Episode 161: Teaching Hard Things Simply

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Hi, and welcome to Teaching Python Podcast. This is episode 161,
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Teaching Hard Things Simply.
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My name's Kelly Schuster-Paredes, and I'm a teacher who codes.
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And I'm Julian Sequeira, and I'm a coder who coaches.
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I'm so excited. We got that right. It's like a thing that we always get scared about it when we come.
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So today, I'm really excited. I'm going to skip over all that fun stuff.
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Today, I'm really excited. We have Jeff Kroom here, and he's an IBM Distinguished
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Engineer, and he's pretty much like a person I stalk online because he has all
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these great videos that I can easily share with teachers.
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And like we were just talking before the show, they're all under like 15 minutes,
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so perfect for teacher bits. Welcome, Jeff.
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Thank you. Thank you. And I'll say right up front that a bit of disclaimer so
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that I stay out of IBM jail, that the views expressed are my own.
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So don't blame IBM or NC State University where I'm an adjunct professor.
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Neither one of them are responsible for it comes out of my mouth.
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Who is responsible, Jeff?
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I don't. I would say me, but people who know me know that I'm not a responsible
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individual. So I guess no one is.
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Perfect. You're going to fit right in with this conversation.
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I have a feeling we're going to have a great time.
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We kick the adults out.
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We kick the adults out, exactly.
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That's right.
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So before we start, because I always do this, so we get off tangent,
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I want to start with the wins of the week. And wins of the week are anything
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that happened at home, at school, at work, in the countryside.
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And we're always going to start with the guest. So, Jeff, go ahead.
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Okay. Well, I had a cool last seven days.
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I went to Austin, Texas, where I did a presentation at a customer seminar and
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then got to go to Cartagena, Colombia.
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I've been to Colombia a few times before, but never to Cartagena and a very beautiful city.
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Got to do a keynote on the topic of AI and jobs. Will AI take or make jobs?
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And the answer is definitely yes, it will.
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I bet you that went over well.
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Just yes. Yes.
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Yeah. And then I was done. And so that was easy work.
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That's crazy. I wish I could get to go to Cartagena. I actually went there one
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time. I loved the Wall City, but no one ever invites me. We have to go to things
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like Orlando to talk. It's not very fun.
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Uh, Julian, you want to go next?
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Yeah, sure. Um, all right. So I, I have a couple of wins, but the,
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the good one with the kids, uh,
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my kids got their mid-year results in for school and they all smashed it.
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So one of them, we've got a bunch of rewards, sorry, awards at a school ceremony,
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uh, yesterday, which was very proud, a very proud moment for us. So that was good.
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Um, and on a personal note for me with, with work, things are changing and going very quickly.
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On one hand, I start a brand new CTO gig on Monday.
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So I'll be the CTO of a financial company starting on Monday, which is very exciting.
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And on the other hand, people in my community have started reaching out for
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help. So yesterday, I was with an elderly lady, 82 years old,
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walking her through password management. She's hard of her sights going.
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And so I you know working through the accessibility stuff but also um,
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educating at the same time that's the relevance to this conversation um and
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answering her ai questions all at once so it was a it's been a good good period of time you're.
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Doing the lord's work.
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And if you watch.
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If you watch any of jeff's I think I feel like jeff is going to say something like pass key not.
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Not I was doing my best you could you could see me biting my tongue why would.
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You give an 80 year old woman a password.
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Yeah yeah.
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Passwords what are.
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Those my my first thing was just not in a notebook okay let's just get it off
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this one piece of paper in front of you that's that's the the bar small wins
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yeah exactly understand,
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you are you know what though jeff his thing she she did ask about pass keys
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so i want to hear about your um you're gonna have to talk about that in a minute then.
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That means we're making.
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Progress. Yeah, exactly. All right, Kel, go for it.
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So, I've had a really fun summer. I've done a couple of presentations,
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but last week, a girl from my gym,
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asked me on a friendly if, and I kind of,
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replicated the friendliness, I needed to work out some ideas because I'm doing
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a presentation for a group called Crew, which is Construction Real Estate Women in Palm Beach County.
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And I was trying to, I was a little bit nervous because it's a pretty big crowd.
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It was like 45 people and we're talking about AI and just how,
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you know, AI can be used as a colleague in the real estate market,
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just kind of helping out some of these companies and firms.
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So my friend at the gym, she's like, hey, come practice with us.
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So I went to her real estate office and it was a really fun time.
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And being able to present to people when I don't know really anything about
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real estate or finance, and I'm just talking about AI, it was just kind of,
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it was a nice feeling that.
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People in industry are experiencing stuff that we've already experienced in
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schools like three years ago and so,
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feeling like the expert in the room even though i didn't know the the subject
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area was just uh another one of those you know wins and here we go so it's kind
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of it was a good win yeah that's cool very nice yeah all right,
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all right let's get to the fun stuff so um i'm just gonna tell a little story
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so i've been you know silently stalking and learning from Joss' videos,
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sharing the little tidbits.
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The first one I shared with the teachers was...
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This video, I think it was where you defined deep learning, AI,
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machine learning. And Jeff, I don't know if you've ever watched any of his videos.
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He has this really cool see-through screen and he writes everything. I want one.
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Yeah, very cool.
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But it's very simple and well-explained and, like I said, short tidbits.
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And so I've been sharing out some of his knowledge pieces to teachers and just
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loving the stuff. And that's where I'm starting with. There's no question.
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I'm going to let you tell us a little bit about everything that you've done.
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And then Julian will go for there.
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Okay.
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You bet. Well, first of all, thank you. Thank you for watching.
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Thank you for the kind words.
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There's, to me, this is a lot of fun. And I know it probably people leave with
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the impression because I'm such an amateur at this that I'm probably doing this in my basement.
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But actually, there's a professional operation behind me supporting me.
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So I go out to the, I live in Raleigh, North Carolina, and I go out to the IBM
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office at Research Triangle Park just about every Friday morning.
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And I record a new video, 10 to 12, every Friday, unless there's some major reason not to.
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So I'm out there doing those. And we have a studio, and it's got professional
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sound, lighting, cameras, all kinds of stuff in there.
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I mean, it's probably $30,000 or $40,000 worth of equipment in there.
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And then we have professional editors that fix things.
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Things and try to make it look presentable because, you know,
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I mean, talk about Mission Impossible. They've got me as source material and
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they have to put enough lipstick on that pig to make it work.
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So that's what we do. Now, what I basically do is I come up with the subjects.
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Every once in a while, someone in IBM will come to me and say,
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hey, we really need you to do this topic or that topic.
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But for the most part, I come up with the subjects myself because Because in
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my day job, my primary responsibility is working with clients throughout the
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Americas, North and South. So that's my whole coverage area is that half of the world.
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And I will run across things that they are curious about, that they don't understand,
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that I can see they're struggling with. And then that often becomes.
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A food for thought for me, oh yeah, maybe I don't understand it either. Maybe I need to learn.
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Well, one of the best ways to learn something is to teach somebody,
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because in order to teach somebody, you have to learn it yourself.
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In learning it deeply enough to be able to explain it, then it causes you to
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dig deeper. So that's, I look at, for me, number one benefit I get from doing
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these videos is it's my own learning.
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It forces me to develop, you know, a structure around the ideas so that I can
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explain them in a way that hopefully a lot of people understand.
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And then there's the number one question that we always get,
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if you've ever seen any of these videos, as Julian told you,
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you know, there's a glass. It looks like I'm riding backwards in the air with
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my left hand in a dark room.
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And none of those things I just told you are true.
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The room's very bright, at least in terms of the lights shining in my eyes.
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I feel like I need sunglasses.
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The background is black, though, so it looks like the room is dark.
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If I write in the air, you can see nothing happens. So I'm obviously not writing
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in the air. There is a glass in front of me, and we use special markers.
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And there's a black light that's built into the glass that goes around the rim of the glass.
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And then these markers fluoresce in the black light. So that's what gives it
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that visual pop that you see.
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But the main thing, the number one question I always get is,
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how did you learn to write backwards?
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And so many people, what kills me is the ones who write in the YouTube comments.
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It's like, one of them I always remember, dude, I can't even concentrate on
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what he's saying. He's writing backwards.
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I'm like, okay, well...
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Sorry to let you down. I can barely write forwards. So I'm definitely not writing
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backwards. And I'm definitely not left-handed.
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So there should be your clue.
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And a lot of people will write in comments like, it looks like,
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because I'm not the only one recording these videos. They go on the IBM technology
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channel. So we have a lot of other IBMers on there.
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And somebody said, it looks like IBM only hires left-handed people.
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And I'm like, okay, that should be your clue. Yeah, that should be the clue
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that tells you, yeah, what we do is we record it. It looks backward if you're
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in the studio looking at this from the other side of the glass.
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And then the editor takes the video and then just flips it like this.
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And now magicians never supposed to tell, you know, how their magic is done.
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Now, you know, the secret and now you'll be able to, you know,
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no big deal. You know, that guy can't write backwards.
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But I did a video where I explained how we make the videos.
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Okay.
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And I love the comments on that. One after another is like, finally, I can sleep.
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This was such relief for people who had been working on this in their head.
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Smoke, mirrors, no, there's no smoke, no mirrors. Just a simple editing trick.
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And then that's how it comes out. So now you know how the sausage is made.
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I thought you had that board. I actually contacted the people where you write.
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And during the videos, it does like the reversed vision on there.
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Here, I thought you had like the $20,000 board. Now I have to tell everybody
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because I went to school and I said, I want one of these.
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If you give me one of these, my trainings are going to be better. I promise you.
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That's right. That's right. Well, there's something. It's really interesting
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there. We have the, I'll say the old fashioned board, the analog board in our
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studio, which is a piece of glass. And we're writing on the markers, aren't it with markers.
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There is a new technology that I have seen. In fact, we had an IBM Tech Exchange
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conference last year in Orlando, and I got to try this out.
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Digital boards, where you don't use markers, you use a stylus.
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And the stylus, as you're drawing...
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You know, you can just choose what color and the stylus will draw it in that color.
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You can also have animations that you put in and all that's done not during
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the post-production, which is what we have to do whenever you see animations in my videos.
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I'm pointing at stuff that's not there and having to pretend it's there and
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hoping the editor doesn't leave me looking foolish when I point at something
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that's not there. And every once in a while they do.
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So I love the digital boards and they're, you know, probably $30,000 or whatever,
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like you're talking about. So they're a lot. The analog boards are not as much.
