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points by hungryhobbit 2 days ago | 0 comments

I don't follow how "we can see tiny stuff" = "it is possible to build closed system"?

Seeing tiny stuff doesn't magically give you enough info for Claude to reason with the system predictably (the way it can with source code).

[–] sumanthvepa 2 days ago

It can start to reason about what it seems under the microscope. You just need to train it on images and data from microscopes.

[–] randomImmigrant 2 days ago

Have you looked at cells and tissues under a microscope? There’s patterns upon patterns inside patterns under and over and through other patterns.

There’s no well-labeled dataset for all this because we don’t know everything we are looking at.

Take a neuron. You can stain it so you can tell apart an axon and a dendrite. But if you don’t it’s very possible to miss or mislabel the axon, because unlike the textbook neuron, the axon isn’t always obviously oriented in a way that promotes ID.

And that’s just one example. With more complex samples, you also have a lot of edges which AI models don’t segment very well.

[–] menaerus 2 days ago

You largely explained a day in life of a SWE working in a complex codebase spanning (tens of) millions lines of code. In complex products we always deal with a great degree of uncertainty and due to the ambiguity and high complexity we usually have no simple way of proving that the thing (TM) we designed is going to be working as advertised in all cases. Because the problem space is in-exhaustive, and to increase the likeliness of the solution we designed, we run the experiments, and not on a sole machine but clusters at data-center scale. Even then you get "bug reports" and that's not because engineers were lazy but because the domain is genuinely complicated, and ever changing.

So, I'd say it's a pretty similar process, and yet we have the AI models now almost completely integrated into the SWE domain remediating significant parts of its complexity.

[–] dekhn 1 day ago

I've done SWE work at the highest level (I ran a job that ran a worker on almost every idle machine at Google, and the workers formed a global torrent for data distribution). If you took all of Google's data centers in the world, along with their networks, it would be roughly the complexity of a few cells in the human body. Fortunately, with my training in biology, working on such simple data centers was pretty easy.

[–] menaerus 1 day ago

I'm not sure what you mean by "running a job that ran a worker". Running the system is vastly different than building and maintaining one. I work on highly available and distributed systems spanning multiple data centers, and single node scalability of database kernels, so I guess I could also say that I've done and still doing work at the highest level but in my case it is the core development. Which one is yours?

[–] dekhn 1 day ago

The system I built was called exacycle. It ran as a borg job in every cluster we had access to, with priority 0 or 1, with N tasks (where N=# of machines in the cluster). Borg ran the "worker" task as long as there were resources free on the machine (often, several workers ran on the same machine, and no workers ran on other machines). There was a global schedulers that picked mini-tasks off a global queue and fed them to the cluster-level scheduler (batches based on instant resource availability, and subject to some data locality optimization) which then fed them to the machine workers.

At peak, we had 1-3 million xeon cores running our tasks (protein folding, protein design, drug docking), 24/7. This was before GPUs were really a thing. Each mini-task was basically a rosetta or gromacs or autodock binary running on local inputs and writing local outputs for ~1 hour using ~1GB RAM and no network. Input and output was via colossus, or distributed through a global torrent network (literally all 1-3 million cores formed a single data transfer group, with data passed between clusters at low probability).

I built most of the code of the system (which depended on Google infra) including the scheduler and the machine worker, and also ran the system as on-call "SRE" for 3 years. During that time, we nearly almost didn't take down the Google global network, and had small latency impacts on many services. Managing to run the system for so long without antagonizing ads or search or youtube was a major challenge.

I would say the complexity of the system I built (considering everything down to the OS) was roughly the level of a few human cells in a plate, in terms of interacting components and the complexity of their interactions.

Here are some of the publications that came from running exacycle (we provided strong evidence that using more compute improves outcomes in molecular simulations)

https://pubmed.ncbi.nlm.nih.gov/24345941/ https://pubmed.ncbi.nlm.nih.gov/24265211/

[–] menaerus 1 day ago

I appreciate the details, very interesting. So from a quick glance my understanding is that exacycle is a task partitioning and dispatching system built on top of existing google infra runtime (borg). Tasks are units that are massively parallel and distributed across the compute, and have no or almost no shared data. Problem space seems to be built around self contained tasks which have well defined input and output, and main engineering challenge in the exacycle itself would be the optimizing the system for maximum resource utilization? It's complex but not as complex as open problem space engineering problems dealing with highly concurrent and distributed workloads with shared state and that are unknown at the time you're designing the algorithm.

Anyway, my point was not in that I would equate software complexity with the complexity found in biology but that I see the same trend and similar type of challenges that can be found in both fields and therefore I see both fields amenable to the AI advancements whereas many people from the thread do not, and I disagree with that.

[–] throwaway132448 1 day ago

What is it with SWEs thinking every problem can be addressed in terms of SWE, while at the same time being unaware of how little they understand the problem they are trying to address?

[–] menaerus 1 day ago

I made a parallel with similar complexity and ambiguity that can be found in SWE discipline too. I tried not to make any hard claims but I did present my opinion on the topic - I don't see a reason why drug discovery field wouldn't benefit from the AI when there're proportionally difficult fields (math, comp.sci, mechanics, medicine) where AI is already making a significant difference. Maybe you think comp biology is of a very different kind in which case you would turn out as ignorant here. lastly, my comment is there to spark the discussion and have some food for thought because not so long ago this type of reaction has been seen in comp.sci circles and math just few days ago.

[–] bijowo1676 1 day ago

because SWEs are simply more superior. it is SWEs who built AI, and not the biology students

[–] meindnoch 1 day ago

It's a kind of Dunning-Kruger effect.

https://xkcd.com/1831/

[–] menaerus 1 day ago

Did I say anything wrong?

[–] randomImmigrant 1 day ago

I work in software now after a decade in a neuroscience lab. This is just utter and complete nonsense. Please stop trying to see everything as analogous to software. It might actually help you solve software problems too, rather than this monomania on reducing the real world to software.

[–] mrtesthah 2 days ago

Do you know what loading a sample into a scanning tunneling microscope involves?

[–] bijowo1676 2 days ago

the transition is in-silico => in-vitro => in-vivo

AI already can do in-silico simulation

add microscopes and tiny precision actuators, and we can build closed feedback loop in-vitro system

once we have in-vitro, it will be easier to go in-vivo

[–] randomImmigrant 2 days ago

Why would number 2, which we’ve been able to do for decades, possible affect number 3 on your list?

[–] bijowo1676 1 day ago

currently we rely on meatbags to do 2 and 3, we have not fully built in-vitro done by Claude Code

[–] randomImmigrant 1 day ago

Untrue. And you also don’t have robitics that can do all kinds of cell bio/tissue bio experiments that require the affordances and skills humans hands have but no robot to date does. Seriously, have you been in a lab and seen the kinds of experiments that get done?

[–] bijowo1676 1 day ago

can today's labs prompt Claude Code to do 1488 experiments in-vitro continiously over 48 hrs ?

[–] randomImmigrant 1 day ago

Are you under the assumption “in vitro experiment” is a small set of skills?