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RECENT POSTS IN THIS TOPIC

Lorraine Ford: on 4/8/17 at 2:36am UTC, wrote Larissa, You attempt to model the generation of improved fitness. The...

Claudio Borsello: on 4/7/17 at 19:04pm UTC, wrote Dear Larissa, I've read with amusing interest your essay. It's a fair way...

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Larissa Albantakis : on 4/1/17 at 18:52pm UTC, wrote Dear Peter, Thank you for you time and the nice comment. A hierarchically...

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FQXi FORUM
May 24, 2017

CATEGORY: Wandering Towards a Goal Essay Contest (2016-2017) [back]
TOPIC: A Tale of Two Animats: What does it take to have goals? by Larissa Albantakis [refresh]
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Author Larissa Albantakis wrote on Mar. 7, 2017 @ 16:35 GMT
Essay Abstract

What does it take for a system, biological or not, to have goals? Here, this question is approached in the context of in silico artificial evolution. By examining the informational and causal properties of artificial organisms (“animats”) controlled by small, adaptive neural networks (Markov Brains), this essay discusses necessary requirements for intrinsic information, autonomy, and meaning. The focus lies on comparing two types of Markov Brains that evolved in the same simple environment: one with purely feedforward connections between its elements, the other with an integrated set of elements that causally constrain each other. While both types of brains ‘process’ information about their environment and are equally fit, only the integrated one forms a causally autonomous entity above a background of external influences. This suggests that to assess whether goals are meaningful for a system itself, it is important to understand what the system is, rather than what it does.

Author Bio

Larissa Albantakis is an Assistant Scientist at the Wisconsin Institute for Sleep and Consciousness, at the University of Wisconsin—Madison. She obtained her Diploma in physics from Ludwig-Maximilians University in Munich in 2007, and her PhD in Computational Neuroscience from Universitat Pompeu Fabra in Barcelona in 2011. She has been at the University of Wisconsin since 2012, working together with Giulio Tononi on Integrated Information Theory, and has recently been awarded a ‘Power of Information’ Independent Research Fellowship by the Templeton World Charity Foundation.

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Rene Ahn wrote on Mar. 8, 2017 @ 01:50 GMT
Hi Larissa,

Nice example, withfun discussion, I am not (yet?) a "Tononi believer" myself, but it does get more convincing perhaps where you explain that more complicated environments give rise to more "integrated" architectures. (when adding more types of blocks etc.)

If there is indeed such a trend (likely) then I wonder whether you investigated a possible connection here with information compression or even Kolgomorov complexity?

Kind Regards

Rene Ahn (2855)

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Author Larissa Albantakis replied on Mar. 8, 2017 @ 05:21 GMT
Dear Rene,

Thank you for your comment and pointing to compression / Kolmogorov complexity. On a practical level there is indeed a connection. In fact we use compression as a proxy for integrated information $\Phi$ in real neural recordings (see Casali AG, Gosseries O, et al. (2013) A theoretically based index of consciousness independent of sensory processing and behavior. Sci Transl Med 5:198ra105.). The idea is that a perturbation will have a complex (incompressible) response in a highly differentiated and integrated system, but will have only a local or homogenous (highly compressible) response in a modular, disconnected or homogenous system.

We also found a correlation between compression measures and $\Phi$ in a study on elementary cellular autonomata (Albantakis & Tononi, 2015).

With respect to the theoretical issues discussed here, intrinsic information and meaning, what is important is characterizing the entire cause-effect structure of the system rather than just its $\Phi$ value (which is just a number). As I argue in the essay, intrinsic information must be physical, and the actual mechanisms of the system matter. By contrast, algorithmic information is, by definition, a measure of extrinsic information: it explicitly disregards the actual mechanisms of the system (neural network) and seeks the shortest program with the same output. For intrinsic information and intrinsic meaning, the implementation matters. To recap the essay, the proposal is that meaning is not in what the system is doing, but in what it is, and algorithmic information only captures the "doing".

I'm looking forward to reading your interesting essay more thoroughly soon.

Best regards,

Larissa



Shaikh Raisuddin replied on Mar. 8, 2017 @ 07:30 GMT
Larissa

Thanks for your reply.

To move from high potentiality to low potentiality is inborn nature of matter and is the inborn goal of matter.

The goal in question is to differentiate between internal potentiality and external potentiality and to steer motion.

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Rene Ahn wrote on Mar. 8, 2017 @ 01:53 GMT
oops, I, mean of course Kolmogorov.

