WEBVTT - AI Education Programs with Intel 0:00:03.640 --> 0:00:07.240 In a world where technology continues to advance at breakneck speeds, 0:00:07.600 --> 0:00:10.000 education is evolving to meet the needs of the future. 0:00:10.880 --> 0:00:13.240 Years ago, if you were told that the average person 0:00:13.280 --> 0:00:17.920 would have a STEM job, that is, a career in science, technology, engineering, 0:00:18.000 --> 0:00:21.080 or math, a natural assumption would be that the world 0:00:21.079 --> 0:00:25.440 had progressed to most people having advanced degrees. On the contrary, 0:00:25.880 --> 0:00:28.600 it is the technology that progresses and with it our 0:00:28.640 --> 0:00:31.440 ability to keep up with the times. When I was 0:00:31.440 --> 0:00:34.360 growing up, our classroom only had one computer for all 0:00:34.400 --> 0:00:37.720 of us to share. Now my children have their own 0:00:37.720 --> 0:00:42.919 individual iPads and laptops to research, create and learn. It's 0:00:43.000 --> 0:00:45.960 amazing how far we have come with using technology to 0:00:46.080 --> 0:00:49.800 educate our young With their early adoption of technology comes 0:00:49.800 --> 0:00:52.800 to added responsibility of preparing them for how to use 0:00:52.840 --> 0:00:57.760 technology for their future occupations. For many students, those jobs 0:00:57.760 --> 0:01:01.480 will begin sooner than graduating from cos Whether it is 0:01:01.640 --> 0:01:04.600 learning to manage a small team of AI powered self 0:01:04.600 --> 0:01:08.320 service machines at the local grocery store, or learning coding 0:01:08.360 --> 0:01:13.000 skills to oversee a large autonomous robotic warehouse. Education is 0:01:13.040 --> 0:01:19.240 shaping a brighter future, one student at a time. Hey, there, 0:01:19.400 --> 0:01:23.240 I'm grain class and this is technically speaking. An Intel podcast, 0:01:23.840 --> 0:01:27.200 the show is dedicated to highlighting the technology is revolutionizing 0:01:27.319 --> 0:01:31.160 the way we live, work and move. In every episode, 0:01:31.240 --> 0:01:34.360 we'll connect with innovators in areas like artificial intelligence to 0:01:34.480 --> 0:01:38.399 better understand the human centered technology they've developed. We tend 0:01:38.400 --> 0:01:43.120 to see an education and STEM that is science, technology, engineering, 0:01:43.160 --> 0:01:47.520 and maths as something that's highly academic, involving several years 0:01:47.520 --> 0:01:50.760 in high education. Most of the guests on the series 0:01:50.800 --> 0:01:53.760 have graduated from some of the top universities around the world, 0:01:54.320 --> 0:01:58.280 or even developing AI tools before they entered college. While 0:01:58.280 --> 0:02:00.800 it is true that AI and technology are being innovated 0:02:00.800 --> 0:02:03.480 by some of the greatest minds in the world, those 0:02:03.520 --> 0:02:07.040 minds aren't always acquiring their skills the same way because 0:02:07.040 --> 0:02:09.440 it's not always about the degrees that you have, but 0:02:09.520 --> 0:02:13.280 your passion for knowledge and your ability to learn. One 0:02:13.280 --> 0:02:16.240 of the recurring themes for me in recording this series 0:02:16.600 --> 0:02:19.480 is how AI tools have made things more accessible for 0:02:19.520 --> 0:02:23.160 people around the world. However, it is important for education 0:02:23.360 --> 0:02:26.400 around AI and machine learning to be accessible as well. 0:02:27.120 --> 0:02:30.240 In this episode, i'll explore how learning AI is becoming 0:02:30.240 --> 0:02:34.280 more accessible with the help of Intel's AI for Workforce program. 