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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 weve been trying to map and understand it.

  • And most recently - replicate it.

  • The brain is certainly a computer

  • 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

  • is passed along the chain.

  • 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.

  • Weve 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

  • how complex it is, how difficult it is.

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

  • The human brain is one of the biggest secrets

  • and mysteries that we have,

  • despite many years of intensive work.

  • 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 neuronsbranches;

  • 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.

  • There are different aspects.

  • 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 molecular profile,

  • 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

  • reconstruct the areas in 3D.

  • 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

  • brain simulations on supercomputers

  • but even the supercomputers struggle.

  • 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

  • where quantum effects take over.

  • 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,

  • but also help build better AI.

  • 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,

  • the world inside your heads,

  • 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

  • all work thanks to neural networks,

  • 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

  • whatever task it was designed for,

  • is a lot of examples.

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

  • whenever you type in cat,

  • 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,

  • it just needs to see it a couple of times in order to quickly grasp the main features

  • that are specific for that animal

  • and then recognize it whenever it sees another individual of the species.

  • The scientists at Heidelberg University are working on a different part of the Human Brain Project.

  • Theyre using the brain maps developed by Professor Amuntsteam

  • to build computer hardware they hope will help AI learn like our brains do.

  • This new hardware is called neuromorphic which means formed like neurons or like the brain.

  • Actually, none of what you see here on the outside is really neuromorphic.

  • You might be tempted to think that this is more or less like the machine that you have

  • at home on or under your desk.

  • This would be true for the outside components

  • but at the heart of the system, there lies a piece of hardware that is fundamentally

  • radically different from the chips in your computer,

  • and that is the neuromorphic heart of the system.

  • The microchips on these wafers look nothing like the entangled web of neurons

  • that we have in our heads.

  • But each component communicates like an individual neuron

  • by sending along spikes of electricity to their many partners.

  • This design immensely increases the operating speed.

  • neuromorphic hardware generates results 10 million times faster than conventional hardware.

  • We certainly believe that this will become a big thing, we will see many applications

  • of these systems for everyday tasks.

  • One of them would be face recognition, pattern recognition in general,

  • speech recognition, the ability to read texts.

  • The ultimate goal, of course, is to create true artificial intelligence.

  • But it's really hard to say by when we will be able to actually copy the brain in an artificial substrate.

  • What we can certainly do and what we are doing right now is - understand

  • particular aspects of computation in the brain.

  • The 4 million artificial neurons packed into this neuromorphic computer

  • are just a tiny fraction of the 86 billion neurons in the human brain.

  • Still, it’s a big step forward for the machines.

  • Even though our knowledge of the brain has increased over the last few decades,

  • it’s still fragmented.

  • If the Human Brain Project is successful, it could bring this knowledge together

  • and encourage research and collaboration across different scientific fields.

  • And so this effort could be just the beginning of the journey.

  • Better understanding the human brain, is really one of the challenges of the 21st century.

  • We have an increasing amount of people suffering from neurodegenerative diseases,

  • suffering from major depression, other psychiatric diseases.

  • We need to have new tools to diagnose and have better therapies for these brain diseases.

  • And since we are living in an aging population,

  • these diseases, of course, play a major role in the future.

The human brain is the most powerful supercomputer in the world.

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あなたの脳のようなコンピュータを構築する (Building a Computer Like Your Brain)

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    林宜悉 に公開 2021 年 01 月 14 日
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