs are thus a limiting factor in every endeavor. We're unable to predict the future because of the halting problem. We're unable to distinguish meaning because doing so is equivalent to solving the halting problem. Et cetera.
This all may be true, but it seems like grasping at straws from a man who has this to say about mathematics: "what I’ve concluded is that actually the mathematics we have today is really just a historical accident: the direct generalization of the arithmetic and geometry that happened to be used in ancient Babylon. So it’s just history that makes the particular axiom systems we’re using seem meaningful to us."
Yes, the halting problem is a very real phenomenon, but the vast majority of the time it doesn't strike in full generality; we can often approximate solutions. And, this is all based on the assumption that the universe itself is subject to Curry-Howard. Maybe, but then again the only evidence we have is that there don't appear to be any NP-complete problems in nature.
Computation and the Future of the Human Condition isn't all bad though. It's a short enough read that you can get through it in one sitting, and it'll definitely provoke interesting thoughts. That being said, it's not Wolfram's best work. A better read is his blog post Showing Off to the Universe (http://blog.stephenwolfram.com/2018/0...) which better details his arguments and is free.
July 27, 2016
Wolfram is lecturing about his "Galilean" discovery looking at the computational universe (cool analogy, nay?). The whole thing converges to his hypotheses about the future of computation (detailed in A New Kind of Science), which is driven by the evolution of human purposes. This is thrilling because nature is the inspiration but it gets even better because he is doing it Feynman-style! No fancy language, no baloney and having fun. His reasoning may widen your view of the future by sharing intriguing ideas that resembles fractals (but no direct reference).
December 17, 2018
Wolfram gets a little bit full of himself (I like the comparison another reviewer makes to Galileo), but I guess in some ways he merits it. An interesting read, and I'll be looking into the longer A New Kind of Science soon.
July 27, 2016
Wolfram shares computational ideas
Wolfram takes us through NKS, computational irreducibility and the experimental discovery process now available to us because of our computational systems. Powerful and useful ideas presented in this short book. Recommended.
Wolfram takes us through NKS, computational irreducibility and the experimental discovery process now available to us because of our computational systems. Powerful and useful ideas presented in this short book. Recommended.
March 15, 2022
4 stars -- Wolfram is a certified genius in about as many ways as society can offer (prodigy, physics wunderkind, Mcarthur Fellow, etc. & possibly even a self-made billionaire)... but while I can tell he is a clear thinker, ( & so his writing is simple in an attempt to be clear to the masses, but..), I found his writing oversimplified & over-generalized. I can tell he is making connections between powerful insights, but I am missing the shock & weight of profundity that usually accompanies revolutionary revelations such as these.
For example, Stephen writes, "... the principle of computational equivalence says that systems found in the natural world can perform computations up to a universal level of computational power, and that most systems do in fact attain this maximal level of computational power. Consequently, most systems are computationally equivalent. "
As short as this book is, I had to stop & unpack simple sentences like that.
I take it that when Stephen says "computation" , he means an algorithmic simple set of rules applied to an input as an operation, after a defined initial set of conditions, whose function yields a specified output. So, like a simple computer program coded in a programming language, there's an initial condition, you give it input, it runs the operations of simple rules, and computes your answer as the output.
So when Stephen says "Computational Reducibility" he means an operational shortcut, like multiplying is faster than counting & adding for a sum. And exponents are faster than a successive series of multiplying & adding layers of squares.
Further, if the behavior of a system is either repetitive or nested--then it will always be computationally reducible. But there is a point where no further short-cuts can be applied. Stephen calls this limit of maximal level of simplification, "Computational Irreducibility". The implied result is that the only way to determine the answer to a computationally irreducible question is to perform, or simulate, the computation.
So, Wolfram proposes the Principle of Computational Equivalence which says that there are basically only two levels of computational power: trivial (obviously simple) and universal (complex & irreducible). And since most natural systems are at the universal level , in other words, computationally irreducible, then their behavior cannot be computed faster than the live outcome. Therefore the dynamics & outcome can not be predicted faster than actually letting it play out.
Therefore all of these natural systems (human brains, weather sysytems, flowing water, etc.) are computationally equivalent and, in fact, these natural systems can compute the same things as a computer; and reflexifively computers can be used to model and simulate these natural systems, but while you can speed up the run time with powerful processors, you still can't outrun the development of the natural system.
