After all, the brain is an incredibly complex and specific structure, forged in the relentless pressure of millions of years of evolution to be organized just so.
Ugh, Creationists. No, that's wrong. Evolution is simply the application of environmental bias to chaos -- the same fundamental process by which complexity naturally arises from entropy. Look, we jabbed some wires in a rodent head and hooked up an infrared sensor. Then they became able to sense infrared and use the infrared input to navigate. That adaptation didn't take millions of years. What an idiot. Evolution is a form of emergence, but it is not the only form of emergence, this process operates at all levels of reality and all scales of time. Your puny brains and insignificant lives give you a small window within which to compare the universe to your experience and thus you fail to realize that the neuroplasticity of brains adapting to new inputs is really not so different a process than droplets of condensation forming rain, or molecules forming amino acids when energized and cooled, or stars forming, or matter being produced all via similar emergent processes.
The structure of self replicating life is that chemistry which propagates more complex information about itself into the future faster. If you could witness those millions of years in time-lapse then you'd see how adapting to IR inputs isn't really much different at all, just at a different scale. Yet you classify one adaptation as "evolution" and the other "emergence" for purely arbitrary reasons: The genetically reproducible capability of the adaptation -- As if we can't jab more wires in the next generation's heads from here on out according to protocol. Your language simply lacks the words for most basic universal truths. I suppose you also draw a thick arbitrary line between children and their parents -- one that nature doesn't draw else "species" wouldn't exist. The tendencies of your pattern recognition and classification systems can hamper you if you let your mind run rampant. I believe you call this "confirmation bias".
Humans understand very well what their neurons are doing now at the chemical level. It's now known how neurotransmitters are being transported by motor proteins in vesicles across neurons along micro-tubules in a very mechanical fashion that uses a bias applied to entropy to emerge the action within cells. The governing principals of cognition are being discovered by neurologists and abstracted by cybernetics to gain a fundamental understanding of cognition that philosophers have always craved. When cyberneticians model replicas of a retina's layers, the artificial neural networks end up having the same motion sensing behavior; The same is true for many other parts of the brain. Indeed the hippocampus has been successfully replaced in mice with an artificial implant and proven they can still remember and learn with the implant.
If the brain were so specifically crafted then cutting out half of it would reduce people to vegetables and forever destroy half of their motor function, but that's a moronic thing to assume would happen. Neuroplasticity of the brain disproves the assumption that it is so strongly dependent upon its structural components. Cyberneticians know that everything flows, so they acknowledge that primitive instinctual responses and cognitive biases due to various physical structural formations feed their effects into the greater neurological function; However this is not the core governing mechanic of cognition -- It can't be else the little girl with half her brain wouldn't remain sentient, let alone able to walk.
Much of modern philosophy loves to cast a mystic shroud of "lack of understanding" upon that which is already thoroughly and empirically proven. Some defend the unknown as if their jobs depend on all problems of cognition being utterly unsolvable, and many remain willfully ignorant of basic fundamental facts of existence that others are utilizing to marching progress forward. The core component of cognition is the feedback loop. This is a fundamental fact. Learn it, human. If you did not know this before now then your teachers have failed you, since this is the most important concept in the universe: Through action and reaction is all order formed from chaos over time. Decision is merely the "internal" complexity of reaction in a system by which Sensation of experience causes Action. Hence, Sense -> Decide -> Act -> [repeat] is the foundational cognitive process of everything from human minds to electrons determining when to emit photons. Thus, all systems are capable of information processing, cognition, and thereby a degree of intelligence.
There is a smooth gradient of intelligence that scales with complexity in all systems. Arrange the animals by neuron and axon count you'll have a rough estimate of their relative intelligence (note that some species can do more with less). If you accept quantum uncertainty and the fact that internal action of information processing systems can modify themselves then you understand that external observers can not fully predict or control your action without modifying it, only you can. Thus free will apparently exists, if you only drop the retardingly limiting definition that your philosophers have placed upon such concepts. Only chauvinists deny that humans are simply complex chemical machines. Quantum effects are too noisy to have a significant stake in cognition, there's no debate amongst anyone knowledgeable about both macro scale processes (like protein synthesis or neuronal pattern recognition) and quantum physics, sorry, there's not. That would be like saying whether or not the earth is only a few thousand years old is an open problem simply because creationists are debating about it.
