It's September of 2023. And we can't go a half day. Without a discussion about artificial intelligence. A few short years ago, nobody even knew what it was. Nobody was talking about AI. And today, everywhere we go, people are talking about artificial intelligence. What is it? What is artificial intelligence? And more important? How do we teach it? That is the topic of today's episode of The Tech Ed podcast. Welcome to the tech ed podcast where we visit with leaders who are shaping, innovating and disrupting technical education, people who are not afraid to think differently, not afraid to try something new, all with the goal of securing the American Dream for the next generation of STEM and workforce talent. Welcome into the tech ed podcast. I am your host, Matt Kirchner. And today it is just the three of us. Yes, that's right. It is me, the audience and artificial intelligence and how do we teach artificial intelligence at every level of education, then what is artificial intelligence? Let's start by answering that second question. What is artificial intelligence? And again, I have to paraphrase my emerging leader in the field of Artificial Intelligence friend, Murtaza Bora, who says that in order to understand artificial intelligence, we have to define both of those words, the first thing we have to do is define what do we mean by intelligence? And Murtaza would tell you that it is a combination of human experience, and learning. Let me give you an example. How do we know not to put our hand on a hot stove? The answer is we learned it one of two ways. We either learned it, and we know it by learning. That is, somebody taught us, Hey, don't put your hand on that hot stove, you are going to burn your hand. And we realize that whoever that individual was somebody we trusted, told us not to do that, we believe that to be true. And we avoided that hot stove, like the plague. The other way that we can learn, or that we can know something, rather, is that we can learn it through experience, we put our hand on the hot stove, and we say, hey, that is hot. And not just right now, and it doesn't hurt just right now, but it is going to hurt for days, and maybe even a week or so if we burn our hand on a hot stove. That is how we learn through experience. So we can either learn by learning that is by gaining knowledge by having somebody or someone or something impart knowledge to us. Or we can learn through experience by putting your hand on the hot stove and realizing that we're never going to do it again. So human intelligence is a combination of these two things. It is a combination of learning and experience, the way we acquire knowledge learning or connecting and linking ideas, which is experience, that is intelligence. That's the intelligence or that second word, the intelligence part of AI. What do we mean by artificial? Well, again, I defer to my friend, Murtaza Bora, who tells me that when we say artificial, the artificial part of artificial intelligence is just a mathematical representation, or an algorithmic implementation of something. So we're using math, and we're using algorithms in order to generate information in order to make learning and intelligence artificial. So what is an algorithm if if our the artificial part is the combination of that mathematical representation and an algorithmic implementation of something? What is an algorithm it's just a set of rules that a computer uses to complete a task, that's all it is, is a set of rules or information or steps that a computer uses to complete a task? That's what the artificial part of artificial intelligence is? So when we combine the two of those artificial intelligence is basically the mathematical representation and algorithmic implementation of human experience in learning. And that is essentially what artificial intelligence is, how do we make a computer represent and implement our own human intelligence and our experience and our learning, and it can get more complicated than that. But for now, we're going to keep it simple. And we're going to leave the definition at the one we just shared with you. So all artificially intelligent systems do four things, every single artificially intelligent system does four things. The first thing it does is it has the ability to see, the second thing it does is it has the ability to think the third thing it does is it has the ability to do or act. And the fourth thing it does is it has the ability to communicate. All artificially intelligent systems have the ability to See, Think, Do and communicate hate every single one of them. So when we put our hand on the hot stove, going back to our example, what is the human being doing the first thing we are doing is we are seeing or we are sensing something, we are sensing that that stove is hot, and it's burning our hand, we realize as we are going through that, that that stove is burning our hand. So we have a series of nerves going from our hand to our brain. And those nerves are saying, Wow, that's hot. And that is how our hand is seeing or sensing its environment. The second thing that happens is that we think so all of those signals go up to our brain, and we say, Wow, that hurts. While that's really, really hot. The third thing we do is that we act, so based upon that information, where our brain is going to send a signal back to our hand to remove it from the point of danger or the point of harm. And then chances are we're going to communicate as well, we're going to communicate through a shriek or through a yell or through bursting out with that pain that we felt when we put our hand on the hot stove. But that's not the only way we're communicating all those that series of nerves is communicating from our hand to our brain, and our brain is communicating back to our hand. So that is our example an intelligent system. And certainly our human body is an intelligent system has the ability to see or sense, it has the ability to