Professorship is still training, and even more costly.
Most professors are in a state where they are a.) bad at managing their lab as they haven’t been trained for it and b.) past their prime when it comes to doing actual science, as they don’t have the time to dedicate to it.
Ok, last thing, and I know it's unpopular, but on "grad students make this place run" and are "workers": when you become a professor, you will learn this is completely untrue. Love our students, but they are *students* and incredibly costly ones. 1/3
@Aging_Scientist I can tell you from my editorial activities so much AI slop is being created and submitted and it increased massively in the past 18 month, most stay unchecked on preprint servers.
The life of a mercenary biohacker isn’t easy
Sure, traveling to elite labs to work on biological millennium problems might seem very sexy and mysterious and cool - and it is
But sometimes I think I might love the Bio-AI game too much. It haunts me in a very sexy and mysterious way
If they discovered the cure for all of these diseases TOMORROW they still wouldn't be treating any patients outside of clinical trials for another decade.
If you work at @AnthropicAI and you want this promise to come true, you need to have a strategy to come to Montana. Don't let the "standard playbook" determine your drug development strategy during the singularity, approach your go to patient strategy with the same sort of innovative mindset you approach everything else with. Reach out to @MontanaETRB or DM me!
Very cool to see some new approaches in classic machine learning.
Another area to improve on would be phylogenetic methods, as for some fields we won’t just get more data e.g the number of vertebrates is around 65,000 and won’t change much.
Introducing e-means, an algorithm beating k-means in 10x fewer epochs.
With order of magnitude wall-clock speedups, e-means is released in FAISS and needs no hyperparameter tuning beyond k. It is robust to various scales and datasets. Still skeptical ? Just try e-means [1/10]🧵
My AI agents found an unmapped Maya site in Mexico, using LiDAR data from a NASA dataset.
72 hours from a random thought on vacation to a reply from Mexico's national archaeology institute.
What a time to be alive…
My AI agents found an unmapped Maya site in Mexico, using LiDAR data from a NASA dataset.
72 hours from a random thought on vacation to a reply from Mexico's national archaeology institute.
What a time to be alive…
"Scientists are people of very dissimilar temperaments doing different things in very different ways. Among scientists are collectors, classifiers and compulsive tidiers-up; many are detectives by temperament and many are explorers; some are artists and others artisans. There are poet-scientists and philosopher-scientists and even a few mystics. What sort of mind or temperament can all these people be supposed to have in common? Obligative scientists must be very rare, and most people who are in fact scientists could easily have been something else instead."
- Medawar
It’s is a very thorough piece that raises many valid points of criticism.
Only thing he should also criticize in it, is the reliance on mice as researcher models, or at least be honest that they are great for academic publishing, but most mouse studies are irrelevant for aging science, unless the goal is to understand mouse aging.
A scorched-earth approach to aging research by Rich Miller 🔥
Pretty much everything gets criticised, from the hallmarks of aging to cell senescence, epigenetic clocks, and cellular rejuvenation.
But Rich makes some very good points. I particularly like the Rolex analogy regarding aging clocks.
If you’re working in aging science, please read it and thank me later! It's an entertaining and much-needed critical perspective.
For the record, I was a reviewer of this paper, and suggested a little more nuance and balance. My suggestions didn't make it to the final version, though. Why be balanced when you can just burn everything in your path! 🔥
https://t.co/I7BUNoK2W2
A scorched-earth approach to aging research by Rich Miller 🔥
Pretty much everything gets criticised, from the hallmarks of aging to cell senescence, epigenetic clocks, and cellular rejuvenation.
But Rich makes some very good points. I particularly like the Rolex analogy regarding aging clocks.
If you’re working in aging science, please read it and thank me later! It's an entertaining and much-needed critical perspective.
For the record, I was a reviewer of this paper, and suggested a little more nuance and balance. My suggestions didn't make it to the final version, though. Why be balanced when you can just burn everything in your path! 🔥
https://t.co/I7BUNoK2W2