0:02:34.600 --> 0:02:37.360 Before we get into it. Let me introduce our guest 0:02:39.280 --> 0:02:42.000 jotting me now is the lead faculty of Intel's AI 0:02:42.120 --> 0:02:46.400 for Workforce program at Chandler Gilbert Community College. Habib Mattah 0:02:47.160 --> 0:02:50.800 Habib was considered a child prodigy in STEM, beginning his 0:02:50.880 --> 0:02:54.960 collegiate career at the Chandler Gilbert Community College before graduating 0:02:54.960 --> 0:02:59.160 from Arizona State University at age sixteen. Following up his 0:02:59.160 --> 0:03:02.600 bachelor's with the mark in Computer Science, Habib went on 0:03:02.639 --> 0:03:05.880 to become a production lead at Intel, where he oversaw 0:03:05.919 --> 0:03:11.080 the analysis of statistics and led a team of manufacturing engineers. Ultimately, 0:03:11.120 --> 0:03:13.519 it was his love for AI and STEM that inspired 0:03:13.600 --> 0:03:17.160 him to transition into education. He wants to be the 0:03:17.200 --> 0:03:20.440 catalyst and making AI tools a part of the education 0:03:20.639 --> 0:03:24.280 system permanently and ensuring that a new generation of kids 0:03:24.280 --> 0:03:31.600 are fully immersed instead education. Welcome have you, It's good 0:03:31.600 --> 0:03:32.200 to be here. 0:03:32.440 --> 0:03:34.640 Wow. I couldn't have written that. That's one of the 0:03:34.680 --> 0:03:38.800 best introductions I've heard ever, So thank you so much. 0:03:39.200 --> 0:03:40.720 It wasn't from chat GPT either. 0:03:41.240 --> 0:03:44.560 I was about to say that, but well, I get 0:03:44.560 --> 0:03:47.080 it up with those jokes already, being an AI professor. 0:03:47.480 --> 0:03:50.440 That's right, that's right. So I mean, I have a 0:03:50.480 --> 0:03:54.680 son who's twelve and he's just started high school. You 0:03:54.760 --> 0:03:58.240 started college at age twelve, and I can't imagine him 0:03:58.360 --> 0:04:02.800 heading off to universe. So your early achievements are really remarkable. 0:04:03.520 --> 0:04:06.920 What did that experience teach you about learning stem tools 0:04:07.080 --> 0:04:09.800 at an early age and how has it shaped your 0:04:09.840 --> 0:04:10.840 approach to teaching. 0:04:11.680 --> 0:04:15.080 So a small correction there, I was one year older. 0:04:15.200 --> 0:04:18.080 I was thirteen when I started, okay, and I was 0:04:18.160 --> 0:04:22.400 that's just, I know, not a big deal in terms 0:04:22.440 --> 0:04:25.919 of age. So I was going into eighth grade, and 0:04:26.240 --> 0:04:27.880 at the time, I always knew that I was going 0:04:27.920 --> 0:04:30.280 to do some kind of engineering because my dad is 0:04:30.320 --> 0:04:35.040 an electrical engineer, and I wanted to see if it 0:04:35.080 --> 0:04:39.360 was possible to accelerate that process. I had no idea 0:04:39.640 --> 0:04:42.040 what the college space would have been like and how 0:04:42.080 --> 0:04:43.800 I would have came out on the other side of it, 0:04:44.279 --> 0:04:47.240 but I knew that my dad worked there at Chandler 0:04:47.240 --> 0:04:49.960 Gilbert Community College as well as my mom does as well. 0:04:50.000 --> 0:04:53.359 She's an English faculty, and so I knew i'd be 0:04:53.360 --> 0:04:56.280 in a safe place going to the community college, and 0:04:56.320 --> 0:05:00.120 I'd be around family when I'm not in classes. So 0:05:00.480 --> 0:05:03.279 I just ventured out into that big world. I didn't 0:05:03.279 --> 0:05:05.880 really even realize that everyone around me was, you know, 0:05:06.000 --> 0:05:08.839 five or six years older. I was so focused on 0:05:09.800 --> 0:05:12.479 reaching that goal of becoming an engineer like my dad. 0:05:13.320 --> 0:05:16.560 And it was actually quite interesting that you said that 0:05:16.640 --> 0:05:19.560 your father was an electrical engineer. My dad was an 0:05:19.600 --> 0:05:22.800 electronic engineer, and I remember spending quite a bit of 0:05:22.880 --> 0:05:26.080 time in his workshops at a young age. Perhaps tell 0:05:26.120 --> 0:05:28.479 me a little bit more, I mean of his influence 0:05:28.600 --> 0:05:30.120 on yourself going up. 0:05:30.640 --> 0:05:33.080 Well, it's an influence of his and as well as 0:05:33.080 --> 0:05:36.360 the culture. My name's Habib, which is Lebanese. It's actually 0:05:36.400 --> 0:05:40.119 my grandpa's name. And part of the Lebanese culture is 0:05:40.120 --> 0:05:44.479 is that you become either a doctor, a lawyer, or 0:05:44.520 --> 0:05:47.720 an engineer, right, and