My actual AI work is extremely hard mental work, harder even than writing, which Harlan Ellison once called the toughest labor he ever performed (way harder than being e.g. a truck driver, which I myself have never done). There was no way I could do it two days in a row—though you’ll note that I say ‘was’ not ‘is’ since it’s important to keep in mind that these things often change over time.
I can attest to that; programming is complex because you formalize solving a problem over just solving it. AI is doubly complex because you’re formalizing how to do that.
AI is way more than twice as complex as ordinary programming. I have written plenty of programs that write programs, dealing with two layers of formalizing solutions, that is not anywhere near AGI. For one thing these programs only generate a certain class of programs. And much more importantly, they are not more powerful than I am so I can actually detect mistakes and fix them after I execute them.
Meh. AI conceptual work can be hard. But in reality, on any programming project where you’re both the brains and the brawn, you’re going to spend 90% of your time doing stupid stuff like writing hundreds of boring little subroutines; investigating different libraries, data sources, and data standards; figuring out which database software gives you the best performance; profiling and optimizing SQL queries; and of course DEBUGGING.
“Exponential” refers to how a quantity relates to another. For example, we would say that (until environmental limits are encountered) a population’s size is exponential with respect to time, and mean, that there is an initial population size P0 at a time t0, and a doubling time T, such that the population at a given time, P(t) = P0 * 2^((t—t0)/T). In computer science, we might say that the time or memory requirement of an algorithm is exponential with respect to the size of a list, or the number of nodes or edges in a graph, which could be represented by a similar equation, assigning different meanings to the variables. (Often, we really the mean the equation to be an approximation, or an upper or lower bound on the actual quantity.)
But if you say that designing and programming an AI is exponentially hard, you have not identified a variable of the problem that is analogous to the time in population growth. “Exponential” is not a vague superlative, it has a precise meaning. If all you mean to say is that AI is much harder than conventional programming, then just say that. Yes it is vague, but that is better than having your communication be more precise than your understanding.
Targeted commenter doesn’t really deserve being hit that hard, but voted up anyway.
The thing I really despise is when people use “exponential” as a superlative to describe fast-growing quantifiable processes that are not known to be exponential.
My actual AI work is extremely hard mental work, harder even than writing, which Harlan Ellison once called the toughest labor he ever performed (way harder than being e.g. a truck driver, which I myself have never done). There was no way I could do it two days in a row—though you’ll note that I say ‘was’ not ‘is’ since it’s important to keep in mind that these things often change over time.
I can attest to that; programming is complex because you formalize solving a problem over just solving it. AI is doubly complex because you’re formalizing how to do that.
AI is way more than twice as complex as ordinary programming. I have written plenty of programs that write programs, dealing with two layers of formalizing solutions, that is not anywhere near AGI. For one thing these programs only generate a certain class of programs. And much more importantly, they are not more powerful than I am so I can actually detect mistakes and fix them after I execute them.
Meh. AI conceptual work can be hard. But in reality, on any programming project where you’re both the brains and the brawn, you’re going to spend 90% of your time doing stupid stuff like writing hundreds of boring little subroutines; investigating different libraries, data sources, and data standards; figuring out which database software gives you the best performance; profiling and optimizing SQL queries; and of course DEBUGGING.
Not that my programs ever have bugs, of course.
If I had to guess, I’d guess that you’re spending your time in Java or C (++|#) :)
True. I should’ve said exponential.
That does not really mean anything.
“Exponential” refers to how a quantity relates to another. For example, we would say that (until environmental limits are encountered) a population’s size is exponential with respect to time, and mean, that there is an initial population size P0 at a time t0, and a doubling time T, such that the population at a given time, P(t) = P0 * 2^((t—t0)/T). In computer science, we might say that the time or memory requirement of an algorithm is exponential with respect to the size of a list, or the number of nodes or edges in a graph, which could be represented by a similar equation, assigning different meanings to the variables. (Often, we really the mean the equation to be an approximation, or an upper or lower bound on the actual quantity.)
But if you say that designing and programming an AI is exponentially hard, you have not identified a variable of the problem that is analogous to the time in population growth. “Exponential” is not a vague superlative, it has a precise meaning. If all you mean to say is that AI is much harder than conventional programming, then just say that. Yes it is vague, but that is better than having your communication be more precise than your understanding.
Targeted commenter doesn’t really deserve being hit that hard, but voted up anyway.
The thing I really despise is when people use “exponential” as a superlative to describe fast-growing quantifiable processes that are not known to be exponential.