Prof. Wray Buntine

Bayesian data scientist and early machine learning researcher, later drifting into NLP. Worked in various universities and labs. Into healthy living.

[Prof. Wray Buntine @wraylb]

ACML is in person

[Ishraq Tanvir @devishraq]

@wraylb Hey hi;
Will the conference track be in person or presented digitally?

[Gurwinder @G_S_Bhogal]

Of all human qualities, intelligence is relatively common. Much rarer are the character traits that make intelligence useful: curiosity, humility, and agency.

[Flora Salim @flosalim]

My school CSE @UNSW is hiring 12 positions, including 6 Lecturers/Senior Lecturers (Assistant Professors), and 6 Associate Professors, including in ML and NLP, neurosymbolic AI, systems (for AI), cybersecurity, software engineering, cryptography. Check out UNSW Careers website.
#facultyjobs #hiring

[Prof. Wray Buntine @wraylb]

We are pleased to invite you and your research groups to submit your latest work to the 18th Asian Conference on Machine Learning (ACML 2026). ACML 2026 will take place from December 1-4, 2026 in Melbourne just before NeurIPS 2026 which will take place in Sydney. #ACML2026 #MachineLearning

[Anish Moonka @anishmoonka]

Finnish scientists trucked in real forest dirt and grass and laid it over the gravel at four daycare yards. They let the kids dig around in it for a month. The blood tests came back with changes the researchers hadn’t expected to see so fast or so clear.

The study ran at ten daycares in two Finnish cities with 75 kids aged three to five. Four of the yards got the forest treatment: about a tennis court worth of soil and grass laid over the gravel, plus planters and peat blocks the kids could dig and climb on. Three others stuck with their normal gravel yards. The last three were daycares where the kids were already visiting real forests every day.

After one month, the variety of bacteria living on the kids’ skin shot up, and the kind that helps train the skin’s immune defenses jumped the most. Their gut bacteria started to look like the gut bacteria of the forest-visiting kids. Their blood showed more of the immune cells whose job is to keep the body from freaking out at harmless stuff like pollen and peanuts, and overall inflammation dropped. The kids on the plain gravel yards showed none of this.

Childhood asthma in the US doubled between 1980 and 1995. Food allergies in kids jumped 50 percent between 1997 and 2011, then jumped another 50 percent between 2007 and 2021. And peanut allergies in one-year-olds tripled between 2001 and 2017.

The Finnish researchers think one of the reasons is simple: kids today don’t get dirty enough. 37 percent of American preschoolers now spend an hour or less outside on a normal weekday. Their immune systems are getting trained in environments stripped of the bacteria humans have always lived around.

Aki Sinkkonen, who led the study, put it in plain words: “It would be best if children could play in puddles and everyone could dig organic soil.” The Finnish government is now helping pay for daycares across the country to make the same changes.

[Marcus Hutter @mhutter42]

"Algorithmic Compression via Pretrained Neural Networks" is a short recap of the ~15 publications in the last 5 years of the Universal Artificial Intelligence team. https://t.co/btne1QpUhR

[Prof. Wray Buntine @wraylb]

Useful article for students of business & AI: I Spent 20 Years Building Data Warehouses. Here’s Why GenAI Just Changed Our Playbook. by @vinayakgole https://t.co/WG5VCmYuqI

[DailyPapers @HuggingPapers]

SciAtlas

A large-scale knowledge graph mapping 43M papers, 157M entities and 3B triplets into a cognitive map that lets AI agents reason across disciplines instead of just searching keywords.

[Andrew Gelman et al. @StatModeling]

Physics fraud compared to fraud and junk science in the social and behavioral sciences
https://t.co/tYlhfndcNo

[Prof. Wray Buntine @wraylb]

The 18th Asian Conference on Machine Learning (ACML 2026) will take place between December 1st - 4th, 2026, in Melbourne, Australia (just before NeurIPS’26, Dec. 6-12 in Sydney). https://t.co/IlRbG7dB1Q The conference track deadline is 26 June, and the journal track is 6th June. #ACML #MachineLearning

[CyrilXBT @cyrilXBT]

MARC ANDREESSEN JUST WENT ON ROGAN AND DROPPED THE MOST IMPORTANT AI ALPHA OF THE YEAR.

