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Human First AI Why Replacing Humans Is a Terrible Business Strategy with Derek Crager S3-EP5 · SpaceTomatoes

• 0:00
  I'm just curious, what happens if somebody asks something that's outside of its purview?
• 0:05
  Say, like, hey, Pachymetra, you want to chat today?
• 0:07
  How are you doing today?
• 0:08
  They're just lonely and want somebody to talk to.
• 0:11
  Does it have something that cues up and says, okay, I can't talk about that?
• 0:15
  Well, it's interesting you said that because mental wellness is huge.
• 0:18
  And it's in HR departments, you know, keeping track of how the workers actually feel day to day.
• 0:25
  Because are they going to come in tomorrow if it's the bottom line?
• 0:28
  Or can we make your day better?
• 0:31
  Good HR departments, good HR employees really do care about the employees.
• 0:40
  So in a situation like that, it's whatever the company.
• 0:58
  All right, everybody.
• 1:08
  Welcome back to another episode of the Space Tomatoes podcast.
• 1:12
  I'm one of your hosts, Havoc.
• 1:14
  I'm the other guy, LX.
• 1:15
  Hello.
• 1:16
  That other guy.
• 1:17
  Welcome back.
• 1:17
  All those things.
• 1:19
  Today we have with us special guest, Derek Crager, founder and CEO of Practical AI,
• 1:24
  creator of PocketMentor, and author of Human First AI. Welcome to the show, Derek.
• 1:32
  Well, thank you both. I appreciate the invite to bring me in.
• 1:36
  Absolutely. So, Human First AI, what is that concept and where does it come from?
• 1:44
  Yeah, absolutely. Well, we're actually in this early path to this AI adoption from the consumer
• 1:49
  level. You know, we're really, this is like our fourth year. And in internet timeframe,
• 1:54
  it really took the internet about 17 years for people to adopt the internet. It came out in 89.
• 2:01
  People really didn't hop on in mass until about 2006. So AI, we're still at this polarization
• 2:07
  point. And if we talk about the adoption, that's the human factor. And there's polarization.
• 2:15
  It's early and it really gets into change management, which is the psychology of adoption.
• 2:22
  And right now, AI is so early, people don't know what they're adopting and they have to know before they can make a choice.
• 2:30
  So the pollers here are God and the devil.
• 2:34
  AI is the end all, the be all on the positive aspect.
• 2:38
  Or, you know, AI is here to take our lives.
• 2:42
  I heard quotes that there's a 10% possibility that AI is going to outlive us.
• 2:48
  So I thought that was interesting.
• 2:51
  But I think we're here.
• 2:53
  I think there's a third path.
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  And that Yogi Berra quote comes to mind, if you know what I'm talking about.
• 2:59
  When you come to a fork in the road, what do you do?
• 3:02
  Well, you pick it up.
• 3:03
  So here we are.
• 3:04
  We don't need to choose all or nothing.
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  We can keep the baby and throw out the bathwater separately.
• 3:11
  And human-first AI does this.
• 3:14
  If not for humans, the business success that we have today across all corporate enterprise wouldn't have happened.
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  It's all been labor and mind of humans.
• 3:25
  So why is the conversation focused on replacing humans if humans is what got us here?
• 3:32
  So does AI do good stuff?
• 3:34
  Oh, my God, absolutely it does good stuff.
• 3:36
  But there's six or seven items that humans will always do better than AI.
• 3:42
  So human-first AI is keeping the humans employed and giving the humans the ability to control what AI does.
• 3:51
  So Practical AI, my company, our philosophy is use AI to think with the human, not for the human.
• 4:00
  That's a big difference.
• 4:02
  and it even goes beyond human in the loop because human in the loop, that's kind of a catchphrase
• 4:08
  that people are using, is like, hey, let's put a human in here. We're going to check on it every
• 4:12
  hour. Oh, you know, I'm tired. Let's check on it once a day. You know what? Nobody's died yet. Let's
• 4:18
  check on it once a week, once a month. It gets to be the point. Human in the loop is just a laugh.
• 4:22
  It really doesn't mean anything. But if we use AI to think with the human, then we can empower the
• 4:29
  humans to do what they've always done well, and that's to make choices outside the box.
• 4:35
  AI is great.
• 4:36
  AI is technology.
• 4:37
  So AI will only do what we program it to do.
• 4:41
  And what do we call it?
• 4:41
  You know, that box on our desk or corporate, you know, the box in the database center.
• 4:48
  And AI can only do what we program it to.
• 4:52
  And by definition, that fits inside a box.
• 4:54
  So what happens when things go wrong in both the white-collar environment and the blue-collar environment is that humans have to take over.
• 5:05
  They have to step in no matter what.
• 5:07
  And if we get used to AI making all of our decisions then our businesses become brittle And so human AI is really empowering the human to be better than they are so they can continue to be the integral cog in the business
• 5:29
  Yeah, it's always been a striking thing for me, right?
• 5:32
  But to see companies looking at replacing humans with AI, and while I can understand as a short-sighted approach where that might be somewhat desirable for a company whose obvious mission is to make dollars for the shareholders, etc.,
• 5:53
  The fact of the matter is our economy relies on these humans and more unemployed humans means less humans to spend on the thing that you're producing, meaning you're literally destroying your own marketplace by replacing the humans with AI, right?
• 6:13
  It's always been a thought for me.
• 6:17
  Well, humans will get replaced through technology.
• 6:20
  It's happened in the past and will continue to happen.
• 6:23
  but it doesn't mean they're going to be displaced entirely.
• 6:26
  If we think back to AT&T days when the operators were replaced,
• 6:31
  you know, they used to do the,
• 6:33
  I think we're all old enough in the room to remember the switchboard operators
• 6:37
  in comedy skits.
• 6:38
  So we're aware of them, whether we used them or not,
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  but switchboard operators that literally plugged in wires to make calls
• 6:45
  were replaced with computers.
• 6:47
  And I think it was NPR that did an episode or a two-part episode saying that what ended up is that those roles got displaced, but the workers ended up in jobs better than they had.
• 7:04
  And then if we go even further back, guys, to the agricultural revolution, let's say, I don't know, as close as 150 years ago, the number was that one farm would feed two families.
• 7:18
  And I don't know about you, brother, but I'm glad I'm not out there detasseling corn and rowing and hoeing behind a donkey because I'm glad we advanced using some technology there.
• 7:30
  And what happened is that the masses ended up finding, I think the agriculture revolution really led to the industrial revolution.
• 7:40
  That's when the Fords, Henry Fords were out there that created assembly lines and brought us humans that hate the sun and the weather and the rain inside to do some work.
• 7:53
  And there was some adoption there as well, but ultimately, quality of life increased and we went on.
• 8:03
  Yeah, no, I definitely see that.
• 8:06
  I think there will be some replacement.
• 8:09
  My hope is that it's not as pervasive as is potential and possible.
• 8:18
  Right. I mean, you have companies coming out that are replacing entire, you know, shops with nothing but robotics, ML, machine language, machine learning and AI. Right. Literally no staff on site. Someone comes, fills the bins and that's it.
• 8:37
  And my fear with that is that we get to a situation where we're replacing all of these entry level positions, which are critical to our economy in that they are the training grounds for new people coming into a workforce.
• 8:58
  Right.
• 8:58
  very very few people start at a you know higher position at a corporation straight out of the
• 9:07
  gate they cut their teeth in retail you know making coffee doing things like this and what
• 9:15
  happens if that all goes away you know what's your experience i've heard tell that there's a
• 9:22
  like drive-thrus that are using that like mcdonald's and stuff i don't go there so i don't
• 9:25
  know, but like my in-laws told me, they hate it because I guess the thing got their order
• 9:31
  wrong a bunch of times.
• 9:32
  So I don't know.
• 9:33
  That's kind of taken away.
• 9:34
  I mean, my first job was at McDonald's.
• 9:36
  So that's kind of taken away from younger people, I think.
• 9:40
  I don't know what other instances there are.
• 9:42
  Well, let me clarify my stake in being here.
• 9:44
  I am not here to defend AI in any way.
• 9:46
  AI is a tool and it can be used good or bad, just like every tool that's out there.
• 9:53
  The headlines read that AI is going to cause our brains to stop functioning.
• 9:59
  And I remember that I had this cool, in seventh grade, I had this cool Casio calculator watch.
