1) Define your domain. AI doesn't mean neural nets. It means making a computer do something that you thought required intelligence. Neural nets are getting pret

1) Define your domain. AI doesn't mean neural nets. It means making a computer do something that you thought required intelligence. Neural nets are getting pretty good at general purpose do-what-I-mean problem solving, though. Do you want to do neural nets or do you want to do AI? 2) Set a goal. Start by figuring out what you want to do. You want to make an algorithm which achieves X, solve for X. 3) Lit review. Find out what work has been done in this particular direction. Don't just skim, understand the work. Re-implement and reproduce the most promising studies just to see if you can get them working. If you can't, that's publishable in and of itself! If no-one's done anything in this direction then this should be easy. 4) Proof of concept. You've determined that a thing is possible and you have a good idea how to do it. Start working on an implementation of the thing. ...this is where business, science and engineering part ways. Science: 5) Once you have a proof of concept that exceeds state of the art in some measurable way, write a paper and publish it. Get funding. Repeat. Engineering: 5) Apply for a job at a company doing similar research using your proof of concept as your resume. No-one cares what grades you got in year 10 geography if you can show them something awesome that you built just now. Business: 5) Either build a minimum viable product yourself, break into a viable market, and then iterate on that while also working yourself to death learning to do all the business stuff you never realised even existed, or use your proof of concept as a pitch deck to get financial backing from some incubator, angel, VC etc. then build your dream for them as well as you.