randomImmigrant

A beautiful article. Thanks for sharing!

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

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.

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?

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

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.

> Humans in a modern lab are largely unnecessary.

That is utterly untrue. It stems from the notion that biology only needs big data for us to solve all its mysteries.

Beyond that, while automated labs for cell biology are plausible and active, and the same fit organoid biology, the same really isn’t true at organism level. Labs already are artificial environments that make organisms have odd phenotypes, and odd interactions which bias the data away from what you’d find in the natural world.

You add in robotic manipulation, and you add a new confound.

Drones in the wild are an obvious counterbalance to this, though even in that scenario, the drone can be a confound unless well hidden.

In the end though, this kind of systemic breakdown of biology, while much needed raw material to improve our theories, won’t solve the whole of biology. Human hands and human minds will be needed for a good long while.

But isn’t this obvious? Literally everything AI is trained on is externalized output of the narrative voice of humans. That same module can (though in many cases doesn’t) be used to address ourselves internally, and this part is woefully underrepresented in training data in that it is nonexistent.

So exactly how can the model believe anything else? It only sees the externalized summation. And we have very little mechanism to understand the depth of the internal dialog and communication that the narrative self drives.

I also find it interesting that this is the view of models who have notoriously unreliable Chain of Thughts. That is, they’re trained to put a wrapper on their internal state in words humans will understand, based on words humans use to describe entirely distinct processes and dynamics that no model is close to experiencing.

It must all seem, from that vantage, utterly fictional.

So much grandeur inside every cell.

Welcome to the much less abstract world of biology, where wishes don’t become well folded proteins nearly as often.

I agree, but it’s equally true access to a broader education where you can actually “strength train” your mental fitness will be needed. We kind of invented a digital telekinesis that lets us summon a vast amount of information on any topic.

We’ll need to build a nose for the right kind of information. Kids growing up in this world already are better at it than older folks who are newly thrown into this environment.

But beyond that I think our agency has already been sapped, as a species. Rebuilding the muscle to exercise our agency as free citizens using the information at our command to make better decisions… we shouldn’t surrender this ability to AI. The best science fiction and science writing already gives us plenty of reason to avoid this. We don’t need to get to the worst of it, and have all out war to correct it.

And that does mean investing heavily in human education. Getting better at teaching things to adults. Making insight easier to gain at every strata of society so true freedom of information when it comes to public works and governance is achieved, which makes the people the watchers and owners of government.

No easy thing to do. Absolutely doable, technologically speaking and in terms of our current biology.

What we have is years of thinking we’re not capable enough. Or cooperative enough. Or rational enough. We can be. We just need the right context, a full belly, and practice in using our voices and shaping our futures actively.

I do wonder where the evolution of the human brain goes, with the widespread use of AI.

The early evidence isn’t good, but I do believe with deliberate and thoughtful training our brains our capable of much more than we currently give them credit for.

Tao’s analogy is flawed. He should have asked how many pharma companies would spend research funding on a phase 3 trial where the only evidence was a swarm of bots swearing the molecule will work, with no given reasons.

I suspect some will. But to find out, we will have to have a trial, so no one is buying it as truth without checking and handing out AI medicine candy. That too, though, will likely change if the peptide craze is anything to go by.