• 10:09
  And people today, you know, current generations, they don't realize that the headlines back in the, what was it, late 70s, early 80s,
• 10:18
  when these calculators came around that we could fit in our pocket or on our wrist that the headlines were hey we can have students using calculators Computers are going to think form and they going to become stupid But what really happened is it elevated the individual So yes let teach
• 10:37
  basic math is what we learned. This is the takeaway, what, now 40 years later or so.
• 10:43
  And the discussion went on for about 20 years. And that's kind of like a psychological adaptation.
• 10:48
  It takes about one generation for the world to adapt.
• 10:52
  And what we found out is that, hey, let's teach children how to do the basic math so they understand what math is.
• 11:00
  But then let's let them use calculators so they can reduce that load on their mind so they can have more free time to spend learning higher level math.
• 11:12
  And that's what happened.
• 11:13
  We came away and the entire world was, I don't know, enlightened might be too strong a word, but the entire world literally got smarter because now all the time that they would spend doing manual addition, subtraction, multiplication, division was spent at a higher level math to understand how to solve problems, not just push numbers together.
• 11:37
  Yeah, no, absolutely. And I see, you know, I mean, I think AI is an immensely powerful augmentation tool. I do obviously see areas where it will replace workers. And I think there's a lot of room for re-skilling.
• 11:55
  um and and i think you know robotics in general has done that and shown us this as well you look
• 12:01
  at manufacturing floors you you pointed out forward right uh the auto manufacturing industry
• 12:07
  was one of the very first places that started replacing workers with automated assembly lines
• 12:13
  but it wasn't a wholesale replacement and you know ostensibly was good for the people that were
• 12:21
  displaced a little further down the line, right? Well, and understand too that any technology that
• 12:29
  comes out, every company is trying to take advantage of it. They figure it's the genie
• 12:33
  out of the bottle and let's just use it and apply it to everything. In my book, I talk about
• 12:39
  when electricity, not invented, but adopted, both residential and commercially, industrially,
• 12:46
  people were throwing electricity as a solution for everything and i kind of point out when a
• 12:54
  company says we're an ai company you might want to stay away from that company because that's like
• 13:01
  saying you know the kroger grocery store is an electric company because they use electricity
• 13:05
  to keep their refrigerators cold and that's what happened back oh gosh 130 120 30 years ago or so
• 13:13
  when electricity was being adopted.
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  Some people, if you ask the random person
• 13:21
  walking down the street,
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  said, when was the electric car invented?
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  They might say like, I don't know, 2000, 2010, 1995.
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  It was originally around 1900.
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  It was when electricity was being adopted
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  because they were using this new technology,
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  industrialized, to try to solve for problems
• 13:41
  that really weren't even there.
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  They're trying to paint with a large brush.
• 13:45
  And that's what's going on today.
• 13:46
  AI is being thrown at every problem before there even is a problem.
• 13:53
  And just to put hopefully the majority, if not all, of your audience at rest here that's concerned about losing their job,
• 14:01
  MIT results indicate that 95% of all corporations that have adopted AI, 95% have been failures.
• 14:12
  There's only been 5% that have an ROI.
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  And that's because they're throwing everything.
• 14:18
  Throwing that spaghetti against the wall, they're just trying to see what sticks.
• 14:22
  And in the end, the human factor is always going to be the human factor.
• 14:28
  And we need the human factor because we do adopt and because humans do adapt.
• 14:33
  And we do think outside the box.
• 14:35
  These are things that computers aren't going to be able to do.
• 14:38
  So it's going to allow us as the individual, whether it's at home or at the shop or the office, it's going to allow us to really get a boost.
• 14:49
  We're going to have to start thinking like managers, project managers, supervisors.
• 14:54
  And we're going to be doing the same work, but we're going to be using new tools.
• 14:59
  And we get new tools all the time.
• 15:01
  And that's all AI truly is.
• 15:03
  It's just a new tool.
• 15:04
  And I encourage everybody just to play with it like it's Play-Doh.
• 15:08
  Just feel it out and see what it is.
• 15:11
  But don't be worried about the wholesale economy losing their job because it's not going to happen.
• 15:17
  There's going to be those that are directly affected.
• 15:20
  But you mentioned Havoc reskilling.
• 15:23
  Reskilling is so much easier to do today than it was 50 years ago that the opportunities are really endless out there for humans We never gonna be put out of work permanently Yeah I don know that I hold quite the same level of
• 15:47
  Optimism?
• 15:50
  Yeah, it's concerning for me. And here's why, right? I mean, gunpowder was created,
• 15:56
  right? Well, before gunpowder, black powder was created and it was used to create these
• 16:02
  beautiful displays with fireworks. And then we decided we could put it in, you know, tube and
• 16:08
  kill people further away. And then dynamite was created. No, we can clear these roads and we can
• 16:14
  do mining operations and make tunnels. So railways don't have to go way around these mountains and
• 16:20
  all this. Oh, but we can put them in bombs and drop them on people. And the nuclear, you know,
• 16:26
  nuclear development came about and it was like oh free energy i mean literally energy for everyone
• 16:33
  on the planet yeah let's put in a bomb and kill a bunch of people you know what is any
• 16:39
  weaponize it come on i mean that's you know where it all ends up eventually yeah let's hope that it
• 16:47
  it you know some of that is is tempered i i think it's too late for that well you know with it
• 16:53
  hope is contagious.
• 16:56
  Let's keep it.
• 16:56
  We're going to have the dogs with the guns,
• 16:58
  robot dogs with the guns.
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  We already do.
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  Pretty soon.
• 17:01
  I know.
• 17:02
  You can have one right in your neighborhood.
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  Watch where you go.
• 17:05
  As long as I can have one,
• 17:07
  it's okay.
• 17:08
  If everyone has one,
• 17:09
  it'll be okay.
• 17:10
  On an unrelated note,
• 17:12
  I don't know if you've seen,
• 17:13
  I guess there's a lot of people cut down these flock cameras with the
• 17:16
  hacks.
• 17:17
  Have you seen that?
• 17:20
  I have.
• 17:20
  I saw there's a,
• 17:22
  a hack that shows
• 17:24
  what was I
• 17:26
  I don't know
• 17:27
  one of my doom scrolling
• 17:28
  videos
• 17:29
  it was like Veritasium
• 17:31
  or something
• 17:31
  they were talking about
• 17:32
  those
• 17:33
  those flock cameras
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  that are all over
• 17:35
  United States
• 17:36
  you know
• 17:36
  we're United States
• 17:37
  everywhere
• 17:37
  but
• 17:38
  they said except for this one town
• 17:40
  and this guy
• 17:41
  he did his
• 17:42
  video
• 17:43
  and
• 17:44
  he said hey
• 17:45
  I actually ended up
• 17:47
  connecting to those cameras
• 17:48
  and I shared them out
• 17:49
  to the public
• 17:50
  and they got mad
• 17:51
  yeah
• 17:52
  I bet
• 17:52
  Yeah, they actually did a really poor job of securing the systems. You actually can get into them and just go peruse all of the artifacts in there. And that put out a number of people have been doing various things.
• 18:11
  I obviously don't want to encourage anyone to vandalize things, and I would certainly never tell someone that a 1,000 nanometer green laser pointed at a flock camera lens would damage it.
• 18:23
  So don't do those types of things.
• 18:25
  I heard a rumor there's like 200 bucks in gold and copper inside all those things.
• 18:29
  I don't know if that's true or not.
• 18:30
  Hey, we'd have to look.
• 18:32
  We really don't know until we test out that theory ourselves, right?
• 18:36
  That's right.
• 18:37
  Somebody's testing it out, I think.
• 18:39
  Not me, but somebody.