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Shaikh Raisuddin wrote on Mar. 8, 2017 @ 05:20 GMT
Larissa Albantakis,

Good essay!

The questions remain are 1) how the system acquire stability with togetherness? 2) what internal state create goal?, 3) what is the internal disciplining principle? and 4) how the system replicate?

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Author Larissa Albantakis replied on Mar. 8, 2017 @ 05:52 GMT
Dear Shaikh,

Thank you! And indeed, those are very important questions. As admitted in the essay, it is still a long way to understand what kind of cause-effect structure would correspond to goals. As part of the integrated information research project, before we get to goals, we are currently exploring what kind of cause-effect structure would be required to have intrinsic information about spatial relations.

With respect to 1), applying the IIT framework, we can assess whether a system is a stable integrated complex across its dynamics (and did so recently for the fission yeast cell cycle network, to appear soon, see ref 18 in the essay). In this way we can also gain insights about which mechanisms contribute to the stability, as opposed to the function of the system.

About 3), the animat experiments show that integrated structures have an advantage in complex environments even if the selection is purely based on fitness. As outlined in the essay, the main reasons are that integrated systems are more economical and more flexible (for more details see the refs given in the essay).

Finally, with respect to 4), in the artifical evolution scenario described, the animats are simply copied into the next generation with a fitness-dependent probability. In general, however, the notion of intrinsic information outlined here applies to artificial systems just as much as to biological systems. Accordingly, being self-replicators is not a necessary requirement for having goals. But of course it is crucial for the question how those system have developed in nature in the first place.

Best regards,

Larissa




Lorraine Ford wrote on Mar. 9, 2017 @ 00:08 GMT
Dear Larissa,

Why is the “fitness” 47% at the start, when there are no connections between elements, sensors and motors? Surely the fitness should be 0 if the Figure 1 model has no connections i.e. if there is no ability to catch food or avoid danger?

If the animats weren’t already fully fit enough to survive in the environment, then how did they survive to generation 2, let alone survive to generation 60,000?

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Author Larissa Albantakis replied on Mar. 9, 2017 @ 00:33 GMT
Dear Lorraine,

Thanks for your thorough reading. The initial 47% are a technical issue. If the animat is just sitting still (which it is without connections) it gets hit by ("catches") some blocks correctly and correctly avoids some blocks. 0% fitness would correspond to doing the task all wrong, i.e. catching all the large blocks and avoiding all the small blocks. One could rescale fitness to 0 for no connections and negative values if they do worse than by doing nothing at all. That wouldn't affect any of the results.

As for your second question, after each generation the animats are selected by the algorithm probabilistically dependent on their fitness. If they all do terribly, then each of them has the same probability of 'reproducing' into the next generation.

The population size is kept fixed at 100 animats. So it can be the case that some animats are copied several times, while others are not copied at all.

The genomes of the animats in the new population are then mutated with low probability, and some of the mutated animat offspring may now have a first connection that allows them to have a little bit higher fitness in generation 1 (or whenever such a mutation first happens).

These slightly fitter animats then have a higher probability of 'reproducing' into the next generation and so on. The way to see this is that it's not the animat itself that is put into the next generation, but its mutated offspring, which can be fitter than its parent.

I hope this made sense! Let me know if you still have questions.

Best,

Larissa




Alan M. Kadin wrote on Mar. 14, 2017 @ 02:40 GMT
Dear Dr. Albantakis,

I read your essay with great interest. Your studies of even very small model neural networks shows clearly that they evolve adaptive behavior which mimics that in biological organisms.

I also address the issue of adaptation in my own essay, “No Ghost in the Machine”. I argue that recognition of self, other agents, and a causal narrative are built into specific evolved brain structures, based on neural networks, which create a sense of consciousness as part of a dynamic model of the environment. The reason that this is such a difficult problem is that we are being misled by the subjective perceptions of our own minds.

Also, I noticed that you work at an Institute for Sleep and Consciousness. In my essay, I cited the work of Prof. Allan Hobson at Harvard, who emphasizes the importance of the dream state as an alternative conscious state that can provide essential insights. Do you have any thoughts about this?