so I had to choose one 0:05:47.760 --> 0:05:50.480 of those, and I wasn't too good with blood, and 0:05:50.520 --> 0:05:55.680 so engineer definitely was that. On top of that, my 0:05:55.800 --> 0:05:59.360 dad would give me little incentives growing up. So I 0:05:59.440 --> 0:06:03.479 was super video games and he would say, okay, I'll 0:06:03.480 --> 0:06:06.840 give you one dollar for every math sheet that you do. 0:06:07.200 --> 0:06:11.000 So I would do these large multiplication tables just so 0:06:11.080 --> 0:06:13.240 I can get like one dollar and save up for 0:06:13.279 --> 0:06:14.200 that game I wanted. 0:06:15.320 --> 0:06:17.600 Ah, that's cool. I mean, I remember my dad giving 0:06:17.640 --> 0:06:21.240 me sort of logic puzzles and numerical puzzles as well. 0:06:21.320 --> 0:06:23.000 Going out. You didn't give you any money though, that's 0:06:23.240 --> 0:06:27.080 crying shame. So yeah, so I'm quite interested in the 0:06:27.120 --> 0:06:31.120 whole accelerated program that you went through. What sort of 0:06:31.160 --> 0:06:33.720 challenges are there for students to get access to that 0:06:33.760 --> 0:06:35.880 sort of program. 0:06:36.240 --> 0:06:40.600 We had found that if I went through homeschooling and 0:06:40.760 --> 0:06:44.160 tested out of homeschooling, then that could get me admitted 0:06:44.160 --> 0:06:47.680 into the community college. Once I started that process, all 0:06:47.720 --> 0:06:50.719 I did was a placement test, which is very standard 0:06:50.760 --> 0:06:54.440 across all community college goers. To do this placement test, 0:06:55.080 --> 0:06:57.760 I tested into like the hundreds, so not even the 0:06:57.800 --> 0:07:02.560 one hundred classes because again I'm thirteen. But within a 0:07:02.640 --> 0:07:06.520 year I was able to begin my program similarly to 0:07:06.560 --> 0:07:09.160 someone who had just got out of high school. From there, 0:07:09.920 --> 0:07:13.720 my memory of the time was very normal, Like the 0:07:13.760 --> 0:07:17.760 students around me were all pretty mature. I was able 0:07:17.800 --> 0:07:20.920 to talk to the other nerds and play video games 0:07:20.920 --> 0:07:24.240 with them, and so I had found myself in a 0:07:24.280 --> 0:07:28.600 pretty fun community that was quite nice. And actually I 0:07:28.680 --> 0:07:32.280 have people that I've talked to in my life that 0:07:32.360 --> 0:07:35.720 have kids and they've had their own kids go through 0:07:35.760 --> 0:07:38.320 that route as well. 0:07:38.440 --> 0:07:42.080 Despite spending most of his childhood becoming a scholar and STEM, 0:07:42.360 --> 0:07:46.480 his passions lie in teaching and helping others. This education 0:07:46.640 --> 0:07:49.120 is valuable to have even when you're not working in 0:07:49.160 --> 0:07:51.840 a STEM field. In such a role as an engineer, 0:07:52.440 --> 0:07:55.480 just having that sort of background can help others better 0:07:55.600 --> 0:07:59.680 understand the technologies we engage with regularly. If you've ever 0:07:59.680 --> 0:08:02.920 had to help a grandparent use a phone or printer, 0:08:03.600 --> 0:08:07.240 you know the exact challenges of helping others become tech savvy. 0:08:09.600 --> 0:08:11.920 In terms of the role now that you have at 0:08:11.960 --> 0:08:14.160 the college, maybe you could give a little bit of 0:08:14.200 --> 0:08:16.200 a summary. What are the courses, what are the programs 0:08:16.200 --> 0:08:17.040 that you're teaching there? 0:08:17.760 --> 0:08:22.240 Yeah, So when I transitioned from working at Intel into 0:08:22.320 --> 0:08:25.840 Chandler Gilbert, Intel approached us saying, we have a high 0:08:25.840 --> 0:08:30.280 school program that we're using in I believe Singapore for 0:08:30.680 --> 0:08:33.680 teaching AI. Is there any way we could take this 0:08:33.760 --> 0:08:35.959 high school program and make it into like a two 0:08:36.120 --> 0:08:41.120 year vocational program. And so my background was in computer 0:08:41.200 --> 0:08:44.120 science and AI, and I had already been working at 0:08:44.160 --> 0:08:47.400 Intel and I had family at Chandler Gilbert. Right, So 0:08:47.440 --> 0:08:50.520 it's like this perfect marriage between the three. And so 0:08:51.400 --> 0:08:55.080 the program I teach at Chandler Gilbert is in essence 0:08:55.120 --> 0:08:59.040 that it's a two year vocational program for someone to 0:08:59.120 --> 0:09:03.720 learn about artificial intelligence. Now, we started this in twenty nineteen. 