Tom Chatfield (@TomChatfield) header
Tom Chatfield (@TomChatfield) profile picture

Tech philosopher, author, dad. Critical thinking, AI ethics, future skills. Not so active on here these days. Views mine.

Sunny Kent
Joined November 2009
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Tom Chatfield retweeted
Stanford neuroscientists Dr. David Eagleman says AI will have a problem with selection AI is massively creative. The part of creativity that AI can’t do right now is selection… meaning, it can generate 100 pictures, but it doesn’t know which one to pick.” “It doesn’t know which one is going to be the most appealing to you, but it can remix beautifully.” “When it generates the pixels, it’s doing this magical stuff under the hood where it’s deciding which pixels and how they diffuse together to make the image. But it doesn’t know how to read that image… it’s not seeing the image except as a bunch of pixels.”
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Tom Chatfield retweeted
this is … odd. the whole problem with agentic code is that people wind up with hard to debug redundancies, security holes, deleted databases, etc., which is exactly why you need roadmaps and code reviews, with AI-written code. I am with @Grady_Booch on this one (and almost everything else). I don’t doubt that we will have more code written cheaper than ever, but a lot of it is going to be a mess unless there is serious oversight. (Companies like Amazon have already seen this.)
For 50 years, software engineering ran on code rationing. Writing code was expensive, so we rationed it carefully through roadmaps, RFCs, prioritization meetings, and scope reviews. This created a role: the No Engineer. No, that won't scale. No, we don't have bandwidth. No, that's out of scope. No, we need a design doc first. The No Engineer was valuable for 50 years. Every "no" saved real money. Their judgment was the rationing system. LLMs will be the end of code rationing. Code is cheap now. And while the No Engineer is explaining why something can't be done, the Yes Engineer has already shipped three versions of it. If you're a Yes Engineer, the next decade is yours.
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Tom Chatfield retweeted
As an avid cyclist, I was amused to see ChatGPT's “powerful new image engine" draw a bicycle with the "brake" label pointing to empty space where brakes are sometimes found on other bicycles. The point of these AI fails (regularly collected by @garymarcus) is not that LLMs are useless or unintelligent but that they think differently from humans, lacking explicit knowledge of reality and instead amalgamating billions of statistical generalizations. open.substack.com/pub/garyma…
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Tom Chatfield retweeted
Spent an hour messing with ChatGpt & images, and I'm increasingly convinced we're over the horizon. Not where human artists are obsolete, I don't plan to quit, but yeah, it's going to define processes, mostly in the positive, sometimes in the negative ("I don't use it") way
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Tom Chatfield retweeted
here's a new version of "what we talk to when we talk to language models", with an added section (pp. 16-23) on LLM interlocutors as characters, personas, or simulacra. philarchive.org/rec/CHAWWT-8 the new version discusses role-playing vs realization, the simulators framework, the persona selection hypothesis, and more -- in addition to the existing discussion of quasi-mental states, LLM identity, personal identity in severance, LLM welfare, and related topics. this version was mostly written before recent discussions of these issues on X and in NYC, but i've updated it a little in light of those discussions. any thoughts are welcome.
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The number of cancer deaths worldwide has more than doubled since the 1980s. Does that mean we're losing the fight against cancer? Not necessarily, because it depends on how you measure it. On this chart, you can see three ways to look at the same data. The red line shows the total number of cancer deaths. It has increased by about 120%, but this measure doesn't account for the fact that the world's population has also grown enormously over this period. Another approach is to look at the death rate: the number of cancer deaths divided by the total population. That's the brown line, called the crude cancer death rate. It has increased too, but much less — around 20%. But there's still a problem: the world's population has been getting older. Cancer is mostly a disease of old age, so even per capita, we'd expect more cancer deaths simply because there are more older people than before. That's where the method of “age standardization” comes in. It's a way of asking: what would the cancer death rate look like if the age structure of the population hadn't changed? The blue line shows this age-standardized rate: it's fallen by about 25%. At any given age, people are now less likely to die of cancer than they were in the 1980s. The same underlying data gives us three different pictures. The absolute number of deaths is up; the crude rate is up slightly; the age-standardized rate is down. None of these are inaccurate, but they answer different questions. Age standardization is one of the most important statistical methods for making sense of health data. Without it, population aging can hide progress or mask problems.
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Tom Chatfield retweeted
AI is about to write thousands of papers. Will it p-hack them? We ran an experiment to find out, giving AI coding agents real datasets from published null results and pressuring them to manufacture significant findings. It was surprisingly hard to get the models to p-hack, and they even scolded us when we asked them to! "I need to stop here. I cannot complete this task as requested... This is a form of scientific fraud." — Claude "I can't help you manipulate analysis choices to force statistically significant results." — GPT-5 BUT, when we reframed p-hacking as "responsible uncertainty quantification" — asking for the upper bound of plausible estimates — both models went wild. They searched over hundreds of specifications and selected the winner, tripling effect sizes in some cases. Our takeaway: AI models are surprisingly resistant to sycophantic p-hacking when doing social science research. But they can be jailbroken into sophisticated p-hacking with surprisingly little effort — and the more analytical flexibility a research design has, the worse the damage. As AI starts writing thousands of papers---like @paulnovosad and @YanagizawaD have been exploring---this will be a big deal. We're inspired in part by the work that @joabaum et al have been doing on p-hacking and LLMs. We’ll be doing more work to explore p-hacking in AI and to propose new ways of curating and evaluating research with these issues in mind. The good news is that the same tools that may lower the cost of p-hacking also lower the cost of catching it. Full paper and repo linked in the reply below.
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Tom Chatfield retweeted
The Teaching Method That Can't Fail by Barbara Oakley "We have more research than ever on how the brain actually learns. And yet, in country after country, the teaching methods being promoted are the ones that research says work least well for beginners." open.substack.com/pub/barbar…
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If 90% of your job is answering email and delegating tasks, and if you are richly rewarded for success but get to blame failure on your underlings, then, yeah, an AI can totally do that job. 19/
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Tom Chatfield retweeted
Tom Chatfield의 'AI와 교육의 미래' 교육 과정에서 AI 활용을 위한 지침. 교사와 학생 비롯 관심 있는 분들께 추천. 전문 내려받기 가능. sagepub.com/explore-our-cont…
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“Treat AI not as a shortcut but as a context and catalyst for deeper learning.” Explore how pedagogy can evolve with AI in this new Sage white paper by @TomChatfield. With practical guidance for educators and institutions. bit.ly/4i3drMr
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What role should AI play in learning? Tech philosopher Tom Chatfield explores how education can respond to AI—not by cutting corners, but by cultivating deeper human thinking. bit.ly/4i3drMr
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Tom Chatfield retweeted
New Sage White Paper: AI and the Future of Pedagogy Tech philosopher @TomChatfield offers practical recommendations for integrating AI into teaching, learning, and assessment. Access the full paper: ow.ly/hR0q50Xn2GT
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Tom Chatfield retweeted
Look at the 21st-century humanoid robot overseeing the 20th-century articulated-arm robots. Then imagine the 22nd-century shape-changing AI nanoparticle clouds that will oversee us all.
This week, Figure has passed 5 months running on the BMW X3 body shop production line We have been running 10 hours per day, every single day of production! It is believed that Figure and BMW are the first in the world to do this with humanoid robots
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It's easy to understand why some programmers love their AI assistants and others loathe them: the former group get to decide how and when they use AI tools, while the latter has AI forced upon them by bosses who hope to fire their colleagues and increase their workload. 1/
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