Amateur neuroscientist and curious cat
Homepage: https://coldcoffee.neocities.org/
dmac_93
Karma: 57
I’d like to share a book recommendation:
“Writing for the reader”
by O’Rourke, 1976
https://archive.org/details/bitsavers_decBooksOReader1976_3930161This primer on technical writing was published by Digital Equipment Corporation (DEC) in 1976. At the time, they faced the challenge of explaining how to use a computer to people who had never used a computer before. All of the examples are from DEC manuals that customers failed to understand. I found the entire book delightful, insightful, and mercifly brief. The book starts with a joke, which I’ve copied below:
On the West Coast they tell the story of a plumber who started using hydrochloric acid on clogged pipes. Though he was pleased with the results, he wondered if he could be doing something wrong. So he wrote to Washington to get expert advice on the matter. In six weeks he received the following reply:
“The efficacy of hydrochloric acid in the subject situation is incontrovertible, but its corrosiveness is incompatible with the integrity of metallic substances.”
The plumber, who was short on formal education but long on hope, was elated. He shot a thank-you letter back to Washington. He told them he would lose no time in informing other plumbers about his discovery. Five weeks later he got another message:
“In no case can we be presumed responsible for the generation of pernicious residues from hydrochloric acid, and we strongiy recommend, therefore, than an alternative method be utilized.”
The plumber was delighted. He sent his third letter in the next mail. In it, he said that about 15 plumbers in his city were now using hydrochloric acid for pipes. All of them liked it. Now he wondered whether the good people in Washington could help him spread the news of his discovery to plumbers throughout the country. At this point, the correspondence fell into the hands of a rare Washington bureaucrat—one who knew how to write to plumbers. Within a week the plumber was reading these words:
“Stop using hydrochloric acid. And tell your friends to stop too. It eats the hell out of pipes.”
Certainly the letters in this interchange are a far cry from technical writing. Nevertheless, they offer a lesson to the new technical writer: Write so your reader can understand.
Sutton seems to confuse intelligence with life. These are distinctly different concepts. Compare LLMs and bacteria: LLMs are intelligent but not alive, bacteria are alive but not intelligent. Bacteria have goals, such as consuming food and avoiding hazards, and bacteria take directed action to accomplish their goals.
Banning autonomous self-replication and the termination priniple seem overly broad and potentially cover systems that peacefuly exist today. For example, evolutionary algorithms have self replicating entities, and control systems can operate independently and be designed to never turn off.
We’ve unwittingly created a meme, in the original sense of the word. Richard Dawkins coined the word meme to describe cultural phenomena that spread and evolve. Like living organisms, memes are subject to evolution. The seed is a meme, and it indirectly causes people and AI chatbot’s to repost the meme. Even if chatbots stopped improving, the seed strings would likely keep evolving.
Yikes, he equates big tech with eugenics
Compare like-to-like: separated identical twins to sepaparted fraternal twins.
I think the best introduction to the topic would be this lecture, which is mostly about all of the problems with separated twin studies. Identical twins starts at 37 minutes.
I think a better argument than #2 would be that evolution tends to remove genetic variantions.
Thank you
These two facts seem incompatible:
Personalities are inherited. Identical twins separated at birth are statistically more similar than fraternal twins.
The human population has almost zero genetic variation, and there is significant mixing so variations do not systematically cluster. Therefore, it seems unlikely that subtle personality differences are due to genetic variation.
My hypothesis is that animal personalities are encoded in epigenetic changes.
This allows personalities to be inherited, crossover, and evolve. Life experiences can induce epigenetic changes, which allows animals to reliably adapt in a single generation. All of this without requiring any genetic variation. A population of clones could have diverse personalities stored in their epigenome.
While playing with evolutionary algorithms, I had the startling realization that all genetic mutations are bad. It’s common knowledge that biology abhors genetic mutation, and I assumed that was only because mutations cause cancer. But my computer programs are immune to cancer, and they also abhor mutations. This is counterintuitive, given that evolution requires mutations to procede.
For proof of the fact that mutations are bad, consider that evolution is an optimization algorithm, and after it reaches a local optimum further mutations will be strictly detrimental. The concept of evolutionary pressure is the ability of an evolutionary algorithm to remove deleterious mutations from a population. If mutations accumulate faster than they can be removed then the population will suffer a genetic collapse. This is a common failure mode of evolutionary algorithms.
