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#05 | From Self-Taught Coder To Chief AI Officer - Jordan Legg
16 Plays11 months ago
In this episode of Nelly Talks, Nelya sits down with the inspiring Jordan Legg, a Chief AI Officer and Founder at Takara.AI whose path from self-taught coder to tech innovator is anything but ordinary. Whether you’re looking for tips to break into the tech industry or tackle the challenges of entrepreneurship, Jordan shares plenty of inspiring insights and practical advice that you won’t want to miss.
Timestamps:
00:00 Nelly Talks Intro
01:08 Meet Jordan Legg
02:20 Did you always know you wanted to go into the tech industry?
04:34 Was your untraditional career path a hurdle for you?
06:37 If not through formal education, how did you learn what you needed to?
09:33 What do you enjoy most about your job? 10:45 Are there any down sides to your job?
12:02 How do you try to achieve a healthy work/life balance in your role?
14:23 Has generative AI's rise in popularity impacted your role?
19:23 Do you feel like there is more representation in the tech industry?
21:48 What do you think about the tech scene in Milton Keynes?
24:26 What advice would you give your younger self?
26:37 Nelly Talks Outro
Key Points:
• Teach yourself to code by using online resources like tutorials and coding platforms
• Focus on gaining practical skills and hands-on experience even if you don’t have a formal education
• Master the fundamentals of coding and AI before relying on advanced tools
• Embrace problem-solving and learn as you go when facing unfamiliar challenges
• Set boundaries for work and prioritise balance to avoid burnout
Let us know what guests you'd like us to interview in future episodes!
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Transcript
Introduction to Nelly Talks
00:00:05
Speaker
I didn't have that sort of bedrock of qualifications or real experience. Designing a solution, that's my favorite. It's like, right, the client's got this issue. How are we going to make this issue go away?
00:00:19
Speaker
Hey everyone, I'm Nellia from Nelly Wax. Welcome to my podcast Nelly Talks, a careers-focused show for those who are considering careers in tech industry, or maybe looking for a change of direction in this space.
Podcast Focus & Guest Introduction
00:00:36
Speaker
We will be interviewing different speakers in this field to understand their journeys, their hurdles of base, how they overcame those, and any tips and hints that they would like to share on the way.
00:00:54
Speaker
Hello, so today we have ah Jordan Lagg with us. Jordan, hi, welcome to Nelly Talks podcast. Hello, how's it going? Great to have you here. um Jordan, it'd be great if you could introduce yourself to our audience.
00:01:08
Speaker
Sure. So my name is Jordan Legge. I'm a co-founder and chief AI officer for Takara AI. I've got a long sort of five, six year history building AI machine learning applications. I previously worked ah as the director of intelligence of my last business where we were generating huge amounts of B2B of sales and marketing leads. And I built a massive machine learning model in NLP and sentiment analysis to do that across loads of LinkedIn data and then When we built Dakara, I want to take everything that I learned and help other people and other businesses go on that journey because that's something that I didn't have in my business, right? Which I think a lot of people are going through that now. What is AI? What's going on? How do I use it? How do I make the best out of it? So that's a bit of info about me. I look after the application development teams here, the AI development team, and a lot of the sort of open source research stuff as well.
00:01:57
Speaker
I'm also a community researcher for ka here for AI. They're a great great group of people. Please join us. um So that's that's about me. Awesome. Thank you so much.
Jordan's Unconventional Path to AI
00:02:07
Speaker
Jordan, I guess, um ah ah obviously, checked you out on LinkedIn. I'm sure many people in our audience will. And ah you became, ah you came into tag at quite an early age. Did you always know that you want to get into tag? AI has been around for a while, perhaps things like general AI are newer things, like going for a bit more of a
00:02:31
Speaker
Hi, thanks to media, et cetera. But do you always know this was going to be a path for you? How did you get into world of tag? And then specifically, how do you get in the world of AI? Yeah, it's it's a great question because you know if you go back to when I was 18, I was working on planes and I was DJing up and down the country. So you know the the the career difference was quite big, but I was always into computers and I learned you know taught myself how to code when I was 14, building websites and stuff. um And then when I was about 19, I spent the sort of year building an algorithm to do a lot of the LinkedIn sentiment analysis stuff um because my father was a sales director at this large tech firm and he asked me,
00:03:08
Speaker
Hey Jordan, you're pretty good with computers. Could you figure out how to do this? And I was like, I'll give it a go. And then that giving it a go ended up as a five, you know, six year business, you know, multimillion pound valuation. um So that was sort of like, it wasn't like a, it wasn't like a single thing that happened. It was like ah a series of different events that led up to that. And then here we are with Takara, which is another sort of small step, which I assume again will be another many, many few steps ah helping people and their businesses. And also.
