pealco

It reminds me of "web3" marketing. My hackles are immediately raised.

Most of my time interacting with this site was spent in developer tools, trying to figure out where the scrolling behavior was coming from. (Couldn't figure it out.) I can't understand why people are still doing this in 2025.

Cool, but as described, this wouldn't work on humans. The light sheet microscopy technique that the suped-up MRI data is paired with to create these images requires tissue to be "cleared" (or made transparent) with solvents, which obviously you can't do with a living human brain. To be honest, I don't quite understand how light sheet microscopy works with living _mouse_ brains.

I was so confused :) "Is the stand doing computations??"

In the video, you keep saying "Turing" (turr-ing), when I think you mean to say "truing" (troo-ing).

In my experience, people like this are dilettantes, who actually have a very shallow understanding of these "ideas" that they're so in love with. They confuse _having heard_ of Obscure Subject with _understanding_ Obscure Subject. If you happen to have a deeper understanding of Obscure Subject and try to engage them in conversation about it, it goes nowhere.

Ted Petrou has written a very detailed critical review of this book. He finds it lacking in certain areas.

https://medium.com/dunder-data/python-for-data-analysis-a-cr...

This doesn't really address your teacher's claim about having to look words up, though. What you want to look at is the distribution of low frequency words across the book. What do the plots look like when you remove proper nouns, functional words (e.g., "the", "and", prepositions) and, say, the top 1000 most frequent words in English?

In what space does the clustering occur? I wasn't able to tell from the post.

What sorts of things does it improve?

> I think Norvig acknowledges the point you are making here, namely that the statistical approach does not explain the cognitive systems behind language.

If that is the case, then the argument that Norvig is making is irrelevant to the argument Chomsky is making. Chomsky simply makes the point that statistical accounts lack explanatory adequacy. As someone who has worked closely with many of his students and who has received extensive training on his scientific program, I can say with confidence that Chomsky would have no objection whatsoever about the usefulness of statistical approaches to linguistic engineering problems. The results speak for themselves. He would go on to say, however, that how well a statistical approach solves a linguistic engineering problem is irrelevant to the question of how humans do what they do.

The answer to the question may well be statistically grounded. That is a valid hypothesis and a logical possibility which should be taken seriously. However, it is incumbent on the proponents of such an answer to provide evidence that it is what humans are doing. Here are some examples of the kinds of evidence necessary:

* evidence that humans are capable of performing the kinds of computations that the statistical approach requires,

* evidence that the statistical approach works with the relatively limited amount of data that a human receives,

* evidence that the statistical approach fails in ways that humans fail

How well a statistical approach succeeds at an engineering task is not an item on this list, simply, again, because engineering tasks are irrelevant to what humans actually do.

Let me specifically say that statistical approaches are not, from the start, ruled out as potential candidates for the algorithms underlying human language. It's just that a case has to be made for them using the right kind of evidence.

Finally, I'll reiterate what others have pointed out: from a scientific perspective, that something is hard to explain doesn't mean that we shouldn't try. And, those that have given up (as you suggest Norvig has) shouldn't fault those who haven't for calling them out on it.

The "pure" photograph is a myth. Every single aspect of taking a photograph imposes the same kind of artificiality that the author claims Instagram filters impose. From the most basic technical considerations: the aperture and shutter speed, to the most practical: what you are pointing the camera at. Even seemingly innocent choices like the kind of camera and lens you use are guilty of this.

With every one of these choices, the photographer creates a distance from the "real" world. A filter is no different. It is merely another tool available to a photographer to achieve his goals.

The bicycle?

I prefer "natural language processing" over "computational linguistics" because NLP/CL has very little to do with the goals of linguistics. Like you mentioned, linguists are after the truth of what actually happens in the mind to make language work. NLP folks are after what works -- any answer will do.

Being practical is fine, of course, but it just irks when NLP is billed as a science that is trying to "discover" something deep and real about the world. You often see young NLP students who take linguistics courses wanting to bring over some of the (cognitive) stuff they learned in these courses over to NLP. This is well-intentioned, but naive. NLP is an engineering discipline and, as such, the answer that provides the best results is the best one regardless of what it's cognitive viability is.

I swear by Notational Velocity (http://notational.net/).