What does it mean for a machine to “understand”?(medium.com) |
What does it mean for a machine to “understand”?(medium.com) |
That said, I have a small problem with the examples presented to say that already machines understand us :)
The article says 'For example, when I tell Siri “Call Carol” and it dials the correct number, you will have a hard time convincing me that Siri did not understand my request"
Let me try to take a shot at trying to explain that Siri did not "understand" your request.
Siri was waiting for a command and executed the best command that matched. Which is, make a phone call.
It did not understand what you meant because it did not take the whole environment into consideration. What if Carol was just in the other room. A human would maybe just shout "hey Carol, Thomas is asking you to come", instead of making a phone call.
If listening to a request and executing a command is understanding, then computers have been understanding us for a long time. Even without the latest advances in AI.
This is the crux of the matter. These voice recognition agents are trained with goal of accurately modelling a function that converts recorded sound to a series of words, and then act on those words to perform the most appropriate action. They are NOT trained to model the entire world, which is an incredibly complex task that no one has been able to formulate as a problem that computers can solve, yet. Humans on the other hand, have a machine that is extremely well-equipped to do just that - the brain. And that is exactly why humans are able to "understand" things, while we feel that machines are not, with our definition of "understand".
In the far distant future, if and when we do figure out a way to model the entire world, come up with suitable objective function, and solve it on a computer, there's no reason why that machine should be any less capable of understanding things than the average human.
We have a very specific set of evolved traits that define our understanding of the universe. A lot of that is social. So our "understanding" of the phrase "call Carol" includes a wide range of social cues about what that means, and your example is perfect: "call Carol" means that I want to talk to her, and that would be better done in person if possible, but that "if possible" has a more-or-less specific range of "if she's within earshot so I can yell for her", which is limited to the range of a human voice (but not the maximum range, like screaming, but just a normal yelling range). Which is less if the door is closed, or there's music playing, or Kevin is trying to nap in the other room. And not at all if we're in a library, or concert, or even a public space where yelling would draw attention. If "call Carol" has to include all of these to qualify for "understanding" then I think I know some people who fail at this test.
My go-to thought experiment on this is Dolphins. Dolphins are intelligent, have language, etc. But their understanding of the world must be so different. Trying to explain to a dolphin what "tripping someone up" means is going to be tricky. They may understand the words, but they'll never understand the concept.
We swim in a sea of social cues and non-verbal communication. We can program an AI to imitate more and more of this, and be aware of more of it, but it's like teaching dolphins about long-distance running. It's never going to come naturally. And they're never going to evolve that understanding naturally (like we do as children) because it's not in their nature. We anthropomophise our machines a lot, and we assume that they'll grow (like children) to grok all of our social cues eventually, because our only experience of similar situations is, well, children. But they're just machines, designed for a single purpose. They're never going to grok this. They're never going to be "like us" and really understand all the social ramifications of "call Carol". At some point I think we're going to have to accept this, and say that the machine understands the phrase "call Carol" enough. TFA draws the line at the machine calling Carol, and that seems reasonable.
The classic analogue is of course the Chinese room argument: https://en.m.wikipedia.org/wiki/Chinese_room
If you could make a machine pass the Turing test it might be intelligent - but no one has, and it's debatable if it's even possible, and it's even more debatable if, hype notwithstanding, the Turing test is even a good test of human-equivalent intelligence, because it ignores side channels that are fundamental to human communication, including tone of voice, posture, and facial expression.
(Yes, people communicate over email/SMS. But no one communicates over email/SMS without an implied social context that hugely limits and simplifies the content of any conversation.)
It's not the "call Carol" problem that needs to be solved. It's the "understand the entire world context well enough to know how to call Carol without being told - which includes being able to research information that isn't already available, and also includes edge cases like 'We went to Carol's funeral last week' and 'Carol had her phone stolen yesterday' and 'Carol is flying to Australia and won't be receiving messages for another 12 hours" and "Carol prefers FaceTime to WhatsApp."
And so on.
Ultimately your toy machine has to show evidence that it understands the entire world and can learn about it like a human can - which includes being able to do original research that isn't a simple literal Google search, parse humour, understand emotional responses and common cultural references, and follow standard social protocols.
That's a much harder problem than having a vaguely plausible limited text-only conversation, whether it's in Chinese, English, or Swahili.