00:03:38
Speaker
you know I'm learning a great deal as well and in in AI and ML space. um you know Now we're talking about genitive AI. Genitive AI has been around probably for like 10 years, but since the transformer came out in 2017, now it's actually pretty good. So it's just really exciting to be ah where I'm at right now, but my path to this was very unconventional. And yeah, I didn't go to uni, didn't do anything
Self-Taught Learning Challenges
00:03:59
Speaker
like that. My A levels are a bit rubbish. So there's really no excuse.
00:04:04
Speaker
And how did you find that? I mean, you know, obviously, kind of, we know what education system is like, at the moment, you know, I've got kind of my scientists, but besides the CECs, and um as as a recruiter, I'm like, like, just just enjoy yourself to something that you love doing, because you know it doesn't always translate into career and also there'll be so many new jobs in the future that like school probably can't even fully prepare you for now you know even as recruit I'm like university is kind of not for everyone but did you find that not going for like a traditional path into tech um was that a hurdle when you was it a hurdle for you before when you were kind of thinking of entering this field does that kind of hold you back in any way now
00:04:49
Speaker
Yeah I think it in some ways it was good because it allows you to be a lot more agile um because you can just go on and do stuff but in that in the same way you don't have really a bedrock of information to rely on ah from being taught that so um you sort of just kind of like you you feel like you're winging it for like five years you never really feel like you're qualified and because you don't have any qualifications and I was young at the same time so you know imagine me i'm twenty i don't have any degrees no qualifications no nothing ah and i'm trying to get you an enterprise to buy a solution from me that i say it's gonna like radically change the way that you know your sales marketing functions gonna work so it's that sort of stuff really help me back because i didn't have that sort of bedrock of qualifications or ah real experience.
00:05:33
Speaker
And of course I was running the business as well so I didn't really know what I was doing there. Yeah, so I'd say the education system didn't work for me because I learned in a very specific way and that's totally fine because most everyone else seems to be okay with it. But I guess the biggest drawback is 10, 12 hour days working harder than everybody else trying to catch up and trying to do stuff as fast as possible.
Modern AI Tools & Work-Life Balance
00:05:54
Speaker
Yeah, so I guess there must have been a lot of learning from what you're saying on the business side, just also on like the tech side as well. yeah ah But perhaps also very relevant learning, you know, in line with what you're actually doing, whereas as we know, kind of the way the degrees are structured to give people great foundation, but that they are generalist, you know, so when I recruit early talent, I find like you could such a huge volume of applications, but a lot of people who don't really know the area they want to go into. And and it almost like I think it's great as a foundation, but then you you kind of you do go into a very specific career path, you know,
00:06:30
Speaker
like graduate scheme or junior engineering, you know, whatever the path is. That's really great to hear. And how did you go about acquiring this knowledge? You know, was it kind of on the fly as you're doing the work and you're kind of picking stuff up? And if so, which sources did you use? Whether that kind of network and community mentorship element, or did you kind of, are you someone who just goes and does research, uses the kind of various databases? what What worked for you? How do you like,
00:07:00
Speaker
navigate for the world of information that's out there now, especially on AMP. Yeah. Well, I mean, today it's a lot easier because, uh, you know, you've, you've got things like Claude and Jack GPT that can do a lot of the heavy lifting for you, right? You know, and even if you give it, you know, research papers and all that sort of stuff, it can summarize them for you and pick out the cool elements. And, you know, it's a big help, big help, but before big LMs, it's sort of like Googling YouTube videos, you know, all the very similar things that you do. If you wanted to do anything like, how do I,
00:07:30
Speaker
You know, repair my washing machine or how do I fix my car or something? it's It's very much the same. And it's like, okay, how do I generate value out of data? Okay. Let me Google around this. Okay. I can do this. I can build this NLP model and then I need to make some splits, validation test splits and okay, great.
