Data Literacy, Product Design and the Many-Faced God
It’s official — AI has gone mainstream. For data scientists, this good news, although there’s some ambivalence about its hype and ethical applications. Companies are taking note — and too often, they hire a data scientist to sprinkle magic machine learning dust and bring the company into the AI era. That, of course, doesn’t work — unless the company is ready to embrace data literacy.
Case in point: Apple recently hired Ruslan Salakhutdinov, a prominent deep learning expert and professor at Carnegie Mellon, my alma mater, to build an AI center of excellence. This is exciting; it’s heartening to see Apple finally paying attention to AI and machine learning — but is it too little, too late?
Building a team that’s doing ‘cutting-edge research in deep learning, machine intelligence, and artificial intelligence’ is not easy — not in this hiring environment. But infusing data thinking throughout a company is orders of magnitude harder. This matters, because data thinking permeates your products and can make them feel “smart” — or not.
Here’s an example.
MacOS Sierra came with a new version of Photos — one with better face recognition and better organization, not unlike Google Photos (which I am a big fan of). I’ve enthusiastically started using it, as it was slowly (and I do mean slowly — over a month) indexing my pictures. Face recognition was disappointing — I was identified as 100 different persons, and another member of my family as over 200. I manually — and painstakingly — merged them, only for more alter-egos to appear as indexing continued. Pretty bad user experience, overall — and one I didn’t have with Google Photos, where I maxed out at a dozen alter-egos. (Sunglasses happen.)
Fortunately, this is the part that Ruslan and his team of machine learning PhDs can fix with better face recognition algorithms that can match Google’s. Unfortunately, that’s the part that’s easy compared to an even bigger problem: data as a second class citizen is reflected in product design. (This is a widespread issue. I’m picking on Apple because I just clicked the wrong thing and lost my selection of 77 faces I was going to merge.)
Consider this: Apple Photos detects hundreds of faces, and I diligently merge & label them. Now, Photos knows a) how many times a person appears in my photos; b) what % of my photos include this person (for every year of my life!); c) how many times I appeared in a photo with them; d) how many times one of my ‘favorites’ appears in a photo with them. You can even imagine building a co-occurence graph and iteratively calculating the strength of those edges, then visualize it à la LinkedIn InMaps. (Imagine, but don’t actually do it. )
Bottom line — Apple Photos should know how important these people are.
And yet — how does Apple Photos order these faces?
Alphabetically.
OK. Maybe they ran user studies and this was the least confusing and less cognitively taxing method. Maybe it’s the easiest way to scan for a person (search is not a first class citizen, either). Maybe it was a very deliberate decision to give more control to the user and explicitly select “favorites”. Maybe the product manager veto-ed an engineer who came up with a measure of importance because it sounded too convoluted. Maybe nobody thought of using these signals because it’s not part of the product/dev culture, or maybe they have — but decided to not even bother bringing it up. As a result, Photos does not feel “smart”. Walt Mossberg said the same thing about Siri.
But you don’t even need AI or sophisticated algorithms to make products feel ‘smart’ — not initially. Often, including in this case, simple counting and division is enough — if you think about it creatively. To do that, you need data thinking to be part of the culture and top of mind, not an after-thought.
PS: How does Google Photos solve this problem? It orders people by frequency, and lets you search for their names. No need to overthink it — and it’s just as easy to understand as alphabetical order.
