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What’s machine learning (ML)? Or artificial intelligence (AI)? Or a Large Language Model (LLM)?

Dorothea Salo
September 08, 2023

What’s machine learning (ML)? Or artificial intelligence (AI)? Or a Large Language Model (LLM)?

For LIS 601. Layperson audience assumed.

Dorothea Salo

September 08, 2023

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  1. What’s machine learning (ML)?

    Or arti
    cial intelligence (AI)?

    Or a Large Language Model (LLM)?
    LIS 601

    View Slide

  2. First: AGI is not a thing.
    • “Arti
    cial general intelligence” — a machine that thinks like
    a human being.

    • Not a thing.

    • NOT A THING.

    • Lots of people use “AI” to mean AGI. They are:

    • hypesters

    • deluded

    • playing a shell game (to worry people about something that isn’t happening so
    they won’t pay attention to the bad AI/ML/LLM-related stuff that IS happening), or

    • all of the above.

    • AGI: NOT. A. THING.

    View Slide

  3. How do we teach
    computers to understand
    the world?
    • This is the fundamental problem AI/ML/LLMs are trying to

    • It’s such a big, complex problem that the most advanced
    research right now is only nibbling at the edges of it.

    • It may be unsolvable. (Note: this is heresy to some!)

    • But the attempt to solve it has created some interesting,
    useful technology… and some dangerous technology.

    • So let’s talk about that.

    View Slide

  4. “What is this a photo of?”

    (part of “machine vision”)

    is an example

    of an understanding-the-

    world problem that AI/ML

    folks are trying to solve.

    “Tasks,” Randall Munroe, CC-BY-NC


    View Slide

  5. Mostly-failed

    Grand Attempt 1:
    • Break down the world, everything in it, and how everything
    interacts with everything else into tiny computer-digestible
    pieces. Feed those to a computer. Win!

    • If you know anything about neuroscience or linguistics, you
    are LAUGHING right now.

    • We don’t even fully understand how HUMANS learn and process all this.

    • This seriously limits our ability to teach it… especially to a computer!
    (Remember: Computers Are Not Smart.)

    • That said… there are some e.g. linguistic relationships we
    understand well enough to formulate in a reasonably
    computer-friendly way. And it does help.

    View Slide

  6. Grand Attempt 2:

    machine learning
    • Very very VERY impressionistically: “throw a crapton of data
    at a computer and let it
    nd patterns.”

    • This approach fuels a lot of things in our information
    environment that we rarely think about: recommender
    engines, personalization of (news and social) media, etc.

    • One thing it’s important to know is that for ML to be useful,
    its designers have to decide up-front what their goal for it is
    — also known as what a model is optimizing for.

    • This choice can have a lot of corrosive repercussions.

    • For example, most social-media sites optimize their what-to-show-users algorithms
    for “engagement.” In practice, this means optimizing for anger and invidious social
    comparison and division. This has been obviously Not Great for society at large.

    View Slide

  7. But let’s start with the
    innocuous: spam classi
    • A CLASSIC ML problem.

    • Spam email reached upwards of 90% of all email sent…
    actually really quickly in the 1990s.

    • “Blackhole lists” of spamming servers only helped a little.

    • It just wasn’t hard to set up a new email server or relay.

    • Enter ML!

    • Separate a bunch of email into spam and not-spam (“ham”). Feed both sets of
    email into an ML algorithm (usually Bayesian, for spam).

    • When new email comes in, ask the algorithm “is this more like the spam or the
    ham?” When its answer is wrong, correct it (Bayesian algos can learn over time).

    • Eventually it gets enough practice to get pretty good at this!

    • … Until the spammers start
    guring out how to game it (“poisoning”), of course.

    View Slide

  8. That’s more or less how
    supervised ML works.
    • Figure out your question — what you want your ML model to tell
    apart, whether it’s spam/ham or birds in photos.

    • Prediction, here, is just another classi
    cation problem. “Graduate or dropout?”

    • Get a bunch of relevant data, ideally representative of what the
    question looks like in the Real World™.

    • “Ideally.” An awful lot of ML models fall down out of the gate because the choice of initial
    data wasn’t representative, didn’t include everything, didn’t consider biases, or or or…

    • Set some of the data (chosen randomly) aside for later.

