I've never felt playing against what is suppose to be an entire room of machines (wether Deep Blue or Watson) to be fair. What would be fair is to limit the total mass of the computer to say 200kg and leave it at that. What is effectively happening is AlphaGo is running on a distributed system of many, many machines. Even Watson took an entire room. Google is paying a premium to push AlphaGo to win.
It's a proof-of-concept. What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go no matter how much computing power we threw at it. Now we can. Give it ten years and, between algorithmic optimizations and advances in processing, you'll have an unbeatable Go app on your phone.
Indeed. The first time a computer defeated a human in Chess it was this[1] size (1997). In 2009 it became possible to fit a grandmaster into this[2].
> Pocket Fritz 4 won the Copa Mercosur tournament in Buenos Aires, Argentina with 9 wins and 1 draw on August 4–14, 2009. Pocket Fritz 4 searches fewer than 20,000 positions per second. This is in contrast to supercomputers such as Deep Blue that searched 200 million positions per second. Pocket Fritz 4 achieves a higher performance level than Deep Blue.[3]
The first steps are always the most inefficient. Make it work, make it right, make it fast.
GM Michael Stean lost to Cyber 176 (a mainframe 'supercomputer') in 1977 (at blitz). AFAIK this was the first time a computer defeated a GM; they began defeating IMs and experts some ten years before that. Kasparov himself lost to Fritz 2 at blitz as early as 1992.
"Under tournament conditions" is the condition everyone forgets. Go AIs were competing with ranked players given handicaps of varying degrees of absurdity.
> Give it ten years and, between algorithmic optimizations and advances in processing, you'll have an unbeatable Go app on your phone.
I find this overly optimistic because of the huge amount of power required to run the Go application. Remember, we're getting closer and closer to the theoretical lower limit in the size of silicon chips, which is around 4nm (that's about a dozen silicon atoms). That's a 3-4x improvement over the current state of the art.
The computer to run AlphaGo requires thousands of watts of power. A smartphone can do about one watt. A 3-4x increase in perf per watt isn't going to cut it.
If there will be a smartphone capable of beating the best human Go players, my guess is that it won't be based on general purpose silicon chips running on lithium ion batteries.
On the other hand, a desktop computer with a ~1000 watt power supply (ie. a gaming pc) might be able to do this in a matter of years or a few decades.
As solid as your argument may be, everyone saw arguments like this over and over. Every single time they were solid. For a time, it was the high frequency noise that would not be manageable (80s), then heat dissipation (90s), then limits on pipeline optimization (00s) and now size constraints on transistors. They were all hard barriers, deemed impossible and all were overcome.
I already know that your answer will be: "but this time it is a fundamental physics limit". Whatever. I'm jaded by previous doomsday predictions. We'll go clockless, or 3D, or tri-state or quantum. It'll be something that is fringe, treated as idiotic by current standards and an obvious choice in hindsight.
Our brains use roughly ~20 watts though, so we know that the power constraints can be overcome, if not in silicon then it may be biological machines we use in the future.
The previous problems were solved because people were willing to spend hundreds of billions of dollars to solve them. And they are still spending that kinds of money.
If the normalcy bias was in effect, they wouldn't be spending that money.
Actually Normalcy Bias may in fact feed that kind of money spending until such time as reality hits. Assuming that people will automatically act more logically when large amounts of money is in play flies in the face of recent history. Just look at the recent housing loan crisis. Normalcy Bias played a part there.
It's certainly possible that we'll break more barriers with clever engineering and new scientific breakthroughs. But that doesn't mean the Normalcy Bias isn't in play here.
Normalcy bias may have people spending lots of money on fabs assuming that the problems would be solved by the time the fabs are built.
However, I'm talking hundreds of billions spent on R&D to specifically to solve problems associated with chip manufacture. It took on the order of 25 years to solve each of the problems listed in the grandparent's post. Nobody would spend that kind of money or time on something that they think somebody else would solve.