0:09:03.960 --> 0:09:07.719 This was before chat GPT and the boom a popularity 0:09:08.080 --> 0:09:12.560 of AI. So what my focus has been since twenty 0:09:12.640 --> 0:09:16.679 nineteen is how can I give my students marketable skills 0:09:17.720 --> 0:09:21.400 while still keeping it accessible Because typically AI is a 0:09:21.440 --> 0:09:24.160 graduate field right now you have to have a master's 0:09:24.240 --> 0:09:27.040 or a PhD to learn about the topic. How can 0:09:27.080 --> 0:09:30.440 I keep this field accessible to learn as well as 0:09:30.600 --> 0:09:33.960 marketable with the skills that they do learn throughout the 0:09:34.000 --> 0:09:38.359 two year program. And so we have six different classes 0:09:38.840 --> 0:09:42.640 that we teach following that goal. Intro to AI is 0:09:42.679 --> 0:09:46.640 one of them, intro to Machine Learning, intro to Natural 0:09:46.679 --> 0:09:50.240 Language Processing, so this is, you know, how does SII 0:09:50.480 --> 0:09:54.280 know what when you say Hey Siri? Or how can 0:09:54.360 --> 0:09:57.280 chat GPT seem to understand the text that you write? 0:09:57.480 --> 0:10:00.640 Then we have computer vision, where it's how do cars 0:10:00.960 --> 0:10:04.080 driving on the road see other cars and pedestrians and 0:10:04.120 --> 0:10:08.320 no one to stop. The last two classes are intro 0:10:08.559 --> 0:10:13.199 to Business Solutions. We actually have an employee from Intel 0:10:13.360 --> 0:10:18.600 teaching that class, giving students important aspects of what work 0:10:18.640 --> 0:10:24.520 looks like in AI, like benchmarking and copyright issues that 0:10:24.559 --> 0:10:27.319 come with data. And then we have a capstone class 0:10:27.320 --> 0:10:30.000 where the students get a whole semester to explore an 0:10:30.040 --> 0:10:30.880 AI project. 0:10:31.520 --> 0:10:35.000 Well that's all in two years. Two years, yes, Well 0:10:35.080 --> 0:10:39.440 that's impressive. And in terms of getting into that sort 0:10:39.440 --> 0:10:42.360 of program, what are the some of the prerequisites that 0:10:42.760 --> 0:10:46.480 students have to have before joining in. 0:10:47.280 --> 0:10:49.959 So we're a community college and we want to keep 0:10:50.000 --> 0:10:54.240 this as accessible as possible. So our first class, Intro 0:10:54.320 --> 0:10:58.880 to AI, also named AIM one hundred. AIM stands for 0:10:59.040 --> 0:11:02.960 AIM machine Learning and it was a funny, you know, 0:11:03.280 --> 0:11:07.520 acronym to put there. But it's no prerequisites to join 0:11:07.559 --> 0:11:12.120 our first course. Now, our next class, AIM one ten, 0:11:12.200 --> 0:11:15.880 which is intro to Machine Learning, has a prerequisite of 0:11:16.600 --> 0:11:21.000 statistics as well as intro to Python, so you'll need 0:11:21.040 --> 0:11:24.160 to know a little bit of Python and statistics. 0:11:24.080 --> 0:11:27.720 Just for everyone's benefit. Python is a popular computing language 0:11:27.760 --> 0:11:30.360 which actually has a lot of free resources for anyone 0:11:30.920 --> 0:11:33.760 to look up and be able to code their own 0:11:33.800 --> 0:11:37.679 AI machine learning programs. So, Habi, do you know if 0:11:37.720 --> 0:11:41.200 this program is trying to be replicated in other community colleges. 