@ggronvall
Professor, @JohnsHopkinsEHE, @JohnsHopkinsSPH, @JohnsHopkins; https://t.co/6Eoxy53kiP
Marc Johnson @SolidEvidenceThere is something really concerning (and disappointing) going on in the US right now. A dangerous and preventable situation. Let me explain. 1/
Marc Johnson @SolidEvidenceThere is something really concerning (and disappointing) going on in the US right now. A dangerous and preventable situation. Let me explain. 1/
Retsef Levi @RetsefLUnlike what @washingtonpost & @bylenasun attempt to promote Demetre Daskalakis is NOT a ‘warrior’ of public health & science but part of the cult that tarnished any trust in public health! The truth is that he actively tried to sabotage the scientific work of ACIP & the COVID Immunization Workgroup! He worked hard to block discussions on any safety & efficacy issue related to potential contaminants in the COVID vaccines, their prolonged immunological effects (IgG4 class switch), the impact of documented frame shifting (production of unintended antigens) and prolonged vaccine injuries. We should all be thankful that is not anymore in position of power!
Citrini @citriniThere’s a really pronounced divide between people who are on the AI side of things and people who are on the biology side of things. Even among people working in AI, the divide persists based on where they’re focused. The AI side, adherents of scaling laws and the bitter lesson, believe that AI getting better means AI being more capable of novel discoveries in medicine (that go beyond discovering new drugs in well understood targets or speeding up dry lab work etc). The biology people think the idea of a bitter lesson in biology is kind of ridiculous and that regardless of AI’s capabilities it’s going to be at least 5-10 years until you could hope to reliably simulate human toxicology in silico (right now AI/ML can’t even reliably simulate in vivo studies like binding affinities or functional assays). Even the most AI pilled of them, who will list all the areas that AI can reduce the time spent on various areas, don’t think the concept of AI discovering novel targets or generating new understanding of causal biology is realistic in the near term. Most, in fact, will readily characterize the assumption that even an “ASI” level model could find cures hidden in our current understanding (without expanding that understanding with significantly more biological data) as patently false. This was a big part of why we wanted to experience it ourselves by using AI for a (very) small portion of what it can do in biology. It was both mind blowing and revealed some poignant insights into how different the problem of making AI better at something like coding is to the problems of biology. Even if it unlocked the ability to generate new molecules at a stunning pace, its simulation (in silico) of binding only had 49% accuracy. And if we wanted to improve that, we’d need to feed it back the data from the lab - data that took about 30x as long to get as it took to generate the molecules. And these were tests in a test tube (turn of phrase), imagine how long it would take if we wanted to improve the model with data that required recruiting humans and monitoring their reactions. Human biology is very complex, and adding a drug - especially a novel drug and doubly so for a novel drug acting on a novel mechanism - means mapping interactions that have nth-order-effects that we don’t have the data to predict. Virtual cells have made advancements enabled by AI, but the perturbations equate to a small fraction of the possible. This is before you even get into curing disease, especially uniquely human diseases. Brain function involves cellular identities that are unique to humans, meaning animal studies are of little use. How long does it take to understand what the side effects of chronic administration of a drug in humans are? AI can speed up analyzing the data, or designing the trial or even getting it approved…but it can’t make a 6 month trial complete in 3 months. I think its important to point out that significant advancements can be made in AI that contribute to biology, while also being realistic about how much it speeds up something as complex as curing a specific cancer or diseases of the brain.
Molecular Microbiology and Immunology @JohnsHopkinsMMIComment "DONE" if you’ve had your flu shot this season! 💉 Flu vaccines help protect against severe illness. Stay ahead of flu season and protect your community. 📍 Insights from Andrew Pekosz, PhD
Dr. Émile P. Torres (they/them) @xriskologyThis is TESCREAL pro-extinctionism. I have been screaming about it for YEARS. The “good” outcome is one in which humanity dies out. I’m fucking serious about this, and people—the media—need to WAKE UP.
The Moscow Times @MoscowTimesOpinion: The death of a laboratory worker in Irkutsk has turned the spotlight on how Russian laboratories can ensure workers' safety and improve their transparency, writes @ggronvall.
POLITICO @politico‘It's time to get a vaccine in place’: RFK Jr.’s views helped stall bird flu plan
Yong-Bee Lim, PhD @FuturistYB@FilippaLentzos and I just published a @BulletinAtomic (thanks, @MatthewField2!) article about @AnnieJacobsen’s recent book: “Biological War: a Scenario”. You can read the article here:
Avi Norowitz @AviN102@ArmsControlWonk Seems wrong to call a person a monster because (I presume) you believe their communications on nuclear war have been bad.
Dr. Jeffrey Lewis @ArmsControlWonkWatching this monster torture another field of study is incredibly amusing. She’s your problem now bioengineers!
Dr. Saskia Popescu @SaskiaPopescuReminder folks - we have a lot of unknowns in this situation. Including if it’s plague (which is manageable btw). Hospital exposures reflect someone seeking medical care when they don’t know what they have. Quarantine is often used haphazardly as a term for general response (contact tracing, prophy, etc.). Lab biosafety failures are concerning and require thorough review. (1/2)
Ashish K. Jha @ashishkjhaWe've been here before. In 1979, anthrax spores escaped a Soviet military facility in Sverdlovsk. At least 66 people died. It took years become public This was all in the old era -- before AI and biological engineering capabilities became widespread
