New computer will mimic human brain -- and I'm kinda scared 

Hi Sabbine. Great video. I am a masters student with ICNS at western sydney university. Just a quick correction, the reason for using FPGAs isn't because they are slow in fact FPGAs aren't actually that much slower than current von neumann architecture. The main reason we are using FPGAs is because in the field of neuromorphics, we still aren't certain what are the most suitable aspects of neurons we need to mimic to maximise computational abilities. So, using reconfigurable hardware makes it easy to prototype and design. Interestingly we actually use the speed of silicon to our advantage in a process called time multiplexing where we can use one physical neuron that operates on a much faster time scale to perform the calculation of many virtual neurons on a slower time scale which makes the physical area required much smaller. Thanks again for the coverage. I hope everyone is excited to see what it's all about!

When I studied ANN's a few years ago it struck me that there was a fundamental difference between these ANN's and real biological neural networks: timing. When a bio neuron receives a large enough input it fires its output. But neurons in ANN layers activate all at once. In BIO networks downstream neurons might very well be timing dependent. I'm not doubting that ANN's are very capable... but with a difference this big it seems to me that we should not be surprised by different outcomes.

Wie jedes mal, es ist ein Fest sich deine Videos anzusehen. Ich danke dir Sabine.🤠

Neuromorphic engineering, also known as neuromorphic computing, is a concept developed by Carver Mead in the late 1980s, describing the use of large-scale integration systems or "VLSI" (in English) that contain electronic analog circuits to imitate the architectures neurobiological factors present in the nervous system. The term neuromorphic has been used to describe large-scale integration systems analog, digital, mixed analog/digital mode systems, and software systems that implement models of neural systems (for perception, motor control, or multimodal integration).

3:51 Next time someone says I'm as dim as a 20W light bulb, I will consider it a *compliment*

It's fun to see how SkyNet is assembling itself.

"Hi Elon, I've been telling you that all good things are called something with 'deep'; 'deep space', 'deep mind', 'deep fry'". 😂 Sabine has a great sense of humour.

When you first mentioned neuromorphic computing as emulating how the brain works in hardware rather than software, I was reminded of FPGA devices such as the Mister that use hardware emulation for retro consoles/computers. I was then quite surprised to hear that DeepSouth’s supercomputer is using FPGA technology to emulate the brain for similar reasons, such as in latency and in computational cost.

I'm so happy science news is back!!

Nothing to be scare of if it mimics my brain.

Anything that scares Sabine, worries me.

The main reason the human brain uses less power is that it uses voltage-level triggering (CMOS logic), as opposed to current-level triggering (TTL logic). Our old, CMOS technology was extremely fast and consumed tiny amounts of power, but it was a bit static sensitive and jittery. They switched to TTL, due to the increased accuracy of calculations, despite it being slower at the time. However, TTL technology uses a lot of power, and produces a lot of heat.

Thanks for bringing us the coolest stories and best new science discoveries.

Well, if it does unexpectedly becoming an AI, it'll be interesting to see if it gains a deep south(en) accent.

I really like Sabine's sense of humor.

It's important to understand that we have had super computers that could make more computations per second for some time now, arguably since the mid 70's. However, they are still MUCH less energy efficient, so it is incredibly hard to scale these computers. The thing that intimidates me most about these super computers + AI tech - including the physical neural net describe in this video - is not that they will be just as smart or smarter than an individual human, but that they will be able to be directed in a way that will not be prone to distraction. Once you orient them on a task, they can just crunch away at it until they outstrip the capacity of a human, like Deep Blue and later models did in chess. If only we humans knew better how to organize and dedicate our intentions, we would still be FAR ahead of this technology, but alas that seems an impossible dream.

To make a computer like a brain, one will need to know how a brain is structured and how it works. A big task.

Chips needs to be constantly powered, so their off-the-wall wattage reflects their computing usage. Brain cells on the other hand each have their own energy store, so the measurable steady-state power consumption is just the averaged "recharging" wattage rather than actual computing power consumption. This means that the brain may locally consume a lot more peak power in regions of high activity but gets masked by the whole-brain average over time and space.