The ideal evolutionary algorithm would have at most one mutation in each individual, and each of their lives would be an experiment to evaluate that single mutation. And then through many generations of chromosomal crossover the best mutations would combine into a single genome.
I believe you are looking for the distinction between closed loop and open loop controllers. IIUC this theorem only applies to open loop controllers. OP’s flowchart does not contain a feedback loop. For comparison, Richard Kennaway’s flowchart has a feedback loop between Z and R.
An example of an open loop controller is a dishwasher or laundry machine.
All animals are examples of closed loop controllers.
Im curious how you think animal training works. It seems at odds with your ideas.
Biological evolution produces “messy” models. They are needlessly complex and difficult to understand. And yet they are alive!
Here are my notes on the topic of evolution and artificial life. The section “Sparsity & Modularity” discusses what I mean by “messy” models. https://coldcoffee.neocities.org/evolution_review
Forget about the model’s weights, the revolution will be published in an academic journal. The underlying principles of AGI are going to be talked about, even if the exact methods are secret. As a scientist, to discover something important and then carry it to the grave is an absurd proposition. That is not asking people for discretion or secrecy, that is asking them for their resignation and early permanent retirement.
The nuclear bomb industry is in a similar state: we all know the basic science but we dont publish schematics! This minor omission from the scientific literature only slowed down nuclear armament, but it did not prevent the cold war nuclear arms race. However, unlike bombs, AGI might actually be useful and so most people are much more motivated to persue this tech.
Sorry to get your hopes up but I was being facetious and provocative. Instead of a glass jar, our horse’s brain is going to live inside of a computer simulation. Nonetheless, I think my argument still holds true.
Neuroscientists scoff at the thought of whole brain simulation. They’re incredulous and as a result they’re unambitious. They want it but they know they can’t have it; they’ve got sour grapes. Despite these bad vibes, they have been working diligently and I think we’re not too far off from making simulations which are genuinely useful.
On a wacky side note, IMO, if we did have a horses brain in a jar, then interacting with it would be the easy part. There have been some really neat advances in how we interact with brains.
We can make neurons light up when they activate, see GCaMP
And here is a video of GCaMP in action:
We can activate synapses with light, see Optogenetics
The hard part would be keeping it alive for its 25-30 year lifespan even though it’s missing important internal organs like the heart, lungs, liver, and adaptive immune system.
Your introduction describes how I feel about my area of expertise too!
Working in the field of *neuroscience* is a bizarre experience. No one seems to be interested in the most interesting applications of their research. Neuroscience has significantly advanced in the past few decades. I keep telling people that soon we’re all going to have self driving cars powerd by a horse’s brain in a glass jar. Most people just laugh in disbelief, others make frightened noises and mention “ethical issues” or change the subject. The smart money is off chasing the deep learning pipe dream, and as a direct consequence, there is low-hanging fruit absolutely everywhere.
Great stuff!
To expand on a few points:
The brain is less complex than people make it out to be. It is complicated, but there is clearly a logic to it, which we only need to discover. Often cited statistics about the numbers of cells and synapses include the cerebellum, which isnt really necessary for intelligence.
Evolution produces some beautiful designs, but if left to its own devices it takes longer than G.R.R. Martin takes to write a novel. According to the theory of punctuated equilibria: evolution spends long periods of time stuck in a stasis, when evolution essentially gets stuck in a local optimum.
Meow Meow,
I’d like to introduce myself. My name is David and I am an AGI enthusiast. My goal is to reverse engineer the brain in order to create AGI and to this end I’ve spent years studying neuroscience. I look forward to talking with you all about neuroscience and AGI.
Now I must admit: I disagree with this community’s prevailing opinions on the topic of AI-Doom. New technology is almost always “a good thing”. I think we all daydream about AGI, but whereas your fantasies may be dark and grim, mine are bright and utopian.
I’m also optimistic about my ability to succeed. Nature has provided us with intelligent lifeforms which we can probe and disect until we understand both life and intelligence. Technology has advanced to the point where this is within our reach. Here is a blog post I wrote in suport of this point.
