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So for example, what kind of understanding do you think is lacking in the visual recognition example? To me it seems that this field is quite well understood.

So for example, what kind of understanding do you think is lacking in the visual recognition example? To me it seems that this field is quite well understood. The net looks at the data and searches for patterns. On first layers simple patterns like contours (which is a simple mathematical function), on higher levels different kinds of dots or smaller objects (objects meaning, a spatial configuration of contours and whitespace from the previous layer), on higher levels relationship of those obects to each other, etc. etc. Pooling and convolution handle recognizing of these patterns in different parts of the image. Final layers do statistical weighing of which patterns were found in which strengths, and generate the most likely result (of in this case, a classification task). I'm not saying it's magic, I'm not saying it is AGI, I'm not even saying it's far from pure statistics, but what part of this process is "hard to understand what the net is doing"? It is all pretty clear at this point? There are online courses the describe every step of this... > These "visualizations" or high level descriptions of machine learning algorithms aren't a form of true understanding... It's just a summary of a class of problems Seems like an arbitrary distinction that you have come up with. I argue that if we can make those nets, if we can control them, if we can predict them - then yes it is true understanding. Now yes the universe is indeed a fractal and you can undersatnd any small particle in a an uncalculable number of ways and claim that your understanding is more true, more profound etc, but what is the point? I think a great cause is to create AGI, to which I still haven't seen any arguments hinting at impossibility of. > The logic of "visual recognition" is not understood by humans. We can only reproduce such logic as a blackbox. A neural net. I really don't understand what you are saying here unfortunately, sounds like a philosophical black hole of overintellectualizing a simple phenomenon. I would say that we understand enough to have practical ability, which is all that matters. > It's a very meta concept and possibly as a result it's also very hard for you as a consciousness yourself to write the exact definition of consciousness or pinpoint exactly what it is. Well sure, I agree with you there. It is already the case for many phenomenon that we can nonetheless control. What is the exact definition of a car? Of a video game? Of a tree? Of a career? Of a family? I don't think humans need exact definitions of things, they can just as well function relatively optimally without them. Not all knowledge is in logical definitions.

crimsonalucard 7y ago
Here's a simple way to put it. If you understood visual recognition. Then you can code up the algorithm by hand, given enough time. If the problem is too complex for you to understand then you can subdivide the problem into smaller pieces and give it to a team of people to code it. You can't currently do either of these things. You require an algorithm to code it up for you. Therefore you don't really understand it. >The net looks at the data and searches for patterns. On first layers simple patterns like contours (which is a simple mathematical function), on higher levels different kinds of dots or smaller objects (objects meaning, a spatial configuration of contours and whitespace from the previous layer), on higher levels relationship of those obects to each other, etc. etc. Pooling and convolution handle recognizing of these patterns in different parts of the image. Final layers do statistical weighing of which patterns were found in which strengths, and generate the most likely result (of in this case, a classification task). I kind of got into this. Peering into the neural network doesn't mean you understand it anymore then peering into the human brain to see what parts light up given certain stimuli. If you can understand it then you can code it up in the same amount of lines of code. You obviously can't and don't understand the algorithm well enough to do this. However you're satisfied by this level of understanding which is fine, but the ultimate goal of neurology is to surpass this level of understanding and THAT is what I am addressing. >Well sure, I agree with you there. It is already the case for many phenomenon that we can nonetheless control. What is the exact definition of a car? Of a video game? Of a tree? Of a career? Of a family? I don't think humans need exact definitions of things, they can just as well function relatively optimally without them. Clearly there is a difference between our lack of an exact definition of a car vs the exact definition of consciousness let's not get into the semantics of the difference here suffice to say that we're both human beings and we understand there is a huge lack of understanding of what consciousness is vs what a car is. >Not all knowledge is in logical definitions. It is. Lack of a logical definition is an indication of lack of knowledge. Your brain clearly has an exact definition of a career given the fact that given an input you can instantly recongize whether something is a "career" or not. The reason why you can't write the exact exact definition down in words is because you lack understanding of the structure of the definition that is being held in your brain. However given all possible inputs your brain will have an output. Given all inputs you will be able to write down a full mapping between inputs and outputs... that is a formal definition of "career"... you just lack the capability/knowledge of simplifying the definition into a single logical statement and understanding what exactly it is... but an exact formal definition exists in your brain. >I really don't understand what you are saying here unfortunately, sounds like a philosophical black hole of overintellectualizing a simple phenomenon. I would say that we understand enough to have practical ability, which is all that matters. What's wrong with over intellectualization? Are you implying that you want less brain power applied to this problem? We need to be stupider to understand something? If you say that practical ability is enough then this argument is over because we are talking about different things. I'm not talking about practical ability I'm talking about understanding of consciousness. The ability to define it and hold the concept in your head or several heads. That's what I'm talking about and that is the ultimate philosophical goal of neurology. A brute force simulation of a neural net which is the current state of things these days is not a form of understanding. Once you understand what it is, you should be able to code it up by hand. Sure we MAY (keyword) only be interested the practical aspects of something like voice recognition. But the question of consciousness is clearly not something we're just interested in emulating. We're interested in understanding it. If we develop 3D printing and scanning to the point where we can reprint the entire atomic structure of things we see in reality this does not mean we understand everything at the molecular level. Practical application != actual understanding. Taking this printer and printing out a human brain doesn't help us understand it at a deeper level just like how a photocopier copying a calculus text book doesn't help us understand calculus. Additionally your "practical" machines will always have limitations if there isn't full understanding of the phenomenon that operates underneath. Self driving cars that deliberately crash into an unrecognized object is an example of unpredictability that comes with lack of understanding.