So in that single sentence above, Stephen is declaring equations themselves to be an unnecessary limit. Wolfram is attempting to take math beyond equations to simulations. And these simulations don't even need computers to run.
Stephen has prioritized algorithms over equations, and simulations over computations. His approach is exploring the computational universe of possible programs as a way to explore all possible mechanisms. In effect, “mining” the computational universe to find useful algorithms; find much more efficient programs —which will typically happen not to be readily understandable by humans.
Stephen Wolfram even goes on to say our future will not be limited by what technology is possible , but rather by what humans think is useful, so the limit is the evolution of human purposes.
Stephen Wolfram is, so far, 20 years into “universe hunting" -- Trying to find the handful of simple rules that crank out our universe. And more, Hunting to find the simple rules that generate the range of all possible universes.
This book is a great quick introduction to his approach of mining Simulations for useful algorithms.
For example, Stephen writes, "... the principle of computational equivalence says that systems found in the natural world can perform computations up to a universal level of computational power, and that most systems do in fact attain this maximal level of computational power. Consequently, most systems are computationally equivalent. "
As short as this book is, I had to stop & unpack simple sentences like that.
I take it that when Stephen says "computation" , he means an algorithmic simple set of rules applied to an input as an operation, after a defined initial set of conditions, whose function yields a specified output. So, like a simple computer program coded in a programming language, there's an initial condition, you give it input, it runs the operations of simple rules, and computes your answer as the output.
So when Stephen says "Computational Reducibility" he means an operational shortcut, like multiplying is faster than counting & adding for a sum. And exponents are faster than a successive series of multiplying & adding layers of squares.
Further, if the behavior of a system is either repetitive or nested--then it will always be computationally reducible. But there is a point where no further short-cuts can be applied. Stephen calls this limit of maximal level of simplification, "Computational Irreducibility". The implied result is that the only way to determine the answer to a computationally irreducible question is to perform, or simulate, the computation.
So, Wolfram proposes the Principle of Computational Equivalence which says that there are basically only two levels of computational power: trivial (obviously simple) and universal (complex & irreducible). And since most natural systems are at the universal level , in other words, computationally irreducible, then their behavior cannot be computed faster than the live outcome. Therefore the dynamics & outcome can not be predicted faster than actually letting it play out.
Therefore all of these natural systems (human brains, weather sysytems, flowing water, etc.) are computationally equivalent and, in fact, these natural systems can compute the same things as a computer; and reflexifively computers can be used to model and simulate these natural systems, but while you can speed up the run time with powerful processors, you still can't outrun the development of the natural system.
So in that single sentence above, Stephen is declaring equations themselves to be an unnecessary limit. Wolfram is attempting to take math beyond equations to simulations. And these simulations don't even need computers to run.
Stephen has prioritized algorithms over equations, and simulations over computations. His approach is exploring the computational universe of possible programs as a way to explore all possible mechanisms. In effect, “mining” the computational universe to find useful algorithms; find much more efficient programs —which will typically happen not to be readily understandable by humans.
Stephen Wolfram even goes on to say our future will not be limited by what technology is possible , but rather by what humans think is useful, so the limit is the evolution of human purposes.
Stephen Wolfram is, so far, 20 years into “universe hunting" -- Trying to find the handful of simple rules that crank out our universe. And more, Hunting to find the simple rules that generate the range of all possible universes.
This book is a great quick introduction to his approach of mining Simulations for useful algorithms.
November 19, 2016
In this essay, Stephen Wolfram discusses computational irreducibly, cellular automata, and areas relevant to the reach of computation. He references frequently his book A New Kind of Science, and the mathematical computation program Mathematica he is responsible for creating, among other accomplishments. He sprinkles enough of his passion throughout the pages to inspire and motivate one to dig deeper.
February 9, 2020
I've read this in just few hours. Reducing reality, including past present and future in a sheer computational frame is a very fascinating concept indeed. And it's already happening in a way, I believe.
April 28, 2020
Probably not the best foray into Wolfram's writings but certainly the easiest.
I'll consider the bargain $1.99 Kindle price as payback for all the podcast content I've consumed.
I'll consider the bargain $1.99 Kindle price as payback for all the podcast content I've consumed.
January 9, 2019
When a brilliant mathematician
Displaying 1 - 9 of 9 reviews