think it has the ability to do or act, and it has the ability to communicate. And in the same way, artificially intelligent systems do exactly the same thing they see they think they act, and they communicate. Let me give you an example that I think a lot of us can relate to, I happen to drive a Chevy Tahoe, I love my truck, I do quite a bit of traveling and the work that I do drive that truck all over the place. I've got two friends that work in the automotive industry who both work in the field of the technology that goes into the sideview mirror on my vehicle. So I have one of those vehicles that I know many others do, that can actually sense when a car is in my blind spot. So if I'm driving down the interstate, and somebody pulls up into my blind spot, kind of near that rear, left driver side quarter panel on my car, I can't see them, I can't see them in my sideview mirror, I can't see them in my rearview mirror, the only way that I can see them is by turning over my shoulder and looking at my blind spot. Well, I can't see that car. But my car knows that it's there. And it knows that it's there. Because the little sideview mirror senses and there's sensors all over the car, that sense that that car is there, and then a little light comes on notifying me that there's somebody in my blind spot. If I ignore that signal, that little signal that comes on, my car will first vibrate. So my seat actually vibrates and alarm goes off. If I for instance, turn my sink my attorney signal on is if I'm going to change lanes while going down the interstate. The second thing that happens is that if I try to change lanes, my car will say, wait a minute, we're going into danger, it will actually try to steer me back into a straight line. And many of you have experienced that while driving your cars have a has this kind of technology. Let's consider what is happening here. I can't see that car. But the sensors on my car can it knows that a car is there. And that is the c part of artificial intelligence. So we have sensors on the car that are seeing that are sensing the environment, it knows that a car is in the blind spot, it sees that it's there. And that is the sea Park. The second thing that happens is that it is thinking it says hey, there's a car over there, it knows that there's a car next to me. And then the system acts it activates a little light on the side mirror. And that is both acting and communicating to me. So it's sending that signal to the side mirror. And it can act even further. If I tried to change lanes, it tries to keep me in my own lane. And it also communicates it communicates to me, like we said via that light on the mirror, it communicates via via the vibrating seat. It communicates via the alarm. And it's also communicating to every other smart sensor and smart device, the entire smart system and my entire entire vehicle. Just that technology gives us a great example of the whole continuum of artificial intelligence, of seeing of thinking of doing and communicate, See, Think, Act and communicate every single artificial intelligence system, does it. But how do we teach it? How do we teach artificial intelligence in the K 12? School District? How do we teach artificial intelligence in our Technical and Community Colleges or for that matter, in our universities in in the workforce? Well, let's start out by understanding that unlike other fads in education, and those of us who have been in education for awhile know that some fads and ways of teaching and ways of delivering knowledge and theories can come and go. I'm of the belief that artificial intelligence is absolutely here to stay. So let me give you an example. Sundar Pichai is the Chief Executive Officer of alphabet formerly known as Google. He is spending all of his time studying this thing these things obviously alphabet doing all kinds of really, really innovative things in the world of artificial intelligence. We don't even have time to get into a few of those on this episode of the podcast, but Sundar Pichai says that artificial intelligence is more profound than electricity, or fire. He says it is the most important thing that humanity has ever worked on. Now let's think about what we mean by profound as electricity or fire. Think about the advent of fire before fire. Human beings had no good way to cook their food before fire, we had no good way to stay warm. When the temperatures drop, presumably all we could do is wrap ourselves in animal fur and trying to hunker down in a cave or other dwelling, and wait out the cold weather. And then fire came along, and the whole world changed man discovered the ability to harness fire and it changed everything. Suddenly, we could cook our food cooking food made it more digestible. It made it more nutritious and it made it way safer. Gathering round of fire became a focal point for community. It built communities as individuals gathered around the fire for warmth, it protected us from predators. And so we can light a fire and protect ourselves from whatever animals that might be lurking in the dark that might be looking to attack human beings and the fire scared those individual animals away. It also allowed us to protect ourselves from insects and pests, thereby reducing disease it was used as a signal for communication. So now with fire and smoke, we could send information across long distances, messages and warnings that kept us safe fire aided humanity in making tools and pottery and ceramics. And later, it allowed us to clear the land for agriculture and allowed us to grow even more food and settle in even more areas. It offered the ability to work metal which brought on the Bronze Age in the Iron Age. And finally it had cultural and spiritual significance. It represented Cultural and Spiritual Significance representing purification, and renewal and transformation. And speaking of transformation, think about how transformative electricity was, as we've talked about