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How AI & Neuroscience can assist on the improvement of each other

Neuroscience & artificial intelligence have to help improve each other

Artificial intelligence humanoid
Artificial intelligence humanoid

Despite their names, artificial intelligence technologies in addition to their component systems, such as artificial neural networks, don t have much to exercise with real brain science. A professor of bioengineering and neurosciences is concerned in comprehending how the brain works as a system—and how we have to apply that information to design and to engineer new machine learning models.

In current decades, brain scientists have learned a huge amount about the physical connections in the brain in addition to about how the nervous system routes information and processes it. But there is still a vast volume yet to be revealed.

At the same time, computer algorithms, software in addition to hardware advances have brought machine learning to formerly unimagined levels of achievement. In addition to other scientists in the field, including a percentage of its leaders, have an increasing believe that finding out more about how the brain processes information could assist programmers to decipher the ideas of thinking from the wet in addition to the squelchy world of biology into all-new forms of machine learning in the digital world.

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The brain is not a machine

Machine learning is one part of technologies that are often branded artificial intelligence. Machine learning systems are healthier than humans at finding complex in addition to subtle patterns in very large data sets.

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These systems seem to be everywhere—in self-driving cars, facial recognition software, financial fraud detection, robotics, helping with medical diagnoses in addition to elsewhere. But under the hood, they’re all really just variations on a single statistical-based algorithm.

Artificial neural networks, the most common mainstream approach to machine learning, are highly interlinked networks of digital processors that accept inputs, process measurements about those inputs in addition to generate outputs. They need to learn what outputs should result from various inputs until they develop the ability to respond to similar patterns in similar ways.

 

If you want a machine learning system to display the text This is a cow when it is shown a photo of a cow, you 'll first get to give it an enormous number of different photos of various types of cows from all different angles so it has to adjust its internal connections in order to respond This is a cow to each one. If you show this system a photo of a cat, it will know only that it’s not a cow—and won’t be able to say what it actually is.

artificial intelligence
artificial intelligence

 

But that s not how the brain learns, nor how it handles the information to make sense of the world. Rather, the brain takes in a very small amount of input data—like a photograph of a cow in addition to a drawing of a cow. Very quickly, in addition to after only a very small number of examples, even a toddler will grasp the idea of what a cow looks like in addition to be able to identify one in new images, from different angles in addition to in different colors.

But a machine is not a brain, either

Because the brain in addition to machine learning systems applies basically diverse algorithms, each excels in ways the other fails miserably. For instance, the brain has to process information efficiently even when there is noise in addition to the uncertainty in the input—or under unpredictably changing conditions.

You could look at a grainy photo on ripped in addition to crumpled paper, depicting a type of cow you had never seen before, in addition to still reason that s a cow. Similarly, you routinely look at partial information about a situation, in addition, to make predictions in addition to decisions based on what you know, despite all that you don't.

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Equally important is the brain's ability to recover from physical problems, reconfiguring its connections to adapt after an injury or a stroke. The brain is so impressive that patients with severe medical conditions have to have as much as half of their brain removed in addition to recovering normal cognitive in addition to physical function. Now imagine how well a computer would work with half its circuits removed.

Equally impressive is the brain s capability to make inferences in addition to extrapolations, the keys to creativity in addition to imagination. Consider the idea of a cow flipping burgers on Jupiter who at the same time is solving quantum gravity problems in its head. Neither of us has any experience of anything like that, but I have to come up with it and efficiently communicate it to you, thanks to our brains.

Perhaps most astonishingly, though, the brain does all this with roughly the same amount of power it takes to run a dim lightbulb.

Combining neuroscience in addition to machine learning

In addition to finding out how the brain works, it’s not at all clear which brain processes might work well as machine learning algorithms, or how to make that translation. One way to sort through all the possibilities is to focus on ideas that advance two research efforts at once, both improving machine learning in addition to identifying new areas of neuroscience. Lessons have to go both ways, from brain science to artificial intelligence—and back, with AI research highlighting new questions for biological neuroscientists.

For instance, in a lab, scientists have developed a way to reason about how each neuron contribute to their overall network. Each neuron exchanges information only with the other specific neurons it is linked to. It has no overall idea of what the rest of the neurons are up to, or what signals they are sending or receiving. This is true for every neuron, no matter how broad the network, so local interactions collectively influence the activity of the whole.

It turns out that the mathematics that describes these layers of interaction are equally applicable to artificial neural networks in addition to biological neural networks in real brains. As a result, we are developing a fundamentally new form of machine learning that has to learn on the fly without advance training that seems to be highly adaptable in addition to efficient at learning.

In addition, we have applied those ideas in addition to mathematics to explore why the shapes of biological neurons are so twisted and convoluted. We have found that they may develop those shapes to maximize their efficiency at passing messages, following the same computational rules we are using to build our artificial learning system. This was not a chance discovery we made about the neurobiology: We went looking for this relationship because the math told us to.

Artificial neural network (ANN)
Artificial neural network (ANN)

Taking a related approach may also notify for the research into what happens when the brain falls prey to neurological and to neurodevelopment disorders. Focusing on the principles in addition to mathematics that AI in addition to neuroscience share has to assist advance research into both fields, achieving new levels of ability for computers in addition to the understanding of natural brains.

Originally posted 2019-07-11 04:47:03.

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