>> Protein folding is exactly the type of world-changing scientific work.

Nope. Not at all. That's a misunderstanding of the goal of science which is to
explain how the world works. AlphaFold, like all neural network models is a
predictive model but has no explanatory power. It can predict the structure of
proteins from sequences but it cannot explain why, or how, proteins fold.
Scientists can still not explain how proteins fold, certainly not by interacting
with AlphaFold.

Why is that a problem? You said you care about results. I linked to the
wikipedia article about the Copernican Revolution. I recommend you read the
wikipedia article on epicycles [1]. To summarise, for hundreds of years, from
Hipparchus of Nicaea, through Claudius Ptolemy and all the way up to Copernicus,
astronomers used systems of epicycles (circles-upon-circles) to explain the
apparent movements of the planets. Epicycle-based models matched observations
very well and predicted the movement of the planets very accuratey because it is
always possible to fit a smooth curve with arbitrary precision given a
sufficient number of epicycles. However, even Copernicus' model that had the sun
in the center of the universe, unlike many earlier models, could not explain
why the planets moved the way they did. A true explanation only became
available when Isaac Newton developed his laws of universal gravitation.

Newton's theory (itself later superseded by Einstein's theory of general
relativity) provided a true explanation of observable phenomena and a way to
calculate better models and obtain more accurate predictions than were ever
possible before. That is the power of explaining and understanding the world and
simply modelling an incomprehensible set of observations, which is all that
neural networks can do, doesn't even come close.

>> I'm not trying to troll you. I just think you are biased in you assessment of
significant results and what it means to be stalled.

ILP is a tiny field with maybe a few dozen people that consistently publish on
the subject. By comparison, there are many tens of thousands of papers published
on deep learning every year, the deep learning conferences receive many
thousands of submissions and research is funded with many millions of dollars by
governments, militaries, and the largest of large technology corporatations, the
Googles and Facebooks etc, and training state-of-the-art models requires many
terrabytes of data, many petaflops of computing power, to the extent that
training such models is now only feasible for the aforementioned large
corporations. Normally, I'd complain that you are comparing apples to oranges.
How can a few people with meager resources be expected to outdo DeepMind and
OpenAI?

Yet the algorithms that I study run on a student laptop, are trained in a weakly
supervised manner, take seconds to train, and generalise robustly from single
examples without any sort of pre-training whatsoever (for instance, the approach
I study, Meta-Interpetive Learning, comfortably and routinely learns recursive
prorgams with arbitrary structure from a single example, without an example of
the "base-case"). These are capabilities unheard of in neural networks that must
be trained on hundreds of millions of examples [2] of actual programs (rather
than examples of inputs and outputs, as in ILP) in order to generate programs
and cannot solve programming tasks, nor generate solutions, unlike the examples
tasks and solutions in their training sets; while in the case of ILP every
programming task solved is, by definition unseen (hence, "weakly supervised"
because examples are programming tasks without examples of the solutions).

That is what it means for a field to be stalled. When a single graduate
student with a seven-year old laptop can solve problems that the dominant
approach cannot even attetmpt (in this case, learning from a single example, without pretraining and with weak supervision).

____________

[1] https://en.wikipedia.org/wiki/Deferent_and_epicycle

[2] https://arxiv.org/abs/1703.07469v1

See Section 4.4. Hyperparameters and Training: "Each minibatch was re-sampled so
the model saw 256 million random programs and 1024 million random I/O examples
during training.".