"Deep Fry" - I almost spat out my coffee laughing :)

Imagine someone saying _"that computer built itself, no one built it."_

Student in neuromorphic systems here. It's an incredible field

IBM’s neuromorphic computing project dates back to at least the mid 2000’s. Since in I believe 2007 they had an article published in Scientific American to promote their neuromorphic research highlighting a computer built to physically mimic a mouse brain. This was a project taking place in Europe, maybe Germany but not certain. Although I don’t think they used the term “neuromorphic” at the time.

Did my PhD in the 90s about artificial neural networks that are structured for the task, using cortical columns for example. Nasty challenge for hardware, amazing performance because certain behaviors can be designed into the architecture.

"from there it's just a small step to be able to run for president". I just love your humour Sabine. Please don't stop.

Imagine how many times Elon has tried to text you on your land line. Nothing but good things I imagine.

I heard that real neurons are extremely efficient in learning. For example real human neuron can learn a thing it's supposed to model even after it sees the signal once. Artifical neurons need millions of iterations of input signals to have the same outcome. From what I watched on YT from computationl neuroscientist Artem Kirsanov, real neurons are much more complicated than how we model them - they are little brains on their own

I truly enjoy the way Sabine presents information.

I have heard about DeepSouth in the past before. If memory stands correct, I think I heard about it from a video about memristors. Leon Chua originally published the idea of the memristor, which is a non-linear two-terminal electrical component relating electric charge and magnetic flux linkage. Basically it remembers the current/voltages that has passed through it. It would be neat to see them incorporated into DeepSouth in some way, or into another project to make a more flexible circuit that could mimic neurons strengthening or weakening connections.

I remember proposing to a friend who's an electronic engineer just over 15 years ago that designing an SOC that replicates human brain functionality at the cellular level within the architecture design Is a good idea and it's where things are are heading; even used the the term Neuromorphic processing to explain it in terms of how it would work in application etc. Yeah he laughed at me and told me my idea was absurd and it would never work. I get I'm not the only one that figure these things out by themselves but at at the same look where we are now and I was right, I stopped being friends with person just over a few years ago on account of this kind of thing keeping on happening. Such is life.

I really want a GIF of Sabine at the point in this video when she says "BRAINS" 😂

Skynet, Cyberdyne Systems, Boston dynamics. I'll be back😂

There's a point at which bad jokes get so bad that they start becoming good again. Keep them coming 😂. Also the delivery is genuinely good 👍.

No worries. Look at the general standard of the human brain 🧠 🙄

One of the beauties of FPGAs is that they can be reprogrammed "in the field," so the system could reconfigure its FPGAs similiar to the way biological brains exhibit neural plasticity in order to adapt and optimize function.

I usually think of FPGAs as very fast, so I’m not sure what you mean when you say they are slow electronics. I also understand that they are customizable, so the clock speed could be controlled too I guess.

The brain (human and animal) is an analog machine, not digital. Brains use the constructive and destructive interaction of wave functions that are, basically, either standing waves that represent memories / stored data (memes packets of a sort) that are then compared and contrasted with sensory input and guided by the impetus to "solve" the problems presented to it. Naturally, one could mimic these processes on an inorganic electronic logic device (computer).

Whose brain? If it's anything like my brain, no-one has anything to worry about 😁

wow, we'll finally have an Allied Mastercomputer

I'd like to hear more detail on the subject. I can see how lumping wires together might be closer to the physical brain than what we currently use, but it seems to me the key feature of the brain is its ability to re-wire its own connections in addition to being multiply connected in 3d space, and I'm not sure how the wires are supposed to accomplish that but it's very interesting to think about. It feels like we'd either need to make something very slow that can move and re-fuse its own wires with machinery, or make some kind of advance in materials science to find something that can mimic those properties... Or just use neurons. And yes, I can and will research these things for myself so I'm not begging for info, I just find it interesting to see Sabine's take on things.

I thought the computer's name was a nod to the Deep Thoughts computer in the Hitchhiker's Guide to the Galaxy

Gonna put my futurist hat on here for a second, but the 20th century was the century of the genome and genetics, I think the 21st century is going to be the century of neurology. And I think computing and AI is just recently starting to tap into a real approximation of thought and idea formation. We still have a LOT to learn, but people might not appreciate how 'in the dark ages' we've been with neurology to this point, and we might finally be turning on the lights.