• 18:40
  so part of your history you you actually worked for amazon i i'm not sure if it was a contractor
• 18:47
  role or you were actually working in the employee of of amazon but you created uh apparently they're
• 18:53
  the best the highest rated uh employee training program in their history uh what how did that go
• 19:00
  well it sounds like it went pretty good uh the way you tell it it sounds like but i mean okay so
• 19:06
  sorry not how did that go but you know what what made it so successful what was it that you did
• 19:13
  with the training program that made it something more than here's your one hour mandatory training
• 19:18
  you have to sit and slog through well it uh it turns out and i did work for amazon directly
• 19:24
  and and i was on the reliability team which is a cool way of saying you know maintenance and
• 19:29
  engineering you know the ones that keep the uh um gosh the robots and the conveyors running that
• 19:35
  sort of thing. And when I got there, I was in the reliability org, which is a smaller org than all
• 19:43
  of Amazon. Amazon employs, I know, let's say a million and a half globally. And I think my group
• 19:49
  at the time, at least for North America, was still around like 45,000 people, which, you know,
• 19:54
  it's good size, even if it was a company, but it was an org within Amazon. And the hiring,
• 20:00
  because it was skilled, you know, skilled, whether it be skilled trades, you know, men and women that
• 20:05
  work with their hands or engineering that's, you know, trained to do specific things, you know,
• 20:11
  on schematics, computers, they would hire in and just expect them to work. So, Amazon would hire
• 20:18
  the people. We kind of had a joke about it. And the joke was, hey, you know, so-and-so's in the
• 20:23
  deep end of the pool, you know, do you think he'll make it out? Well, hey, he's looking good.
• 20:28
  Looking good. Oh, nope, he went under. He's back up. He's back up.
• 20:32
  Nope, went down, down for the count. Well, we got to get another one.
• 20:37
  And the numbers tell the truth here. I think the attrition rate was around 82 or 83%,
• 20:44
  which means for every 100 people that Amazon hired in that organization,
• 20:50
  internal that 83 would just leave on their own accord. They just didn't make it. And that becomes
• 20:56
  expensive. So it's unfortunate that the role of education is put at the bottom of the rung and
• 21:04
  that lever isn't pulled until there's a business need, strong enough business need. So they asked
• 21:12
  me and they didn't tell me I was the last person they asked, but I think it was December 14th when
• 21:18
  I got a call and they said, hey, I think it was what, 2020, 2021 in December when I got the call.
• 21:27
  And this was during this COVID pandemic that a lot of people were talking about. And Amazon had
• 21:34
  committed in North America, we had 500 warehouses or what we call fulfillment centers. And in one
• 21:43
  year. It took 25 years to get to 500. And in one year, their plan was to double that to 1,000.
• 21:51
  And so I got this call on December 14th and says, hey, Derek, we're doing this thing. And they gave
• 22:00
  me the details and they thought, do you think we should train them, these new hires? Because if
• 22:05
  we're doubling the amount of centers, fulfillment centers, we're going to have to double the
• 22:10
  employees to go, let's say from, I don't know, 45,000 and 90,000 or some rough doubling of
• 22:17
  employment. I said, yeah, that'd be a good idea. And it's a phone call. So I'm wondering if it's
• 22:22
  tongue in cheek that the question came about. They said, great, do it. And I thought, yeah,
• 22:28
  just I had that. I said, okay. I said, how about some particulars here? And I said, when would you
• 22:35
  like to see this program up and running? They said, oh, I don't know, January 1st. And, you know,
• 22:41
  it's corporate America and it's December 14th and corporate America takes the last two weeks of
• 22:48
  December off, at least in North America we do. And I thought, okay. I got that extended out to,
• 22:56
  I think like January 18th or something. So I got me two or three more weeks to get started.
• 23:01
  but that was the first class that came in.
• 23:07
  And so, frankly, we started.
• 23:10
  I had some connections, some people that helped launch some centers,
• 23:16
  and I just said, hey, meet me here.
• 23:18
  And actually, one of the guys lives in Florida.
• 23:22
  He said, hey, you come to Tampa, Florida.
• 23:24
  I'll be down there and help out.
• 23:26
  So I said, heck yeah.
• 23:27
  So we just brought everybody to Tampa, Florida.
• 23:29
  This is when all the restaurants are closed.
• 23:31
  Hotels are nearly closed.
• 23:33
  And we rented out a restaurant in the hotel we were at.
• 23:36
  And we started our first class.
• 23:38
  And our first class was, I think, 24 people.
• 23:43
  Eventually, the next class, it got up to 40 and 40 and 40 consistently.
• 23:47
  But that class started at 24.
• 23:49
  And it was two weeks long.
• 23:51
  And we said, all right, what should we talk about?
• 23:53
  And we literally put it to a vote.
• 23:55
  What don't you know?
• 23:57
  And we talked about it.
• 23:58
  And then that evening, there's about four or five of us.
• 24:00
  So we'd sit around the hotel lobby or we'd talk over dinner and say, hey, what did we talk about today?
• 24:07
  All right, let's put that down.
• 24:09
  Because our presentation slides, our PowerPoint was literally just welcome to class.
• 24:17
  And so each class every day and every week and then class after class, the secret was listening to the people that are actually doing the work and finding answers.
• 24:28
  and it sounds, I don't know, maybe a little too easy, but a lot of people overlook easy
• 24:37
  and we just made it happen. We listened to the people, we filled it in, and I believe in continuous
• 24:43
  improvement and that's what we did. The class was continually improved from class to class to class
• 24:49
  to class. I made sure that everybody had connections with everybody else from class.
• 24:54
  We were bringing in people that didn't normally work together because we might have managers from different locations that, you know, each one manager per shift or one manager per location.
• 25:06
  So they didn't have that camaraderie out there.
• 25:10
  So they were literally, you know, like I described earlier, in the deep end by themselves.
• 25:14
  so the moral of the story is you give humans the right information at the right time they can do
• 25:21
  some magnificent magnificent uh successful uh projects that's what we did wow that truly is an
• 25:30
  it's a wonderful take there really honestly you know you i can't even count the number of
• 25:40
  trainings that I've gone to. And I don't think I've ever been engaged in the beginning of a
• 25:47
  training to ask, well, what do you know? What don't you know? What do you need help with?
• 25:52
  Right Uh which that right there seems like just such a practical thing to do but I never seen it Never once Well it worked
• 26:07
  It worked.
• 26:08
  I did not pretend to have all the answers.
• 26:11
  We all stayed vulnerable.
• 26:14
  And the first day of class was actually never about Amazon stuff.
• 26:18
  It was always about get to know yourself and get to know other people.
• 26:21
  And anybody that's had to take any part of a leadership training, that's effectively what all the training revolves around.
• 26:30
  Hey, everybody's different.
• 26:32
  So let's just, you know, put up, you know, three legs to a table or a chair because it takes more than one to hold it up.
• 26:40
  That's wonderful.
• 26:41
  What are some of what are some of the your favorite lessons from that period?
• 26:47
  And what would be like one big takeaway you got from the experience of developing this, implementing it, learning through it, etc.?
• 26:58
  Well, for myself, I think my big takeaway was everybody can contribute.
• 27:08
  And we've had individuals that spent a long time with Amazon.
• 27:11
  We had people that came in from different industries come in to Amazon as brand new employees.
• 27:19
  But the goal was the same.
• 27:20
  It was to come up with a system that allowed each person to maximize their potential.
• 27:27
  So there was no one way is the right way.
• 27:32
  There was no my way or the highway.
• 27:35
  It was, hey, let's flex, let's listen in.
• 27:38
  And that really started on day one of the programs because we would meet in person.
• 27:42
  And we would have, I would get there early and we had organized, gosh, hotel lobbies, restaurants, etc.
• 27:51
  You know, I think I would have tables and chairs that were on seven foot centers.
• 27:55
  So everybody had their personal space.
• 27:58
  I think six foot was a number everybody was throwing around.
• 28:02
  We were all wearing masks and eventually, you know, eye shields or goggles.
• 28:07
  And that made communication much more difficult, at least in the early years.
• 28:13
  So maybe that's what forced us to be patient.
• 28:18
  I mean, I really don't know.
• 28:19
  But the takeaway is the same either way.
• 28:22
  Everybody's voice counts and makes sure that everybody gets a chance to speak up.
• 28:29
  I'm a little bit curious.
• 28:30
  I don't know if you might mind sharing a little bit behind the curtain.
• 28:33
  And when my wife is looking at pajama pants at like 10 o'clock at night and she hits the click to buy it now and they show up in the morning at like 6 a.m., how does that all work?
• 28:45
  Like when she goes to return it, where does that stuff go?
• 28:48
  Well, I'm just from personal experience.
• 28:50
  Yeah, absolutely.
• 28:51
  Absolutely.
• 28:51
  So it's all logistics, which is a fancy way of saying truck locations.
• 28:56
  And so when they say, or when I talked about earlier, the fulfillment centers, it's essentially warehouses.