People have probably spent billions of dollars to find a cure for cancer, but there isn't one that works for all cancers and most are still very bad news.
Say you spent a hundred billion dollars to extinguish the sun- that wouldn't work. How much money you spend is irrelevant when you're up against what people call "hard physical limits".
Isn't our inability to cure all cancers a limitation of our knowledge more than a hard physical limit?
I've read several articles saying that different cancers are not exactly the same disease, but more like different diseases with the same symptom (uncontrolled tumor growth) and different etiology, even sometimes different from person to person, not just from tissue to tissue. This was said to be a reason that a general cancer cure is so elusive. But is it really thought of as impossible, not just elusive?
Maybe our inability to extinguish the sun is also a limitation of knowledge more than a hard physical limit!
Even if I'm right about this, your description of the situation would still be accurate in that there would be no way to simply throw more money at the problems and guarantee a solution; there would need to be qualitative breakthroughs which aren't guaranteed to happen at any particular level of expenditure. If people had spent multiples of the entire world GDP on a space program in the 1500s, they would still not have been able to get people to the moon, though not because it's physically impossible to do so in an absolute sense.
There is a lot of room for improvement with the implementation. The way we are using deep neural networks at the moment is exellent for prototyping, but far from optimal. For instance, this paper http://arxiv.org/abs/1511.00363 shows that you can replace floating point operations by simple bitwise operations without losing too much precision in DNNs for image recognition. Together with a better (that is, compiled, instead of interpreted) representation of the inference step I would expect an order of magnitude improvement at a small loss in precision. More software tuning, especially the kind of low-level optimizations that most chess programs do, should yield another big improvement.
Finally, the hardware we are using to run these programs is insane. Sure the silicon is approaching some hard physical limits, but your processor spends most of that power trying to make old programs run fast...
My prediction is that with enough ressources it is possible to write a Go AI which runs on general purpose hardware that's manufactured on current process nodes and fits in your pocket.
I don't think you appreciate how much of this is good algorithms, and how little you need sheer computing power to get good results.
If you look at http://googleresearch.blogspot.com/2016/01/alphago-mastering... you'll find that Google's estimate of the strength difference between the full distributed system and their trained system on a single PC is around 4 professional dan. Let's suppose that squeezing it from a PC to a phone takes about the same off. Now a pocket phone is about 8 professional dan weaker than the full distributed system.
If their full trained system is now 9 dan, that means that they can likely squeeze it into a phone and get a 1 dan professional. So the computing power on a phone already allows us to play at the professional level!
You can get to an unbeatable device on a phone in 10 years, if self-training over a decade can create about as much improvement they have done in the last 6 months, AND phones in 10 years are about as capable as a PC is today. Those two trade off, so a bigger algorithmic improvement gets you there with a weaker device.
You consider this result "overly optimistic". I consider this estimate very conservative. If Google continues to train it, I wouldn't be surprised if there is a PC program in a year that can beat any Go player in the world.
You're right, it won't be a general purpose computing device the way we conceive of it with the von Neumon architecture.
It'll likely be hardware that can be generalized to run any kind of deep net. The iPhone 5S is already capable of running some deep nets.
As a friend mentioned, it isn't the running of the net, it's the training that takes a lot more computational power (leaving aside data normalization). A handheld device that is not only capable of running a deep net, but also training one -- yeah, that will be the day.
There are non von Neuman architectures that are capable of this. Someone had figured out how to build general-purpose CPUs on silicon made for memory. You can shrink down a full rack of computers down into a single mother board, and use less wattage while you are at it.
This really isn't about having a phone be able to beat a Go player. Go is a transformative game that, when learned, it teaches the player how to think strategically. There is value for a human to learn Go, but this is no longer about being able to be the best player in the absolute sense. Go will undergo the same transformation that martial arts in China and Japan has gone through with the proliferation and use of guns in warfare.
