""Imprecise facts"? What do you mean?"

That most AI problems deal with uncertainty that's harder to deal with in first-order logic. You gave a good example with NLP where the early stuff I looked at tried logic approaches to find they fell flat due to context & language's probabilistic nature. All kinds of things in game AI turned out that way. Even some stuff that would seem true or false, like "am I being attacked?", wasn't so clear when bluffing was involved. I couldn't imagine how to handle a Poker game in Prolog with any success. That's significant given a combo of math, human BS, and luck it brings is common in many AI problems. For these reasons, people were moving from first-order logic even in my day to fuzzy logic, probabilistic models, neural networks, and machine learning techniques.

"The subject is AI and most of the curriculum was machine learning."

Now, remember that I said one or more subfields (or trends more accurrately) are usually very popular in AI. Anything now considered machine learning seems to be it these days. One other thing to factor in is the old adage in AI that any AI technique becoming very popular or effective is no longer called AI. So, as get up to date here, we have to consider that machine learning or AI labels might have changed in how they're applied + AI stuff getting non-AI names.

"Expert systems" were a huge thing back then but today they just call them business logic, business process management, workflow, whatever. Usually lots of If-Thens or Case Statements. Decision-making was AI but now the field is called planning, solving, optimization. Methods were largely search with heuristics and/or constraint solvers. Winners in timetabling at least were the same type last I checked. Game pathfinding another example. Automatic programming was a combo of high-level descriptions (eg 4GL), templates, synthesis, and so on. Still is although they call them IDE's with code generators now. Computer vision and pattern matching were definitely machine learning with statistical models and such. NN's were really different from most things in how they worked, have more resources now, and I usually hear deep learning but maybe they're called machine learning now. Chatterbots were highest performers in conversational style with many methods. Machine-to-machine and some to human communication was (and mostly still is) done with finite-state machines. Hardware synthesis tried all kinds of things but geometic methods with search + heuristics wins to this day. Knowledge storage, querying, and so on mostly went into database tech for best results & performance due to easier model and machine implementation. Some still did logic languages (eg Prolog, Datalog), encoding in neural nets, lots doing RDF/OWL/whatever, and so on.

So, that's off the top of my head different areas I learned about with techniques and new labels that appeared. Have any of these that weren't using machine learning techniques gone mostly into that in academia and/or commercial implement that you're aware of? While we're at it, do you have a link that lists the best AI labs so I can check on that and do a general update of my knowledge later on?

"So it's a big deal that Sussex seems to have completely given up on it. "

Normally I'd think it was chasing fads given the bandwagon effect that deep learning in particular is having. This time, though, I saw enough logic programming experts give up on it to know either the hardware or the approach itself isn't up to most AI challenges. Time showed it was probably approach itself. So, I agree Sussex moving on is very significant.

"But, really- AI is now machine learning. Nobody is willing to try anything else."

Non-ML is dominating in terms of highest performers in planning, synthesis, and databases. Those I know that are getting tons of research. They just don't call it AI anymore despite it being artificial tools replacing intelligence of humans that do such work. Genetic Programming's "Humies" awards probably deserve some mention given it's not really traditional AI or what you'd think of in machine learning. It's its own thing. There could be others. I agree that machine and deep learning are the big things getting vast majority of effort... damn near drowning everything else out... but others are still there. I already mentioned that name changes but I think it also depends on your university. I noticed in the past a lot and now a little that specific universities get big on specific approaches in general or subfields. Yours might have went all in on the trend more than another. A survey would be nice at this point that covered the activities I mentioned above that were called AI in the past.

"I'm not sure what's the situation with Lisp."

Me either. For AI and other research, most academics write tools with whatever they like best or others are using. For production, they usually code it in a common language for that like C++, Java, etc. One planner for timetabling college exams I recall was designed/prototyped in LISP then coded to C++ for release. The point of bringing up LISP is that DSL's, either executing or generating for specific languages, let you do every part of something as diverse as Watson in the same, common language with benefits of others selectively. For a Prolog example, you might parse in data with FSM's in C generated from LISP DSL, do simple filtering/preprocessing with safe 3GL, handle queries with Prolog style, and do user interaction with event-driven model. All integrated into same data structures, function calls, whatever through common language that's easy to parse/transform. Most apps don't need such power but a full AI might find it useful. Many machine learning techniques can just use a 3GL, though.

Unrelated, how far along are you on porting your prior work to open-source Prolog?