many times on this podcast, the electricity brought on the second industrial revolution. Before electricity, we lit our homes with what with fire. After electricity, we can lead our homes with electrical lighting before electricity, no electrical stoves, no appliances, no dishwasher. Think of everything that electricity made possible. Lighting, electronics, first radios and then TVs and later computers and smartphones, transportation, electric trolleys and trams and electric trains and none of that was possible before electricity in the world of medicine and health care things like X rays, and MRIs were not possible before electricity, our ability to communicate first through the telegraph, and then through the telephone. And now, the internet is totally transformed entertainment as cinemas came online, and people could go and watch movies back in the day when moving pictures were just coming out refrigeration and air conditioning, heating, ventilation, air conditioning, food preservation, and finally in the area of research, where we electrified our laboratories and our labs and our analytical instruments. And that changed everything. Just like the discovery of fire. Harnessing electricity changed our entire world in almost every way imaginable. So, again, credit where credit is due the examples of how fire and electricity rocked our world did not come. Those examples did not come from Me. They came from chat GPT I entered in the question of how these two things changed the world. And the answers came out immediately. I used artificial intelligence to be able to deliver that last part of our presentation. By the way, what is Chet GPT? I know we were talking all the time here in September of 2023. About that relatively new technology, certainly in the way that it's been made available to the common person so that somebody like me, could use it to generate the information on electricity or fire. What is chat GPT the GPT in chat GPT stands first for generative that means it produces content. That's pretty simple. The P stands for pre trained, that means that data scientists are taking huge seas of data, we're producing tremendous amounts of data now more than ever before, by orders of magnitude, taking these huge datasets and training chat GPT to gather all that information and all the all the data primarily from the internet that it analyzes when we ask it a question. And finally transformer is the tea part of chat GPT which is using specialized algorithms to find patterns in that data and then deliver the results to us. So By the way, that is just like what Murtaza Bora taught us using math and algorithms to implement human experience and learning that is exactly what we're doing when we're implementing artificial intelligence and using a tool such as chat GPT. So Sundar Pichai of Google tells us that this technology, this technology of artificial intelligence, is more transformative than electricity, or fire. Are you convinced as I am? That maybe just maybe we should be teaching this at every level of education? I think we're in agreement that we need to the question is, how do we do it? And as you might expect, I have some ideas. So let's go back to what do all artificially intelligent systems do, we already agreed that they can all see or sense their environment, they can think they do or act based upon the process of thinking, and they have the ability to communicate with each other, just like my truck driving down the interstate realizes that there's a vehicle next to me realizes that it presents a potential danger to me or potential unwanted situation. It prevents me from doing something dangerous, and it communicates the situation to me and to all the other smart systems in the entire vehicle. That is the See, Think, Act and communicate continuum of artificial intelligence. But just like that, there are great ways for us to teach that continuum to our students, let's just explore a few of them. And first, teaching artificial intelligence is more important than you think it might be if you have made or are making the right investments in your educational learning centers. One of the reasons that we here at the Tech Ed podcast are such huge advocates for implementing authentic Industrial Technology at every level of education. As we transform and secure the American Dream for the next generation of STEM and workforce talent is that schools that have that technology, already implemented in their environments are able to back by the right curriculum and the right elearning and the right teacher training, perfectly deliver education on artificial intelligence in a ways that inexpensive 3d printer cannot do in a way in ways that vinyl cutters for example, not that there's anything wrong with having them in a Fab Lab, for instance. But it's really hard to teach AI on a vinyl cutter, or a little router in these types of technologies, we need to have authentic industrial technology, and real world technology that is loaded up with real world sensors and devices and the ability to communicate that we're going to talk about here in just a moment. Let's take for example, an industrial robot, the typical industrial robot at a learning center in education is loaded up with a ton of smart technology, sensors and devices that are measuring things like force and temperature and disturbances and moisture. And gathering all that information. In real time, we can take that robot and we can connect it to a programmable logic controller, or we can connect it to a computer network. And then we can gather that information, we can start to acquire that data. And then we can connect that PLC or that computer network to the cloud. So now we can take the data that makes sense to send to the cloud. And we can discern that data and send it from the edge from our little robot or from our programmable logic controller up to the cloud, where cloud based software can compare the data from our little