Rather, what we're really talking about is a shot at having AIs do things that we never thought they could do -- handle ambiguity. What I think we will see is -- not the replacement of blue collar workers by robots -- but the replacement of white collar workers by deep nets. Coupled with the problems in the US educational system (optimizing towards passing tests rather than critical thinking, handling ambiguity, and making decisions in face of uncertainty), we're on a verge of some very interesting times.
Please do elaborate. I try to base my assumptions (which I accept may turn out to be completely wrong) on physics and experience in working in semiconductors.
I just don't see a 1000x+ decrease in the power required happening in a decade or two without some revolutionary technology I can't even imagine. Is this what you meant? I'm sure most people couldn't imagine modern silicon chips in the 1950s vacuum tube era. But now we're getting close to the theoretical, well-understood minimums in silicon chips, so another revolutionary step is required if another giant leap like that is to be achieved.
> physics and experience in working in semiconductors
> without some revolutionary technology I can't even
> imagine
I suspect (in the nicest possible way) that in a lineup of your imagination (on current assumptions) vs the combined ingenuiety of the human race driven by the hidden hand, the latter wins.
exDM69 never said it's not gonna happen, he just said that it's not going to happen in ten years, and I agree with him. Revolutions never occurs that quickly. To achieve that we don't just need an improvement of the current state of the art, we need a massive change and we don't even know what it's going to look like yet ! This kind of revolution may occur one day but not in ten year.
And it could even never happen, remember that we don't have flying cars yet ;)
It seems to me that the people who say "it won't happen" do tend to have a much better reason to say it won't happen (or rather that it _probably_ won't happen) than the people who insist the next big revolution is just round the corner just because the last big revolution did happen.
The optimistic position is a bit like saying: "I 've lived 113 years, I'm not going to die now!". It's entirely possible for a trend to reverse itself. If machine learning has taught us something is that background knowledge (in this case, of processor technology) gives you much better results than just guessing based on what happened in the past.
Heterogenous computing is already used in AlphaGo (and your smartphone). 3d chips will come to mainstream devices in a few years, but will give "only" a modest performance boost, say 2x or so.
The rest of the technologies you mention have great potential but will they be available in a smartphone in one decade? I don't think so.
You might ultimately only need some specialized "neural processing instruction set" for either the GPU cores or for the CPU cores. Or at least, I don't see any obvious obstacles to that.
I feel the same way about the chips reaching their physical limits. But I keep waiting for a new way we use them. We used to just churn out MHz and that was the metric. Then we got hyper-threading, multi-cores, GPU and other specific processors and new ways of programming to go with it all. I imagine we'll see the same. Just like the brain has different areas of processing, I'm hoping we'll see the same in silicon chips. Just like how we offload work from the general purpose cpu to the more efficient purpose build gpu or sound card etcs. Not saying every computer is going to have a GO chip in it, but maybe someday we'll have machine learning processors or who knows what. But yeah the advancements will be new designs and new ways of processing instead of more power.
Sure. But so far, we've found that revolutionary step every time we've hit these sorts of walls, and if I was a betting man I'd wager we'll do the same again.
Right, but just as a contrast: Technological progress speed has been at an all-time high since the begin of the industrial revolution.
It might as well slow down again and we have to remember that most humans in history saw little to no advances in technology over their lifetime.
I'm excited for the possibilities modern science opens up but I also think we might reach a point where fundamental progress stalls for a century or two.
A Go app likely wouldn't rely on the native processing power of the smartphone.
An AlphaGo app could be created today for a smartphone. The bottleneck isn't the phone it's the cost of the cloud computing resources behind it.
Perhaps a combination of Moore's law and economy of scale would make it affordable sooner than we think.
The Xbox One, for example, already subcontracts difficult problems out to Azure.
No, they haven't shown that the same kind of intelligence required to play go can be implemented in computer software. The methods AlphaGo uses are not the same as the intelligence a human uses at all. What they have done is prove an implementation of computer go in software is capable of beating a human player, not that they have implemented the same kind of intelligence as the human player.
