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was a genius in making people feel comfortable with new tech, but the danger was that users were likely to default to out-of-date behaviors.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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As someone who has spent a lot of time thinking and building in AI education, and sees huge potential, I have been shown this headline a lot
I am sure Alpha School is doing interesting things, but there is no deployed AI tutor yet that drives up test scores like this implies.
I am not doubting their test results, but I would want to learn more about the role AI is playing, and what they mean by AI tutor, before attributing their success to AI as opposed to the other dials they are turning.
Google has been doing a lot of work on fine-tuning Gemini for learning, and you can see a good overview of the issues and approaches in their paper (which also tests some of our work on tutor prompts). arxiv.org/abs/2412.16429
I suspect that a lot of "AI training" in companies and schools has become obsolete in the last few months
As models get larger, the prompting tricks that used to be useful are no longer good; reasoners don't play well with Chain-of-Thought; hallucination rates have dropped, etc.
I think caution is warranted when teaching prompting approaches for individual use or if training is trying to define clear lines about tasks where AI is bad/good. Those areas are changing very rapidly.
None of this is the fault of trainers - I have taught my students how to do Chain-of-thought, etc. But we need to start to think about how to teach people to use AI in a world that is changing quite rapidly. Focusing on exploration and use, rather than a set of defined rules.
“GPT-4.5, Give me a secret history ala Borges. Tie together the steel at Scapa Flow, the return of Napoleon from exile, betamax versus VHS, and the fact that Kafka wanted his manuscripts burned. There should be deep meanings and connections”
“Make it better” a few times…
It should have integrated the scuttling of the High Seas Fleet better but it knocked the Betamax thing out of the park
🚨Our Generative AI Lab at Wharton is releasing its first Prompt Engineering Report, empirically testing prompting approaches. This time we find: 1) Prompting “tricks” like saying “please” do not help consistently or predictably 2) How you measure against benchmarks matters a lot
Using social science methodologies for measuring prompting results helped give us some useful insights, I think. Here’s the report, the first of hopefully many to come. papers.ssrn.com/sol3/papers.cf…
This is what complicates things. Making a polite request ("please") had huge positive effects in some cases and negative ones in others. Similarly being rude ("I order you") helped in some cases and not others.
There was no clear way to predict in advance which would work when.
The significance of Grok 3, outside of X drama, is that it is the first full model release that we definitely know is at least an order of magnitude larger than GPT-4 class models in training compute, so it will help us understand whether 1st scaling law (pre-training) holds up.
It is possible that Gemini 2.0 Pro is a RonnaFLOP* model, but we are only seeing the Pro version, not the full ultra.
* AI trained on 10^27 FLOPs of compute, an order of magnitude more than then GPT-4 level (I have been calling them Gen3 models because it is easier)
And I should also note that everyone now hides their FLOPs used for training (except for Meta) so things are not completely clear.