ISTM there's no more "understanding" involved in this than when I touch the Contacts icon on my screen, then "C", "A", "R", etc until Carol's entry is displayed, and then I touch the Phone icon to initiate a call.
The fact that the interface used was sound-waves that the device recognised as matching the keyword "call" and the contact-list entry "Carol", rather than my finger touching specific areas of the screen, may be a handy feature. Of course it's a triumph of signal processing, fuzzy recognition, etc. But there's no more "understanding" involved than in the touch-screen version of the action, or in typing a command and parameter into a terminal window.
I think this is a reasonable thing to say, in the limited way he has defined ‘understanding’. People forget what a titanic achievement that user interfaces that allow us to communicate our intentions to a computer and receive a relevant response actually are, whether it’s using a voice or clicking a button.
The problem with the hype is that we are nowhere close to building systems that understand anything.
All we've built are calculators on steroids so far.
For example, take the classical AI knowledgebase fragment, "bird is animal that flies". If I ask example of bird, it can say "eagle", and exhibit some understanding. We can then probe further and ask for a bird which is not an eagle. If it says "bat" or "balloon", it exhibits that it still doesn't understand birds quite right.
In particular, if the description is nonsensical and thus impossible to understand, we cannot give any examples.
This idea was really inspired by the study, where they asked people to recognize nonsensical and profound sentences, describing certain situation. The profound are the ones where you can create a concrete instance of the situation.
On the other hand, machines still perform actions that one could call 'stupid'. When alphago was losing in the fourth match against Lee Sedol it would play 'stupid' moves. These were, for instance, trivial threads that any somewhat accomplished amateur go player would recognize in an instant and answer correctly.
Humans, and also animals, have a hierarchy in their understanding of things. This maps on brain structure too. Evolution has added layers to the brain while keeping the existing structure. In this layered structure the lower parts are faster and more accurate but not as sophisticated. Stupidity arises because of a lack of layeredness so when the goal of winning the game is thwarted the top layer doesn't have any useful thing to do anymore and it falls back on a layer behind that. For alphago pretty much the only layer behind its very strong go engine is the rules of go. So, even when it is losing it will never play an illegal move but it will do otherwise trivially stupid things. For humans there is a layer between these things that prevents them from doing useless stuff. For living entities this is essential for survival. You can be forgetful of your dentist appointment but it is not possible to forget to let your heart beat. It seems that this problem could be mended by putting layers between the top level algorithm and most basic hardware level such that stupid stuff is preempted.
I think this behavior is less 'stupid' than it appears. When human beings play Go, the points matter even to the loser, and everyone goes home when it is over. There is life outside of Go. To Alpha Go, Go is it's entire universe. Part of the way it was trained was competing against other instances of itself, a sort of Thunderdome where the loser doesn't get to continue existing, and doesn't contribute to future generations. To Alpha Go, defeat is death. The behavior we observe when losing is nigh-certain has a human equivalent, we call it desperation. Alpha Go is trying moves that can only possibly work if the opponent makes a catastrophic blunder, which is incredibly unlikely, but it's the only shot it has.
Google Search doesn't, but Google Assistant does. I posed the exact queries suggested by the article and the second query of simply the word "when" did give the correct answer (May 11 1997).
I wonder if now it would correctly take the previous context into account. Google has been working a lot on improving their search and assistants to be "conversational". [1] looks like one of the results of this endevour.
[1] https://cloud.google.com/dialogflow/docs/contexts-overview
It’s like saying “my calculator lets me type ’1 + 2 =’ and gives me the answer ‘3,’ so it seems to understand that question, but when I look at the calculator I see there’s no ‘sqrt’ button that would show me the square root of 3.”
The fact that my basic calculator doesn’t have a “sqrt” button is pretty irrelevant to how well it “understands” how to add two numbers together.
I think what they were trying to get at is that understanding is stateful.
For example, imagine a system that has as input the picture of a human face in RAW format. If the system runs the picture through JPEG compression, for example, and returns something substantially smaller, it has shown some understanding of the input (color, spatial repetition, etc).
A more advanced system, with more understanding, may recognize it as a human face, and convert it to a template like the ones used for facial recognition. It doesn't care about individual pixels anymore, or the lighting, just general features of faces. It understands faces.
An even more advanced system may recognize the specific person and compress the whole thing to a few bits.