robot with data from 10s of 1000s of other robots that are also cloud connected in the same way. And in the same sense that chat GPT has data scientists that have created algorithms that sift through tons and tons of data, we can do the same thing with that industrial data from that little industrial robot that is sitting in a school district and a high school and a middle school at a technical college, anywhere in the world. And now we can take all of that data and understand how on a macro level, that data can be analyzed to predict the future. Let me continue with our example. So now I've got a robot sitting in my classroom. And there's a sensor on one of the servo motors in that robot. And we realize that when the resistance in that servo motor reaches a certain point, almost without exception, that servo motor will fail within the next three months. And we're doing that at the cloud because we're measuring 10s of 1000s of other robots. And so we know that the servo motor at the edge, that little servo motor in our classroom is likely to fail. And so we can predict its own future failure, we can predict the future failure that servo and we can send information either to the robot to the PLC or to the operator or the robot that says hey, you better plan to replace that servo motors sometime in the next several months, because the truth of the matter is that that is going to fail. We know that based upon analyzing all this data, that is what We call predictive analytics. So think about what we just did here, we basically use cloud based software gathering information from all of these other robots. And we have our servo motor that says, hey, this servo is getting a little bit hot, because we have a temperature sensor there. And so we're first thing we're doing is seeing again, in our same continuum, See, Think, Act and communicate, the first thing we do is see, we see force or resistance, we can see the fact that there is a resistance in that servo motor, the second thing that we're doing is thinking, wow, that servo motor is getting hot, or there's a lot of force that is being created in that servo motor, the third thing we do is we act we can send information and order the new motor. And the fourth thing we do is that we communicate, so we're communicating from the robot up to the cloud, and then from the cloud back to the robot, back to the individual operator of that robot, or, for that matter, a teacher or student in the classroom. So that is artificial intelligence, it's hard to teach that on a cheap 3d printer, I'm just being honest, that we can on an advanced one, we can certainly do that with more advanced technology in the 3d printing space. But we can do it on a robot. And we can do it on all kinds of other equipment and systems, you just have to know where to look for it. So let's talk a little bit more about some examples of how we can teach AI. And if you're starting to understand the idea, the idea is that we can go through our educational institution, look at the current equipment, look at the systems that we're teaching on, and find ways to teach the continuum, find ways to teach the See, Think, Act, and communicate continuum. And and we can do this at every level of education, we can do it in K 12, we can do it at our Technical and Community Colleges, we can do it at our universities, we can do it in industry, we can teach AI, we can teach the continuum of See, Think, Act, and communicate. So a couple other examples. I've gotten really, really fascinated over the course of the last couple years with 3d scanning. So if you're not familiar with this technology, we can take a scanner, either a handheld scanner or a scanner at the end of a robot and scan a 3d object. Think about any object in front of you right now, whether it's your computer, your smartphone, a coffee mug, a light, anything in your office, anything in your classroom, anything wherever you are today. And think about being able to scan that. So running a hand scanner, or putting a scanner at the end of a robot and just scanning that environment, we can create a CAD file, we can create a 3d STL file of that entire object using that 3d scanner. So just by scanning the object, now I have a CAD file, I can go into CAD software and I can manipulate that object. And then I can take that CAD file and send it off to a 3d printer, or we can send it off to a machining center, or we can send it off to a metal farmer and actually form that product form that object using artificial intelligence. And here's how we do it. At the end of the scanner, the scanner is white light and a vision system and a camera that is how we are sensing that is how we are seeing we are using those technologies. As part of the see Think, Act communicate continuum. The next thing we're doing is that we have the ability to think we are using cloud based software that is gathering that information. And it can be edge based software as well. We can have the software on a computer on a on a PC, and so on. But we're using software. And that software is using what we are seeing using the sensors and it is thinking about what it is that it is seeing and is using that software to create that 3d model. The third thing that we're doing is that we can output that STL file that CAD file. So we're acting we are taking the information that we have sensed or thinking about it. And then we are using that to create an output in the form of a CAD file. And then we're using all kinds of communication devices. We're communicating via USB, we're communicating via wired communication and coaxial communication in many cases, RF and Wi Fi communication. So just by explaining to a student how that 3d scanner is using artificial intelligence to do what it does, we're teaching the AI continuum. Another great example that we can use is this whole idea of programming and coding. Almost every school that we go into has some version of a computer science or data