Impressively promising work, thanks & good luck! Is there anything a layperson can do to help you reach your goal?
Ilio
Karma: 253
More specifically, if the argument that we should expect a more intelligent AI we build to have a simple global utility function that isn’t aligned with our own goals is valid then why won’t the very same argument convince a future AI that it can’t trust an even more intelligent AI it generates will share it’s goals?
For the same reason that one can expect a paperclip maximizer could both be intelligent enough to defeat humans and stupid enough to misinterpret their goal, e.g. you need to believe the ability to select goals is completely separated from the ability to reach them.
(Beware it’s hard and low status to challenge that assumption on LW)
Yes, that’s the crux. In my view, we can reverse…
Inability to distinguish noice and patters is true only for BBs. If we are real humans, we can percieve noice as noice with high probability.
… as « Ability to perceive noise means we’re not BB (high probability). »
Can you tell more about why we can’t use our observation to solve this?
That’s an interesting loophole in my reasoning, thanks! But isn’t that in tension with the observation that we can perceive noise as noise?
(yes humans can find spurious patterns in noise, but they never go as far as mistaking white noise for natural pictures)
Yes, although I see that more as an alternative intuition pump rather than a different point.
A true Boltzmann brain may have an illusion of the order in completely random observations.
Sure, like a random screen may happen to look like a natural picture. It’s just exponentially unlikely with picture size, whereas the scenario you suggest is indeed generic in producing brains that look like they evolved from simpler brains.
In other words, you escape the standard argument by adding an observation, e.g. the observation that random fluctuations should almost never make our universe looks obeying physical laws.
One alternative way to see this point is the following: if (2) our brains are random fluctuations, then they are exponentially unlikely to have been created long ago, whereas if (1) it is our observable universe itself that comes from random fluctuations, it could equally have been created 10 billions years or 10 seconds ago. Then counting makes (1) much more likely than (2).
0% that the tool itself will make the situation with the current comment ordering and discourse on platforms such as Twitter, Facebook, YouTube worse.
Thanks for the detailed answer, but I’m more interested in polarization per see than in the value of comment ordering. Indeed we could imagine that your tool feels like it behaves as well as you wanted, but that’s make the memetic world less diverse then more fragile (like monocultures tend to collapse here and then). What’d be your rough range for this larger question?
This is sort of what is customary to expect, but leaning into my optimism bias, I should plan as if this is not the case. (Otherwise, aren’t we all doomed, anyway?)
In your opinion, what are the odds that your tool would make polarization worse? (What’s wrong with keep looking for better plans?)
Nothing at all. I’m big fan of these kind of ideas and I’d love to present yours to some friends, but I’m afraid they’ll get dismissive if I can’t translate your thoughts into their usual frame of reference. But I get you didn’t work this aspect specifically, there’s many fields in cognitive sciences.
About how much specificity, it’s up to interpretation. A (1k by 1k by frame by cell type by density) tensor representing the cortical columns within the granular cortices is indeed a promising interpretation, although it’d probably be short of an extrapyramidal tensor (and maybe an agranular one).
You mean this: “We’re not talking about some specific location or space in the brain; we’re talking about a process.”
You mean there’s some key difference in meaning between your original formulation and my reformulation? Care to elaborate and formulate some specific prediction?
As an example, I once gave a try at interpreting data from olfactory system for a friend who were wondering if we could find sign of an chaotic attractor. If you ever toy with Lorenz model, one key feature is: you either see the attractor by plotting x vs vs z, or you can see it by plotting one of these variable only vs itself at t+delta vs itself at t+2*delta (for many deltas). In other words, that gives a precise feature you can look for (I didn’t find any, and nowadays it seems accepted that odors are location specific, like every other sense). Do you have a better idea or it’s more or less what you’d have tried?
Is accessing the visual cartesian theater physically different from accessing the visual cortex? Granted, there’s a lot of visual cortex, and different regions seem to have different functions. Is the visual cartesian theater some specific region of visual cortex?
In my view: yes, no. To put some flesh on the bone, my working hypothesis is: what’s conscious is gamma activity within an isocortex connected to the claustrum (because that’s the information which will get selected for the next conscious frame/can be considered as in working memory)
I’m not sure what your question about ordering in sensory areas is about.
You said: what matters is temporal dynamics. I said: why so many maps if what matters is timing?
Why do we seem to have different kinds of information in different layers at all? That’s what interests me.
The closer to the input, the more sensory. The closer to the output, the more motor. The closer to the restrictions, the easier to interpret activity as latent space. Is there any regularity that you feel hard to interpret this way?
Finally, here’s an idea I’ve been playing around with for a long time:
Thanks, I’ll go read. Don’t hesitate to add other links that can help understand your vision.
I’m willing to speculate that [6 Hz to 10 Hz ]that’s your ‘one-shot’ refresh rate.
