Part of our problem is the way we think. I am a person. I am not a complex adaptive system. And yet I am. I am made of entities. There is a messaging bus, the entities sense, act and interact. But I don't think of myself as a CAS or talk about We. Wecellfs?
Perhaps this a Sapir-Whorf thing. Our language limits what we can think. What is the difference between a pile of ants and an ant colony? A colony is collection of entities, but what do we call the entity that is the colony? Are the ants smart or is the colony smart.
How else could it be? At some level, it would inevitably be a top-level aggregation "creating a unity that doesn't really exist". The alternative would be for the whole brain to be a single elementary particle!
Also, I think you are talking about the corpus callosum for the 'bus' right?
As described in Marvin Minsky's fascinating book "Society of Mind" ...
Your adaptive system has a very complex model of the environment. You can model yourself as an agent in the environment, and you identify as parts of that agent. I say “parts,” because there is a ton of thinking and actions that your adaptive system performs which you do not identify as you.
I agree that, in general, we humans, downgrade the importance of external stimulus and interactions with our environment (including other people). My two cents is that this is downplayed where we live in cities and don't move too much, once you move to very distanct places and cultures (and not assuming yours is the best one) more things tick in the brain.
that said, based on the status quo we definitely don't spend enough resources on making sure we can peacefully and sustainably live next to others.
I am a collector of theories of consciousness :) assuming your quote above is making reference to the "scale" at which "self" is understood, you might be interested in this theory:
Information Closure Theory of Consciousness (2020) https://www.researchgate.net/publication/342956066_Informati...
This reddit comment sums it up better than the paper seems to be able to: https://www.reddit.com/r/MachineLearning/comments/dco3t1/com...
> Consciousness (at least, consciousness(es) that we are familiar with) seems to occur at a certain scale. Conscious states doesn't seem to significantly covary with noisy schocastic activities of individual cells and such; rather it seems to covary at with macro-level patterns and activities emerging from a population of neurons and stuffs. We are not aware of how we precisely process information (like segmenting images, detecting faces, recognizing speeches), or perform actions (like precise motor controls and everything). We are aware of things at a much higher scale. However, consciousness doesn't seem to exist at an overly macro-level scale either (like, for example, we won't think that USA is conscious).
However I would like to mention that sometimes we do think so, as in "the will of the party", at least in some language's context.
Fun fact, when I tried to find similar sentence like "the will of Democratic/Republican Party", google returns 5 results for the former but followed by voters/members and thus not what I want, for the latter, there is no results at all. But as I find "the will of the party", I find an abstract of some paper from my area.
Maybe party is too small for this. It seems like "the will of the nation" is widely used.
Complex adaptive systems can be nested. Human families, communities, societies, governments all form greater gestalts in which humans, themselves complex adaptive system are a part of.
Individuals are smart, committees are dumb.
Fundamental particles must be geniuses.
But a human isn't a bunch of individual cells, it is a cell that cloned itself many times. Those cells all have the same base code and can thus become an intelligent committee.
Amen. You could easily teach quite intricate biology in grade school, if you focused on a fascinating example or two. How many more people would be inspired, rather than bored?
> I discovered a very strange phenomenon: I could ask a question, which the students would answer immediately. But the next time I would ask the question – the same subject, and the same question, as far as I could tell – they couldn’t answer it at all!
> Then I say, “The main purpose of my talk is to demonstrate to you that no science is being taught in Brazil!”
But each test had one or two questions where you had to put together the knowledge, not just regurgitate, and that student consistently failed those question on each and every test.
Yet the student got top scores on each and every test, because the accumulated number of points was enough to get them into the top bracket.
I was so annoyed with that, asking the teacher how they could get top scores while clearly demonstrating they didn't understand the subject matter. Of course, all in vain.
edit: Great read BTW
Biology is just astoundingly complicated, especially micro-bio.
Lets look at the 'Central Dogma' of biology as a point to focus on a bit. It's the idea of 'information' transfer. DNA gets decoded into RNA which then gets decoded into Protiens, right? Easy peasy little discussion. You go into how DNA works a bit, it's structure, it's functions. Then you go a bit more into RNA and the various sub types, how the decoding proteins work, Slicer and Dicer, etc. You then talk about how three letter codons work to make amino acids, how you transport the mRNA out of the nucleus, etc. At each step you take a look at how the thing works and you mention some other launching off points for more research if the kids are interested. This is how a lot of education works, things like cooking, math, history, etc.