crimsonalucard 7y ago

Here's a simple way to put it. If you understood visual recognition. Then you can code up the algorithm by hand, given enough time. If the problem is too complex for you to understand then you can subdivide the problem into smaller pieces and give it to a team of people to code it. You can't currently do either of these things. You require an algorithm to code it up for you. Therefore you don't really understand it. >The net looks at the data and searches for patterns. On first layers simple patterns like contours (which is a simple mathematical function), on higher levels different kinds of dots or smaller objects (objects meaning, a spatial configuration of contours and whitespace from the previous layer), on higher levels relationship of those obects to each other, etc. etc. Pooling and convolution handle recognizing of these patterns in different parts of the image. Final layers do statistical weighing of which patterns were found in which strengths, and generate the most likely result (of in this case, a classification task). I kind of got into this. Peering into the neural network doesn't mean you understand it anymore then peering into the human brain to see what parts light up given certain stimuli. If you can understand it then you can code it up in the same amount of lines of code. You obviously can't and don't understand the algorithm well enough to do this. However you're satisfied by this level of understanding which is fine, but the ultimate goal of neurology is to surpass this level of understanding and THAT is what I am addressing. >Well sure, I agree with you there. It is already the case for many phenomenon that we can nonetheless control. What is the exact definition of a car? Of a video game? Of a tree? Of a career? Of a family? I don't think humans need exact definitions of things, they can just as well function relatively optimally without them. Clearly there is a difference between our lack of an exact definition of a car vs the exact definition of consciousness let's not get into the semantics of the difference here suffice to say that we're both human beings and we understand there is a huge lack of understanding of what consciousness is vs what a car is. >Not all knowledge is in logical definitions. It is. Lack of a logical definition is an indication of lack of knowledge. Your brain clearly has an exact definition of a career given the fact that given an input you can instantly recongize whether something is a "career" or not. The reason why you can't write the exact exact definition down in words is because you lack understanding of the structure of the definition that is being held in your brain. However given all possible inputs your brain will have an output. Given all inputs you will be able to write down a full mapping between inputs and outputs... that is a formal definition of "career"... you just lack the capability/knowledge of simplifying the definition into a single logical statement and understanding what exactly it is... but an exact formal definition exists in your brain. >I really don't understand what you are saying here unfortunately, sounds like a philosophical black hole of overintellectualizing a simple phenomenon. I would say that we understand enough to have practical ability, which is all that matters. What's wrong with over intellectualization? Are you implying that you want less brain power applied to this problem? We need to be stupider to understand something? If you say that practical ability is enough then this argument is over because we are talking about different things. I'm not talking about practical ability I'm talking about understanding of consciousness. The ability to define it and hold the concept in your head or several heads. That's what I'm talking about and that is the ultimate philosophical goal of neurology. A brute force simulation of a neural net which is the current state of things these days is not a form of understanding. Once you understand what it is, you should be able to code it up by hand. Sure we MAY (keyword) only be interested the practical aspects of something like voice recognition. But the question of consciousness is clearly not something we're just interested in emulating. We're interested in understanding it. If we develop 3D printing and scanning to the point where we can reprint the entire atomic structure of things we see in reality this does not mean we understand everything at the molecular level. Practical application != actual understanding. Taking this printer and printing out a human brain doesn't help us understand it at a deeper level just like how a photocopier copying a calculus text book doesn't help us understand calculus. Additionally your "practical" machines will always have limitations if there isn't full understanding of the phenomenon that operates underneath. Self driving cars that deliberately crash into an unrecognized object is an example of unpredictability that comes with lack of understanding.