I would say that an OCR scanner understands the alphabet and how text is laid out, GPT-2 understands the relationship between words and how text is written. And a physics simulator understands basic physics because it can approximately compress a sequence of object movements into only initial conditions and small corrections.
Lossy compression makes this concept non-trivial to measure, but it's still a world's away from the normal philosophical arguments.
If someone ask why you like ice-cream, you can tell a nice story about the hot summers during your childhood, but the reality is that sugar and fat are very useful.
If a the autopilot of a Tesla hit someone, the error report is "Fatal error 0xDEADBEEF: coefficient 742 > 812".
If a person hit someone the explanation is "It was dark and near a curve. I was texting that is totally safe. I got distracted by reindeer nearby. And I snoozed and was thinking about reaching a handkerchief".
Human understanding has been wrong often enough, missing enough crucial context to be dangerously hillariously wrong even amongst the "experts" of the day who came closest.
The isn't some epistemological nilhism but to point out that understanding is incomplete for everyone and just because a given intelligence subset doesn't match with our assumptions doesn't mean it is wrong - although it also isn't always right.
There are projects doing video and text understanding. I think the trick to efficient generalization is to have the representations properly factored out somehow. Maybe things like capsule networks will help. Although that my guess is that to get really sort of componentized efficient understanding neural networks are not going to be the most effective way.
This sounds a bit like a studying for a test taking. What if we made a definition and then worked successfully to reach the state when, according to this definition, the system "understands". Can we expect to be satisfied with the result in general, outside of the definition?
The definition of understanding could be tricky, as history suggests. Other than "to understand is to translate into a form which is suitable for some use", there could be many definitions. Article itself brings examples of chess playing or truck driving which were considered good indicators, yet failed to satisfy us in some ways.
Maybe we should just keep redefining "understanding" as good as we can today, and changing it if needed, and work trying to create a system "good", not necessarily "passing the test"?
But I have to disagree with this (because of course I do):
>> For example, when I tell Siri “Call Carol” and it dials the correct number, you will have a hard time convincing me that Siri did not understand my request.
That is a very common-sense and down-to-earth non-definition of intelligence: how can an entity that is answering a question correctly not "understand" the question?
I am going to quote Richard Feynman who encountered an example of this "how":
After a lot of investigation, I finally figured out that the students had memorized everything, but they didn’t know what anything meant. When they heard “light that is reflected from a medium with an index,” they didn’t know that it meant a material such as water. They didn’t know that the “direction of the light” is the direction in which you see something when you’re looking at it, and so on. Everything was entirely memorized, yet nothing had been translated into meaningful words. So if I asked, “What is Brewster’s Angle?” I’m going into the computer with the right keywords. But if I say, “Look at the water,” nothing happens – they don’t have anything under “Look at the water”!
https://v.cx/2010/04/feynman-brazil-education
In this (in?) famous passage Feynman is arguing that students of physics that he met in Brazil didn't know physics, even though they had memorised physics textbooks.
Feynman doesn't talk about "understanding". Rather he talks about "knowing" a subject. But his is also a very straight-forward definition of knowing: you can tell whether someone knows a subject if you ask them many questions from different angles and find that they can only answer the questions asked from one single angle.
So if I follow up "Siri, call Carol" with "Siri, what is a call" and Siri answers by calling Carol, I know that Siri doesn't know what a call is, probably doesn't know what a Carol is, or what a call-Carol is, and so that Siri doesn't have any understanding from a very common-sense point of view.
Not sure if this goes beyond the Chinese room argument though. Perhaps I'm just on a diffferent side of it than Thomas Dietterich.
I think the key ingredient is 'being in the game', that means, having a body, being in an environment with a purpose. Humans are by default playing this game called 'life', we have to understand otherwise we perish, or our genes perish.
It's not about symbolic vs connectionist, or qualia, or self consciousness. It's about being in the world, acting and observing the effects of actions, and having something to win or lose as a consequence of acting. This doesn't happen when training a neural net to recognise objects in images or doing translation. It's just a static dataset, a 'dead' world.
AI until now has had a hard time simulating agents or creating real robotic bodies - it's expensive, and the system learns slowly, and it's unstable. But progress happens. Until our AI agents get real hands and feet and a purpose they can't be in the world and develop true understanding, they are more like subsystems of the brain than the whole brain. We need to close the loop with the environment for true understanding.
