Will we ever get out of the GLUT era of ML? They say jobs like physicians and lawyers can get replaced by ML. Seems like that’s because of the GLUT nature of those jobs, rather than about ML itself.
I guess a better question is at what point is the model no longer considered GLUT. Is intelligence ultimately a form of GLUT? Let’s say you figured out the gravitational constant. Well, good for you. The process of deduction is basically looking for patterns in a look up table right? You do a bunch of experiments that tries to cover all edge cases, then you draw some generality out of your process. Without the generalization, you’d have to always go back to the look up table, but now you have a nice little equation that you can just plug in the values. No more GLUT, woohoo! Is this intelligence?
The generalization (coming up with algorithms, proofs, equations, etc) is just part of a process that allows you to use it in a simplified form as part of knowledge building process. The knowledge we build has become the intermediate look up table replacing the older, less efficient way of obtaining the same information. You can use the fact force = mass * acceleration or some constant equivalent of that equation without actually knowing the equation yourself, you are just less efficient at using it than in its simplest form. The person who generalized these knowledge aren’t special or anything. It’s just that they happened to be the ones doing it instead of someone else. Other people might be doing something else. The work is so rewarding, that’s why we encourage people to pursue this line of work, you don’t even need much monetary or other related type of incentives for people who are capable of this type of work to want to work on this. Monetary incentives tend to favor the needs of the population, and most people don’t really have any immediate need for this type of work done. They are usually enjoying the far derivatives of these knowledge that they can benefit from.
So to build an AI, if you hard code the math symbols and their operations, is that any different than the type of model representation they would learn unsupervised in an open environment? They don’t even have to operate on a world model, they can strictly be confined to the math space. Seems like that’s what MATH learners are doing. These machines probably will never derive equations like F=m*a but they will have some internal representations of it in their models if they were to do well on MATH data set or the likes.
If we build a neural net that itself has built a computer/neural net, would that be a 3 layer deep recursion?
Will we ever get out of the GLUT era of ML? They say jobs like physicians and lawyers can get replaced by ML. Seems like that’s because of the GLUT nature of those jobs, rather than about ML itself.
I guess a better question is at what point is the model no longer considered GLUT. Is intelligence ultimately a form of GLUT? Let’s say you figured out the gravitational constant. Well, good for you. The process of deduction is basically looking for patterns in a look up table right? You do a bunch of experiments that tries to cover all edge cases, then you draw some generality out of your process. Without the generalization, you’d have to always go back to the look up table, but now you have a nice little equation that you can just plug in the values. No more GLUT, woohoo! Is this intelligence?
The generalization (coming up with algorithms, proofs, equations, etc) is just part of a process that allows you to use it in a simplified form as part of knowledge building process. The knowledge we build has become the intermediate look up table replacing the older, less efficient way of obtaining the same information. You can use the fact force = mass * acceleration or some constant equivalent of that equation without actually knowing the equation yourself, you are just less efficient at using it than in its simplest form. The person who generalized these knowledge aren’t special or anything. It’s just that they happened to be the ones doing it instead of someone else. Other people might be doing something else. The work is so rewarding, that’s why we encourage people to pursue this line of work, you don’t even need much monetary or other related type of incentives for people who are capable of this type of work to want to work on this. Monetary incentives tend to favor the needs of the population, and most people don’t really have any immediate need for this type of work done. They are usually enjoying the far derivatives of these knowledge that they can benefit from.
So to build an AI, if you hard code the math symbols and their operations, is that any different than the type of model representation they would learn unsupervised in an open environment? They don’t even have to operate on a world model, they can strictly be confined to the math space. Seems like that’s what MATH learners are doing. These machines probably will never derive equations like F=m*a but they will have some internal representations of it in their models if they were to do well on MATH data set or the likes.
If we build a neural net that itself has built a computer/neural net, would that be a 3 layer deep recursion?