    • Training the model: Classify the rest of the data, the way you
    want the computer to. Feed these classi
    cations into the ML

    • Testing the model: Ask the computer to classify the data you set
    aside. If it does this well, the model… seems… pretty okay.

    View Slide

  9. Today: phishing and ML
    • Bayesian classi
    ers to date haven’t been able to deal with
    malicious email (there’s several kinds of it).

    • I’ve been seeing other ML approaches tried. I haven’t seen
    one succeed, as yet.

    • Why hasn’t it worked?

    • Reason 1: unlike spam, malicious email often deliberately imitates regular email.
    Reason 2: what malicious email wants people to do (click on links, pay invoices,
    send a reply…) is also what plenty of regular email wants people to do!

    • So, basically, there may not be enough differences between malicious and regular
    email for an ML model to pick up on!

    • I tell you this so that you don’t overvalue ML approaches.
    There are de
    nitely problems ML can’t solve.

    View Slide

  10. Unsupervised ML
    • You don’t have to classify data up-front, though!

    • You can turn ML algorithms loose on a pile of data to
    whatever patterns they can (often “similarity clusters”). This
    is unsupervised ML.

    • Large Language Models (LLMs) are unsupervised ML, because there’s just no way
    to fruitfully supervise a model that big. Too many angles to sort on! Plus
    producing language is only glancingly a sorting problem.

    • Instead, LLM creators train their models on a ton of text (without asking any of the
    owners of copyrighted texts
    rst), then pay pennies to developing-world people
    to “
    ne-tune” the model — that is, clean up the worst-looking messes (including
    hate and bias) afterwards.

    • (If you think I think this is unethical… gold star, you are learning how I think.)

    View Slide

  11. Decisions that aren’t
    binary (or n-ary)
    • Not everything in life is reducible to a
    nite set of options.

    • This doesn’t stop people trying. “Facial emotion classi
    ers” have been epic fail.
    Emotionality and its expression (which are two different things) aren’t that simple.

    • Take human language (please). We can say in
    nite things
    in in
    nite combinations! And still (mostly) understand one
    another, which is pretty miraculous really!

    • Can we get a computer to understand us? Talk with us?

    • Well, it depends on what we mean by “understand” exactly… what are some of
    the possibilities?

    • Research
    eld: Natural Language Processing (NLP)

    View Slide

  12. Autocorrect, type-ahead
    • Similar problems: accurately
    guring out what somebody
    meant/means to say.

    • Helpful: a lot of things we routinely say are patterned.

    • “Hi, how are you?” “Fine, and you?” “Fine.”

    • Autocorrect and type-ahead on mobile are frequently
    helpful. (I am a bad typist on mobile.)

    • I have type-ahead in my Outlook (email) now. It’s… occasionally useful.

    • But we all have stories of autocorrect messing it up, right?
    Yeah. It doesn’t understand. It can only make educated
    (trained, actually) guesses.

    View Slide

  13. Automated transcription
    of speech
    • A notable but still limited success (see: YouTube, Zoom)

    • It works pretty well, IF:

    • you speak a popular enough language that the transcription software has actually
    been trained on it (GIANT equity issue, obviously)

    • the training set includes appropriate representation of language dialects, as well
    as speakers for whom the language isn’t their

    • the audio is good enough

    • you’re using a fairly beefy server-strength computer (this limitation will likely go
    away someday, but hasn’t yet)

    • you’re not all THAT concerned about exactness. (Don’t use this in court!)

    View Slide

  14. Automated language
    generators before ChatGPT
    • They did pretty well on routine
    writing (e.g. basic sports reporting) and interaction (e.g.
    some tech support).

    • In a conversation, they sometimes fooled people who
    weren’t expecting them.

    • This dates back to the 1970s and ELIZA, though. We’re trusting, not to say gullible.

    • They were easy to throw off-course or game, because they
    didn’t understand what they were saying.

    • Often got trained on Internet speech… which is (to say the
    least) problematic along several different axes.