The idea that a new self- sustaining meaning generation can arise out of the interlocking mechanisms of a computer is an interesting one. As we see self driven car CEOs describe some of the most advanced systems we have, requiring to be run in controlled environments and balking at the infinite complexity of real life, are we really building computer systems that are anything more than an incredibly sophisticated loop?
My point is that humans are also highly-sophisticated, biological machines, so if you say machines cannot "understand", you are making the same claim for humans as well.
Making the claim about what a human is in the absolute, is more about what you fill the unknown with than the nature of a human.
Understanding is the difficult question. I would argue the understanding people want out of machines is the ability to generate, use and self-manage tools and that the machine knows the tool's place or context under a human value, story or intent and adapt to the implications of that higher order. That in the most exaggerated sense would be perceived as a machine that understands, but of course people mean different things when they say that.
You've rigged this up to operationalize it for current digital machines.
"Understanding", "Intelligence", etc. is a feature of animals in their environment. We need to begin there; and that is what we are talking about.
We "understand" how to drive as a dog "understands" how to play fetch. Understanding is not ever going to be a trivial rule that some digital system may instantiate.
It will always require direct causal contact with an environment. In my view "understanding" is "competent play in a changing environment" -- ie., the ability to modify the environment as it changes in accordance with your goals.
This rough definition is inspired by work in animals to understand the role of the neocortex, and animal learning, and the role of consciousness therein. Roughly: consciousness is "perceptual and cognitive intelligence grappling with environmental change".
I am agnostic regarding that, as I don't think there is any evidence that they do not attempt to build models that are consistent representations of reality.
I am assuming, based on my own experience, they also have this "internal lightbulb" going on when they think they have built the correct model. But whether they are actually cognizant of it (self-aware), I have no idea. (I guess what I am saying is that understanding and self-awareness are two different things.)
On my reading list is "The proper treatment of events", a book which "studies the semantics of tense and aspect" within a formal framework of constraint logic programming[1]. There is other similar work in this area, like "Good-enough parsing, Whenever possible interpretation:a constraint-based model of sentence comprehension"[2].
[1] http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.10.... [2] https://hal.archives-ouvertes.fr/hal-01907632/file/CSLP-Blac...
Question: What is an example of a bird? Answer: An egret. Question: What is another example? Answer: Canaries.
Seems to do fine. I don't really have a stop though, so it goes on making up new questions on it's own. Make of it what you will. Very few of the answers are correct or even coherent enough to be correct: https://hastebin.com/agululiqif.txt
I do like this one though:
Question: Who is the inventor of the English ham? Answer: Poor old Francis Bacon.
Above, I am talking in the narrow sense. So the fact that the model itself is wrong shouldn't be an issue. But in the broad sense, we could say that understanding is ability to convert between intensional and extensional (ostensive) representations (models) of the world. Finding an example from intensional representation is just one task that is required.
Edit: nvm, I think I found it : http://journal.sjdm.org/15/15923a/jdm15923a.pdf
But perhaps I wasn't clear, the study doesn't say this, but it was rather my own experience with the BS sentences in that study that led me to the observation that they have an empty set of examples if we take them as a constraint satisfaction problem of sorts.
> The opposite of a fact is falsehood, but the opposite of one profound truth may very well be another profound truth. - Niels Bohr
And, in fact, it is my rule of thumb test if something is a profound truth.
It like saying that red-headed people doesn't have a soul - there is no way to disprove that assertion.
Does that seem dangerous to anyone else?
I also don't see any distinction between "qualia" and "soul" other than spelling, but perhaps it's because I don't have one.
Finally, I have this question for Searle: Say you understand English. Does any specific neuron in your brain understand English? No, the larger system of neurons+neuronal connections does, so why doesn't the system of grad student+book understand Chinese?
All it shows is that after hundreds of years, we still don't know how to explain or quantify human consciousness.
Qualia is generally argued by Sam Harris to be simple or reductionist elements of our human experience we can all agree humans share. Burning your finger on a hot stove and recoiling is a conscious experience every human shares.
The soul includes way more ideas and depends on who you talk to. The word has been overloaded a bunch, but generally can be said to include a higher spiritual aspect.
I have also somewhat responded before to Chinese room argument with this comment: https://news.ycombinator.com/item?id=20864005
We don't know.
I'd suggest that anyone who purports to give a definitive answer to that is in fact making a leap of faith - in one direction or another.
