randomImmigrant

Yeah and warming up my eye/brain isn’t a pretty thought. Head impacts brain function, and the last thing I want is a part of my brain getting more sustained heat than the rest.

Really cool. I’m especially drawn to this as the time from thought to mouthing the words is so much faster than speaking out loud, for me.

But I’d be concerned both about battery life, and the long term health consequences of having an RF chip so close to my eyes/brain. Have you explored these aspects much?

I think the “Long Industrial Revolution” framing is the correct one. I found this paragraph to be particularly interesting:

“Briefly: LLMs create social relations between their users and the authors of the text in their training corpora. With the right access to the model and the corpus, one can trace the connections from system output back to individual source texts and their authors (Grosse et al., 2023). These social relations are mechanically mediated, giving users the illusion that they are interacting with just the machine and not an assemblage of people. But mediated social relationships and their illusions are a common fact of modern life. The social relations created by LLMs in turn cut across, and interact with, other social relations, including those shaped by other social technologies.”

Whatever else LLMs are, they are this too. This is a descriptor of the information system in the context of the broader information ecosystem it inhabits, and I think way too much of current AI discourse simply ignores this, either from a belief that humans being “less intelligent” won’t matter and will be outcompeted, or, more chillingly, where shaping the information ecosystem is seen as a priority with AI as the tool to enable it, sapping agency and control away from the public.

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.

So why are they continuing when they say alignment isn’t working? If for all these years they have feared misaligned AI, and now they say their alignment science is less than good, the most obvious conclusion is to stop pushing in the same direction.

This doesn’t mean they fold as a company, or give up on AI, or stop working. But what exactly is the explanation for continuing to train ever larger frontier models in the current context?

I agree there’s no proper plot. That doesn’t mean the AI companies are right, and the reason is in point no. 2 in his list:

The AI company leaders giving these warnings were giving the exact same warnings before they had AI companies - and in many cases, before they worked in the industry, or before the industry existed.

And that doesn’t stand the sniff test, because:

1. There’s a strong linguistic silos around these AI leaders and many of their employees. Assumptions couched in language that should be rigorously questioned is instead sometimes axiomatic. 2. It’s hard to understand why, if you had the same exact fear before starting the AI company, you started it in the first place, let alone continue even as you bemoan these fears. 3. Why, if this path is so dangerous, these leaders don’t consider alternate paths to building AI. Why are all their investments in this one way to do intelligence? Surely we will benefit from having multiple routes to choose from?

The absence of choice and the piling of inevitabilities is why these warnings don’t add up. That the same drum has been banged by the same people for a few decades without input from other fields and sources is what makes all this seem religious prophet adjacent rather than technical assessments by thoughtful engineers and scientists.

Good list. Some additional thoughts:

1. Find patient organizations/support groups for your cancer. They often have excellent collections of info, from some of the sources covered in the article, and can help with finding the right doctor, or the right place to get a second opinion, or financial assistance. Some also list recruiting clinical trials.

2. Try and find local support groups. Other patients are often going to give you way better ideas and suggestions that you never thought of. It also helps enormously to have other folks going through similar journeys to talk to.

3. If you can afford it, Sloan Kettering does a remote consultation, where your primary oncologist can also be present, and they review all tests, suggest new ones, go over the treatment plan, etc. They do this in some international markets too. Pricey AF, but worth it for complex cases.

Lovely work and lovely images and videos. But two things stand out:

1. The transgenesis was successful but initially at least induced behavioral changes. Over multiple generations these hopefully attenuated, but that’s the price of a model system: you poke the fundamentals and sometimes it can change what matters to what you’re studying.

2. I wonder how much being in the wild affects the quality and variety of camouflage, that will be sanded down in lab grown cuttlefish. They have fewer textures they experience, and rarely do their camo in the lab when under true predatory threat. For a question like: does the camouflage adjust when the cuttlefish is tracking the movement of a nearby predator, and what’s the neurological basis of that, are going to necessarily remain hard to answer in a lab.

Still, fascinating work that I hope teaches us a lot about these amazing creatures and ourselves.

There are certainly theories to that effect. But whether you stretch it to consciousness or not, each cell actively defines its boundary, and this boundary can change over time, to accommodate experience, and to promote anticipation.

Cells are autopoetic. They write their own experience.

No. It is the cells definition and construction, one it spends considerable effort maintaining and the moment it stops maintaining it, it loses life. There’s really no two ways about it. You need a buffer from the environment to be alive. Otherwise thermodynamics will take its course right away.

A single cell is quite literally defined by its membrane that… separates it from its environment.

I think yes, your early experience does a lot to set your clock, though there is plasticity/remodeling possible.

As for Sweden: the low light in winter definitely impacts the clocks function. But still not constant darkness, since even a bit of sunlight is some signal to the clock, and it’ll entrain.

High level: the net of extracellular goo around neurons inside which synapses sit (called peri-neuronal nets/PNNs) that keeps them stable day to day and sets thresholds for their “learning” based remodeling, appears to be structured by astrocytes, the third cell in many synapses, that sends out “tentacles” (not the official term, just a good visualization) to sheathe many synapses.

When the circadian clock of just these astrocyes is disrupted, after birth, the PNNs got thinner and didn’t have rhythmic structural remodeling.

Synaptic strength in these knockouts went up, but long term potentiation, a major mechanism for memory in the synapse, gets weaker. The mice had impaired learning and memory performance also.

tldr: it’s not all about the neurons. The surroundings also remodel with time, and shape the neurons themselves. Also, biological synapses are ridiculously more complicated and dynamically very different from the “weights” of a model.

That’s the endogenous period, that really only comes out as is in total darkness over many days. If the hour a day shift keeps happening even when you have access to sunlight, that would be interesting, though I’m not a clinician. Does the activity pattern drift till you’re in anti-phase to the solar day?

Generally the endogenous clock is approximate (hence the word circadian, about a day). But that’s because the day length has been constantly lengthening since animals evolved. Too slowly for us to notice, but the proteins that handle clocking are fairly ancient (though there are additional copies in humans compared to a fruit fly, say), and they aren’t locked in to favor a rotation period for Earth because there hasn’t been a stable period over long stretches.

The clock entrains to the environment, though. The more the daylight brightness to night time darkness shift in intensity, the higher amplitude their rhythms are, generally. Other signals also entrain them, like social activity, and the gut clock is a whole other complexity (kinda downstream of the brain clock but it can feed right back and wreck the central oscillation).

Any number of these factors can cause circadian effects, from a flatter rhythm, changed period, entrainment mismatch, etc.

In rare cases when a circadian gene itself is mutated, then you can have the clock period stretched significantly, and then synchronizing to the 24 hour day becomes really hard.

Be sure to use local coding agents! Not much use spending more to do more private sequencing then exposing the data to the internet some other way.

I’ve personally not (yet) sequenced my genome. That’s where my personal decision is at, and will remain till data ownership for genomic data isn’t guaranteed. That one big part of me I don’t want to surrender to the all consuming digital aether until I absolutely need a test.

When that time comes, I’d try get a whole genome assay. It’s pricier, but the data is gonna be there as more and more evidence about various polymorphisms come to light. Equally, such data is potent in the wrong hands. D