alevskaya

It is definitely not the first time. Years ago while doing a ranger shift my friend witnessed a young person fall to their death while climbing a structure. I remember another incident of someone jumping off a dome while psychotic that I suspect was fatal. You need to be sober and capable to climb sketchy stuff.

"they can be small molecules that inactivate essential molecular targets in humans" - aka a poison. I mean we deal with the reality of poisons plenty fine right? This isn't x-risk stuff.

I was an experimental synbio person for decades, and I did plenty of microbial pathogen work. I've also worked on frontier LLMs for the last decade. All of this stuff is gated by experimental effort, not AI!

Because fatality and virulence stand in fundamental tension with each other when it comes to epidemiology. You can't magic that tension away or just invent other incompatible properties of a hypothetical virus (stealth, latency, virulence, fatality, etc etc etc) just because you want to tell a ghost story. Biology: (immunology, cell biology, virology) impose constraints!

I worked in immuno-oncology: cancer vaccines work in melanoma where many things work because of the neoantigen abundance, they've generally been very mixed in efficacy. And even BionTech's BNT111 failed in melanoma! We have hope for these approaches but the reality of this stuff is way more nuanced than you think it is.

Alphafold can't reliably predict thermal energy landscapes or make functional predictions - and how could it? It wasn't trained on anything that could capture structure - function relationships.

Again, most people just have no idea how hard - fundamentally hard - molecular physics is to predict, and how necessary experiments are for any development of biological systems.

You can read up on the synthesis of sarin on wikipedia.

I was a biophysicist and experimental biologist for decades, I designed viral vectors and cell therapies, etc. What protects us here isn't our ignorance vs an "ASI".

It's the fact that the complexity of molecular physics scales exponentially in particle number. No amount of thinking is going to punch through that. You've got to do experiments. The slow loop through reality is not optional, and in the case of "superviruses" would require a lot of iterative experimental development in humans.

(And please knock off the bio infohazard act - I routinely get asked about scenarios that AIxBio safety people cook up and they always involve some howler misunderstanding of basic facts in biology or medicine. Just talk about an idea if you have it.)

I was a genetic engineer for ~20 years and have worked on frontier LLMs for the last 8. I used to engineer viral vectors and studied how to evade human immune systems for gene therapies...

The biorisk scenarios that the AI safety folks flog are fever-dreamed fantasies that have only the most tenuous connection to biological reality. As someone who cares about the real bio-risks of natural pathogens, I get pretty tired of fear-based marketing pretending that AI is a bigger threat than, say, animal agriculture.

I do think it's a lot simpler than the problem Itanium was trying to solve. Neural nets are just way more regular in nature, even with block sparsity, compared to generic consumer pointer-hopping code. I wouldn't call it "easy", but we've found that writing performant NN kernels for a VLIW architecture chip is in practice a lot more straightforward than other architectures.

JAX/XLA does offer some really nice tools for doing automated sharding of models across devices, but for really large performance-optimized models we often handle the comms stuff manually, similar in spirit to MPI.

Nothing fancy. I made these with some pretty simple hand written scripts in javascript rendering to canvas: lots of fiddly little boxes moving around are simpler to script than to hand animate. (If I were to do much more of this I might rewrite these in blender since it has much nicer authoring tooling and export control.)

Quantum mechanics is needed to explain any microscopic phenomena in chemistry and biology - that is not at all in dispute.

The odd set of claims is that somehow biology has 1) figured out how to preserve long-range entanglement and coherent states at 300K in a solvated environment when we struggle to do so in cold vacuum for quantum computing and 2) somehow still manages to selectively couple this to the -known- neuronal computational processes that are experimentally proven to be essential to thought and consciousness.

This more or less amounts to assertions that "biology is magic" without any substantive experimental evidence over the last thirty years that any of the above is actually happening. That's why most biophysicists and neuroscientists don't take it at all seriously.

This referenced paper seems like primarily a theoretical modelling paper (almost all of its figures are simulations?) that contains as far as I can read 3 (!) actual experimental measurements in bulk on a fluorospectrophotometer. The claim is that the observed increased fluorescent quantum yield (QY) of microtubules over tubulin can be explained by the ideas in their simulations.

It's hard to buy that their proposed stories are the simplest explanation for these few measurements. Much more boring phenomena can influence QY. e.g. simply occluding fluorophores from the bulk solvent can have a huge influence on QY and spectra. (I used to design biological fluorescent reporter reagents...)

This seems like a theoretical modelling paper (all of its figures are simulations?) that contains as far as I can read 3 (!) actual experimental measurements in bulk on a fluorospectrophotometer. The claim is that the increased fluorescent quantum yield (QY) of microtubules over tubulin can be explained by the ideas in their simulations.

It's hard to buy that their proposed stories are the simplest explanation for these few measurements. Much more boring phenomena can influence QY. e.g. simply occluding fluorophores from the bulk solvent can have a huge influence on QY.

The static shape of a protein doesn't automatically give you a prediction of its functional properties. There's a hell of a lot more biophysics going on that we have no predictive models for that are needed to understand catalysis, allostery, assembly, etc etc etc. We don't even have good comprehensive data for any of that (compared to sequences or structure) to model with.

Fold prediction is an incredibly useful tool for scientists and genetic engineers to help design new proteins, but it doesn't magically solve molecular or cell biology. Designing new functions and mechanisms is still going to involve a huge amount of labor and brute-force experimentation.