> What they've proved is that the same kind of intelligence required to play Go can be implemented with computer hardware. Before now, software couldn't beat a ranked human player at Go no matter how much computing power we threw at it.
I don't think that's quite true as a description of what we knew about computer Go previously, though it depends on what precisely you mean. Recent systems (meaning the past 10 years, post the resurgence of MCTS) appear to scale to essentially arbitrarily good play as you throw more computing power at them. Play strength scales roughly with the log of computing power, at least as far as anyone tested them (maybe it plateaus at some point, but if so, that hasn't been demonstrated).
So we've had systems that can in principle play to any arbitrary strength, if you can throw enough computing power at them. Though you might legitimately argue: by "in principle" do you mean some truly absurd amount, like more computing power than could conceivably fit in the universe? The answer to that is also no; scaling trends have been such that people expected computer Go to beat humans anywhere from, well, around now [1], to 5 to 10 years from now [2].
The two achievements of the team here, at least as I see them, are: 1) they managed to actually throw orders of magnitude more computing power at it than other recent systems have used, in part by making use of GPUs, which the other strong computer-Go systems don't use (the AlphaGo cluster as reported in the Nature paper uses 1202 CPUs and 176 GPUs), and 2) improved the scaling curve by algorithmic improvements over vanilla MCTS (the main subject of their Nature paper). Those are important achievements, but I think not philosophical ones, in the sense of figuring out how to solve something that we previously didn't know how to solve even given arbitrary computing power.
[1] A 2007 survey article suggested that mastering Go within 10 years was probably feasible; not certain, but something that the author wouldn't bet against. I think that was at least a somewhat widely held view as of 2007. http://spectrum.ieee.org/computing/software/cracking-go
[2] A 2012 interview though that mastering Go would need a mixture of inevitable scaling improvements plus probably one significant new algorithmic idea, also a reasonably widely held view as of 2012. https://gogameguru.com/computer-go-demystified-interview-mar...
"Recent systems (meaning the past 10 years, post the resurgence of MCTS) appear to scale to essentially arbitrarily good play as you throw more computing power at them. Play strength scales roughly with the log of computing power, at least as far as anyone tested them (maybe it plateaus at some point, but if so, that hasn't been demonstrated)."
This is exactly the opposite of my sense based on following the computer go mailing list (which featured almost all the top program designers prior to Google/Facebook entering the race). They said that scaling was quite bad past a certain point. The programs had serious blindspots when dealing with capturing races and kos[1] that you couldn't overcome with more power.
Also, DNNs were novel for Go--Google wasn't the first one to use them, but no one was talking about them until sometime in 2014-2015.
[0] Not the kind of weaknesses that can be mechanically exploited by a weak player, but the kind of weaknesses that prevented them from reaching professional level.
> Play strength scales roughly with the log of computing power
To be fair, a lot of the progress in recent years has been due to taking a different approach to solving the problem, and not just due to pure computing power. Due to the way go works, you can't do what we do with chess and try all combinations, no matter how powerful of a computer you have. Using deep learning, we have recently helped computers develop what you might call intuition -- they're now much better at figuring out when they should stop going deeper into the tree (of all possible combinations).
There've definitely been algorithmic improvements, but from what I've read so far, the change in search algorithms, from traditional minimax search to MCTS, has been the biggest improvement, more than deep learning.
The game itself, however, scales exponentially, and there's nothing to do about that, so if you enlargen the board, no computer... And no human may be able to play it well.
The achivement was a leap towards the human level of play (and quite possibly over it). There might be additional leaps, which will take AIs WAY beyond humans, but none of those will scale linearily in the end. (And yeah, I guess you didn't want to say that either)
Then you can say that, in 10 years, if we indeed have reached that point. Otherwise it'd just an empty prediction, and his perfectly valid point stands.
The real achievement is in the algorithm. To make an analogy, the accomplishment of putting a man on the moon required that we understand enough to make a rocket. We could have put hundreds of car engines together but that wouldn't ever have gotten us to the moon.
