A mind map is a type of chart that organizes information visually. Typically, a mind map will stem from a central idea, with supporting ideas branching out from it in a nonlinear order. There are plenty of mind map templates available online. But, most of them are boring and they all look the same. Place that central idea in the middle of your mind map and branch out your supporting ideas around it. Focus on one keyword for each idea. If you plan on sharing your mind map in a presentation, blog post, or any form of longer content where you want to keep readers engaged, come up with a design concept for your mind map.
Your design concept will determine what kinds of supporting visuals you include, what colors you use, and how you choose to lay out your mind map. Pro Tip: You can change photos easily in the Venngage editor. I dragged it onto the canvas and resized it to fit the mind map.
It took a couple of clicks to do. The supporting ideas in the mind map above are all different colors, which helps them stand out from one another. The tool can grab a color palette automatically from any website or you can set the colors manually. Whatever you like! A neutral background with a few accent colors will prevent your mind map design from looking cluttered and overstuffed.
Visual hierarchy is all about creating different visual weights by varying size, shape, color, position, and density. For example, in this mind map template, the perceived density of the shapes creates two levels of hierarchy—a dense, filled and visually salient central idea surrounded by lighter, outlined ideas.
Put more simply, more ink commands more attention!
Another way to create a visual hierarchy is to use different sized shapes in your mind map, like in this example:. In the mind map template below, the supporting ideas storage, forms, and generation connect to the central idea with double lines:. Once again, the more ink there is in one place, the more attention drawn to that part of your mind map. All through the book I was wondering why, with a title like this, there were no references to the very interesting research that's publicly accessible done at the Allen Institute. As it turns out, the reason was in the final chapter - Paul Allen had earlier criticised his "laws" as not being actual physical laws in one of Kurzweil's earlier books.
I'm imagining that at this point he must be really drunk to dedicate a whole chapter to saying how Paul Allen was wrong in saying that. I don't normally write lengthy reviews of books that I've read but this was so bad that I felt obliged to warn others not to waste their money and time and saving some trees in the process. I'd give this book zero stars if I could. View all 4 comments. Dec 23, James Dittmar rated it it was ok Shelves: popular-science. I like Kurzweil. But I thought he did a little too much boasting and did not provide enough details.
First half of the book: it appears that we can model the brain with hierarchical hidden Markov models better than we can with neural nets. Some back of the envelope calculations show that Hidden Markov models may contribute to the functioning of the brain. Ok, so far so good.
Second half of the book: wildly uneven coverage of a wide range of topics in neuroscience philosophy, such as identity, fre I like Kurzweil. Second half of the book: wildly uneven coverage of a wide range of topics in neuroscience philosophy, such as identity, free will, and consciousness. Kurzweil likes to frequently mention all of the contributions that he has made to AI. I think this could have been toned down a little bit. View all 3 comments.
Feb 19, Ryan rated it liked it Shelves: nonfic-computation. Speaking as a software engineer who has a fascination with AI, I largely agree with Kurzweil's glowing assessments about the future of machine intelligence, though I'd probably push his timeframe back a few decades and could do with a bit less of his self-promotion.
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Though there's a lot we still don't understand about how the human brain operates, neuroscience and computer science are starting to form the same fundamental insights about how intelligence "works", whether it's represented as neurons or a mathematical process. In a truly intelligent machine, data from the outside world is taken in by a large, hierarchical array of pattern-recognizers, which gradually rewire themselves to better anticipate the messy-but-hierarchical patterns of the real world visual squiggles to letters, letters to words, words to syntax, syntax to meanings, meanings to relationships, relationships to concepts, concepts to insights -- and back down again.
To some extent, the software world has already made useful progress in this direction. Still, there's plenty here for a general audience, when he gets away from the geekery. Kurzweil is passionate and pretty convincing about his belief that even limited gains in awareness of how the human brain works still provide AI researchers with some powerful springboards, and that, conversely, advances or missteps in AI teach us more about the brain. As he points out in discussing Watson, the IBM computer system that famously won on Jeopardy after acquiring most of its knowledge from scanning natural-language documents the sampling of questions it got right is impressive , things have already come a long way.
