The article asks "Why so cautious?" Well ... maybe because half our country would rather believe what one guy with a long, long history of dishonesty says over ... you know ... reality?
Now, to be clear I still 100% fault Google for giving in to them (and for renaming established geographic landmarks like Lake Ontario). The "do no evil" company has become the "how evil can we get away with" company, and that's not even a recent change.
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.
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.
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.
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?
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)
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.
Yes, we explicitly avoided any kind of high concurrency/parallelism. That was an intentional decision, although note we did have a global torrent network across all the workers, whose topology (node interactions) formed an extremely complex graph.
I used to work in the MPI world on supercomputer jobs and it was miserable. I also worked on other high concurrency systems at Google (helped run the Ads DB, did training and engineering of Sibyl). I continue to insist these systems are incredibly simple compared to even the most basic human cells interactin g.
Well, we have seen AlphaFold solving a decades old protein folding problem. There's no reason for me to believe, with all the AI advancements I've seen in the meanwhile, that we will not solve other problems in this field too.
AlphaFold didn't solve a decades old protein folding problem. They substantially improved protein structure prediction but with important limits.
THe primary bottleneck for a drug is getting it through clinical trials. No matter how much you speed up lead detection, development, and in vivo studies, the bottleneck will still be clinical trials. Now, if we could make lead detection better at generating molecules that get through clinical trials, that's great- but it would take decades to know for sure. If we could make development less likely to produce toxic molecules that trigger the immune system, sure that's great- but again, we're talking decades before we know if it worked.
Attempting to improve clinical trials- well, those are already fairly well designed and run, but it's likely you could make improvements there too that increased efficacy. But again, you're still just dealing with the FDA, filing tons of paperwork, and tons of people looking at the paperwork. Unless you fix those systematic issues, we're not going to accelerate drug approvals.
> AlphaFold didn't solve a decades old protein folding problem. They substantially improved protein structure prediction but with important limits.
That is a bit of a semantic stretch and calling it substantial improvement for a 50-year (?) old computational bottleneck in structural biology is a bit of an understatement too. It has been a true breakthrough solving a problem humanity as of then thought it was impossible to tackle.
I definitely see a huge role for AI in the lab. Don’t get my wrong at all. The physical labor in the biology lab would go a lot smoother if I knew the data analysis pipelines could be handled with AI.
I shudder to think where my phd would have gone if I’d had AI. And having had to work with jury rigged microscopy rigs and patch clamping rigs, I’m fascinated to see where physical AI for actuators and control systems goes. Certainly I’d be using Claude to improve the control UX to my taste so I could wander my samples with greater control, save time and reduce error.
But I don’t see how any current AI system will have the curiosity and drive. You need to create conditions for meaningful experiments to be done and a mechanistic way of doing this doesn’t work. The most elegant experiments find ways to cut to the core of the emergent behavior, around all the simultaneous changes happening in different levels of the system from which said behavior arises.
You simply cannot see it all. Not every AI can, we are measurement limited. We can track and measure some dynamics, and the art of the experiment lies in thinking through the connections, then picking the right places to look to catch the system in the act. And you better do it so the failure guides the next experiment, and the next and the next, till you finally have sliced through the complexity and found some key nodes that hold the keys.
And usually after all that, you find your key node is regulated by the key nodes of a few other grad students. The complexity here is in part because the entire system is in constant conversation across levels of organization. The causal flow is looped and is also very sensitive to the environment.
AI has a big role to play, but I doubt it’ll parse it without humans at play. It simply cannot invent a slight modification of a microscope and physically connect the new lens element in the right place, calibrate it, all in time for the biology grad student next door to bring some cells to image that are a mix of 40 days old (no contamination! Magic!) vs 30, 20, and so on, to do a very expensive experiment (in terms of labor and time and rats and materials).
So much can go wrong on so many vectors. Which is also what pushes humans to their creative best. And AI will help a lot. But till you have a robot that can drive, but also do mouse dissections and staining and manipulate a microscope with faulty parts to get a clear image, you’re going to need humans to study biology.
When you invent these super robots that somehow slide through all this physical labor without consuming a whole lot of power, we can talk.
Meanwhile, at the cutting edge, where neither procedure nor mass produced machinery exists to do the whole thing, the reality is, we already use a lot of fancy tech in biology, and can use more, and AI can help keep the focus on the actual physical experiments that take so much care and attention.
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?
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.
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.
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?
Does it matter? It's like saying "we fireproofed 1 of 200 apartments in a building" ... well great, but you still can't sell that building until you fireproof the other 199.
Same issue here: no one in America can even buy these things (practically-speaking), let alone drive them legally in US streets until China starts building them to conform to US safety standards.
Until that happens, we're just seeing nifty demos of impressively cheap cars ... that are also (subtly) very dangerous.
BYD is currently the second biggest selling vehicle manufacturer (not just EV) in Australia after Toyota and ahead of Ford. It is also the only one increasing sales in the top three. All the vehicles sold here meet our strict design rules. There are some notable US vehicles that even with substantial engineering modifications probably couldn't be made compliant with our safety regulations.
Perhaps they're just 'nifty demos' in the eyes of the US market, but in countries like Australia, where BYD is already the 2nd best selling car brand, they are very much not that.