AlphaGo utilizes the "Monte Carlo tree search" as its base algorithm[1]. The algorithm has been used for ten years in Go AIs, and when it was introduced, it made a huge impact. The Go bots got stronger overnight, basically.
What novel thing AlphaGo did, was a similar jump in algorithmic goodness. It introduced two neural networks for
1) predicting good moves at the present situation
2) evaluating the "value" of given board situation
Especially 2) has been hard to do in Go, without playing the game 'till the end.
This has a huge impact on the efficiency of the basic tree search algorithm. 1) narrows down the search width by eliminating obviously bad choises and 2) makes the depth at where the evaluation can be done, shallower.
So I think it's not just the processing power. It's a true algorithmic jump made possible by the recent advances in machine learning.
Especially 2) has been hard to do in Go, without playing the game 'till the end.
This is what struck me as especially interesting, as a non-player watching the commentary. The commentators, a 9-dan pro and the editor of a Go publication, were having real problems figuring out what the score was, or who was ahead. When Lee resigned the game, it came as a total surprise to both of them.
Just keeping score in Go appears to be harder than a lot of other games.
Score in Go is captured stones plus surrounded empty territory at the end of the game. Captures are well defined when they happen, but territory is not defined until the end.
The incentive structure of the game leads to moves that firmly define territory usually being weaker, so the better the players, the more they end up playing games where territory is even harder to evaluate.
> but that wouldn't ever have gotten us to the moon
Fitting analogy. There was a line in the film Blood & Donuts about the moon being ruined when they landed on it, which I couldn't really feel until today.
A top smartphone chess program can beat pretty much all but the best few players in the world. Do you think it's fair to pit a 150 gram device against a 70 kg human?
Although a fairer comparison would be against 1 kg of brain. Comparing against a human would need to include all the infrastructure for the device such as energy production or the manufacturing equipment required.
But nevertheless, fitting so much computing power in such a small device is a great achievement.
Not really, the brain to operate also needs all the other systems. Just as a CPU needs all the other parts to function. And including the manufacturing equipment is just as false, in the sense that you would have to include his mother (as biological manufacturing)
The human can run off resources that are available "in the wild", self-repair, and self-replicate at better than 1:1 (that is, a group of n humans produce >n offspring), whereas the smartphone needs a huge amount of infrastructure to repair it and produce new ones.
I don't think any mass comparison is really meaningful, mind, but it's not that simple.
Advanced chess players require a society which produces enough surplus to afford enough leisure to allow someone to not only produce a brain not damaged by starvation, but to allow them to use that brain to learn chess at a high level. It took a very long time for humans to get to that level, even though chess is a fairly old game.
My point is, humans "in the wild" likely didn't have any equivalent to chess, because they didn't have sufficient leisure time. Chess is a product of an environment that's just as "artificial" as the one which produced cell phones.
Games, including complex ones, go back a long way in human history. I've seen various claims about how much free time people have in primitive societies and I don't know enough to really know which are correct. The modern style of chess play relies on having openings books and computer assistance, but that's less true of go, which AIUI is learned largely through practice and a cultivation of taste and instinct (and the pieces can just be a set of stones and a grid scratched in the dirt).
Games may have existed, but the relative skill level of the players was likely a lot lower when people weren't spending as much time mastering the game, spreading and consuming strategy knowledge, and constantly holding events to compete and refine the best players.
I think the entire analogy is stretched a little thin of the players requiring all of this, but I also think the original attack on the Go AI based on it's mass is off base as well.
The story of Chess, according to Iranian mythological sources (recounted in Shahnameh) is that it was presented to the Iranian Court by the emissaries of the Indian Court, as a 'semantic puzzle' invented by Indian sages. (These games, it should be noted, were pedagogical in nature and used as symbolic means of training monarchs by the intellectual elites.)
The response of the Iranian sages was the invention of Backgammon, to highlight the role of Providence in human affairs.