0:11:41.880 --> 0:11:45.760 That's actually at the heart of AI for Workforce. So 0:11:45.920 --> 0:11:49.160 I'm the lead faculty at Chandler Gilbert, but I played 0:11:49.480 --> 0:11:53.240 a role in helping advise AI for Workforce and now 0:11:53.240 --> 0:11:57.160 they're their own separate entity. So what we had at 0:11:57.160 --> 0:12:02.640 our campus last week was a summit hosted are at 0:12:02.840 --> 0:12:06.480 Channel Gilbert campus called the AI Teaching and Learning Summit, 0:12:06.960 --> 0:12:09.760 So we had one hundred different folk faculty and administrators 0:12:09.800 --> 0:12:13.160 from across the country and even Canada come to our 0:12:13.240 --> 0:12:17.800 campus and try and learn about building their own AI 0:12:17.840 --> 0:12:22.200 programs within their institutions. Well, one of the leaders of 0:12:22.360 --> 0:12:26.400 AI for Workforce came and talked about I think they've 0:12:26.440 --> 0:12:30.400 reached somewhere between thirty two to forty eight community colleges 0:12:30.440 --> 0:12:34.680 in the country. They've almost hit every single state in 0:12:34.800 --> 0:12:38.120 terms of a community college within the state. So they 0:12:38.120 --> 0:12:41.960 have that many community colleges that have at least taken 0:12:42.080 --> 0:12:45.160 the INTEL training that they have available now, which is 0:12:45.200 --> 0:12:50.720 available for free, to build programs within their own college. 0:12:50.920 --> 0:12:55.360 In terms of who I see that has full fledged programs, 0:12:56.080 --> 0:12:59.320 think there's only like four or five colleges that I'm 0:12:59.360 --> 0:13:03.240 aware of in the country that have an associates or 0:13:03.240 --> 0:13:06.800 a certificate in AI at a community college level. 0:13:09.559 --> 0:13:23.120 We'll be right back after a quick break. Welcome back 0:13:23.240 --> 0:13:29.480 to Technically Speaking, an Intel podcast. I'd like to get 0:13:29.480 --> 0:13:32.280 your thoughts on any data or trends in the job 0:13:32.320 --> 0:13:38.120 market around the significance of learning about AI, and is 0:13:38.160 --> 0:13:41.520 that demand still there and do you see that growing 0:13:41.559 --> 0:13:44.760 and in which areas and which industries do you see 0:13:44.760 --> 0:13:48.880 the best potential for your students going through that program. 0:13:49.200 --> 0:13:52.240 It's a hard marker to pin on right now because 0:13:52.280 --> 0:13:55.640 it's such an emerging field. There's different routes you could 0:13:55.720 --> 0:13:59.400 see AI, this large field of AI going right now. 0:14:00.160 --> 0:14:03.760 So I'll start from the most beginner level. You have 0:14:03.800 --> 0:14:07.240 people who are like prompt engineers who use something like 0:14:07.360 --> 0:14:12.040 chat GPT, these very industry wide models and are able 0:14:12.080 --> 0:14:17.679 to interface with that system such that they get autonomous outputs, 0:14:17.800 --> 0:14:21.400 automatic outputs that increase productivity and reduce the amount of 0:14:21.440 --> 0:14:23.960 work that someone would need to do. Right That prompt 0:14:23.960 --> 0:14:28.480 engineer is a great field for someone who's entry into 0:14:28.680 --> 0:14:31.200 the AI space. Right It doesn't need nearly as much 0:14:31.280 --> 0:14:35.480 math or programming even and there's actually a lot of 0:14:35.600 --> 0:14:40.000 drag and drop interface to perform AI modeling platforms where 0:14:40.040 --> 0:14:42.680 you can kind of input data, drag and drop what 0:14:42.720 --> 0:14:47.360 you need and then get some meaningful output. I can't 0:14:47.720 --> 0:14:51.720 say that I target that too much right now because 0:14:53.080 --> 0:14:56.000 there's not a lot of stability there just yet. Chat 0:14:56.120 --> 0:15:01.920 GPT is still relatively new, no code tools, it's very specified, 0:15:02.480 --> 0:15:05.640 So I focus more on level two. I would say, 0:15:06.360 --> 0:15:11.560 you can hear my video games speak come out. Level 0:15:11.600 --> 0:15:16.840 two is they have some coding. They're like a software developer, 0:15:17.440 --> 0:15:22.479 but more equipped to tackle problems that could evolve automation 0:15:22.960 --> 0:15:27.680 of let's say text and images, have some of the 0:15:27.760 --> 0:15:32.400 data analytics background as well to analyze and process data, 0:15:33.040 --> 0:15:36.560 come up with systems that automatically process that data and 0:15:36.600 --> 0:15:40.040 give meaningful output. So they have to have a little 0:15:40.040 --> 0:15:43.520 bit of math to understand how that data is being 0:15:43.560 --> 0:15:45.880 inputed and what the story is behind the data is 0:15:45.880 --> 0:15:48.120 what I typically say. And then they have to have 0:15:48.120 --> 0:15:52.640 some programming, because if you stick with only no code tools, 0:15:52.680 --> 0:15:55.480 you're very limited to what those no code tools can offer. 