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I mean, they're, I don't know, I don't know for sure. It depends on the size,
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but it's a fraction of that cost for sure.
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You know, less than $10,000 for most of them. But here's the downside of the
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analog boards that people don't think about is that there's one thing you never
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see us do in those videos. Erase.
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Mm.
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Okay, this is not like a dry erase board where, you know, you fill up the space
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and then you can just erase it and then you can reuse the space. No, this is a glass.
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So when you try to erase on it, it smears.
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Smudges, yeah.
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Then you get to erase it again. Then you get to erase it again.
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It's like, you know, five minutes of erasing to clear the board.
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And nobody wants to watch you do that on camera.
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Yeah.
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So that's why I don't recommend those for live presentations.
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Yeah.
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You know, you would have the problem, you need to flip it, but,
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you know, that can be done with the camera.
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But the digital board would allow you to do those kind of things and do them more in real time.
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And then the other thing that's different is, you know, if you think about the
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board space is this big, well, I'm taking up a third of it with me being in
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whatever section I'm in. And you don't want to write over your face. That looks really dull.
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And that's a rookie mistake. You'll see the people who are rookies writing on
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their face because to them, they're just looking at it.
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And you do that on a regular dry erase board.
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But on these, you want to use the margins, you know, the sides,
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or either you stand over to the side and put it all here. So there's a whole lot I've learned.
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Good Lord.
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And a lot of mistakes I've made along the way.
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Not to mention all the topics.
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Yes.
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When you're thinking about teaching these difficult topics, because you go from
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everything. I know you're an expert in cybersecurity, is that correct?
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Yeah, that's my main thing.
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But then you have to talk about everything else that encompasses tech.
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I've seen you've talked about quantum computing and everything.
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Yeah.
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When you go to build out these, and this is, I guess, like a really...
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Hard thing for teachers, is trying to tease out what's the most important,
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what is it that they really need to know, and how can you get it in a place
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where everyone's going to understand?
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What's your trick? Because I think you are quite the experienced educator when it comes to that.
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Well, thank you. I'll tell you, there's a lot of learning I had to do,
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and other people taught me, and then I learned from mistakes that I made as
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well, that it's different when you're in YouTube land than when you're in the
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classroom or when you're at a conference presenting. And I do all of those.
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We're used to, you know, if you've got a classroom, you've got a captive audience.
00:16:03.959 --> 00:16:05.553
So they kind of have to be there.
00:16:06.643 --> 00:16:10.479
If you're in YouTube land, you've got about 15 seconds.
00:16:10.479 --> 00:16:14.989
And if you don't grab them, They're out the door Onto the next video So one
00:16:14.989 --> 00:16:18.219
of the things That you can't do And I see this as a rookie mistake People will
00:16:18.219 --> 00:16:21.059
start these videos And they're standing there And they're introducing their
00:16:21.059 --> 00:16:25.108
topic And just talking So then it's just a talking head And a black background,
00:16:25.760 --> 00:16:30.335
Again, you're losing people. They are not walking. They are running for the
00:16:30.335 --> 00:16:34.676
exits at that point because YouTube land is short attention span.
00:16:35.465 --> 00:16:40.905
So that's the thing I had to learn to adjust on is that I need to get something
00:16:40.905 --> 00:16:43.085
happening on the board, something for people to look at.
00:16:43.525 --> 00:16:46.575
They don't want to just look at this. I mean, nobody wants to look at this.
00:16:46.802 --> 00:16:50.675
So I need to give them something else to distract them and, you know,
00:16:50.675 --> 00:16:56.735
watch this, you know, give them some bright, shiny object to engage them and
00:16:56.735 --> 00:16:58.510
then try to keep things moving.
00:16:58.852 --> 00:17:02.265
Because the videos, some of these are less than 10 minutes.
00:17:03.085 --> 00:17:06.495
I try not to ever get them beyond 15, but sometimes, you know,
00:17:06.495 --> 00:17:09.058
every once in a while they'll go longer. But, you know, if you're in a classroom,
00:17:09.325 --> 00:17:11.895
you've got them for an hour or two hours or something like that.
00:17:11.895 --> 00:17:16.325
So when I'm teaching at NC State University, I mean, I've got the hour.
00:17:17.185 --> 00:17:24.015
Or longer. And my approach is different, where you don't just take your PowerPoints
00:17:24.015 --> 00:17:27.725
or your slides and then put them onto that format.
00:17:28.125 --> 00:17:31.610
It doesn't work that way. So there's a lot of rethinking you have to do.
00:17:32.385 --> 00:17:35.365
One of the things that occurred to me as saying, I didn't come up with this,
00:17:35.365 --> 00:17:39.465
but I really love it, because we're trying to go for brevity,
00:17:39.465 --> 00:17:44.465
but still have something that as substantive is that if I'd had,
00:17:44.465 --> 00:17:46.905
it would have, I'd have made it shorter if I'd had more time.
00:17:48.225 --> 00:17:50.162
Which seems counterintuitive, of course.
00:17:50.676 --> 00:17:55.795
You know, I can put together a one-hour PowerPoint presentation with less time
00:17:55.795 --> 00:17:59.745
than it takes me to put together a 10-minute lightboard presentation.
00:17:59.745 --> 00:18:01.105
Lightboards are what we call those things.
00:18:01.696 --> 00:18:04.845
Because I got to figure all the things I'm taking out and what's going to be
00:18:04.845 --> 00:18:09.125
the flows and what's going to be the color of markers I use and how am I going to lay them out?
00:18:09.125 --> 00:18:13.785
Because I don't want to spend time, you know, even little things like capping
00:18:13.785 --> 00:18:17.834
and uncapping the markers that we've all been taught to do so they won't dry out.
00:18:18.293 --> 00:18:20.145
Nobody wants to listen to you go, hurry.
00:18:21.405 --> 00:18:24.905
As you're doing that with these markers. So I always tell the rookies,
00:18:24.905 --> 00:18:29.082
take the caps off and put them somewhere else so you're not even tempted to do that.
00:18:29.433 --> 00:18:33.155
And line your markers up in the order in which the colors you're going to use
00:18:33.155 --> 00:18:36.125
them and try to make the colors mean something.
00:18:36.865 --> 00:18:40.915
And sometimes I'll change in my script and say, okay, no, I need to switch to
00:18:40.915 --> 00:18:45.705
a different color because I've overused that one. Or here I want red to mean
00:18:45.705 --> 00:18:47.545
this and green to mean that, this sort of thing.
00:18:48.065 --> 00:18:51.975
So there's a lot that goes into it. But for me to do a 15-minute video,
00:18:51.975 --> 00:18:57.065
usually two to three hours, I would say on average, sometimes longer,
00:18:57.405 --> 00:19:02.182
of just trying to come up with a general script and outline for a 15-minute video.
00:19:03.005 --> 00:19:09.618
Yeah, that makes sense. Well, I think it was the most recent one on the humanities that you put out.
00:19:10.485 --> 00:19:12.965
I watched that last night because Kelly sent it to me.
00:19:12.965 --> 00:19:15.285
Oh, good. So I found somebody that did.
00:19:15.665 --> 00:19:16.265
I loved it.
00:19:16.785 --> 00:19:17.305
You're the one.
00:19:17.305 --> 00:19:20.105
Yeah, I was the one who said, how are you writing backwards?
00:19:21.125 --> 00:19:22.225
Yeah, right, right, right.
00:19:22.225 --> 00:19:24.715
Actually, that was the first video, because I watched a few of your videos now,
00:19:24.715 --> 00:19:27.215
and that was the first one where I kind of went, wait a minute,
00:19:27.215 --> 00:19:28.234
did he just ride backwards?
00:19:29.165 --> 00:19:32.865
Yeah. That was the first one that even triggered. But no, I think,
00:19:34.114 --> 00:19:38.166
like, that one was so, it hit home a lot with me because, you know,
00:19:38.485 --> 00:19:42.182
last week I gave an AI presentation down in Sydney, and...
00:19:43.099 --> 00:19:45.926
This is the kind of stuff I brought into it, where I said, look,
00:19:46.094 --> 00:19:51.219
the human aspect, the experience, the lived experience, our understanding of
00:19:51.219 --> 00:19:54.289
literature and just human history and knowledge, our,
00:19:55.208 --> 00:19:58.759
experience as humans is not replicated by AI.
00:19:58.759 --> 00:20:02.509
It's not there. It's just this database of information, right?
00:20:02.509 --> 00:20:07.729
So I won't repeat what you said, but it was just such a enlightening,
00:20:08.569 --> 00:20:13.389
well put together talk that part of me was like, I should have just shown them
00:20:13.389 --> 00:20:19.229
this last week because it was exactly what I try to articulate, but I couldn't.
00:20:20.827 --> 00:20:25.794
First of all, have you had any feedback on that video specifically?
00:20:25.794 --> 00:20:31.044
Because that was one that wasn't overly technical and more about our experience
00:20:31.044 --> 00:20:34.564
as human beings. And I loved it for that reason, because I'm the mindset guy.
00:20:34.564 --> 00:20:37.954
But yeah, so tell me, any feedback on that one?
00:20:37.954 --> 00:20:42.410
Yeah, yeah. And I'll even tell you the story behind why I decided to do that one in particular.
00:20:43.434 --> 00:20:46.894
So you're right. My background is cybersecurity. That's my PhD.
00:20:47.574 --> 00:20:52.114
And that's what I do in my day job. It's what I teach. I've also had an abiding
00:20:52.114 --> 00:20:56.414
interest in AI ever since I was an undergrad student back in my computer science
00:20:56.414 --> 00:20:59.714
days, a thousand years ago, writing my dinosaur to class.
00:21:00.174 --> 00:21:06.614
So as AI finally became real, I was like, wow, at long last,
00:21:07.074 --> 00:21:12.684
the thing that is so fascinating to me is AI and cybersecurity.
00:21:12.684 --> 00:21:15.814
So a lot of times I'll do videos that talk about the cross-section,
00:21:16.134 --> 00:21:17.832
the confluence of those two topics.
00:21:18.057 --> 00:21:24.174
But there's enough things in AI that, to me, I thought a lot of people are missing the point on.
00:21:25.394 --> 00:21:30.074
I love what AI can do, and I hate what AI can do because it can do some amazing
00:21:30.074 --> 00:21:31.942
things and it can do some awful things.
