🎧 A computer learns about ingredients and recipes | Eat This Podcast

Listened to A computer learns about ingredients and recipes by Jeremy Cherfas from Eat This Podcast


Perhaps you've heard about IBM's giant Watson computer, which dispenses ingredient advice and novel recipes. Jaan Altosaar, a PhD candidate at Princeton University, is working on a recipe recommendation engine that anyone can use.

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Back in February I had retweeted something interesting from physicist and information theorist Michael Nielsen:

I found the article in it so interesting, there was some brief conversation around it and I thought to recommend it to my then new friend Jeremy Cherfas, whose Eat This Podcast I had just recently started to enjoy. Mostly I thought he would find it as interesting as I, though I hardly expected he’d turn it into a podcast episode. Though I’ve been plowing through back episodes in his catalog, fortunately this morning I ran out of downloaded episodes in the car so I started streaming the most recent one to find a lovely surprise: a podcast produced on a tip I made.

While he surely must have been producing the episode for some time before I started supporting the podcast on Patreon last week, I must say that having an episode made from one of my tips is the best backer thank you I’ve ever received from a crowd funded project.

Needless to say, I obviously found the subject fascinating. In part it did remind me of a section of Herve This’ book The Science of the Oven (eventually I’ll get around to posting a review with more thoughts) and some of his prior research which I was apparently reading on Christmas Day this past year. On page 118 of the text This discusses the classic French sauces of Escoffier’s students Louis Saulnier and Theodore Gringoire [1] and that a physical chemical analysis of them shows there to be only twenty-three kinds. He continues on:

A system that I introduced during the European Conference on Colloids and Interfaces in 2002 [2] offers a new classification, based on the physical chemical structure of the sauce. In it, G indicates a gas, E an aqueous solution, H a fat in the liquid state, and S a solid. These “phases” can be dispersed (symbol /), mixed (symbol +), superimposed (symbol θ), included (symbol @). Thus, veal stock is a solution, which is designated E. Bound veal stock, composed of starch granules swelled by the water they have absorbed, dispersed in an aqueous solution, is thus described by the formula (E/S)/E.

This goes on to describe in a bit more detail how the scientist-cook could then create a vector space of all combinations of foods from a physical state perspective. A classification system like this could be expanded and bolted on top of the database created by Jaan Altosaar and improved to provide even more actual realistic recipes of the type discussed in the podcast. The combinatorics of the problem are incredibly large, but my guess is that the constraints on the space of possible solutions is brought down incredibly in actual practice. It’s somewhat like the huge numbers of combinations the A, C, T, and Gs in our DNA that could be imagined, yet only an incredibly much smaller subset of that larger set could be found in a living human being.

Small World

The additional byproduct of catching this episode was that it finally reminded me why I had thought the name Jaan Altosaar was so familiar to me when I read his article. It turns out I know Jaan and some of his previous work. Sometime back in 2014 I had corresponded with him regarding his fantastic science news site Useful Science which was just then starting. While I was digging up the connection I realized that my old friend Sol Golomb had also referenced Jaan to me via Mark Wilde for some papers he suggested I read.

References

[1]
T. Gringoire and L. Saulnier, Le répertoire de la cuisine. Dupont et Malgat, 1914.
[2]
H. This, “La gastronomie moléculaire,” Sci Aliments, vol. 23, no. 2, pp. 187–198, 2003 [Online]. Available: http://sda.revuesonline.com/article.jsp?articleId=2577 [Source]

🎧 Podcast Directories | Why Can’t We … ?

Listened to Podcast Directories from Why Can't We ... ?, August 19, 2016
Every year there are millions of podcasts published by tens of thousands of people in hundreds of languages, yet there are really just three podcast directories where people are able to go and look for new shows to enjoy. The vast majority of podcast players will read a directory listing from iTunes in order to provide the most comprehensive search, but none seem particularly good at recommending shows. Given how just about every other service we use online has some sort of algorithm in place to show us music, movies, TV shows, advertisements, and social accounts we might be interested in, why is podcast discovery still such a complicated endeavour?

There are obviously a lot of problems with the podcast ecosystem, and primary among them is podcast discovery and curation. I really wish there were more people working on this problem. Wouldn’t it be nice to have an indieweb solution?

It also makes me wonder what happened to audio platforms like Seesmic, Audioboo.fm, and Cinchcast which made uploading audio pretty simple, though I suppose that there wasn’t much of an audience for that type of audio, in part because the production value and actual content often wasn’t very good. Perhaps things like Soundcloud or streaming video/audio services like UStream have replaced them, but for any kind of bandwith, the cost of hosting goes up, but this also has the economic value of making the quality go up because it requires a bigger investment in production too.