0:15:56.080 --> 0:16:00.000 With programming, it opens the doors. So that's level two 0:16:00.080 --> 0:16:02.720 and that's where I try and keep my students. These 0:16:03.040 --> 0:16:07.440 concepts in AI can get very sticky mathematically very quickly, 0:16:07.920 --> 0:16:12.560 and I can't expect a two year student to be 0:16:12.840 --> 0:16:17.320 at that level of foundation. So then level three is 0:16:18.240 --> 0:16:23.560 that they have a very solid mathematical foundation to where 0:16:24.280 --> 0:16:27.600 pretty much any AI algorithm I throw at them they 0:16:27.640 --> 0:16:31.560 can at least orient themselves to quite quickly. Someone with 0:16:31.600 --> 0:16:35.120 a master's background or a bachelor's background can do this, right. 0:16:35.480 --> 0:16:37.520 You come into a new class and it's like, oh, 0:16:37.720 --> 0:16:40.440 that's just the formula I've seen before kind of but 0:16:40.640 --> 0:16:41.640 just in a different way. 0:16:41.840 --> 0:16:42.520 Ye gotcha. 0:16:43.000 --> 0:16:48.880 And then the programming expertise of let's say, Okay, we're 0:16:48.880 --> 0:16:50.400 not going to do this in Python, now we're going 0:16:50.440 --> 0:16:53.160 to do this in R. They know so many programming 0:16:53.240 --> 0:16:56.200 languages at that point that they can kind of easily 0:16:56.240 --> 0:16:59.120 switch between the two for a quick prototype. So I 0:16:59.120 --> 0:17:01.280 see that as level three three, So right now, I 0:17:01.280 --> 0:17:02.240 target that level too. 0:17:02.680 --> 0:17:05.320 Okay, just previously, you talked a little bit about some 0:17:05.400 --> 0:17:09.119 of the student projects that can be quite exciting for 0:17:09.480 --> 0:17:11.880 both the teachers and the students, a like, is there 0:17:11.920 --> 0:17:14.320 just one that is top of your brain right now 0:17:14.359 --> 0:17:17.960 that is quite exciting that you're working on with your students. 0:17:18.640 --> 0:17:22.320 So the one I do is I have them create 0:17:22.359 --> 0:17:26.200 an automatic bubble sheet scanner. So they take a photo 0:17:26.240 --> 0:17:28.880 with their phone and they should be able to grade 0:17:29.480 --> 0:17:32.919 a bubble sheet just based off of a photo. So 0:17:33.000 --> 0:17:36.280 I teach them all about things like how to detect 0:17:36.280 --> 0:17:39.880 the bubbles on a sheet, how to know which position 0:17:40.359 --> 0:17:43.199 is where on the sheet. It doesn't automatically tell you 0:17:43.320 --> 0:17:45.960 where it is, so you have to do that and 0:17:46.000 --> 0:17:49.080 then sort them in a list that the top left 0:17:49.119 --> 0:17:51.919 is zero and then go on from there all the 0:17:51.920 --> 0:17:54.760 way down. Gotcha, And then to know whether or not 0:17:54.800 --> 0:17:59.720 it's filled. So that's about an eight week process, not 0:17:59.800 --> 0:18:03.000 that one project, but what leads up to that project. 0:18:03.640 --> 0:18:06.959 So then let's jump into I guess the capstone projects 0:18:06.960 --> 0:18:09.160 where my students are out in the wild West, right. 0:18:09.800 --> 0:18:13.719 I have students who do stock market prediction brain tumor 0:18:14.119 --> 0:18:18.399 detection based off of MRI or CT scans. One of 0:18:18.400 --> 0:18:21.159 my students is a musician, so he likes to handwrite 0:18:21.160 --> 0:18:23.600 his musical notes and he wants to be able to 0:18:23.600 --> 0:18:28.120 take a picture and have it be electronically printed. I've 0:18:28.160 --> 0:18:32.240 had a student who won actually an Intel competition taking 0:18:32.560 --> 0:18:38.160 brain wave EEG data and trying to detect if there 0:18:38.480 --> 0:18:42.000 is an epileptic seizure occurring within that data. I think 0:18:42.040 --> 0:18:46.000 what I find is is that I'm always amazed that 0:18:46.040 --> 0:18:50.399 I give them these little seedlings of knowledge and then 0:18:50.440 --> 0:18:54.520 that capstone project comes around and they just grow without 0:18:54.600 --> 0:18:55.639 me even being there. 