Artificial Intelligence! Okay, okay so that was pretty clickbait-y but first, hear me out —
To be honest, when I was in high school, I had no idea what an engineer even did. Bridges? Trains? Endless Newton’s equations for balls bouncing, presumably so we can play better sport? I skipped advanced mathematics and physics because well, only engineers would ever need that! Fast forward four years, a Linguistics degree with a handful of languages and a library full of physics and tech books devoured with a kind of bittersweet excitement, I was torn. What an incredible world we live in but why didn’t I choose to study this stuff?! Should I go on with languages and academia knowing I might not have the practical impact I wanted to have in the world? Or should I dive into engineering where I would be miles behind, “there is a lot of maths” (so I was told like it was a bad thing) and who even knew if I would enjoy it?
Fast forward another four years, after some incredible projects, intense up-skilling and long nights studying things of questionable usefulness, I emerged with a sense of pride but a semi-deflated spirit. During my degree, I kind of skipped over all that abstract statistics stuff because well, only statisticians would use that, right? And as fate would have it, my final year thesis relied quite a lot on Machine Learning. Guess I learned who else needed those statistics!
A few years later, after the deflated spirit re-inflated and the burning student urge for cash was gone, I’m doing this Machine Learning stuff as my full-time job. Job isn’t the right word. It’s more like my reason to wake up every day and passionately pour over code to make it do dope shit that will save lives. The point of all this is that I didn’t know about this stuff when I started and I was totally wrong about the things I needed to learn. There was no one to SHOW me what interesting and impactful things I could do (because telling is different to showing) or that I was missing out by not learning to engineer and program earlier. And when I started, I couldn’t make machines learn to save my life, but I kept trying, learning, devouring those articles and tutorials with the same fervor as I had before I started engineering. And what I realised is that this field is huge. The possibilities are ridiculously endless.
Literally. You can MAKE pictures from words describing stuff. You can fill in the missing parts of a face. Recreate the sound of someone’s voice (almost). Understand Mandarin in a busy street. Learn what cancer looks like where human beings might fail (working on it). Drive a car. Lawyer the crap out of things after reading entire swaths of legislation like perusing a magazine. Make endless administration obsolete (jokes, the bureaucracy will never go away). Sequence genomes and gut biomes and anything else we have the data for. Rival top pop stars for music generation (on its way). What a time to be alive.
There is so much going on in this space, it’s overwhelming but as I look around me, in the office, at the meetups, at the local startups, it hits me… It’s a sea of dudes. Where are all the other women? Am I not looking in the right places? Or are they just not here yet? Are they going to miss out on this colossal opportunity to shape the future? Should we be encouraging more women, especially ones with programmer backgrounds, to get into ML and AI while the field is still growing?
Reasons not to bother are all too common such as women just aren’t interested (yeah, like me in high school with no idea what amazingness was out there) or they’re not cut out for it (because we can’t survive ‘competitive’ workplaces like Uber? Yeah, right.). Opposition to promotion of tech enthusiasm amongst women doesn’t even make sense given our pioneering role in the history of computers and software engineering. Ada Lovelace, widely regarded as the first computer programmer in history, Hedy Lemarr, without whom we’d have no WiFi, and the inspiring ladies, like Valerie Thomas and Margaret Hamilton, whose contributions at NASA are out of this world. It’s only now that we’re being reminded of these female tech heroes through content like The Bletchley Circle and Hidden Figures, which show their highlight their awesome contributions and the struggles associated with them.
But you know what? Why wouldn’t you encourage more women to do ML and AI? To take some of the most cutting edge tech of our time and change the world with it? And to solve global problems that would keep them financially set up for life? If you knew the tech wave was coming and could completely wipe out certain jobs, making them obsolete as well as impact all other areas of life, you wouldn’t just tell your sons about it. You’d tell your daughters too.
So what are we doing about this? You know, the fact that by 2020, women globally will face five jobs lost for every one gained as a result of AI developments (WEF) and yet only a fraction of ML and AI engineers are women (only 17% of graduates in Computer Science are women and not all CS grads do ML and AI). There are more than enough people telling women to get into STEM and decrying the lack of diversity, so we want to bring change. There’s already a company retraining coal miners to be programmers, and we can certainly do the same. Instead of just finding women who want to be AI engineers, we want to up-skill any woman with a passion for their project or field and show them how to build AI models that are relevant to what they’re doing. It’s just one of the ways we can take the women who will be hardest hit by automation and train them to fill emerging engineering jobs, starting with things they love.
What can you do to support us?
1. The easiest thing to do is support this idea by voting here — http://s.mri.ai/becks. The prize will go to getting this change instigated and you don’t have to fork out a dime.
2. Pledge your time, knowledge, money, resources or whatever it is you have so that once we get the ball rolling, we’ll know exactly who wants to be involved.
3. Tag the women in your life most likely to be impacted by the AI revolution. We need their input on what this change should look like, where their passions lie, what they want to solve in their jobs, what their concerns are, what they love and hate about technology.