Neuromorphic computers are devices that try to achieve reasoning capability by emulating a human brain. They are a different type of computer architecture that copies the physical characteristics and design principles of biological nervous systems. Although neuromorphic computations can be emulated, it's very inefficient for classical computers to simulate. Typically new hardware is required.

The first neuromorphic computer at the scale of a full human brain is about to come online. It's called DeepSouth, and will be finished in April 2024 at Western Sydney University. This computer should enable new research into how our brain actually functions, potentially leading to breakthroughs in how AI is created.

One important characteristic of this neuromorphic computer is that it's constructed out of commodity hardware. Specifically, it's built on top of FPGAs. This means it will be much easier for other organizations to copy the design. It also means that once AI starts self-improving, it can probably build new iterations of hardware quite easily. Instead of having to build factories from the ground up, leveraging existing digital technology allows all the existing infrastructure to be reused. This might have implications for how quickly we develop AGI, and how quickly superintelligence arises.

#ai #neuromorphic #computing

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0:00 Intro
0:26 Contents
0:36 Part 1: What is neuromorphic computing?
0:55 Copying biological nervous system
1:45 Spiking neural networks vs regular ones
2:12 Example: playing simple games vs simulations
2:55 Other neuromorphic hardware
3:34 Organoid intelligence is the opposite approach
4:35 Part 2: Brain-scale neuromorphic computing
5:01 Example: simulating a brain with a supercomputer
5:45 Power consumption of brains
5:59 Brain storage: 2500 TB
6:19 DeepSouth: First neuromorphic computer at brain scale
6:56 DeepSouth capabilities
7:22 Built on mainstream FPGAs
8:29 Hardware design: FPGAs and ASICs
9:28 Because it uses FPGAs, DeepSouth is replicable
10:16 Neuromorphic architecture (computing paradigm)
11:10 Comparison with quantum computing
11:40 Part 3: Impact on AGI development
12:17 Energy consumption of AI
13:33 GPUs compared with neuromorphic computers
14:15 GPU analogous to FPGA (programmable)
15:00 AI can optimize its hardware more easily
15:47 New architectures are likely to arise
16:16 Conclusion
17:11 Accelerating the development of AGI
17:40 Commodity hardware
18:07 Outro