Organizing the world of fonts with AI

https://medium.com/ideo-stories/organizing-the-world-of-fonts-with-ai-7d9e49ff2b25

by brudgers • 9 years ago

48 19 9 years ago

nerdponx2• 9 years ago

This is really fun and interesting work, but it also goes to show how diluted the term/buzzword "AI" really is.

This is barely even machine learning (what exactly is being "learned" here?). 5 years ago this would have been "dimension reduction and clustering".

ghusbands• 9 years ago

AI has always been a much more general term in Computer Science than in popular use. Look at historic papers to see.

halflings2• 9 years ago

What is being learned is an embedding (lower-dimension representation) that generalises well enough to group "similar" fonts together.

Both dimensionality reduction and clustering are important parts of machine learning: even if some of the dimensionality reduction might be 100% mathematical, most of the recent ones involve statistics and some engineering, hence being included under the ML umbrella.

Eridrus• 9 years ago

T-SNE doesn't learn an embedding or generalise at all. There is no way to add new points to his plot without running the entire T-SNE algorithm again from scratch.

Though who even cares if it is AI or not, it's new and looks useful.

photoJ• 9 years ago

Dimension reduction and clustering have long been the focus of AI/ML. Reference, Bishops book an early 2000's classic, Pattern Recognition and Machine Learning, chapter 9.1 is k-means clustering.

azinman24• 9 years ago

People started using machine learning as a term to differentiate the goal from actual intelligence. They're not the same. Early work in AI was generally about achieving goals (aka A*, blocks world, etc), which is not the same thing as dimensionality reduction or clustering.

nerdponx2• 9 years ago

Metallurgy is an important aspect of high end bicycle design. But you would never argue that metallurgy is bicycle design.

ju-st• 9 years ago

https://en.wikipedia.org/wiki/AI_effect

brudgers3• 9 years ago

One of the properties of artificial intelligence is that in the rear view mirror it is always unimpressive "standard programming practice." Searle's Chinese Room [1] probably describes the problem well enough -- once people are familiar with the mechanism that solved a difficult problem for humans, the machine around the mechanism no longer seems intelligent.

In the early days of artificial intelligence, the general problem solver's unguided search of an exponential solution space was cutting edge (and GPS was non-exhaustive despite searching a combinatorial explosion of potential solutions). A decade the A* algorithm [2] that's in beginning game tutorials was cutting edge research and today, Eliza's pattern matching against natural language powers the 'bot revolution'.

I suspect that in a few years people will say "That's a convolutional neural network, not AI."

[1] https://en.wikipedia.org/wiki/Chinese_room

[2] https://en.wikipedia.org/wiki/A*_search_algorithm

azinman24• 9 years ago

I'd say right now a convolutions neural net by itself isn't AI. That's a how not a what.

When I think of AI, I think of hard AI, which includes the ability to learn new things, extrapolate, integrate across senses, and generally reason like an animal (ideally human) brain would.

It's currently unknown what will achieve (if anything) true AI. Maybe a NN? Maybe a really fancy use of A* search? Some crazy rules engine with a trillion rules a la Searle's Chinese room? It doesn't really matter how in the end as long as you achieve the what (AI).

This typography post is not even remotely close to it, nor is wit.ai, or generally almost all things hyped as AI. It's really frustrating to me having actually focused on studying AI and neuroscience to see these labels just haphazardly applied to literally anything. I'm fine with the title of machine learning, as that appropriately sets expectations. At least in the post the author used ML even if nothing was learned because that's closer to what occurred.

singham• 9 years ago

I had seen a similar research project by people from Microsoft. They had mapped fonts onto a higher dimensional data. Then you can generate new fonts on the fly by specifying a new point and getting a font for that.

https://github.com/erikbern/deep-fonts

tyingq• 9 years ago

I'm surprised it was all driven solely by rendering a single word "handgloves". The diagram hints that it might have even been an all-caps "HANDGLOVES". I wonder if the results would have been better with a longer phrase that included other characters, or some mix of lower/upper case.

haddr• 9 years ago

Very nice and promising thing. I would suggest performing a second iteration with the following improvements:

- classify on all characters (not only one word)

- add some more fonts, at least those most popular ones. I tried with a few very popular (i'd say classic) fonts, but none was found

ryanmarsh• 9 years ago

I would imagine most designers would prefer to see something like "RSOgfcq" if they're font surfing. An 'A' conveys so little about a typeface.

halflings2• 9 years ago

Agree that "a" is not helpful, but "RSOgfcq" (or an actual word) is too long to use in such a map.

azinman24• 9 years ago

Doing that forces you to focus on the trees instead of the forest. If you use an actual English word then you can compare gestalts, which is typically more important. That allows you to understand the emotional impact, etc.

Organizing the world of fonts with AI

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