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Users debate AI terminology and share AI/ML learning progress

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 15 sources

One user is advocating for more precise terminology, suggesting that Large Language Models (LLMs) should not be broadly categorized as 'AI' but rather as a specific type of Machine Learning. Other posts detail a user's ongoing learning journey in AI and ML, focusing on statistical concepts like hypothesis testing, chi-square tests, and comparing means and proportions. Another post highlights a blog entry discussing the use of '99%-tools,' which likely refers to tools with a high degree of uncertainty or imprecision. AI

Summary written by gemini-2.5-flash-lite from 15 sources. How we write summaries →

IMPACT Promotes more precise language in AI discussions and shares foundational statistical learning for AI/ML practitioners.

RANK_REASON The cluster contains opinion pieces and personal learning logs related to AI and ML terminology and concepts, rather than a specific event or release.

Read on Mastodon — sigmoid.social →

COVERAGE [15]

  1. Mastodon — sigmoid.social TIER_1 · [email protected] ·

    Stop naming Large Language Models as AI - you are better than that! :revblobfoxdrakedislike: What type of Machine Learning (ML) LLMs fall under and how ML maps

    Stop naming Large Language Models as AI - you are better than that! :revblobfoxdrakedislike: What type of Machine Learning (ML) LLMs fall under and how ML maps to specific type of definition of Artificial Intelligence - read on my Wiki :blobcatreading: Be smarter than the whole t…

    LINKS source.gyt.is/…/Large-Language-Models source.gyt.is/…/Machine-Learning source.gyt.is/…/Artificial-Intelligence
  2. Mastodon — sigmoid.social TIER_1 · [email protected] ·

    Day 86 of learning AI/ML I studied Chi-square tests (tables & association) • Frequency & contingency tables • Chi-square test for homogeneity • Chi-square test

    Day 86 of learning AI/ML I studied Chi-square tests (tables & association) • Frequency & contingency tables • Chi-square test for homogeneity • Chi-square test for independence • Testing relationships between variables # LearnInPublic # AI # ML

  3. Mastodon — sigmoid.social TIER_1 · [email protected] ·

    Day 81 of learning AI/ML I studied Hypothesis testing for a mean • Writing hypotheses (mean) • Conditions for t-test • When to use z vs t • Calculating t-statis

    Day 81 of learning AI/ML I studied Hypothesis testing for a mean • Writing hypotheses (mean) • Conditions for t-test • When to use z vs t • Calculating t-statistic • Finding & comparing p-values • Making conclusions from test # LearnInPublic # AI # ml

  4. Mastodon — sigmoid.social TIER_1 · [email protected] ·

    Day 80 of learning AI/ML I studied Hypothesis testing for proportions • Constructing null & alternative hypotheses • Conditions for z-test (proportion) • Calcul

    Day 80 of learning AI/ML I studied Hypothesis testing for proportions • Constructing null & alternative hypotheses • Conditions for z-test (proportion) • Calculating p-value from z-score • Making conclusions from test results # LearnInPublic # AI # ML

  5. Mastodon — sigmoid.social TIER_1 · [email protected] ·

    Day 79 of learning AI/ML I studied Hypothesis testing (errors & power) • Type I error, type II error (false negative) • Power of a test (detecting true effect)

    Day 79 of learning AI/ML I studied Hypothesis testing (errors & power) • Type I error, type II error (false negative) • Power of a test (detecting true effect) • Trade-off between errors & significance • Real-world consequences of decisions # LearnInPublic # AI # ML

  6. Mastodon — sigmoid.social TIER_1 · [email protected] ·

    Day 78 of learning AI/ML I studied Hypothesis testing @khanacademy Unit 12 • Idea behind hypothesis testing • Null vs alternative hypothesis • p-values & signif

    Day 78 of learning AI/ML I studied Hypothesis testing @khanacademy Unit 12 • Idea behind hypothesis testing • Null vs alternative hypothesis • p-values & significance levels • Estimating p-values (simulation) • Using p-values to draw conclusions # LearnInPublic # AI # ML

  7. Mastodon — fosstodon.org TIER_1 · [email protected] ·

    # ThoughtProvoker 🤔 I am # antiAI in the sense that the disruptive introduction of # LLM platforms into society, and damn the externalities, makes # AI utterly

