hazrmard

This is my takeaway:

Authors analyzed ~300 workers doing ~1000 tasks. They collected self-reported measure of using critical thinking [yes/no], and estimate of cognitive effort of doing the task. Authors also measured workers' confidence in themselves doing the task or the AI doing the task.

They found: if someone is confident that AI can do the task, they report using critical thinking less, even though they think the task is easy. When someone is confident in doing the task and in evaluating AI responses, they use critical thinking more, even though they think the task is harder.

I think the paper is saying: low-domain expertise (read: confidence) + unrestricted AI tooling can eliminate the human from the loop. The fallout is better automation or unreliable systems. We need to resolve this ambiguity.

The question is: should low-domain expertise be coupled with more restricted AI tooling as a critical thinking forcing function? Are schools/workplaces right in restricting AI so students/new workers can reach the point of high domain expertise?

A breakdown of why the awarded work, Ice Cube, is significant:

  - A neutrino is an elementary subatomic particle. Neutrinos are produced by nuclear reactions inside stars, supernovae, radioactive decay. They are one of the most abundant particles in the universe.
  - Neutrinos are known as "ghost particles". They have 0 charge and near-zero mass. They only react with the weak nuclear force and gravity. Incredibly hard to detect. Trillions can pass through a whole planet without hitting a single atom!
  - Neutrinos give us a pristine snapshot into the origin of the universe. They have travelled billions of years and trillions of miles without interacting with anything. Unless...we catch them!
  - Ice Cube does this. Located in Antarctica, the project turns a cubic kilometer of ice into a neutrino detector. How? Scientists drilled boreholes 2.5km deep into the ice and placed 5000 optical detectors to catch a neutrino interaction.
  - When a neutrino, rarely, collides with an atom, it produces charged particles. In ice - not vacuum! - certain particles can travel faster than light. This produces something similar to a sonic boom. A faint, blue glow known as Cherenkov radiation (see it in action https://youtube.com/watch?v=hSuSG19Pcoc).
  - IceCube was first to detect neutrinos coming from outside the solar system, establishing the source of high energy cosmic radiation. It also opened a whole new chapter of neutrino astronomy.

This is not intended as a product launch. I think this is a preemptive bet on a conjunction of the following happening in time for a future product launch:

  1. Cost to launch / mass lowering due to technological advancements.  
  2. Mass / compute lowering due to technological advancements.  
  3. Compute hardware evolution stagnating to the pace, relative to compute payloads, where CapEx is amortized over longer duration.  
  4. Compute & energy hardware reliability improving so OpEx is lower, and remaining useful life predictably keeps hardware running over its designed lifetime.

(I work on #4).

A wishy-washy inequality (per time, factoring out launch cost from OpEx):

    Revenue - CapEx/lifetime - (launch_cost + OpEx) * (1 + failures) > 0

Presumably, any one of these could see a x100 improvement and make the whole equation in the green. But, I believe this is a low-earth-orbit-shot bet on the sum getting better.

Note: I did 1+failures to denote baseline cost (initial launch, nominal OpEx).

Turns out the balance of my PhD thesis was coming up with ways to cheer up disappointed drones >:(

I resonate with the author's point that I can modify my own website to my will.

I use hugo to build my website and I wrote my own theme. It's clunky, but control over the design/build lets me:

  - Put PyIodide into posts to make ML code explainers interactive (https://iahmed.me/post/poor_mans_autograd/)
  - Inject custom widgets to illustrate theoretical ideas (https://iahmed.me/post/surprise-derivation-entropy/)
  - Have multiple layouts to suit my needs (https://iahmed.me/resume/ rendered from json at build time for SEO)

On the flip side, I'm not good at SEO :) It works because SEO is not my primary goal, but I'd love to have a bigger audience. The flexibility I'm afforded is an ok compromise.

Does the peak starting in mid-2020 correspond to mass speculative / adversarial / bandwagon hiring by tech companies during the pandemic?

Looks fun, but I don't quite understand this product:

  - Do the buttons map to configurable skills / prompts?
  - Is it meant to be used remotely with some independence (like codex remote), or is it a peripheral like a trackpad?

You'd be right to be surprised by lack of mention of quaternions. I (blog author) was not too familiar with them when I did the work this post is based on. With the benefit of hindsight, I may yet revise the post.

Hello, blog author here. I agree the math appears overkill. I wrote this as a learning aid for myself with the benefit of hindsight of having worked with some drone sims. I wanted to dispel any doubt in my mind that I could derive drone physics from basic principles, instead of copy-pasting state equations. I went further and tried to motivate transport theorem, rotational analog of F=ma etc from scratch.

In summary: I take F=ma and extend it for rotational motion. (1) Calculating linear motion when the vehicle containing sensors is rotating. (2) Calculating rotation of the vehicle itself due to thrust/yaw force acting about its center of mass.

I'll echo what the other commenter said: this is no way PhD math. It may appear so - but I'm only being verbose with simpler concepts like cross products and rotation matrices.

It's my blog. Bartosz Ciechanowski's work was my inspiration!

Good work! I wonder if meta-learning can play a better role here compared to heuristics or hindsight. MAML requires hessians, but first-order MAML or Reptile variants could help apply layer-wise adjustments to learning rates.