00:21:33.014 --> 00:21:37.097
Any dual-use technology is like that, can do good, can do bad.
00:21:37.409 --> 00:21:43.914
Well, so I was doing a talk for the University of North Florida University talking
00:21:43.914 --> 00:21:49.711
about AI and jobs and this kind of subject, and in particular...
00:21:50.193 --> 00:21:53.901
Talking about how we need to, as educators, in fact, there's a video I have
00:21:53.901 --> 00:21:55.601
on AI and the future of education,
00:21:56.321 --> 00:22:01.421
where I make a very unpopular stance that instead of us saying,
00:22:01.421 --> 00:22:06.171
keep AI out of the classroom, you know, because the fear is if we let students
00:22:06.171 --> 00:22:09.551
use AI, then they're not going to learn anything. They're just going to copy and paste.
00:22:09.935 --> 00:22:13.141
Valid fear. But we're going to have to get over that.
00:22:13.561 --> 00:22:19.791
We're going to have to adapt to that. Because as I asked my students last semester,
00:22:19.791 --> 00:22:24.101
I said, how many of you, and this is college level, how many of you have a professor
00:22:24.101 --> 00:22:27.321
that ever said you can't use a calculator in class?
00:22:28.633 --> 00:22:32.611
And they all laughed. I mean, nobody does that. Now, if you're in elementary
00:22:32.611 --> 00:22:35.101
school and you haven't shown proficiency
00:22:35.421 --> 00:22:38.343
with arithmetic, absolutely you shouldn't be using a calculator.
00:22:38.661 --> 00:22:42.681
But once you've got proficiency, you should use the tools that are available to you.
00:22:43.226 --> 00:22:48.591
If we require students not to use the tools, the best tools available to them,
00:22:48.591 --> 00:22:50.656
then we're preparing them for the jobs of yesterday.
00:22:51.421 --> 00:22:54.238
And they will not be suited for the jobs of today and tomorrow.
00:22:54.523 --> 00:22:58.197
Because their boss is never going to come to them and say, I want you to do
00:22:58.614 --> 00:23:03.241
X, Y, and Z and don't use AI. No boss is going to say that.
00:23:03.681 --> 00:23:05.656
So why would I as a teacher say that?
00:23:06.921 --> 00:23:11.161
Now, there's a whole lot about how we should use AI in the classroom.
00:23:11.161 --> 00:23:14.723
And a lot of people are doing a lot of thought on this. I'm not saying I have all the answers.
00:23:15.200 --> 00:23:18.881
What I'm convinced of, absolutely convinced, is that we can't say no to this.
00:23:19.461 --> 00:23:23.461
We have to figure out how to incorporate it as a tool, just like we did with
00:23:23.461 --> 00:23:25.463
calculators, just like we've done with a lot of things.
00:23:26.424 --> 00:23:31.330
So I was giving that talk to University of North Florida, and it came to the Q&A session.
00:23:31.810 --> 00:23:34.550
And of course, I've been talking about using AI throughout all of this.
00:23:35.010 --> 00:23:37.350
And someone raised their hand. Now, I'm doing this remotely,
00:23:37.350 --> 00:23:39.636
so I can't really see all that well who it is.
00:23:40.710 --> 00:23:44.652
And she said, I'm an English professor here, and how dare you?
00:23:46.030 --> 00:23:47.330
And I was like, uh-oh.
00:23:48.950 --> 00:23:53.520
She said, why would students ever read these great works of literature if they
00:23:53.520 --> 00:23:56.810
could just go to AI and ask it, you know, essentially for the summary.
00:23:56.810 --> 00:24:00.308
And I was like, okay, all right, all right. Yeah. You raise a good point.
00:24:01.410 --> 00:24:04.250
And I tried to answer it as best I could during, in the moment.
00:24:04.790 --> 00:24:10.617
And I think I gave a passable answer, but what she said really stuck with me
00:24:10.900 --> 00:24:14.630
because I thought, yeah, there is a lot more to this.
00:24:15.250 --> 00:24:18.980
And I can imagine if I was an English teacher, I'd be feeling very marginalized
00:24:18.980 --> 00:24:22.760
in all of this STEM, STEM, STEM for everything, you know, Science,
00:24:22.760 --> 00:24:24.131
technology, engineering, and math.
00:24:24.990 --> 00:24:28.800
That's everything. And then I got to thinking, yeah, you know what?
00:24:28.800 --> 00:24:33.668
AI doesn't know what to do. We tell it what to do.
00:24:34.190 --> 00:24:39.790
I mean, once we tell it, it will go do it. But we decide what and we decide why.
00:24:40.690 --> 00:24:44.046
And why is a question of meaning and purpose.
00:24:44.644 --> 00:24:48.790
And you know what you don't learn in calculus class is purpose.
00:24:49.235 --> 00:24:53.580
What you don't learn in computer science class is meaning. What you don't learn
00:24:53.580 --> 00:24:58.810
in, you know, all of these STEM classes in engineering are the whys.
00:24:59.690 --> 00:25:04.800
So if we're going to guide this AI, we need to be, and now that we have this
00:25:04.800 --> 00:25:08.950
tool that is so powerful, we need to be really careful about the why before
00:25:08.950 --> 00:25:10.818
we turn this monster loose.
00:25:11.390 --> 00:25:16.610
And so my contention, and that's what this video is about, is that the humanities
00:25:16.610 --> 00:25:21.796
have never been more important than they are today because the humanities are where we learn the why.
00:25:22.203 --> 00:25:24.942
You know, where do we learn, first of all, what's true and what isn't?
00:25:25.761 --> 00:25:31.247
Well, that's philosophy. You know, that's where we look at logical proofs and things like that.
00:25:32.107 --> 00:25:36.657
Where do we learn, you know, about man's humanity against man and these kinds
00:25:36.657 --> 00:25:38.550
of eternal struggles? Well, that's in literature.
00:25:39.003 --> 00:25:44.187
You know, where do we learn about the past so that we don't keep making the
00:25:44.187 --> 00:25:46.328
same mistakes in the future? Well, that's history.
00:25:46.828 --> 00:25:51.977
And I could go on. You know, those are all humanities, and those are all incredibly
00:25:51.977 --> 00:25:54.587
important in how we wield this tool.
00:25:55.067 --> 00:25:59.638
So that was what motivated me. So my hat is off to that English professor.
00:25:59.848 --> 00:26:03.227
And I even called her out when I posted to LinkedIn. I said special thanks to
00:26:03.227 --> 00:26:08.346
her for motivating me on this topic because I believe that. Now,
00:26:08.509 --> 00:26:11.603
some of the commenters really liked it, okay?
00:26:12.127 --> 00:26:16.917
Some of them said, oh, great, here we go. Another liberal. We have too many
00:26:16.917 --> 00:26:19.807
of those in our education. I was like, what in the world?
00:26:20.567 --> 00:26:25.357
This was not a political statement. The humanities are something.
00:26:25.357 --> 00:26:28.777
I mean, one guy even made a comment about, yeah, here we go.
00:26:28.787 --> 00:26:31.827
Another God in the gaps argument. That didn't hold up well or something.
00:26:32.207 --> 00:26:37.237
And I said to him back, the reason you even know about that reference is because of the humanities.
00:26:37.237 --> 00:26:39.847
Oh, nice. Excellent.
00:26:40.527 --> 00:26:42.567
So anyway, so many.
00:26:42.567 --> 00:26:47.337
So many thoughts. And I wish I had the whole time just to go into all the things
00:26:47.337 --> 00:26:49.461
in my head. Two things, though.
00:26:51.353 --> 00:26:57.468
AI, I just got sent yesterday or last night, an English teacher sent me a notebook
00:26:57.468 --> 00:27:02.328
LM. It's an idea he's going to try, I guess, and coming up. And he put the telltale
00:27:02.328 --> 00:27:04.698
heart in there. And, you know, and everybody raids this short story.
00:27:05.098 --> 00:27:10.578
And all the English teachers are, like, diving deep into the hidden analogies
00:27:10.578 --> 00:27:14.008
and whatever else is in there, metaphors in this thing. And as a student,
00:27:14.008 --> 00:27:15.498
you're just like, yeah, he murdered somebody.
00:27:15.898 --> 00:27:18.708
But as I was playing with this notebook LM.
00:27:18.708 --> 00:27:19.358
Spoiler alert.
00:27:19.358 --> 00:27:23.638
Spoiler alert. But it was pretty cool because you could ask,
00:27:24.038 --> 00:27:26.918
I don't know what he put in there as the sources because I didn't look,
00:27:27.258 --> 00:27:31.838
but it was really diving into what the meaning and what was the real point that
00:27:31.838 --> 00:27:34.718
English teachers always use this short story.
00:27:34.718 --> 00:27:38.668
So I think there's a lot to be said about things that can help and learn.
00:27:39.278 --> 00:27:41.588
I think that's great. In fact, one of the things when I was talking to that
00:27:41.588 --> 00:27:47.058
English professor, I told her, look, there's some works of literature that I
00:27:47.058 --> 00:27:49.908
think are just, you know, transformational. When I read that book,
00:27:49.908 --> 00:27:52.090
it changed me. I'm not the same person anymore.
00:27:52.500 --> 00:27:56.638
And then there's some others that English teachers made me read that I'll never forgive them for.
00:27:58.098 --> 00:27:59.848
And Moby Dick is one of those.
00:27:59.848 --> 00:28:01.038
But I'm sure if you had...
00:28:01.378 --> 00:28:02.538
I don't think anyone should have to read that.
00:28:02.538 --> 00:28:07.128
I'm sure if you had an AI where you can ask it questions about only Moby Dick,
00:28:07.128 --> 00:28:11.758
you would be able to understand the meaning of life now because of that.
00:28:12.658 --> 00:28:14.088
Exactly. And wouldn't it be
00:28:14.088 --> 00:28:18.084
great to have that tutor help you if you're trying to read through this.
00:28:18.269 --> 00:28:21.708
And it doesn't make any sense. And Shakespeare doesn't make sense to me until
00:28:21.708 --> 00:28:24.365
I get my head in that space. And sometimes I never can.
00:28:24.800 --> 00:28:27.778
If it could, okay, what in the world did he mean by that? Oh,
00:28:27.778 --> 00:28:30.808
okay. Well, this is what those words meant in those days.
00:28:31.691 --> 00:28:36.277
I can't just call up the professor every time I stumble over one of those things.
00:28:36.277 --> 00:28:36.457
Exactly.
