I believe you are referring to recognizing smile or frown on facial expression? But that seems to move away from my original point on recognizing objects, which seems to be hypothesized being handled by the ventral system ?
I don’t want to open this can of worm, I am now wondering what is considered as emotion then since similar to intellgence I can’t really seem to find a commonly agreeable definition. Say if a C. elegans is in a toxic environment and the body is under tremendous “stress”, is that considered as emotion? Is surprise a emotion or just the brain trying to align the prediction with reality? If an organism can express just “stress” and “surprise”, do they consider having emotion? And the most important question, if an organism does not express emotion, does that mean it cannot have intelligence?
I think you stated it clearly already here, I believe you are describing visual attention, not recognition. You have to be able to recognize certain part of your visual is an object before you can pay attention to it. But I would like to stay focus on the recognition part.
In a sense I am still on the same side as Francois Chollet on the relationship between emotion and intelligence. But at the same time I suppose I need a good understanding on what emotion really is first, is it just different chemicals released by the body that will influence the brain’s operation just like the basic architecture, or it is something else?
I can 't help thinking that doesn’t seem to align with the engineering approach. I agree with starting out small. But how are you to go build something when you don’t know what you are building? What tricks are you showing when you don’t know what that trick is?
You were asking for a laundry list for a sim of intelligence or intelligent object. It is right that you have to know what you are trying to build but in Intelligence’s case it’s a different story. No one knows what intelligence really is but we can feel/identify it. Would a laundry list when implemented result to intelligence? Or can intelligence be reduced to properties such as listed above? I don’t think so, it will just complicate things. So IMO to make it simple for a simulation, identify the problem, provide a solution (e.g. algorithm) and a good test of intelligence is to ask beings who can identify intelligence by evaluating the sim’s/solution’s behavior - this is easier than a laundry list.
The problem with the five criteria in the first post is they-re so generic that a raspberry pi with attached camera and motion detection&recording software, arguably fits all intelligence criteria:
Perceiving and memorizing information that’s quite obvious that it does.
De-memorizing it’s just erasing old recordings in order to keep free space available for new.
Predicting is less obvious but the basic motion vector algorithm in the GPU does exactly that - it does not record frames entirely, the video compression algorithm stores predictions on how does current frame reshapes in the following frame. That checks exactly next prediction alignment with the next perception thing.
The last one - deriving information is too vaguely expressed. A simple xor of two files, or any computing process in that respect, derives new information from previous one. “Mash up” doesn’t mean much we tend to use it whenever we don’t really understand what actually happens. “Chemicals got mashed up and first living cell appeared. Mashing up must be an essential ingredient of life”.
I think maybe this continues to be an issue on the discussion with different objectives. I think I have stated mine quite clearly that my intention is not going to build a usable AI where I am only interested to build a simulation of biological intelligence (and not human either). I know many of the members in this forum are very much only interested in human intelligence because it is more interesting. But in my opinion, understanding human intelligence is a very very complex topic. I am not religious but when I look at all the species around and the commonality of the neuron structure I can’t help thinking there were designers experimenting and improving the design from single cell to c elegans to octopus and finally us. If that is true, I am sure they would fail miserably if they just focused on building human intelligence right from the start. I kinda like one of the statements from this article (although commercial), the reductionist approach:
“Ambitious scientific projects, such as the human connectome project and the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative, are looking to answer these very complex questions through direct imaging of human brains. Meanwhile, other researchers are taking a reductionist approach to unlocking the secrets of the brain. A reductionist approach is one that seeks answers to highly complex questions, such as how does the human brain work, in the simplest model available, such as C. elegans. The C. elegans nervous system is extremely simple when compared to the human brain. A C. elegans hermaphrodite has just 302 neurons while the human brain is estimated to be made up of 100 billion neurons”
When I look at the progress of the european brain project after so much money and time invested and still not much of a breakthrough, I do believe going from a reductionist approach might be more practical. And there is a very big issue on using computer hardware to simulate biological intelligence with quadrillion synaptic connections. You mentioned before you are planning to use a floor of computers and I am not sure if you are planning to do it yourself or not but I can’t help feeling pessimistic about that approach. I am lucky in a sense to have a small team that has already done a pumping engine for a high volume real-time analytics service with single digit microsecond latency, back off idle strategy, and back pressure that I can use to bridge the differences between computer hardware and biological architecture (I am hoping this will be the only part that does not adhere to the biological design). But even that I am not sure if it can even evolve to simulate octopus’ intelligence.
I do like your definition. In a sense I could relate my list with yours to a certain extent. I am not sure about “useful ways” and “best action” because they sound a bit on the subjective side. And I am going to invite you to be open mind if see if you can break down your definition further and deeper with first principal, particularly around “useful ways” and “best action”. And what I learn so far is, don’t worry if others think it is too simplistic, being complicated is never a good thing. And if you are going to, please share your update. Would love to learn more from that. And I truly think it is important to continuously work and refine the definition of intelligence as the basis of the work in every step of the way.