00:07:47
Speaker
I can then train it. How do I train it? What format should I put the data set in? I don't know. Where should I host it? I don't know. So it's just a, it's just a series of, I don't know. And then just finding out the answers and then storing it up here. And that's basically it, which is like an incredibly tiresome process, but it's the only way that I can really learn. And that's just the way that it is. Yeah. And it's the same today.
00:08:08
Speaker
Yeah, no, that's awesome. I love it how you just say like you just build an NLP model by watching stuff. Like I think clearly you've got a very technical mindset because I know people who like 10 years into their tech career and probably could not do that sometimes without like a team, et cetera. And I agree with you. I think I use like Chad Gipiti, you know, Gemini, other kind of other tools to almost like navigate me through the wealth of information. like I remember like maybe seven years ago I'd have to go to like events like I don't know for computer vision for example like CVPR and you literally need to go to like there'll be this boards where PhD researchers would talk about their papers and you'd have to go look before the paper was actually released and published outside of events.
00:08:57
Speaker
And that would take another like six, 12 months. So before the information came out, it'd be such a long cycle. And then of course the the the paper itself is not easily accessible to a lay person. It's not written in a way. So you like having this like AI summarizing it, picking up the key insight is so amazing. Even like for someone who's like a recruiter, and I'm not a tech person, but just to learn about it. And I think it makes this information more accessible. So it's not closed off in like a,
00:09:24
Speaker
you know, big research communities or certain research school centers where only like PhD folks kind of. Yeah, I agree. So that that that's been really awesome to say. I know you were a couple of hats, you look after various sort of areas of the business. yeah What do you enjoy about your role the most at the moment?
00:09:43
Speaker
Oh, probably designing a solution. That's my favorite. It's like, right. The client's got this issue. How are we going to make this issue go away? Like, so model selection, model design, architecture. That is probably my favorite piece because it's the, it's the biggest sort of like, uh, problem solving piece where you go out and you try and go, okay, these are the KPIs for the client. They need like 8% confidence on these documents or something, or they need.
00:10:08
Speaker
this thing to go away and we need a human loop feature to have a view on it but to get it as high as possible maybe we should use this model like a unit model or maybe we should use detail or maybe we should use an LLM or maybe we should use just standard NLP or sentiment analysis or something like that so That's probably my favorite part is just ripping apart ah problems and trying to find a design solution for it. your Your eyes have literally lit up when you started talking about solutions. I so love it. And I think this probably something that can connects a lot of people in tech. They love complex problems, right? And when you find a role where you can do that, you can earn a living, exit, you know, it's kind of win-win. That's awesome. and
00:10:45
Speaker
Are there any kind of downsides to your current role, whether it's kind of, you know, from a technical perspective or just the whole kind of running your own business, you know, kind of the logistics of being not just looking after your role or potentially looking after to teams, customers and anything else that, you know, falls on your labs when you're running your own business?
00:11:05
Speaker
Uh, I'm pretty busy right now. Like I'm working a lot of hours right now, which I wish I didn't have to do, but I just have to do it. And so you have to just kind of sacrifice other things sometimes that I like doing. That's probably the biggest downside. Everything else is pretty good. Love talking to clients, love building teams, love making friends, love writing code. That's what I like to do. But yeah, the the long hours at the moment, like, yeah, it's, it's, I'm trying not to get burned out. That's what I'm trying to say. Yeah. And that was my next question. Like what, how do you balance because being like,
00:11:35
Speaker
a fan of the business, running your business, you know, there was a lot of glamour attached to it. And quite often people don't talk about the downside because, you know, you you are basically doing a couple of people's work just as of one person and and very few people who can do that. What do you think for someone who could potentially consider it, you know, start up on their own, they have amazing idea, they're thinking, you know, perhaps I'm doing something else or maybe I don't know, I'm working for corporate for someone else and considering that move. How do you try and achieve balance? What kind of things you do and prioritize not to get burnt out and still kind of enjoy the journey?