    # ThoughtProvoker 🤔 I am # antiAI in the sense that the disruptive introduction of # LLM platforms into society, and damn the externalities, makes # AI utterly inhumane technology. What I do not think is very effective, is the anti-AI # activism that involves attacking or 'ostrac…

  8. Mastodon — fosstodon.org TIER_1 · [email protected] ·

    Day 85 of learning AI/ML I studied Chi-square tests (categorical data) • Inference for categorical data • Chi-square distribution (intro) • Goodness-of-fit test

    Day 85 of learning AI/ML I studied Chi-square tests (categorical data) • Inference for categorical data • Chi-square distribution (intro) • Goodness-of-fit test • Chi-square statistic • Interpreting results # LearnInPublic # AI # ML

  9. Mastodon — fosstodon.org TIER_1 · [email protected] ·

    New Blogroll Post “Working with 99%-tools” by Abhinav Tushar «Full post on the site» # Llm # Personal # Ai # Ml # blog # indieweb https:// lepisma.xyz/journal/2

    New Blogroll Post “Working with 99%-tools” by Abhinav Tushar «Full post on the site» # Llm # Personal # Ai # Ml # blog # indieweb https:// lepisma.xyz/journal/2026/05/01 /working-with-uncertain-tools/index.html?ref=blr.indiewebclub.org

  10. Mastodon — fosstodon.org TIER_1 · [email protected] ·

    Day 84 of learning AI/ML I studied Comparing means • Statistical significance (real example) • Difference of sample means distribution • Confidence interval for

    Day 84 of learning AI/ML I studied Comparing means • Statistical significance (real example) • Difference of sample means distribution • Confidence interval for difference of means • Hypothesis test for difference of means # LearnInPublic # AI # ML

  11. Mastodon — fosstodon.org TIER_1 · [email protected] ·

    Day 83 of learning AI/ML I studied Comparing population proportions • Comparing two population proportions • Hypothesis testing for proportions • Interpreting s

    Day 83 of learning AI/ML I studied Comparing population proportions • Comparing two population proportions • Hypothesis testing for proportions • Interpreting statistical significance • Drawing conclusions from experiments # LearnInPublic # AI # ML

  12. Mastodon — fosstodon.org TIER_1 · [email protected] ·

    Day 82 of learning AI/ML I studied Hypothesis testing (summary) • Hypothesis testing & p-values • One-tailed vs two-tailed tests • z vs t statistics • Small vs

    Day 82 of learning AI/ML I studied Hypothesis testing (summary) • Hypothesis testing & p-values • One-tailed vs two-tailed tests • z vs t statistics • Small vs large sample tests • Proportion hypothesis testing # LearnInPublic # AI # ML

  13. Mastodon — mastodon.social TIER_1 Русский(RU) · [email protected] ·

    Large Models, Small Tokens. LLMs - The Battle for Context (Part 1) Why Understanding Tokens, Weights, and Dictionaries is Key to Productive Work with AI Agents. Chapter 1

    Большие модели, маленькие токены. ЛЛМ - битва за контекст (ч.1) Почему понимание токенов, весов и словарей — ключ к продуктивной работе с AI-агентами. Первая глава цикла “Битва за контекст”. https:// habr.com/ru/articles/1033230/ # ai # tokenizer # llm

  14. Mastodon — mastodon.social TIER_1 · [email protected] ·

    Instead of "don't ask, don't tell" the PR templates should contain "I swear I didn't use any LLM or other so called genAI for this PR, on my honor" and see who

    Instead of "don't ask, don't tell" the PR templates should contain "I swear I didn't use any LLM or other so called genAI for this PR, on my honor" and see who removes it! # AI # noAI # LLM # LLMs # rust # rustlang

  15. Mastodon — mastodon.social TIER_1 · 0xchitra ·

    Day 87 of learning AI/ML I studied Inference for regression slope • Inference about slope (linear regression) • Conditions for valid inference • Confidence inte

    Day 87 of learning AI/ML I studied Inference for regression slope • Inference about slope (linear regression) • Conditions for valid inference • Confidence interval for slope • t-statistic for slope • Using p-value to conclude # LearnInPublic # AI # ML

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