Except nearly none of what I just said about the 'Central Dogma' is considered true anymore. Sure, some of it is, but the vast majority of how proteins get made is not encompassed in it. Nearly the entirety of modern micro-biology is all about the 'exceptions' to the 'Central Dogma'. So much so that you can't really even say that there is any appreciable difference between RNA and proteins anymore. Every week, and I am not joking here, there is at least one new paper detailing some hybrid mess of RNA and proteins that has critical importance in how we understand how even the most common parts of a cell works. It's to the point that I would not call the 'Central Dogma' and outright lie, but more of a useful fiction.
Like saying that a 'for loop' is how the internet works. Yes, there are 'for loops' in the internet, yes they are critical, yes, you need to learn about them. But no, you cannot teach someone about the internet via a fascinating example or two about 'for loops'.
Understanding biology is Hard, it is the end result of 4+ billion years of literal life and death. It is not something that can be done in a few examples. Even an understanding at a 12 grade level does in fact take a full school year to get to, and even then, it's just the barest launching point into the wider field. The OP s wrong. Full Stop. You do need to learn the names of these things, you do need to get down and do the work of learning all the facts, you do need to fill your brain with these things that are going to affect you as the world gets more and more complicated, you do need to connect this incredibly vast amount of information together. It is going to affect you or the ones you love.
Edutaiment is not the way forward here. Hard work is.
My own research centered on one subset of functions within E. coli. I was lucky that I found a carefully engineered subset of plasmids and adaptions of E.coli, that could be mathematically modelled [2] [3]. I didn't have to know the whole functioning of E. coli. I didn't have to use mathematics beyond algebra. That is, no calculus was needed. The key task was to put together the quantitative research of about half a dozen labs. Okay, I had a "mountain" of articles to read. And it took 5 years of effort. But it was only doable, because I was modelling a carefully constrained subset of cellular functions.
The only really important detail that wasn't in the original dogma is reverse transcriptase, and they added a dotted line to support that once it was found in physical form.
> You do need to learn the names of these things, you do need to get down and do the work of learning all the facts, you do need to fill your brain with these things that are going to affect you as the world gets more and more complicated, you do need to connect this incredibly vast amount of information together. It is going to affect you or the ones you love.
Do you mean molecular biology instead, which includes the study of central dogma?
(That's a common terminology hiccup, lots of people get this wrong)
OTOH, English really needs another word, meaning "like intelligence, but it could be simulated by an analog computer with a good handful of of discrete components".
People tend to underestimated cells just like you do here.
This article breaks down how the cell behaves down the the molecules. Once each part is pulled apart and examined, where did the 'intelligence' go.
The single cell, looks 'intelligent'. But, when it is all pulled apart we don't find it. It is just chemicals, reactions, physics.
Then, scale that up to multi-cellular organism, then human, its all just mechanistic, chemicals, physics. So where did the intelligence come from? Humans are also just twitching flagella.
This article just makes it a more stark idea, because a single cell appears 'intelligent', but we can pull it apart and examine the constitutive parts, the chemical, molecules.
So there is not much wiggle room for philosophy or souls. It looks intelligent, but look, we can peer under a microscope and don't see the intelligence.
I'm not thinking of simulating the whole cell. And last I heard, a DC full of computers can't fully simulate one sucrose molecule.
All this language is just confusing. In a chemical gradient sense, molds and yeasts solve tough problems all the time, but it's not much more than physics
It's also seems odd to call it "baffling" when they 100% understand how it works!
dude, I am an analog computer with a good handful of discrete components and I'm definitely intelligent
In a given teaspoon of ocean water in the top layer, there are millions of bacteria (in soil there can be up to 1 billion). Each one lives for a day or two before it either divides or is consumed, with a handful of mutations at each round of division. So ~200 divisions a year, for three billion years, with selection stochastically whittling out the few good mutations the crop up now and then, in a diversity of changing ecosystems and you end up with where we are today. Oh, and the occasional horizontal gene transfer for extra spice.
Obviously that is a large hand wave -- the numbers above are from today's environment; early on the biotic density was lower. But the large numbers swamp things. The only real mystery is how things initially got started. But again, it is hard to imagine the time scale involved and the wide variety of environments that exist over the time to imagine the happy accident where the first self-replicating molecule just happen in the right environment that was stable enough for long enough for that self replication to gain traction.
Evolution producing a complicated, half non-working, incomprehensible, "everything interacts with everything else in a chaotic and unpredictable to us way", is the EXPECTED outcome.
It's similar to how many big programming projects become spaghetti messes of half integrations and barely functional parts hooked together half-hazardly where every feature relies on a bit of code nobody understands. It's an "iterate on the stuff that works" process, except the machinery inside a cell is way more effective and tolerant of such a regime than our stupidly strict programming languages and computers.
