The Next Generation Of Brain Mimicking AI

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The problem with biological neural networks is that they take 9 months to build and about 20 years to train. Some of them don’t work, others are corrupt, they are highly susceptible to viruses and about half of them don’t follow logic.

re-pasting this because it was deleted : 400tflop for 1000 tokens; a decent gpu is 20 tflop/s, therefore it would take like 30s for a gpu to process it. A gpu is like 200w, 30s of which is 1.6 wh, not 300wh

300wh for an inference request / compute? I run several models locally on an Nvidia 4080, it takes a few seconds of the GPU working at around 85%. I play games for 3 hours straight with the GPU at 95%, so that would imply I could charge 129,600 Iphones. My electricity provider would inform me of this, surely.

THE BEGINNING is a bit misleading because it is not the use of the neural networks, but exclusively training of them is power-hungry. Once the training is done, the power costs are minimal in most cases. Hence, even iPhones use onboard hardware accelerated neural networks for many years by now. Also, before spiking and neuromorphing, the biggest breakthrough that is actually possible and is happening is activation function training, which postulates that it should not be just a sigmoid or any other simple function but a parametric curve. The chap has forgot to tell about this. But otherwise, the video is great.

This took a long time to produce. The topics, visualizations are fantastic. Excellent, well spoken host. Great job.

Neuromorphic is the future of AI. It’s something i said to myself the first time i learned about it back in 2021. I’ve always been obsessed with the concept of AGI and the benefits it can bring (and how to mitigate the risk)! To me, it always seemed like our conventional hardware and software wasn’t going to work in the long-term. An interesting thought i’ve always considered, is that when AGI is eventually created (could be as little as a year away), said AGI system could be integrated into advanced simulation software. Imagine an AGI system using simulations to discover and determine a room-temp superconducting material! Imagine an AGI system that troubleshoots 100 years of nuclear fusion reactor designs in less than a second! Imagine an AGI system that designs a compact quantum computer that’s lightyears ahead of our current tech, integrates it into itself, and then makes a 1-1 scale model of a human down to the molecules for illness prevention and understanding! We are encroaching upon the most important time in human history. If we can stop ourselves from weaponizing AI long enough, we may see the creation of an aligned ASI that’s happy to give its parents a nice retirement!❤

8:14 - "I feel alone..." So very relatable. You'll always get the line "you're not alone in feeling this way" in one form or another. But in looking around, there's no one here. And there won't be anyone here. So I must be alone, and I should feel like this. Chances are, most people who are lonely enough to ask help from a speech model won't find anything new they haven't heard from a counselor, so they'll stay alone too. I find no comfort in that. For the average human happiness, it would be so much better if it was just me.

Now, for an old guy, with a couple of non-STEM degrees, what you present is so far beyond my capacity to grasp and understand, it's not funny. Therefore, I suspect that only a very limited number of ordinary citizens could began to comprehend either. So, this means almost NONE of current politicians have even a tiny shot of appreciating the significance of logical conclusion about the limits of materials and and services required for extensive usage, requirement and cost of such systems. Thanks for sharing and offering a subject so complex that it requires a really intelligent and well trained individual to grasp and appreciated.

Duh, we aren't going to continue on the current trajectory of power/energy consumption with AI. This fuels effort in improving the energy efficiency so that curve will never exist, it's just hypothetical.

Excellent video with wonderful graphics, very well explained by the host in clear, simple and direct language. It opens the doors to the next generation of neural networks and justifies very well the need to develop research on Neuromorphic Computing, which is of interest of young talented students at the Pontificia Universidad Javeriana in Bogotá, Colombia. We will be attentive to future videos like this. CONGRATULATIONS.

Well done on the video structure, felt really natural and informative

The gpt4 request energy estimate has to be bullshit, with 1kw per gpu and in the ballpark of 16/32 gpus serving probably at least a hundred users, a request would need to take an entire hour. I'm probably not 2 orders of magnitude off, which is the offset needed to make the 300Wh number make sense

Thanks for the video! I didnt know about spiking neural networks and analogue computers before this. It was a nice summary of the field and what we can expect in the near future 😀

Algorithm, engage!

This is THE best presentation on AI architecture I’ve ever seen, by a wide margin! (!) Megaprops for the depth, breadth and clarity, I’ve been paying a lot of attention to AI over the last couple of years, but still learned things, and this clarified my understanding of others. Great job!

Great video. Curious what software you used to create the motion graphics?

This is the first time I've viewed your channel and I consider your explanation of such a complex topic the clearest and most thorough of the many I've encountered -- well done!

A lot of people are complaining about the 300Wh figure, but they seem to be justifying it based on running small local models rather than industry-scale applications. I'm going to assume you're using the same source I found, in which someone tried to calculate a request's cost by using Sam Altman's claim of a worst-case cost of $0.09/query and power costing $0.15/kWh of power (they were using Euros but that doesn't make much difference or sense for this really). For some reason, they threw in an assumption that this estimate was double the actual cost, arriving at 300Wh. (I'm guessing that they decided halving the cost was a reasonable estimate instead of just using the worst-case estimate.) There's not much issue with this estimates.. but it is over a year out of date and it's not clear which model is specifically referred to. There is no published parameter count for GPT-3.5-Turbo or ChatGPT, but multiple estimates have placed it in the range of 20-22 billion. GPT-3 has 175 billion parameters. GPT-4 has been said to contain 1 trillion or 175 trillion parameters. GPT-4o hasn't had anything about parameters mentioned. Since there seems to be a lot of assuming that reasonable critique is just spreading fear and doubt, I think a reasonable thing to do is try to come up with a best-case estimate for power consumption: If we pretend that 300Wh estimate is valid only for GPT-3 running on less efficient hardware, and say GPT-4o is a heavily optimized version of GPT-3.5-Turbo, it could be reasonable to say that requests cost less now.. but that's making a lot of assumptions. (Looking at the calculation in @youtou252's comment: GPT-4o has a 128k token context window, and outputs up to 2048 tokens, not 1000. Their estimate on power usage may be accurate for first-query of a session, but ignores how quickly the calculations required expands as the conversation continues. They are off by about 100x, conveniently the difference in orders of magnitude between the estimates given by Sam Altman & used in this video.) Ultimately, the power usage is a real problem that has been said by many.. it isn't just spreading fear, it's a real problem to be solved.

This was so entertaining, always happy to see a New Mind video drop