And there's no reason to believe that the rapid convergence won't continue, especially in the post-cloud computing world. And Kurzweil wades a little bit into the philosophy of consciousness, exploring some its more paradoxical aspects in light of what science knows about the human brain. For example, it's been shown that the two cerebral hemispheres, in patients with a severed connection, operate almost as two separate brains. Yet, each one still seems to think it has a conscious link to the other. Maybe such individuals are more like two people in one body, but don't realize it?
Eerie, huh? His other thought experiments are nothing new, but still fun. Everyone should know what the Chinese Room is. His enthusiasm for the topic can be quite inspiring. This is a fascinating look into how our brains operate, and how the first synthetic brains have been operating, and will operate as they become more sophisticated and, eventually, sentient. Nov 10, Charlene rated it it was amazing Shelves: technology , neuroscience , innovation , constructs , decision-making , philosophy , favorites. Well, I am simply in love with Kurzweil.
How could I not be? This was one of the best books on Philosophy of Mind that I could imagine reading. Early on in the book, Kurzweil respectlfully disagreed with Steven Pinker, and imo, setting himself apart from the good genes crew Dawkins et. He went on to take his lucky reader on a tour of the future of the mind, teaching them about everything that has been done to date to try to create a mind. In , I took a cognitive science class that fea Well, I am simply in love with Kurzweil. In , I took a cognitive science class that featured a lot of Kurzweil's work, as well as many other things included in this book.
I later took two courses in Philosophy of Mind. All of these courses focused heavily on AI. I loved those classes so very much and this book brought everything flooding back. In fact, this very book was written not by hand, but was dictated using Dragon Dictation which is a product of HMMs. Kurzweil also provided his reader with a short but excellent history of Philosophy of Mind by including Jackson's Mary The Knowledge Argument , Searle's Chinese room, Chalmers zombies, and Dennett's ideas about all of that.
I was sad that he didn't include Andy Clark, but even with that oversight, it was one of the best and most relatable summaries of Philosophy of Mind that I have read. He took out the jargon and, instead, made every concept easy enough for a middle schooler to grasp, yet interesting enough for academics. Kurzweil chose the most interesting bits of neuroscience to include in this book, all of which are still exciting in I can only imagine what I would have felt like if I had read this book in I would have been blown away. The efforts to create a mind have been ongoing for decades.
There is not stopping it, much to the chagrin of many. If you want to be informed about how this process works, read this and Kevin Kelly's The Inevitable. They pair nicely with one another. View 2 comments. Dec 22, Andrej Karpathy rated it liked it. Kurzweil's book offers an overview of the biological brain and briefly overviews some attempts toward replicating its structure or function inside the computer.
He also offers his own high-level ideas that are mostly a restatement of what can already be found in other books such as Hawkins' On Intelligence with a few modifications he admits this himself though at one point, for which he gets bonus points. Finally, he applies his Law Of Accelerating Returns LOAR to field of AI and produces Kurzweil's book offers an overview of the biological brain and briefly overviews some attempts toward replicating its structure or function inside the computer.
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By the end, you're almost convinced we're almost there! The bad: First, his own theories are extremely vague and half-baked though I forgive this. If he knew more he would be busier with things other than writing this book and essentially reduce to some form of Hierarchical Hidden Markov Model. That's not especially exciting, I think most researchers in the field will agree on such high-level things. All in all, I would recommend this book to anyone who's interested in some pointers to our efforts to replicate a brain in the computer, who wants to learn a bit about the biological brain, or who's into the philosophy of it all.
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Feb 20, Nathaniel rated it it was ok Shelves: non-fiction , science. I'm just going to warn everyone at the offset: this book triggered my grumpy, cane-waving, "you kids get off my lawn" reflexes pretty hardcore. So, buckle up. If you ever need a really clear example of how intelligence and wisdom are not the same thing, this book is a great place to get started.
I don't for an instant doubt that Ray Kurzweil is a very, very smart guy. Almost certainly smarter than I am. The problem is that, like quite a lot of people who have had a super-abundance of success--a I'm just going to warn everyone at the offset: this book triggered my grumpy, cane-waving, "you kids get off my lawn" reflexes pretty hardcore. The problem is that, like quite a lot of people who have had a super-abundance of success--a dearth of healthy failure leads to a superabundance of confidence.
Basically everything that is wrong in this book stems from that. So let's get started. Two of Kurzweil's motivating points are that: 1. Except, of course, that if you went back in time 2, years and explained Bernoulli's principle to people, they couldn't exactly go out and build a Same principles here: even if PRTM is correct, the idea that it's sufficient to build human-level AI is totally unsubstantiated. We'll get to some specific deficits shortly. As for the second, here's his argument: Let's think about what it means to be complex.
