Why AI doesn't follow length instructions, the best $40 you can spend, and more

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Welcome to AI Goes to College, the podcast that helps higher education

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professionals navigate the changes brought on by generative AI.

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I'm your host, doctor Craig Van Slyke.

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The podcast is a companion to the AI Goes to College newsletter.

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You can sign up for the newsletter at ai goes to college dot com

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slash newsletter. Welcome to episode 4 of

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AI Goes to College. In this episode, I

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talk about generative AI's paywall problem,

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Anthropic's release of some excellent new Claude models that

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actually beat GPT. Talk about Google's

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bad week. And why generative AI doesn't

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follow linked

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versus few shot prompting and the best $40 you can

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spend on generative AI. It is not what you expect.

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I also wanted to give a shout out to Rob Crossler. If you haven't checked

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out his interview on AI Goes to College, you ought to. It's very

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interesting. Rob is a smart guy. Before we

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get into the main content though, I wanna thank Grammar Nut for catching

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a small typo on the AI Goes to College website.

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It's really great that some of the readers and listeners are so sharp eyed.

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I very much appreciate it. So here's my

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rant of the week. Large language models, as some of

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you probably know, are trained on data, and data

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that aren't included in the training data aren't reflected in the output.

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This means that biased data on the input side is reflected in biased

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output on the output side. So none of this is groundbreaking.

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We've talked about this before. You probably talked about this before with

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others. Demographic based bias

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is well known and it's a serious ethical issue related to generative

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AI. But when I was playing around over the last couple of

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weeks, it occurred to me that biased training data results in a different

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problem for academic researchers. I'm speculating here

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because I really don't know exactly what data the various large language

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models are trained on, but it seems to me that the training

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data may underrepresent articles from top academic journals,

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which is a huge problem. And here's why.

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A lot of top journals are behind paywalls of various sorts.

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For example, if I want to access a recent article

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from MIS Quarterly, I either have to get it through

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Association For Information Systems or maybe through my

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library. And a lot of top journals are like

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that. I'm not sure in other fields, but that's certainly the case in

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my field and I suspect it's the case in most fields. You know,

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sometimes we can get lucky lucky and an article's available through Google Scholar

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or some other non paywalled repository like

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ResearchGate or something like that. And eventually many

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of these articles make their way around the paywalls to become more freely

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available. But if those articles weren't available as part

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of the training data, then their

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findings may not be reflected when you interact with that

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large language model for your research.

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Now the training data may include abstracts or citations

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from articles that are included, but

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those are limited. The abstract only contains so much

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information and so you're really not going to get the full

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representation of the article. And since many of our top

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journals are behind paywalls, you may miss out

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on articles that are in top journals. That's kind of been

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my experience so far with perplexity. I think this

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problem is even bigger for disciplines that rely more on books than journal

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articles. Now, I'm not sure about the extent of the problem, but

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it's something to be aware of. For now,

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I advise caution. Look, generative AI

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is great. I'm a big believer in it, but it really

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isn't a shortcut for the hard work of scholarship.

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You still have to put in human thought and effort.

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So the big news item over the last couple of weeks is Anthropic releasing

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some new Claude models. This is a little bit

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complicated, so I'll refer you to the newsletter which is available

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at ai goes to college.com.

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But here's the low down. Anthropic,

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who's a competitor to OpenAI and produces

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competitors to JetGPT, released 3 new

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models: Claude 3 Haiku, Claude 3 Sonet,

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and Claude 3 Opus. I don't think Haiku is available

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yet, but Sonnet, Sonnet, and Opus are.

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I kind of like the names, since they're loosely

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the one that's the most capable. Both SONNET and

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Opus have 200,000 token context

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windows. So the context window is how much data the model

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can consider when it's creating its output. You can think of it as

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the model's memory capacity. Roughly, this is very

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roughly, a 200 k context window should be

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able to handle 3 to 400 pages of text. Now there are a lot of

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variables there. Opus can even

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go further for some uses. According to Anthropic, it

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can have a context window of up to 1,000,000 tokens,

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which is huge, but right now that's reserved for special

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cases. You can get the 200,000 context

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window for Claude 3 directly from Anthropic,

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or you can get it through Poe, and you know I'm a big fan of

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poe.com. But in Poe at least, Claude

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3 OPUS and Claude 3 OPUS 200 k are different

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models. Claude 3 OPUS is a smaller

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model than Claude 3 Opus 200 ks. Yeah. It gets

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confusing, but if you're trying to deal with large

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documents, either just default to the 200 k version

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or try the non 200 k version and see how things go. If they don't

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go well, then try the 200 k version. There's some

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limits through Poe of how many interactions you can have with Claude in a

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24 hour period, but they're pretty liberal, so I don't

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think it's going to be a big big deal. I don't think you're

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going to run into the usage limits for most use cases.