00:12:15
Speaker
Yes, I think, you know, what what worked well with me in my last business at the moment, you know, we've been, the car has not been going that long. So it's just like, we're all tryharding and working as much as possible to get to a place where we can then
Impact of Generative AI in 2023
00:12:26
Speaker
sort of wind it back. But in my last business, where I got it to is I got it to more of a nine to five model where, you know, once it hit five o'clock, that was it. I'm not touching it anymore. And I'm going to do literally anything else because otherwise, you know, your whole life just revolves around work and there's no sort of like, there's no beginning and end to it. So your brain just doesn't understand, you know, especially if you work in one room, like you sleep, you work, you do all this stuff in one room. It's like, it just doesn't, it just doesn't work. Your brain just gets burned out. There's no end ah to it. So that's where I want to get to with Takara. I'm hoping I can get to that in probably like three or four months. um So let's hope I last that long. Yeah, no, I'm sure you will. The sparkle is still there. And that's a thing. I think it's challenging, you know, when you really enjoy what you do, sometimes work doesn't feel like work, but of course, like everything else that you enjoy in life. I know if you're in fitness, pets, kids, yeah other friends, you know, eating out, right? So easy just to just work for your lunch. and just Exactly. Yeah, I think I've started ah my own business network just over a year ago.
00:13:30
Speaker
And I got to the point where I'm, I've just hired my first full time person. Wow. Congrats. Thank you. I'm starting to bring some interns on board. You know, when you go through the stage where you're like, I think this is in like a sane state where.
00:13:46
Speaker
Um, it actually makes sense to me. You've got the customers, but yeah, like really, really, it's, it's just a bit of a, like a hard graph before you get to that. But yeah, I am hoping, uh, for that, uh, nine to five as well. Soon, all these people kind of get embedded and started. Awesome. And as you've mentioned, as I've mentioned, you know, AI has been here for a while, but kind of 2023 has been massive around.
00:14:10
Speaker
the attention the generative AI got, you know, like you can say, you can call it hype, you can call it rise, you know, whatever terminology you want to use, I don't think it's hype because I think we know it's here to stay. Has generative AI specifically more recently or that kind of increase in publicity impacted your role or your business? Any other developments in your field outside of that that really excite you and you think will be you know, defining for the area that you work in. Because, you know, from recruitment perspective, every company now tries to bring LLM engineers and malengineers. Jenny, I prom engineers, some of the jobs that like did not exist, you know, a couple of years ago. um So but interesting to see how have you been impacted from your personal side in terms of the tools that you've seen come through, but also from the business side, given there is much more hype around
Diversity and Inclusion in Tech
00:15:04
Speaker
this now.
00:15:04
Speaker
I'd say there's two aspects to it. i You know, the barriers to entry for working with machine learning and and AI in general has got so much better. You know, companies like Hugging Face have made amazing Python libraries that make it super easy to build like image generation pipelines, LLM pipelines, all this stuff. It's really, really good now. Whereas before you'd have to write probably 50 lines of like TensorFlow or PyTorch code just ah just to make it happen. ah Now it's probably like,
00:15:31
Speaker
25 maximum and it's really nice it looks good it's repeatable that's what you like um so that's probably the the best at the moment that's that's what really excites me is lowering that barrier to entry people to work with this stuff because that's what we need that's what you need as well right if you're recruiting people for for these roles if you can make it easier for people to do those roles then you can recruit people faster and you it's it's Everyone's life is just a lot easier. um The other things I think are going to be huge for our industry is multimodality ah and like multilinguality. If we can get both of those like to a really high standard, all sorts of things open up. right you know With multilinguality, you've basically democratized AI to everyone on Earth, regardless of language.
00:16:12
Speaker
Um, and that means that, you know, people that want to sell products in China can have perfect translations into English. Um, people that want to sell things in Zimbabwe to worldwide audiences, they totally can. And if you want to do customer service across like 9,600 languages, wicked. That's like super powerful. That's and it's so inherently human. And then multimodality is like, you know,
00:16:34
Speaker
Why are we just talking about just text with LMs, right? And text and just generate images. And maybe you share an image sometimes. But once you give all this information to the LM, right, which is your reasoning engine, I guess, ah once it can see and hear and experience what around us, your training data is a test is effectively infinite, because you can train it on everything. And if you think you can train on everything, then the model quality could technically become infinitely good and that excites me so much because the models are already so good and the fact that we're only really just scraping the internet and just plugging that into the model and they're that good so far imagine what happens when it starts training off like the physics of our world around us and how our world looks and sounds
00:17:19
Speaker
and the temperature and the humidity and all these things, right? So that's, for me, that's like my, in five years is going to be super interesting from that perspective. Yeah. Awesome. I think like improving access is huge, you know, if the technology and kind of the need for talent also drives it, you know, the companies will follow because ultimately in in this field, it's, there's so much innovation that happens. It's not necessarily someone who went to the best uni or work for X company that can pick this up.
