louise VS thelma

and this is a cool repo to track: https://github.com/stassa/louise

From what I understand this is a relatively new approach to ML? Has anyone heard of this? I was hoping to get a general feel for what people in the industry believe for the perspectives of this approach. If you're curious, here's an implementation of MIL.

Thanks, that's a nice example.

>> For an example. The potential hypothesis here are pre generated, but you can imagine an algorithm or adapt an existing one with a tight generalise/specialise loop.

Yes! I'm thinking of how to adapt Louise (https://github.com/stassa/louise) to do that. The fact that s(CASP) is basically a Prolog-y version of ASP (with constraints) could make it a very natural sort of modification. Or, of course, there's always Well-Founded Semantics (https://www.swi-prolog.org/pldoc/man?section=WFS).

Well, since we're going a little mad with speculation in this thread, I have to point out that true one-shot learning (as opposed to "one-billion-plus-one-shot") works just fine, but only in the symbolic machine learning paradigm. For example, see:

https://github.com/stassa/louise#capabilities

In particular the second example listed there. A trivial example, but one that cannot be reproduced by current approaches without big-data pre-training.

I bring up the 80/20% training/test split that is standard in machine learning because I remember an interaction with my supervisor at the start of my PhD. In one of our meetings my supervisor asked me about the details of some experiments I was running, with a system called metagol (linked from the Louise repository above). En passant, I mentioned that I was training with ta 20/80% training/test split and my supervisor stopped me to ask me if I thought that was a standard setup for machine learning. Thinking he meant the splitting of my training data to training and testing partitions I asked, a bit bemused, that yes, of course, that's the standard thing. To which my supervisor laughed and replied "I don't think so". Later of course I realised that he meant that the done thing in machine learning is to use most of the data for training and leave as little as possible for testing.

In Inductive Logic Programming, it's typically the other way around, and the datasets are often a few examples, like a dozen or so. Of course our systems don't do the spectacular, impressive things that deep learning systems do, but then again we don't have a dozen thousand graduates racing to out-do each other with new feats of engineering. Which is a bit of a shame because I think that if we had no more than a thousand people working on ILP, we 'd make huge progress in applications, as well as in understanding of machine learning in general.

Oh well. It's probably all for the best. Who wants to build genuinely useful and intelligent systems anyway?

Note you can do machine learning of logic programs. My PhD research:

https://github.com/stassa/louise

In which case it _is_ machine learning and it still really works :D

If you want to auto-write Haskell, use MagicHaskeller:

http://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskeller.ht...

And if you want to auto-write Prolog, use my own Louise:

https://github.com/stassa/louise

My guess is the end result of all this "AI" assisted code-generation is that it will have the same impact on the software engineering industry as spreadsheets had on accounting. I also believe that this AI-powered stuff is a bit of a "two-steps forward, one step back" situation and the real innovation will begin when ideas from tools like Louise [1] are integrated into the approach taken in Codex.

When VisiCalc was released departments of 30 accountants were reduced to 5 accountants because of the improvement for individual worker efficiency, however accounting itself remains largely unchanged and accountants are still a respected profession who perform important functions. There's plenty of programming problems in the world that simply aren't being solved because we haven't figured out how to reduce the burden of producing the software; code generation will simply increase the output of an individual software developer.

The same forces behind "no-code" are at work here. In fact I see a future where these two solutions intermingle: where "no-code" becomes synonymous with prompt-driven development. As we all know, however, these solutions will only take you so far -- and essentially only allow you to express problems in domains that are already well-solved. We're just expressing a higher level of program abstraction; programs that generate programs. This is a good thing and it is not a threat to the existence of our industry. Even in Star Trek they still have engineers who fix their computers...

[1] - https://github.com/stassa/louise

There was an earlier system called Thelma (https://github.com/stassa/thelma), an acronym for "Theory Learning Machine". Then I created a new system and, well, I couldn't resist a bad pun. They're a bit of a tradition in ILP.