“One of the benefits of having an explicit, formal definition of intelligence, is to identify what general principles underpin it. A precise definition and measure serve a North Star for research”
And thanks to Mark, I highly recommend reading Francois’ paper.
I respect others’ objectives (genuinely), but mine is really about building a simulation on the biological intelligence, really about how a network of biological neurons can form to show sign of intelligence (not human level).
“One of the benefits of having an explicit, formal definition of intelligence, is to identify what general principles underpin it. A precise definition and measure serve a North Star for research”.
For simulating biological intelligence, it is important to take “What” and “How” together. I have stated my objective very early on was to build a software simulation on biological simulation. The “What” is the list of fundamental elements of the definition. The “How” is to build a software version of neuron, with dendrites and axon, with the simulation of the chemical to electric signalling, along with the mechanism of growing and pruning of the dendrites within the network of neurons to be able to satisfy the “What”, not the human level type of “What”. but the lowest possible level of “What”, is what I am going after.
Because of this context in mind, I hope you can see no sense of bringing raspberry pi and camera into it.
I am delighted to follow up on this. Much of AI work revolves around reward functions and feedback. I do think that this is important and I can point directly to the functions that nature has used to define “useful ways” and “best action.” It may not be the answer you are looking for but I will stand by it. Given that there are massive ecological niches to fill, evolution ALWAYS picks the solution that creates critters that has DNA that survives to reproduction. The “useful ways” and “best actions” are the ones that support this reward function. EVERY ONE of your ancestors, without fail, in the most difficult situations, have always made the correct choice that insured that they were able to survive long enough to reproduce. If this took the right neural substrates, and a culture that insured that they were trained with the the right skills, evolution supported making this happen.
Group selection is a debatable topic but I see it fitting with this definition.
Let’s say someone created a machine with human-level intelligence. Do you think your Laundry List will still be the same? Do you think “formal definition” of intelligence will hold? Global non-consensual definition of a thing means we fundamentally as human beings do not know that thing, but of course we can pretend we know it because we can make judgment to it.
In any case all my opinion, but I can assure you a laundry list would not be very useful.
I think it was Jeff who said we need a brain theory to build machine intelligence? Neuroscientists don’t have it, biologists don’t have it, psychologists don’t have it. We don’t have a brain theory, klaar!
As I have understood this in detail Numenta’s way of building the brain theory isn’t simply a process of listing data or facts such as listing the properties of “intelligence”. It appears to be much more realistic to me that their “laundry list” is a product of experimentation, simulation, trial-and-errors, and knowledge acquired in brain science (as constraints). The process is not like having a list beforehand similar to software development. It is more of an evolution of ideas and experimentation. For example the current grid cells imply location-based encoding in the brain, from what I’ve known this was not predetermined during the HTm days.
Thanks for the advice. At the same time, I don’t think anyone can argue the impact from Hubel and Wiesel’s own reductionism trap. Mistake is a new information for the next success.
I am not sure if I would disagree with what you said.
In Jeff’s “On Intelligence” book, he stated very early on on page 6 in his book “What is intelligence” and “It is the ability to make predictions about the future that is the crux of intelligence”. So very clearly it was quite important to define what intelligence is, or largely is, before anything else. And I have tried very hard to further break down the “make predictions” into my list of fundamental elements for the definition of intelligence.
When JH wrote the book the idea that the brain modeled virtually everything and that the core of that modeling was sequence/prediction was not a mainstream idea.
He pounded on that unique aspect as the central focus of the book, to the exclusion of all the related concepts that make prediction into a functioning system.
I don’t fault this intense focus - a book has to be about something and broadening the concepts to include all the ancillary systems would have diluted and perhaps buried this important concept.
Now that this message has been delivered it’s time to turn attention to some of the other important bits. I have tried to share the main features of these systems in this forum; this is my attempt to put down the results of decades of reading and reflections on consciousness and intelligence.
I personally see the features you are looking for as emergent properties of these very complex systems. I don’t think it is possible to model these in a way that would be more than a toy without duplicating much of these systems.
How much of a computer do you have to simulate to capture the “exel-ness” of the computer?
Searching for the “intelligent part” as an isolate feels to me like taking the drum apart to see what makes the noise. Good luck with that.
I do think that is the essence of my point. Biological intelligence, particularly human intelligence, is such a complicated subject. Without intense focus on the fundamentals, it would be very difficult to build simulation to understand it further.
I think I have what I need for my Q1 and can prepare for my Q2. Appreciate everyone’s help on this and will continue to need such generous help.
