Is DNA the Blueprint of Intelligence?

[apeiron]

I don't understand how anything can be said to be intelligent in the physicalist perspective. Isn't everything just unfolding according to the laws of physics?

This makes the huge (if standard) assumption that the "laws of physics" currently capture everything that needs to be known about complex systems (and even simple systems).

If you have a physicalist theory of meaning, as opposed to information, then what is it?

An example of attempts to frame such a theory would be CS Peirce's semiotics for example.

Rosen's modelling relations is allied with this project.

Anticipation as well.

What is intelligence? Why are savant's not really intelligent? Why are supercomputers measureably dumb? Information theory can't tell you. A theory of meaning is what you would need.

And a physically-grounded one would be a real achievement.

 

[imiyakawa]

This makes the huge (if standard) assumption that the "laws of physics" currently capture everything that needs to be known about complex systems (and even simple systems).

Every single measurement/prediction has complied to physical law thus far e.g. evolution of the wavefunction complies to the shrodinger equation, planets move in their orbits according to einsteinian geometry (although this is just an approximation, really). What observation hasn't conformed to physical law?

Moreover, we can see extremely simplistic systems as conforming to physical law through direct observation. What possible reason do we have for expecting that if we just double the complexity of the system (or keep adding increasing orders of complexity), some other "causal thing" has an influence on matter rather than just physical law. If we keep adding complexity, what logically sound reason do we have to expect that this system will suddenly become unpredictable in QM sense?

I see no reason to expect that this would occur, and thus how is asserting the inverse a huge assumption? Given what I've stated, I'd say what I said was more like a sound conclusion awaiting counter-evidence if its falsity is even to be considered.

 

[imiyakawa]

Double post, sorry.

 

[binbots]

It seems no matter how much we debate the intelligence there is no difference between us and the most simple creature. To say a cell is not as smart as us because it can't predict future events is an assumption that we know the future. That we are the only ones to learn from past events is to say evolution does not work on this same system.
Just like we humans strive to survive by using technoligy, to potect us and feed us. Cells created us for the same purpose. If we were to create AI we would just be the next step in evolution. When we create AI it will wonder who created it and why. Just the way we look at cells and DNA. We are ourseleves AI here to create Better AI for the purpose of survival.

 

[TheStatutoryApe]

It seems no matter how much we debate the intelligence there is no difference between us and the most simple creature. To say a cell is not as smart as us because it can't predict future events is an assumption that we know the future. That we are the only ones to learn from past events is to say evolution does not work on this same system.
Just like we humans strive to survive by using technoligy, to potect us and feed us. Cells created us for the same purpose. If we were to create AI we would just be the next step in evolution. When we create AI it will wonder who created it and why. Just the way we look at cells and DNA. We are ourseleves AI here to create Better AI for the purpose of survival.

Evolution, properly, is not "learning". If it gets cold and 50% of your single celled species dies out because they were unable to survive the weather change it is not because they did not learn something and the other 50% did not survive because they had learned something. One half was simply capable of surviving and the other not. The survivors could not have "taught" their less fortunate brethren to survive. If there could be said to be any sort of 'intelligence' there it would not be invested in any individual organism but rather spread out across the species. But we will come across the same problem comparing species to species as one dies out and another does not we can not necessarily say that one failed to "learn" while the other did not. It would seem to me that "intelligence" begins when information can be acquired, utilized, and transmitted by individual organisms. When an existing organism that is not capable of surviving can become capable and share that capacity with other existing organisms.

 

[apeiron]

Every single measurement/prediction has complied to physical law thus far e.g. evolution of the wavefunction complies to the shrodinger equation, planets move in their orbits according to einsteinian geometry (although this is just an approximation, really). What observation hasn't conformed to physical law?

We have a collection of partial models of reality and no complete theory of everything.

As you have said, QM lacks a story on the observers that give meaning to the wavefunction. Einstein offered higher accuracy than Newton, but is gravity a force, spacetime curvature, or what?

So you can't argue that there is a completeness about physical theory which makes it certain higher level emergent properties of complex systems can be broken down to "pure physics".

You ought to read Schroedinger's What Is Life? for a classic text making the case that "biology is larger than physics".

Theoretical biology is concerned with things like theories of meaning. I realize most other physicists seem to be on a mission to erradicate such things from scientific modelling.

Which is why we hear so much rubbish on issues like freewill, consciousness - and here, intelligence.

 

[apeiron]

Evolution, properly, is not "learning". If it gets cold and 50% of your single celled species dies out because they were unable to survive the weather change it is not because they did not learn something and the other 50% did not survive because they had learned something.

Wrong. Learning does involve the elimination of information. Babies have a proliferation of synapses and prune to fine-tune.