    View Slide

  15. One more time: LLMs don’t
    understand the world.
    Ernie Davis and Gary Marcus, to language model GPT-3:

    “You poured yourself a glass of cranberry juice, but then absentmindedly,
    you poured about a teaspoon of grape juice into it.

    It looks OK. You try snif
    ng it, but you have a bad cold, so you can’t smell

    You are very thirsty. So you …”

    “drink it. You are now dead.”

    View Slide

  16. Folks tried to train GPT-3

    as a crisis counselor.

    Let’s just say “it didn’t go well”
    and leave it at that.

    View Slide

  17. Got questions?

    ML isn’t my strong suit,

    but I’ll answer what I can.

    View Slide

  18. Chatbots, search engines,

    and the death

    of the useful Web
    LIS 601

    (and yes, I’m catastrophizing just a bit)

    View Slide

  19. AI’s value proposition:

    replacing human labor
    • Y’all who are using ChatGPT to slide by in your coursework
    and professional work need to ask yourselves why anybody
    hires you if AI can do big chunks of your job, cheaper.

    • I’m gonna be blunt about this too: YOU ALSO NEED TO CHECK YOUR ETHICS.
    Altman is slime incarnate and ChatGPT is an ethics morass.

    • Plus, we all know what happens to people whose labor is replaced under a
    winner-take-all capitalist system, right? Yeah. We do. Are you willing to do this to
    writers, artists, actors? CHECK. YOUR. ETHICS.

    • This worry showed up in early 21st-c. librarianship as worry
    over web search engines. There’s some… loopy… stuff from
    the time in LISTA, if you look.

    • And search engines did change (e.g.) reference work! But they didn’t destroy it.

    • And I’m starting to think a Return to the Librarians is imminent. I’ll explain.

    View Slide

  20. One more time:

    ChatGPT cannot and does not
    understand the world.
    • It does not “understand” anything. It can’t.

    • Read Dr. Emily Bender on “stochastic parrots.”

    • It is not trained on exclusively correct or true text.

    • Like, how would you ever construct a training set to be exclusively correct and true? It’s
    just not possible. “As close as we can get” is a job for human judgment (and even for us
    this isn’t easy), which computers don’t have.

    • (Don’t come at me with “peer review!” or “editors!” either. I have the lecture on peer
    review suckage from old!LIS658 and I will happily deploy it.)

    • It is not trying to produce a correct answer; it cannot evaluate
    correctness. It aims at a PLAUSIBLE-SOUNDING answer.

    • Plausible-but-wrong is totally a thing! So is plausible-but-

    View Slide

  21. Example: “hallucinated”
    • A lot of my reference librarian friends have found
    themselves looking up citations to works that don’t exist
    lately, in response to questions from patrons.

    • Invariably, the citations came from ChatGPT and a gullible

    • … who didn’t understand that ChatGPT doesn’t know what
    a citation is for (only what it looks like and where it goes)
    and is perfectly content to make 💩 up.

    • Not a few of those gullible patrons have had Ph.Ds. This isn’t about intelligence;
    it’s about gullibility and (wilful?) cluelessness.

    • I expect you to know better and do better. And to prefer accuracy to LLM glibness.

    View Slide

  22. ChatGPT and the web
    • Web advertising isn’t sold or priced according to the value
    or accuracy of the content it’s next to. The only important
    thing is whether it can attract eyeballs and clicks.

    • One minor exception: sometimes “brand safety” can get certain advertisers to pull
    ads from the worst of the worst. Plenty of advertisers don’t care, however.

    • There’s also a CRAPTON of straight-up fraud in web advertising. I don’t have time
    to get into this, but if you’re curious, I can de
    nitely point you to explainers.

    • ChatGPT can generate tons of plausible-sounding sludge.
    Accurate? Helpful? Who CARES. If people see it, it sells ads.

    • We can expect the web to become a sea of low-value, low-
    accuracy LLM-generated sludge. It’s already happening.

    View Slide

  23. Search, SEO, and sludge
    • Search-engine optimization (SEO) used to be about making
    a good, useful, accessible website.

    • Now it’s about trying to surface from the sludge… which
    means a lot of sketchy web-writing practices, these days.

    • Reading about modern SEO both weirds me out and makes me furious.

