Tesla gets a lot of credit today, but this paper shows Edison mastered the psychology of new technology. To get people to use scary electricity he made it feel the same as the gas they knew. Gas lights gave off light equal to a 12 watt 💡 so Edison limited his 💡 to 13 watts. 1/5
As another example, lampshades weren't needed for an electric light. They were originally used to keep gas lamps from sputtering. Edison used them as a skeuomorph (a design throwback to an earlier use) by putting them on electric lights. Not required, but comforting to have. 2/5
He also developed the electric meter as a way of charging (because gas was metered) and insisted on burying electric wires (because gas was underground).
The fascinating thing was the trade-off: it made the technology more expensive and less powerful, but more acceptable. 3/5
Interestingly, Tesla (the company) learned the lessons Tesla the person did not. Electric cars could have plugs anywhere, so why does charging a Tesla feel like putting gas in a regular car? It’s skeuomorphic, linking the old to the new! 4/5
The process Edison used, called "robust design," helps make new technologies palatable. The classic article by Douglas & @andrewhargadon is extremely readable, and explains a lot about how design helps new technologies get adopted. 6/6 psychologytoday.com/sites/default/…
The lesson is worthwhile for anyone creating new technologies. Apple famously used skeuomorphic design in the original iPhone to make a series of complex apps easier to understand & work with at a glance. medium.com/@akhov/apples-…
One final note on Edison (for now). He was such a superhero to the public that there were contemporary science fiction novels about him teaming up with Lord Kelvin to conquer Mars.
Edison was a genius in making people feel comfortable with new tech, but the danger was that users were likely to default to out-of-date behaviors. As an illustration, here is a sign from Hotel del Coronado, the 1st electrified hotel (the work was overseen by Edison himself).
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I don’t know if LLMs can reason in theory, but they seem to “reason” in practice.
Ask Claude a Fermi problem: How many telephone poles are there in Manhattan? (Guess: 440. Actual: 0). On Staten Island? (Guess 19000-2859. Actual: 27,137) Queens? (Guess: 51-85k. Actual: 103k)
It doesn’t seem to have the number handy, but, even if it did, next token prediction should mean it reasons forward, not backward from a conclusion.
They are obviously not good at all sorts of problems (logic puzzles, etc.) but they do well at many others (Fermi problems, etc).
Reminders about how to stay sane on Twitter: 1) You don't have to weigh in on anything you don't want to (or don't know anything about) 2) You should block more 3) You don't need to share your real life 4) Delete a lot of drafts 5) You can delete tweets people take the wrong way
6) Don't be a jerk. Think twice before quote-tweeting to dunk on people 7) Emotional contagion online has support in the academic literature. You don't need to keep the chain of bad feelings going 8) Twitter people do not represent real-life views, don't take it too seriously
9) Either be very trigger-happy to curate your For You page or stick to your Following page. Algorithms optimize for engagement. Things that make you mad engage you, and then you will be shown more things that make you mad 10) You cannot judge real-life consensus based on Twitter
Write me a corporate memo covering the following points, integrating them together in a good way:
-Forgive me for the murder I committed
-We will hit quarterly steel crumpet goals
-The fish people are here to evaluate our offer. Make sure to speak to them only in fish. Include some fish people sayings
-Do not touch the vortex in the break room. Explain why. -We have pivoted from a NFT company to an artisanal pickle shop. Give details about their similarities.
-Anyone not acknowledging Pluto is a planet will be fired. There will be a series of tests.
The legal and PR teams have asked you to walk everything back (even though everything that happened was real, and everyone knows its real), each point for a different reason, write that memo make the excuses elaborate and obviously transparent and over-the-top.
the floor is now lava, explain why, give new procedures, spin it positively. its actual lava. not fake, not a joke. lava.
(OMG, it has it in for Steve, hilariously, see the last memo)
So how is Runway AI able to model fluid dynamics reasonably well?
The prompt: "A highly detailed portrait of a marble eagle with honey oozing down. Cinematic, highly detailed, film grade."
It isn't perfect (the viscosity seems to change a bit, etc), but why does it work at all?
This is going to be one of those posts where the reactions are divided between "this isn't anywhere as good as you could get with a physics model of fluid over a surface" & people who say "wait, how does a text-to-image model have anything like consistent physics for new scenes"
That prompt: "overhead shot: Two pirate ships sailing back and forth in a cup of coffee in a storm. Cinematic, highly detailed, film grade."
The Civ series is sort of like Ender’s Game, but for management rather than murdering aliens. Business school students who were good at Civ V also turn out to be better planners, organizers, and problem-solvers, in this small experiment.
Other games work as effective tests of fluid intelligence:
⌨️Performance in MOBAs like League of Legends and Dota correlates with intelligence
🎮Performance in FPSs like Battlefield, Destiny, and (likely) Fortnite don't show the same pattern
This qualitative research paper argued that the the skills of a raid leader in World of Warcraft & MMOGs work for offline leadership skills as well. Good guild leaders make good real leaders.
AI detectors have high flaw positives & teacher intuition seems to work even worse: “Here we show in two experimental studies that novice and experienced teachers could not identify texts generated by ChatGPT among student-written texts.”