We learn skills like driving by learning what features of the environment to ignore.

Individuals may reproduce with variable success. But the species genome as a whole is the entity to which we would attribute adaptive or anticipatory change - learning or intelligence in every day language.

 

[apeiron]

...for the purpose of survival.

Again, the purpose is better thought of in terms of the second law. Life is dissipative structure whose purpose is accelerating the degradation of entropy gradients.

http://www.nesh.ca/jameskay/www.fes.uwaterloo.ca/u/jjkay/pubs/Life_as/lifeas.pdf

http://www.library.utoronto.ca/see/SEED/Vol2-3/2-3%20resolved/Salthe.htm

http://www.mso.anu.edu.au/~charley/papers/LineweaverEgan2008v2.pdf

 

[TheStatutoryApe]

Wrong. Learning does involve the elimination of information. Babies have a proliferation of synapses and prune to fine-tune.

We learn skills like driving by learning what features of the environment to ignore.

Individuals may reproduce with variable success. But the species genome as a whole is the entity to which we would attribute adaptive or anticipatory change - learning or intelligence in every day language.

As I noted, the 'intelligence' would have to be attributed to the species as a whole. An individual specimen of DNA or any single cell would not necessarily be considered 'intelligent' in and of itself. It would be like calling a punch card 'intelligent'. And I still think that there are issues with attributing 'intelligence' in this fashion. Pruning information in and of itself can not be considered 'intelligence' I do not think. Nor would I consider random mutation and accidentally novel combinations of genes to be the acquisition of information.

 

[JoeDawg]

But the species genome as a whole is the entity to which we would attribute adaptive or anticipatory change - learning or intelligence in every day language.

Genomes don't anticipate.

 

[noblegas]

I think that intelligence is the application and understanding of knowledge. knowledge is just knowing a bunch of facts but not necessarily how those facts have arise or why they exist. knowledge is trivial ; By trivial, I mean knowledge is what you would memorize for a trivia contest like jeopardy. an intelligent person would understand how the pythagorean theorem is derived, not just tha the pythorgeroean theorem is known by the equation a^2+b^2=c^2

 

[apeiron]

Genomes don't anticipate.

How do they know the kind of body you would need then?

Encoding for a pair of legs, for example, is a prediction about something.

 

[TheStatutoryApe]

How do they know the kind of body you would need then?

Encoding for a pair of legs, for example, is a prediction about something.

How is reading code anticipatory?

 

[apeiron]

How is reading code anticipatory?

The code is the anticipatory statement.

 

[JoeDawg]

The code is the anticipatory statement.

That's what you call an abuse of teleology.

The code exists through random mutation and natural selection, not design or purpose.
Genomes don't think, you're anthropomorphising.

 

[apeiron]

That's what you call an abuse of teleology.

The code exists through random mutation and natural selection, not design or purpose.
Genomes don't think, you're anthropomorphising.

No it is a generalisation of a psychological term towards a general physical meaning.

This is what is happening out there in a world which you may not be aware of.

From semiotics we move to biosemiotics and hopefully eventually to pansemiotics.

This is current work within theoretical biology.

You may object, but it would help if you read the references first.

 

[JoeDawg]

You may object, but it would help if you read the references first.

What you describe is an abuse of teleology, pure and simple.

As to your references, and whether they actually support what you claim, you haven't convinced me it would be worth my time.

 

[apeiron]

What you describe is an abuse of teleology, pure and simple.

As to your references, and whether they actually support what you claim, you haven't convinced me it would be worth my time.

Statements - even restated - don't make an argument. And why even start conversations when you have no follow through? A very weak response.

 

[JoeDawg]

A very weak response.

You get what you pay for.

 

[apeiron]

You get what you pay for.

I'm sure that would be wounding if it made sense.

 

[apeiron]

Further examples of actual papers that some cannot be bothered to read because their views are already decided...

http://bib.oxfordjournals.org/cgi/reprint/2/3/258

With the availability of quantitative data on the transcriptome and proteome level, there is an increasing interest in formal mathematical models of gene expression and regulation. International conferences, research institutes and research groups concerned with systems biology have appeared in recent years and systems theory, the study of organisation and behaviour per se, is indeed a natural conceptual framework for such a task. This is, however, not the first time that systems theory has been applied in modelling cellular processes. Notably in the 1960s systems theory and biology enjoyed considerable interest among eminent scientists, mathematicians and engineers. Why did these early attempts vanish from research agendas? Here we shall review the domain of systems theory, its application to biology and the lessons that can be learned from the work of Robert Rosen.