00:28:36.917 --> 00:28:40.297
So it could be very helpful and augment that intelligence.
00:28:40.297 --> 00:28:43.943
Hey, I want, I'm sorry, I'm going to jump in, Shilling, because I have all these questions in my head.
00:28:44.251 --> 00:28:48.527
So part of my role now, because it's kind of like a funny to me,
00:28:48.527 --> 00:28:51.797
I don't think it's funny to everybody else, but because I taught Python,
00:28:51.797 --> 00:28:55.652
because I'm a person probably like everyone, like the two of you,
00:28:55.977 --> 00:28:57.237
where I just love learning,
00:28:57.697 --> 00:28:59.537
I got dumped into AI.
00:28:59.537 --> 00:29:02.496
So every day has been like a major learning moment.
00:29:03.042 --> 00:29:07.887
And since you're the cybersecurity guy, and since I talk to a cybersecurity
00:29:07.887 --> 00:29:13.277
team every Friday and we dissect the subprocessors and where's my AI model and
00:29:13.277 --> 00:29:15.817
where my OWASP risks are.
00:29:15.817 --> 00:29:16.877
I'm sorry you have to do that.
00:29:17.237 --> 00:29:20.282
Apologies. But it's actually fun.
00:29:21.097 --> 00:29:24.157
My people are difficult ones to deal with. I'm just going to tell you.
00:29:25.577 --> 00:29:30.557
What do you so and I think this is something that most schools do not have as
00:29:30.557 --> 00:29:36.737
a person that is actually loves talking about these things but what could you say to,
00:29:37.457 --> 00:29:42.287
some educators who don't have any idea about all the cyber security risks what
00:29:42.287 --> 00:29:48.017
are like some top three things that they should look for um,
00:29:49.503 --> 00:29:51.732
Yeah, without expertise.
00:29:52.487 --> 00:29:56.848
Yeah, well, so watch all of my videos. That would be the thing I would say,
00:29:56.848 --> 00:30:00.228
right? That's not self-serving, is it? No, they're good.
00:30:00.908 --> 00:30:02.008
I would second that.
00:30:02.768 --> 00:30:07.428
There you go. No, no, some of them would not be necessarily all that on target,
00:30:07.428 --> 00:30:09.257
but hopefully some of them would be.
00:30:09.375 --> 00:30:12.730
Um, I, I think I look at it this way.
00:30:13.072 --> 00:30:17.068
Um, cybersecurity is a really interesting topic to me, but it,
00:30:17.068 --> 00:30:21.500
I understand it's an acquired taste for others and most just never quite acquire it.
00:30:21.860 --> 00:30:25.518
Uh, but for me, it was, it was always interesting from the start because I like
00:30:25.518 --> 00:30:29.697
taking things apart and knowing how things work and how things cannot work.
00:30:30.039 --> 00:30:33.238
And of course hackers are taking things apart and making them not work.
00:30:33.603 --> 00:30:38.238
So I like the hard challenge of trying to figure out what they're going to do
00:30:38.238 --> 00:30:41.648
and then try to keep them from doing it, which is an even harder problem than
00:30:41.648 --> 00:30:42.810
actually taking it apart.
00:30:44.012 --> 00:30:46.758
But the kinds of things that I think people need to focus on,
00:30:47.106 --> 00:30:49.558
we still have a lot of problems and we mentioned this, you know,
00:30:49.558 --> 00:30:52.609
about pass keys, you know, passwords.
00:30:52.928 --> 00:30:58.568
Everybody's got passwords and I think everybody hates them and I don't blame,
00:30:58.888 --> 00:31:00.086
you know, They're terrible.
00:31:02.068 --> 00:31:05.448
They used to be the least worst alternative, but we have better alternatives now.
00:31:06.628 --> 00:31:09.108
And those things are called pass keys. And if you want to know about it,
00:31:09.108 --> 00:31:10.726
I've got a couple of videos on them.
00:31:10.893 --> 00:31:14.628
But in general, you'll find more and more sites offering these.
00:31:15.128 --> 00:31:18.388
And one of the big reasons, without getting into technical details,
00:31:18.525 --> 00:31:25.330
about why they're useful is that the number one cause of data breaches for the last few years,
00:31:25.868 --> 00:31:31.118
And IBM just came out with a report today on the cost of a data breach for 2026.
00:31:31.118 --> 00:31:35.618
And it was number one in terms of cost of data breach. It was number one in
00:31:35.618 --> 00:31:37.388
terms of frequency, phishing attacks.
00:31:38.165 --> 00:31:43.408
So phishing attacks are largely ways to try to steal your password,
00:31:43.408 --> 00:31:44.562
your login credentials.
00:31:45.629 --> 00:31:49.230
If the best way to make sure your password isn't stolen is to not have one.
00:31:50.049 --> 00:31:55.789
And a passkey is not easily stolen. So this thing is essentially phishing resistant.
00:31:56.199 --> 00:32:00.479
That is one of the biggest things that we could do if we were to get more and
00:32:00.479 --> 00:32:04.729
more people to adopt passkeys and use them in place of passwords.
00:32:04.729 --> 00:32:06.569
And the great thing is you don't have to remember them.
00:32:07.102 --> 00:32:10.950
You know, they get stored on your device and you unlock the device with your
00:32:11.234 --> 00:32:14.829
face print, you know. And I don't know about you, But so far,
00:32:14.829 --> 00:32:17.129
I've never forgotten to take my face with me everywhere I go.
00:32:17.769 --> 00:32:18.439
Happens once in a while.
00:32:18.439 --> 00:32:21.649
Right there on the front of my head. Yeah, yeah, right, right.
00:32:22.869 --> 00:32:29.349
So that's, I think, a really, it's, if you're offered the opportunity and more
00:32:29.349 --> 00:32:33.328
and more retail sites, for instance, are offering this, use it. I think that's a big one.
00:32:33.593 --> 00:32:36.939
So I learned, sorry, Julian, this is going to be all about me and Jeff today.
00:32:36.939 --> 00:32:42.009
Sorry. That's right. I just broke my face. I just broke my face out. Your face is great.
00:32:46.109 --> 00:32:46.550
Sorry.
00:32:46.748 --> 00:32:47.149
You go you go all right.
00:32:47.149 --> 00:32:51.679
All right so i just learned and i don't know if this is a real thing but like
00:32:51.679 --> 00:32:58.665
with it's called what blast radius and it was kind of linked into the idea of the canvas hack,
00:33:00.089 --> 00:33:04.589
and and so my my take is i like we want to put it behind uh,
00:33:05.341 --> 00:33:10.710
behind put our apps and all our stuff behind sort of like a walled vendor that
00:33:11.140 --> 00:33:16.549
doesn't ingest any of the data but just kind of talk you know allows that talking,
00:33:17.398 --> 00:33:24.719
from the app to the LTI or whatever whatever connection they have and um is
00:33:24.719 --> 00:33:29.287
that like a thing now because for example canvas got hacked right a couple yeah
00:33:29.558 --> 00:33:30.789
whatever a couple months ago,
00:33:31.445 --> 00:33:35.353
And with everything that's connected, then they had to worry about these blast
00:33:35.353 --> 00:33:41.153
radiuses. Is that something you think like is going to be more encompassing
00:33:41.153 --> 00:33:42.533
maybe for schools? Oh, yeah.
00:33:42.533 --> 00:33:46.163
We'll see more of that. We've had it ever since we started really moving into
00:33:46.163 --> 00:33:49.564
cloud apps. We've had that as a major exposure.
00:33:50.034 --> 00:33:55.914
And we have it even more now with the public chatbots that most people use.
00:33:56.110 --> 00:33:58.531
And I keep trying to advise everyone.
00:33:58.985 --> 00:34:03.633
Anything you put into a public chatbot, you should consider to be public information.
00:34:04.517 --> 00:34:08.403
And I don't think people have that message yet because they can use what you
00:34:08.403 --> 00:34:10.617
put in to train their models.
00:34:11.058 --> 00:34:13.893
And if you're putting insensitive information into those, you know,
00:34:13.893 --> 00:34:16.863
you say, well, hey, look, I've got a spreadsheet with all my student grades
00:34:16.863 --> 00:34:20.853
and I'm just going to upload that here and have it, you know, munch on it for me.
00:34:21.333 --> 00:34:26.256
Well, okay, if that was okay for you to send out to the world, you're good.
00:34:26.749 --> 00:34:31.953
But my guess is no. So what we need to be doing is having private instances
00:34:31.953 --> 00:34:34.073
of these, and you have to pay extra.
00:34:34.573 --> 00:34:37.233
I mean, those public chatbots are free, so I get that.
00:34:38.493 --> 00:34:44.599
And I use them, but everything I put in them, I'm okay if everybody reads about me.
00:34:44.988 --> 00:34:48.907
You know, like if I'm going to ask it a medical question, it's going to be about my broken shoulder.
00:34:49.249 --> 00:34:53.249
It's not going to be about something I consider to be personal as an example, okay?
00:34:53.615 --> 00:34:58.033
I don't care if anybody knows. I did break my shoulder in January. Okay, fine.
00:34:59.033 --> 00:35:04.393
I put that out there. But those are the kinds of things that we have to be more
00:35:04.393 --> 00:35:08.933
aware of. And therefore, like if you're talking about Canvas or at NC State
00:35:08.933 --> 00:35:12.133
where we use Moodle, we'll be switching over to Canvas, I think, coming up.
00:35:13.733 --> 00:35:17.503
All of those services, we put our data in the cloud and then we don't see what
00:35:17.503 --> 00:35:21.408
happens to it after that. Now it's all trust. And,
00:35:21.929 --> 00:35:26.876
A lot of times that trust is misplaced. A lot of companies will,
00:35:27.296 --> 00:35:30.556
they spend, and this is true of most organizations, they will spend most of
00:35:30.556 --> 00:35:34.631
their effort in putting functionality into an app, into a service.
00:35:35.316 --> 00:35:39.836
And it turns out they really need to be putting at least as much into securing it.
00:35:40.476 --> 00:35:44.056
And they usually don't. Because securing it doesn't sell more licenses.
00:35:44.776 --> 00:35:49.596
There's a financial disincentive to do security right until they get caught.
00:35:50.776 --> 00:35:54.376
And that's what happened. And now all of this happened. But until then,
00:35:54.856 --> 00:35:58.166
you know, it's everybody's riding fast. And, you know, as I said,
00:35:58.166 --> 00:35:59.297
go fast and break things.