It’s possible. I don’t think there was relevant human data in Walter Freeman time, so I’m willing to speculate that’s indeed the frame rate in mouse. But I didn’t check the literature he had access to, so just a wild guess.
the imagery of the stage ‘up there’ and the seating area ‘back here’ is not at all helpful
I agree there’s no seating area. I still find the concept of a cartesian theater useful. For exemple, it allows knowing where to plant electrodes if you want to access the visual cartesian theater for rehabilitation purposes. I guess you’d agree that can be helpful. 😉
We’re not talking about some specific location or space in the brain; we’re talking about a process.
I have friends who believe that, but they can’t explain why the brain needs that much ordering in the sensory areas. What’s your own take?
But what is [the distributed way]that?
You know backprop algorithm? That’s a mathematical model for the distributed way. It was recently shown that it produces networks that explains (statistically speaking) most the properties of the BOLD cortical response in our visial systems. So, whatever the biological cortices actually do, it turns equivalent for the « distributed memory » aspect.
Or that’s my speculation.
I wonder if that’s too flattering for connectionism, which mostly stalled until the early breakthrough in computer vision suddenly attract every labs. BTW
A few comments before later. 😉
What I meant was that the connectionist alternative didn’t really take off until GPUs were used, making massive parallelism possible.
Thanks for the clarification! I guess you already noticed how research centers in cognitive science seem to have a failure mode over a specific value question: Do we seek excellence at the risk of overfitting funding agency criterion, or do we seek fidelity to our interdisciplinary mission at the risk of compromising growth?
I certainly agree that, before the GPUs, the connectionist approach had a very small share of the excellence tokens. But it was already instrumental in providing a common conceptual framework beyond cognitivism. As an example, even the first PCs were enough to run toy examples of double dissociation using networks structured by sensory type rather than by cognitive operation. From a neuropsychological point of view, that was already a key result. And for the neuroscientist in me, toy models like Kohonen maps were already key to make sense of why we need so many short inhibitory neurons in grid-like cortical structures.
Going back to Yevick, in her 1975 paper she often refers to holographic logic as ‘one-shot’ logic, meaning that the whole identification process takes place in one operation, the illumination of the hologram (i.e. the holographic memory store) by the reference beam. The whole memory ‘surface’ is searched in one unitary operation.
Like a refresh rate? That would fit the evidence for a 3-7 Hz refresh rate of our cartesian theater, or the way LLMs go through prompt/answer cycles. Do you see other potential uses for this concept?
We’ve got to understand how the memory is structured so that that is possible.
What’s wrong with « the distributed way »?
When I hear « conventional, sequential, computational regime », my understanding is « the way everyone was trying before parallel computation revolutionized computer vision ». What’s your definition so that using GPU feels sequential?
Thanks, I didn’t know this perspective on the history of our science. The stories I most heard were indeed more about HH model, Hebb rule, Kohonen map, RL, and then connexionism became deep learning..
If the object tends toward geometrical simplicity – she was using identification of visual objects as her domain – then a conventional, sequential, computational regime was most effective.
…but neural networks did refute that idea! I feel like I’m missing something here, especially since you then mention GPU. Was sequential a typo?
Our daily whims might be a bit inconsistent, but our larger goals aren’t.
It’s a key faith I used to share, but I’m now agnostic about that. To take a concrete exemple, everyone knows that blues and reds get more and more polarized. Grey type like old me would thought there must be a objective truth to extract with elements from both sides. Now I’m wondering if ethics should ends with: no truth can help deciding whether future humans should be able to live like bees or like dolphins or like the blues or like the reds, especially when living like the reds means eating the blues and living like the blues means eating the dolphins and saving the bees. But I’m very open to hear new heuristics to tackle this kind of question
And we can get those goals into AI—LLMs largely understand human ethics even at this point.
Very true, unless we nitpick definitions for « largely understand ».
And what we really want, at least in the near term, is an AGI that does what I mean and checks.
Very interesting link, thank you.
Fascinating paper! I wonder how much they would agree that holography means sparse tensors and convolution, or that the intuitive versus reflexive thinking basically amount to visuo-spatial versus phonological loop. Can’t wait to hear which other idea you’d like to import from this line of thought.
I have no idea whether or not Hassibis is himself dismissive of that work
Well that’s a problem, don’t you think?
but many are.
Yes, as a cognitive neuroscientist myself, you’re right that many within my generation tend to dismiss symbolic approaches. We were students during a winter that many of us thought caused by the over promising and under delivering of the symbolic approach, with Minsky as the main reason for the slow start of neural networks. I bet you have a different perspective. What’s your three best points for changing the view of my generation?
(Epistemic fstatus: first thoughts after first reading)
Most is very standard cognitive neuroscience, although with more emphasis on some things (the subdivision of synaptic buttons into silent/modifiable/stable, notion of complex and simple cells in the visual system) than other (the critical periods, brain rhythms, iso/allo cortices, brain symetry and circuits, etc). There’s one bit or two wrong, but that’s nitpicks or my mistake.
The idea of synapses as detecting frequency code is not exactly novel (it is the usual working hypothesis for some synapses in the cerebellum, although the exact code is not known I think), but the idea that it’s a general principle that works because the synapse recognize it’s own noise is either novel or not well known even within cognitive science (it might be a common idea among specialists of synaptic transmission, or original). I feel it promising, like how Hebb has the idea of it’s law.