All of YVLIAZ's Comments + Replies

I just want to point out some nuiances.

1) The divide between your so called "old CS" and "new CS" is more of a divide (or perhaps a continuum) between engineers and theorists. The former is concerned with on-the-ground systems, where quadratic time algorithms are costly and statistics is the better weapon at dealing with real world complexities. The latter is concerned with abstracted models where polynomial time is good enough and logical deduction is the only tool. These models will probably never be applied literally by engineers, bu... (read more)

There is difference between "having an idea" and "solid theoretical foundations". Chemists before quantum mechanics had a lots of ideas. But they didn't have a solid theoretical foundation.

That's a bad example. You are essentially asking researchers to predict what they will discover 50 years down the road. A more appropriate example is a person thinking he has medical expertise after reading bodybuilding and nutrition blogs on the internet, vs a person who has gone through medical school and is an MD.

I think you are overhyping the PAC model. It surely is an important foundation for probabilistic guarantees in machine learning, but there are some serious limitations when you want to use it to constrain something like an AGI:

1. It only deals with supervised learning

2. Simple things like finite automata are not learnable, but in practice it seems like humans pick them up fairly easily.

3. It doesn't deal with temporal aspects of learning.

However, there are some modification of the PAC model that can ameliorate these problems, like learning with membership ... (read more)

I would definitely recommend learning basics of algorithms, feasibility (P vs NP), or even computability (halting problem, Godel's incompleteness, etc). They will change your worldview significantly.

CLRS is a good entry point. After that, perhaps Sipser for some more depth.

Yes, so the exact definition of "have-to" and "want-to" already present some difficulties in pinpointing what exact the theory says.

In my personal experience, it's not so much "fear" than fatigue and frustration. I also don't feel that my desire to read reduces; it stays intense, but my brain just can't keep absorbing information, and I find myself keep rereading the same passages because I can't wrap my head around them.

I can see this theory working in several scenarios, despite (or perhaps rather because of) the relative fuzziness of its description (which is of course the norm in psychological theories so far). However I have personal experiences that at least at face value don't seem to be able to be explained by this theory:

During my breaks I would read textbooks, mostly mathematics and logic, but also branching into biology/neuroscience, etc. I would begin with pleasure, but if I read the same book for too long (several days) my reading speed slows down and I start f... (read more)

I bought these with a 4 socket adapter. However, I think my lamp can't power them all. Does anyone know a higher output lamp?

Actually I'm not even sure if that is how lights work. If someone can explain how I can the power that goes to the light bulbs, it'd be greatly appreciated.

I'm going out on a limb on this one, but since the whole universe includes separate branching “worlds”, and over time this means we have more worlds now than 1 second ago, and since the worlds can interact with each other, how does this not violate conservation of mass and energy?

The "number" of worlds increases, but each world is weighted by a complex number, such that when you add up all the squares of the complex numbers they sum up to 1. This effectively preserves mass and energy across all worlds, inside the universal wave function.

In contrast with the title, you did not show that the MWI is falsifiable nor testable.

I agree that he didn't show testable, but rather the possibility of it (and the formalization of it).

You just showed that MWI is "better" according to your "goodness" index, but that index is not so good

There's a problem with choosing the language for Solomonoff/MML, so the index's goodness can be debated. However, I think in general index is sound.

You calculate the probability of a theory and use this as an index of the "truthness"