00:35:59.586 --> 00:36:01.946
Well, no, please not with my data.
00:36:01.946 --> 00:36:02.696
Yeah.
00:36:02.696 --> 00:36:07.980
So that's a risk. And it's a risk that will continue as we use more and more AI.
00:36:08.305 --> 00:36:12.396
And if you're going to use AI with sensitive information, you need to have your
00:36:12.396 --> 00:36:19.126
own organization's private instance of that so that your data is your data,
00:36:19.126 --> 00:36:21.096
not your data is training their model.
00:36:22.217 --> 00:36:26.066
And you still have to trust that those companies are not using it,
00:36:26.066 --> 00:36:28.466
that their terms and conditions, they're actually sticking to that,
00:36:28.466 --> 00:36:29.856
right? That's exactly right.
00:36:30.336 --> 00:36:33.236
At some point, you have to trust someone. You do.
00:36:33.716 --> 00:36:39.306
To the extent possible that you can have the information encrypted and you have
00:36:39.306 --> 00:36:42.116
the keys, then you don't have to trust quite as much.
00:36:42.476 --> 00:36:46.476
Yeah, that's right. So because, and you mentioned at the start of this with
00:36:46.476 --> 00:36:49.309
your background and what you currently do day to day at IBM,
00:36:49.727 --> 00:36:52.647
you know, you're talking to customers, enterprise clients and everything.
00:36:53.298 --> 00:36:56.374
Based on what you were talking about with cybersecurity and,
00:36:56.374 --> 00:36:58.510
Kelly, what you brought up with the blast radius,
00:36:59.793 --> 00:37:06.864
where are you kind of seeing people stop their cybersecurity efforts when it
00:37:06.864 --> 00:37:08.609
comes to their AI stacks?
00:37:09.504 --> 00:37:12.664
Like it's easy to go okay we need to make sure the logins are secure and all
00:37:12.664 --> 00:37:17.144
that kind of stuff but as you get further down and you have llms having blanket
00:37:17.144 --> 00:37:19.366
access to things or agents having,
00:37:20.022 --> 00:37:25.885
external internet access while also having internal access to databases and everything do you see,
00:37:26.680 --> 00:37:29.914
is there a trend or is there something you see more often where people aren't
00:37:29.914 --> 00:37:31.324
thinking there's a trend okay you're.
00:37:31.324 --> 00:37:33.734
Saying what the trend is they're not doing any.
00:37:33.734 --> 00:37:34.224
Security.
00:37:38.664 --> 00:37:42.558
I hate to be, you know, a doomsdayer, but I'm just telling you the truth.
00:37:42.901 --> 00:37:47.154
I mean, look what happened. There was a story that broke just last week and
00:37:47.154 --> 00:37:51.451
we talked about it on today's podcast on the IBM security intelligence podcast
00:37:51.840 --> 00:37:54.435
about the hugging face attack.
00:37:54.929 --> 00:37:59.804
OpenAI was running one of their cyber-capable models.
00:38:00.124 --> 00:38:05.493
A model is designed to be able to break things and find where breakages are,
00:38:06.264 --> 00:38:09.957
to try to do an exercise. And this agent...
00:38:11.440 --> 00:38:15.390
Kind of went rogue. I mean, it was told what its objectives were and it tried
00:38:15.390 --> 00:38:17.960
to achieve those objectives, even if it meant cheating.
00:38:18.540 --> 00:38:24.350
And that meant cheating meant using a vulnerability in hugging face,
00:38:24.350 --> 00:38:27.551
another company entirely, and breaking into their system.
00:38:28.040 --> 00:38:34.070
So here we had an agent that essentially went rogue and attacked from one legitimate
00:38:34.070 --> 00:38:35.740
company to another legitimate company.
00:38:36.380 --> 00:38:40.850
Now, if that can happen, that's where, you know, okay, ooh, this thing just
00:38:40.850 --> 00:38:43.660
slipped out of the lab, you know, kind of thing.
00:38:44.200 --> 00:38:49.590
But the bad guys have access to this technology too. And agents are becoming
00:38:49.590 --> 00:38:50.661
more and more available.
00:38:51.120 --> 00:38:56.820
Now, I'm not against these things. That's the hard thing is to try to strike the right balance.
00:38:57.280 --> 00:38:59.700
I don't want to tell people don't use agents because they're dangerous.
00:38:59.700 --> 00:39:03.930
I want to tell people they're dangerous and here's how you need to use them if you're going to.
00:39:04.300 --> 00:39:08.517
But agents are built on a lot of assumptions.
00:39:09.120 --> 00:39:14.460
An agent is basically a model that uses tools in a loop autonomously.
00:39:14.773 --> 00:39:18.160
So what could possibly go wrong? Well, models hallucinate.
00:39:18.580 --> 00:39:22.994
Models can be prompt injected. The tools they use could be compromised.
00:39:23.860 --> 00:39:28.747
The tools they use could belong to someone else who is a hacker who's just collecting,
00:39:29.420 --> 00:39:31.060
credentials that you're feeding into them.
00:39:32.140 --> 00:39:35.780
It's doing this in a loop, which means it can do it at machine speed faster
00:39:35.780 --> 00:39:40.380
than you can intervene and it's doing it autonomously. So it's not asking your permission.
00:39:41.390 --> 00:39:42.562
All of that is a,
00:39:43.241 --> 00:39:46.656
If you don't do this right, this is what I call a risk amplifier.
00:39:46.656 --> 00:39:46.946
Yeah.
00:39:47.646 --> 00:39:51.686
And we have to do this right, which has been the subject of a lot of videos
00:39:51.686 --> 00:39:55.106
I've talked about. So I'm preaching on this one, you know, constantly trying
00:39:55.106 --> 00:39:58.020
to make sure we get it right. But I don't think most people are.
00:39:58.298 --> 00:40:03.196
Yeah. I joke and I used to tell our tech director that he was the department
00:40:03.196 --> 00:40:06.386
of no, but I've become the department of no. Because the more I learn,
00:40:06.786 --> 00:40:09.226
the more I'm like, no, we don't need that. No.
00:40:12.266 --> 00:40:16.306
And that's the problem. security people become that. I did a video where,
00:40:18.366 --> 00:40:25.526
I, and this was not my idea. I actually heard a former CIO of the state of North
00:40:25.526 --> 00:40:31.021
Carolina say this. I don't even know where she is now, but she said, don't say no, say how.
00:40:31.805 --> 00:40:35.606
And I like that better. If you say no, people will do it anyway,
00:40:35.606 --> 00:40:37.772
and they'll do it in the most insecure way possible.
00:40:38.125 --> 00:40:44.065
If I say how, then maybe I can get them to do it right. So maybe for instance, I say,
00:40:44.907 --> 00:40:49.487
No to using public chatbots with AI, with confidential information.
00:40:49.907 --> 00:40:54.397
But the how means we've set up a private instance where you can go knock yourself
00:40:54.397 --> 00:40:56.727
out. So everybody use this one instead.
00:40:57.387 --> 00:41:01.337
But if I just say no and don't give an alternative, oh, they're going to do it anyway.
00:41:01.337 --> 00:41:06.297
Yeah, I'm trying that with that whole new Claude release all for K-12 educators.
00:41:06.297 --> 00:41:08.008
They've released a free.
00:41:08.199 --> 00:41:14.827
They said, hey, all K-12 educators, if you give us your driver's license picture
00:41:14.827 --> 00:41:19.947
and prove that you're an educator with your paycheck stub, we're going to give you something.
00:41:19.947 --> 00:41:23.847
It's like these silly requirements. We're going to give you a free.
00:41:23.847 --> 00:41:27.727
Until somebody hacks in and gets the identity database. Thank you.
00:41:28.547 --> 00:41:31.407
That's the one. And then they're like, and it's completely safe.
00:41:31.407 --> 00:41:36.807
We've written it so that you as teachers can put your grade books, your teachers counts.
00:41:36.807 --> 00:41:40.837
But we're doing it for teachers and not for districts. So the teachers came
00:41:40.837 --> 00:41:44.957
out to me. I'm like, listen, no to Claude. And I love Claude.
00:41:44.957 --> 00:41:48.457
And I'm not saying anything about it. I know I'm going to assume positivity
00:41:48.457 --> 00:41:50.623
that they're doing this as a positive thing.
00:41:51.917 --> 00:41:54.836
Just trying to put that out there. in the space.
00:41:55.133 --> 00:41:55.987
I'll give the benefit of the doubt.
00:41:56.247 --> 00:41:56.867
I'm going to give the benefit of the doubt.
00:41:56.887 --> 00:41:58.307
But that doesn't mean they're going to get it right.
00:41:58.787 --> 00:42:02.173
And I said, you know, I'd rather pay 20 bucks a month for $20.
00:42:02.701 --> 00:42:06.939
For every one of you, because they don't even offer that enterprise yet for K-12.
00:42:07.107 --> 00:42:12.665
I'd rather them pay the 20 bucks a month for their own little one that they want.
00:42:13.105 --> 00:42:17.185
And then we worry about it. But it's just, it's become quite a thing.
00:42:17.185 --> 00:42:22.055
And hope that you're not just paying 20 bucks a month for just more tokens in
00:42:22.055 --> 00:42:24.735
the same service with the same level of security.
00:42:24.735 --> 00:42:25.195
Exactly.
00:42:25.195 --> 00:42:28.911
Because that's the other thing you kind of have to vet with these things.
00:42:29.865 --> 00:42:33.265
That, you know, so, I mean, even better still is if you can say,
00:42:33.265 --> 00:42:36.964
look, we're going to have a private instance in our own cloud space,
00:42:37.289 --> 00:42:39.205
and we're going to run those models over here.
00:42:40.245 --> 00:42:41.045
Yeah, you're going to come and
00:42:41.045 --> 00:42:45.373
teach me from IBM Watson. I went to IBM conference in Orlando last year.
00:42:45.606 --> 00:42:49.705
You come and teach me and sit me down with IBM Watson, and I want to build my own instance.
00:42:49.705 --> 00:42:50.905
There we go.
00:42:50.905 --> 00:42:51.885
He can fly me over as well.
00:42:52.345 --> 00:42:54.255
Julian wants to join too. We're going to have her.
00:42:54.305 --> 00:42:57.135
Come on, come on. This year, the conference is in Atlanta.
00:42:57.135 --> 00:43:00.767
Oh, well, might have to go there. Yeah. So...
