Data visualization inspiration thanks to DALL-E: how Rothko, Basquiat, Picasso, and Monet would create an academic chart.
A few more sources of data visualization inspiration: Bar charts as stained glass in an old cathedral. As a page from the Voynich Manuscript. As ancient stone monoliths on a grassy plain. Made of great columns of fire in the sky at the end of the world. cdn.discordapp.com/attachments/10…
Bar charts made out of cake. In the style of Klimpt. As a Persian rug. Out of writhing tentacles.
Bar charts in the style of the a 1950s comic book, by Leonardo da Vinci, made of Jello, on a knight's shield
Scientific diagrams created by Vincent van Gogh, in the style of the Egyptian Book of the Dead, made of smoke and fire, haunted by ghosts.
Bar charts in the style of Magritte, a burning post-apocalyptic city, a Byzantine mosaic, an 80s punk album cover.
Bar charts made of books. Bones. Charcuterie. Tiny fuzzy monsters.
Bar chart by Lisa Frank, in a book of dread prophecy, traced by the masts of tall ships in a Turner painting, outlined by tornadoes.
Bar chart as cave painting, as Brutalist architecture, drawn by Studio Ghibli, in a frame of a Wes Anderson movie.
Bar chart as drawn by Dali, in the style of DALL-E (I asked it to create a bar chart in the style of AI), composed of (creepy) dollies, made out of dal.
Bar charts in the style of Keith Haring. As a traditional Chinese landscape. Carved into the rock of an alien planet. As a Hieronymus Bosch painting.
Bar charts as a Cézanne still life. As a scene in a Michael Bay movie. As a couture dress. Out of art deco furniture. (All of these are done in Midjourney, which I used for the first time yesterday!)
Since people keeping asking for these, here are all the images I posted (plus some leftovers), maximum resolution, under creative commons attribution license. Enjoy! drive.google.com/drive/folders/…
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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.