• 29:06
  And they go, her order goes in, it gets picked, and it gets routed through, most of the time, the least busy location that is within, you know, whether one day's drive, two days drive, etc.
• 29:19
  and then once it gets routed there there are pickers on the floor humans that go through and
• 29:26
  they go to a certain aisle certain shelf and they they see this on their scanners the scanners that
• 29:31
  like like they're using clothing stores to scan the tag and it's got the little computer screen
• 29:38
  on it just like three inches by three inches they go through and they pull whatever was ordered those
• 29:44
  pajamas that you like, you know, with the cool colors.
• 29:48
  So it's actually humans pulling it out of the bins.
• 29:50
  Humans pulling it out of the bins.
• 29:52
  So it's kind of like how were the pyramids built, you know, with a lot of work.
• 29:55
  And that's what Amazon does at the base level.
• 29:59
  Now, once it gets pulled, it goes into a specific line.
• 30:06
  It's like a checkout.
• 30:07
  They just dump entire carts because they actually pull for multiple customers.
• 30:11
  So one picker, and that's an official title, one picker picks orders for five people, 10 people, 20 people, whatever they can fit the cart.
• 30:22
  They take it to a specific line.
• 30:24
  And now that it's all QR and barcoded, that line, when it comes through, they'll scan that barcode and they say, oh, this is for Dallas, Texas, and it's this address for this person.
• 30:38
  So they just grab it.
• 30:39
  They box it.
• 30:40
  The label prints automatically, which printing the labels and stamping the labels on the box is one of the most difficult and frustrating points of this entire system.
• 30:49
  But it's packaged by hand.
• 30:52
  It either gets taped up by hand, which is rare, or it gets folded and then a box taper tapes it automatically.
• 30:59
  But once it's taped, it goes down conveyance that scans the barcode and routes it to the right truck.
• 31:06
  So if you ever been at a warehouse and you see all these truck doors each one of those truck doors is going to a different location And it most easily understood the logistics is most easily understood if we say it like going to the airport
• 31:23
  Like if I'm going to fly to see my daughter in Seattle, hopefully I can take a direct route.
• 31:30
  But if I don't have a direct route, I might be routed through Dallas or Phoenix and it's a second
• 31:35
  route. So that's what happens. The box gets transferred from that immediate warehouse
• 31:40
  to a truck going in your house's direction. And then once it gets there, it gets dropped off at a
• 31:48
  delivery center, which is a smaller center with not much mechanical. It's really all, again,
• 31:57
  individuals moving the boxes. And then it's very much like a FedEx or a UPS, which they can also
• 32:06
  deliver. It can be barcoded for UPS and UPS will deliver the package. Or Amazon more and more
• 32:12
  delivers their own trucks. You see the cool smiley face buildings are on the side of the trucks.
• 32:19
  But it's all the same. They just grab the box and they deliver it. And the secret is having
• 32:25
  warehousing close enough to your location that they can make it available and get it to you
• 32:31
  within whatever time limit that is i see yeah it's pretty amazing the logistics that they
• 32:39
  they put into this i i have to imagine it's it's not quite but approaching the level of like airline
• 32:46
  logistics right uh that that i think is probably a lot more challenging just because of the the
• 32:53
  strict timing on everything, but still very, very complex stuff. So you also created this
• 33:02
  thing called Pocket Mentor, which I guess was world's first AI-based mentor. What made you come
• 33:11
  up with that idea and what did you kind of want that to be used for? Thank you for asking the
• 33:18
  question because I think it's really important. I think it's an effective use of AI in that
• 33:23
  it amplifies the human. And for Amazon and other companies, I've done a lot of training.
• 33:33
  Ultimately, my feather in my cap was what I did at Amazon that last time. And my takeaway,
• 33:40
  again, is giving the right person the right information at the right time. So what Pocket
• 33:44
  mentor does um imagine if um oh my god how about how about this throwback let's let's set it aside
• 33:53
  for a moment do you remember traveling across the country before gps before google maps oh yeah
• 34:01
  road atlas yeah road atlas triple a you know trip books that sort of thing and map quest and what
• 34:08
  would happen if you had an accident ahead that blocked the route you would have to stop pull over
• 34:15
  And so now you're not moving toward your destination or your goal.
• 34:18
  You have to figure out on your own how to get where you need to get.
• 34:23
  Now, if you're delivering a package, whether it be for Amazon or UPS or for any shop out there,
• 34:29
  the customer doesn't care if you ran into traffic.
• 34:32
  They're going to go, oh, you know, we're running into traffic.
• 34:35
  Let's give it another day.
• 34:37
  We won't be mad.
• 34:37
  No, they want service.
• 34:39
  So GPS, whether your flavor is MapQuest or Google Maps or Apple Maps, it tells you, and it actually routes, it's proactive with the GPS.
• 34:54
  And it says, hey, that route is blocked.
• 34:58
  Here's two alternatives, and we can choose and go.
• 35:02
  That's really how PocketMentor works.
• 35:03
  That was part of the inspiration there.
• 35:05
  But let's say you're on the job, again, blue collar, white collar, doesn't matter.
• 35:10
  And you're on the job and most of the time you're either using your hands or your eyeballs or both.
• 35:16
  And if you had to stop and look something up, well, now you're not moving toward your destination.
• 35:23
  So what Pocket Mentor does, it's actually a voice media that allows you to just press a button on your headset or on your phone.
• 35:34
  And it's called Pocket Mentor because it fits on your phone.
• 35:37
  It's not an app.
• 35:38
  It can work through a webpage or it can work through a telephone call.
• 35:41
  So you don't even need Wi-Fi or internet, but it's your buddy.
• 35:45
  It's your safety net.
• 35:46
  And now I'm kind of flashing back to the 1970s and 80s where help, I've fallen and I can't get up.
• 35:52
  Remember when that started?
• 35:54
  Maybe I picked the wrong name.
• 35:56
  Maybe I should have went with that route, but essentially it's the same thing.
• 35:59
  You hit the button and you start talking to your mentor.
• 36:02
  And this mentor, it's AI handles the translation between the human and the data set, the knowledge base.
• 36:10
  And that knowledge base, it's not the World Wide Web.
• 36:13
  You're not going to get crazy answers that the headlines talk about.
• 36:16
  It's going to be programmed with only the data for your job role.
• 36:21
  And so you say hey help Where does this package go What is the three process to reset the computer or to reset the conveyor And it will guide you through it Things that you responsible as a human to
• 36:37
  do anyway. And sometimes we just have a hiccup. We have a brain fart. I mean, really, our brains
• 36:44
  weren't meant for long-term memory. So if we can fill in that long-term memory with computer
• 36:50
  support, well, then we can go about our day and be productive. And that's what Procket Mentor does.
• 36:55
  It fills that gap when you say, help, I've fallen and I can't get up.
• 37:01
  So it's specifically for vocational specific applications rather than general users or
• 37:08
  can general users make use of it as well? Well, it's because it's trained on a specific topic,
• 37:15
  then we just have to ingest that knowledge base, whatever it is,
• 37:20
  that cool technology, ingest, give it that data or database to run from.
• 37:26
  So if it's for Ford Motor, it's going to be for a specific job.
• 37:32
  If it's for Starbucks, how do they make that macchiato, et cetera, et cetera,
• 37:39
  thing again, then it'll walk you through it.
• 37:41
  Um, for home, it, uh, I really don't have a, uh, like a civilian use or a consumer use.
• 37:49
  Um, but, uh, it's really is just as easy as, as trading that box and then talking to it.
• 37:56
  Sounds like something that could be used pretty much in any field.
• 37:59
  I mean, I, you know, in my head, I, as you were going through the scenarios, I'm thinking
• 38:03
  of a framer, you know, building a home, right.
• 38:06
  and they come across a situation, they're like, oh, crud, what's the code on this?
• 38:12
  Pocket mentor. Hey, pocket mentor, what's code on this type of framing with this,
• 38:17
  you know, obstacle, et cetera. Oh, here's the code for that in your location. Boom. All right.
• 38:22
  Now you've got it. Exactly right. It's having the right information at the right time. And
• 38:28
  the neat thing about using AI to deliver that information is that YouTube is great, right?
• 38:35
  YouTube has taught a lot of people and given people the confidence to go out, change the brakes on their car if they've never done it before.
• 38:44
  You know, replace the belt on their clothes washer if they've never done it before.