Alan Kadin

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Author Larissa Albantakis replied on Mar. 15, 2017 @ 04:18 GMT
Dear Dr. Kadin,

Thank you for your interest! Indeed, sleep is a very interesting state for consciousness research as it is possible to compare conscious vs. unconscious levels in the same state using so-called non-response paradigms. Taking consciousness as phenomenology, dreaming clearly counts as being conscious. I also happened to notice that the scientific american article about sleep you cited in your essay in fact describes research performed at the Wisconsin Center for Sleep and Consciousness (Please see our website http://centerforsleepandconsciousness.med.wisc.edu/index.htm
l for more interesting experimental work being done in this field.)

It was a pleasure reading through your essay, and I hope you found the notion of causal control/autonomy advocated in my essay of interest. While the dynamical system as a whole (including the agent) may be dynamically determined, from the intrinsic perspective of the agent itself in its current state within that environment, there are causal constraints on its mechanisms from within the agent and from the environment. In this way, systems with the right kind of recurrent connections can causally constrain themselves above the background of influences from the environment.

The animats are so relevant to ideas and theories about "dynamic models of the environment" as they provide an excellent model system to test the proposed ideas. What kind of mechanistic structure would be necessary to have any kind of "model of the environment"? Do the simple animats have it, some of them, or not? And if not, then why not? What is missing?

Best regards,

Larissa




Simon DeDeo wrote on Mar. 15, 2017 @ 14:26 GMT
Dear Larissa,

It was fun to catch up on your animats work. You make an unusual move here—at least from the point of view of many biologists, who follow Dan Dennett and like to reveal goal-directed behavior to be nothing but selection. We take the "intentional stance" because it's so useful as a prediction tool.

By contrast, you want to locate goals through the causal powers that a...

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Author Larissa Albantakis replied on Mar. 16, 2017 @ 05:04 GMT
Dear Simon,

Good to hear from you. Your comment made my day, as you indeed captured the essence of my essay. The animats are such a great model system as they force one to consider the implementation of suggested potential solutions to intrinsic meaning, based on "information processing", "models about the environment", etc. Most of the time these ideas are presented abstractly, sound...

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James Arnold wrote on Mar. 16, 2017 @ 00:24 GMT
Hello Larissa

Your project sounds fascinating, and must have been enjoyable.

As you know, crucial element in the experiment is the designer's goal. Without the designer there is no seeking, and no experiment.

I'm not suggesting a religious significance to seeking, or intention, but rather, that there seems to be a presumption that seeking and avoiding, however rudimentary, can develop in a truly deterministic system. Goal-seeking behavior may seem unproblematic in a deterministic world just because it has emerged in ours, but try an experiment of any complexity without programming an appearance of goal-seeking and watch how many generations it takes for it to emerge on its own(!)

You write of "goal-directed behavior" that "by the principle of sufficient reason, something must cause this behavior." You might be interested in my essay about spontaneity being more fundamental than causation, that it may be causally influenced, but essentially free of causation.

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Anonymous replied on Mar. 17, 2017 @ 05:03 GMT
Dear James,

Thank you for your comment and taking the time to read my essay! Indeed, in these artificial evolution experiments, some kind of selection bias has to be assumed that leads to certain systems being preferred over others. In the absence of biased selection, causal structure may emerge, but will not be stable for more than a couple of generations.

I read your essay about spontaneity with much interest. A possible connection could be that in the described causal analysis we assume any element within the system that is not being constrained as maximum entropy and the cause-effect power of a mechanism is evaluated also in comparison of maximum entropy. Certainly though my analysis starts by assuming physical elements with at least two states that can causally constrain each other and leaves room for more fundamental concepts.

The point I want to make with the essay is actually quite similar to Searl's Chinese Room argument, but aims at a partial solution at least. The two animats perform the same task, but in the feedforward case there is no system that could possible have any understanding of the environment (or anything else), as there is not system from the intrinsic perspective in the first place. This animat would correspond to the lookup tables. The other animat does have a small but nevertheless integrated core that constrains itself and thus at least forms a minimal system that exists from the intrinsic perspective above a background of influences from the environment.

Best regards,

Larissa

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Author Larissa Albantakis replied on Mar. 17, 2017 @ 05:05 GMT
Sorry, somehow I wasn't locked in.

Larissa




Satyavarapu Naga Parameswara Gupta wrote on Mar. 16, 2017 @ 09:38 GMT
Dear Larissa Albantakis,

Nice essay on animats,

Your ideas and thinking are excellent for eg…

By examining the informational and causal properties of artificial organisms (“animats”) controlled by small, adaptive neural networks (Markov Brains), this essay discusses necessary requirements for intrinsic information, autonomy, and meaning.

