Good Old Fashioned AI is dead, long live New-Fangled AI

Post by setr » Tue, Nov 15, 2022, 9:59 PM UTC

shafoshaf wrote: post #23

triska wrote: post #2

The "new-fangled" AI, as the article calls it, is often useful when the stakes are low, and you can accept mistakes in outcomes. Examples of such applications are: trying to determine which of your friends occur in a photo, which movies a subscriber may be interested in, or which action could lead to victory in a computer game. Getting a rough translation of a newspaper entry, as mentioned in the article, is also a g…

The only barrier for higher stakes applications is going to be the frequency of errors. Flying an airplane or running a factory has a lot less margin for error, but humans don't do those things perfectly either (Chernobyl, Three Mile Island, Union Carbide-Bhopal disaster). It doesn't have to be perfect, just better than humans. And in fact, I'd argue that by having no deterministic outcomes prevents systemic failure,…

Post by tomrod » Tue, Nov 15, 2022, 10:05 PM UTC

time_to_smile wrote: post #10

People spend an awful lot of time talking about current successes in AI without often reflecting on how much (or little actually) AI impacts their lives. Despite all of the energy put into current gen AI, as far as every day impacts the biggest things I can think of are: - Spam filtering/email sorting - Web search - GPS/Wayfinding - Voice assistants These are the only practical applications of "AI" that I use more or…

Post by cscurmudgeon » Tue, Nov 15, 2022, 10:09 PM UTC

blueyes wrote: post #4

triska wrote: post #2

The "new-fangled" AI, as the article calls it, is often useful when the stakes are low, and you can accept mistakes in outcomes. Examples of such applications are: trying to determine which of your friends occur in a photo, which movies a subscriber may be interested in, or which action could lead to victory in a computer game. Getting a rough translation of a newspaper entry, as mentioned in the article, is also a g…

GOFAI was never more than a rules engine. If-then statements. Agree with you about probabilistic AI being useful in low-stakes situations, at least at first.

Post by tjnaylor » Tue, Nov 15, 2022, 10:12 PM UTC

wwwtyro wrote: post #5

Why do the eyes in the generated images always look a little off? Most facial features usually appear photorealistic to me, but the eyes always have a little smudge or something in them that gives them away.

  *However, in the case of AI painting, it will almost certainly change the coloring of the left and right eyes and how to add highlights . Humans can understand that ``the left and right eyes have the same physical shape and are placed in the same situation, so there is naturally a consistency there. '' I don't understand the theory "I don't really know what an eye is, but it's something like this that's placed around here, isn't it?" Still, it looks like it, so humans can recognize it as eyes, but there are still many defects in the details.

  Among them, the most distinctive feature is the “ highlight that melts into the pupil and breaks the pupil ”. Humans know that ``first there is the eyeball, there is the pupil in it, and then the surrounding light is reflected to form a gloss'', so ``the highlight does not block part of the pupil. It can be understood as a matter of course that the shape of the pupil itself does not collapse, even if the AI     does the same, but AI that learns only by looking at the final illustration can understand the ``logical relationship between the whites of the eyes, pupils, and highlights''. I don't recognize anything . Or rather, I can't. I didn't give it as data.

  The unnatural deformation of the pupil is also one of the judgment materials. Humans know that "the pupil is originally a perfect circle", but AI trained by looking only at the final completed illustration does not know "the original shape of the pupil" . Therefore, such an error occurs.

  Another feature of AI drawings is that they often subtly change the color of the left and right eyes . Of course, there are characters with different eye colors on the left and right (heterochromia), but in most cases , characters designed that way can be clearly recognized as having different colors . It is one of the criteria for judging that the colors are similar at first glance, but if you take a closer look, they are different.

  However, even if there is such a character, it is not strange, so it is not an important basis. Also, it is natural for the color of the left and right eyes to change depending on the surrounding environment, so be careful not to make a mistake.*

Post by ShamelessC » Tue, Nov 15, 2022, 10:29 PM UTC

nonrandomstring wrote: post #21

falcolas wrote: post #19

Earlier quoted context omitted.

So, there's an area of research that's under way called "AI Assurance" which seeks to answer many of these questions. Some things they're attempting: - Creating explainable outcomes by tracing the inner works of ML models. - Looking for biases in models using random inputs & looking for biased outputs. - Using training sets with differently weighted models to find attacks and biases. etc.

The tragedy is that GOFAI did all these things as built-ins. Procedural expert systems have been doing introspection, backtracing, declaring confidence intervals etc since the 1960s. Layering "assurance" on top of inherently jittery statistical/stochastic and neural systems seems to misunderstand how these models evolved, where they come from and why there are alternatives.