The Mathematics of Life - where I explore how fractals determine how long complex biological systems (like us) last and how to get more out of your 2 billion heartbeat budget https://t.co/KUf8m3aRh6
@makemytrip@IndiGo6E the flight from kozikode to vishakhpatnam has been cancelled on 4th dec by @IndiGo6E bearing the pnr number V773NZ . I have raised the refund for @makemytrip and it’s been 20 days and they keep saying @IndiGo6E hasn’t processed the refund
This paper from Harvard and MIT quietly answers the most important AI question nobody benchmarks properly:
Can LLMs actually discover science, or are they just good at talking about it?
The paper is called “Evaluating Large Language Models in Scientific Discovery”, and instead of asking models trivia questions, it tests something much harder:
Can models form hypotheses, design experiments, interpret results, and update beliefs like real scientists?
Here’s what the authors did differently 👇
• They evaluate LLMs across the full discovery loop hypothesis → experiment → observation → revision
• Tasks span biology, chemistry, and physics, not toy puzzles
• Models must work with incomplete data, noisy results, and false leads
• Success is measured by scientific progress, not fluency or confidence
What they found is sobering.
LLMs are decent at suggesting hypotheses, but brittle at everything that follows.
✓ They overfit to surface patterns
✓ They struggle to abandon bad hypotheses even when evidence contradicts them
✓ They confuse correlation for causation
✓ They hallucinate explanations when experiments fail
✓ They optimize for plausibility, not truth
Most striking result:
`High benchmark scores do not correlate with scientific discovery ability.`
Some top models that dominate standard reasoning tests completely fail when forced to run iterative experiments and update theories.
Why this matters:
Real science is not one-shot reasoning.
It’s feedback, failure, revision, and restraint.
LLMs today:
• Talk like scientists
• Write like scientists
• But don’t think like scientists yet
The paper’s core takeaway:
Scientific intelligence is not language intelligence.
It requires memory, hypothesis tracking, causal reasoning, and the ability to say “I was wrong.”
Until models can reliably do that, claims about “AI scientists” are mostly premature.
This paper doesn’t hype AI. It defines the gap we still need to close.
And that’s exactly why it’s important.
It’s been a tough few months for Sonos. A redesign of the app caused outrage. So when a friend tipped me off to the r/Sonos subreddit filled with 261K angry people, I braced for impact. I found the expected complaints—but I also noticed they really liked an employee named Keith.
Mobile ALOHA's hardware is very capable. We brought it home yesterday and tried more tasks! It can:
- do laundry👔👖
- self-charge⚡️
- use a vacuum
- water plants🌳
- load and unload a dishwasher
- use a coffee machine☕️
- obtain drinks from the fridge and open a beer🍺
- open doors🚪
- play with pets🐱
- throw away trash
- turn on/off a lamp💡
Project website: https://t.co/9rzIX8wLEp
Co-lead @tonyzzhao, advised by @chelseabfinn
(amazing photographing from @qingqing_zhao_ )
My father's simplicity is legendary. But today I was stumped. 20 years ago I bought a thin flannel jacket for Rs 150 or so in Sarojini. In a couple of years as I made slightly better salary, I bought better clothes and left that jacket at home. Today my parents sent me pics