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>Out of curiosity, what are some of these concepts that you have stumbled upon that you would put in this category (preferably at least somewhat related to buil

>Out of curiosity, what are some of these concepts that you have stumbled upon that you would put in this category (preferably at least somewhat related to building AI or new technology in general)? I am sure there are some but it would be interesting to see specific examples. Visual recognition. Voice recognition. Language Recognition. Self driving. Music Composition. We have failed in all these areas to use modularization to simplify these problems into something we can model as a theoretical whole. We turn to neural nets not to "understand" the problem but to build systems that can solve the problem while bypassing understanding. >I simply haven't seen anything that would indicate that it would be impossible to understand consciousness in the same practical mannter as the way we understand for example cars now If we have that level of understanding then we should be able to modularize the problem into smaller units of complexity so that a team of people can build the solution component by component. So far for the examples I listed above we don't have that level of understanding. We could employ the same techniques used for language recognition to understand the human brain... by building a neural net.... but that kind of defeats the point right? The neural net may simulate consciousness but it doesn't exactly let you (or a team of people) understand it... Also for your comment on the black box nature of neural nets.... I think we can both agree that the high level understanding or "insights" we gain are at best very high level. The logic of "visual recognition" is not understood by humans. We can only reproduce such logic as a blackbox. A neural net. 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. For example voice recognition and visual recognition are two very different problems but they are both "visualized" and modeled in the exact same way. You can probably also model consciousness as a problem that consists of a multi-dimensional set of points where you need to find the best fit curve. This does not mean you understand it. You can also probably peer into these artificial neural nets and see what components are doing what similar to how we can peer into a human brain and see which parts of a brain light up when you give it stimuli. Again, it doesn't mean you have true understanding. This is not to discount the entire field of neurology as a whole. Clearly there are bit and pieces we can understand. I am saying the ultimate goal of understanding consciousness may be fruitless. 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.

Erlich_Bachman 7y ago
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

Erlich_Bachman 7y ago

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