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Ethan Mollick Profile picture
@emollick
Apr 1, 2021 • 7 tweets • 4 min read • Read on X
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A brief thread on color differentiation!

We have a huge number of words for color in English, as Ingrid Sundberg's Color Thesaurus shows. 1/n ingridsundberg.com/2014/02/04/the…
But different cultures group colors differently. This chart based on research by Gibson and Conway shows the difficulty of communicating a color. The colors to the left are easiest to communicate since they are most distinct (white and red in English). 2/n theconversation.com/languages-dont…
But the emotional associations with colors are remarkably similar across cultures. 3/n
You can test your own ability to differentiate among colors by sight with this puzzle game. 4/n kuku-kube.com
The rest of the thread describes real colors, but this image can generate impossible-to-render colors too:
🔵Stygian blue: a blue both totally blue & impossibly black
🔴Self-luminous red: a red that is brighter than white
🟠Hyperbolic orange: more orange than 100% orange color
5/
And there is always, always an appropriate xkcd, in this case a survey determining consensus viewsof what name to give each color rendered on a screen. blog.xkcd.com/2010/05/03/col…

You can play with these using the color controversy game (via @mattgroh) 6/ colorcontroversy.com
Colors can have a big influence on how we feel about the world. Here’s an example of how much choosing the right color matters, even where you wouldn’t expect it. 👇

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More from @emollick

Ethan Mollick Profile picture
Mar 8
“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… Image
It should have integrated the scuttling of the High Seas Fleet better but it knocked the Betamax thing out of the park
Dang, Claude. This is just half the thing.

Full story here: docs.google.com/document/d/1-h…Image
Image
Read 5 tweets
Ethan Mollick Profile picture
Mar 4
🚨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 Image
Image
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. Image
Read 4 tweets
Ethan Mollick Profile picture
Feb 25
The lack of benchmarks for writing, telling stories, persuasion, creativity, emotional intelligence, perceived empathy, and doing office work are...

(1) holding back AI advances, (2) hiding big differences between models & (3) obscuring how good these models are for real work
If you want to influence the future, now is the time to release a really good benchmark.
We are getting AIs optimized for coding, doing graduate level math, multiple choice exams, and also counting the r's in strawberry.
Read 4 tweets
Ethan Mollick Profile picture
Feb 16
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.
Read 4 tweets
Ethan Mollick Profile picture
Feb 11
There is a lot of important stuff in this new paper by Anthropic that shows how people are actually using Claude.
1) The tasks that people are asking AI to do are some of the highest-value (& often intellectually challenging)
2) Adoption is uneven, but many fields already high Image
Image
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Paper: assets.anthropic.com/m/2e23255f1e84…
This is just based on Claude usage, which is why adoption by field is less of a big deal (Claude is popular in different fields than ChatGPT) than the breakdowns at the task level, because they represent what people are willing to let AI do for them.
Read 5 tweets
Ethan Mollick Profile picture
Feb 10
Thoughts on this post:
1) It echoes what we have been hearing from multiple labs about the confidence of scaling up to AGI quickly
2) There is no clear vision of what that world looks like
3) The labs are placing the burden on policymakers to decide what to do with what they make Image
Image
I wish more AI lab leaders would spell out a vision for the world, one that is clear about what they think life will actually be like for humans living in a world of AGI

Faster science & productivity, good - but what is the experience of a day in the life in the world they want?
To be clear, it is completely possible to tell a very positive vision of the future of humans and AI (heck, just steal from The Culture or Long Way to an Angry Planet or something), and I think that would actually be a really useful exercise, showing where the labs hope we all go
Read 4 tweets

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