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What is a big deal is this ever enlarging context

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window. That just opens up a lot of interesting

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possibilities. For example, club 3

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should be able to summarize and synthesize across multiple

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journal articles in a single session. I haven't tested this out

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yet, but I'm going to soon and I will certainly let you know how it

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goes. The other big thing about Claude 3 is that

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according to Anthropic, Claude 3 outperforms

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GPT 4 across the board. Now if you go to the

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newsletter there's a nice little table, it's

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from Anthropic though, that shows a bunch of popular

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benchmarks and how well Claude

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3 and the various models and GPT 4 and

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Gemini Ultra and Pro did. And

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in every single instance, Claude 3 was

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better. Now who knows? You know, is Anthropic

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cherry picking here? Maybe, but even if they are, the

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performance is quite intriguing. And I think

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it bodes well for the future of these models.

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Competition is gonna push everybody. Google and Anthropic and

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OpenAI, they're all going to push each other and whenever Apple gets into the

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game and Meta and on and on and on. So I think the

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competition is good. It's gonna help us push the boundaries of what's

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possible with these models. Okay. Let's switch

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to another little bit of kind of amusing but

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insightful news. Google had a bad week.

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This is a couple of weeks ago by the time you listen to this.

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So the basic problem was that Gemini was creating some, shall we

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say, interesting images. America's founding fathers as

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African Americans, the pope is a woman, and there were some

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others. Of course, the world being what the world is,

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there was a bunch of outrage over this. Although I

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kind of chuckled and I think a lot of other people did. So

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according to Google, the problem came when

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they were fine tuning Gemini's image generation model, which is

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called Imagen 2. So the fine tuning was

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intended to prevent the tool from, and I'm quoting here,

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creating violent or sexually explicit images or depictions

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of real people. Engineers were also trying to

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ensure gender and ethnic diversity, but Google

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seems to have overcorrected, which resulted in some of these curious

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and historically inaccurate images. I think we need to get

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used to this. Widespread use of generative AI is

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still very new and we're still trying to figure out how to

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implement appropriate guardrails. In fact, there's not even

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widespread agreement on what those guardrails ought to be. So

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we're gonna continue to see these sorts of problems. There'll be a problem,

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There'll be an over correction. These are going to go back and forth swinging

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like a pendulum until we eventually find the equilibrium and the right

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balance between freedom of use and freedom from harm.

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I'm pretty confident that we'll get there eventually, but it's

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gonna take a while. So when you see these kinds of problems, it's

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good to be aware of them, but don't get unduly upset that

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there's some right wing or left wing conspiracy going on. I

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think most of it is just honest engineers trying to find the right balance

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between freedom of use and freedom from harm.

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So in the meantime, I think one of the big

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messages that I wanna take away and I want you to take away from this

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is be careful of relying on generative AI for anything important or

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anything or anything that might be seen by the public

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unless there's human review. The human in the loop is

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critical especially at this stage of generative AI.

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So make a human check part of the process whenever you use generative

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AI for anything important. Maybe it wasn't practical

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for Google, but for most of us, it will be. If you

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want some more details, there are a couple of links to articles about this whole

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brouhaha in the newsletter. Again, the newsletter's

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available at aighostocolllege.com. You really should subscribe.

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Okay. So here's my first tip of the week. And this

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comes from listener Ralph Estep, who's also a friend of

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mine, who sent me an email asking me why generative AI

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is so bad at following instructions about length.

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By the way, Ralph has a really good daily podcast that focuses

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on financial health. You ought to check it out. It's,

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available at askralphpodcast.com. It really

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is very good, and we all need to keep an eye out on our financial

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health, especially given inflation and some of the uncertainties in the

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world. So you have probably experienced the length problem of

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generative AI. You tell Chad GPT or Gemini or Claude or whomever to

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produce an output of 500 words, and there's no telling how

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long the output will be, but I'll bet it's not 500 words.

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When I try this, I get 200 words. I might get 750 words. And this

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can be kind of frustrating. So I wanted to understand what

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this is all about. So I asked Gemini, why is this a

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persistent problem with generative AI tools? I

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actually kind of liked Gemini's response, so I put it

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verbatim in the newsletter. I even put a link to the conversation in

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the newsletter, so you ought to check it out. But here's the bottom

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line. What you need to do is give it a range, not a

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target. So don't say 500 words, say between 3

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56 100 words, or something like that. You can provide

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examples of writing that fits the length that you want. These can

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be templates that AI can follow. Another good

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approach is to start small and then build up. Ask for a

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short summary first and then ask for more detail on specific

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points. This gives you more control. And

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so, how you phrase your request might also make a

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difference. And this is according to Gemini. If you

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say summarize this topic in about 400 words, it might