00:43:01.672 --> 00:43:08.188
Quick question. You're coding language in cybersecurity, or is there like a
00:43:08.188 --> 00:43:11.218
coding back there, or is it consulting more so now?
00:43:12.158 --> 00:43:12.638
Yeah. So,
00:43:14.758 --> 00:43:19.338
it's interesting. After doing 200 videos, basically every question anyone ever
00:43:19.338 --> 00:43:21.368
asks, I can say, I did a video on that.
00:43:22.293 --> 00:43:26.678
And I did a video on that. The most important programming language of the AI era.
00:43:26.998 --> 00:43:27.278
Is English.
00:43:27.338 --> 00:43:27.938
Are you ready for this?
00:43:27.938 --> 00:43:28.388
English.
00:43:29.723 --> 00:43:29.958
It is.
00:43:30.038 --> 00:43:31.118
You said that on last night's video.
00:43:31.178 --> 00:43:33.438
If you think of, yeah, okay.
00:43:33.438 --> 00:43:34.998
We've been watching your videos.
00:43:35.458 --> 00:43:36.168
Okay, somebody.
00:43:36.168 --> 00:43:37.101
We know you, Chip.
00:43:38.038 --> 00:43:42.038
Yeah, great. I mean, think about it. The reason we invented programming languages
00:43:42.038 --> 00:43:47.618
in the first place is because we speak, you know, these high-level natural languages, English.
00:43:48.338 --> 00:43:50.758
Somebody may say, why are you talking English? It could be Spanish.
00:43:50.758 --> 00:43:55.687
Yes, it could. So, we'll use English as the stand-in for this. But natural language.
00:43:57.378 --> 00:44:02.638
Computers didn't understand that. So we invented programming languages as something in between.
00:44:03.098 --> 00:44:09.223
So you translate your thoughts, your intents into instructions, which is code.
00:44:09.520 --> 00:44:11.814
And then the computer knows how to run the code.
00:44:12.410 --> 00:44:17.038
Well, now with AI, generative AI, being able to understand natural language
00:44:17.038 --> 00:44:21.263
natively, well, we can skip that middle step in a lot of cases.
00:44:21.385 --> 00:44:27.458
We can go straight from intent to outcome, intent to results, and not have to code.
00:44:27.858 --> 00:44:31.178
And more and more, and this is the thing, I don't mean to sound like an IBM
00:44:31.178 --> 00:44:35.178
commercial here, but the big emphasis IBM's doing these days is on this product
00:44:35.178 --> 00:44:38.110
we have called Bob, which is a coding assistant.
00:44:38.370 --> 00:44:42.998
But it's more of a coding assistant for enterprise environments and does a whole
00:44:42.998 --> 00:44:46.997
lot more than just write code for you because any old chatbot can do that.
00:44:47.718 --> 00:44:52.337
But that's the future. Now, can it write perfect code? No.
00:44:53.040 --> 00:44:57.248
You know what? Neither can people. So that's not the standard.
00:44:57.701 --> 00:45:03.684
Can we write more code? Yes. Can we generate it? Can we do iterate on it faster? Yes.
00:45:04.084 --> 00:45:08.963
But then we've got to spend more time on making sure it did what we asked.
00:45:09.944 --> 00:45:13.464
And we need to move security. We've been saying we need to shift left.
00:45:13.464 --> 00:45:15.684
The video I'm going to do on Friday is on this topic.
00:45:16.684 --> 00:45:19.944
Shift left security. Get security earlier in the process.
00:45:20.364 --> 00:45:22.744
You know, it should be all the way in the requirements phase.
00:45:22.744 --> 00:45:27.464
But in the development process, don't wait until the end and then do the security testing.
00:45:28.024 --> 00:45:33.064
Start doing that early. And if AI is generating the code, we can use AI to test
00:45:33.064 --> 00:45:35.484
the code as well and look for vulnerabilities.
00:45:35.484 --> 00:45:39.784
Oh, my gosh. You just made me think of another question. So no code, like a no code apping.
00:45:40.104 --> 00:45:46.304
If you're coding with English and you're using, say, like Cloud Code or Lovable or something.
00:45:46.304 --> 00:45:49.864
Although the AI may create code and then the code runs. But in some cases,
00:45:49.864 --> 00:45:51.244
it doesn't have to. It can just do it.
00:45:51.860 --> 00:45:56.914
But if you wanted to build in security in the front, say you're working...
00:45:58.307 --> 00:46:03.304
With a code. That's the thing. Most people don't really know what they're building
00:46:03.304 --> 00:46:08.894
for security when they build with the English or the AI. So they kind of just
00:46:08.894 --> 00:46:09.987
say, hey, make it secure.
00:46:11.394 --> 00:46:13.713
That's like saying, here's my essay, make it better.
00:46:15.394 --> 00:46:17.494
Yeah. Sprinkle magic pixie dust.
00:46:17.494 --> 00:46:17.994
Exactly.
00:46:17.994 --> 00:46:21.929
Yeah. No, that's not going to be enough. I can assure you that.
00:46:22.634 --> 00:46:25.302
But my example is, Like I remember when I was in high school,
00:46:25.598 --> 00:46:31.244
one of the first programming assignments I had was to write a program using
00:46:31.244 --> 00:46:34.474
the basic programming language. There was a programming language called basic
00:46:34.474 --> 00:46:36.442
that nobody uses anymore.
00:46:37.714 --> 00:46:40.116
And find all the number of ways you can make change for a dollar.
00:46:40.840 --> 00:46:42.514
Okay? So that was the assignment.
00:46:42.854 --> 00:46:46.374
Well, I figured out how to do that and wrote that, and it's not a big deal.
00:46:46.734 --> 00:46:47.928
It's actually quite easy.
00:46:48.614 --> 00:46:52.514
Probably 12, 15 lines of code to write something like that.
00:46:53.339 --> 00:46:56.914
Now I can go to a chatbot and say, tell me all the ways to make change for a
00:46:56.914 --> 00:46:58.434
dollar. And it could just spit it out.
00:46:58.834 --> 00:47:02.624
It doesn't have to create code as an intermediate step. So that's what I'm saying.
00:47:02.624 --> 00:47:06.742
In some cases, it can give us the answer, the result without code.
00:47:06.986 --> 00:47:10.594
In other cases, we'll use it to actually write code because we want this to
00:47:10.594 --> 00:47:14.784
be, you know, something that is more predictable and more deterministic than
00:47:14.784 --> 00:47:18.424
a chatbot would be because, you know, how you can ask them the same question
00:47:18.424 --> 00:47:20.790
on different days and sometimes get slightly different answers.
00:47:21.377 --> 00:47:25.174
Sometimes that's charming. Sometimes that's annoying, right?
00:47:25.574 --> 00:47:30.454
Yeah. So, okay. So I've got a question then before we wrap it up.
00:47:30.454 --> 00:47:31.854
I think we're getting close to time.
00:47:33.534 --> 00:47:37.078
With all of this layer with AI and cybersecurity in there as well,
00:47:37.891 --> 00:47:42.067
And you want to move it to the front, which I actually, you can't see this for
00:47:42.067 --> 00:47:46.657
everyone listening, but I was drawing up a process diagram for an app.
00:47:46.657 --> 00:47:47.127
Look at that.
00:47:47.127 --> 00:47:51.357
An app I'm building. And there's the security abstracted, you know,
00:47:51.697 --> 00:47:55.087
out of this, let's just build an agent kind of thing, which is what they said.
00:47:55.087 --> 00:47:55.377
Good for you.
00:47:55.397 --> 00:47:55.937
Good for you.
00:47:56.197 --> 00:47:56.437
And I'm like, no.
00:47:56.437 --> 00:48:00.837
No, we need an MCP server here. I'm going to write very explicit scripts in
00:48:00.837 --> 00:48:01.556
here, you know. Excellent.
00:48:02.237 --> 00:48:06.887
Um, so that's, that's all there, but how would you suggest the people listening
00:48:06.887 --> 00:48:10.897
and learners out there who are thinking, starting to switch onto this and go,
00:48:10.897 --> 00:48:13.777
Oh crap, I didn't really need to think about this stuff as they're seeing more
00:48:13.777 --> 00:48:17.067
breaches and like hearing about the hugging face thing and all of that.
00:48:17.416 --> 00:48:21.037
Um, aside from your channel, which is amazing, by the way, I actually brought it up here.
00:48:21.037 --> 00:48:23.827
So I have the link to your channel. We can put in the show notes after,
00:48:23.827 --> 00:48:29.447
but, um, is there like a formal at this point in time, given this is all relatively
00:48:29.447 --> 00:48:33.607
newish, is there any formal learning people can do for this where they might
00:48:33.607 --> 00:48:35.144
get a cert? Is there anything like that?
00:48:35.505 --> 00:48:42.877
Yeah, yeah, there are some. In fact, there's a skillsbuild.org is actually a website that IBM runs.
00:48:43.277 --> 00:48:48.337
And there's a lot of training on there that we make available to the education market all for free.
00:48:49.197 --> 00:48:52.657
Training in AI, training in cybersecurity, training into a lot of different
00:48:52.657 --> 00:48:55.117
disciplines. So that'd be my first stop.
00:48:55.777 --> 00:49:01.127
Second would be Coursera.com, which they've actually taken some of my videos
00:49:01.127 --> 00:49:02.377
and made them into courses.
00:49:02.377 --> 00:49:02.897
Oh, cool.
00:49:03.237 --> 00:49:07.333
And, you know, people who have a Coursera, you know, subscription or whatever,
00:49:07.669 --> 00:49:11.977
they can, and they added some assignments to it that thankfully I don't have to grade.
00:49:13.237 --> 00:49:17.297
And then after you finish those courses, you can get a completion certificate.
00:49:17.737 --> 00:49:19.877
So that's kind of like the next level up from it.
00:49:20.617 --> 00:49:25.237
So those things are available. And I do think an advantage we have is right
00:49:25.237 --> 00:49:28.657
now with the internet like it is, it's never been easier to learn.
00:49:29.097 --> 00:49:31.348
We have so many resources available to us.
00:49:31.767 --> 00:49:36.495
I learned so much on YouTube myself from other people.
00:49:37.855 --> 00:49:41.975
I look at the IBM videos from my colleagues. I also look at lots of other sources,
00:49:42.555 --> 00:49:45.495
and learn so many things. It's such a great resource, I just think,
00:49:45.495 --> 00:49:47.811
in general. So there's all of that.