I suspect that the video originally posted on Weibo, the video in which all the text is in Chinese, might not be aimed at Americans. I think they are targeting a different market with this. Perhaps China.
A Los Angeles Superior Court jury on Friday ordered General Motors to pay a record $4.9 billion in damages to two women and four children who were trapped and burned when the gas tank of their 1979 Chevrolet Malibu exploded after a rear-end collision on Christmas Eve six years ago.
US safety standards are _different_. Car standards are particularly messy in that a car that's road-legal in the EU probably isn't in the US and vice versa. Mostly small stuff; for instance they'll have different side mirrors.
Producing a version of these cars for US standards likely would not be difficult (every car manufacturer has to deal with about four standards regimes); it's just that they're no reason to do so currently.
It's almost like there are different-sized Discords out there, and some people (e.g. you) use only the small ones, while other people are different, and some of them use large Discords.
To be fair, it's not that simple, and having read the article you should know as much instead of painting this false argument.
What ELSE did they wipe?
>the phone was rife with “numerous sexually explicit messages and images” sent to the victim as well as “significant personally identifying information about her” is not clear error. The record reveals that these conversations occurred over multiple apps on the phone and that Stathas also received explicit photos of the minor. The court did not clearly err by crediting the government’s assertion that “no procedure short of digitally wiping” the phone would eliminate all criminal or harmful material.
So a convicted child molester used multiple phone apps to contact his victim, and instead of going into every app on the phone to see if it had child sex abuse evidence the police just wiped his phone. The court said (more or less) "that's not unreasonable: cops have better things to do than hand wipe criminals' phones of criminal evidence".
Now is there a larger question here? Sure ... but lets not pretend police clearing evidence of child sex abuse from a phone (which the convicted child sex abuserclaimed contained evidence ... for a lawsuit that very likely would never happen) is as simple as "police deleting evidence of their wrongdoing".
It’s called mockery. In the most implausibly well intentioned case where that’s not the intention, it could be called building a strawman. There is no way to do either within HN guidelines.
As the courts are finally starting to take public notice of again, scale matters.
Imagine a world where housing is free, where anyone without can request a primary residence in a location of their choosing and -after twenty four hours- receive one free-of-charge that any member of today's working class would declare to be solidly of above-average quality.
In this world, would police who were tasked with destroying evidence of child molestation from the home of a convicted child molester be in error if they burned the molester's house down, rather than going through the trouble of locating just the documents, trinkets, & etc. that they were tasked with removing? [0]
I mean, what's the harm, right? The child molester can get a good house for nothing with a day's notice.
To make the analogy explicit: just because the child molester can re-use the phone doesn't make torching everything on that phone the correct thing to do. The fact that their mass destruction of that guy's property also destroyed evidence that was relevant to a civil lawsuit only makes things worse for them... maybe not legally, but definitely from a "That shit ain't right and shouldn't be happening!" standpoint.
I hope that the courts treat the relevant evidence as being deliberately destroyed, should the molester bring his civil suit.
> The court said (more or less) "that's not unreasonable: cops have better things to do than hand wipe criminals' phones of criminal evidence".
Yeah, this court is using bogus logic. If the cops don't have time for it, then they should be required to delegate to specialists. Mass destruction of one's papers and other personal effects in order to destroy a tiny slice of them is wildly out of line.
[0] The child molester lives alone, so the majority of the property in his house happens to be his... not that the torch squad bothered to check.
The victims weren't even accused of a crime, let alone convicted, and the police destroyed their house without compensation. They lost in court, because of the important American principle that the police can do what they like and owe nothing to the citizenry.
Ugh. That's pretty typical for this generation's Supreme Court.
That reminds me of the time -IIRC- Scalia asserted that it was A-OK for the cops to "happen" to kill a fleeing suspect by dropping a tree on his moving car, even if they expected that the falling tree would strike the car.
If I'm attributing the decision to the right guy, I'm sure he would have loved to have gotten a chance to exonerate all those ICE agents who've -collectively- gone on a murder spree here "recently".
depending on system, there should be an option to sync to an earlier image before the offenses occured, however you cant really be sure there isnt some enablement still hidden in the device so its faster and more convenient, to just wipe everything. i cant say i have a lot of sympathy, but thats what happens when you make your daily carry phone into a crime phone.
Good luck trying to get YouTubers to stop making endless content about these products, and, most importantly, good luck getting YouTuber fans to stop demanding this type of content.
Connecting the world via the web was always gonna result in unbreakable monopolies. We screwed ourselves and, honestly, there’s no way back now.
Yes. Hence the privileged should fight for privacy and security on behalf of everybody and not spread the nihilsm. For example, by supporting user-respecting companies or NGOs fighting for user rights.
Are there known cases of people killed or attacked for their support of digital freedoms? I don't mean becoming famous for political actions or organizing large events. I mean buying non-mainstream devices or sending a few dollars to EFF.
Now, to be clear I still 100% fault Google for giving in to them (and for renaming established geographic landmarks like Lake Ontario). The "do no evil" company has become the "how evil can we get away with" company, and that's not even a recent change.
But why? That part seems pretty clear.
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