Some of the animats even...

view entire post


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Member Tommaso Bolognesi wrote on Mar. 17, 2017 @ 14:47 GMT
Dear Larissa,

nice and dense essay! One of the aspects that intrigued me most and that, I believe, adds much originality to your work, is the attempt to tackle goal-oriented behaviour under the perspective of the ‘intrinsic’ features of the agent - beyond what appears to the external observer. However, I’m still trying to understand clearly the sense in which the use of internal cause-effect information, based on conditional state distributions and the IIT tools, should yield a ‘more internalised’ notion of goal-oriented behaviour for an open subsystem than, say, the plain detection of a local entropy decrease. In which sense is the former more internal? Does it refer to an issue of internal interconnection architecture, high Phi values, and ultimately growing consciousness?

One of the most attractive (at least to me) hard questions related to the 2017 Essay Contest is the difference between re-acting and acting: when and how does the ability to act spontaneously, as opposed to reacting (to, say, the arrival of pieces of different sizes) arise in artificial or natural systems? As far as I have seen, none of the essays has tackled this issue directly. What (new?) information-theoretic 'trick' is required for obtaining an animat that starts doing something autonomously and for no reason, i.e., not as a reaction to some external stimulus? In your opinion, is it conceivable to characterize (and synthesize) this skill just in the framework of IIT [… yielding an animat that stops catching pieces and says “Larissa, give me a break!” :-] ?

Another small question: in the simulation of [8] it seems that fitness increases visibly, while Phi doesn’t. In general, shouldn’t one expect them to roughly grow together?

Thank you!

Tommaso

http://fqxi.org/community/forum/topic/2824

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Author Larissa Albantakis replied on Mar. 17, 2017 @ 15:45 GMT
Dear Tommaso,

Thank you very much for your comment and insightful questions. By contrast to something like measures of local entropy decreases, the IIT formalism does not just yield a quantity (integrated information) but also a characterization of the system, its cause-effect structure, which is the set of all system mechanisms that constrain the past and future states of the system...

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Member Tommaso Bolognesi replied on Mar. 20, 2017 @ 16:20 GMT
Thank you.

Making a choice based on internal memory, as opposed to being triggered by external events, is certainly a step towards autonomy, but again you need some internal trigger that induces you to look up that good or bad experience in your memory, compare with the current situation, and decide how to (re)act. You mention that 'doing something for no reason' - perhaps the perfect form of agency - could be achieved with just a little noise inside the system. I also thought about this. You mention it cursorily, but I wonder whether this couldn't in fact be the key to implement agency. Quantum fluctuations have already been envisaged (e.g. by Lloyd) as the random generators at the basis of the computational universe edifice: maybe they play a role also in triggering reactions that appear otherwise as self-triggered, spontaneous actions.

Best regards

Tommaso

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Author Larissa Albantakis replied on Mar. 20, 2017 @ 16:49 GMT
Dear Tommaso,

Noise could play an important role for innovation, exploration, and creativity. Yet, if you take autonomy to be causal power of the system itself, noise would not count since it doesn't actually come from within the system but literally out of nowhere. The causal power of the system itself would go down with noise, just as it would decrease through external inputs that drive the system. But I think the divide is just that we have two different views on autonomy (paralleled by the different possible views on free will). One emphasizes the 'free' part: 'being able to act otherwise', making choices without reason. The other emphasizes the 'will' part: 'being determined by oneself as opposed to outside stimuli'. A willed decision would be one that strongly depends on you, your memories, and internal structure, and your best friend can easily predict your choice. This latter sense of autonomy is possible in a deterministic world.

Best regards,

Larissa




Peter Martin Punin wrote on Mar. 17, 2017 @ 18:09 GMT
Dear Larissa,

I carefully read your essay. Your approach and mine are radically different, but this precisely could be a sufficient reason to have a good discussion.

Your essay has a great merit. You honestly describe the constraints a given system has to master so that we can ascribe to the system in question. “ A system can only ‘process’ information to the extent that it...

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Author Larissa Albantakis replied on Mar. 26, 2017 @ 17:23 GMT
Dear Peter,

Thank you very much for your insightful comment. I now had the time to read your essay too and liked it a lot. I completely agree that there is a fundamental problem how selection can arise in the first place. I hope I made this clear in my essay at the very beginning. In my work, I program selection into the world. What I want to demonstrate is that even if there is a clear cut selection algorithm for a specific task, this doesn't necessarily lead to fit agents that have intrinsic goals. As you rightly point out it is a big question where such selection mechanisms arise from in nature.