However the bar is way below that at the moment, and masquerading as "intelligence".
Current machine learning (ie., mere statistical) approaches to AI, that do not explicitly aim to dynamically model environments/goals/behaviour/etc., aren't even meeting an extremely minimal notion of intelligence.
We have at the moment "smart rocks". Electrical current "tumbles down" a "digital mountain" and we all it's path "smart" because it has useful outcomes. Equally, a rock rolling down a hill finds an optimal path -- it aint "smart".
We should look at what the rock does when you start adpating its environment: eg., create a little dip in the mountain side; it gets trapped. A mouse doesnt get trapped in a dip, it continues to explore -- why?
Because animal behaviour is inherently exploratory of the enviornment. A mouse doesnt "solve" a maze, it intelligently navigates it -- so that when unexpected change occurs, it isn't "broken".
At the moment, all AI systems radically break when such changes occur -- because they are statistically trained on mere data. They arent dynamically model building. They aren't in an environment. They're just rocks rolling down a hill.
I can say I do, but what reason do you have to believe me?
Still, it remains a philosophical problem, even more so for animals or robots like Data. That's what Ned Block called The Harder Problem of Consciousness. But here I think we just have to accept that our knowledge of others and the world lacks certainty. We trust our senses and inferences to form a reasonable view of the world, but we can never be sure.
But given the constraints of ML/ai you will eventually have a bounded container where an ai car can operate and where it can't. The car will be tasked with looping through that environment from job to job then back to recharge at it's base station. For all the sophistication of getting the car on the road and working it won't really be making up it's own story through the world nor will it understand the greater context of it's actions. The pattern recognition in CV is great, but it is fed by humans, so the meaning that a tree should be avoided has been initially put in by a programmer, even if the car in the moment chooses to avoid the tree by itself. The car is crunching meaningless numbers like a pipe directs water.
So when people say a machine "understands something" it can't ever really be true because all of our machines don't know what is going on in the world, they only know what numbers they see and how to behave when those numbers change. At the very bottom it's electricity looping through logic gates and that same principle is repeated all the way up to a car that loops through it's environment and comes back.
If all the humans left the planet, the car wouldn't be described as understanding the world, it'll be seen as a generic device sitting in a garage somewhere waiting for orders from a human. If you fill the earth with aliens the CV breaks not having seen aliens before, the roads get changed over time by nature, the high detail mapping it relies on fails. The cars "understanding" only exists as an outcome of electric impulse. It doesn't understand and never could. We are building more and more sophisticated loops, and I'm glad, but to think computers can understand is a doomed project. They will never "get" the values, intents and stories we put in them. Computers will forever be a labour of love is not able to regress into understanding what we mean it to be.
The atom in the molecules in the neurons of your brain are bounded by the laws of Physics. They can't disobey them, they are as free as the coefficient of the ML tables.
> If all the humans left the planet, the car wouldn't be described as understanding the world, it'll be seen as a generic device sitting in a garage somewhere waiting for orders from a human.
Unless some car have setup an alarm to go to pick you from work at 5pm, you are not there but it goes anyway. After some time (1 hour?) it gives up and return home to get charged and wait for the next day. The waiting time depend on the weather (if it is cold or rainy) and the battery charge and perhaps the congestion of the roads.
Once per year they go to the robot-mechanic for the anual service. They also go when a tire or something get broken. They can call the autonomous crane in case it is needed. During the repairing time, they call a replacement and send all your info and schedule, so you would not miss your appointments (in case you were still there).
The car also negotiates automatically the insurance with the company web service, and pays the registration fees. Your autonomous house pays the electricity bills. Until your bank account is empty.
If you have some money in a good investment found this can last for a long time, until your car is too old and decides to retire and buys a replacement.
We are still very far from this scenario, but it is not so difficult to imagine that a bunch of small features compose nicely.
Somewhat related: https://en.wikipedia.org/wiki/Hachikō
It’s more accurate to say the Chinese room computes results which humans recognize as successful translation from English to Chinese. The understanding is all on the side interpreting the output.
Data participates in human society and he has a human-like body. Data also has subjective experiences, as evidence by his dream sequences in one episode. Whereas the Chinese Room is just following a bunch of rules for translation. But Data doesn't merely translate from one set of symbols to another given a large set of rules. He learns by interacting with people and his experiences as an android. From that we could say understanding is the result of an embodied social activity that the Chinese Room completely lacks. Whatever the Chinese room is said to be doing, that's not the same as understanding language.
