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  • We often talk about how traditional computing is reaching its limit--there's a threshold

  • we can't move past without making some seriously big changes to the way we structure computers.

  • One of those exciting ways is by making physical computers a little more like human brains.

  • We introduced this concept in more detail here, but a quick recap: this kind of computing

  • is called neuromorphic computing, which means designing and engineering computer chips that

  • use the same physics of computation used by our own nervous system.

  • This is different from an artificial neural network , which is a program run on a normal

  • computer that mimics the logic of how a human brain thinks.

  • Neuromorphic computing (the hardware version) and neural networks (the software version)

  • can work together because as we make progress in both fields, neuromorphic hardware will

  • probably be the best option to run neural networks on...but for this video, we're

  • going to focus on neuromorphic computing and the really exciting strides that have been

  • made in this field in the past year.

  • See, traditional computers 'think' in binary.

  • Everything is either a 1 or 0, a yes or a no.

  • You only have two options, so the code we use and the questions we ask these kinds of

  • computers must be structured in a very rigid way.

  • Neuromorphic computing works a little more flexibly.

  • Instead of using an electric signal to mean one or zero, designers of these new chips

  • want to make their computer's neurons talk to each other the way biological neurons do.

  • To do this, you need a kind of precise electric current which flows across a synapse, or the

  • space between neurons.

  • Depending on the number and kind of ion, the receiving computer neuron is activated in

  • some way--giving you a lot more computational options than just your basic yes and no.

  • This ability to transmit a gradient of understanding from neuron to neuron and to have them all

  • working together simultaneously means that neuromorphic chips could eventually be more

  • energy efficient than our normal computers--especially for really complicated tasks.

  • To realize this exciting potential, we need new materials because what we're using in

  • our computers today isn't gonna cut it.

  • The physical properties of something like silicon, for example, make it hard to control

  • the current between artificial neurons...it just kind of bleeds all over the chip with

  • no organization.

  • So a new design from an MIT team uses different materials-- single-crystalline silicon and

  • silicon germanium layered--on top of one another.

  • Apply an electric field to this new device?

  • You get a well-controlled flow of ions.

  • A team in Korea is investigating other materials.

  • They used tantalum oxide to give them precise control over the flow of ions...AND it's

  • even more durable Another team in Colorado is implementing magnets to precisely control

  • the way the computer neurons communicate.

  • These advances in the actual architecture of neuromorphic systems are all working toward

  • getting us to a place where the neurons on these chips can 'learn' as they compute.

  • Software neural networks have been able to do this for a while, but it's a new advancement

  • for physical neuromorphic devices--and these experiments are showing promising results.

  • Another leap in performance has been made by a team at the University of Manchester,

  • who have taken a different approach.

  • Their system is called SpiNNaker, which stands for Spiking Neural Network Architecture.

  • While other experiments look to change the experiments we use, the Manchester team uses

  • traditional digital parts, like cores and routers--connecting and communicating with

  • each other in innovative ways.

  • UK researchers have shown that they can use SpiNNaker to simulate the behavior of the

  • human cortex.

  • The hope is that a computer that behaves like a brain will give us enough computing power

  • to simulate something as complicated as the brain, helping us understand diseases like

  • Alzheimer's.

  • The news is that SpiNNaker has now matched the results we'd get from a traditional

  • supercomputer.

  • This is huge because neural networks offer the possibility of higher speed and more complexity

  • for less energy cost, and with this new finding we see that they're edging closer to the

  • best performance we've been able to achieve so far.

  • .

  • Overall, we're working toward having a better understanding of how the brain works in the

  • first place, improving the artificial materials we use to mimic biological systems, and creating

  • hardware architectures that work with and optimize neural algorithms.

  • Changing computer hardware to behave more like the human brain is one of a few options

  • we have for continuing to improve computer performance, and to get computers to learn

  • and adapt the way humans do.

  • While scientists make computers that work like brains, put your brain to use by building

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  • It looks like it's gonna be a wild ride ahead, you guys.

  • I think you should probably subscribe to Seeker so you can always know when something new

  • and exciting happens as we progress along this brain-mimicking path, and for even more

  • on this subject, may I suggest you check out this video on neural networks?

  • Thanks for watching.

We often talk about how traditional computing is reaching its limit--there's a threshold

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Neuromorphic Computing Is a Big Deal for A.I., But What Is It?

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    joey joey posted on 2021/04/14
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