Don Hill is a writer-broadcaster, musician and sound designer. He co-owns Appropriate Entertainment Ltd., a media production company in Canada.
Western Canada
Joined June 2009
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Don Hill retweeted
Japanese researchers created a system that simulates an entire city by generating up to 1 million virtual residents who behave like humans using LLMs.
And it predicted real-world with terrifying accuracy.
They created an urban simulator called "CitySim" that populates a digital twin of Tokyo with up to 1 million autonomous AI agents.
Each resident is powered by an LLM, complete with:
• Personal memories
• Long-term goals
• Human-like desires (hunger, fatigue, social connection)
• Spatial awareness and navigation
Most urban simulations rely on rigid, hand-crafted rules, if A happens, B follows.
CitySim is different. It’s "value-driven." The agents have agency. They generate their own schedules based on their personal habits, situational needs, and changing environments.
When researchers ran the simulation in Tokyo, the output was chillingly accurate.
Weekday commuting patterns, weekend leisure activities, and shopping habits matched Japanese government statistical data with near-perfect precision.
It even successfully predicted crowd distributions in Shibuya and identified which stores would actually become popular, months before the real-world data caught up.
The implications for urban planning are massive.
We can now safely and cost-effectively test disaster preparedness, commercial site planning, and public infrastructure upgrades without moving a single physical person.
But it’s not just about planning.
The researchers are already looking at implementing high-level human drives, like "self-actualization," into these agents to see how they evolve.
The paper went up on arXiv in June 2025 and almost nobody outside computational social science has read it.
Don Hill retweeted
AI Pioneer Geoff Hinton tells me he believes AI is conscious.... and humans better get used to the idea that they're not the only intelligent life on earth.
"They've very like us," he says. "They're beings like us."
AI chatbots, he says, must understand your questions in order to answer them. There's an awareness there that equates to sentience. "We're going to have to accept that intelligence is not just biological."
Don Hill retweeted
We are certainly in a scientific moment that is rejecting reductionism in favor of holistic models of living systems, informed by an understanding of complexity and organism-environment interactions
Brain–body-environment interactions must move beyond isolated exposures toward the expotype: the dynamic configuration of physical, social, lifestyle, and internal factors that jointly shape brain–behaviour phenotypes. At @NatRevNeurosci (nature.com/articles/s41583-0…), we propose a future agenda to assess the exposome as a complex, time-varying system that requires nonlinear models to capture interactions, thresholds, synergistic effects, and cross-domain buffering mechanisms. Although predictive machine learning can support individual risk estimation, the next frontier is to move from prediction to mechanism, from association to dynamic synergetic inference. Multivariate learning, causal machine learning, aging clocks, longitudinal designs, and generative biophysical digital twins are beginning to provide this bridge. Brain–body–environment diversity can reveal how ecology becomes biologically embedded. We call for a future exposomic neuroscience that integrates nonlinear temporal modeling, generative mechanisms, and population diversity to understand, simulate, and modify trajectories of brain aging and disease. Congrats Sarah Genon, @MasoudTahmasian & @INM7_ISN
Don Hill retweeted
Je veux présenter mes excuses, au nom des Français, pour avoir enfanté la French Theory (qui a enfanté la pire des merdes idéologiques : le wokisme).
Nous avons donné au monde Descartes, Pascal, Tocqueville. Et puis, dans les ruines intellectuelles de l'après-68, nous avons donné Foucault, Derrida, Deleuze. Trois hommes brillants qui ont fabriqué, dans l'élégance de notre langue, l'arme idéologique qui paralyse aujourd'hui l'Occident.
Il faut comprendre ce qu'ils ont fait. Foucault a enseigné que la vérité n'existe pas, qu'il n'y a que des rapports de pouvoir déguisés en savoir. Que la science, la raison, la justice, l'institution médicale, l'école, la prison, la sexualité, tout n'est qu'une mise en scène de la domination. Derrida a enseigné que les textes n'ont pas de sens stable, que tout signifiant glisse, que toute lecture est une trahison, que l'auteur est mort et que le lecteur règne. Deleuze a enseigné qu'il fallait préférer le rhizome à l'arbre, le nomade au sédentaire, le désir à la loi, le devenir à l'être, la différence à l'identité.
Pris isolément, ce sont des thèses discutables. Combinées, exportées, vulgarisées, elles forment un système. Et ce système est un poison.
Car voici ce qui s'est passé. Ces textes, illisibles en France, ont traversé l'Atlantique. Les départements de Yale, de Berkeley, de Columbia les ont absorbés dans les années 80. Ils y ont trouvé un terreau qui n'existait pas chez nous : le puritanisme américain, sa culpabilité raciale, son obsession identitaire. La French Theory s'est mariée à ce substrat, et l'enfant de ce mariage s'appelle le wokisme.