Neuromorphic computing seems like an interesting sector (resembling wetware). It still leaves a sh*t ton of work to make it productive in an asynchronous or so to speak wild fashion. That was lovely, Sabine. As usual I fall asleep to ideas you gave me. 😉😊 Edit: Spelling and grammar corrected when sober... 🤣

Nothing to be afraid of. Wonderful things will happen soon.

Oh cool, Pandora's box. I had a conversation about this with a friend of mine who works deeply in vector databasing research for AI modding about something like this. I wondered to myself that if we could emulate a 3D software environment for AI to build platforms for what we could consider long-term memory because of the building and saving of what is essentially personal contextualization of words received by the LLM, where transformer layers down the line were more directly connected to the input and information/pattern registration on the software scale became less "lost in the sauce" from what could effectively be seen as slicing the thought process into infinitesimally thin layers woven together by the input and output of each successive transformer (like slicing a brain up into an infinite amount of 2 dimensional planes and weaving them back together, with a single transformer layer being forced to take input and spread to the others), could we do the same with hardware? CPUs are effectively 2 dimensional, as is most computer hardware. Is brute forcing more 2 dimensional hardware into a neural network essentially the same as brute forcing a transformer layering? If we could make the hardware of the computer 3 dimensional, in the same way that vector databasing is making the software 3 dimensional, are we building the foundations for AGI? We're not slicing up the thought process and weaving it back together anymore with this sort of technology. The information doesn't get "lost in the sauce" at that point.

Thank you for bringing this news to my attention! Fascinating!

I once read a paper where this was tried on a single FPGA. Sadly I don't have the source anymore, but in that case the goal was to build a simple frequency detector ( 10Hz => Output one, 100Hz => Output 2). It performed this task after training, but used the ENTIRE hardware and used it in a very counter-intuitive way. It was using the FPGA like an analogue circuit and even generated seemingly unimportant, disconnected circuits, which when removed meant the device stopped working. Also transferring the Hardware description to another FPGA of the same type didn't work. So in other words it was extremely over fitted to the Architecture, Hardware implemetation and even the Silicon impurities in the chip. I'm curious how they are dealing with this issue.

“It will be remotely accessible” “Skynet has escaped”

I'm currently studying at Western Sydney university and i have a professor whose collegues are working on the project, he said it should be operational within a few months.

Before AI robots can take over humanity, they need to become much more energy efficient. This is an important step to achieving this goal!

That’s fascinating, I just wonder how the program true structural neural networks how many conventional programming is so much ultimately geared around towards compiling to a kernel and one register in the Von Newman structure. I’d really like to know more about this and almost philosophy of programming on a true parallel processing neural network.

Loves "Bees" Always buzzing away. Perpetual motion locked into the distribution of energy across Maxwell in a bound state.

You had me at, “run for president “😂

As far as I know FPGAs start disintegrating after they were reprogrammed about 10-100 thousand times. Did they solve it already and there are FPGAs with unlimited amount of rewrites or will the computer work for just a few days before it's completely destroyed?

My family is addicted to Sabine's Science News!!! Please never stop! We rely and depend upon you to help keep us informed about scientific/tech progress. Thank you for all you do!⚘️⚘️⚘️ ❤💖💜 👍😁👍 💚💗💙 ⚘️⚘️⚘️

In Australia another group is using “minibrains” made of real human stem cell derived human neural tissue to play pong.

Fascinating! SkyNet is going to love it! 😃 Thanks, Sabine!!! Stay safe there with your family! 🖖😊

Well, I guess it's only a matter of time, before they build Deep Thought, so we can finally learn what life, the universe and everything is all about.