Another way to put it is that language isn't equivalent to symbol manipulation, even though it makes use of symbols, or a least since the written word was invented.
Unfortunately this is a recursive question, because the only device we have for exploring the difference between a brain and a computer is our brains. Thus, I believe the Chinese room experiment is rightly composed of as a thought experiment - what other means do we have for assessing our difference from computers other than our intuition?
What I'm trying to get at is deeper. I guess it's a question of philosophical form. Can you grow an software package to the point of transcending a looped format? Usually a program has our goals and desires established in the coding process and we may through in some qualitative checking functions. We then compile it into a binary form that runs on a CPU that has a clock. That CPU always runs, and the human relevant meaning in the code like function names, the human interpretation of images, video, maps and sound was evaporated only leaving streams of binary. The binary flows through logic gates that act like plumbing tools. The tools can check their own output and proceed down different qualitative paths.
ML as a form grinds out the problem of optimizing the path through those logic gates against qualitative checks. Then we store the working model and loop it at runtime.
So why can you give a human the idea 'making cars can be sold and get you laid' and the human will change their entire career, living location and lifestyle to suit a better economic output, but the program cannot reason/create a form that is not a loop?
If we give a car body sensors and body 'brains' it can synthesize many different perspectives at once. Tactile door handles could give fingerprint/heartbeat/temperature senses on the human driver, as one tiny example. You could program in assumptions about what a high temperature human needs and wants. You could give the car every kind of imaging sensor, air quality sensors, moisture level sensors. You could track and synthesize all that data across time and evolve in a sense for when it's going to rain like ants have, or whatever. It could 'feel' the world. But it would still be that sheepdog waiting at home.
Could it anticipate your needs? Only as a historical projection and whatever you program in. Can you infer human intent, thought or value from sensors? Computer vision applied to human faces or voices? I don't think so.
Humans use different forms of language to transmit intent, values, stories, feelings. The idea that we could have a language or sensor inference that we talk to the car with that will perceive and adapt to the conflict our own mind is wrestling with and seeks to solve is difficult. Google's automated hair dressing appointment booker is cool, it is extending the breadth of voice commands a computer can respond to without having to understand what the words mean or the conflicts implied in understanding their meaning but only how they should be plumbed around as electric bits.
I guess the endless hope is that we just have enough quantity of processed information we can build a machine that you can interface with, it'll solve the problem and that you don't need to know how the innards of the machine work. Which always seems like a plausible goal until it isn't. Web apps break, the internet can behave unpredictably, washing machines require cleaning and soap/ washing knowledge, cars break. Stuff that can be ignored is usually because we pay others to fix the problems quietly. Shifting the burden of understanding how to deal with looped quantative machines to the capatalist/currency system, another quantative system.
The challenge of allowing a human to ignore the new loops one must learn the structure of and thus be able to say the machine 'understands' me instead of I having to understand it, is a forever doomed hope that we benefit from trying to solve.
This looping, CPU, "programming it in", and app concept are not the direction machine learning is going in. That's not how deep learning and neural networks work. You can integrate them with an app and do looping yes, but you can also just connect them to each other. No looping, no "programming it in", no apps.
Frankly I'm not saying ml is programmed in, only the initial conditions are, which is where the meaning is. We have hired a lot of low income earners to classify images for image recognition, which is the outsourcing of discerning meaning from the CPU to the he human. These kind of broad discussions don't go anywhere here, I should go somewhere more philosophical.
You should take a look at the play analysis of the games of AlphaStar made by Beastyqt https://www.youtube.com/watch?v=uaJYF4iSvNs . He absolutely anthropomorphize the Protoss AlphaStar and says that it thinks this and then it thinks that. That version plays nice and use interesting moves. If that is thinking and understanding is an open question. (My answer is "just a little".)
If you have time you can see the Terran and Zerg version of AlphaStar. He is not happy with them. The Zerg version is a one trick pony that can almost be programed as an expert system. The Terran version doesn't play very well, because it's very difficult to know where to put the buildings and Siege Tanks.
It's interesting to see the difference in the anthropomorphized analysis.
I don't expect to see ever a 100% bug compatible model of the brain. I expect to see some system that does a somewhat similar calculation and produce results that look similar to the result in the brain. Something like the eye and a camera.