Whenever we have guests or new people in the lab, we make a round of introductions and everyone describes their research in three sentences or less. When it comes to my turn, we have a running gag. Sometimes I just say, “All of the above,” or just “Evolution in general.” Other times I say, “Evolution of intelligence, animal behavior, and game theory,” or if the crowd is more open minded, I might add “consciousness” or “graph theory.”
While “All of the above” may cover the areas I publish in, it does not really describe what I am actually working on, or what I think I am really good at. This becomes readily apparent when reading through job advertisements, in particular reading job posts about German faculty positions.
The usual German job post reads something like this:
“Professor for fluid dynamics in viscous low temperature fluids”
“Mathematical Theory of Economics in curved space systems”
“Marine Biosystem, conservation and exploration of Baltic micro island shores”
“Evolution of the Gtrc gene family in mammalian cell lines”
“Mobile communication platforms and crowd sourcing in underrepresented minorities”
Imagine you run into someone at a conference, and the person introduces themselves with the line, “I work in limbic slow stream micro-biomes and their interplay with slug gut parasites.” You would smile, say something about how interesting that sounds, hope that you don’t sound overly sarcastic, and start talking about your own work.
When did we stop working on actual questions? When did we start hiding in these narrowly defined micro niches? Even worse, the search committees seem to look for people that fit in these micro niches. Someone like me, who has essentially two backgrounds, and works as a multidisciplinary (not just interdisciplinary) researcher at the intersection of biology, computer science, and cognitive science, already has problems fitting in. I am not going to work on something overly specific just so I can avoid competition, or to bluff everyone into thinking that just because my research field is narrow it must be important.
I think I have an idea. I hereby announce my new job description: The Big Question! Just like the buzz word Big Data, which seems to beat everyone into submission, I work on the Big Question: How Modularity, Representation, Epistasis, Fitness Landscapes, and Game Theory all come together in the Evolution of Behavior, Intelligence, and Consciousness.
Cheers Arend – The Big Question Researcher
but fluid dynamics at “low temperatures” strikes at the biggest questions we know how to ask. In particular, what aspects of nature are continuous and which are discrete? In fluid dynamics the continuous can be easily modeled, laminar flow and linear functions. At some velocity, and with some irregularities in the flow, turbulence develops. In turbulent flow the CS people throw up their hands and say there isn’t enough computing power: initial conditions and molecular-scale geometries wreak havoc on attempts at prediction.
luckily nature is unfathomably complex, though the complexities often emerge from simple principles. anyway, the ‘german faculty positions’ gave me a chuckle. May your research be fruitful and your heart guided by the beauty of understanding.
I think we both have the same job description! The Big Question: consciousness, and intelligence for humans, robots, AI, and other conscious dynamic systems. All of this seems obvious to me, but so far no academic agrees with my views.
Which are: the brain algorithm is muscular – culturally trained unconscious muscle activation patterns, when activated become conscious when we perceive the results of the muscle activations. We then consciously adjust the unconscious patterns, until the task is accomplished. The adjusted patterns are retained in unconscious muscle memory. Since perception is active, images are intertwined with the muscle patterns.
Of course the hurdle for academics is that the vocal muscle patterns are how we access the culture, where all knowledge resides. Thus verbal logic is cultural peer pressure (superstition), until empirically validated (science). Language is not based on logic, and words do not represent any entity – material or abstract. They are muscular actions in the culture. Consciousness is the perceptual leg of the algorithm.
The brain has no representations, and does no computations, and contains no knowledge. Brain function is dynamic; the algorithm cycles at probably 10-100 Hz, so that the movie in the mind appears continuous. Thus, input and output are not separate, but each is part of the other. Brain function is not a sandwich with a computational middle layer; perception and action are dynamically connected, in real time.
This solves, I believe, all problems in neuroscience understanding of brain function. Meaning is grounded in the culture, which we access by muscular actions. Mirror neurons are eliminated, since the speaker’s words adjust the unconscious patterns of both speaker and listener.
And it brings humans back down to earth, on the same evolutionary path as all the other mammals.
I would be greatly interested in your comments!
Donald Wilhelm, III
Great description. Not sure if I understand your point well enough, but it certainly is an interesting perspective and makes me smile just reading your words. I recently just graduate from college and do not dare to proclaim anything in front of full-time researchers. But I understood your words and think it’s a fabulous way to phrase the idea about intelligence, or say, universal/general intelligence.
My own little research involves evolution of lambda calculus functions. Lambda Calculus is a pure symbolic language where each word/element/sentence in it, is guaranteed to carry no semantic meaning, except it’s symbolic/syntactic “shape”. Lambda calculus is actually even “lower-level” than muscular activities in your story.
I believe, It is, actually, the lowest level we can achieve in an axiomatic system. Lambda calculus is equivalent to Turing Machine, so technically, I can rewrite anything computable in this universe in a sentence in lambda calculus. For those things not describable in lambda calculus, I deem them in the realm of gods. No intelligence should or attempt to perceive it.
There are only three kinds of symbols in lambda calculus. And each carry no meaning. The dynamics in lambda calculus is achieved by only one rule, the rule of substitution, which I consider, the most important necessary rule for any kind of intelligence to form. It is so primitive, so pure, and so close to god. Yeah we can encode/rewrite everything into lambda calculus sentences. Alonzo Church (inventor of Lambda Calculus) has an encoding scheme to convert the problem domain of natural numbers into lambda sentences. I used it to evolved problems solvers for simple regression problems.
But why would you do that? After all, I am just a human perceiving meaning from bunch of meaningless symbols. Did I find the source of wisdom? Surely I can gain the greatest intelligence by exploiting this source with extreme computational power, and solve the hardest problems! The source though, infinite as it is, would never show me any sign of tiredness, and universal as it is, would never show me any sign of loneliness or sympathy. My intelligence comes from this source, so does the intelligence of any rock.
The low level always constitutes the high level. The lower the level, the harder for the high level to interfere. The lowest level is divine, by the rule of the universe, and could never be tampered with.
There might be many gods, but this universe only has one form of intelligence.
When you’re working on such fundamental ideas, why would you want to limit yourself at all…
Not to diminish your larger point, which is excellent by the way, you might also occasionally use the loaded word/field “Complexity” which reasonably descriptive. Or maybe Evolutionary-ITBiophysicist?