All it requires is mutations, time and selection pressure.
The Blind Watchmaker is still a good read.
In order to evolve such a system all you need is for the separate components to be useful. A cell laying still and multiplying is useful enough, so that is the baseline. Then adding a flagella to move randomly so it can move away from its waste product and keep hitting new nutrients is also useful. From there it can start to detect waste and move when it is near waste and stop moving when it is near food. Then yo just continue such steps, not very hard to imagine compared to imagining macro evolution.
In your case, why would a flagella be useful if it's not propelling something? A flagella is only useful as a component of something, but not by itself.
I suspect what you're really saying is "Will you still respect me for being a creationist". And the answer is, LOL of course not. Nobody is entitled to have their wacky ideas be respected. A lot of the "free speech" complaints are really demands that other people treat your bullshit with respect, which is an absurd demand.
But if my suspicion above is way off, please tell me. I am curious why anyone would say what you said.
Both are faith based responses to questions we cannot answer any other way. Getting caught up in absolutes thinking your interpretation is the gold standard is a sign of an unrefined critical thinking process.
But of course, every neuron in the brain is bafflingly complex and we still don't know or understand how that complexity manifests itself in thought and intelligence. Given physics and the interactions of "things", every cell in the brain is more complex than the LLMs we're using today. Not to say that every cell is capable of producing the same output as an LLM of course, just that the behavior that it contributes to the overall system is that complex.
Indeed.
Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes: https://www.amazon.com/Biophysics-Computation-Information-Co...
- In the example case of a CNN, the total weights of a kernel is of [Input Number of Channels * Input Kernel Size * Number of Filters], which can be a pretty small amount when it comes to for example a 3x3 kernel with 3 channels with 128 filters coming to a total of 3,456 parameters (3 * 3 * 3 * 128), however in the case of an ANN the same filter is strided across the entire 2D input feature map (or 3D for 3D CNNs). If the input image is of HD resolution of 1280 * 720 and the stride is 2 across both dimensions, then the number of strides is 230,400. The effective number of parameter activations is 796,262,400 (3,456 * 230,400). The reason for this example is that it is sort of a known thing for likely decades now that CNNs are inspired in part by the human visual cortex [0]. For the human visual cortex which needs to be fast, there cannot be parameter sharing across a single kernel, and likely the weights would need to be parallelized to an extent, which would theoretically imply duplicating the weights across the human brain. Thus, the advantage out here lies with ANNs.
- The neurons in the human brain would have to have a certain level of redundancy in place due to the constant cellular repair work.
- The neurons in the human brain can seemingly only be updated by Hebbian learning rather than direct updates which is in the case of the computer memory of ANNs.
- Finally, a significant part of the human brain is for non-logical but environmental reasons, such as movement and touch, and non-logical things such as fear, jealousy, lust, etc; parts which ANNs do not need to possess in the same way (eg: the fight-or-flight response of the amygdala part of the brain).
And the scale invariance of nature is clearly visible here. The cell is "small" compared to human scale but it is as complicated as any machine existing in human scale. There is no absolute small or big in nature.
And the "in silico" experiments are probably a bit of a sleeper for people outside the field. It is really obvious how improvements in computing power will have/had a transformative impact on this field. To go from poking out random molecules and growing dangerous things in a pitri dish to fast computer simulations from DNA seems like quite a big jump in how quickly the field can learn.
there's also an absolute big, known in cosmology, far beyond the scales of galaxies, galaxy clusters, etc... it's your mom
Also scale is subject to physical limitations. Bones can only carry that much weight - chemical processes are limited by - for example - maximum energy dissipation rate.
Or did you understand that and were wondering if we'd ever coopt the bodies mechanisms to create familiar logic gate based compute? Personally I doubt that we'd use already familiar transistors because the process requires ultra pure materials that are modified in very thin layers using gasses to scrape or place individual layers, but maybe we'd find a mechanical analogue expressible via protein, or at that point use purpose built neurons instead.
[1] https://www.amazon.com/Song-Cell-Exploration-Medicine-Human/...
- The emperor of all maladies - about cancer research, for which he got a Pulitzer
- The gene - about the evolution of the field and the discoveries and what is the latest thinking
How close are we to being able to make a map of all atoms within a cell? There are 1E23 atoms in 1 ml of water, and an ecoli is about 500nmx500nmx1um. That means there are only about 2E10 atoms in the whole cell!
Would it be possible to somehow freeze a whole cell, then use an electron beam to knock off and identify (via mass) every atom there?