00:49:48.837 --> 00:49:55.885
I think wanting to learn and focusing on learning and having that kind of lifelong
00:49:55.885 --> 00:49:59.919
learner mindset is super critical to this.
00:50:00.615 --> 00:50:04.541
Because I remember when, again, back when I was an undergrad,
00:50:05.315 --> 00:50:07.072
I took a course in computer graphics.
00:50:07.576 --> 00:50:11.853
And now back then, again, we're talking dinosaur days. So computer graphics
00:50:11.853 --> 00:50:15.593
was a really new thing. I mean, we're talking Pong level graphics here,
00:50:15.593 --> 00:50:19.903
if you even know what that game is. But I mean, we're talking really crude stuff.
00:50:20.543 --> 00:50:25.113
And the professor I had that taught that was a really good professor.
00:50:25.113 --> 00:50:26.343
I'd had him for other courses.
00:50:27.903 --> 00:50:32.363
But I could tell he had just read one chapter ahead in the textbook.
00:50:32.727 --> 00:50:35.543
That's what he knew about this. That's what qualified him.
00:50:36.023 --> 00:50:39.333
I mean, his experience. But he didn't know any more about graphics other than
00:50:39.333 --> 00:50:41.923
he had read one chapter ahead in the textbook from us.
00:50:42.323 --> 00:50:46.283
Because I realized when I read that chapter, he was almost quoting it verbatim.
00:50:46.803 --> 00:50:53.364
So, okay. But the reason I bring that up is I feel like we're all a little bit like that now.
00:50:53.618 --> 00:50:58.213
What qualifies you as an expert these days, especially in AI,
00:50:58.213 --> 00:51:02.513
a fast-moving topic like this, is you read one chapter ahead in the textbook
00:51:02.513 --> 00:51:03.819
compared to everybody else.
00:51:04.423 --> 00:51:08.184
And nobody has finished the book. because it's still being written.
00:51:08.863 --> 00:51:14.443
So there's, you know, we have to be careful that we don't, and I love using
00:51:14.443 --> 00:51:17.263
analogies, that we don't outrun our headlights on this.
00:51:17.783 --> 00:51:22.443
And a lot of people are doing that, you know, but that's the thing.
00:51:23.083 --> 00:51:27.163
You keep pushing forward into the darkness, trying to fill it with more knowledge.
00:51:27.683 --> 00:51:31.833
And again, I think we're in a great place to be able to learn because there's
00:51:31.833 --> 00:51:36.082
never been more resources available to us than now, but it's moving fast.
00:51:36.395 --> 00:51:39.673
So if you don't like learning, this is not your era.
00:51:39.743 --> 00:51:41.423
This is not for you. I love that.
00:51:42.063 --> 00:51:46.583
I recommend you build a time machine and go back in time because it's not going to slow down.
00:51:47.003 --> 00:51:48.833
I actually really love that because
00:51:48.833 --> 00:51:54.063
you know what? I think that summarizes 26, 27 for a lot of teachers.
00:51:54.772 --> 00:51:59.403
I think we need to put that on there that if you don't love learning,
00:51:59.403 --> 00:52:03.683
you're in the wrong place kind of thing. Because if we don't get that through...
00:52:04.531 --> 00:52:07.548
Not just the teacher's heads, because that's kind of easy. We go,
00:52:07.548 --> 00:52:11.098
oh, yeah, of course. But I think reminding kids,
00:52:13.498 --> 00:52:17.318
that even though AI can do this stuff, if you're not learning with it or learning
00:52:17.318 --> 00:52:21.278
from it, you're really not doing too well for the future.
00:52:22.118 --> 00:52:25.518
Yeah. I think there's a push and pull on this. You know, the kids maybe don't
00:52:25.518 --> 00:52:29.228
necessarily want to learn. The teachers like learning. They've chosen this as
00:52:29.228 --> 00:52:31.090
their profession because they love learning.
00:52:31.678 --> 00:52:36.923
But then on the other side, what the kids are better at is flexibility and adaptability.
00:52:37.438 --> 00:52:38.858
And a lot of times the teachers are not.
00:52:39.698 --> 00:52:43.458
And I find they've become very resistant. They learned things in a certain method,
00:52:43.818 --> 00:52:46.936
and they assume that's the same method that we'll always have to carry forward.
00:52:47.156 --> 00:52:51.480
You know, I gave an example in the video on AI and the Future of Education,
00:52:51.724 --> 00:52:53.548
where I start off writing in cursive.
00:52:53.849 --> 00:52:59.158
And I say, you know, look, there was a time when that was a really important skill to have.
00:52:59.158 --> 00:53:03.178
It just became a log. again, and they want to play a law again in Florida.
00:53:03.578 --> 00:53:04.018
No, fine.
00:53:05.398 --> 00:53:10.448
Fine. Why don't they go ahead and teach all the kids how to put shoes on horses, too?
00:53:10.448 --> 00:53:13.078
I think so. Because that's going to be around as relevant. We might as well,
00:53:13.078 --> 00:53:17.768
you know. I mean, you know, we got to train some more OCR better with some really,
00:53:17.768 --> 00:53:19.438
really scribbly cursive heads.
00:53:20.158 --> 00:53:24.278
I mean, no, the bottom line is, you know, we free up, educate.
00:53:24.278 --> 00:53:27.298
We have precious little educational time. Instructional time.
00:53:27.758 --> 00:53:31.568
Is that really where we want to spend it? is that the hill you want to die on
00:53:31.568 --> 00:53:34.978
cursive i'm going to recommend that that not be the.
00:53:34.978 --> 00:53:38.818
One although it was very embarrassing when my son went for his driver's license
00:53:38.818 --> 00:53:41.518
and they told him to sign for his driver's license and he didn't know how to
00:53:41.518 --> 00:53:44.758
sign it and he starts printing like a two-year-old i'm like what is that,
00:53:47.458 --> 00:53:48.138
and you know.
00:53:48.478 --> 00:53:51.028
His kids are going to look back and say what was this.
00:53:51.028 --> 00:53:56.605
Scrawl that all these old people did why didn't they know how to write what is a pen and,
00:53:57.121 --> 00:53:58.649
What is a pen? Yeah.
00:53:59.419 --> 00:54:04.469
Well, most of what we write these days is not with a writing instrument.
00:54:04.869 --> 00:54:06.149
Most of it's done with a keyboard.
00:54:06.149 --> 00:54:07.879
Yeah, our thumbs. On our thumbs.
00:54:07.879 --> 00:54:13.219
Yeah. And my grandmother got awards for penmanship when she was in school.
00:54:13.219 --> 00:54:13.559
Yeah.
00:54:13.559 --> 00:54:18.679
Okay, nobody cares now. I'm sorry, but they just don't because when I type it,
00:54:18.679 --> 00:54:20.229
it looks the same as when you type it.
00:54:20.709 --> 00:54:25.229
You know, that skill that used to be really important in the past is not as important in the past.
00:54:25.569 --> 00:54:31.365
So, teachers have to be adaptable and flexible and looking forward.
00:54:31.469 --> 00:54:35.450
I mean, if we're the educated ones, we've got to lead people in that area,
00:54:35.786 --> 00:54:39.752
not just teach them this way because this is the way it's always been done.
00:54:40.158 --> 00:54:42.562
If we're smart, we know that's not an answer.
00:54:42.734 --> 00:54:46.839
Yeah. No, I love that. And that's actually why the only reason I drew on that
00:54:46.839 --> 00:54:50.279
piece of paper for that process flow I was putting together is because I didn't
00:54:50.279 --> 00:54:53.089
have a $30,000 electronic board.
00:54:53.589 --> 00:54:54.619
Oh, okay.
00:54:54.619 --> 00:54:55.878
So if you want to send one over.
00:54:56.033 --> 00:55:02.227
That's all. So, yeah, I hear the plea for a GoFundMe. Yeah, exactly.
00:55:02.471 --> 00:55:06.781
Can you imagine if we had a studio or a podcast? I would actually,
00:55:07.104 --> 00:55:09.002
you know, we'd go back to live streaming.
00:55:09.269 --> 00:55:11.389
If I'd be over there every week to record.
00:55:11.389 --> 00:55:11.869
It'd be great.
00:55:12.409 --> 00:55:13.029
Oh, sure.
00:55:13.809 --> 00:55:17.189
So just one last question from me, and it's kind of tongue-in-cheek,
00:55:17.189 --> 00:55:19.949
but you said you like to use analogies, right?
00:55:20.849 --> 00:55:24.729
And quotes and things like that. last week in my presentation,
00:55:24.729 --> 00:55:29.549
I was saying AI is not an oracle, right? It's not a know everything kind of
00:55:29.549 --> 00:55:33.729
thing. And I wanted to put a picture of the oracle from the matrix.
00:55:34.409 --> 00:55:37.997
And then I thought to myself, I don't know who's going to understand this in the room.
00:55:38.368 --> 00:55:42.339
So do you have, as you put your stuff together, do you ever have those moments
00:55:42.339 --> 00:55:46.929
where you think about your audience and think, who's going to get this? Is this going to flop?
00:55:46.929 --> 00:55:50.269
Constantly. Constantly. Because here's the thing.
00:55:50.789 --> 00:55:55.581
Um, so I, just to give you a general idea of, of how long I've been around,
00:55:55.889 --> 00:56:00.129
I got my, my bachelor's in computer science in 1984.
00:56:00.129 --> 00:56:00.419
Okay.
00:56:00.889 --> 00:56:01.276
So.
00:56:02.252 --> 00:56:04.592
That's when I'm talking about dinosaur era.
00:56:04.932 --> 00:56:10.122
Now, I'm up talking to my students every week, and I mean-
00:56:11.227 --> 00:56:16.607
When I was already 20 years into my IBM career, they were not yet zygotes.
00:56:17.407 --> 00:56:21.627
So they're not getting the reference that I'm making on these things.
00:56:22.447 --> 00:56:26.057
And I'll put these out there. And like what I just said to you, you all laughed.
00:56:26.057 --> 00:56:28.507
I'm a former biology teacher. That's why.
00:56:29.987 --> 00:56:35.647
I just get blank stares. And then I'm like, okay, I guess I have to explain this to you, don't I?
00:56:35.647 --> 00:56:38.483
No, they have it on recording. They're like, what's zygote?
