"Software experts are—of necessity— comfortable with high cognitive friction. They pride themselves on their ability to work in spite of its adversity.

Normal humans, who are new users of these products, lack the expertise
to judge whether the cognitive friction is avoidable."
"High cognitive friction polarizes people: it either makes them
feel frustrated & stupid for failing, or giddy with power at overcoming the extreme difficulty.

These people either adopt cognitive friction as a lifestyle, or
they go underground and accept it as a necessary evil."
"You can predict which features in any new technology will be used & which won’t. The use of a feature is inversely proportional to the amount of interaction
needed to control it.

In other words: if a feature is useful & requires no work to employ, it is used 100% of the time."
"Naïve user = code for "incompetent user.” And certainly these
people are made to feel stupid, but they are not. The bad design of the interaction is at fault.

"Power user" = person who has been hurt so
many times that the scar tissue is thick enough to no longer feel the pain."
"One of the most expensive items associated with hard-to-use software is technical support.

Imagine how much more effective your software development efforts
would be if you could avoid spending *over five percent* of your net revenue on technical support."

• • •

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More from @DynamicWebPaige

4 Sep
"I get taught life lessons all the time. Probably the most important one is that people will tell you [that] you can’t do something, and you have to ignore them because you can."

- Alan Cooper, @ComputerHistory Oral History (2017)

archive.computerhistory.org/resources/acce… Image
"Are you working to become a good ancestor -- to make something truly interesting -- or are you just working to make money?

Because you can make money by burning villages down and rebuilding them; that’s a good way to make money, but it’s not a good way to be a good ancestor." Image
"If you don’t contribute, don’t pay attention and you don’t give back then it can go away in the blink of an eye...

And so I’m rededicated to this notion of no: if you want a democracy it doesn’t mean you live in a democracy.

It means you participate in it or you lose it." Image
Read 5 tweets
10 Jul
A thread:

College had always been very grad-school-focused, for me: do the NSF REUs, do the industry internships, take the GRE, get a PhD, be lady Carl Sagan.

But halfway through senior year, my mom got very sick (heart issues); and I needed to get a big-girl-job to support us.
Role: developing geophysics plug-ins & geoprocessing scripts at Chevron.

That job isn't something that I would have selected, by a long mile—but it let me experiment with machine learning, with data science, and with tools like Spark, on massive amounts of accelerators/compute.
Chevron also paid for me to go to grad school, at night (carbonate geology, computer science), and offered a flexible every-other-Friday-off schedule...

...which meant that I had time to go to conferences, contribute to open-source, found a PyLadies chapter in Houston, more.
Read 6 tweets
2 Sep 19
Hey, internet: just in case you're not one of those engineers who hears "Hey, compilers!" and comes running delightedly, here's a thread on why I think #MLIR is going to transform the machine learning industry:

(1/n)

for more technical details, ref: ai.google/research/pubs/…
We're seeing an explosion of domain-specific accelerated hardware (yay!).

@GoogleAI's TPUs are an example of this, sure; but you also have @Apple's specialized chips for iPhones, @BaiduResearch's Kunlun, and literally *thousands* of others.

The way that you program for
(2/n)
@GoogleAI @Apple @BaiduResearch these chips right now isn't easy.

Developer-facing tooling is essentially non-existent (or quite bad, even for TPUs); many models are built, and then found to be impossible to deploy to edge hardware; etc.

You also have very rad, but specialized types (e.g., bfloat16).

(3/n)
Read 10 tweets
31 Dec 18
had a really odd dream that I co-founded a startup

pitch: applying transfer learning concepts to education

(ex: if you're an engineer who is adept at natural language processing, what additional machine learning concepts would you need to master to solve computer vision tasks?)
🧠 we used @KhanAcademy data to train the models (zero idea how imaginary-startup got access to it, but 🤷‍♀️)

the dream ended with our acquisition by @Coursera, who eventually seized complete control of the undergraduate (101- and 201-level) and enterprise education markets 💪
learning paths were also personalized for each user

so if Billy learned best when reading, then trying a hands-on activity, then a video + another hands-on activity, that was his path

but if Maria could grok concepts with only a hands-on activity, that was her curriculum design
Read 5 tweets
30 Nov 18
✨💡 This is an ace idea from @sarah_edo! 💕

👩‍💻 Be on the lookout for a @TensorFlow Advent Calendar tomorrow, as well, highlighting meaningful, high-impact projects and papers from our community. If you'd like for yours to be considered, please shoot me an @-mention!
#TFadvent begins today! 😄

For our first project, I'd like to highlight this accessibility example from @shekitup that uses @TensorFlowJS to interpret sign language—and then translates those signs into input that can be used by a home assistant! 🗣️✨

medium.com/tensorflow/get…
🎁 Day #2 of #TFadvent:

🤖 Check out this project from a recent hackathon at @ColbyCollege! The team trained a @TensorFlow model to learn muscle movements, then used that model to send signals to a prosthetic arm, controlling one finger at a time. ✨

👉: m.facebook.com/notes/daviscon…
Read 32 tweets
26 Nov 18
Noogler training doesn't start until tomorrow (😭), so have spent the day derping around @Stanford!

(I think that this was the first building I ever wandered into, back in July 2010 - was presenting a poster at the Lunar Science Forum, and had 30min to dash on/off campus. 🤗💕)
👩‍🎓HOW DO YOU DO, FELLOW KIDS #ClassCrashing
💕Best decision ever: your girl just accidentally walked into Chris Piech's "Probability for Computer Scientists" course, and today's lecture is on Naive Bayes.

🎶Homeboy even has a @Spotify playlist running for students as they file into the auditorium! web.stanford.edu/class/cs109/
Read 7 tweets

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