Maybe not fully "correctly"!

In

https://news.ycombinator.com/item?id=21949722

are some arguments about the term data structure. For that argument, what I have in mind is more general. So, with generality beyond data structures we can have relational database schema (apparently Microsoft's SQL Server documentation has a different meaning of schema) which can be new.

Or the intelligence needs to store and manipulate data. To store is to use a data structure of some kind, and to manipulate is to use some algorithms.

So, the challenge is to find the data structure and corresponding algorithms.

My prediction is that in the 10 years we will do that.

My reasoning:

(1) Mice, rats, kittens, puppies, ..., and much more including humans do a lot of it, that is, what humans do with intelligence.

(2) The babies of these species learn starting with relatively very little or nothing.

(3) The learning is fairly simple, e.g., does not require a lot of computing or data.

(4) Once the learning gets going, especially in humans, the amount of learning grows quickly from building on past learning, new data, and new thinking.

(5) We can guess that especially early on the learning is relatively simple -- that keeps the intelligence relatively stable.

Well I'm predicting that from essentially just (1)-(5) we can get the human level intelligence I mentioned, i.e., we will find the appropriate data structures and algorithms.

If there were a lot of value in my thinking about (1)-(5), NSF and/or DARPA would fund me to pursue what I outlined; since I'm sure they would not fund me, we, including me, can conclude that there is not a lot of value in my thinking. Maybe that does not make my thinking wrong; it might be right but just so far incomplete!

So, why not the short summary of GOFAI "someday"? Because I'm guessing that data structures, algorithms, (1)-(5), and 10 years will be at least a significant part of what will be sufficient for GOFAI!

In particular, what I explained seems to be more than just "someday" because:

(A) I believe that current work in deep learning neural networks, which obviously DO have some applications, might have some role in some relatively autonomous and non-conscious parts of GOFAI, maybe similar to some of what is crucial in some insects. So, I don't see the current work in neural networks as very relevant to the GOFAI I am predicting. So, I'm pruning off that stream of work for progress toward what I'm predicting. Again the neural network DOES have some applications.

(B) I worked for some years in AI in IBM's Watson lab. My view is that none of that work is at all relevant to what I'm predicting. So, I'm pruning off that stream of work for progress toward what I'm predicting.

So, with (A) and (B), I'm saying two paths to avoid. If I'm right, then avoiding (A) and (B) would have some evidence of being good contributions.

But no doubt like nearly everyone else, I want practical results faster than my prediction promises so don't want to work on that prediction. And I am not working on that path and, instead, am pursuing my startup which DOES have some pure and applied math at its core but is MUCH more likely to work and work much faster than my AI prediction!

But here HN asked for some predictions, so I made some!