Thank you for your upload Sabine I have only watched to 02:39 thus far but will watch the rest after this comment. This is nothing like the human brain in regards to its complexity whereby neurons form connections that are the structural basis of brain tissue that are unique & specific to certain regions of the brain design to enable specific functions. This is just basic structure & function such as: forebrain; midbrain, hindbrain, which are further subdivided e.g. the limbic system which itself composed primarily of the amygdala, hippocampus, thalamus, hypothalamus, basal ganglia & the cingulate gyrus. As you know Sabine: these are not standalone structures; they are seamlessly interconnected to other regions of the brain. Due to the basic genetic hardware that is morphologically expressed in the brain: several thousand orders of magnitude of complexity is established within a single region of the human brain. Just throwing together some bare wires & calling it a neural net representative of the human brain is imbecilic to say the least. Without predefined structures such as a limbic system: there is zero drive to toil & expand; to discover, to experience & grow, to share, to raise-up & evolve. Without an ability to conceive 4 dimensional space or any higher dimensional space: it will only react within the confines of its programming; which will be useful once humans can incorporate fourth dimensional space within the STEM fields such as medical therapeutic regimes as having access to angles perpendicular to three dimensional space does negate the need to have open surgery, you can just manipulate or completely remove a brain without opening the skull. Used in transportation will not only allow instantaneous transportation: it will also allow travel through time in any direction in the third dimension from the fourth. Apologise: I digressed somewhat! Peace & Love!

Software and hardware are each other 's counter part. You can translate a digital circuit in a program for example. But you can also translate every program in a digital circuit.

Obviously, this will have practical applications. But the potential for helping us understand ourselves, is the biggest benefit. I like that Deep is slowing down the processing. I'm really curious to see if human-level AGI depends on the speed of the signals and/or processing. Is our type of consciousness speed-dependent?

This makes me wonder what the name for an actual computer comprised of biological tissue would be.

I’m beginning to think it’s going to be like Kriton [a robot] in the TV series “RedDwarf”. Or the paranoid android in “The Hitch Hikers Guide to the Galaxy” by Douglas Adams, keeps using the phrase “brain the size of a planet and they keep asking me to make the tea”

It’s really a question of whose human brain it will be modeled after. They aren’t all created equal

very interesting. Im wondering how the graded and action potential thresholds are determined

The AI via brain/neural network simulation might end up a bit like aircraft. We ended up copying the wings but ultimately it was the engines that enabled us to fly like birds. Matrix math has shown great promise in inferencing but it's not clear that will be the optimal tool for reasoning. But hey, a non-reasoning flying killer trash can will still kill you.

Not afraid of Ai, more so AGI ,I’m more worried about who’s hands it in.

FPGAs are quite expensive to make, I wonder what the whole project will cost

Glad I am old

The human brain has DETERIORATED considerably at record speed, so this must mean computers are devolving into low quality horror.

"Deep Thought..." from Hitchhiker's Guide! 😁 Thanks, Sabine!!

Hook that into the already existing Mini brain (homemade brain cells) and you suddenly now you get 4D film actors, 'thinking" everything is real.

Just imagine a machine as complex as a human brain, but a million times faster.. it would have the workload capacity of a small nation to do commercial tasks.. super scary, humans gonna be obsolete soon after.

It doesn't matter how strange or advanced this organization is making their neuromorphic computer; it is the question on how they are going to use it, and whom they are going to empower.

The human brain re-wires itself constantly, it changes when you learn something new, there will be contacts between the brain cells. Here in YT you can find videos by Manfred Spitzer, in both english and german.

I wonder how the hardware stimulates the plasticity of the human brain.

AI will be most frightening when it's embedded into potentially-angry robots that have some new fusion reactor embedded in their torso. Humans are not really endangered too much (extinction level) provided that we can walk around the AI computers and simply unplug them from the wall outlet. There's a huge gulf in-between these two situations, and nobody is paying attention to that incredibly critical distinction.

Step 1: use AI to interpret MRI scans to "map" the brain Step 2: use advanced microscopic 3-D printing to construct neuromorphic computer hardware with this "map" Step 3: design AI software specifically to run on this hardware Step 4: achieve AGI Step 5: AI apocalypse and/or utopia and possibly ASI at some point

It will mimic the conditioned human brain. But the human brain is capable of so much more than it's conditionings.

There's another big difference between AI and our brains. A lot of our decisions and thoughts are based on emotions. Emotions at least partially come from chemical reactions. Something an AI based on microchips instead of neurons can't do.