We might ask, is a forest complex? The answer depends on the perspective you choose to take. You could note that there are many thousands of trees in the forest and that each one is different. You could then go on to note that each tress has thousands of branches and that each branch is completely different.
Then you could proceed to describe the convoluted vagaries of a single branch. Your conclusion might be that the forest was complexity beyond our wildest imagination. But such an approach would literally be a failure to see the forest for the trees. Certainly there is a great deal of fractal variation among trees and branches, but to correctly understand the principles of a forest you would do better to start by identifying the distinct patterns of redundancy with stochastic that is, random variables that are found there.
It would be fair to say that the concept of a forest is simpler than the concept of a tree. This is analogy is, shall we say, strained. If all you care about is geometry, then yes: you could use procedural generation to generate a reasonable facsimile of a forest without trying to minutely recreate an actual forest. This is the idea behind the video game No Man's Sky , which uses procedural generation to create a universe "which includes over 18 quintillion 1.
A tree is an organism, and a forest is a superorganism of even greater complexity even if you only consider the relationships between the various trees and ignore all the other creatures inhabiting it. In short, this is just wishful thinking presented as an argument. There's an awful lot of pseudoscience like this in the book. He spends some time estimating the total number of patterns that a human brain needs to memorize, patterns for everything from the shape of the letter "a" to rules for driving safely.
Unsurprisingly, his estimate of the number of patterns we need to memorize and his estimate for the number of discrete pattern-recognition unites in the neocortex coincide. This is convenient for his theory, but useless for any other purpose because he had defined his terms so loosely if at all that the explanation is entirely a black-box, while the method used to derive it is no more scientific than the infamous Drake equation.
Another argument he repeats several times is that the brain really can't be that complex because there's only but so much information related to the brain in our DNA. This is an extremely problematic assertion, because it leaves out the very serious possibility of pointers. Think about it this way: we can easily compute how much memory it takes to send a string of English characters.
If you use a typical encoding, you spend about bytes per character, so a thousand characters is about a kilobyte. Now consider that I encode random gibberish and send it. How much information have I sent? Or imagine I write something meaningful, but that I send it to someone who doesn't speak English? How much information has been transmitted? Now imagine that I send it to someone who does speak English. Obviously this person--receiving a meaningful message--gets a lot more information out of what I've sent than the previous two.
And so obviously measuring how much information is available for transmission isn't the same thing as measuring how much information is received. The English speaker is interpreting my message against a vast library of linguistic data that they already have in mind, and so they're getting a lot more out of it.
Or, to make this example really extreme, suppose that my message says, "You should look up this article on wikipedia" and then provides a URL. This is what I really mean by a reference. It's possible to send a small message that refers to a much larger amount of information stored elsewhere. Effectively, this is what DNA is doing, since it's basically saying "here's how to build proteins" and then relying on the information encoded in physics--which dictates the behaviors and interactions of those proteins--to reference a vast library of information.
How much data it takes to send instructions to build a brain via DNA and how much data we would need to replicate a mind in some other substrate are entirely different questions. The only way we could be assured of needing no more information than is available in the DNA is if we were actually building a biological brain or, at least, simulating one at the atomic level, which is exactly what Kurzweil insists we don't have to do.
In other words: more wishful thinking. The real irony here, of course, is that Kurzweil refers to these concepts in the book. He understands that human intelligence is important because it allows us to store information "in the cloud. Instead of transmitting knowledge via DNA, we transmitted it via spoken language, and so we could store a lot more. Than we figured out writing, etc. He's familiar with all of this, so he should understand that the same could be true of DNA.
But the most egregious problems occur when Kurzweil ventures outside of science entirely and starts talking philosophy. This is a common problem with science writers, by the way. They really tend to lack a fundamental sense of humility when treading outside their own specialized domains. For example, he has a chapter on theories of consciousness where--after summarizing some of the competing views--he basically waves the entire topic aside: "These theories are all leaps of faith Perhaps the most embarrassing section was his discussion of Descartes' famous " cogito ergo sum.