0:18:55.840 --> 0:18:59.200 Yeah, Habib, you mentioned a lot of people need graduate 0:18:59.280 --> 0:19:02.920 level degrees to work in AI. Right now, what path 0:19:02.960 --> 0:19:06.120 have you seen your students go through after these courses? 0:19:06.840 --> 0:19:09.120 Have they said it's opened any doors for them? 0:19:09.600 --> 0:19:12.479 So one of the big challenges we're trying to tackle 0:19:12.720 --> 0:19:17.080 on the community college AI education side is pathways for 0:19:17.160 --> 0:19:21.280 students upon graduation. Right it's pretty bleak in terms of 0:19:21.359 --> 0:19:24.280 having a two year degree and meeting minimum jow brecks. 0:19:25.440 --> 0:19:28.959 Our college actually is beginning to offer or developing a 0:19:29.000 --> 0:19:32.680 bachelor's degree in AI to alleviate that issue. But I'm 0:19:32.760 --> 0:19:36.959 seeing that they're still finding positions because of how marketable 0:19:37.000 --> 0:19:40.159 AI skills are. Right now, it may not be, you know, 0:19:40.200 --> 0:19:44.400 an engineer, but it could be, Hey, here's an entry 0:19:44.480 --> 0:19:47.360 level role at a company, but it's easier for them 0:19:47.400 --> 0:19:50.280 to get through the door because they have these marketable skills. 0:19:50.760 --> 0:19:53.760 And in your view, how do you see the future 0:19:53.800 --> 0:19:58.440 of AI in the workplace And what's your number one 0:19:58.560 --> 0:20:03.760 reason that you tell the younger generation and students why 0:20:04.080 --> 0:20:06.320 AI tools are so important to learn. 0:20:07.119 --> 0:20:11.200 You know, when AI was first emerging, let's say chat 0:20:11.280 --> 0:20:14.719 GPT right into popularity, everyone was like, AI is going 0:20:14.760 --> 0:20:19.359 to replace jobs, and actually there's been a new keynote 0:20:19.400 --> 0:20:23.000 I guess that people have been saying, which is people 0:20:23.040 --> 0:20:27.160 who have AI skills will be better equipped than those without. 0:20:27.760 --> 0:20:32.000 I completely agree on that note, because we work so 0:20:32.119 --> 0:20:35.640 much with technology. If we're better to interface with that technology, 0:20:35.720 --> 0:20:37.920 we're going to be that much more productive. We can 0:20:38.000 --> 0:20:41.280 tackle much more complex problems in a shorter amount of time, 0:20:41.640 --> 0:20:45.800 and so I see the future workforce being able to 0:20:45.840 --> 0:20:50.120 be once again more productive utilizing these tools. I mean 0:20:50.320 --> 0:20:53.240 when you're typing an email and gmails like hey, here's 0:20:53.280 --> 0:20:56.280 the rest of that sentence. Yeah right, it's just so 0:20:56.480 --> 0:20:59.840 convenient and it helps ease the amount of hand to 0:21:00.080 --> 0:21:02.960 paper work we have to do. And we can now 0:21:03.000 --> 0:21:06.320 be more creative with that time and solve more nuanced, 0:21:06.480 --> 0:21:10.600 more complex problems because we have these systems better ready 0:21:10.600 --> 0:21:14.480 to assist us. So someone like me and you can 0:21:14.520 --> 0:21:17.320 do a lot more with this lifespan that we have. 0:21:18.040 --> 0:21:22.320 We can develop, create ID eight more things because we 0:21:22.400 --> 0:21:23.480 have more time to do so. 0:21:25.600 --> 0:21:28.119 The way Habib looks at AI as a tool in 0:21:28.160 --> 0:21:31.200 assisting the workflow reminds me of the invention of the spreadsheet. 