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work better than write 400 words on this topic. It's

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gonna take practice, so I

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just wouldn't rely on it ever to give me a specific number of words. But

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as you practice, you can find a way to get it closer

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and closer to the link that you want. This is kind of a good

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thing for those of us who are instructors because it's gonna

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make students actually work a little bit instead of just spitting

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out 500 word papers. Okay. Here's

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a very useful tool. There's a little bit

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of a pun there. More useful

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things, which comes from the creator of

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one useful thing, which is a newsletter I really like,

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Ethan Moloch and Lillek. I think it's

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Lillek, Moloch. They've got a new website called More Useful

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Things. You can go to more useful things dot com and find it

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there. It includes an AI resources page

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that, no surprise, includes some pretty useful AI

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resources. There are 3 sections. 1 is a

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pre order section for Ethan's book, Co Intelligence Living and

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Working with AI. I pre ordered it, and I think it's probably gonna be pretty

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good. There's other resources that has some stuff

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like an AI video, not an AI video, a video

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on AI and links to some of their research

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on AI. But what I really want to talk to you about is their

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prompt library. Their prompt library

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includes instructor aids, student exercises, and some

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other stuff. The instructor aid prompts are really pretty

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good, but they are long and they they are

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complex. For example, they've got one that will create a

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simulation and that prompt is over 600 words

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long. Look. There's nothing wrong with this. In fact,

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complicated prompts are often very effective, especially for

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more complicated tasks. But I want to be careful

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here. I don't want you to look at the complexity of these prompts and

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go, oh good Lord. I'm never going to be able to learn this.

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Don't have to know how to write those prompts, especially not at first.

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You can accomplish a lot with pretty simple

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prompts. So both simple and complex prompts have their

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places. You can start off simple, everything will be fine.

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But you really ought to check out more useful things. Even if it just

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gives you some ideas about how generative AI can be used, it's

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worthwhile checking out if for no other reason than that.

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So check it out. Okay. On to the next topic.

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Recently I was listening to an episode of Dan Shipper's How You

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Use Chat GPT. It's really good. I think it's on YouTube. I

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listen to it as a podcast. Dan was interviewing Nathan

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Lebens on how he uses chat gpt as a copilot for

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learning. Episode was very interesting. Check it out.

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But what caught my attention was a discussion of something called chain

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of thought versus few shot prompting. This is a

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little advanced, so I want you to stay with me

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here. But if it gets to be too much, just move on to the next

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segment. Few shot prompting is pretty easy to understand.

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You just follow your task description with a few examples.

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So let's say that you wanna create some open

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ended exam questions. Do you give chat GPT

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or Gemini or whomever, whomever, whatever? Is it

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whomever or whatever? Oh, that's scary. You give the

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tool of choice your prompt, say, create some open

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ended exam questions on this topic

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and give it some parameters, and then you give it 2 or 3 examples.

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Now what the AI tool will do I'm gonna say chat gpt here

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to just make it easy. What chat gpt will do is it will look at

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and analyze your examples and try to create questions

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that are similar to your examples. Sometimes just giving

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a single example is really useful. They call that one shot prompting.

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Chain of thought prompts are a lot more complicated.

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The main idea is that you ask chat gpt

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to think aloud. So I'm gonna give you an example of a chain of

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thought prompt, and this one was produced by Chatt GPT.

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Explain the concept of chain of thought prompting using the chain of thought

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approach. I thought that was clever. Begin by defining what chain

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of thought prompting is. Next, break down the process into its

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key components explaining each one step by step.

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Then illustrate how these components work together to guide an AI in

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processing and responding to complex tasks.

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Finally, conclude by summarizing the advantages of using chain of thought

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prompting in AI interactions, especially in educational

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contexts. And then the result was pretty long. I'm

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gonna have to send you the newsletter send you to the newsletter to check that

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out, but this can be

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a good way to really get chat

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gpt to do more complicated

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things. I don't use chain of thought prompting very

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much. I think few shot prompting works really

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well, but few shot prompts

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require knowing what good output will look like.

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If you're not sure what you want, you might consider chain of thought

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prompting. But if you're a beginner, stick with

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few shot prompting. Even one shot prompts

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really are quite useful. I use that quite a bit actually.

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Okay. So there are a couple of messages here. First, keep

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things simple when you can. Simple is often very

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effective. 2nd, don't be afraid to experiment with different

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approaches and even to blend approaches. So

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you can decide. Your prompts can be simple or they can be complicated.

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All right. Here's my favorite little part of this episode.

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This is, I think, the best $40 you can spend, and it's $40

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a year, on your generative AI use and productivity.

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Look. I know monthly software subscriptions are totally out of

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hand. I don't even wanna know how much I'm spending at 5,

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10, 15, $20 a pop every month. But

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despite this, I recently added a $40 per year subscription,

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and it's already proven to be one of my best software investments ever.

