animals-and-machines

_bibliography/references.bib

@@ -496,3 +496,26 @@
   year={1953},
   publisher={Shahnamah Press Bombay}
 }
+
+@misc{enwiki:1120152608,
+    author = "{Wikipedia contributors}",
+    title = "Intelligence --- {Wikipedia}{,} The Free Encyclopedia",
+    year = "2022",
+    url = "https://en.wikipedia.org/w/index.php?title=Intelligence&oldid=1120152608",
+    note = "[Online; accessed 15-November-2022]"
+  }
+
+@misc{techcrunch-stability-ai,
+  author={Wiggers, Kyle},
+  title={This startup is setting a DALL-E 2-like AI free, consequences be
+    damned},
+  year={2022},
+  url="https://techcrunch.com/2022/08/12/a-startup-wants-to-democratize-the-tech-behind-dall-e-2-consequences-be-damned"
+}
+
+@article{khan2012energy,
+  title={Energy Consumption Of The Human Body},
+  author={Khan, Donish},
+  year={2012},
+  url="http://large.stanford.edu/courses/2012/ph240/khan1/"
+}

_posts/2022-11-15-animals-and-machines.md

@@ -0,0 +1,107 @@
+---
+layout: post
+title: "Animals and Machines: A Misled Comparison"
+subtitle: "I find comparing animals and machines absurd"
+date: 2022-11-13 00:00:00
+permalink: animals-and-machines/
+categories: personal, science
+published: false
+math: true
+author: Mahdi
+---
+
+I find comparing animals and machines absurd, because of course, animals win!
+What am I talking about here, what am I comparing? I've had multiple occasions
+where I have had to defend the stance that animals, and in general, biological
+beings are much more efficient and intelligent than human-made
+machines and AI. Let's first set the stage.
+
+## Intelligence
+
+What do I mean when I talk about intelligence? I think the definition I find on
+Wikipedia is a fair one:
+
+> Intelligence has been defined in many ways: the capacity for abstraction,
+logic, understanding, self-awareness, learning, emotional knowledge, reasoning,
+planning, creativity, critical thinking, and problem-solving. More generally, it
+can be described as the ability to perceive or infer information, and to retain
+it as knowledge to be applied towards adaptive behaviors within an environment
+or context. {% cite enwiki:1120152608 %}
+
+## Efficiency
+
+This I define as the ratio of useful output from a system to the amount of
+energy it needs to do carry the action.
+
+# An Absurd Comparison
+
+"This AI is much more intelligent than dogs", or even in more extreme cases "This
+AI is better than humans!".
+
+Some AI achievements are _impressive_, for sure. Stable Diffusion or Dall-E
+achieve impressive results. GPT-3 can be impressive sometimes, self-driving cars
+are also sometimes impressive, but being impressive is not the same as being
+intelligent or efficient, let's dissect what goes on behind such impressive
+feats of AI, and then we can look at the factors in the open.
+
+## Stable Diffusion
+
+What does it take for Stable Diffusion to create an image given some text?
+
+The dataset used to train Stable Diffusion is the
+[LAION-5B](https://laion.ai/blog/laion-5b/) dataset with 5.85 billion image-text
+pairs. {% cite techcrunch-stability-ai %}
+
+This means, we first had to have 5.85 billion images made by humans, and then
+labelled by humans, that's a ton of energy and time spent on the training data
+of this model.
+
+To train the model, 100 Nvidia A100 GPUs were used, for a total of 150,000
+GPU-hours, at a cost of $600,000. {% cite techcrunch-stability-ai %}
+
+Let that number sink in, 150,000 GPU-hours were required to train this model
+with 5.85 billion images. Nvidia A100 GPU has a max thermal design power (TDP) of 300W, and
+while TDP is not the best measure of actual power consumption, it can serve as a
+ballpark. So this GPU uses 300W of power per GPU hour, which is 45000kW for
+150,000 GPU hours, this is discounting the energy consumption of all the other
+components of the computers training stable diffusion.
+
+## Human
+
+Now imagine I asked a human artist to draw the same image I asked of Stable
+Diffusion. I'm pretty sure this human has not seen 5.85 billion images with text
+prompts, and I'm also pretty sure they have not had to spend $600,000 for
+training (including surviving and feeding themselves), and they also did not
+have to use as much energy as 150,000 GPU-hours of Nvidia A100s. A human body
+consumes food to generate energy, and the basic amount of energy consumption of
+the human body is about 4kJ/kilogram of body weight and daily hour {% cite
+khan2012energy %}. To get watts per hour, we can use the formulas below:
+
+Power in watts $P_W$ is equal to the energy in joules $E_j$, divided by the time period in
+seconds $t_s$:
+
+$P_W = E_j / t_s$
+
+So given that I weigh 70kg, my body consumes around 280kJ per hour, plugging
+into the formula:
+
+$P_W = 280000 / 3600$
+
+$P_W = 77.7$
+
+So my body consumes somewhere around 77.7 watts per hour, that's only 680652 watts or
+680kW per year! With this consumption, I could live 66 years before I would
+consume the same amount of energy as the training procedure of Stable Diffusion.
+
+I hope we can agree that as impressive as Stable Diffusion is, it does not beat
+a good human artist, and it sure is not as efficient as a human. I think to say that any
+AI is smarter than humans in any subject, must take into account the efficiency
+of the system as well.
+
+## Hummingbird
+
+My favourite example when it comes to comparing animals and machines, is the
+tiny hummingbird, which I think is more impressive than any machine made by
+humans, let me explain!
+
+{% bibliography --cited %}