Previous studies suggested that teaching kids chess improved a whole lot of outcomes, from math skills to logic to academic achievement.... except that almost all of these studies were small. A large randomized trial with 4,000 students finds no advantages to learning chess 1/2
The findings are interesting in itself (I don't have to feel guilty about not teaching my kids chess!), but it also has a bigger point: small samples sizes (and the file drawer problem where null results aren't published) result in accidental bias. 2/2 muse-jhu-edu.proxy.library.upenn.edu/article/706374…
Also, playing an instrument also has no effect on cognitive development (though music is nice for its own sake!)
On the plus side, being good at chess does make you better at beating people at chess, and that isn’t true for all games. This is an objective ranking of games by whether you win by skill or luck. Chess requires the the most skill, poker is more luck. econstor.eu/bitstream/1041…
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This is, in fact, what early acceleration of science looks like with jagged AI.
Writing abstracts takes a lot of time, many scientists are bad writers and clearly written abstracts are important. As long as the authors check over the results, this is a good use case for AI.
We do not yet have true autonomous science or breakthrough ideas from AI, but AI provides time savings throughout the process when used carefully by humans: data cleaning, exploratory analysis, writing, pushing back on ideas, (deep) research - all can be helpful when used well.
Kinda amazing: the mystery model "summit" with the prompt "create something I can paste into p5js that will startle me with its cleverness in creating something that invokes the control panel of a starship in the distant future" & "make it better"
This is through LMArena, where you are given random models to test. You will likely get a chance to use "Summit" fairly often (it came up three times in my six attempts): lmarena.ai
They are often far worse at getting AI to do stuff than those with a liberal arts or social science bent. LLMs are built from the vast corpus human expression, and knowing the history & obscure corners of human works lets you do far more with AI
These are systems that respond to human writing and (often) techniques that apply to human psychology.
Everyone now has a machine that makes words, images, video, sound where the limit is often your own ability to imagine something new (or invoke old ideas others do not know).
The Math Olympiad is great, coding is important, accelerating science has tremendous value.
But LLMs give a chance for both cultures to contribute in ways that have not been possible for a long time.
X (and other social media sites) make our 1990s optimism about the Information Age seem silly.
Even with all of the world's information a click away (& a free AI that can help explain that information in a personalized way), half-mangled anecdotes with no source win every time.
It really is not what most people who was working on building the early web in the late 1990s were expecting. Universal access to information was going to transform everything, creating widespread learning and bridging divides.
It really is shocking how much that didn't happen.
The fact that people use the internet mostly for entertainment isn't a weird or surprising
But you also have access to courses on every topic by experts, every major out-of-copyright book, can talk to people from anywhere, etc. The impact of that is smaller than I once expected.
So, OpenAI Deep Research can connect directly to Dropbox, Sharepoint, etc.
Early experiments only, but it feels like what every "talk to our documents" RAG system has been aiming for, but with o3 smarts and easy use. I haven't done robust testing yet, but very impressive so far.
I think it is going to be a shock to the market, since "talk to our documents" is one of the most popular implementations of AI in large organizations, and this version seems to work quite well and costs very little.
I am sure the other Deep Research products will be able to do the same soon, and, while I am sure there are hallucinations (haven't spotted any yet, though), this seems like an example of how the LLM makers can sometimes move upstream to the application space and take a market.
Very big impact: The final version of a randomized, controlled World Bank study finds using a GPT-4 tutor with teacher guidance in a six week after school progam in Nigeria had "more than twice the effect of some of the most effective interventions in education" at very low costs