• 38:48
  But what happens when you go into a process and whatever you're fixing or repairing or building brand new doesn't follow?
• 38:59
  Maybe it didn't read the manual that you're reading.
• 39:01
  So what happens?
• 39:02
  So AI can actually create on-the-fly instruction from the core instructions to help you through those special scenarios.
• 39:17
  In my head, I'm already thinking like auto mechanics, like a flow chart for diagnostics and stuff, or HVAC technician or any kind, even a lot of the blue-collar trades, that would be a pretty good application, I think.
• 39:30
  Yeah, absolutely. And my background, I actually started a blue collar. I don't think anybody's immune anymore that college doesn't have the payback that college once did. And we're seeing the boomer generation leave skilled trades positions, whatever the skilled trades are, whether they're building trades where you're doing contract work or you're in-house doing work.
• 40:00
  And we are millions shy of having enough people for the trade.
• 40:05
  So my initial impetus was really to how can I use AI in a way that there is zero possibility it can replace humans.
• 40:16
  And that would be to help trade those in the trades, to have that right information at the right time,
• 40:22
  because it's the training that is most costly.
• 40:30
  because when you bring an apprentice in,
• 40:32
  you essentially have to pay for the apprentice
• 40:34
  and they don't know anything.
• 40:36
  There's no ROI on a zero ROI, negative ROI.
• 40:40
  So if we can train people faster
• 40:42
  to do what they want to do and need to do,
• 40:45
  it makes the world a better place all over.
• 40:49
  It gets them on board faster.
• 40:50
  So it does reduce the cost for businesses
• 40:53
  to get their people trained,
• 40:55
  but at the same time, for the person themselves,
• 40:57
  There's surveys they give, I think, officially every 10 years or so, and they interview.
• 41:03
  They say, hey, where do you work?
• 41:05
  Why do you work there?
• 41:06
  What's the number one reason?
• 41:08
  And I know I'm 58, and I have never heard a year where money, I work here for the money,
• 41:15
  has been higher than three.
• 41:17
  It's usually about fourth on that list.
• 41:20
  And the number one is typically, I want to feel productive.
• 41:25
  I want to do something.
• 41:26
  I want my life to have meaning. And so if we can give that information to them that they need so they can perform, it makes them feel.
• 41:39
  more mentally healthy, and it's a benefit to the businesses that employ those people.
• 41:46
  So it truly is a win-win-win all the way around.
• 41:51
  Well, I will say there's one downside.
• 41:54
  Yeah.
• 41:54
  Definitely one downside.
• 41:56
  Having worked in the trades myself, if a person were equipped with PocketMentor,
• 42:02
  it would be very challenging for the customary expected and really just part of the initiation
• 42:12
  when you sent the new guy out to find the door stretcher i mean he's gonna go well you know
• 42:19
  there's no such thing well you're supposed to go out to the truck and be looking for it for an hour
• 42:25
  you know what i mean i'm just saying hey we can program a snipe hunt anytime and we could
• 42:33
  randomize it there you go you got you got to have the hazing for the new guys it's just part of the
• 42:39
  gig right hey you know go look on the shelf give me some blinker fluid it's over there by the oil
• 42:43
  yeah you're saying that in jest but i truly believe there's value to that it helps uh bonding
• 42:48
  helps camaraderie now i don't believe anybody should die from it but uh you know if you don't
• 42:53
  get those spark plugs changed in that diesel, then by the end of the day, then you might as
• 42:59
  well not come back tomorrow. Right? Yeah. Awesome. So your company, Practical AI,
• 43:08
  coming from where you were, what was the impetus for starting this company and
• 43:15
  what is it about? What is it that makes Practical AI different in a world full of AI stuff?
• 43:23
  I think practical AI is AI that you don't really see. It's AI that does something. It's not headlines. We're not looking to change the world. I mean, we are looking to change the world, but the only way we're going to do that is through solid practical and pragmatic use of whatever tools and technologies that come to us.
• 43:47
  So practical AI, I was very conscious, conscientious about choosing that name because there has to be practical application.
• 43:58
  And if you can't understand it, well, then you're probably trying to create a solution that doesn't need to exist.
• 44:04
  so when you when you are engaged with a customer implementing you know something
• 44:13
  what uh i guess what does that look like how do you uh everyone wants ai right i mean everyone
• 44:21
  wants ai and they wanted to solve all the things they wanted to do everything uh and i think this
• 44:26
  is where that you know 89 to 95 percent failure rate comes in is there's no there's no actual plan
• 44:34
  or idea what the vision is for the end goal, right?
• 44:40
  So how do you deal with that?
• 44:44
  A new customer comes in and they want the world
• 44:47
  and you have to kind of take them down a funnel.
• 44:53
  Temper their expectations.
• 44:54
  Yeah, take them down a funnel to find what is your vision,
• 44:58
  what is the ultimate goal of what you're trying to implement here.
• 45:02
  Well, that's that human factor. And the human factor, it drives it all. Technology cannot outpace the human factor, good or bad. So if a company has SOP, standard operating procedures, we can take that and I can have for them a running pocket mentor by the end of the day.
• 45:28
  But if we get to a company and we say, okay, what do you want to do?
• 45:32
  Well, we want to do this role.
• 45:33
  And you say, okay, what does that role do?
• 45:36
  Well, I don't know.
• 45:36
  Hey, Jim, what do we do here?
• 45:39
  If it comes down to one of those.
• 45:41
  So even though we're a technology company, a lot of that interaction is just standard boots on the ground.
• 45:50
  Let's find out what you actually do and how you do it.
• 45:54
  Because we need to have the answers.
• 45:58
  And the neat thing about it is that once those answers are documented, everybody has access to them across the realm.
• 46:08
  I like Ford's CEO because he is really a big sponsor of skilled trades.
• 46:14
  He says, hey, we need them.
• 46:16
  We've got to train them.
• 46:17
  We're short on them.
• 46:18
  And every time he talks about them, I read his article, listen to it and such.
• 46:22
  And I really think it is important that we get that right information to the right people at the right time so they can do the human things that make us all successful.
• 46:37
  Wonderful, wonderful.
• 46:39
  So you were diagnosed neurodivergent later in life.
• 46:46
  And I wondering how did that play into your founding of the company and Pocket Mentor and your book that it coming right
• 46:59
  It's not out quite yet?
• 47:01
  The audio book is out.
• 47:04
  The print version will be out later this summer.
• 47:07
  But yeah, the Human First AI is available now.
• 47:09
  And since you mentioned it, I'll give every listener that you have a free chapter.
• 47:15
  they can just go to my site and read the first chapter for free. They don't even need to pop in
• 47:19
  their email address. It's all easy. Wonderful. So how did that play into the founding of the
• 47:28
  company, the creation of these tools? And how has this helped you in your journey with your
• 47:36
  neurodivergent aspects of life? Well, the fact that I was diagnosed at age 50, I started thinking
• 47:44
  that maybe there's another category I belonged to back in my 40s, but still a good chunk of my
• 47:52
  productive adult life, I spent in the self-help aisle almost permanently reading, listening,
• 48:03
  watching. Why is this round peg not fitting in the round hole? Well, it turned out I wasn't a
• 48:09
  I was a square peg and I didn't realize it. So because I couldn't connect and my neurodivergence diagnosis at age 50 was autism, ADHD and dyslexia.
• 48:24
  So the dyslexia kind of gives me the reason why I could never finish a book in school for a book report, just because everything, you know, these letters keep jumping around.
• 48:37
  I don't know.
• 48:38
  I thought my eyes were just twitching.
• 48:40
  I really did.
• 48:40
  But when I was diagnosed, I did some actual crying, tears falling from my eyes for about two weeks.
• 48:52
  Just tears of pain, tears of joy, tears of relief, because now I had a name and a category, and I could seek information that helped other people apply it to myself, new information in a different way.
• 49:08
  and I was working at Amazon at this time.
• 49:12
  It was early in my last Amazon tenure
• 49:15
  and I embraced it.
• 49:18
  I was at the point where I'm just exposing myself,
• 49:21
  sharing my vulnerability, I think is what people say.
• 49:24
  And I put it on my email tagline
• 49:27
  and I essentially said,
• 49:28
  hey, I'm autistic, I'm different.
• 49:30
  If what I said isn't clear,
• 49:33
  well, that's probably just me.
