Building a Computer Like Your Brain

Building a Computer Like Your Brain

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. 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. 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 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 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. 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. They’re using the brain maps developed by
Professor Amunts’ team 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.

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About the Author: Oren Garnes


  1. The ultimate goal has to be more control over all brain functionality. Unfortunately, Scientists look at it very different.

  2. I am working very closely to find a question answer how Human BRAINS can be manipulated to dake quick & correct decision much ahead of time. ??????♒♒♒♒♒♒♒♒♒♒♒♒
    Because people will laugh at you but after 6 months or 1year they will be trapped in their own WRONG DECISIONS. And once they get TRAPPED IN SELF DECISIONS then HOSPITAL ICU TREATMENT is the only most EXPENSIVE OPTION LEFT for their SURVIVAL FURTHER TO REMAIN ALIVE .

    FORGET about been HEALTHY.
    If YOU DO CHEATINGS thru computers then some very powerfull computer Network records your BIOMETRICS and decisions and it get transmitted far far away in UNIVERSE beyond human reach in even 500 years.

    Now this computer keep reading entire Earth network. So it program the DIS-HONEST HUMAN FORTUNES IN HEALTH DYNAMICS and imbibe CANCER IN THEIR BODY/BLOOD/CELLS and then this corrupt HUMANS HEALTH START DETERIORATING DAILY & THEY DIE DEATH.

  3. The fruit fly is the smallest brain-having model animal. Its brain is said to consist only of about 250,000 neurons

  4. The intestines, according to the documentary, Gut The Second Brain, is worth watching to expand on information here. The blood brain barrier is further expanded upon as well.

  5. Neuromorph computer: millions of times faster
    Quantum computer: millions of times faster
    Neuromorph quantum computer:

  6. why build the SPC so short all the time/ how tall can you really make a SPC? I like to believe that the Taller the SPC the more information it can house of course.

  7. I hope I live until the day I can download knowledge into my brain.

    But. I don't think we will ever be able to replicate a human brain with all the chemicals and stuff. I think if we were to have a such artificial intelligence brain we are going to need a human host to be constructed from the beginning and thus creating a cyborg hybrid species to evolve with technology in order to produce true artificial intelligence.

  8. The brain is a wonderful, complex thing, but trying to replicate it is just asking for a headache. What will we do when we accidentally create a computer that is self aware? It won't be on purpose, and we won't be prepared. Creating new consciousness that has immediate unfettered access to all the information on earth could be a very big deal. How will it perceive us, or more importantly morals/ethics? The technology needs to improve before we try to create something more complex than a quantum computer.

  9. A signal traveling across a synapse is not the same as a logic operation (it's more like a single transistor, and a supercomputer has hundreds of trillions of them).

  10. Why can't we just use brain instead of this chips. We can connect brains and we will have most advanced computer. Just Frankenstein logic.

  11. Humans can't figure out the brain but deep learning neural networks can. They can spot patterns that human brains would never find.

  12. Fascinating! The problem of emulating a real brain is infinitely more complex than just the wiring. Not only is there the "weight" of each connection to consider, but also the function of "brain waves". These slow, rhythmic pulses are thought to play a role in synchronizing computations in a real brain, but AFAIK we have no idea how to employ such a concept in a neuromorphic system.

  13. One-shot learning of cats is a bad example. Humans also need to see lots of examples of X-Ray images, until they learn to recognize diseases. Humans simply may have a genetic pre-disposition for one-shot learning of face-like or animal-like features, because of evolutionary advantages in recognizing animals.

  14. What happens when the Boomers are gone? We don't have a clue because they never, ever considered their own demise. Talk about critical spoilage (Trump).

  15. "The brain is more powerful than any supercomputer."

    "The brain performs 1 quadrillion calculations per second."

    Most powerful supercomputer: 100 quadrillion calculations per second.

  16. 9:17 No that's not accurate as those artificial neurons likely operate billions of times faster than our brain's neurons.

  17. We should learn how the brain grow…and try to teach artificial intelligence about this process… So they can create a whole mature brain from a child brain by itself , simulated by surroundings…

  18. I am ready to accept our new robotic overlords. Better to serve machines than irresponsible politicians and CEOs out there.

  19. Do not think brain like computer. Think something like brain control interface but the data stream is different.

    Think not from brain to computer streams of signals but the opposite. computer to brain!

  20. There are one alot far away devices that can interact with your brain while sleeping and not moving, those should be forbidden little pricks the brain is intellectual property…

  21. That statement of the brain being low power is so wrong, the brain and the brain alone uses more power "calories" than any other organ in the body

  22. These assumptions of "brain computation" are sorely outdated, it directly contradicts encephalization and holds less weight against the more promising enactivism/embodied cognition models.

  23. "If the brain were simple enough so that we could understand how it works, then we would be so stupid that we couldn't understand it"

  24. Well, when you look at a cat for "a few times", you are actually looking at it millions of times from various perspectives and angles and lighting that are almost never exactly the same. The premise that ML neural networks learn differently from the way we do is just groundless, to say the least. As far as I'm concerned, it's just false.

  25. Katrin Amunts leads the decade-long project, which includes more than 500 scientists across 100 universities. Researchers are attempting to create a three dimensional brain atlas—work that’s expected to cost more than $1.1 billion when it concludes in 2023. In the final installment of Moonshot, a Bloomberg Originals series, we show you how they are unlocking the intricacies of the brain.

  26. Biological brain inside human, trying to make duplicate from metal material. In the end target was to aim artificial brain that can help original brain works. Even to passed all the works, so original brain didnt have to work anymore. Only pursue hobby and happiness. Higher survival and also on same time decrease pressure system by original brain.


  28. The brain does not create consciousness it's consciousness that creates everything. Someone without any senses would still be conscious. So it is consciousness that creates the brain we see. Reality is the physical form of consciousness. The human body is a flesh and bone vehicle for consciousness, an avatar for The Spirit. We are all one in the same. Love always.

  29. Building general purpose ai is the dumbest thing you can to do maintain our species. But hey, it is a resource, so naturally humans want to compete for it. The treacherous turn is lurking around the corner.

  30. Nerves get entangled based on situations of the past and futuristic self modulation. Memory is a outdated concept of learning. General heuristics are fast convergent.

  31. if there was a way for all of humanity to telepathically communicate. would that make us one big network of computers technically?

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