00:17:48
Speaker
I think you say if you have people who love problem solving. and have tech mindset, like they have that curiosity mindset, I think we will see more more inclusivity, more hiring um in this way, that will be just something the industry will need to do, hopefully, yeah not like a, you know, cringy logos and like videos on the websites, but generally, like driven by what the sector needs. So i i'm I really hope the diversity um of talent will be like it just a natural side product of us actually
00:18:20
Speaker
needing to look at new skill sets and accelerate how the skill sets are trained. Because, you know, a lot of companies with current tech teams, workforce just don't have some of those skills, you know, it's like some engineers have very narrow, like, I don't know, one specific oo language that they've been taught in union, that's what they use. And yes, they are subject matter to experts in that.
00:18:41
Speaker
but that's not necessarily how like, you know, Gen AI works and that's not necessarily how we scale. I'm really excited about it as well. Just as a recruiter, it's been like, I've been watching this for many years happening now it just kind of going for hyper growth and all companies like, okay, how are we gonna adjust our hiring and our tech?
00:19:00
Speaker
And, um you know, I've been in the reactive stage, hopefully it will be like in a proactive planning stage now, ah that kind of geni, I've kind of settling down a little bit, kind of know what to expect.
Milton Keynes Tech Scene
00:19:12
Speaker
i'm And from your kind of ah perspective, um if you focus on a topic of like, diversity and inclusion, you know, it it it is kind of a thing in the tech industry. Do you feel like there is more representation in the tech sector now than perhaps when you were starting out you know in terms of whether it's a local scene here kind of in Milton Keynes or generally kind of across UK internationally what you're saying do you think like there is
00:19:42
Speaker
um more diverse talent in this field now? I'd say yes. I think, well, it always can be better. I think from a global standpoint, it's way, way better, um you know, from working in KFRAI on the community team. There's like 3,500 people from around the world. You know, there's like 96 different countries in there.
00:20:02
Speaker
ah It's about as diverse as you can really get. um I'll say like most of the machine learning and AI community is like, guys, I'll say that much. that's a That's just a given. So it can always be better. I just don't know how we solve that. That's not that's not really a question that I can really answer. And of course, you know when when we're trying to hire, we we try and hire the the best people for the job and and and do what we can.
00:20:26
Speaker
Yeah, I love that because, you know, so often I come into company like we need to hire more eggs or what I'm like, we should just have like increasing open process that encourages everyone to apply without barriers, and then focus on whoever's best qualified. And you know, like, it's about actually giving access to everyone, you know, like the whole equity and equality, versus saying, Oh, I need to have x type of person at a final interview, you know, like the first, the first day and I'm because I think when you start speaking to like women in tag, any and underrepresented groups, they don't want to be like
00:21:04
Speaker
just chosen because they're part of the staff. Who would want that? yeah Yeah, exactly. And then, you know, you don't want to have that imposter syndrome that I know you spoke about when you like gone into tag, you think like, am I just here for that reason? So again, like, I think um the whole like open sourcing of tools of data will really help improve access and more people can actually play around with tags.
00:21:26
Speaker
get the skills and actually enter this workforce. But there's that's the idea. Let's see, let's see how we do. Let's hope this happens in our lifetime, because it's going to happen, you know, in a year or two. Awesome. And if you kind of look at the local tech scene in Milton Keynes, I know I've seen your kind of your name pop up, the company's name pop up to various like STEM events.
00:21:49
Speaker
etc. What do you think about like the taxi and the Milton Keynes? Do you see any opportunities and if you look at the kind of particular projects that excite you? Uh, so to be fair, Milkenes is great. Love Milkenes. And the tech scene is probably the best in the Midlands. Like bar none. Uh, you know, you got Amy down the, down, down the road from, from KU that they're brilliant. Uh, you've got Bletchley Park as well, which is, I think they should make that like a startup incubator for AI, to be fair. Cause it's the birthplace of, you know, intelligent computing. So.