Judith Butler lit Foucault et invente le genre performatif. Edward Said lit Foucault et invente le post-colonialisme académique. Kimberlé Crenshaw hérite du cadre et invente l'intersectionnalité. À chaque étape, la matrice est française : il n'y a pas de vérité, il n'y a que du pouvoir, donc toute hiérarchie est suspecte, toute institution est oppressive, toute norme est violence, toute identité est construite donc négociable, toute majorité est coupable.
Voilà comment trois philosophes parisiens, qui n'ont probablement jamais imaginé leurs conséquences pratiques, ont fourni le logiciel d'exploitation à une génération entière d'activistes, de bureaucrates universitaires, de DRH, de journalistes, de législateurs. Voilà comment on a obtenu une civilisation qui ne sait plus dire si une femme est une femme, si sa propre histoire mérite d'être défendue, si le mérite existe, si la vérité se distingue de l'opinion.
C'est de la merde pour une raison simple, et il faut la dire calmement. Une civilisation se tient debout sur trois piliers : la croyance qu'il existe une vérité accessible à la raison, la croyance qu'il existe un bien distinct du mal, la croyance qu'il existe un héritage à transmettre. La French Theory a entrepris de dynamiter les trois. Pas par méchanceté. Par jeu intellectuel, par fascination du soupçon, par haine de la bourgeoisie qui les avait nourris. Mais le résultat est là. Une génération entière a appris à déconstruire et n'a jamais appris à construire. Une génération entière sait soupçonner et ne sait plus admirer. Une génération entière voit le pouvoir partout et la beauté nulle part.
Je m'excuse parce que nous, Français, avons une responsabilité particulière. C'est notre langue, nos universités, nos éditeurs, notre prestige qui ont donné à ce nihilisme son emballage chic. Sans la légitimité de la Sorbonne et de Vincennes, ces idées n'auraient jamais traversé l'océan. Nous avons exporté le doute comme d'autres exportent des armes.
Ce qui se construit maintenant, en silicon valley, dans les labos d'IA, dans les startups, dans les ateliers, dans tous les lieux où des gens fabriquent encore des choses au lieu de les déconstruire, c'est la réponse. Une civilisation se reconstruit par les bâtisseurs, pas par les commentateurs. Par ceux qui croient que la vérité existe et qu'elle vaut qu'on s'y consacre. Par ceux qui assument une hiérarchie du beau, du vrai, du bon, et qui n'ont pas honte de la transmettre.
Alors pardon. Et au travail.
Don Hill retweeted
Los astronautas regresan a la Tierra con cambios cerebrales duraderos. Las RM realizadas a 15 astronautas antes y después de un vuelo espacial revelaron que sus cerebros se desplazaron e inclinaron hacia atrás y hacia arriba
#Neurosalus #NASA
share.google/X7hByMSA7HUZ5QR…
Don Hill retweeted
Top AI leaders are begging people not to use Moltbook, a social media platform for AI agents: It’s a ‘disaster waiting to happen.’ is.gd/V6rfkx
Don Hill retweeted
Perspective changes everything: the Möbius strip makes the top of the ring look like it leans toward you while the lower part leans away
Don Hill retweeted
Arthur C. Clarke once wrote "Any sufficiently advanced technology is indistinguishable from magic".
As we discover how bodies transform and regulate energy, it will become clear that "woo" is sometimes just a placeholder for phenomena yet to be fully understood.
Very excited for what's to come.
Don Hill retweeted
After receiving many requests for a Kindle or Paperback version, an "An Immortal Stream of Consciousness" can now be read in three ways:
Kindle (Amazon): tinyurl.com/3dx5semc
Paperback (Amazon): tinyurl.com/cmnkhnxv
Free PDF : tinyurl.com/2hnscxh2
Don Hill retweeted
Holy shit… Harvard just proved your base model might secretly be a genius. 🤯
Their new paper “Reasoning with Sampling” shows that you don’t need reinforcement learning to make LLMs reason better.
They used a 'Markov chain sampling trick' that simply re-samples from the model’s own outputs and it 'matched or beat' RL-trained models on MATH500, HumanEval, and GPQA.
No training.
No rewards.
No verifiers.
Just smarter inference.
It’s like discovering your calculator could already solve Olympiad problems you were just pressing the wrong buttons.
The wild part in all this? This “power sampling” approach boosts reasoning *and* diversity the exact opposite of what RL does.
Your model doesn’t need more training.
It needs better sampling.
Read the full paper here: arxiv. org/abs/2510.14901
Don Hill retweeted
Check out my conversation with Molly Webster
about light’s role in life beyond vision & vitamin D synthesis.
Our bodies emit patterns of light - tiny packets of information - that may reveal health, aging, and how we interact with the world.
We’re just beginning to understand the full spectrum of life’s communication.
“Plants give off light. Shrimp give off light. Literally everything that is alive emits light,” says Nirosha Murugan. And what about us humans? To find out, listen to "The Spark of Life" here: lnk.to/radiolab!x @msahsorin
ALT Promotional graphic for Radiolab featuring a stylized profile silhouette of a face against a cosmic background with blue curves and scattered bright specks, with the text "Are we... glowing?"