I can imagine a remarkably ignorant person who decided to invent meaning for a quote without bothering to look it up online might come up with such a silly theory, but to claim it is "generally interpreted" this way is only to reveal a deep chasm of ignorance on fundamentals of Western philosophy. To his credit, Kurzweil goes on to say that "reading this statement in context of his other writings I get a different impression," whereupon he gives the correct summary of Descartes' point. However, getting the point correct doesn't really make up for claiming as your own interpretation the meaning of a phrase that 1 is abundantly clear in context and 2 appears in introductory philosophy textbooks everywhere.
As I stated: it's probably just a case of overconfidence. Kurzweil is probably really quite brilliant in his area of expertise e. This is compounded when, a few pages later, he seriously attempts to defend his Law of Accelerating Returns a generalized version of Moore's Law as being just as much a real "law" as the laws of thermodynamics. I'll leave out a detailed rebuttal of this point, because I think for most people the silliness--and vanity--of this position are self-evident.
All of this culminates in his final attack on John Searle and his Chinese room thought experiment. Searle is one of the most respected philosophers alive today, and Kurzweil is someone who thinks stating the obvious and accepted interpretation of philosophy's most famous three words is somehow his own invention. Naturally, this does not end well. Inside this room sits a man who speaks not a single word of Chinese. Occasionally, someone will write a question in Chinese and submit it through a slot.
The man then looks through his library of symbols to find an input that matches. When he does, he copies down the output and slides that paper back out of the window. To an outside observer, it looks as though they can simply ask the room a question in Chinese and get an answer. So they might naturally assume that the person in the room speaks fluent Chinese. Of course, having to wait minutes or even hours to get a reply might spoil the illusion, but the point of this example is to make an analogy for a computer, so you can imagine the person looking up the Chinese symbols is really, really fast.
Of course, we know that he doesn't understand a word. And so--according to Searle--the fact that an AI program could for example pass the Turing test wouldn't guarantee that the AI understood anything at all. Kurzweil's rebuttal to the most famous argument against strong-AI is to simply state that--while the man doesn't understand Chinese--the man and the room taken together do.
This is not bad as a starting point to try and respond to Searle, but it's just that: a starting point. It's not clear to me at all that a library of books "understands" information just because it contains information. Especially if the librarian happens to be unable to read any of the words in his library! Suffice it to say: I didn't find this book very convincing. I find the overall topic interesting, and I do think that Kurzweil's explanation of how the neocortex works is entirely plausible. I found it convincing, anyway. This PRTM pattern recognition theory of mind seems entirely plausible as an explanation for how the neocortex handles knowledge--both learning and recall--and I do think that his basic aspiration to use that as a basis for constructing cybernetic brain-enhancements has some realistic promise.
I say, some because I think he's skipping over some really, really hard problems, especially how to integrate an artificial neocortex with a biological one. The neocortex is impressively plastic, but not infinitely so, and it's not at all clear to me that this is a trivial problem. Moreover, creating an auxilliary neo-cortex is very, very, very far from creating a stand-alone AI.
As Kurzweil admits, even if you constructed an artificial brain he seems to think you could get away with just a neocortex you're going to have to teach it. But he views this as a symbol matter of pattern recognition. This is obviously false, because a lot of what is required for growing a healthy human mind includes things like love and empathy.
He suggests that waiting to teach a nascent AI in real time would be tiresome, so we should speed up the clock cycle and fast-forward our artificial brain through years of development in years of real time. Would anyone like to propose a method of providing a simulated version of years of love and healthy attachment to a computer simulation at 10x speed? Because we know--from tragic natural experiments--that a lack of love and nurture leads to severe developmental problems both emotionally and intellectually.
There's some really, really interesting material here. And if Kurzweil was willing to show some humility in dealing with experts outside his field--and maybe a little bit of humility with his own visions--it could have been a fascinating and influential book. As it stands, however, his achievements are overshadowed by his unjustified arrogance.
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Nov 16, Aaron Thibeault rated it really liked it. As time marches on and technology advances we can easily envision still more impressive feats coming out of AI.
And yet when it comes to the prospect of a computer ever actually matching human intelligence in all of its complexity and intricacy, we may find ourselves skeptical that this could ever be fully achieved. There seems to be a fundamental difference between the way a human mind works and the way even the most sophisticated machine works--a qualitative difference that could never be breached.
