Reading Time: 4 minutes

This week I changed things up a bit! I decided to record a video in my treehouse where I’ve been having quite a few meetings recently. It’s been really enjoyable to work from there given the summertime and it’s been fun to see others in their different working environments too. 

The video covers it all, but should you prefer reading over watching, here’s the take-away: 

In this week’s video, I talk about something that I’ve been hearing about a lot from our clients and people I speak with; the notion of automation. With the pandemic and how it’s affecting the world and the economy, a lot of companies are looking to automate some of their processes. And I started to think about that word automation. It is a word I don’t use very often – I usually use something like “personalization” or “optimization”. But so I started to think about what automation really means in terms of A.I. and it’s what I will talk about briefly today. 

Automation Processes 

To illustrate the value of automation, let’s think about an online shopping experience for a moment. If someone is shopping on a site, let’s call this person Sarah, and she has a question, she might look down in the lower right of the screen for that little window that says: “Hey if I can help just click here and ask a question”. After submitting her question, Sarah gets a response from customer agent Tom. And Tom looks at some of her browsing history and says: “Hi Sarah, based on what you’ve been browsing for the last few weeks we’re going to recommend this item to you” and he shares the item link. And Sarah thinks “well what a great experience but I don’t quite like this, do you have something in blue maybe?” And so Tom does further research, looking through all the available items whilst referencing Sarah’s browsing history, and he sends her back a link to a blue item she will probably like. This level of personalization by a customer agent would be insane right? If you really want to grow your business, it’s nearly impossible to have that level of personalization. It would never scale.

So what you do is look for something that can be automated. This is where a chatbot that can do all the same things as Tom would be of great value – looking through someone’s browser history and providing a solid recommendation. It’s what a lot of sites do without even having a chatbot! And that’s what we spend a lot of our time doing: building in that type of personalization using A.I. Whether it’s a product recommender or something to help increase engagement by just showing someone the right experience online, all of the things that we do day to day can be put underneath this umbrella of automation. It’s really about taking processes that can be done very well by a human, one-on-one, and then extending them out to a much larger user base so they can potentially help tens of thousands, if not millions, of people at the same time. 

As I mentioned, this notion of automation is something we’ve been hearing a lot about recently. This might be due to the fact that data science is being managed from much higher up. We’re seeing a lot of c-suite people managing data science projects which I think is an absolutely wonderful fit because, in my opinion, who else is better suited to understand how systemic optimization can conserve different parts of a company and how to deploy this type of optimization that we do? How do we take a process and scale it outside of what humans can do? And with the emphasis that COVID-19 has put on our systems and infrastructure – how do we provide this level of service to more people at a time through automation? 

And that is what A.I. is really there for. I’ve always said that A.I. is not a technology nearly as much as it’s there to enhance products, which is why I love it when I see data science either directly under say a chief product officer, the head of product for a company, or the CEO. I think it’s a great position since having that global view of what can make products better is really, really important for data science. 

That’s my take on automation! If you agree or disagree, please let me know and if you’d like to talk about any ways to help automate the processes in your company please feel free to reach out to us. I hope you’re all doing well staying safe and staying healthy. 

Take care,

– Zank

Of Interest

How Instacart Fixed its A.I. and Keeps up With the Coronavirus Pandemic
Like many companies, online grocery delivery service Instacart has spent the past few months overhauling its machine-learning models because the coronavirus pandemic has drastically changed how customers behave. Starting in mid-March, Instacart’s all-important technology for predicting whether certain products would be available at specific stores became increasingly inaccurate. Read about how they fixed it here:

What a Machine Learning Tool That Turns Obama White can (and Can’t) Tell us About A.I. Bias
It’s a startling image that illustrates the deep-rooted biases of A.I. research. Input a low-resolution picture of Barack Obama, the first black president of the United States, into an algorithm designed to generate depixelated faces, and the output is a white man. What’s causing these outputs and what do they really tell us about A.I. bias?

 10 Wonderful Examples of Using A.I. for Good
One of the many benefits of using artificial intelligence is to help us view societal problems from a different perspective. While there’s been much hubbub about how A.I. might be misused, we must not overlook the many ways A.I. can be used for good. Our global issues are complex, and A.I. provides us with a valuable tool to augment human efforts to come up with solutions to vexing problems. Here are 10 of the best ways artificial intelligence is used for good.