Best regards,

Larissa




Don Limuti wrote on Mar. 18, 2017 @ 04:56 GMT
Hi Larissa,

I was pleasantly surprised reading your essay. Reminded me of "Vehicles" by Valentino Braitenberg only with the vehicles replaced by animats which are much more interesting goal directed creatures.

Many other scientists would be very tempted to say this completes the essay question by saying that the MUH (Mathematical Universe Hypothesis) is true. And I was completely surprised by: "While we cannot infer agency from observing apparent goal-directed behavior, by the principle of sufficient reason, something must cause this behavior (if we see an antelope running away, maybe there is a lion). On a grander scale, descriptions in terms of goals and intentions can hint at hidden gradients and selection processes in nature, and inspire new physical models."

I believe you agenda is something like: Let us pursue this concept of agency and see where it takes us. This is the essence of science.

Thanks for your excellent essay,

Don Limuti

Question: Is there a way to "play" with your animates online?

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Author Larissa Albantakis replied on Mar. 19, 2017 @ 16:42 GMT
Dear Don,

Thank you for your nice comment. The artificial evolution of the animats takes quite a bit of computational power, so there is no easy way yet to play around with them. However, there is a little video of one evolution and the behavior of one animat on http://integratedinformationtheory.org/animats.html

There is, however, an online interface to calculate the integrated information of little systems of logic gates: http://integratedinformationtheory.org/calculate.html

Best regards,

Larissa




Member Ian Durham wrote on Mar. 20, 2017 @ 01:56 GMT
Hi Larissa,

I wrote you a longer e-mail that I just sent, but in general I found your essay well-written and extremely stimulating. I’m still not entirely convinced that you’ve answered your own question concerning whether or not systems can have “goals.” You suggest that perfect fitness is a goal, but to me, a goal is an internal thing whereas it would seem to me that perfect fitness is largely a response to external stimuli (and by external, I include things like viruses and illness since I’m thinking of goals as related to consciousness here). But maybe I'm wrong. Who knows. Nice essay, though.

Ian

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Author Larissa Albantakis replied on Mar. 20, 2017 @ 18:06 GMT
Hi Ian,

Thanks for your comment. I'll be answering your email shortly. For the discussion here, I agree with you that having goals is necessarily intrinsic. That's why I put 'goal' in quotes any time that I referred to it as 'apparently having goals as subscribed to the agent by some outside observer'. The essay tries to make the point, that neither of the animats actually intrinsically has the goal of perfect fitness, although an outside observer would be tempted to describe their behavior as 'having the goal to catch and avoid blocks'.

I then give a necessary condition for having any kind of intrinsic information, that is being an integrated system that is to some extent causally autonomous from the environment. I moreover claim that the only way to find intrinsic goals is to look at the agents' intrinsic cause-effect structure and that correlations with the environment won't get us there. What kind of cause-effect structure would correspond to having a goal intrinsically I cannot answer (yet). But there is hope that it is possible since we know that humans have goals intrinsically.

Best,

Larissa




Stefan Keppeler wrote on Mar. 20, 2017 @ 17:31 GMT
Dear Larissa,

this is a nice summary of some of your own and related work. Now I want to learn more about integrated information theory. Thank you!

After reading many essays here I start seeing crosslinks everywhere...

When you wrote "Think of a Markov Brain as a finite cellular automaton with inputs and outputs. No mysteries." it immediately reminded me of Joe Brisendine's description of bacterial chemotaxis.

And later, when you wrote "one might ask whether, where, and how much information about the environment is represented in the animat’s Markov Brain" I had to think of Sofia Magnúsdóttir's essay who qualitatively analyzes the role of models which an agent must have about its environment.

I'd love to replace (in my essay) my clumsy conditions of being "sufficiently rigid and sufficiently flexible" by something less vague; maybe concepts from integrated information theory could help.

Cheers, Stefan

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Vladimir F. Tamari wrote on Mar. 21, 2017 @ 08:54 GMT
Dear Larissa,

I read your essay with interest but found the technical descriptions of the animats technically beyond my comprehension, although I am very interested in Cellular Automata CA which seem to resemble Markov Brains? Anyway you have certainly attempted a serious answer to the essay question.

My Beautiful Universe Model is a type of CA.

