[jsomers]

Hi, author here. This article was many years in the making. It's ostensibly a story about reading minds but really it's about the unreasonable power of high-dimensional vector spaces.

That made it pretty tough to write: how do you explain dimensionality reduction, PCA, word2vec, etc., and the wonders of high-dimensional "embeddings" (of the sort you find in deep neural nets) when a lot—or all—of these ideas might be new to the reader? I'm not sure—but this was my attempt!

[boldslogan]

Hey I liked all the examples.

Thought you might like this one: A Geometric Analysis of Five Moral Principles (OUP 2017)

Ethics using vectors or from a description of the technique: The geometric approach derives its normative force from the Aristotelian dictum that we should “treat like cases alike.” The more similar a pair of cases are, the more reason do we have to treat the cases alike. These similarity relations can be analyzed and represented geometrically. In such a geometric representation, the distance in moral space between cases reflects their degree of similarity. The more similar a pair of cases are from a moral point of view, the shorter is the distance between them.

[enugu]

That was a fascinating article, I liked how you covered the human element in how it helps the paralyzed and the intuitions/visuals of the researchers in the field.

Future applications can be good or bad, but of course that makes it even more important to record the early history of the field and these kind of articles will also help in start the ethics discussion at an earlier stage.

[Jun8]

Great article! I think you’ve done a great job of introducing these difficult concepts in simple language. I saved to Pocket and it’s already got a Best Of label there.

PCA (KLT) can be introduced as a generalization of the Fourier Transform. This can follow from using a cocktail mix analogy to Fourier Series. When I was a TA this was the approach I took with students, which seemed to make things easier for them.

An introductory post on PCA vs FA is here: https://towardsdatascience.com/what-is-the-difference-betwee...

Personal note: Susan Dumais, mentioned in the article also did great early work in text summarization, just after she joined Microsoft. I tried using some of her approached in video summarization in my PHDin early 2000s. How time flies.

[canjobear]

I don’t think explaining it as a generalization of the Fourier transform is going to help very much with New Yorker readers.

[ReaLNero]

PCA applies an orthogonal linear transformation, while FA uses a series of coefficients to scale a sequence of functions, which are then integrated. They are similar in use but very different in method. Calling one a generalization of the other seems misguided?

[aaaaaaaaaaab]

Ummm… the Fourier Transform is an orthonormal linear transformation.

[bertil]

Great article! I’m developing Machine learning systems and my partner is working on psychiatric use of Deep-Brain stimulation, so a rare moment that we can share.

Very minor point: the King - male + female = Queen is a good example, but widely decried as not true by specialists. I don’t have much better examples (I haven’t been able to tell if Paris - France + England = London, for instance) but if you reuse that story, it makes sense to investigate that myth. There’s a lot there too.

[anigbrowl]

I think you did a very good job - it captured the feeling that it's almost sorcery which still hits me any time I successfully apply it, without getting bogged down in technicalities. I think it's OK to be superficial as long as you give people enough information to look up and learn more about it. Mentioning word2vec will certainly give interested readers a head start.

[a-dub]

i find the simplest way to explain pca to a general audience is to draw a ellipse of points off center and tilted in 3d space, and then draw plots for x, y and z. then center the ellipse and rotate the axes to match the major and minor axes of the ellipse and then show how it can be drawn in just x and y and that those x and y plots are far easier to interpret. done.

[mvkel]

It’s cool to see you here! Fascinating article. I had no idea this was being pursued in an applied way; assumed it was all theoretical. Exciting!

[Hizonner]

Why did you write an article glorifying people who are working as hard as they can toward dystopia?

[ausbah]

why are you asking a loaded question?

[revolvingocelot]

I'm not sure if you're reading the same Hacker News that I've been, but mine's mostly been about the confluence of surveillance capitalism, laissez-faire treatment of vulnerabilities in the tech stacks that power it (or even eg absentmindedly putting customer data into an unsecured S3 bucket), and the inability/unwillingness of governments or regulatory bodies to do anything about any of it. In light of these modern realities, I have difficulty believing in a positive final form of this technology. "Mobile pocket telephones" have evolved into "expensive powerful swiftly-obsolescent general-purpose computers mostly used for providing telemetry on the user to unaccountable corporations". Even if the HN crowd end up being able to opt out of the worst aspects of this, like one can with a modern smartphone via GrapheneOS or whatever, we still have to live alongside everyone else who can't.

I can think of lots of nefarious uses for this sort of thing, and I'm just some asshole who's read some science fiction. The real nefarious uses will be architected by people much smarter than me, whose moral difficulties will be dismissed by The Profit Motive, psychopathy, or both.

[shadowgovt]

And here I am thinking about the potential benefits to para- and quadriplegics of circumventing a damaged spinal cord if only we could reliably interpret signal from the brain.

For all the bad you've listed, there's a reason people voluntarily choose to carry those surveillance devices in their pockets: the boons outweigh the ills by an order of magnitude. They're rarely dwelt upon because they're ubiquitous... much like nobody bothers to extol the virtues of fire.

We talk here about what's wrong because there is room for improvement, not because we should halt progress.