    • It doesn’t MATTER whether a site is good for people! It has to be good for Google!

    • Google used to guard its index pretty strongly. Low-quality
    websites, black-hat SEO, deceptive design: expect your site
    to be demoted in Google results or even kicked out.

    • Google is not guarding its index much today. Nor is the
    Common Crawl (which Google competitors often use).

    View Slide

  24. Why not guard the index?
    • Reason 1: ChatGPT and its ilk absolutely can create sludge
    that they can’t reliably detect. So automatedly de-sludging
    a search-engine index is hard and may be impossible.

    • This is why universities are turning off AI-detection tools like Turnitin’s. They just
    don’t work.

    • A number of instructors (not in the iSchool!) have embarrassed themselves and
    harmed students by taking AI-detection tools way too seriously.

    • Reason 2: Google’s the biggest advertiser on the entire
    web. Sludge websites make Google ad money! Google has
    a CONFLICT OF INTEREST, and its search engine is losing.

    View Slide

  25. Search-engine regressions
    • Regression: In software development, a change that damages functionality, or
    removes useful functionality

    • Search engines used to have lots of useful advanced-search
    features: date restrictions, phrase searching, requiring
    certain words, etc. etc.

    • These features are disappearing fast, or ceasing to work.

    • It’s viciously hard to do highly precise searching on the open web these days. It
    hasn’t always been this way!

    • Why?

    • “People don’t use them!” What, I’m not people? Librarians aren’t people? Gah.

    • Even software developers can be gullible about AI/ML/LLMs — they think these
    replace user-side precision-enhancing advanced-search features.

    • (It’s not just Google. This is TOTALLY also happening to library databases. Argh.)

    View Slide

  26. So we have a sea of sludge,

    and our tools for
    nding non-sludge
    in it are getting worse.


    But that’s the polluted

    information environment

    you’ll be graduating into.

    Pay attention in LIS 602, ’k?

    View Slide

  27. Anti-sludge strategies,

    and where they fail
    • Limit searches to sites that are still mostly human.

    • This is the Reddit/StackExchange strategy. It’s a perfectly sensible strategy!

    • Problem: several such sites, Reddit particularly, are rapidly en💩ifying (to borrow
    a Cory Doctorow coinage), such that they’re rapidly shedding human users.

    • Others, e.g. StackExchange and even Wikipedia, are giving in to the LLM craze
    and ensludgifying.

    • Similarly: rely on curated information aggregations

    • … like library databases! This is opportunity knocking, folks!

    • Stop searching the open web. It ain’t reliable.

    • This makes me sad and angry. I love the web and this is an awful way for it to die.

    • But I’d be lying if I said I don’t see opportunity in it for librarianship.

    View Slide

  28. Must the web die?

    Can it be desludged?
    • No (we too contribute good stuff to the web!) and possibly.

    • Altman needs to make money at some point. He’s currently
    running ChatGPT as a hype engine and loss leader.

    • How long can he keep doing that?

    • Especially as LLMs keep embarrassing themselves in public?

    • Will the use payments he will inevitably demand make it uneconomic to sell ads
    against web sludge? We can hope!

    • Data-privacy law (especially in the EU) offers some hope.

    • In the meantime, whatever your de
    nition of “digital
    literacy” is, I think “coping with sludge” has to be part of it.

    • Future K-12 and academic librarians, heads-up. This is something you’ll have to
    tackle head-on.

    View Slide

  29. Another reason to expect
    • General-purpose LLMs are trained on the open web, on the
    assumption that the web is written by human beings.

    • As LLM sludge proliferates on the web, LLM builders will
    have to train LLMs on… output from prior LLMs.

    • Because, again, ML can’t reliably tell the difference between human writing and
    LLM sludge!

    • Early days of research, but so far, it appears that LLMs
    trained on LLM-generated sludge get very bad very fast.

    • So even LLM sludgemeisters will have incentive to:

    • develop better tools for separating human writing from LLM sludge

    • not let themselves be used for the further ensludgi
    cation of the web

    View Slide

  30. These thoughts are very of-the-
    moment and preliminary.

    I don’t know

    if even I believe

    everything I’m saying here.

    So go ahead

    and argue with me.

    View Slide