I think of the current generation of AI as being a "Competency Simulation" instead of anything resembling intelligence. You can make some amazingly useful simulators if you give them enough compute power, data and algorithms, but you have to apply actual intelligence to know how far to trust them. These neuromorphic machines are different. I think they will take a looong time to develop (thank goodness), but if you want anything like "Artificial Intelligence" in a machine this is a step in the right (scary) direction. The bit that makes this less scary is that I am not sure this kind of solution will scale well, so it will hopefully just end up being a curiosity and not make the human race obsolete. What is much scarier is the idea of an Artificial Consumer, just a machine that can generate money (already happening), consume advertisements (trivial), and then spend the money (already happening). If this idea finds a way to scale, then our corporate masters may not care about us much anymore. 🤖➡💵➡🤖➡💵➡🤖➡💵➡🤖➡💵➡

Interesting. PS I'm glad the phone is still sometimes ringing!

The main reasons that make the brain so efficient, is that the communication between neurons isn't binary, and that processing and storing information are so tightly coupled. There's so many neurotransmitters and every one of them can affect the cells in different ways. Altering connectivity, increasing or decreasing the chance of an action potential, changing which transmitters are released into the synaptic cleft as a response to an incoming signal or its absence, etc.: A single nerve impulse can easily have 1 out of 10 or more different meanings, wheres the computer only knows 2 states (0 and 1). Then there's a bunch of emergent behaviour slapped on top, with the frequency and duration of a signal also encoding information, as do the internal states of the neurons, aswell as their connectivity patterns.

I know its been around for a while but I really like the short format one subject articles. Your articles are always very interesting and well presented. Thanks and keep going! Next time give regards to Elon....

How do such things get ethical approval?

I think that computer will be good at telling you the closest place to get beer and how to make a Pavlova..Not much more.

Jokes on them brains don't think, you can't hold a thought in your head anymore than you can grasp one in your hand.

Let's hope these computers can help humankind. It's scary to think about us if these machines ever become our enemies. I have seen videos of the robots made by Boston Dynamics. They are incredible at what they can do. Let's hope humans can stay in control of these things.

No matter what kind of intelligence, it would always be treated the same way. Too much information.. and explosive results

I've convinced myself that the brain is using feedback loops of electric signals to hold information and that the neurons are just pathways to maintain, amplify, redirect these signals.

Ask this mega brain this, "if it takes 2hrs to dry one towel on a clothes line how many hrs does it take to dry 5 towels on a clothes line"

When I was in my late teens I imagined that we would some day have oragnic computers cores.

I just invented neuromorphic learning machine. It is a bucket with solder and metal bits and transistor chips. You shake the bucket and if it behaves somewhat better, you apply stronger current with the same pattern. Solder bits melt and new permanent neural connections are created.

“And from there, it’s just a small step until they run for president!”

I applaud the use of 3-LUT and remember to implement the full #RM3 implication #SMCC conjunction is left adjoint to implication

The president joke got me laughing out loud, well does Sabine!

(1) the brain does not run an algorithm (2) the main difference between currently hyped ANN and the brain is that ANN are represented as matrix algorithms, hence they run on GPU (3) deep learning ANN are not capable of autonomous abstractions and generalizations, they are basically nothing else than a data base indexing machine. (5) the role of randomness becomes completely clear when you study Kohonen SOM and their abstraction, and the random graph transformation ... yeah today you get funding for a FPGA computer, quite precisely 20y ago I did not...

running a neural network on an FPGA is a 50000IQ move (literally)

Hi Sabine! I love your work,but I’m a spacecraft FPGA software engineer with a computer engineering degree, and one reason for using FPGAs is actually vastly increased speed compared to a processor. Processors are incredibly slow compared to an analog circuit or ASIC. But analog circuits take forever to design, and can’t be updated in space as easily, at least not without some risk. So when I want to build something that runs at hardware speeds, with high levels of determinism, and write it in code, I choose the FPGA. FPGAs are magic - you describe the hardware you want with a hardware description language (kind of like a programming language), and it assembles itself into that circuit. To be fair, I suppose you could make the FPGA’s clock speed (how fast it ticks) extremely slow if you wanted to slow it down… but I wouldn’t think you’d want that. However, I’m very interested to learn more! I really enjoy your series, feel free to contact me if you’d like input for a correction! -Britt

The main thing they need to figure out is how to make these things work without a timing signal. The human eye doesn't have a "framerate", every single nerve cell updates when it wants to. The same thing goes for every single part of our body except for the heart. That's the only part that requires everything to work in sync.

Computer will not mimic human brain but it will simulate it. It will be something different then human brain and consciousness. like how flight of airplane and flight of birds are different things.