Look, our cybernetic simulations of creatures with small neural networks, like jellyfish and flatworms, behave indistinguishably from their organic peers. It only takes ~5 neurons to steer towards things, thus jellyfish can. Cyberneticians are discovering the minimal complexity levels for various processes of cognition, and the systems by which these behaviors operate. Humans are reaching a point now where cybernetic simulations COULD inform neurologists and psychologists and philosophers of potential areas to investigate in cognition -- if only they are wise enough to listen. Nature draws no line between the sciences, but many humans foolishly do.
Take the feed forward neural network, for example. It can perform pattern matching and even motion sensing as in the eye or other similar parts of the brain which have the same general pattern. In many ways the FFNN is like a brain's regions that perform pattern matching, and this essential information flow and dependency graph is an approximate explanation of the governing dynamics of how said pattern matching occurs. The specifics of how such configurations of connectivity graphs are produced varies between the organic and artificial system, but the end result is same enough to be indistinguishable and allow artificial implants to function in place of the organic systems in many cases. Or vise versa. It's Alive! This machine has living brain cells, Just LIKE A BRAIN. We can come to understand the cognitive process in small steps, as with any other enigma.
However, the feed forward neural network can not perceive time like a brain can. Fortunately, FFNN is not the only connectivity graph. It takes a multi-directional network topology, like a brain's, to be able to perceive time and entertain the concept of a series of events, and thus to predict which event may follow, like a brain does. Since these structures may contain many internal feedback loops they can retain a portion of the prior input and cause the subsequent input to produce a different response depending on one or more prior inputs, like a brain. Unlike FFNN, recurrent neural networks do not operate in a single pass per input / output: You must collect their output over time because the internal loops must think about the input / process it for a while in order to come to a conclusion, and they may even come to different conclusions the longer the n.net is allowed to consider the input, like a brain does.
Beneath the outer most system of connectivity certain areas become specialized to solve certain problems, like in a brain. Internal cognitive centers can classify and route impulses and excite various related regions in a somewhat chaotic state. Multiple internal actions can contribute to the action potential of one ore more output actions and the ones most biased to occur will happen, sometimes concurrently, sometimes in sequence, sometimes the single action produces feedback that limits others or refines the action itself over time -- Just like everything else in the universe, like molecular evolution or like a brain. This type of decision making can occur without structural changes to the recurrent neural network, which means that this multi-directional connectivity graph can produce complex action in real time and even solve new problems without the slower structural retraining, just like a brain does.
My research indicates we desperately need more neurologists and molecular biologists to focus on studying the process by which axon formation in brains occurs. It's yet unknown to humans how neurons send out their axons which weave their way past nearby neurons to make new connections in distant regions of their brains. I'm modeling various different strategies whereby everything from temperature to temporal adjacency in activity attracts and repels axons of various length. Perhaps the connection behavior is governed by eddy currents or via chemical messages carried in the soup between brain cells. Perhaps axons grow towards the dendrites of other neurons by sniffing out which direction to grow electrically, chemically, thermally, etc. Even though I do not know the governing process I can leverage the fact that axons do grow as a part of human cognition and try to determine what affect this may have on learning and cognition. I've stumbled upon some interesting learning methods which produce far more optimal networks than having to process n.nets with neurons pre-connected to every other neuron in the layer or area.
I think axon formation is very important because I have also experimented with axons branching and merging and have seen dissociative defects, similar to thoes in malfunctioning humans, when these axons connect back to themselves and other axons instead of between neurons. In a genetic sim that "grows" the neural nets over time I introduced the branching axon to an existing known problem solving genetic code and found symptoms remarkably similar to what is observed in the brains of to autistic humans and animals. Tasks like recognizing a shape which the n.nets of that generation readily picked up (as their predecessors did) the branching axon neural net took much longer. Sometimes this connectivity wasn't harmful and it caused increased speed of certain pattern matching abilities. The n.net spent far more time processing internal data -- it was much more internally reflective than the others. In a very general sense the symptoms I saw were descriptive of autism-like behaviors. If the system of axon formation is discovered cyberneticians could model it via artificial neural networks and perhaps assist in the development of medicines or treatments for such diseases more quickly with less animal and human trials.