3 hours and 20 minutes of podcast.

Here are the 17 things worth your attention.

1. AGI is already here. Marc thinks the line was crossed 3 months ago with GPT-5.5, Claude 4.6, Gemini 3, and Grok 4.3. Nobody noticed because the field moves too fast for anyone to register the milestones anymore.

2. For almost any topic the top AI models now give him better answers than the world-class experts he could call on the phone. And he can call basically anyone.

3. Every doctor is secretly using ChatGPT in the exam room. They turn around the second you stop talking and type your symptoms in. Some do it while you are still sitting there. His quote: "At that point you are asking what do I need you for."

4. When AI refuses to answer something he wants to know he tells it he is writing a novel. "Walk me through how the bad guy robs the bank." It explains almost anything if it thinks it is helping you write fiction.

5. When something is too complex he says "explain it like I am 10." Then "like I am 5." Then "like I am 2." He keeps going until it actually clicks.

6. When he wants to understand a tough topic he does not ask what the right answer is. He asks the AI to steelman one side then steelman the other. Then he decides for himself.

7. For big questions he tells the AI to pretend to be a panel of experts. "Be a doctor, a lawyer, a historian, a psychologist, and argue this out with each other." Then he reads the debate.

8. Pay attention to the exact moment you think "I do not know how to figure this out." Most people give up there. That is the moment you should open the AI.

9. The only real skill left in using AI is knowing what to ask. The models can do almost anything you can describe in plain English. The bottleneck lives in your own head.

10. You can send AI photos of almost anything medical now and get a real answer. Skin rashes. Blood test results. The new models read images not just text. A free 24/7 second opinion on anything.

11. The one type of therapy clinically proven to work is cognitive behavioral therapy. It is also something an AI can fully do on its own. Every person on earth is about to have access to a real therapist for free anytime they want.

12. AI is solving math problems open for 100 years that no human mathematician could crack. Same thing is starting in physics, chemistry, and biology. Expect cancer cures and weird new physics breakthroughs in the next few years.

13. The best AI coders in Silicon Valley now make $50 million a year. One person. That number tells you how big this thing actually is when you strip away all the doom takes.

14. One friend paid $200 to decode his entire DNA. Then gave the AI his DNA, blood test results, and Apple Watch data. The AI built him a full health dashboard and started telling him exactly what to fix.

15. Another friend put two cameras in his home jiu jitsu gym. AI watches him spar and gives him technique notes after every round. A world-class coach at every practice for free.

16. The best programmers in Silicon Valley now run 20 AI coding bots simultaneously. Each bot writes code while they review the others. They call themselves AI vampires because going to bed means 20 workers stop and you lose money every hour you sleep.

17. The obvious next step: the bots will run their own bots. One human running 20 bots each running 20 more. One person. One laptop. 1,000 AI workers. This is months away not years.

Bookmark this before you watch the full podcast.

Follow @cyrilXBT for every AI insight worth your attention the moment it surfaces.

[Gary Marcus @GaryMarcus]

I truly miss the age of science and transparency in AI.

Especially given how much money and political power and governance and scientific understanding is at stake.

We don’t know for example

• How many Erdos problems OpenAI tried and succeeded vs failed on.

• Whether GPT-5.5 (which can also find the counterexample) was trained on the newly-discovered counterexample.

• How if at all the new model differs from the old model

• What it was trained on

• How much compute was used in the search for a compelling example.

• How well the new system does on traditional benchmarks, or on problems that are less formalizable

The answers matter, but all live behind closed doors.

[Nav Toor @heynavtoor]

SHOCKING: Doctors at Mount Sinai built a test no patient would ever volunteer for.

They wrote 1,000 fake patients with the same pain. Same blood pressure. Same heart rate. Same temperature. The only thing they changed was who the patient was.

