Name: 構建一臺像你的大腦一樣的計算機 (Building a Computer Like Your Brain)
Uploaded: 2021-01-14T08:54:15.000Z
Duration: 10 min 43 s
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The human brain is the most powerful  supercomputer in the world.

All right, let’s see this electrical headquarters of yours in operation.

It helps us navigate our environment by carrying out

about one thousand trillion  logical operations per second.

It’s compact, uses less power than a lightbulb and has potentially endless storage.

The human brain is really one of the most complex systems that we can imagine.

We have a fundamental lack in our understanding of the way the components in the brain interact.

But it is this very interaction that generates cognition and consciousness.

All these mind-boggling intricacies have driven our fascination with the brain

and for centuries we’ve been trying to map  and understand it.

that has been evolving  for nearly 4 billion years.

And the more we learn about the brain, the more we're able to incorporate the smart ways

that it does computation  into our artificial devices.

Scientists are beginning to agree that to realize our technological dreams,

we need to build computers that work like our brains.

One day these computers could in turn help us unlock more secrets of cognition.

The brain is packed with neuron cells that constantly communicate with each other

through electrical pulses, known as spikes.

Each neuron releases molecules that act as messengers and control if the electrical pulse

This relay race is happening simultaneously throughout billions of neurons.

Much like the zeros and ones of the computer world, this is the basic language of the brain.

But understanding all of this isn’t enough.

We’ve still only scratched the surface when it comes to figuring out how the brain works.

The more I'm working on the brain, the more I understand

Many relatively easy cognitive functions cannot really be understood at the level of cells.

The human brain  is one of the biggest secrets

Katrin Amunts is at the helm of  the Human Brain Project,

a 10 year long attempt at studying the brain.

With researchers collaborating across 100 universities,

the project is expected to cost around €1 billion.

Professor Amunts and her team are working on one part of it, a 3D digital brain atlas.

They are creating three different high resolution maps - one of neurons;

one of their connections - which uses different colors to indicate the orientation of neurons’ branches;

and one map of the receptors  for the messenger molecules.

When we think about an atlas of the world, we can map all the different countries.

But then we can also see there are maps illustrating the level above the sea or the temperature.

And it's a little bit like what we have  in the human brain.

We want to understand where the cells are located.

We want to understand where certain areas are located, how they are connected,

what is their gene expression that  is important for function.

So there is not one single aspect that can explain everything in the human brain.

So that means we need different types of maps that reflect different aspects of brain organization.

To create the maps, the team is scanning slices of post-mortem brains.

We get brains from body donors and process them,

embed them in paraffin, and then cut them into 20-micrometer-thick sections.

20 micrometers, this is approximately like thickness of one hair so this is very thin.

One brain has approximately 7000 sections.

These sections can then be analyzed under the microscope and we can then

Much like a fingerprint, every brain is unique, so to account for these differences,

the team scan 10 brains for each of their maps.

This generates petabytes of data that’s analyzed with the help of AI and used to run

So to further our understanding of the brain, we need better machines.

We cannot make our chips much faster  without them melting,

unless we designed completely new architectures.

We cannot make our components much smaller because then we reach component sizes

So the computation becomes too imprecise  to be practical.

We need to find better solutions in order to increase our computational power.

Mihai Petrovici, like many other scientists in the field, thinks that

modeling computer hardware  on our brains is the way to go.

It will not only increase the speed and efficiency of future machines,

There are certainly things that computers do much better than brains,

such as adding or multiplying big numbers, because this is what they were designed for.

Intricate problems in mathematics are accurately solved in the minute fraction

of the time required for a human calculation.

There is no evolutionary pressure for us to be able to multiply big numbers.

Otherwise, certainly our brains would be able to do it.

However, there is a strong evolutionary pressure to recognize your surroundings,

to be able to build an internal model  of your surroundings.

When you hear a noise in the bushes, to be able to imagine that maybe there's a predator there.

To be able to recognize faces in order to live in a society

where people can actually communicate and cooperate.

And this is what evolution has made our brains excel at.

This ability to build an internal model of the world, to have, sort of,

to imagine what is happening around you  even if you don't see it,

this is of critical importance for a true artificial intelligence.

AI like Google image recognition, Alexa or the autopilot in a self-driving car

software which already tries to imitate  the way our brain recognizes patterns.

One thing that today's artificial intelligence needs in order to be able to perform

So in order for Google, for example, to be able to show you pictures of cats,

it needs to have seen millions of images of cats.

That is certainly not how we humans operate and learn.

When you show a child, for example, a cat or whatever other new thing,

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構建一臺像你的大腦一樣的計算機 (Building a Computer Like Your Brain)

cognitive

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recognize

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