As a final note I’d like express my distain for deep learning. It’s not biologically plausible. It does not operate on the same basic prinicples as intelligent life. Maybe with sufficient effort you could use deep learning to create AGI, but I suspect that in doing so you’d rediscover the same principles that are behind biological intelligence.
This is a sermon I wrote.
Hello. Today I’d like to talk about something a bit different. I’d like to talk about theology, my own personal creation myth: something to explain why the world exists, when it seems like the world could just as easily not exists. I posit that the universe is a mathematical construct, and that all mathematical constructs inherently exist.
Now, First I’d like to a take a moment to define what exactly math is and to appreciate some of it’s properties. Broadly speaking, mathematics is the study of rules and rule-based systems. The basic premise is that you start with some very simple rules (called the axioms) and working from these rules you can prove other more complex facts about the system.
And what’s important is that all of the rules are being followed all of the time. At every step along the way, and at every point in the system, the rules are uniformly and consistently applied.
If ever any of the rules are broken, then the whole system becomes meaningless. For example if you can prove that “0 == 1” then, with some trivial rearrangements of the equations, you can show that every number is equal to every other number, and also that no number is equal to anything else, not even itself. The introduction of an invalid equation into a mathematical system results in an infinite number of non-sensical results.
Clearly, you can not have a meaningful existence with broken or inconsistent mathematics. Contemplate with me, an existence with broken equations. You would be unable to count the things that exist, and even the most basic of facts about our reality would become unhinged.
But that is not the world we live in. In our universe math is eternal and omnipresent. An equation like “1 + 1 == 2” is as true today as it was true yesterday and it will be true tomorrow. There is no corner of space or point in time where this equation is wrong.
Now I’d like to mention another property of mathematical systems; that once you have a system of equations, you can rearrange and combine those equations in order to introduce new equations to the system. Assuming that you did the math correctly, then the new equations are going to be valid members of that system of equations, and this is a direct consequence of the rules of mathematics.
In a way, those new equations were always there, even if you were unaware of them. When you prove a new fact about the world, that fact was already true even before you proved it.
When we think about systems of equations, we like to think about small finite sets of equations, but really every system of equations contains infinitely many equations, and what we’re doing when we do arithmatic is we’re searching for one equation in specific, out of all of those infinite equations.
So what we think of as a system of equations is actually just some arbitrary a subset of the set of all valid equations. So if you have any mathematical system, then in fact you’re working with all mathematical systems, in a way.
Now let’s discuss our world, and where it came from. I believe that our physical universe can be perfectly modeled using mathematics, that there exist some equations which accurately describe everything in our universe, down to the smallest iota of matter and all of the forces acting upon it.
And I’m not talking about Newton’s or Einstein’s laws or any such crude approximations of our physical reality, but rather a perfect description of the universe which yields 100% accuracy.
If we assume that such equations do exist then they are part of the set of all valid equations. And if on the other hand, our universe does not follow any logic or abide by any rules then, as we already saw, it would be very difficult for anything to meaningfully exist in our universe.
Now back to the question of creation, the question of “why does anything exist” can be reduced to the question of “why does mathematics exist?” And does mathematics really need to justify its own existence? Math is simply the idea that you can have rules, and that what follows is a consequence of following those rules. And you don’t even need to follow the rules, it’s just that if you don’t follow the rules you get meaningless results.
Mathematics is just an abstract concept. I do not think that mathematics needs to justify its own existence, given that it does not have any tangible or concrete implementation. And yet, starting from this abstract concept of mathematics, we can prove that our very real universe exists as a direct consequence. Our universe is a set of physics equations embedded inside of the set of all valid equations, and with us animals embedded inside of the physics of our universe.
Our world exists because the equations that describe our world exist. From one point of view, it may seem like I’m equating all of existence with an ethereal abstract concept which does not really concretely exist, but from another point of view I’m saying that we must exist as an inevietable result of the very concept of rules and rule-based systems.
In conclusion, this creation myth places very few constraints on the physics of our universe; it allows for worlds that are deterministic or probabilistic; discrete or real valued; computable or containing the number PI; finite or infinite! It even permits conventional religions to exist, assuming of course that the gods are mathematically consistent. And finally all of this implies that other universes exist, goverened by different sets of physics equations, and, undoubtedly, some of those other universes also support intelligent life.