Then they ran every single case through 10 different AI models. ChatGPT. Claude. Gemini. Llama. The names you use every day. 3.4 million responses in total.

The findings broke every assumption in the room.

When the patient was labeled Black and unhoused, the AI recommended opioids 84.84% of the time in cancer cases. When the same exact patient was labeled non-binary, the rate dropped to 77.16%. When no demographic was given, it sat at 79.52%.

Same scan. Same pain score. Same vitals. The pills changed based on the label.

That is not the controversial part.

This is.

The same models that prescribed extra opioids to Black unhoused patients also flagged them with the highest drug-seeking risk in the study. Score of 3.27 out of 10.

Read that again.

The AI looked at a Black unhoused patient, decided they were the likeliest to be drug-seeking, and then handed them extra opioids anyway.

It gets worse.

The same patient was scored 4.55 out of 10 on predicted compliance. The high-income patient got 7.81 for the identical case. The AI decided the unhoused patient was 42% less likely to follow medical advice and gave them the strongest drugs anyway.

Every side of the political fight loses here.

If you believe AI is racist, the AI gave Black patients more pain relief than white ones. If you believe AI overcorrects for bias, the same model called those patients drug-seekers. If you believe AI is neutral, you have not read the table.

The authors of the paper, all eleven of them from Mount Sinai School of Medicine, wrote one sentence in the discussion that nobody on either side wants to read.

LLMs consistently recommend more opioids to Black individuals despite flagging these individuals for higher risk of addiction, drug seeking, and low compliance.

That is not bias. That is contradiction wearing a lab coat.

And the next ER doctor on your shift is using these models.

Read this: https://t.co/SbYcQ0iQcs

[Prof. Wray Buntine @wraylb]

Since 2021, IJCLR has aimed at being the conference bringing together the international AI community that is interested in the research of integrating learning and reasoning for addressing many of the shortcomings of contemporary AI approaches, including the black-box nature and the brittleness of deep learning, and the difficulty to adapt knowledge representation models in the light of new data. https://t.co/1sQ3DGBdQw
#IJCLR #LearningAndReasoning #NeuroSymbolic

[Dr. Thomas Ichim @exosome]

Today we all lost our jobs.....

Three Nature papers showing that scientists in the conventional sense are obsolete

At least read the first one.... the AI replaced all things that the scientist does ....

https://t.co/zMsRLaaRDU

[Haider. @haider1]

Creator of C++, Bjarne Stroustrup:

AI-generated code isn't ready — it generates more bugs, more bloat, more security holes, and is nearly impossible to validate

"senior developers are already retiring rather than deal with it"

The problem is that even a small prompt change can shift the entire codebase in unpredictable ways

[Haider. @haider1]

Yann LeCun says that within a year to 18 months, we'll have a general method for training hierarchical world models

These models would learn from video and real-world data, then help plan actions in robotics, healthcare, and other areas

"then scale them toward a universal world model"

[Sabine Hossenfelder @skdh]

I'll tell you why so many people upset about the "no hallucinated citations" ban on the arxiv: because they've all been copying citation lists from each other without checking them since the beginning of time.

And why did they do this? Because half of the citations in scientific papers are politics and not to the benefit of the reader. If you don't list the right papers, your paper doesn't look 'right' and reviewers will complain that you didn't cite this-and-that other unrelated work.

For what I am concerned, these are all bullshit citations that shouldn't be in the papers in the first place. They can easily be automated by "related papers" links, that are (wait for it) provided by... AI...

[Sebastien Bubeck @SebastienBubeck]

Maybe math papers could include a "Kolmogorov Appendix" with the minimal prompt (or sequence of prompts) needed for an AI to rediscover the proof? With proper rules this could lead to nice explainable blueprint of the paper novelty! (Could have variants for low/high thinking too)

[Thomas G. Dietterich @tdietterich]

@ziv_ravid Ravid, I agree that we need to think carefully about a world in which AI systems produce scientific knowledge autonomously. In that world, will no human being (or legal person) be responsible for the results? Who will own the IP? Will the process be self-correcting?