• 49:34
  Just reach out, we'll straighten it all out.
• 49:36
  And by me sharing that, I think it took a chip off my shoulder for one.
• 49:46
  So it allowed me to understand that not everybody is going to understand me and I should be okay with that.
• 49:53
  But at the same time, it put other people unnoticed by seeing that.
• 49:57
  They thought, oh, and now I'm just kind of imagining this conversation in their mind.
• 50:04
  oh, that's why Derek did that the other day. There's a reason. He's just not being negative.
• 50:11
  He was actually trying to solve a problem. So I think embracing that and letting others around me
• 50:18
  know rather than trying to keep it in, I think that was a big platform and a launch point for
• 50:23
  what later became a foundation for being vulnerable in the program, sharing what I know,
• 50:30
  more importantly, sharing what I don't know. Because there's always that the more we know,
• 50:39
  the more we realize that we don't know. It's very eye-opening. And I think that was a very
• 50:45
  big part of it right there, Havoc, was that just learning that this is who I am and trusting that
• 50:53
  that's who I'll always be. I think the big question a lot of people might have,
• 50:59
  and especially people that might have some of these conditions themselves.
• 51:02
  How do you go for 50 years undiagnosed?
• 51:05
  And what actually brought about a diagnosis?
• 51:09
  I'm sure it was probably a weight lifted off your shoulders when you found that out.
• 51:14
  Well, it was a weight lifted off my shoulders for sure
• 51:16
  because now I had a group I could identify with and connect with.
• 51:22
  But I was born in 67.
• 51:24
  um i am only about a generation away from hilltop people so uh you know it's not something that you
• 51:32
  talked about not something people even knew about the term neurodiverse and neurodivergent wasn't
• 51:37
  even coined until the late 90s and that was really kind of like the umbrella for all us weird people
• 51:43
  that are out there um and i i love the fact that it was that we start talking about you know us
• 51:49
  weird people because i i truly think that uh if we counted all the neurodivergence in the world
• 51:54
  I don't think we would be the minority.
• 51:57
  I think we're probably the majority out there.
• 51:59
  We all different in our own way And my family history it turns out once I found out
• 52:10
  on my dad's side, at least, we have a high, and it is genetic, the autism side has been
• 52:18
  traced genetic. We have a high rate of autism, about three times normal. And that correlates,
• 52:25
  or maybe it's just coincidence, we have a high rate of suicide on my dad's side of the family,
• 52:29
  too. But growing up, it was man's man's world, right? You didn't speak about problems, you just
• 52:37
  solved them. And especially when I went to work in trades, I'll tell you, the people out there
• 52:44
  in the building trades, I would probably trust a lot of them with my life, but then others,
• 52:50
  I wouldn't trust at all. So it's a wider gamut. And again, it's the Wild West is what
• 52:59
  it was. So my time really was keep my mouth shut. And how I think how I eventually got on the train
• 53:07
  for diagnosis was it was just out of self-preservation and pursuit. I think I had some
• 53:13
  time once my kids got grown up that I started looking for answers in new places and I finally
• 53:19
  got there. But back in the 60s, 70s, even 80s, people just weren't talking about this stuff.
• 53:26
  all right yeah and especially in the trades i like i said i worked trades uh for a number of
• 53:34
  years masonry and and you know it is one of those things you just there's no like nobody cares about
• 53:42
  how you feel nobody cares about you know an owl you got i remember working concrete and i got a
• 53:49
  cut pretty bad cut on my hand at one point and one of the guys looked at me and he goes
• 53:53
  here and slap some, uh, slap some cement into my hand, dry, you know, cement, Portland cement from
• 54:02
  the bag, slap that on there, rubbed it in through some duct tape around it and goes, you're good to
• 54:07
  go. And then, you know, that's kind of how the trades works, right? So, oh, well, I'm not, it's
• 54:12
  not, it doesn't matter, right? The concrete moves from here to there. Concrete goes in this form
• 54:17
  and that's it. Nobody cares about anything else, you know? Yeah. Did Havoc, did you work out of
• 54:23
  hall or did you work for uh one company no i i worked for a private uh private contractor so
• 54:30
  we we mainly uh we did do some commercial work but never never worked uh union so uh we would go
• 54:38
  you know do piece work for some of the large communities i was living in southern california
• 54:43
  at the time so a lot of buildings just popping up like i mean insane like popcorn just pop pop pop
• 54:51
  And, you know, we'd come up and there's a need for someone to do tile for bathrooms and you just do piecework. Right. But most of what we were doing was custom. So we'd go into neighborhoods with new homes, people buy the homes. We'd go pitch, you know, you know, patios in the backyard, outdoor kitchens, water features, you know, you know, whatever. Right. Brick entryway, whatever.
• 55:16
  yeah cool i'll just tell my wife earlier today we were talking about my time back in the day and
• 55:23
  i uh i tried coming out of high school i was uh kicked out of house when i was 18 so i was trying
• 55:28
  to go to college on my own pay for it on my own live on my own and then uh i was pointed toward
• 55:33
  the trade so i i added that uh and i was trying to go through trade school and uh and at work at
• 55:40
  at the same time of college eventually dropped the college part back then and
• 55:44
  And so I worked out of the hall for the first, I don't know, five or six years, which meant that I might have 14 W-2s at the end of the year.
• 55:53
  I might have a job that's a day long or a week long or a month long.
• 55:57
  Never had anything more than six months.
• 55:59
  And it was, you know, you show up or if you don't show up, hey, don't come back tomorrow.
• 56:03
  There's no reason to.
• 56:05
  You know, they cut bait and go.
• 56:07
  And it definitely was Wild West scenario.
• 56:10
  And like you said about, you know, when you got that cut, hey, patch it and go on.
• 56:15
  You know, you don't want to be a problem.
• 56:16
  What's that?
• 56:17
  There's a Japanese proverb talks about, you know, nail, a nail sticking out is going to get whacked.
• 56:24
  So we try not to be that nail that sticks up and causes problems.
• 56:29
  Yep, absolutely.
• 56:31
  Move on, get it, get whatever it is.
• 56:33
  Just fix it and move on.
• 56:35
  And if you can't, well, then, you know, we'll, we'll contact you and we maybe when we have something else.
• 56:40
  You know, just buy. That's pretty awesome. So what are, what are, I guess, some of your,
• 56:49
  your favorite, cause I have to imagine that you're, you stay in contact with people when
• 56:55
  you contract with them for, you know, an implementation. What are some of your favorite
• 57:00
  success stories? And you don't necessarily have to go into the organization because confidential
• 57:04
  information, et cetera, but what are some of your favorite success stories and, and, you know,
• 57:09
  how did that translate for you you know business Well thinking through one that bubbles up is it was a success story there was a uh a
• 57:25
  chain of schools i say school they were like uh it wasn't kinder care but it was child care um
• 57:32
  child care slash education all in one so um they uh they they were actually looking to sell their
• 57:39
  company. They had, it was, gosh, I think like 83 locations and all their knowledge was in the brain
• 57:47
  of the main person. And they might've had one manager, might have. I mean, you go into any
• 57:53
  retail establishment, you're lucky to find somebody that knows what's going on there.