Famous inventor and futurist Ray Kurzweil begs to differ. To begin with--despite the richness and complexity of human thought--Kurzweil argues that the underlying principles and neuro-networks that are responsible for higher-order thinking are actually relatively simple, and in fact fully replicable. Indeed, for Kurzweil, our most sophisticated AI machines are already beginning to employ the same principles and are mimicking the same neuro-structures that are present in the human brain.
Beginning with the brain, Kurzweil argues that recent advances in neuroscience indicate that the neocortex whence our higher-level thinking comes operates according to a sophisticated though relatively straightforward pattern recognition scheme. This pattern recognition scheme is hierarchical in nature, such that lower-level patterns representing discrete bits of input coming in from the surrounding environment combine to trigger higher-level patterns that represent more general categories that are more abstract in nature.
The hierarchical structure is innate, but the specific categories and meta-categories are filled in by way of learning. For example, if a letter is obscured, but the remaining letters strongly indicate a certain word, the word-level recognizer might suggest to the letter-recognizer which letter to look for, and the letter-level would suggest which strokes to look for.
Kurzweil also discusses how listening to speech requires similar hierarchical pattern recognizers. Kurzweil's main thesis is that these hierarchical pattern recognizers are used not just for sensing the world, but for nearly all aspects of thought. For example, Kurzweil says memory recall is based on the same patterns that were used when sensing the world in the first place. Kurzweil says that learning is critical to human intelligence.
A computer version of the neocortex would initially be like a new born baby, unable to do much. Only through repeated exposure to patterns would it eventually self-organize and become functional. Kurzweil writes extensively about neuroanatomy , of both the neocortex and "the old brain". Kurzweil next writes about creating a digital brain inspired by the biological brain he has been describing. One existing effort he points to is Henry Markram's Blue Brain Project , which is attempting to create a full brain simulation by Kurzweil believes these large scale simulations are valuable, but says a more explicit "functional algorithmic model" will be required to achieve human levels of intelligence.
Kurzweil touches on some modern applications of advanced AI including Google's self-driving cars , IBM 's Watson which beat the best human players at the game Jeopardy! He contrasts the hand-coded knowledge of the Douglas Lenat 's Cyc project with the automated learning of systems like Google Translate and suggests the best approach is to use a combination of both, which is how IBM's Watson was so effective. Kurzweil thinks the human brain is "just" doing hierarchical statistical analysis as well. In a section entitled A Strategy for Creating a Mind Kurzweil summarizes how he would put together a digital mind.
He would start with a pattern recognizer and arrange for a hierarchy to self-organize using a hierarchical hidden Markov model. All parameters of the system would be optimized using genetic algorithms. He would add in a "critical thinking module" to scan existing patterns in the background for incompatibilities, to avoid holding inconsistent ideas. Kurzweil says the brain should have access to "open questions in every discipline" and have the ability to "master vast databases", something traditional computers are good at.
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He feels the final digital brain would be "as capable as biological ones of effecting changes in the world". A digital brain with human-level intelligence raises many philosophical questions, the first of which is whether it is conscious. Kurzweil feels that consciousness is "an emergent property of a complex physical system", such that a computer emulating a brain would have the same emergent consciousness as the real brain.
This is in contrast to people like John Searle , Stuart Hameroff and Roger Penrose who believe there is something special about the physical brain that a computer version could not duplicate. Another issue is that of free will , the degree to which people are responsible for their own choices. Free will relates to determinism , if everything is strictly determined by prior state, then some would say that no one can have free will. Kurzweil holds a pragmatic belief in free will because he feels society needs it to function. He also suggests that quantum mechanics may provide "a continual source of uncertainty at the most basic level of reality" such that determinism does not exist.
Finally Kurzweil addresses identity with futuristic scenarios involving cloning a nonbiological version of someone, or gradually turning that same person into a nonbiological entity one surgery at a time. In the first case it is tempting to say the clone is not the original person, because the original person still exists. Kurzweil instead concludes both versions are equally the same person.
He explains that an advantage of nonbiological systems is "the ability to be copied, backed up, and re-created" and this is just something people will have to get used to. Kurzweil believes identity "is preserved through continuity of the pattern of information that makes us" and that humans are not bound to a specific "substrate" like biology.
The law of accelerating returns is the basis for all of these speculations about creating a digital brain. It explains why computational capacity will continue to increase unabated even after Moore's Law expires, which Kurzweil predicts will happen around