00:56:39.187 --> 00:56:44.787
Yeah, right, right. So, yes, there's a lot of analogies I would like to make,
00:56:44.787 --> 00:56:48.997
and I have to try to figure out what my audience is, and are they old enough
00:56:48.997 --> 00:56:50.287
to understand these things?
00:56:51.227 --> 00:56:58.587
When I was in Cartagena last week, I was talking about the movie 2001 A Space Odyssey.
00:56:59.247 --> 00:57:02.587
And, okay, so that movie came out in, I think, 1969.
00:57:03.907 --> 00:57:09.287
And, you know, I realized most of the people in here probably weren't alive yet.
00:57:09.767 --> 00:57:13.257
So they had they haven't seen this movie they probably some of them probably
00:57:13.257 --> 00:57:17.427
most of them probably not even heard of it uh so i'm gonna have to explain to
00:57:17.427 --> 00:57:22.547
them what that reference is about you know so yeah so yes but um,
00:57:23.267 --> 00:57:27.587
but yeah there's there's a lot of those i do think analogies are really powerful
00:57:27.587 --> 00:57:32.547
though because we take something that we know already and we're comparing it
00:57:32.547 --> 00:57:34.607
to something that we don't know and
00:57:35.047 --> 00:57:39.134
basically saying oh yeah you kind of already know this because it's a lot like that.
00:57:39.295 --> 00:57:42.267
But then, of course, analogies, you stretch them too far and they break down.
00:57:42.687 --> 00:57:47.307
So they're not always a perfect replication, but they're good for a quick illustration.
00:57:47.307 --> 00:57:49.557
And I think most people can latch onto them.
00:57:49.557 --> 00:57:53.427
Okay, real quick, because we have like a couple minutes and we're usually trying
00:57:53.427 --> 00:57:56.927
to cut it an hour because Julian has- That's because.
00:57:56.927 --> 00:58:00.767
You don't have me as a guest. You're able to do that.
00:58:01.087 --> 00:58:07.127
Sometimes we just go over. For kids, and this is kids pre-college,
00:58:07.447 --> 00:58:10.305
right? Kids graduating out of,
00:58:11.234 --> 00:58:17.741
school next year, 12th grade. And what are three things, because you're in college,
00:58:17.741 --> 00:58:23.227
you're a professor, what are three things that you wish every senior leaving,
00:58:23.900 --> 00:58:28.211
would have in their kind of back pocket? Besides a brain.
00:58:28.211 --> 00:58:30.321
Only three?
00:58:30.321 --> 00:58:31.021
Only three.
00:58:31.381 --> 00:58:32.282
Only three.
00:58:32.444 --> 00:58:33.401
We have to keep it short.
00:58:34.301 --> 00:58:38.341
Yeah. Okay. I'm going to go with communication.
00:58:39.580 --> 00:58:42.570
Incredibly important, especially for people in the STEM fields.
00:58:43.661 --> 00:58:48.991
I can remember, I think a lot of my students even now would be happy to have
00:58:48.991 --> 00:58:52.361
a career where we put them in a closet, slide pizzas under the door,
00:58:52.361 --> 00:58:56.041
and they give us code out the other side. Okay. So what do I do?
00:58:57.521 --> 00:59:02.811
I don't ask them to write code. 20% of their grade is class participation. And most of them hate it.
00:59:04.041 --> 00:59:08.341
They're super introverts and they do not want to talk. And that's exactly why
00:59:08.341 --> 00:59:09.161
I'm going to make them do it.
00:59:09.661 --> 00:59:12.651
Because that's going to be the difference between who wins and who doesn't.
00:59:12.651 --> 00:59:16.611
We've got AI that can do a lot of that, slide the pizzas under the door.
00:59:16.611 --> 00:59:17.861
It doesn't even require pizza.
00:59:18.261 --> 00:59:23.271
So it can just give us code. So the ability to communicate is going to be incredibly
00:59:23.271 --> 00:59:29.030
important. The things that are human, unique, we need to lean into. I think,
00:59:30.009 --> 00:59:33.773
being curious is a big part of this as well.
00:59:34.789 --> 00:59:39.449
One of my favorite quotes here also that I saw recently is, curious people don't
00:59:39.449 --> 00:59:41.769
get replaced, they get liberated.
00:59:42.537 --> 00:59:46.659
And that's what AI will do for us if we're the curious type,
00:59:46.659 --> 00:59:50.029
because we'll move on. As it starts to do the grunt work for us,
00:59:50.029 --> 00:59:54.288
we'll move on to the next interesting thing and continue to win with that.
00:59:54.513 --> 01:00:00.290
And then the most important skill of the AI era is a third C. critical thinking.
01:00:01.269 --> 01:00:04.673
We need to not trust everything that comes out of AI because sometimes it's wrong.
01:00:04.992 --> 01:00:11.649
It will hallucinate, which is a convincing inaccuracy that it tries to tell us.
01:00:12.209 --> 01:00:16.591
Sometimes it's been manipulated to tell us things. Sometimes it just makes mistakes.
01:00:17.652 --> 01:00:21.959
What we wanted to do and when we wanted to do it and why we wanted to do it,
01:00:21.959 --> 01:00:24.689
again, all going back to humanities kinds of questions.
01:00:26.089 --> 01:00:31.189
I think that kind of critical thinking and deciding The main thing I try to
01:00:31.189 --> 01:00:35.049
get across to my students in one of the courses that I teach on secure thinking
01:00:35.049 --> 01:00:37.487
is just because you can do something doesn't mean you should.
01:00:37.852 --> 01:00:41.809
You could write enough code to do anything you want, but should you?
01:00:42.289 --> 01:00:45.239
What are the unintended consequences if you were to do that?
01:00:45.239 --> 01:00:48.769
How could it be used and abused?
01:00:49.409 --> 01:00:53.689
And let's try to put in the safeguards. So critical thinking,
01:00:54.049 --> 01:00:58.389
I think, is also more important than ever. And where do we learn critical thinking?
01:00:59.029 --> 01:01:03.339
Again, I'm going back to the humanities. So it's gonna be a balance.
01:01:03.339 --> 01:01:09.670
We don't want just a bunch of techie engineer types who don't understand the larger context.
01:01:09.803 --> 01:01:15.533
We've gotta be more versatile and spread our knowledge around and lean into the things,
01:01:15.998 --> 01:01:19.662
that ai can't do so that would be my advice.
01:01:19.662 --> 01:01:22.098
Very cool julian you have any one last.
01:01:22.377 --> 01:01:25.762
No no no i i love it this that was the perfect ending i.
01:01:25.762 --> 01:01:32.032
Think perfect ending so yeah so great um so if people wanted to reach out to
01:01:32.032 --> 01:01:34.706
you is there an easy way to get in contact with you.
01:01:35.031 --> 01:01:39.622
Linkedin's probably the easiest yeah if you can find me uh there's a there's
01:01:39.622 --> 01:01:45.742
another jeff crum if you if you google me and he is an evangelist and motivational
01:01:45.742 --> 01:01:47.562
speaker in California. That's not me.
01:01:48.102 --> 01:01:53.522
So, you know, just, but you can Google me or you can find me on LinkedIn.
01:01:53.522 --> 01:01:56.482
It'll be pretty obvious, which if you got the right Jeff Kroon or not.
01:01:56.482 --> 01:02:01.622
Excellent, excellent. I can attest to that. On a Saturday or Sunday morning,
01:02:01.622 --> 01:02:04.852
I was like, oh, and you did reach back. You're like me. I'm like,
01:02:04.852 --> 01:02:06.562
who's on LinkedIn this early like me?
01:02:07.982 --> 01:02:09.152
Anything to share, Julian?
01:02:09.571 --> 01:02:12.942
No, no, I'm good. This has just been inspiring for me. I'm definitely going
01:02:12.942 --> 01:02:15.582
to go through the rest of your videos. Good luck.
01:02:15.582 --> 01:02:17.042
There's like 5,000 of them.
01:02:18.462 --> 01:02:21.942
It's a cure for insomnia. That's not much I'm sure of.
01:02:23.122 --> 01:02:26.322
I went to bed super early last night after watching your video, so you might be right.
01:02:28.262 --> 01:02:29.162
You're making my point.
01:02:29.162 --> 01:02:32.742
Exactly. No, I'm just really appreciative of what you're doing,
01:02:32.742 --> 01:02:37.362
Jeff, because as I'm seeing over here with a lot of the companies I'm talking
01:02:37.362 --> 01:02:41.792
to is exactly the kind of pain point you're solving for people.
01:02:41.792 --> 01:02:46.065
So love it. Absolutely love it. Thank you for joining us.
01:02:46.547 --> 01:02:46.832
Cool.
01:02:46.832 --> 01:02:48.092
Thank you all. I enjoyed it.
01:02:48.092 --> 01:02:51.022
Well, hopefully, maybe I'll try to get to Atlanta and go to IBM.
01:02:51.022 --> 01:02:54.002
I only went for one day last year because I was like, it's just a train ride,
01:02:54.002 --> 01:02:57.692
but maybe we'll see each other because I learned a lot in just one day at IBM.
01:02:57.692 --> 01:02:58.782
So hopefully- If you get there.
01:02:58.782 --> 01:03:03.442
Definitely look me up and I'd say that to anybody, the IBM Tech Exchange Conference in Atlanta.
01:03:03.442 --> 01:03:03.762
Excellent.
01:03:03.762 --> 01:03:05.354
Do I have one in Australia yet?
01:03:06.487 --> 01:03:08.686
No, I'm afraid. Not that I'm aware of.
01:03:08.686 --> 01:03:09.216
You should found it.
01:03:09.316 --> 01:03:11.436
Maybe they do, but I'm not invited, I guess.
01:03:12.536 --> 01:03:13.516
Only Americas.
01:03:14.216 --> 01:03:19.006
Yeah, yeah. They have higher standards for that one than to let me have.
01:03:19.006 --> 01:03:20.066
Yeah, that's true.
01:03:20.066 --> 01:03:24.409
All right. We're going to wrap it up. Otherwise, we'll talk forever.
01:03:24.711 --> 01:03:27.836
All right. So for Teaching Python, this is Kelly.
01:03:28.136 --> 01:03:29.617
And this is Julian. Signing off.
01:03:30.267 --> 01:03:31.696
Oh, he remembered. Well done.
01:03:32.056 --> 01:03:33.046
Got it. Got it.
01:03:33.056 --> 01:03:33.456
Got it.
01:03:36.322 --> 01:03:36.693
Peace.