↳ In-reply-to » For the innocent bystanders (because I know that I won’t change @bender’s opinion):

@movq@www.uninformativ.de Gemini liked your opinion very much. Here is how it countered:

1. The User Perspective (Untrustworthiness)

The criticism of AI as untrustworthy is a problem of misapplication, not capability.

• AI as a Force Multiplier: AI should be treated as a high-speed drafting and brainstorming tool, not an authority. For experts, it offers an immense speed gain, shifting the work from slow manual creation to fast critical editing and verification.
  
• The Rise of AI Literacy: Users must develop a new skill—AI literacy—to critically evaluate and verify AI’s probabilistic output. This skill, along with improving citation features in AI tools, mitigates the “gaslighting” effect.
  

2. The Moral/Political Perspective (Skill Erosion)

The fear of skill loss is based on a misunderstanding of how technology changes the nature of work; it’s skill evolution, not erosion.

• Shifting Focus to High-Level Skills: Just as the calculator shifted focus from manual math to complex problem-solving, AI shifts the focus from writing boilerplate code to architectural design and prompt engineering. It handles repetitive tasks, freeing humans for creative and complex challenges.
  
• Accessibility and Empowerment: AI serves as a powerful democratizing tool, offering personalized tutoring and automation to people who lack deep expertise. While dependency is a risk, this accessibility empowers a wider segment of the population previously limited by skill barriers.
  

3. The Technical and Legal Perspective (Scraping and Copyright)

The legal and technical flaws are issues of governance and ethical practice, not reasons to reject the core technology.

• Need for Better Bot Governance: Destructive scraping is a failure of ethical web behavior and can be solved with better bot identification, rate limits, and protocols (like enhanced robots.txt). The solution is to demand digital citizenship from AI companies, not to stop AI development.

↳ In-reply-to » I'm in an article in Quanta Magazine! It's about the bizarre world of algorithms that re-use memory that's already full. https://www.quantamagazine.org/catalytic-computing-taps-the-full-power-of-a-full-hard-drive-20250218/ I'm the one with all the snow in the background.

@lyse@lyse.isobeef.org I am a big fan of “obvious” math facts that turn out to be wrong. If you want to understand how reusing space actually works, you are mostly stuck reading complexity theory papers right now. Ian wrote a good survey: https://iuuk.mff.cuni.cz/~iwmertz/papers/m23.reusing_space.pdf . It’s written for complexity theorists, but some of will make sense to programmers comfortable with math. Alternatively, I wrote an essay a few years ago explaining one technique, with (math-loving) programmers as the intended audience: https://www.falsifian.org/blog/2021/06/04/catalytic/ .

(#2024-09-24T12:44:35Z) There is a increase in space/memory for sure. But calculating the hashes also takes up CPU. I’m not good with that kind of math, but it’s a tradeoff either way.

@movq@www.uninformativ.de Haha! yeah sounds about like my HS CS program. A math teacher taught visual basic and pascal. and over on the other end of the school we had “electronics” which was a room next to the auto body class where they had a bunch of random computer parts scavenged from the district decommissioned surplus storage.

The advanced class would piece together training kits for the basic class to put together.

So today’s #adventofcode was solved with no programming. Just a bit o maths and wolfram/alpha

↳ In-reply-to » I used to be a big fan of a service called cocalc, which you could also self host. It was kind of an integrated math, data science, research, writing, and teaching platform.

@prologic@twtxt.net It was super useful if you needed to do the sorts of things it did. I’m pretty sad.

At its core was Sage, a computational mathematics system, and their own version of Jupyter notebooks. So, you could do all kinds of different math stuff in a notebook environment and share that with people. But on top of that, there was a chat system, a collaborative editing system, a course management system (so if you were teaching a class using it you could keep track of students, assignments, grades, that sort of thing), and a bunch of other stuff I never used. It all ran in a linux container with python/conda as a base, so you could also drop to a terminal, install stuff in the container, and run X11 applications in the same environment. I never taught a class with it but I used to use it semi-regularly to experiment with ideas.

I used to be a big fan of a service called cocalc, which you could also self host. It was kind of an integrated math, data science, research, writing, and teaching platform.

I hadn’t run it in awhile, and when I checked in with it today I found their web site brags that cocalc is now “extensively integrated with ChatGPT”.

Which means I can’t use it anymore, and frankly anyone doing anything serious shouldn’t use it either. Very disappointing.

Posted to Entropy Arbitrage: Colors for Programmers https://john.colagioia.net/blog/2020/03/11/colors.html #techtips #programming #design #color #format #math

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