What is the "DT" mentioned in this article? I have no prior knowledge of that acronym.

Perhaps http://en.wikipedia.org/wiki/Design_and_Technology ?

Almost certainly, given that the author seems to have significant connections to the UK.

He refers to it again later; "Engineering apprenticeships have been decimated, and even the old metalwork shops in schools have gone, replaced by ‘craft, design and technology’, which seems to mainly involve making things of cardboard."

When I was in secondary school twenty+ years ago, we had both: Design & Technology (AutoCad !) and a full metalwork shop with everything except oxyacetylene torches. There's nothing like hands on experience when it comes to making real things.

I'd love to know whether all that stuff is still there.

We had hand draw pictures and woodworking. Still fun though.

Design Technology - it's drawing pretty pictures (sorry `design concepts`) of what you would make if your school still had proper shop class.

It is to "metal work" what "information technology" is to programming.

My son has just started doing Industrial Arts at school. A lot of it just seems to be dreadful spouting of design and architecture jargon.

But what do people want from a wheel? How do they relate to it? What colour should it be?

3d printers to the rescue?

At the risk of being annoying, virtually all the research done in linguistics departments anywhere in the world is research in the cognitive science of language, so a social science, not a humanity. This certainly includes Berkeley around 1970 or so (assuming that's when Wall was there).

I use xelatex in my work and it's still embarassingly fragmented and outdated. We need to start over on a new TeX-like project (also so that it can be ported to mobile).

People who think "going to college is for chumps" (and there are a lot of you on HN) should do this instead. It's not that hard to graduate college in 2-3 years.

I'm not an expert on this, just a linguist who happens to code a lot, but there is some serious work on the complexity of frequency ordering. The algorithms proposed by R. Rivest (1976, Communications of the ACM) produce near-optimal frequency orders online, so if you can settle for "near optimal" then the problem is hardly traveling salesman as this author claims.

Even with a traveling salesman problem you can get "near-optimal" results with an algorithm that has a reasonable run time. It's still important to note that you have such a problem though, as the fact that you're settling for "near optimal" needs to be understood.

You can actually find the global optimal solution for surprisingly large real world instances. They do tens of thousands of cities.

Lots of interesting stuff here. The Concorde solver is probably the state of the art. http://www.tsp.gatech.edu/concorde/index.html

I happen to know that they do this at the Linguistics Data Consortium (http://www.ldc.upenn.edu/), at least with cable news shows. They mostly do that to obtain data for languages with more minimal resources though, and for the purposes of transcription, not for speech recognition qua engineering research. The real issue though is the research community is interested in increasing the accuracy of recognizers on standard datasets by developing better models, not increasing accuracy per se. Having used more data isn't publishable. Further, in terms of real gains, the data is sparse (power law distributed), and so we need more than just a constant increase in the amount of data. This issue is general to any machine-learning scenario but is particularly pronounced in anything built on language.

Some related papers ~ Moore R K. 'There's no data like more data (but when will enough be enough?)', Proc. Inst. of Acoustics Workshop on Innovation in Speech Processing, IoA Proceedings vol.23, pt.3, pp.19-26, Stratford-upon-Avon, 2-3 April (2001). Charles Yang. Who's afraid of George Kingsley Zipf? Ms., University of Pennsylvania. http://www.ling.upenn.edu/~ycharles/papers/zipfnew.pdf

sexist title (and the use of a COOKING METAPHOR for chrissakes). surely this is why there are so few women in computer science.

It's not notable that he was able to explain it to his wife because she's a woman, it's notable because she's not in the field. This is really pretty obvious (and explicitly stated in the article).

this has been posted like ten times

It is the first time that I have seen it though -- and I check HN far too frequently.