I was interested that you were a sleep researcher - I have recently been interested in how the brain generates and perceives dreams, and noted some interesting observations experienced on the threshold of waking up when I saw ephemeral geometrical patterns superposed on faint patterns in the environment. As if the brain was projecting templates to fit to the unknown visual input.

Another more severe experience along these lines was 'closed eye' hallucinations I experienced due to surgical anesthesia. which I documented here. The anaesthesia seems to have suspended the neural mechanism that seem to separate dreams from perceived reality and I could see both alternately while the experience lasted.

I wish you the best in your researches. It is probably probably beyond your interest but do have a look at my fqxi essay.

Cheers

Vladimir

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Author Larissa Albantakis replied on Mar. 24, 2017 @ 03:39 GMT
Dear Vladimir,

Thank you for your comment and that you took the time to read my essay. Indeed, Markov Brains are very related to cellular automata, the only difference is that each element can have a different update function and that the Markov Brain has inputs from an environment and outputs to an environment (but this could also be seen as a section of a cellular automata within a larger system).

I am very sympathetic to the idea that the universe is in some ways a giant CA. Partly because it would make the connection between my own work and fundamental physics very straightforward, and partly because of the underlying simplicity and beauty.

I am not really a sleep researcher myself. Yet, dreams are an important part of consciousness research. You might find the following work by my colleagues of interest: http://biorxiv.org/content/early/2014/12/30/012443.short

It shows that the responses to seeing a face while dreaming for example are very similar to those of actually seeing a face while awake. Being awake can in this view be seen as a "dream guided by reality". At least some hallucinations then are a mixture of the two states.

All the best,

Larissa



Vladimir F. Tamari replied on Mar. 30, 2017 @ 08:03 GMT
Thank you Larissa for your response and references. It is amazing how much information brain imaging has provided, and yes dreams and reality are inextricably linked by the neural mechanisms that perceive them- the details of how that actually works out is of interest. In the half-awake perceptions I have mentioned and with eyes wide open and the mind alert, I can actually see ephemeral geometrical shapes that the mind seems to throw at , say, a patch of light in the ceiling, as if it is trying to identify or classify it in some way.

I suspect that in normal vision incoming signals are constantly being studied in the same way as perception takes its course. This can be a while field of experimental study, using dark-adapted subjects shown very faint images and seeing if such visual inputs (or outputs?) are seen. Have you come across anything like this elsewhere?

Best wishes

Vladimr

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Gary D. Simpson wrote on Mar. 23, 2017 @ 02:42 GMT
Larissa,

We are Borg. Species a1-c3, you will be assimilated. We are Borg. Resistance is futile:-)

Many thanks for an essay that was both enjoyable and enlightening. I wonder if the animats figure out that they are size 2?

Are there any simulations where the animats of size 1 and size 3 also evolve using similar rules? BTW, what would an animat of size 1 eat? Are there any simulations where the animats can cooperate to attack larger animats? Maybe I run from a lion but me and my buddies will attack a lion if we've got some weapons ..... and have been drinking some courage:-)

You clearly present the meaning of useful information and the difference between information and being ... that is a key concept that many of the essays do not present.

Best Regards and Good Luck,

Gary Simpson

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Author Larissa Albantakis replied on Mar. 24, 2017 @ 01:34 GMT
Hi Gary,

Thank you for your time and the fun comment.

We are looking at social tasks where more than one animat are interacting in the same environment. There are interesting distinctions that need to be explored further. Something like swarming behavior may require very little integration as it can be implement by very simple rules that only depend on the current sensory input. Real interaction, by contrast, increases context dependency and thus on average lead to higher integration. All work in progress.

Best regards,

Larissa




Jochen Szangolies wrote on Mar. 23, 2017 @ 09:30 GMT
Dear Larissa,

thanks for a genuinely insightful essay. At several points, I was afraid you'd fall for the same gambit that's all too often pulled in this sort of discussion---namely, substituting meaning that an external observer sees in an agent's behaviour for meaning available to the agent itself. At each such juncture, you deftly avoided this trap, pointing out why such a strategy just...

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Author Larissa Albantakis replied on Mar. 26, 2017 @ 23:35 GMT
Dear Jochen,

Thank you for reading and the nice comment. I have finally had the time to look at your essay and indeed I think we very much start from the same premise that meaning must be intrinsic. First, to your question: Feedforward structures have no integrated information (by definition), because there is always elements that lack causes or effects within the system, no matter how the system boundaries are drawn.

