snek_boi

Sorry if you know this game, but I've met plenty of people who haven't and I've never seen it mentioned online until I searched for it today: Contact!

It sounds like you really care about fairness, in the sense of giving credit to the hard work behind learning. Do you know the phrase “dead metaphor”?

Came here to say this. I would like to know the definition (and its theory behind) to have a conversation about it, but I won’t watch three hours of a video to get the answer (or not!).

Totally. The history of intelligence has sadly also been the story of eugenics. Fortunately, there have been process-based theories and contextual theories that have defined intelligence in more humane and useful ways. In this view, IQ tests do not measure an underlying characteristic, but a set of mental skills. Seen this way, intelligence becomes something people can gain with nurturance. If you’re interested, check out Relational Frame Theory.

Ah, I see how my wording was confusing. I mean planning in the sense of “How will we complete the work that we already committed to?” and “What will we do today to achieve our Sprint goal?”

I arrived at the word planning because Scrum is sometimes described as a planning-planning-feedback-feedback cycle. You plan the Sprint, you plan daily (Daily Scrums), you get feedback on your work (Sprint Review), and you get feedback on your process (Sprint Retrospective).

lol I hope your standups are not actually like this! The purpose is to, as a team, plan what the team will do today to achieve the Sprint goal

Professionals have large networks of neurons. They are sturdy and efficient from repeated use. Memory palaces help to start the construction of these large networks of neurons. Afterwards, as another commenter noted, the knowledge is deeply processed. Mnemonics are replaced by networks of meaning. It is no longer “This algorithm rhymes with tomato”, but “This algorithm is faster if the data is stored in faster hardware, but our equipment is old so we better use this other algorithm for now”.

Broadly, the progression of learning is: superficial learning, deep learning, and transfer. Check out Visible Learning: The Sequel by John Hattie for more on this.

Edit: To directly answer your question, experts have so many sturdy neural hooks on which to hang new knowledge that mnemonics become less and less necessary. Mnemonics may be particularly helpful when first learning something challenging, but are less necessary as people learn.

You could also check out a paradox called the expert paradox. We used to think memory is boxes that get filled. This idea was directly challenged by Craik and Lockhart’s Levels of Processing. Levels of processing supports the idea that “the more you know, the faster you learn”. Note that this is domain-specific. In other words, an expert in dog training won’t learn quantum mechanics faster than anyone else.

I’d say feeling admiration for others. People who are kind, patient, insightful, and critical thinkers. People who look at how political goods (including wealth) are distributed and can think critically about it. Nutomic and Dessalines for sure.

I see your concern for truth in any scenario, and I agree validity should be a constant consideration! However, bias and astroturfing are different. Bias is the lens that we use to look at reality. Astroturfing is forcing lenses onto many others without them knowing. It is a deliberate campaign.

This is accurate for neoclassical economics. However, I wonder how the comic would change with a nuanced understanding of how neoclassical economics differs from classical economics.

Ultimately, yeah. The article points out that the way they want to do it is with unique designs, carbon neutrality, and transparency in the production chain.

I agree that we shouldn't jump immediately to AI-enhancing it all. However, this survey is riddled with problems, from selection bias to external validity. Heck, even internal validity is a problem here! How does the survey account for social desirability bias, sunk cost fallacy, and anchoring bias? I'm so sorry if this sounds brutal or unfair, but I just hope to see less validity threats. I think I'd be less frustrated if the title could be something like "TechPowerUp survey shows 84% of 22,000 respondents don't want AI-enhanced hardware".