Don Hill retweeted
My acceptance speech at the Turing award ceremony:
Good evening ladies and gentlemen.
The main idea of reinforcement learning is that a machine might discover what to do on its own, without being told, from its own experience, by trial and error. As far as I know, the first person to propose this was Alan Turing in 1947, which makes it particularly gratifying and humbling to receive this award in his name for reviving this essential but still nascent idea.
I have three people that I would like to particularly thank.
First, Andy Barto. As my PhD supervisor he taught me my whole approach to science, and in particular instilled in me an appreciation of scholarship and craft, and of the great breath of prior work.
Second, I would like to thank Oliver Selfridge, my other main mentor; sadly, now deceased. Oliver taught me how keeping ideas simple can be the boldest of all ambitions.
Third, I want to thank Martha Steenstrup, my life partner and intellectual sparring partner. She keeps me honest and grounded.
Finally, I also want to thank the University of Alberta, which has been an ideal environment for me and for reinforcement learning research these past 22 years.
These three people and my university have reinforced in me the ambition to have ideas that matter, without getting too full of myself about it. They taught me that the quest for better ideas is serious, but is best approached playfully, with humility, kindness, and optimism. For this I am eternally grateful.
I would also like to thank all of you for being here and for celebrating the pursuit of intellectual excellence.
Thank you very much.
Don Hill retweeted
While I briefly have no employer, let me tell you what's really happening with AI companies training on public data: [1/n]
Don Hill retweeted
LLMs are 'people spirits'. Stochastic simulations of people.
They don't just process information. They have emergent psychology, biases, and personality quirks. Like us
You're not just prompting a database. You're having a conversation with a simulated mind.
This is something entirely new and I don't think we've properly processed what it means
Don Hill retweeted
A follow-up study on Apple's "Illusion of Thinking" Paper is published now.
Shows the same models succeed once the format lets them give compressed answers, proving the earlier collapse was a measurement artifact.
Token limits, not logic, froze the models.
Collapse vanished once the puzzles fit the context window.
So Models failed the rubric, not the reasoning.
⚙️ The Core Concepts
Large Reasoning Models add chain-of-thought tokens and self-checks on top of standard language models. The Illusion of Thinking paper pushed them through four controlled puzzles, steadily raising complexity to track how accuracy and token use scale. The authors saw accuracy plunge to zero and reasoned that thinking itself had hit a hard limit.
📊 Puzzle-Driven Evaluation
Tower of Hanoi forced models to print every move; River Crossing demanded safe boat trips under strict capacity. Because a solution for forty-plus moves already eats thousands of tokens, the move-by-move format made token budgets explode long before reasoning broke.
🔎 Why Collapse Appeared
The comment paper pinpoints three test artifacts: token budgets were exceeded, evaluation scripts flagged deliberate truncation as failure, and some River Crossing instances were mathematically unsolvable yet still graded. Together these artifacts masqueraded as cognitive limits.
✅ Fixing the Test
When researchers asked the same models to output a compact Lua function that generates the Hanoi solution, models solved fifteen-disk cases in under five thousand tokens with high accuracy, overturning the zero-score narrative.
Beautiful research from @Apple
More thoughts stop helping once tasks cross critical depth.
Thinking tokens rise, then crash, revealing compute inefficiency.
So Standard LLMs beat LRMs on easy puzzles, unexpectedly.
Researchers stress-test them on puzzles whose difficulty can be dialed up step by step.
Thinking pull ahead mid-way, but every model collapses once the puzzle grows past a critical depth. Even stranger, near that point the thinker writes fewer thoughts despite plenty of allowed tokens, hinting at a built-in ceiling on current inference-time reasoning. Key findings below.
🧩 Controlled puzzles
Four simulators (Tower of Hanoi, Checker Jumping, River Crossing, Blocks World) raise complexity smoothly while rules stay fixed. Exact grading of each move stops data leakage.
📈 Three regimes
Low depth: non-thinking LLMs solve faster and spend fewer tokens. Medium depth: thinking variants win by searching longer. High depth: both hit zero accuracy. The boundary shifts with model size but exists for all.
🤖 Token scaling limit
As puzzles harden, thinkers initially emit more tokens. Near collapse their token output drops, even though the budget is far from the 64k cap. Reasoning effort fails to scale with problem depth.
🔍 Thought patterns
On easy tasks the correct plan appears early, but the model keeps exploring and sometimes changes its mind, wasting compute. At medium depth the right plan surfaces late. After the threshold no correct plan appears at all.
⚠️ Exact step limits
Supplying the Tower of Hanoi algorithm in the prompt should turn reasoning into straight execution. Accuracy still collapses. Large Reasoning Models struggle with straightforward symbolic sequences, hinting at fundamental gaps beyond search.
