I’m the only data scientist at my company. It allows me to have a huge amount of breadth in my work, which is great, but it leaves me few people to really nerd out with. I mean the type of nerding out that’s specific to data science- there’s definitely a lot of nerding out that goes on with respect to other topics, which is also great.
In an effort that appears selfless but is really quite selfish, I decided to start a data science lunch and learn series. The goal of the series is to help my company enhance its data driven decision making across all teams, not just the data science team. The more people thinking critically about data and highlighting potential datasets and business opportunities the better. That’s the selfless part, me volunteering my time to teach my coworkers. The selfish part is that really, I just want people to be on the same page as me when it comes to data science, so I can bounce ideas off of them, and they highlight opportunities in our business that may be useful to explore. It’d be even better if they started to do their own analyses. I wouldn’t mind having my 60 talented colleagues do my work for me.
My idea was to have 5 sessions:
- The first would be an intro to the whole topic, since many people don’t even know what data science really means (myself included, sometimes?).
- The second lesson, (which at the time of this writing has yet to be taught) will be a discussion of case studies of basic models, and some more cutting edge stuff, with example code in Python. The point being, while the math may be complicated, often times an off the shelf implementation will do the trick.
- The third will be an optional lesson with more math, and talking about models. Apparently people want more math (see the image below).
- The penultimate lesson will be hands on, with all of us doing some exploratory data analysis together, likely on some grocery dataset, since our company is in the grocery space.
- The final lesson will involve us all building a model together, probably to predict if someone will buy peanut butter or not in a given week, or something like that. The reason I choose peanut butter is because I used this example repeatedly in the first lesson, and it seemed to go over well. It’d be nice to tie things together by referring back to the beginning.
The first talk, in 3 parts
My goal for the first session was to set the stage for what’s to come next. I split the talk into 3 parts:
- The story of the “Godfather of Deep Learning,” Geoffrey Hinton, his relation to Toronto, and why everyone should know who he is and be proud to be living in such a great technology hub.
- A walk-through of the definitions of data science, machine learning, in an attempt to recreate the process I went through when I was first trying to wrap my head around all these terms. This is where the “AI or marketing hype?” part came in.
- Why we should all care. I used an example borrowed from a talk I went to by a venture capitalist: In the 90s, companies wondered how to build their internet strategy. In the 2000s, having an internet strategy was a given, but the next big question was how to build a mobile strategy. Now in the 2010s, the question isn’t about an internet or mobile strategy, those are a given. The question is how to harness “AI.” AI is going to go the way of internet and mobile- it’ll be integrated into all businesses. In the future, every company will be an AI company, in some respect. So even if you don’t care about AI, you’ll have to care.
AI or marketing hype?
I was nervous about this part of the talk. I wanted to get a discussion going, and make a point that the term “artificial intelligence” means different things depending on who you’re talking to. A computer scientist would give a very different answer than a non-technical marketing manager. And there’s nothing wrong with that! (Or so I told them. Full disclosure, I’m not a huge fan of the term AI, but I understand that it’s taken on a different meaning in recent years).
This “game” was supposed to be controversial. Supposed to be. It ended up being very… uncontroversial. But still fun.
The game was as follows. I’d show the audience different images of products that could be viewed as either “AI” or “marketing hype,” depending on who you’re talking to. I’d stand there, and let them discuss among themselves, and wait for their answer: was it AI or marketing hype?
Here’s some examples of what I showed my coworkers, along with their responses:
Old school Amazon product recommendations.
Audience’s answer: marketing hype. (Same as my answer).
2. Autonomous vehicles.
Audience’s answer: marketing hype. (Not the same as my answer).
3. Roomba (the vacuum cleaner that remembers the layout of your living room).
Audience’s answer: marketing hype. Definitely marketing hype. (Not my answer).
4. Google’s autocomplete/sentence suggestion.
Audience’s answer: AI (after some discussion). (Not the same as my answer).
Audience’s answer: Marketing hype. (My answer: it depends).
So what is AI?
At the end of the game, I told the audience what sort of product I believe merits the term “AI.” I told them that I think of AI as a machine learning driven system that interacts with the real world. Self driving cars, Amazon’s drone delivery system, a Roomba, Hanson Robotic’s Sophia, I’d call all that AI. Anything else I would call simply a machine learning driven product. (Really, I’d prefer not to use the term AI for any technology. But if I had to, it’d be the definition I’ve given above. And using this definition provided a good narrative for my talk).
Interestingly, my audience didn’t agree with me. The game was controversial, but not in the sense I was hoping for. I was hoping the audience would disagree with each other, but really, they disagreed with me. I was the one standing up there saying “AI exists!” while they were there unanimously saying “there’s no such thing as AI.” I guess that’s the perspective of a (front end/back end) developer. The non-technical audience members either stayed quiet, or seemed to agree with their coworkers. And I agree with them too, but I also think that the term has changed a lot over the years. What it meant five years ago probably isn’t the same as what it’ll mean in another five years. The field is changing fast.
Parting thoughts: I think I did well!
I was surprised by several things:
- People actually showed up. Mostly those in technical roles, but a few senior people as well.
- There was a seemingly unanimous response that AI is just hype, and a marketing term, and that AI does not exist, and cannot exist.
- The fear of math during the presentation (I showed them a picture of a computational graph showing back propagation), but the hunger for more math a few days after.
- The interaction of the audience, with me and with themselves, and the laughing. I always try to get the audience to laugh when I present!
- Everyone was interested and engaged from the start of the talk. People really do have an interest in data science.
- The fatigue it would bring. I haven’t had to present like that since I graduated from my Master’s program (about 18 months ago). I forgot how much fun but tiring speaking can be.
Run a lunch and learn! It’s fun!
I highly recommend every data scientist consider running a lunch and learn session at their company. It’s the perfect selfish and selfless act; a continuous learning and knowledge sharing opportunity on both ends. You get to practice public speaking, improve your communication skills, teach your coworkers, and share some laughs. Most importantly, it’s a lot of fun.
Also published on Medium.