The origin is unclear. The fact that the thematic of a game of war is prevailing now, to me means that it might have as well been in the beginning. Actually it shows at least that those semantics are relevant to war, and to live, so what I was saying stands.
Of course it involves war, but note how they teach the young prince it is (a) better to let the Vazier (your Queen) do all the heavy duty lifting, and (b) it is perfectly honorable to hide behind fortifications in a castle.
The self-repair and self-replicate come at a very hard cost, in the sense that it requires food, water and oxygen, while machines only need electricity. And the replication is actually incomplete in the sense that it starts in a very small state where it needs the three resources to actually become a complete human (adult) and dies if not taken care by a third-party in such early state (parents).
Plus, not far away in the future we will be able to connect an smartphone to a 3D circuit printer and print a new one, to achieve 'self-replication'
Seems like a pretty arbitrary limitation. 70 years ago Colossus filled an entire room, now it can be emulated on a Raspberry Pi. The really groundbreaking part is the algorithm.
1202 CPUs and 176 GPUs is the figure mentioned in the Nature paper. But it's important to understand that this is the computer used to train the networks used by the algorithm. It took about 30+ days worth of wallclock to train it. That's about 110 megawatt-hours (MWh) worth of energy required!
During the play, the computational requirements are vastly less (but I don't know the figures). It's still probably more than is feasible to put in a smartphone in the near future. Assuming we get 3x improvement in perf per watt from going to ~20nm chips to ~7nm chips (near the theoretical minimum for silicon chips), I don't think this will work on a battery powered device. And CPUs are really bad at perf per watt on neural networks, some kind of GPU or ASIC setup will be required to make it work.
That's not correct; those numbers refer to the system requirements while actually playing. To quote from the paper:
> Evaluating policy and value networks requires several orders of magnitude more computation than traditional search heuristics. AlphaGo uses an asynchronous multi-threaded search that executes simulations on CPUs, and computes policy and value networks in parallel on GPUs. The final version of AlphaGo used 40 search threads, 48 CPUs, and 8 GPUs. We also implemented a distributed version of AlphaGo that exploited multiple machines, 40 search threads, 1202 CPUs and 176 GPUs.
In fact, according to the paper, only 50 GPUs were used for training the network.
The cumulative amount of person-hours that went into training Lee Sedol (All the hours spent training his instructors, sparring partners, developing Go theory, playing out, and drawing inferrences from the outcomes of long-dead expert players) is probably more then 500 years. AlphaGo, on the other hand, had to start from scratch.
Given the rules, and a big book containing every professional go game ever played, and no other instruction, it's not entirely clear to me that Lee Sedol would be able to reach his current skill level in 500 years.
And thus why we're not destined to compete with AI, that 110MWh worth of training time can be instantly available to all other Go bots. If only I could have access to a Grandmaster's brain when I needed it!
Are they vastly less, though? The core of the algorithm is still a deep Monte Carlo Tree Search which AlphaGo gets quite a boost on computationally for being able to fire it off in parallel. It's obviously incorrect to take the training system and assume it's identical to the live system, but I think it's disingenuous to say the live system didn't have some serious horsepower.
Yes, for neural networks usually training them takes many orders of magnitude more resources than just using them.
For this particular example, training a system involves (1) analysis of every single game of professional go that has been digitally recorded; and (2) playing probably millions of games "against itself", both of which require far more computing power than just playing a single game.
I'm very aware of that. What I'm saying is that AlphaGo is not merely a neural net reporting best moves directly off the forward propagation. There are two nets which essentially act as proposal distributions for an exploration/exploitation tradeoff in the search space of game trees by which AlphaGo reads positions essentially out to the end of the game and ranks them by win rate (this is Monte Carlo Tree Search). The net moves are "nice" (I think they run at like 80% win rate against some other Go AIs? Maybe I'm misremembering) but the real heart of what makes AlphaGo play well is the MCTS which requires some vast resources to execute—live resources.