It’s stupid expensive though, and you can only really identify whole proteins. But you can do that in context, which is massive
The first is cell specialization, particularly neurons. It seems like nature really came up with a universal neuron. There aren't neurons for eyesight vs thinking, etc. They've experimented with this on frogs where they've reweired the optic nerve to a different part ofd the brain and the frog seems to see just fine. They've even added an eye and the frog seems to cope and use it just fine.
The second is the OpenWorm project [1]. This is an attempt to simulate a relatively simple organism with IIRC ~280 neurons. Despite lots of effort, the simulated version just doesn't match up to the real thing. In artificial neural networks we have a stupidly simplified model of neurons that tends to get reduced to a binary signal and an activation function. Thius can do a lot but it's clearly wholly inadequate for any realistic modelling. The protein interactions in a cell are mind-bogglingly complex.
The third is the three-body problem. To summarize, we have a general solution for the grvity interactions of two bodies. Add one more and we don't. We have classes of solutions but no general solution. This is why JPL needs to use supercomputers to calculate flight plans with a relatively low number of bodies. We see a relatively simple set of interactions lead to massive complexity with protein folding. I imagine that it just won't be computationally viable to simulate even a single realistic cell given all th einteractions that go on. We're simply left to make estimations.
> We don’t yet have the technology to just observe all of the activity inside a living cell. That Goodsell painting above that shows the crowded cytoplasm packed with proteins is an artistic composite—backed by rigorous research to be sure—because there’s no way to capture all the different players in situ at once.
> A group at University of Illinois at Urbana-Champagne uses atomic-scale molecular dynamics simulations, in software, to understand structural details
> It’s a world that’s hard to see; sometimes you just have to imagine what’s going on down there, and back up those imaginings with the right experiments.
> One reason I’m particularly attracted to studies of E. coli chemotaxis is that it’s an early star of what’s been called “in silico” biology. It’s been the subject of many computer models.
Honest, at least.
If I knock up something in excel, and other people use that thinking it is how things are, but do not refer to the reality the model represents because there is no way to do that, how useful is the model?
Have you ever looked at old medieval beastiaries? Information is being relayed, but how useful is it?
https://alexanderadamsart.wordpress.com/2019/07/30/the-besti...
Without a means to view the underlying thing the model is meant to represent, to check and correct one's misunderstandings, how useful can the model be?
We're still quite far from replicating this kind of tech.
Well, if the Roomba could exchange genetic information with the surrounding population and adapting to a changing environment would give the appearance of intelligence and design.
Perhaps I’m misunderstanding you, but to me it seems like you have no argument with evolutionists. Your beliefs seem to permit evolution. I think your disagreement is actually with people that see evolution as evidence for atheism.
Creationists can't see the forest for the trees.
A person is billions of billions of more effective cells than an E.coli cell: still our sense of smell, drive and memory do not seem to be billions of billions times more efficient.
Instead of thinking in terms of a discontinuity between animals or putting humans categorically different, Bonner builds this idea of a continuum instead for both culture and learning. Of course there are differences,
https://press.princeton.edu/books/paperback/9780691023731/th...
This post of course goes deep in the rabbit hole so to speak.
[1] https://www.amazon.com/Biophysics-Computation-Information-Co...
"Quantitative modeling of bacterial chemotaxis: Signal amplification and accurate adaptation, Yuhai Tu"
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3737589/
The main points are:
* Both receptor cooperativity and accurate adaptation can be described quantitatively by simple mathematical models.
* An integrated model (the “standard model”), which contains both signal amplification and adaptation, is developed to predict responses of it E. coli cells to any time-dependent stimuli quantitatively.
* Exponential ramps induce activity shifts, which depend on the ramp rate through the methylation rate function F(a).
* Responses to oscillatory signals reveal that E. coli computes time-derivative in the low-frequency regime.
* E. coli memorizes the logarithm of the ligand concentration and the Weber-Fetcher law holds in E. coli chemotaxis.
It also goes into cooperative phase transitions in the receptor complexes as a means of signal amplification, using the same model as in Ising ferromagnetic spin-spin interactions in physics.
I guess 'naming things' isn't just hard in CompSci.
So I believe intelligence arises from the cells and is an essential function of life, not only an emergent phenomena. The organs serve as division of labor amongst the cells in community for what they are already originally capable of themselves.
More musings in this direction https://sites.google.com/site/pablomayrgundter/mind
Kinda felt similar to the cell comms. I wonder what interesting distributed coordination ideas we could learn in distributed systems computing from cellular biology.