00:22:21
Speaker
So I love Milton Keynes. Got a real love for it. It's a lot better than, you know, ah other places. Like i I live closer to like Northampton. So Northampton has no tech scene. We are the tech scene. Northampton is terrible.
00:22:35
Speaker
Yeah, I remember looking because I, one of my clients is Google cloud, um, GCP, uh, like a consultancy and we were looking at like, uh, Google cloud developer meetups. And when I found one like locally, I was like, Oh my gosh, Milton Keynes has one. I'm living here for seven years. I have never known this. And then I think it's amazing to see like the tech companies give back to community us not being seen, I guess like the whole, like Northampton, Milton Keynes as it just overflow town commuter town from London. Actually.
00:23:05
Speaker
attractant talent to here and building business here is exciting. um So when I thought of hiring, I have to hire people from here because ultimately I think there will be loads of more companies. yeah Like you can save your birthplace of AI here, you know, with Alan Turan's Institute. I mean, I think there are some other universities might disagree kind of in Manchester, etc. But, um you know, yeah why not have something like EF or YC?
00:23:35
Speaker
kick it off here because great facilities, great history.
Advice for Tech Newcomers
00:23:38
Speaker
And it's still close to like, you know, all your central London hubs, et cetera. Of course. Yeah. Let's see. i'm um I'm a big fan of Milton Keynes and obviously it it would help certainly for us not but having to track it to London didn every time. Yeah, I know. Cause we're the same. It's like, oh, you want to go see clients and you want to go do meetups and you want to meet like everybody else is in the know. You got to go to London. That's basically it.
00:24:01
Speaker
Yeah, exactly like so it's great to see Microsoft AI meet up and Leslie Park, that's right, which I go to, you know, just even actually to educate, like, you know, this grade that is kind of supported by Milton Keynes College. ah Yeah, this did not exist not that long a ago. So hopefully it'll go from strength to strength.
00:24:18
Speaker
And it's been great. You've shared so many insights. To wrap us up, Jordan, I'm just keen to hear from you. What advice would you give to your younger self, like starting up in this ah world of tech and AI specifically, and also advise that perhaps people now audience can use when they're considering either entering AI field or perhaps, you know, kicking off their own business because they've got this amazing idea.
00:24:46
Speaker
ah What tips and hands have you picked up on the way that have been really helpful? I would say one thing that really hurt me was relying too much on frameworks, like coding frameworks and stuff like that. Learning the actual base code and how to do stuff like that is so much better, because you can basically code around any problem that you have. Now, there's a little bit of bloatware in AI now. It's very easy to go down. I'm going to use Haystack, and I'm going to use this, and I'm going to use this, and I'm going to use this, and you never understand behind the scenes of how the model gets loaded.
00:25:16
Speaker
how checkpoints are made, how it's quantized, all this stuff. And then when you get a problem, you can't fix it because you just don't understand it. so you know And that's that's hurt that hurt me a little bit in the in the back because it was easier and it was quicker to stand things up. But then once once I wanted to customize it past that or do something different or there was an issue, it really, really hurt me. So the advice I give to myself and anyone else is like,
00:25:39
Speaker
learn it the right way, understand the basics, and then you can go from there. And once you have that really great knowledge, you can rely on frameworks, because it doesn't matter. It's like, okay, I'm gonna use this to save time in this area, or I'll use one LM to train another one, or I'll distill this one down. And your life is just a lot easier. And if it's easier, then it takes less time. And if it's less time, then you can do a nine to five and not a nine to nine.
00:26:03
Speaker
Yeah, yeah, that's that's a great tip. I think it's really nice to learn from other people's kind of lessons versus, you know, do it the hard way, which I think a lot of, ah you know, people in tech have to do, like, how do you make yourself more efficient and and actually do the job that you find most enjoyable versus, you know, just kind of.
00:26:22
Speaker
ah certain tasks we have to do because we have to pick things up. So that's awesome. Thank you so much. It's been lovely to have you on the podcast. Thank you so much for sharing your journey. so Thanks for having me. I can't wait and see you do great things and get to that nine to five place that I'm sure we be all want to get to. And yeah, um like all the best and I'm sure we'll see each other soon. Thank you so much.
00:26:47
Speaker
thank you to all our lovely listeners for sticking with us i hope you found this useful and please do share like etc check out our next episode that comes out in two weeks time