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Alright. So what is this great investment?

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Well, it's for a text expander, and I wanna give a shout out here to

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Dave Jackson of the school of podcasting who talked about this on one of his

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episodes. And so a text expander just

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replaces a short bit of text with a longer bit.

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For example, if I want to type the web address for AI Goes TO College,

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I type semicolon uaig. U is

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short for URL. And the text expander, it just gives the full

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address. The semicolon here is used to indicate that

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what follows is gonna be or could be an abbreviation for a text

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snippet. The semicolon works well because it's usually followed by

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a space rather than characters, but you could really use anything you

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wanted. Now this doesn't save me a lot of time,

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but it saves me 5 or 6 seconds every time I wanna

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type the AI goes to college website.

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And it's long. You know, it's got the HTTPS, colon, etcetera,

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etcetera, etcetera. It just takes a while.

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I have to give people my biography

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periodically. Matter of fact, once a month or so somebody asks for

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it. So normally I go find the Word file and attach it to an

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email or copy and paste it into the message. Now I just type semi colon

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bio and my bio pops up. And I

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use this for student advising. I use this for grading.

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If you're a professor, you have to grade a lot

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of kind of projects, that sort of thing. A text expander

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will change your life when you have to grade. My spring

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class has 85 students in it, and I'll grade 85

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projects for twice. What is that, A 170

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projects? And a lot of the feedback will be

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exactly the same. You know, they forget to use the 1,000 separator,

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the comma, or their their spreadsheets aren't formatted

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well, that sort of thing. Well, now I can just in a few characters

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pop in the feedback and the number of points I'm taking off for that.

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So what does this have to do with generative AI? Well, as you start

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I Now I just type semicolon w d y

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t. I have one for please critique

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this text. I have one for this is really

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lazy. I have one for thank you. And you'll

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find more and more and more uses for a text expander once you get

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into it. So I use generative AI for a lot of

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tasks, so this helps a lot. But it also helps when I

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need to provide some context. So for example, I try

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to tell generative AI whether I'm working on something related to my teaching or

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one of my podcasts or this newsletter. When I'm working

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on the newsletter, I have a whole blurb. It's, I

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don't know, 5 or 6 sentences long. Now, I just type

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in an abbreviation. That's it. So it's

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semicolondaig, which just means

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description of AI goes to college and this whole

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bit of context pops up. So if you're not using a

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text expander, you really ought to consider it. I use one called you're

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gonna love this text expander. I use it. That's the one that

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costs about $40 a year. I use it because it's cross platform.

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It'll work on a Mac. It'll work on a PC. I think it may even

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work on smartphones, although I haven't tried that yet.

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So I really would encourage you to consider making that

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investment, not just for generative AI, but for your

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use in general. Okay. All right. Last thing I

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wanna talk about is the interview I had

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AI Goes to College podcast, which is surprisingly

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available at ai goes to college.com. We talked a

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lot about a lot of different things. Rob is a really smart guy. He's got

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a lot of experience. He's a department chair. He's a fantastic

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researcher. So he's used generative AI in a lot of

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different contexts. We talked about how he's using

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AI to create more creative assignments and to generate questions,

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how he's helping students learn to use AI tools tools to

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explore and understand important concepts. We talked about the

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importance of being willing to experiment and fail with

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generative AI. We discussed

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why it's important to help students become confident but critical

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users of these fantastic new tools, and we talked about a lot of other

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things. So go to ai goes to college.com/rob,

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r o b, and you can check out the entire interview. I'd

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love to hear what you think about it. You can email me at

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craig@aighostocollege.com. Let me know if you've got

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any ideas for future episodes or if there's something you wanna see in

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the newsletter, and you might get featured. Alright.

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That's it for this time. Thank you. Thanks for listening

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to AI Goes to College. If you found this episode useful, you'll love

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00:25:02,140 --> 00:25:05,815
the AI Goes to College newsletter. Each edition brings you

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00:25:05,815 --> 00:25:09,575
useful tips, news, and insights that you can use to help you figure out what

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00:25:09,575 --> 00:25:13,335
in the world is going on with generative AI and how it's affecting higher

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00:25:13,335 --> 00:25:16,960
ed. Just go to ai goes to college.com to sign

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00:25:16,960 --> 00:25:20,799
up. I won't try to sell you anything, and I won't spam you or share

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00:25:20,799 --> 00:25:24,345
your information with anybody else. As an incentive for

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00:25:24,345 --> 00:25:28,025
subscribing, I'll send you the getting started with generative AI

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00:25:28,025 --> 00:25:31,830
guide. Even if you're an expert with AI, you'll find the guide

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00:25:31,830 --> 00:25:34,330
useful for helping your less knowledgeable colleagues.