S.A. is talking about general-purpose AI (position 2 in the RFS). This means processing natural language. There is a lot of progress but it's just slow so it's almost invisible.
Also it's a very difficult field of science. Now you need to be proficient in AI, machine learning, computational linguistics, linguistic corpora research, cognitive sciences, statistics, and sometimes physics if the text changes over time. Of course, you also need to be a good programmer. This combination of skills is very rare. Thus, very slow progress.
I suggest to start with well defined practical problems. For example, no one seems to do much with user generated reviews. There is some sentiment analysis but that is just a binary text categorization problem - not even close to general purpose AI.
It would be much more interesting to show a seller a time ordered stream of clustered reviews that depict only the most representative review for each cluster. This way a seller can see how his/her fixes/changes impact user reviews. Also it would be a great source for features and bug fixes requests. This is an ideal testing bed for clustering, novelty detection, categorization and mild inference. The inference is required because of sparseness of data.
This would create a good data set for a more general purpose AI. We would have reviews and text documenting changes and improvements of a new version of a product. Now the computer could start learning the dialog between users and product developers. Then, we are just one more step from statistical inference based question-answering system. Not a brute force system like "Watson" or a hand crafted rule base system like "Siri".
[EDIT:] I was thinking more about a decision support system that can recommend product changes. But in a way that maximizes customer satisfaction and minimizes the cost of implementation. The dialogue between past changes and customer reaction would give us the surface that needs to be optimized. This would generalize well to other domains where there is a text for request and a text for response - just to name one: clinical text in healthcare (position 5 in the RFS).
Also it's a very difficult field of science. Now you need to be proficient in AI, machine learning, computational linguistics, linguistic corpora research, cognitive sciences, statistics, and sometimes physics if the text changes over time. Of course, you also need to be a good programmer. This combination of skills is very rare. Thus, very slow progress.
I suggest to start with well defined practical problems. For example, no one seems to do much with user generated reviews. There is some sentiment analysis but that is just a binary text categorization problem - not even close to general purpose AI.
It would be much more interesting to show a seller a time ordered stream of clustered reviews that depict only the most representative review for each cluster. This way a seller can see how his/her fixes/changes impact user reviews. Also it would be a great source for features and bug fixes requests. This is an ideal testing bed for clustering, novelty detection, categorization and mild inference. The inference is required because of sparseness of data.
This would create a good data set for a more general purpose AI. We would have reviews and text documenting changes and improvements of a new version of a product. Now the computer could start learning the dialog between users and product developers. Then, we are just one more step from statistical inference based question-answering system. Not a brute force system like "Watson" or a hand crafted rule base system like "Siri".
[EDIT:] I was thinking more about a decision support system that can recommend product changes. But in a way that maximizes customer satisfaction and minimizes the cost of implementation. The dialogue between past changes and customer reaction would give us the surface that needs to be optimized. This would generalize well to other domains where there is a text for request and a text for response - just to name one: clinical text in healthcare (position 5 in the RFS).