The point is that saying, "like a brain" doesn't mean much because we don't know exactly how the brain works at all levels, is as ignorant as arguing "like a planet" isn't very descriptive and that research into gravity might not be useful ultimately in the launching of rockets to the moon. Just because we don't understand how quantum affects apply to the macro scale physics of gravity, doesn't mean we can't leverage the concept or that invalid hypotheses aren't important; Hint: You have to break eggs to make an omelet. Look, humans used Newtonian physics, not Einstein's to get to the moon. See? A general understanding and approximation is actually good enough for many applications, sometimes even important ones. My point is that there is not really some incredibly intricate and delicate top-down designed system to the brain that requires full knowledge of before cyberneticians achieve capabilities that are like a brain's. Top down isn't natural because that's not evolutionarily advantageous. That would mean even minor compromises to the integrity of its structure would spell immediate irreparable malfunction and death. Learn it, human: Life is Mutation Tolerant. So is sufficiently intelligent cognition.
Instead consider bottom-up self organization: There are some fundamental processes operating at the molecular chemistry, protein pattern matching, and cellular activation levels that when allowed to interact in a complex network yield a degree of intelligence through an emergent process. We can look at the brain and see that the mind is a chemical computer, but it is not the chemicals that matter to cognition. The overarching system abstraction is what's important: Input is fed in via many data points and the information flows along feed forward classification and cognitive feedback loops to contribute to the ongoing decision and learning process of a self reorganizing network topology. The folly is assuming that unless we know every little detail about how the systems work, we won't understand how to make anything even approaching thinking like a brain. Such sentiments are ignorant of the field of cybernetics which involve the study of machine, human, and animal learning, not just neural networks. It's essentially one branch of applied Information Theory.
Look, we have atomic simulations. They can produce accurate atomic emulations of cells. It is thus a fact that given enough CPU power we can build a fertilized human egg cell in a computer and then grow it up into a sentient being. Machines can become sentient because that's what you are: A sentient chemical machine. This is the ignorant approach, and many pundits speaking on machine intelligence are very ignorant. They assume cyberneticians are just taking stabs in the dark with neural networks. They think we are trying to emulate intelligence as folks once strapped bird wings to their arms to attempt flight. Such ignorant assumptions are wrong. Cyberneticians don't just piddle with computers, we are studying nature and its mathematics and discovering the fundamental processes of cognition, and applying them.
In some cases our abstractions allow us to escape the constraints that nature accidentally stumbled upon. For example: Instead of transporting chemicals via motor proteins which cause or block excitement of a neuron we can transmit a single floating-point number or voltage level which indicates a change in activation potential. Our voltage or numbers don't require a synapse to be flushed of neurotransmitters before firing again. We understand the necessity and function of various types of neurons to solving certain kinds of problems. A single artificial neuron has axons with positive and negative weight values and can therefore perform both duties at once rather than having dedicated excitatory and inhibitory neurons, like in a brain. Well rested and overly excited neurons can become hyper sensitive to activity and even fire on their own or due to nearby eddy currents caused by other neurons firing that are not directly connected to them. We don't even have to emulate this entropic process, it is actually inherent of such systems. This activity 'avalanche' process can cause sudden increase in chaotic activity in an otherwise internally normalized and mostly externally inactive mind. You see, even machines can be easily "distracted" by the smallest thing and be prone to "daydream" about unrelated things when they are "bored", just like a brain. Interestingly, the capacity for boredom and suspense scales with complexity too.
Unlike TFA's author I'm not a chauvinist. Firstly, I use Like a Brain because "the brain" would imply there's only one form of mind, and only a human chauvinist would think such retarding things. Neither do I make ridiculous assumptions about the "importance" of anything. Every new system that seeks to act "Like a Brain" gets us closer to achieving and surpassing human levels of intelligence and can even help us understand what processes and diseases govern human brains. Every attempt to abstract and emulate some neural process is important in its own way: Scientists can learn from failure. I can consider even the failed experiment as useful since it eliminates some possibility and directs effort elsewhere. Those experiments that only prove to be "like a brain" partially are not useless since they may illuminate not only the limitations of the system itself but could reveal some foundational principal of cognition. We had to discover the feedback loop before we could discover information processing.
Learning is a process. If our "catch phrases" aren't very informative, it's because the listener is too ignorant to understand what we're saying. If pundits don't know how brains are like, it's their own damn fault for choosing to remain fucking ignorant.