science program where students are learning how to program and how to code in various software. And so we can first of all think about how a coding software is seeing it is seeing based upon the use of a keyboard it is seeing based upon the use of in some cases a game controller or a mouse. And so we are taking those devices out on the edge and we are entering information into software. In this case, you know, think about a basic coding course we might be using Java or we might be using C sharp software that a student is getting exposed. As with doing that coding and teaching that particular computer program, and that particular in that particular language, how to think. And then we can think about how do we act? How do we, for instance, create a video game, using the information that we are gathering from our mouths from our keyboard, using the software, and the programming knowledge that we have gained? Now we can create, for instance, a video game, develop a video game, and that student has that experience. And then we're communicating how are we communicating well, from the output input device, from our mouse to our keyboard, to our computer, from our keyboard, from our computer, rather, in many cases, to the cloud, and teaching that entire continuum using just a simple program around game development in a computer coding class, but not just teaching the student about computer science and not teaching them just about coding and programming, but helping them understand how would their learning fits into the See, Think, Act, communicate continuum. A couple other examples. We have been really fascinated with drone technology here at The TechEd Podcast. And we're talking both about unmanned aerial drones, we're talking about unmanned ground drones, that drone is just something that has the ability to sense its own environment, to make its own decisions to avoid danger. Again, that whole See, Think, Act, communicate, continuum. And drones are a great way to do that. If you think about a typical drone or an advanced drone, we have camera systems, we have vision systems on that drone. Think about when that's flying, we've got proximity sensors, we've got line following sensors, we have limits, switches, we have gyroscopes, all of these technologies sitting out at the edge that have the ability to see or sensor environment. From that we are taking all of that information. And we are sending it to some version of a motor control module, some version of an Arduino, some version of a microprocessor on that drone that is gathering all this information about how the drone is flying about how it's balanced about what it's sensing about what it is seeing. And using that software, then to keep that drone in flight. And to keep it balanced, we can teach that so we can teach the sensing, now we're teaching the thinking, and then we're thinking of the act. So as we are seeing things as we are seeing things in the environment, sending that to the microprocessor, the drone is making decisions on its own about which propellers to turn which rotors to turn at which rates to make sure that the drone stays in balance, or turns the way that we want it to turn, it stays in flight. And then it communicates it communicates using Bluetooth it communicates using radio frequency communicates using GPS, it communicates using telemetry. So just on a drone, yes, we can teach a student how to build a drone. Yes, we can teach them how to fly a drone. Yes, we can teach them how to program a drone. In the process, we must teach them how artificial intelligence is at work in that drone, whether it's a ground drone, or an aerial, drone, and so on. We already talked about how we can teach us on robotics. So if we have a robot, that's a smart robot, and it's sensing its own environment, it can predict its own future failure. In order its own replacement parts, we can do the same exact thing on a machining center, assuming that it is equipped with the right technology, assuming that it is equipped with the right sensors and the right ability to gather and acquire data from itself on things like speeds, and feeds, and temperature, and so on. And so we can gather all of this information and send that information up to the cloud and create huge datasets and compare it to datasets that are being created by other machine tools across other educational institutions. And use that information to predict how the machining center or the machine tool is going to perform all of these different ways that we can use to teach the whole continuum of artificial intelligence from sensing, to thinking to doing to communicating just a couple more examples. I was at a high school just a couple short weeks ago, that is actually going to use an autonomous mobile robot to take product or to take items from the office to a classroom. So a teacher orders something or something gets delivered for a teacher or for their classroom. It arrives at the school office, they are going to use an autonomous mobile robot meaning a robot that will drive around the school to deliver that package. How cool is that to be working and living and being educated in a high school where you're using autonomous mobile robots to deliver products to your classroom? Well, here's the thing on that autonomous mobile robot, there is a ton of artificial intelligence going on. And we have an opportunity now to teach students how that autonomous mobile robot that will literally find its way to a classroom on its own. How that autonomous mobile robot uses artificial intelligence to do its work. In other words, we can tell that robot that we want it to go to a certain classroom, that robot has already mapped its own environment. So it's using Oct. kinds of artificial intelligence Lidar and 3d cameras that are sensing its own environment that that is the sea part of artificial intelligence, it is taking that information, and it is sending it to a fleet management software. So