Feynman is no random celebrity. In addition to be a renowned physicist, his famous "Feynman Lectures" and his thoughts on pedagogy are similarly legendary.

So far, no one has mentioned "Bug Algorithms", which have a similar structure of (1) walk in the direction of the goal, (2) walk around obstacles as they are encountered, (3) leave the obstacle to proceed when some condition is met. They are very simple to implement (though not optimal) and there are a number of variants to play around with. Howie Choset has some good lecture slides that describe them [1]. However, as some others have mentioned, something like Jump Point Search [2] is likely a better option given the described scenario.

[1] https://www.cs.cmu.edu/~motionplanning/lecture/Chap2-Bug-Alg... [2] https://en.wikipedia.org/wiki/Jump_point_search

The idea that this is a drop in replacement for numpy (e.g., `import cupy as np`) is quite nice, though I've gotten similar benefit out of using `pytorch` for this purpose. It's a very popular and well-supported library with a syntax that's similar to numpy.

However, the AMD-GPU compatibility for CuPy is quite an attractive feature.

Similar: XKCD conducted a color survey back in 2010 [1]. The results are detailed and the writeup quite interesting.

[1] https://blog.xkcd.com/2010/05/03/color-survey-results/

I'd also like to mention the classic book "Reinforcement Learning" by Sutton & Barto, which goes into some relevant mathematical aspects for choosing the "best" among a set of options. They have a full link of the PDF for free on their website [1]. Chapter 2 on "Multi-Armed Bandits" is where to start.

[1] http://incompleteideas.net/book/the-book-2nd.html

Doesn't Slack violate that constraint? Slack allows (or at least seems to) using iOS system emoji to respond to messages.

This is true enough, though remember that material properties change dramatically when you start moving through wavelengths by orders of magnitude. Silicon is transparent in the mid-infrared, which is what makes silicon photonics possible [1]

[1] https://en.wikipedia.org/wiki/Silicon_photonics

This is a cynical take on what I think is a meaningful benefit that the above commenter is talking about. If you use ChatGPT to learn concepts and approach it with the caveat that you should make sure you understand what you are trying to learn (something that helps to verify that what is being presented to you by the system is "accurate"), it's a fantastic resource. That isn't to say it's true for learning all things, but I'm generally impressed and optimistic for ChatGPT to be as valuable a teaching/learning resource as any other I might find on the web.

I have some mixed feelings about this. I used to do this ~10 years ago when I was a distance runner, but now exercising so hard will throw my body off in other ways and I don't feel good having pushed so hard. Perhaps this is exactly the point you're making, but I don't target zone 5 for long these days. Deep sleep, sure, but at what cost to the rest of my body?

Agreed! I've never seen something like this before, but it seems that all articles in this (nascent) journal have the same sort of information. Does anyone know if this is standard across journals for research in this space?

It seems like using np.random.choice is indeed a slow way to get a grid in which 5% of the values are 1. I would recommend using np.random.rand(size) >= mutations:

  > python3 -m timeit 'import numpy as np; mutations=0.05; rows=10; columns=10; np.random.choice([0,1], p=[(1-mutations), mutations],size=(rows,columns))'
  50000 loops, best of 5: 8.88 usec per loop`

  > python3 -m timeit 'import numpy as np; mutations=0.05; rows=10; columns=10; np.random.rand(rows,columns) <= mutations'
  200000 loops, best of 5: 1.06 usec per loop

Excited for this, but I think with all this conversation about the role an AI assistant should play in work and development, this line feels incomplete to me:

> Think of Claude as a friendly, enthusiastic colleague or personal assistant who can be instructed in natural language to help you with many tasks.

It omits that the colleague may have outdated knowledge or not understand whatever problem you give it. The colleague's "enthusiasm" should be tempered with oversight so that the outputs they produce are not directly used without scrutiny. It seems that most people using these tools increasingly understand this, but to leave it off the website seems ... sloppy at this point.

Edit: upon logging in, I'm greeted by a warning "It may occasionally generate incorrect or misleading information, or produce offensive or biased content."

Rewatching Person of Interest now. Amazing how relevant it is; aged incredibly well.