431–440 of 589 posts
> and diminished sense of ownership over their own writing. Anecdotally, this is how I felt when I tried out AI agents to help me write code (vibe coding). I always review the code and I ask it to break it down into smaller steps but because I didn't actually write and think of the code myself, I don't have it all in my brain. Sure, yes I can spend a lot of time really going through it and building my mental model bu…
I mean, ultimately, I didn't write it myself. It's more of a "remix" of other people's code. Or like if I translated this comment into French. It wouldn't improve my French so why would vibe coding be expected to improve one's programming ability?
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> LLMs, by their very nature are probabilistic. Probabilistic is NOT deterministic. Although I'm on the side of getting my hands dirty, I'm not sure if the difference is that different . A modern compiler embeds a considerable degree of probabilistic behaviour.
> A modern compiler embeds a considerable degree of probabilistic behaviour. Can you give some examples?
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>These 54 participants were between the ages of 18 to 39 years old (age M = 22.9, SD = 1.69) and all recruited from the following 5 universities in greater Boston area: MIT (14F, 5M), Wellesley (18F), Harvard (1N/A, 7M, 2 Non-Binary), Tufts (5M), and Northeastern (2M) (Figure 3). 35 participants reported pursuing undergraduate studies and 14 postgraduate studies. 6 participants either finished their studies with MSc…
The experimental setup is hopelessly flawed. It assumes that people’s tasks will remain unchanged in the presence of an LLM. If the computer writes the essay, then the human that’s responsible for producing good essays is going to pick up new (probably broader) skills really fast.
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As the other comment said, LLMs are not an abstraction. An abstraction is a deterministic, pure function, than when given A always returns B. This allows the consumer to rely on the abstraction. This reliance frees up the consumer from having to implement the A->B, thus allowing it to move up the ladder. LLMs, by their very nature are probabilistic. Probabilistic is NOT deterministic. Which means the consumer is neve…
>LLMs, by their very nature are probabilistic. So are compilers, but people still successfully use them. Compilers and LLMs can both be made deterministic but for performance reasons it's convenient to give up that guarantee.
This article was probably written by AI, because anyone with half a brain could not read the study and come to the same conclusions. Basically, participants spent less than half an hour, 4 times, over 4 months, writing some bullcrap SAT type essay. Some participants used AI. So to accept the premise of the article, using an AI tool once a month for 20 minutes caused noticeable brain rot. It is silly on its face. What…
Problem with LLMs is, when you pass hours feeding prompts to solve a problem, you actually did help (a lot!) to produce the output.
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do you feel this way about every study with N~=54? For instance the GLP-1 brain cancer one?
You'll need to specify the study, I see several candidates in my search, several that are quite older. Generally, yes, low N is unequivocally worse than high N in supporting population-level claims, all else equal. With fewer participants or observations, a study has lower statistical power, meaning it is less able to detect true effects when they exist. This increases the likelihood of both Type II errors (failing t…
The study showed that 0 of the AI users could recall a quote correctly while more than 50% of the non AI users could.
A sample of 54 is far, far larger than is necessary to say that an effect that large is statistically significant.
There could be other flaws, but given the effect size you certainly cannot say this study was underpowered.