• 57:57
  But they had very few people that the minority knew what was going on everywhere. So now they're
• 58:04
  selling this or they want to sell it. And people were interested in buying, but they were hesitant
• 58:13
  because once you buy a business, the knowledge that runs especially a service business like this
• 58:20
  walks out the door. So a lot of times there's contracts as, hey, the CEO and if it's a big
• 58:29
  company, the VP, a number of named people agrees to sign on and be consultants for a week, six
• 58:37
  months, six years, whatever that is. And what we ended up doing, we ended up documenting all the
• 58:44
  knowledge, all their SOPs. So it actually, it made them work better because now everybody had access
• 58:51
  to the information, but at the same time, it allowed them to market themselves with, you know,
• 58:57
  holding their head high because when we sell, it's going to be not just the buildings and the
• 59:03
  clients that are ephemeral, right? They can disappear at any time, but you have the knowledge
• 59:09
  and anybody in your organization can talk to Pocket Mentor and ask questions and get the same
• 59:15
  answers I would give to you one-on-one. Now, you know, some people say that, you know, it's never
• 59:21
  going to be a hundred percent human. Well, you know, that's fine. We don't need to be a hundred
• 59:25
  percent human as long as we can you know follow that that Pareto rule right 80 20 if we can cover
• 59:30
  80 of the answers in 20 of the time well then that helps everybody and because of what they did
• 59:37
  education anything education I'm fanatical about I think there's no substitute for education I mean
• 59:44
  if there was we wouldn't know right we wouldn't be educated on it
• 59:48
  i'm i'm sure that i'd really like to see how that pocket mentor works because i think in the back of
• 59:58
  our brain at least me i think of my wife trying to call the pharmacy and her yelling at the thing
• 1:00:02
  no speak to a pharmacist no i don't want the photo department like and getting in a fight with the
• 1:00:10
  thing that i'm sure yours is much better than that well it's it's funny that you say that because a
• 1:00:15
  lot of those problems aren't from ai they're uh ivr which i the acronym escapes me right now but ivr
• 1:00:23
  is that uh yeah it's intersome interactive uh voice recognition ivr uh see i knew i'd get there
• 1:00:33
  edit out that hiccup will you but uh those ivrs they they look for key words and if they don't
• 1:00:41
  hear the key words, then they're just computers talking. They're recordings that cause them
• 1:00:46
  problems. The AI that's out there, it's actually just now, in the past 12 months,
• 1:00:54
  is where it's really reached out to a level where if it's implemented correctly,
• 1:00:59
  the AI will actually make those interactions more enjoyable and fruitful.
• 1:01:06
  So one of my frustrations with that IVR is, well, even like on generic voicemail, you go to leave a voicemail.
• 1:01:14
  The first thing I want to know after I left the message is, which button do I need to push, if any, to save the voicemail?
• 1:01:21
  But no, it tells you that last.
• 1:01:23
  It says, press one if this is important.
• 1:01:26
  Press two if it's not important.
• 1:01:29
  Press three, you know, if you'd like to hear your message.
• 1:01:31
  And, you know, like 30 seconds later, it says, or hang up to end this call and save your message.
• 1:01:38
  You know, so they're all different, too.
• 1:01:40
  Do I press pound?
• 1:01:41
  Do I press start?
• 1:01:43
  Do I just hang up?
• 1:01:44
  And if AI can actually facilitate that, like you said, when it's done correctly, it's a tool.
• 1:01:52
  And, yeah, I can shoot you some numbers and you can try out.
• 1:01:56
  I think my first pocket mentor was how to fold a paper airplane.
• 1:02:03
  Really?
• 1:02:05
  Interesting.
• 1:02:06
  That is kind of interesting.
• 1:02:08
  So how do you limit?
• 1:02:11
  So one of the big things that's always frustrating about AI, I think for everyone, right, is the, I mean, one of the big benefits of it is its ability to, you know, do inference, right?
• 1:02:24
  but also one of the big detriments is it can infer a thing
• 1:02:29
  that don't actually exist, like maybe bits and pieces do in different areas, but yeah,
• 1:02:37
  it all fits together. So here, how did you go about limiting the inference, which could lead to,
• 1:02:46
  you know, probably not disastrous, but bad information coming through?
• 1:02:52
  Absolutely. So if I forget what your question is, remind me, but I want to preface this by a little
• 1:02:58
  story. So IBM, back when they were doing AI before us consumers could play with AI,
• 1:03:08
  they had this thing called Big Blue and they were teaching it chess. And Gary Kasparov is
• 1:03:15
  who they challenged and Gary Kasparov kicked Big Blue's butt. And so they said, well, let's try
• 1:03:22
  this again. And then Garry Kasparov kicked their butt again. But eventually, IBM's AI beat Garry
• 1:03:29
  Kasparov. And so that was like a telling point. Computers have finally gotten smarter than AI
• 1:03:36
  or smarter than humans is what the headlines read. But if we go back and analyze what happened,
• 1:03:44
  Garry Kasparov at the time was the number one ranked chess player in the world. Those guys and
• 1:03:51
  gals are pretty smart, right? They analyzed the decisions that were made by the computer and they
• 1:03:59
  found out that the computer only made the optimum decision or the best decision or the right
• 1:04:03
  decision two out of three times. So what that means is that even the best chess player in the
• 1:04:10
  world would make the best decision less than two out of three times, maybe like one out of two or
• 1:04:16
  a little more. So if we take this perfection with a grain of salt and if we want to validate,
• 1:04:26
  is AI worth it or is AI working? We have to understand that we're not getting 100% accuracy
• 1:04:33
  right now. Even if we're on the line, even if we call and we say, hey, how am I supposed to do this?
• 1:04:40
  I forget. In manufacturing, I spent a lot of time in automotive manufacturing. If the line goes down,
• 1:04:48
  they want it up and running. They don't care who gets it up and running, but depending on the
• 1:04:52
  problem, a lot of time it's John, right? And if it's five o'clock, John's already home for the day,
• 1:04:58
  so John gets a phone call because John is the subject matter expert, the SME, and John gives
• 1:05:05
  them the answer. Now, will that answer be the right answer? Well, it's probably going to be the
• 1:05:10
  best answer. But if we've already looked at the data and that humans aren't right 100% of the time,
• 1:05:18
  John's probably going to be right, but even his answer could be wrong. And that's what we need
• 1:05:23
  to look at. So if we're looking to amplify humans, we want to do at least as good as the human does.
• 1:05:31
  If we can just do that to support the human and speed up the answer, it's a win.
• 1:05:37
  And at the same time, AI, computers in general, their scalability, John isn't always going to be available at 5 p.m. on a Wednesday, you know, because it could be 2.30 a.m. on a Saturday.
• 1:05:50
  and your low-ranking operator or millwright or repairman is going to be there on their own,
• 1:05:59
  and they only have about six months' experience, and they don't know how to answer that question.
• 1:06:04
  So they could call John. I tell you, John's been around long enough. He's not going to answer that
• 1:06:09
  phone on a Saturday night. So how do they get that answer? AI will allow them to scale that answer.
• 1:06:15
  Now, your question, if I recall correctly, is how do we know it's given the right answer?
• 1:06:21
  And the way we know it giving maybe not 100 of the time the right answer but the best answer is that we reduce the knowledge set to the smallest size possible So if we stay in automotive manufacturing we might give specifics on how to change brakes on a Chevy versus how to change brakes on a Ford There a lot of similarities but there might be
• 1:06:46
  certain springs or clips that are different. So we don't even include information that is not
• 1:06:53
  applicable to that specific role. And this is where AI succeeds is when we limit its responsibility.
• 1:07:01
  It's AI, in my belief, will never be available to one instance answer every question in the world.
• 1:07:10
  It won't.
• 1:07:12
  But if we reduce the specificity of the knowledge to as small a data set as possible, something that is specific for one role, one job, then it will be right 99 plus percent of the time.
• 1:07:26
  is there any research or thought put into what the voice model used sounds like whether it's a
• 1:07:35
  male voice or a female voice or even the accent or anything or are they all pretty much just
• 1:07:40
  standard well see uh now you're into my daughter's realm uh she's a psychologist and but she fell into
• 1:07:46
  uh into technology she's been working technology front they call that human factors hf and it's that
• 1:07:54
  interface, what is best for the human? Now, 20 years ago, 50 years ago, 100 years ago,
• 1:08:02
  if we're being taught how to do a new job, whether it's at home, on the farm, or Ford Motor,
• 1:08:10
  we're going to be told one way, and we have to figure it out on our own.
• 1:08:14
  And we either adapt or we don't. And about half to two-thirds can get it on the first try,
• 1:08:19
  But then the other half to one third may not figure it out or it may take too long to figure it out, then they lose their job.
• 1:08:29
  So it's getting that information in the shortest amount of time that's important.
• 1:08:35
  So what AI allows us to do is specialize.
• 1:08:38
  Today, we are at a one size fits one role.
• 1:08:43
  And if I have a very, we have IQ, which is intelligence quotient, like how much do we know?
• 1:08:51
  But then there's also EQ, which came about about 30 years ago or so, the emotional quotient.
• 1:08:56
  It's how well I can connect to you as a human to human.
• 1:09:00
  And if I stumble upon you and you fell off your bicycle, I'm going to treat you like you just fell off your bicycle.
• 1:09:10
  That's the context.
• 1:09:11
  and if you just won the lottery, I'll go have a beer with you. Hey, congratulations. Nice job.
