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

GOFAI "aged out" of the AI label and became: compilers, databases, search algorithms, planning algorithms, programming languages, theorem proving, a million things that are still commonly used in NLP/CV/robotics, comp arch, etc. Aka, most of what makes computers actually useful.

If something's over 30 years old and is still called AI, that's just shorthand for saying it's a failed idea that in the best case hasn't had its moment yet.

> If-then statements.

99.999% of the software I use is just if-then statements.

(Also, this is like saying that deep learning is Linear Algebra.)

I have a different take on this. I think this technology will allow more people, not less, to make money as a living (so, professionally) in a visual arts related industry. So I'm broadening the field to include not just "artists" but "commercial art" as well (designers, commercial illustrators, video/film post-production, etc.).

The reason is that it changes and lowers the bar to entry for these fields, automates away a lot of the labor intensive work, thereby lowering the cost of production.

Whenever something becomes cheaper (in this case, labor for art), its consumption increases. So in the future, because producing commercial art is so much cheaper, it will be consumed a lot more.

At the same time, we're not at the point where we can actually remove humans entirely from the process. AI generated art is a different process and requires a different skillset, but it still requires skill and learning to do well.

The analogy would be something like a word processor reducing the number of secretaries needed in the workforce, but increasing the number of office workers. People no longer need someone to take notes / dictation, but all kinds of new workflows emerged on top of the technology, and almost all office workers need to know how to use something like a word processor.

Therefore, the opportunity here to do is to build tooling that make it easier and more accessible for more people to work with AI image generation.

Disclaimer: I'm doing exactly that (building tooling to make content generation easier and more accessible) with https://synapticpaint.com/

Some other applications that you may use daily are translate and face unlock/recognition.

It’s interesting that you mention search and spam filtering which both include an adversarial component. It seems to me that the adversarial AI has become better, in line of expectation from the democratisation of AI tools and knowledge.

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.