0:21:31.680 --> 0:21:34.720 My grandfather worked at the bank for forty years in 0:21:34.840 --> 0:21:38.760 the same desk, the same chair, pouring over bank ledges 0:21:38.800 --> 0:21:42.280 with paper and pencil, ensuring all figures balanced exactly to 0:21:42.320 --> 0:21:45.720 the scent. With the invention of computerized adding machines and 0:21:45.760 --> 0:21:50.199 then later spreadsheets on personal computers, there's laborious efforts that 0:21:50.320 --> 0:21:54.800 my grandfather previously undertook had now become so quick and accurate. 0:21:55.600 --> 0:21:58.800 He retired before the advent of these technologies, but I 0:21:58.840 --> 0:22:01.720 often think how helpful those tools would have been for him. 0:22:02.000 --> 0:22:04.800 AI in the workplace empowers everyone to focus on what 0:22:04.840 --> 0:22:08.399 they do best. Learning these AI tools, like learning how 0:22:08.440 --> 0:22:11.800 to use a spreadsheet, gives employees an added edge in 0:22:11.880 --> 0:22:18.200 how they work as individuals and within their teams. I'd 0:22:18.240 --> 0:22:21.600 just like to get your thoughts about the AI tools 0:22:21.640 --> 0:22:24.040 for the non engineers and non tech people. 0:22:24.800 --> 0:22:28.040 Well, I think I've been giving this rose colored glasses 0:22:28.320 --> 0:22:32.399 look onto AI right where AI is always beneficial and 0:22:32.480 --> 0:22:35.000 always used in the right way. But we know that's 0:22:35.040 --> 0:22:38.280 not the case. I think that it's important to know 0:22:38.880 --> 0:22:43.119 what algorithms and what AI can do because our number 0:22:43.240 --> 0:22:47.600 one interface with AI every day is things like the 0:22:47.600 --> 0:22:51.360 Internet and social media. And so I have students who 0:22:51.400 --> 0:22:54.600 may be dealing with some kind of addictive behavior and 0:22:54.640 --> 0:22:59.680 they don't realize that AI recommendation systems that are used 0:22:59.680 --> 0:23:03.119 in sol social media are constantly feeding them content that 0:23:03.200 --> 0:23:06.520 may make them feel stuck in that frame of mind 0:23:06.680 --> 0:23:10.000 and that addictive cycle. And so I think it's important 0:23:10.080 --> 0:23:13.720 for the general public to know what AI thinked imagery 0:23:13.760 --> 0:23:17.960 looks like, what algorithms are out there, and how they 0:23:18.119 --> 0:23:21.639 use your preferences to feed you more content that you like, 0:23:22.680 --> 0:23:27.359 just for our own mental wellbeing. So I see my 0:23:27.520 --> 0:23:30.240 class AA in one hundred as a place where people 0:23:30.280 --> 0:23:33.200 can come and learn about these technologies so that they're 0:23:33.200 --> 0:23:36.920 better equipped to personally manage their interface with them right 0:23:36.960 --> 0:23:37.840 mentally manage it. 0:23:38.440 --> 0:23:42.159 Yeah, and are there any ethical considerations you try and 0:23:42.200 --> 0:23:45.760 emphasize when teaching your course to your students. 0:23:46.200 --> 0:23:50.200 So I try to give my students a broad overview 0:23:50.600 --> 0:23:54.600 of AI ethics, because again, you could dig into one 0:23:55.240 --> 0:23:57.520 quite a bit and there's still a ton of content 0:23:57.640 --> 0:24:02.040 left there. So I started talking about, like what topics 0:24:02.080 --> 0:24:06.000 there are in AI ethics. There's privacy and surveillance, there's 0:24:06.119 --> 0:24:11.520 manipulation of behavior, there's jobs and autonomy. So those are 0:24:11.600 --> 0:24:14.639 all like separate topics that I go over. Then I 0:24:14.640 --> 0:24:17.840 can take a step further and I talk about frameworks. 0:24:18.400 --> 0:24:22.480 So what frameworks are out there in terms of AI ethics, 0:24:22.560 --> 0:24:26.159 Like the European AI Act came out and they have 0:24:26.200 --> 0:24:28.840 a framework for how they want to regulate this technology. 0:24:29.320 --> 0:24:32.560 Now we're seeing policy on the America side on AI. 0:24:33.720 --> 0:24:37.560 My last little tidbit in the AI ethics realm is 0:24:38.119 --> 0:24:41.800 trying to dispel some of the fear. I am personally 0:24:41.800 --> 0:24:44.640 of the belief that there's not some looming AI monster 0:24:45.119 --> 0:24:48.560 coming to eat us, and if there would be, we 0:24:48.600 --> 0:24:51.080