• 1:09:18
  What a lucky cat you are. And AI is in a position where whether we're neurodiverse,
• 1:09:27
  neurodivergent, or what's neurotypical is the normal people is by the big category here.
• 1:09:35
  AI doesn't care because AI will adapt much quicker than a human will to the context.
• 1:09:43
  What type of questions you ask, what type of answers you expect, and it will keep track
• 1:09:49
  of every answer that AI gives from the SOP, from the company database.
• 1:09:55
  If the human says, now, wait a minute, can you explain that to me again?
• 1:10:00
  Or can you tell me how that works with a blue, whatchamacallit, instead of a red, whatchamacallit?
• 1:10:07
  Then AI will rephrase.
• 1:10:09
  And AI will adapt to that individual to have that best friend, I've known you all my life communication.
• 1:10:18
  So the communication value is just the efficiency just skyrockets because it is that one size fits one.
• 1:10:25
  And AI can do that for pennies on the dollar versus putting humans in to stand by, we'll
• 1:10:34
  take your call, operators are standing by.
• 1:10:36
  That's what I was going for, right?
• 1:10:40
  So the way you trim down the inference problem is literally your LLM I assuming is like you said you saying okay well this is a Ford plant I don need to include Chevy
• 1:10:53
  I don't need GM.
• 1:10:54
  I don't need, you know, Dodge.
• 1:10:55
  I don't need any of this stuff, Chrysler, et cetera.
• 1:10:57
  I just need to train it only on the Ford specific data and maintain just that subset of data documents, you know, maybe their document repository, et cetera.
• 1:11:09
  and thereby you limit the amount of inference that can be brought up by AI
• 1:11:14
  by not allowing it access to non-relevant material.
• 1:11:21
  Absolutely. We do the same thing with humans.
• 1:11:23
  And that's really where this adapts from.
• 1:11:25
  My takeaways at my time at Amazon and the successfulness of the program that I had there
• 1:11:31
  and then using AI for what AI can do, the technology of it is adapt and deliver that information.
• 1:11:38
  But just like you would take a Ford to a Ford mechanic, a Chevy mechanic might fix the Ford.
• 1:11:48
  But some of us says, no, no, I'm going to the expert because the Ford mechanic is going to know more than the Chevy mechanic does.
• 1:11:56
  So yeah, we're following the same model as humans do.
• 1:12:00
  We're just amplifying those human abilities.
• 1:12:05
  That is pretty cool.
• 1:12:06
  very cool i'm just curious what happens if somebody uh asks something that's outside of
• 1:12:13
  its purview uh say like hey pocket betra you want to chat today hey how you doing today they're just
• 1:12:18
  lonely and i want somebody to talk to does it have a something that cues up and says okay i can't
• 1:12:24
  talk about that well it's interesting you said that because mental wellness is huge and it's uh
• 1:12:29
  in hr departments you know keeping track of how the workers actually feel day to day because are
• 1:12:35
  Are they going to come in tomorrow if it's a bottom line?
• 1:12:38
  Or can we make your day better?
• 1:12:41
  Good HR departments, good HR employees really do care about the employees.
• 1:12:49
  So in a situation like that, it's whatever the company contracts for.
• 1:12:56
  It's their information.
• 1:12:58
  We don't create the information.
• 1:12:59
  we give away for, we give a new interface to that information for their employees so they can,
• 1:13:07
  it feels more like osmosis than, you know, be standing and being blared at for a week and then
• 1:13:13
  go out and try to do a job. So if they ask a question that's out of context, it's only out of
• 1:13:18
  context if it's outside the SOP. And, but we do have those mental awareness and wellness
• 1:13:28
  triggers in there that can do whatever the company designed. We can have it chat back a time or two,
• 1:13:36
  or we can just say, hey, I think you better talk to John or Peggy or Mary over in HR.
• 1:13:43
  You should defer there. So it is programmable. It's guardrails to as tight as you want to run
• 1:13:51
  the guardrails, just like any piece of software is. So it can either engage or redirect, but there's
• 1:13:59
  always going to be a limitation on how far it engages because it is important to keep that
• 1:14:04
  knowledge pool as small as possible. Interesting. Very cool. Well, I think this is probably a good
• 1:14:13
  place to start wrapping up a little bit here. One of the things we like to do with all of our
• 1:14:19
  guess, is give them an opportunity to give back to someone that was influential to them.
• 1:14:24
  And throughout our lives, we have a figure that had a massive impact on us. It could be a sixth
• 1:14:31
  grade science teacher, historical figure, an author, it could be mom. But who would you like
• 1:14:37
  to shout out? Well, it's posthumously, but Kim Chavez. She was my first supervisor when I was at
• 1:14:46
  Amazon.
• 1:14:47
  And she gave me some insight because you know working at Amazon and the you know kind of a white collar crowd and one of the largest companies in the world that is just all over you got to produce or you gone type of scenario
• 1:15:01
  She gave me some advice, and it was treat the job like it's a business, which did two things.
• 1:15:11
  One, she said, keep track of the good things that you do because nobody else cares.
• 1:15:15
  and when it comes time for selling your program or selling yourself,
• 1:15:22
  you have valid data points to share.
• 1:15:26
  And that was one point.
• 1:15:27
  The other point is that she has an autistic child herself.
• 1:15:34
  And so she was actually a couple years younger than I,
• 1:15:38
  but I always looked at her as a mother figure
• 1:15:42
  because she treated me with, I don't know, kid gloves.
• 1:15:46
  I still feel like I'm a kid with this autism.
• 1:15:50
  It's hard for me to say I'm the best person in the room,
• 1:15:54
  even if I am, which it might happen, but it's rare.
• 1:15:58
  But she would do what good leaders do,
• 1:16:03
  and that is stand up for your employees
• 1:16:05
  and protect their backsides,
• 1:16:07
  but allow them to grow and perform to what they're capable of.
• 1:16:13
  And under her guidance and her shield of protection at a company like Amazon,
• 1:16:21
  she took a lot of hits for me.
• 1:16:23
  But the payoff allowed me to build something that had never been built at Amazon
• 1:16:28
  because she took care of some of the business stuff that I wasn't comfortable or capable of doing.
• 1:16:35
  It allowed me to be creative and to continue to grow something good.
• 1:16:40
  And I admire her for the business acumen that she had and admire her for the person to person, personability and love she had for other humans.
• 1:16:53
  It's awesome.
• 1:16:54
  I think you're the first guest we've ever had that shouted out a supervisor.
• 1:16:59
  It's a pretty cool thing.
• 1:17:00
  And actually, I started in my brain thinking about it and I'm like, wow, there are a few people that I would probably shout out to in that same role.
• 1:17:09
  Very cool.
• 1:17:10
  So where can people find you?
• 1:17:12
  What do you have going on?
• 1:17:14
  Where you said the audio book is released.
• 1:17:17
  Where is the best place for someone to find that?
• 1:17:20
  And yeah.
• 1:17:22
  Yeah, absolutely.
• 1:17:24
  Hopefully you'll have room in the show notes.
• 1:17:26
  We can put a couple of URLs underneath the audio.
• 1:17:30
  But if you Google me, I come up pretty readily.
• 1:17:35
  PracticalAI.app is the business home for my company.
• 1:17:43
  And the book, everybody can get a free read of the first chapter at HumanFirstAI.net.
• 1:17:50
  Awesome.
• 1:17:51
  So PracticalAI.app or HumanFirstAI.net.
• 1:17:59
  Go check them out.
• 1:18:00
  Thank you.
• 1:18:01
  It's good stuff.
• 1:18:02
  Thank you very much for giving us some of your time.
• 1:18:04
  We really appreciate it.
• 1:18:06
  And thank you for giving us some of your time as well.
• 1:18:09
  You know where you can find us.
• 1:18:11
  We're everywhere.
• 1:18:12
  Podcasts can be found as well as YouTube and, you know, other social media sites.
• 1:18:16
  If you like this, give us a thumbs up.
• 1:18:18
  If not, give us a thumbs down.
• 1:18:19
  Leave a comment.
• 1:18:21
  If you have an idea for a show, topic, guest, throw that in the comments.
• 1:18:26
  We'll reply and be happy to try and work on getting something done.
• 1:18:30
  And remember, wherever you are, whenever you're watching this, have an amazing day.
• 1:18:39
  Thanks for coming by, Derek. See you, everybody.
• 1:19:00
  .