I was actually thinking primarily of distributed training time for the networks and playing time for the system, rather than the number of GPUs running this particular match. Also, I thought the number of GPUs in October was more on the order of 1,000? Happy to be told I'm mistaken though.
What a strange sentiment. You would only delay the inevitable outcome. Sure, it wouldn't win now, but processing power will become stronger and machines get smaller. What was the point?
Chess engines and processing power have since then advanced to a point where my phone can now reliably beat Carlsen. There is no reason to suppose Go is different in that respect. In 10 years, DeepMind will fit into a phone.
It's way more about algorithmic improvements than hardware improvements though.
Deep Blue evaluated 200 million positions per second. I don't think top programs of today could get to 2 million positions per second on a smartphone (I get about 10 million pos/second on my i7 3770 quad). It's all about improvements in search algorithms as well as position evaluation.
True. Without hardware improvements the processors that can evaluate 2 million positions per second while 'fitting' into a phone (qua processing power and power usage) would not exist though.
Besides disagreeing with you, this actually isn't true at all. In competition, AlphaGo doesn't rely on particularly expansive hardware. For training, yes, but not for playing.
Considering the complexity of the human brain, it seems only fair to balance out a competitor's handicap in some way. Your idea seems to anticipate the logical progression of these tests: "Nature made this mind inside this small object, the brain, why don't we do that?" Regardless, the trend of course is toward miniaturization. I see news like this recent story: "Glass Disc Can Store 360 TB" http://petapixel.com/2016/02/16/glass-disc-can-store-360-tb-...
to back up imagined futures like the film Her: https://youtu.be/WzV6mXIOVl4 (and that film doesn't even address whether the OSes are connected through a wireless network).
This stuff is happening fast, and we might have found ourselves, historically, in a place of unintelligible amounts of change. And possibly undreamt of amounts of self-progression.
It's the software that's impressive. Why does how many physical computers it takes to run the software matter? It's physical footprint will almost certainly shrink as computers get more powerful.
That's based on the assumption that computing must exist as silicon transistors. When would we have reached the bounds of computation based on physical limits if we had stuck with vacuum tubes. The point is computation is an abstract concept and not tied to the physical medium that we use.
Of course there are physical constraints on computing, but measuring by weight is rather stupid. Measuring the energy consumption seems to be a way better metric (even though "computation per energy" is clearly a human win).
Not to mention that we suddenly forgot that computers have their own units of measurement, such as clock speed (hertz) and memory size (bytes).
> (even though "computation per energy" is clearly a human win)
Is it? The problem here is it is really hard to compare the TCO. For example prime human computation requires years and years of learning and teaching, in which the human cannot be turned off (this kills the human). A computer can save its state and go in a low or even a zero power mode.
>such as clock speed (hertz) and memory size (bytes).
Which are completely meaningless, especially in distributed hybrid systems. Clock speed is like saying you can run at 10 miles per hour, but it doesn't define how much you can carry. GPUs run a far slower clock speed than CPUs, but they are massively parallel and are much faster than CPUs on distributed workloads. Having lots of memory is important, but not all memory is equal and hierarchy is even more important. Computer memory is (hopefully) bit perfect and a massive amount of power is spent keeping it that way. That is nice when it comes to remembering exactly how much money you have in the bank. Human memory is wonderful and terrible at the same time. There is no 'truth' in human memory, only repetition. A computer can take a picture and then make a hash of the image, both of which can be documented and verified. A human can recall a memory, but the act of recalling that memory changes it, and the parts we don't remember so well are influenced by our current state. It is this 'inaccuracy' that helps us use so little power for the amount of thinking we do.
TCO? I'm mentioning solely the electricity the machines consumes (by machine I mean both the human brain and the computer).
Are the units I proposed perfect for the job? Of course not, just look how much you wrote. But I bet that if you do the same "thoroughly" analysis for measuring computing by weight you'll be able not only to write a fat paragraph such as your last one, you can write a whole book on who wrong/meaningless/stupid it is (not that anyone would read such book though).
