animals-and-machines
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year={1953},
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year={1953},
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publisher={Shahnamah Press Bombay}
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publisher={Shahnamah Press Bombay}
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}
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}
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@misc{enwiki:1120152608,
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author = "{Wikipedia contributors}",
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title = "Intelligence --- {Wikipedia}{,} The Free Encyclopedia",
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year = "2022",
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url = "https://en.wikipedia.org/w/index.php?title=Intelligence&oldid=1120152608",
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note = "[Online; accessed 15-November-2022]"
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}
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@misc{techcrunch-stability-ai,
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author={Wiggers, Kyle},
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title={This startup is setting a DALL-E 2-like AI free, consequences be
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damned},
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year={2022},
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url="https://techcrunch.com/2022/08/12/a-startup-wants-to-democratize-the-tech-behind-dall-e-2-consequences-be-damned"
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}
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@article{khan2012energy,
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title={Energy Consumption Of The Human Body},
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author={Khan, Donish},
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year={2012},
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url="http://large.stanford.edu/courses/2012/ph240/khan1/"
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}
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_posts/2022-11-15-animals-and-machines.md
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_posts/2022-11-15-animals-and-machines.md
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---
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layout: post
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title: "Animals and Machines: A Misled Comparison"
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subtitle: "I find comparing animals and machines absurd"
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date: 2022-11-13 00:00:00
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permalink: animals-and-machines/
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categories: personal, science
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published: false
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math: true
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author: Mahdi
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---
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I find comparing animals and machines absurd, because of course, animals win!
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What am I talking about here, what am I comparing? I've had multiple occasions
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where I have had to defend the stance that animals, and in general, biological
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beings are much more efficient and intelligent than human-made
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machines and AI. Let's first set the stage.
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## Intelligence
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What do I mean when I talk about intelligence? I think the definition I find on
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Wikipedia is a fair one:
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> Intelligence has been defined in many ways: the capacity for abstraction,
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logic, understanding, self-awareness, learning, emotional knowledge, reasoning,
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planning, creativity, critical thinking, and problem-solving. More generally, it
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can be described as the ability to perceive or infer information, and to retain
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it as knowledge to be applied towards adaptive behaviors within an environment
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or context. {% cite enwiki:1120152608 %}
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## Efficiency
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This I define as the ratio of useful output from a system to the amount of
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energy it needs to do carry the action.
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# An Absurd Comparison
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"This AI is much more intelligent than dogs", or even in more extreme cases "This
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AI is better than humans!".
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Some AI achievements are _impressive_, for sure. Stable Diffusion or Dall-E
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achieve impressive results. GPT-3 can be impressive sometimes, self-driving cars
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are also sometimes impressive, but being impressive is not the same as being
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intelligent or efficient, let's dissect what goes on behind such impressive
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feats of AI, and then we can look at the factors in the open.
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## Stable Diffusion
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What does it take for Stable Diffusion to create an image given some text?
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The dataset used to train Stable Diffusion is the
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[LAION-5B](https://laion.ai/blog/laion-5b/) dataset with 5.85 billion image-text
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pairs. {% cite techcrunch-stability-ai %}
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This means, we first had to have 5.85 billion images made by humans, and then
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labelled by humans, that's a ton of energy and time spent on the training data
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of this model.
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To train the model, 100 Nvidia A100 GPUs were used, for a total of 150,000
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GPU-hours, at a cost of $600,000. {% cite techcrunch-stability-ai %}
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Let that number sink in, 150,000 GPU-hours were required to train this model
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with 5.85 billion images. Nvidia A100 GPU has a max thermal design power (TDP) of 300W, and
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while TDP is not the best measure of actual power consumption, it can serve as a
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ballpark. So this GPU uses 300W of power per GPU hour, which is 45000kW for
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150,000 GPU hours, this is discounting the energy consumption of all the other
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components of the computers training stable diffusion.
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## Human
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Now imagine I asked a human artist to draw the same image I asked of Stable
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Diffusion. I'm pretty sure this human has not seen 5.85 billion images with text
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prompts, and I'm also pretty sure they have not had to spend $600,000 for
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training (including surviving and feeding themselves), and they also did not
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have to use as much energy as 150,000 GPU-hours of Nvidia A100s. A human body
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consumes food to generate energy, and the basic amount of energy consumption of
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the human body is about 4kJ/kilogram of body weight and daily hour {% cite
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khan2012energy %}. To get watts per hour, we can use the formulas below:
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Power in watts $P_W$ is equal to the energy in joules $E_j$, divided by the time period in
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seconds $t_s$:
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$P_W = E_j / t_s$
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So given that I weigh 70kg, my body consumes around 280kJ per hour, plugging
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into the formula:
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$P_W = 280000 / 3600$
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$P_W = 77.7$
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So my body consumes somewhere around 77.7 watts per hour, that's only 680652 watts or
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680kW per year! With this consumption, I could live 66 years before I would
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consume the same amount of energy as the training procedure of Stable Diffusion.
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I hope we can agree that as impressive as Stable Diffusion is, it does not beat
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a good human artist, and it sure is not as efficient as a human. I think to say that any
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AI is smarter than humans in any subject, must take into account the efficiency
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of the system as well.
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## Hummingbird
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My favourite example when it comes to comparing animals and machines, is the
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tiny hummingbird, which I think is more impressive than any machine made by
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humans, let me explain!
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{% bibliography --cited %}
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