I think the role that the replicators take in your essay is taken by a mechanism's cause-effect repertoire in IIT. By being a set of elements in a state, these elements constrain the past of the system and the future of the system, because they exclude all states that are not compatible with their own current state. The cause-effect repertoire is an intrinsic property of the mechanism within the system. It's what it is. However, by itself, a mechanism and it's cause-effect repertoire do not mean anything yet. It is the entire structure of all mechanisms as a whole that results in intrinsic meaning. For example, if there is a mechanisms that correlates with 'apples' in the environment, by itself it cannot mean apples. This is because the meaning of apples requires a meaning of 'fruit', 'not vegetable', 'food', 'colors' etc etc. Importantly, also things that are currently absent in the environment contribute to the meaning of the stuff that is present. The entire cause-effect structure is what the system 'is' for itself.

What is left to be demonstrated by IIT is that it is indeed possible to 'lock in' the meaning of a mechanisms through the other mechanisms in the cause-effect structure. There is work in progress to demonstrate how this might work for spatial representation.

Best regards,

Larissa



Jochen Szangolies replied on Mar. 27, 2017 @ 08:40 GMT
Dear Larissa,

thanks for your answer! I will definitely keep an eye on the further development of IIT. Is there some convenient review material on its latest version to get me up to speed?

Your mention of 'absences' as causally relevant evokes the ideas of Terrence Deacon, I wonder if you're familiar with them? He paints an interesting picture on the emergence of goal-directedness ('teleodynamics', as he calls it) from underlying thermodynamic and self-organizing ('morphodynamic') processes via constraints---thus, for instance, self-organization may constrain the thermodynamic tendency towards local entropy maximization, leading instead to stable structures. These constraints are then analyzed in terms of absences.

Cheers,

Jochen

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Robert Groess wrote on Mar. 28, 2017 @ 20:22 GMT
Dear Larissa Albantakis,

Thank you for your wonderfully readable and equally rigorous essay on the Tale of Two Animats. The depth of your analysis on artificial "intelligence" is impressive and I also appreciate the point you make regarding, "physics, as a set of mathematical laws governing dynamical evolution, does not distinguish between an agent and its environment." I have not seen that particular perspective before. Thank you for the fresh insight and entertaining read and I have also in the meantime rated your essay.

Regards,

Robert

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James Lee Hoover wrote on Mar. 28, 2017 @ 21:24 GMT
Larissa,

A clever presentation with perhaps a human-paralleled animat development. Do we assume the same primitive neurological processes (humans 1.5 million years ago ( use of fire, how does the reproduction fit in)in the animats' beginnings?

In my essay, I say this about AI systems: "artificially intelligent systems humans construct must perceive and respond to the world around them to be truly intelligent, but are only goal-oriented based on programmed goals patterned on human value systems." Not being involved in evolutionary neuroscience, I doubt the truly causally autonomous capabilities of animats, but perhaps the future. I know we should never judge future events based on current technology and understandings -- a type 0 civilization that we are.

Your causal analysis and metaphorical venture in AI evolution are forward thinking and impressive.

I too try to apply metaphor -- amniotic fluid of the universe to human birth and dynamics:I speculate about discovering dark matter in a dynamic galactic network of complex actions and interactions of normal matter with the various forces -- gravitational, EM, weak and strong interacting with orbits around SMBH. I propose that researchers wiggle free of labs and lab assumptions and static models.

Hope you get a chance to comment on mine.

Jim Hoover

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Peter Jackson wrote on Mar. 30, 2017 @ 10:35 GMT
Larissa,

Interesting experiment, findings and analysis, well presented. More a review than an essay perhaps but I do value real science over just opinion. The findings also agree with my own analysis so my cognitive architecture is bound to marry with it!

You covered a lot but your plain English style made it easy to follow. Bonus points for both!

My own essay agrees many...

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Author Larissa Albantakis replied on Apr. 1, 2017 @ 18:52 GMT
Dear Peter,

Thank you for you time and the nice comment. A hierarchically layered architecture is certainly the way to go for increasingly invariant concepts based on more lower level specific features. I.e. a grid of interconnected elements may be sufficient to intrinsically create a meaning of locations. Invariant concepts like a bar or a pattern will definitely require a hierarchy of levels.

