Recently, we were joined by staff for a casual chat about women in STEM, hosted by Kelly Williams, Senior front-end developer, and joined by Kat Hempstalk, VP of Data & Analytics, and Ali Meredith, Analyst in Data & Analytics.
Ali. Tell us a little bit about your background.
Alli: My background is not like Kat’s, I didn’t come deliberately into machine learning. I didn’t study it. I’m self-taught. So when I was little, I didn’t want to be a machine learning engineer or anything. You’d think so, right?
When I was little, I wanted to be a lawyer, which is not a good choice for me. I went to university, I went to law school, and then I, much to my parents’ disgust, decided that I was not going to be a lawyer.
And at that point I didn’t, I still didn’t know what machine learning was and although it definitely existed, I was just a bit behind the times. All I decided was that I wasn’t going to play it safe, so I made a decision to do something different and I ended up here.
What about you, Kat?
Dr. Kat: Yeah, I don’t think AI was really an option when we were little. It certainly wasn’t a career choice that you could have had and it’s funny because, in the fifties and sixties, computer programming and computer science were actually quite popular amongst women. I guess that disappeared over time.
I think I’m a little bit of an outlier because I got into computing quite early on. For me, machine learning was something that I got into at university. I look back and think I was quite fortunate to have been exposed to it – kids these days are exposed to it far more than we ever were.
That said, kids growing up with ipads and tablets may not be aware of what’s underlying the fact that they can watch their show on Netflix. They’ve got all the technology there that we didn’t have, but they probably don’t understand how it works. And if they were alive to that as an option, that for their career that they could actually make those things work and make the magic happen, then I think we would see a lot more women choosing to go into technology.
Ali, what triggered the move into machine learning?
Ali: When I finished law school, it was around 2012. At that time, there was machine learning in other industries, but not in law. In law, we had these sort of very archaic platforms that had no machine learning on them. For example, I spent some time working in document review. And at the time, if you wanted to read through a hundred thousand documents, you’d have to get a team of a hundred lawyers into a small room, and they’d have to read every single document.
You could perhaps keyword-search and pull the ones that had, the relevant keywords to the top, but that’s a very blunt tool because if you’re searching for something like the word, for example, “Term” the term of an agreement that would be used in other contexts the terms and conditions, or it could be pulled into other parts of a word.
But during my time working there, that sort of just gradually changed. And instead of having people, their system started using AI and machine learning. You could reduce the number of people working and replace it with systems. It was a natural progression for me [into this field].
What has been the highlight of your career [apart from working at LawVu with all of us]?
Dr. Kat: I’ve had the benefit of working in a lot of different industries and the one I started in, I think is still by-in-large my favorite. That’s not to say I want to go back there. Just that it’s something that stuck with me.
I worked in agriculture, to begin with, applying algorithms and computer science to problems involving cows and dairy farming and a little bit of pasture growth as well, and got to play with some drones. What really stuck with me was the hands-on aspect of it.
I’d always envisaged myself working in an office and I was sitting at a desk. Comfortable warm environment, hot cup of tea, nice and safe. And here I was getting up at four o’clock in the morning, going out to a farm, and getting covered in cow droppings.
But actually, I learned a lot from that hands-on experience. Sometimes we don’t see the processes that have gone into producing the data, because that’s outside of the systems that we’re working with. I saw the process before it got to the machine learning system. You can’t get a cow to say cheese, and you need them to look at the camera, right? So there are all these things that you have to do before you’re even ready to begin. I can’t think of another role that I’ve ever had where it’s had that same focus on getting the data, at an actual physical hands-on level.
What’s the best way that we can encourage more women into AI and machine learning?
Dr. Kat: I think I’m a little bit of an outlier because I got into computing quite early on. For me, machine learning was something that I got into at university. Computer science in general was something that I was aware of from primary school age, at a time when computing really wasn’t popular for anyone, never mind girls. So I think I’m a little bit of an outlier in that sense, but I certainly look back and think I was quite fortunate to have been exposed to it. Kids these days are exposed to it far more than we ever were.
But, I think some of the underlying things that may not seem so obvious for the kid who has got the iPad, you know, watching Netflix, is that they’ve got all the technology there that we didn’t have but they probably don’t understand how it works. And if they were alive to that as an option, for their career, they could actually be someone who makes those things work and make the magic happen. If we could do that, then I think we would see a lot more women choosing to go into technology.
Ali: Yeah, absolutely. I agree. It’s changing. When I was 18, I didn’t know what it was.
Any sort of career in STEM would have been… just not an obvious option. Obviously, it needs to be an obvious option. And I guess the way we do that is by making ourselves more visible to young girls who are thinking about it, so they’d know that a career in tech is something they can tangibly do and is an option for them.