The work of Robert Rosen is important in that he not only identified
the weaknesses of our common approach to represent natural systems but he also
outlined a possible way to transcend the reactive paradigm in order to obtain
representations of anticipatory systems.

One of Rosen's achievements is that he introduced a formalism rich enough in entailment to allow final causation without implying teleology. The conceptual framework in which he developed his relational biology is category theory.

His conceptual framework arose from a criticism of the transfer of principles of Newtonian physics to biology. It is in this context that his work deserves renewed interest in the postgenome era of biology and bionformatics. One of the challenges for the emerging field of systems biology is then to link abstract mathematical models, like for example (M,R)-systems, to specific current problems of genomics. An important difference from the 1960s is the availability of three types of gene expression data at different levels: genome level (sequences), transcriptome level (microarrays) and proteome level (mass spectroscopy, gel techniques).

 

[apeiron]

http://www.zbi.ee/~kalevi/textorg.htm

Signs appear as a result of the categorisation process which takes place with the interaction of texts. This can be interpreted as a primary form of anticipation learning. The behaviour of the sequential organic molecules with a high combinatorial potential gives rise to several features which are isomorphic with those of semiotic systems, and text. Organism is a text to itself since it requires reading and re-presentation of its own structures for its existence, e.g., for growth and reparation; it also uses reading of its memory when functioning. This defines an organism as a self-reading text. Anticipation is a property which primarily appears in autocatalytic cycles. For textual autocatalytic systems, anticipation could be represented as a sign.

 

[JoeDawg]

http://www.zbi.ee/~kalevi/textorg.htm

Well of course there is a teleology to biological replication.
But evolution is more than just replication.

Genomes don't select for two legs, all they do is implement a selection. The 'selection' for two legs, happens because of a random mutation, and then enviromental pressure, within populations(not genomes).

Its only after the selection process, that you get a coherent implementation of genomic 'text'.

I don't think your refs are saying what you seem to think they are.
And if those other refs are, I think they are wrong.

 

[binbots]

Is it not possible to see oursleves as AI instead of intellegent. I mean if we create a robot with AI would he not ask and seek all the questions and answers we do. WHo create me? Where did I come from? etc. I see us as amazing biological machines designed by our cells for our cells. The machine became so advanced that it started to question its own existence and thought of itself as a single being. Even though we know we are made up of cells and even more bacteria. Just like evolution gave us legs and eyes, we with this AI survived and began to know things that our cells could not comprehend. We are not smarter just have better tools to observe. Just like if we create AI machines they will start to understand things bigger than us. Intellegence is not something that can be measured. It just is survival of the WHOLE SYSTEM of evolution.

 

[apeiron]

Well of course there is a teleology to biological replication.
But evolution is more than just replication.

You are mixing up development and evolution. Modern biology is learning how to make that distinction. That is the point of Rosen's MR systems for example.

If you are to generalise psychological notions like intelligence to DNA - which was the OP - then intelligence is displaying adaptive behaviour. Adaptation of mental states involves the two aspects of ideas and impressions, anticipations and learning. There is a cycle of activity, an interaction between expectations and what happens.

To apply this to DNA, we would have to shift scale from individuals to the species level of adaptation of course. So the genome is that generalised scale of intelligent and responsive knowing.

Anticipation then relates to the developmental aspects. Learning would be selection - across many individuals and many generations.

You perhaps still want to say the selection, the learning based on "random" mutations come first. But it is really the anticipations that provide the context for any learnings. The output precedes the input - which is what non-systems thinkers feel is so "wrong".

And I say "random" as randomness is always the product of constraint. Genetic variety is gaussian - constrained to a single scale. Unbounded variety is more likely to show a geometric mean. So again, a prediction is being made via anticipatory constraint.

An excellent paper on the patterns of nature is...
http://stevefrank.org/reprints-pdf/09JEBmaxent.pdf

 

[apeiron]

Is it not possible to see oursleves as AI instead of intellegent. I mean if we create a robot with AI would he not ask and seek all the questions and answers we do.

The desire to see all reality in terms of machines continues to be science's biggest philosophical error.

Machine models can be useful - for making machines for example. But the problem displayed over and over in these forums is the inability to think about nature in any other terms.

 

[JoeDawg]

You perhaps still want to say the selection, the learning based on "random" mutations come first.

Yup. Because evolution may have feedback loops, but it progresses linearly.

 

[apeiron]

Yup. Because evolution may have feedback loops, but it progresses linearly.

Which invalidates the idea that anticipation precedes learning how?

 

[JoeDawg]

Which invalidates the idea that anticipation precedes learning how?

Sigh.

 

[apeiron]

Sigh.

Sigh away. Beats having to support your quick assertions with arguments and citations. At least you are aware now there is a rather large literature on these issues.