Among other things, for a smartphone to be bang on trend these days it needs, a tall 18:9 display with minimum bezels (with a notch thrown in for good measure), a superb camera system and Artificial Intelligence and/or Machine Learning.
Artificial Intelligence and Machine Learning are buzzwords being adopted and applied throughout our smartphones makeup, from the System on a Chip, all the way through to the operating system. So, is it just marketing hype, science fiction or is there fact being the fiction? Read on, and we’ll provide a straightforward, and where possible jargon-free overview.
So what’s the difference between Artificial Intelligence and Machine Learning
Artificial Intelligence is best characterized as the ability for a machine to exhibit practices including learning, behavior, and communication with no discernible difference from ourselves. Surely this belongs in the realms of science fiction? Well, if we ended here, then we'd agree but let’s dig deeper.
aking the AI generalization (General AI) described above let’s narrow it down and pick a specific area that is more relevant for our subject matter - for example, Image recognition, and we’ll call this Narrow AI.
Now, our smartphones didn’t all of a sudden develop the ability to recognize and differentiate between a car and a plate of food overnight.
It was taught. The ability for a smartphone to ‘truly’ learn something new in its purest form, that is, without intervention, is still a ways off.
I was part of a team that launched a loyalty card scheme for a major UK retailer, which today has circa 16 Million card holders. Now imagine the volume of data that we were collecting. A customer database containing all the information that all 16 million provided during the registration process including gender, age, children, address which we only added to overtime. A transaction database where every item purchased including date, time, store associated with that customer.
What insights and intelligence our systems would give us - but the reality was somewhat different. We didn’t arrive at the office one day, to discover our insight systems had given a ‘truth’ or trend that we hadn’t contemplated. No, our insights were directly answering questions that we asked - how many women, matching a particular demographic, hadn’t purchased a specific brand of perfume for example. We could take that insight and attempt to brand switch them. This was data mining, albeit on a massive scale with rules and logic created by us.
Now back to our smartphones, this is where Machine Learning enters the frame. Taking a practical example - Apple Photos People Album and let’s assume for one moment that we’ve never ‘tagged’ anybody previously.
When you first view the People Album it only shows photos where the geometry of a ‘face’ has been identified, no names.
Pick a face with no name, select it and give it a name.
It will then attempt to confirm that another face is this person, at this point it’s only got the first face to work with so the offered up face the second time may be way off. So you tell it ‘yes’ or ‘no’ and repeat.eat.
With every iteration it’s learning more and more about that face from different angles, varying hairstyles, and what happens as it ages and so on. You, therefore, reach a point where you take a picture of that person, and it’s automatically tagged with the right name.