there's cloud based software that is managing, in that case, the particular autonomous mobile robot. And so that cloud based software is gathering all this information. And it is helping that robot make decisions. If a student wants in front of the robot, it needs to stop, if it is going to becomes up to a closed door, it needs to stop. If it's coming up to an area where it needs to turn the corner, it needs to be able to turn the corner, if it is coming to an area if I've got two of these working in the same space, and it's going to risk a collision with another autonomous mobile robot, the two robots will know that before that collision, not just happens, but I'll know that before they even start down the same hallway. And one of them can wait out of the way or can reroute itself as efficiently as possible. So that is the think part of our continuum. I've got the Lidar and the 3d cameras gathering information, sending it to the cloud based software. The cloud based software then is making decisions. It is saying, Amr you need to stop, you need to turn a corner you need to wait, that is the act part. And by the way, all this communication is going on between the autonomous mobile robots between the autonomous mobile robot and the cloud between the office that sending the package to the classroom, and the autonomous mobile robot itself. Here again, another great opportunity for us to teach artificial intelligence, See, Think, Act, and communicate. Finally, and I'll tell you, this is probably my favorite way to teach artificial intelligence. And I'm gonna go back to where we started with our good friends at quants are in Markham, Ontario, who have come up with a system called the self driving car STEM experience. These are self driving cars that drive on a track, we can put them in a STEM classroom. And these are not your typical remote control cars. These are loaded with a ton of sensors, a ton of LiDAR, GPS, all kinds of technology, all kinds of sensing technology that allow them to see and they're driving about the track, they can follow a particular line, we can put a yield sign in front of them, and using classification learning, they will yield to another vehicle, all these vehicles are communicating with each other in real time and with the clouds. So the student has a dashboard, where they're learning how that vehicle is seeing, they can literally look right through the camera on the dashboard on a tablet, and see what the vehicle is seeing. As the vehicle drives around the track, he can look at the code behind how the vehicles are thinking and how they're making decisions and how cloud based software and edge based software and microprocessors on the cars themselves are using all of this data that they're gathering to make decisions, then we then we think, okay, we need to do something, I'm coming up to a yield sign and there's another car coming in another direction, I need to yield way to that car, I'm driving down the road, and I'm getting too close to the car in front of me, I need to slow down, I'm not getting to my destination fast enough, I need to speed up. And if I do speed up, I'm using more energy. And so I need to calculate how much energy I'm consuming all of these things that we're doing in terms of the think, to do continuum. And finally, the ability of these things to communicate with each other, with the cloud with the software that the student is using absolutely unparalleled way of teaching our whole continuum, our whole continuum of See, Think, Act, and communicate. And if it feels like I've used those four words, way too many times that is on purpose, we are trying to drive home how very important it is this continuum of artificial intelligence, as I've said many times, seven times seven different ways seven different examples, way more than seven times of how we teach the artificial intelligence continuum. So what does that mean for our teachers and our educators? The truth of the matter is that we need to look for in our classrooms, in our schools, and with our partners, technologies that can help us teach that continuum technologies that are using smart sensors and smart devices, technologies that are using edge based and cloud based software to think technologies that are then causing whether it's an autonomous vehicle, a robot, a machining center, a 3d scanner, to do something and to act based upon what it is analyzing and what it is seeing. And finally, we need to teach that communication continuum, whether that's USB or Bluetooth or Ethernet, or computer networking are how we're communicating information between intelligent devices between intelligent systems and up to the cloud. This is really, really exciting stuff. Like we said, Sundar Pichai, the CEO of alphabet says that artificial intelligence is more transformative than fire or electricity. He says it is the most important thing that humanity has ever worked on. I happen to agree with him. I think after Listening and chatting about this artificially intelligent world that we are going to be living in and that we are already living in. I think many of us will agree that we absolutely, positively have to bring this into the classroom, it is absolutely the future. And the students that learn this information and learn and have these experiences are going to be so much better prepared for the world of artificial intelligence than those who don't, it is absolutely going to transform our world. So let's figure out ways to bring this technology into the classroom. Let's teach our students how every single artificially intelligent system, sees thanks, acts and communicates. And when we do, our students will be way further ahead. I'm excited about this new world of artificial intelligence. I hope you are too and it's been a pleasure to have you with us on the tech ed podcast. Thanks for joining us for this episode of The Tech Ed podcast. 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