As for the statement about information of the system about itself in its current state: This is simple logic and certainly has been voiced before, I think with respect to turing machines and cellular automata, Seth Lloyd also mentioned a similar idea but in terms of prediction (that a system can never predict its entire next state). Note that I meant the entire current state, not just part of it. Yes, the system can have memory of course. But it is important to realize that any memory that the system has, has to be physically instantiated in its current physical state. So all there is at any given moment is the current state of the system and any measure that compares multiple such states is necessarily not intrinsic.

Best regards,

Larissa



Peter Jackson replied on Apr. 4, 2017 @ 11:39 GMT
Larissa,

Yes, I understand. Well explained, thanks.

I see your score now slipped down again! Too many 'trolls' applying 1's (Mines had 11, but I refuse to respond). Normally scoring gets crazy in the last few hours!

I hope you get to read, score and comment on mine (not long now!) I think you could bring a good perspective to the hypotheses which I think are complementary to your analysis.

Very Best wishes

Peter

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Dizhechko Boris Semyonovich wrote on Apr. 2, 2017 @ 05:56 GMT
Dear Larissa

I appreciate your essay. You spent a lot of effort to write it. If you believed in the principle of identity of space and matter of Descartes, then your essay would be even better. There is not movable a geometric space, and is movable physical space. These are different concepts.

I inform all the participants that use the online translator, therefore, my essay is written badly. I participate in the contest to familiarize English-speaking scientists with New Cartesian Physic, the basis of which the principle of identity of space and matter. Combining space and matter into a single essence, the New Cartesian Physic is able to integrate modern physics into a single theory. Let FQXi will be the starting point of this Association.

Don't let the New Cartesian Physic disappear! Do not ask for himself, but for Descartes.

New Cartesian Physic has great potential in understanding the world. To show potential in this essay I risked give "The way of the materialist explanation of the paranormal and the supernatural" - Is the name of my essay.

Visit my essay and you will find something in it about New Cartesian Physic. After you give a post in my topic, I shall do the same in your theme

Sincerely,

Dizhechko Boris

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Torsten Asselmeyer-Maluga wrote on Apr. 5, 2017 @ 18:25 GMT
Dear Larissa,

very interesting essay. I wrote my PhD thesis about physical models of evolution including the evolution of networks. Evolution is goal-oriented. Here, there are two processes, mutation and selection. Mutation produces new information (=species) and selection is a global interaction among the species giving a goal to the process. In a more refined model of Co-evolution, the selection itself is formed by the interaction between the species, so again you will get a direction or goal. So, I think from this point of view, your model perfectly fits.

Maybe I have one question: you are an expert in networks and I wrote about the brain network and its dynamics (using methods from math and physics). Please could you have a look on my essay?

Thanks in advance and good luck in the contest (I gave you the highest rating)

All the best

Torsten

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Dizhechko Boris Semyonovich wrote on Apr. 7, 2017 @ 04:51 GMT
Dear Sirs!

Physics of Descartes, which existed prior to the physics of Newton returned as the New Cartesian Physic and promises to be a theory of everything. To tell you this good news I use «spam».

New Cartesian Physic based on the identity of space and matter. It showed that the formula of mass-energy equivalence comes from the pressure of the Universe, the flow of force which on the corpuscle is equal to the product of Planck's constant to the speed of light.

New Cartesian Physic has great potential for understanding the world. To show it, I ventured to give "materialistic explanations of the paranormal and supernatural" is the title of my essay.

Visit my essay, you will find there the New Cartesian Physic and make a short entry: "I believe that space is a matter" I will answer you in return. Can put me 1.

Sincerely,

Dizhechko Boris

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Claudio Baldi Borsello wrote on Apr. 7, 2017 @ 19:04 GMT
Dear Larissa,

I've read with amusing interest your essay. It's a fair way to tell valuable concepts.

I also love computer simulations of authoms, in order to understand complexity, that infact could be the result of few very simple rules acted by a multiplicity of individuals.

If you have time to have a look of my paper you could find it interesting.

Best regards,

Claudio

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Lorraine Ford wrote on Apr. 8, 2017 @ 02:36 GMT
Larissa,

You attempt to model the generation of improved fitness. The overall animat model system is given a highest-level ruling algorithm and given the equivalent of initial values. Each animat model has controlling “Markov brain” logic gate algorithms, and probably another higher-level algorithm controlling the “Markov brain” algorithm.

But it is an invalid assumption to consider that algorithms must already exist in primitive living things, so your model cannot be considered a model of actual reality.

It is unfortunate that you conclude so much from so little evidence.

Lorraine

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