This looks like a coding language that can take distributions, and output co-ordinates or arguments, and then perform algorithmic transforms on them. i think. Way over my haircut. Looks like an efficient way to work with sets tho.

"These source-to-source transformations perform exact inference as well as generate probabilistic programs that compute expectations, densities, and MCMC samples. The resulting inference procedures run in time comparable to that of handwritten procedures. "

https://arxiv.org/abs/1603.01882

Note that DeepMind's two big successes (Atari and Go) come from scenarios that are perfectly simulable in a computer. That means they can generate an arbitrarily large number of data points to train their massive neural networks. Real world ML problems almost all have strict limitations on the amount of training data that is available.

The infographic on that page is amazing.

I used to worry that dysgenesis was leading us towards a world in which everyone was really dumb. That fear has been at least partially alleviated by new research showing that more educated people are having more kids. But now I worry that dysgenesis is leading us towards a world in which everyone is really sick.

Historically, human reproduction used the following strategy: have 6 or 8 kids, and the healthiest 3 or 4 would make it to adulthood. Now couples have 2 or 3 kids, and they almost all make it to adulthood. But that implies that lots of marginally-healthy children are surviving, thanks to medical technology, and so the gene pool is getting less healthy.

Look around you and count the number of people who have some kind of debilitating allergy, chronic illness, or mental health condition. Does it seem scary to you? What if that percentage goes up dramatically in the future, while the conditions themselves also get worse?

I think too that Jaynes failed in his attempt, but just because he died too, too soon.
Otherwise, have he lived a long life, I believe we would have had much more advancement in the field. To the present moment, nobody seems interested in bringing forward his vision, not even his closest student (Bretthorst, who edited the printed version of Jaynes' book).

Having said this, I believe you are wrong, because while it's true that two different agents can come up with two different prior for the same problem, they can do so only if they have different information (or, being the same thing, have different beliefs about it). Otherwise one is violating either common sense or coherence or basic logic.
There lies, I believe, all the meaning of objective Bayesian probability: a collection of methods, possibly with a unifying framework such as minxent, that allow to uncover the information hidden in a problem formulation, or to reveal why two different subjective priors actually differ.

How would you write a better "Probability theory, the logic of science"?

Brainstorming a bit:

• accounting for the corrections and rederivations of Cox' theorem

• more elementary and intermediate exercises

• regrouping and expanding the sections on methods "from problem formulation to prior": uniform, Laplace, group invariance, maxent and its evolutions (MLM and minxent), Solomonoff

• regroup and reduce all the "orthodox statistics is shit" sections

• a chapter about anthropics

• a chapter about Bayesian network and causality, that flows into...

• an introduction to machine learning

Thanks for the long answer! I just looked at the Cambridge prices for overseas students and it made me feel poor. Might as well seen a 500,000 ILS debt in my bank account.

I live in Israel and maybe I should study here. None of my family has any education though so I'm not really sure what to do. Do you know any universal things I should look for when considering higher education? ('Is it worth it?' sounds like a good question now..)

Coincidentally, I've recently been toying with the idea of setting up a consulting company which would allow people who want to work on "indy" research like AI safety to make money by working on programming projects part-time.

The key would be to 1) find fun/interesting consulting projects in areas like ML, AI, data science and 2) use the indy research as a marketing tool to promote the consulting business.

It should be pretty easy for good programmers with no family obligations to support themselves comfortably by working half-time on consulting projects.

I read a bit of what you previously wrote about your approach but I didn't read your full book.

I think a bunch of Quantified Self applications would profit from good compression. It's for example relatively interesting to sample galvanic skin response in very short time intervals of 5ms. Similar things go for accelerometer data. It would be interesting what kind of data you can draw from the noisy heart rate data on smartwatches with shorter time intervals.

Smart watches could easily gather that data with shorter time accuracy than they currently do but they have relatively limited space.

In practice I think it will depend a lot on how easy it is to use your software.

Maybe you could also have a gamified version. You have a website and every week there's a dataset that get's published. Only have of the data is released. Every participant can enter their own model via a website and the person who's model compresses the unreleased part of the data the best wins.

Great. I'm interested. Performancewise it may not be the best possibility, but for reusability it's good. I wonder about the overhead of your abstraction.