Isn’t about time you started teaching your digital home assistant some new tricks?
By: Enrique Dans
Tags: Alexa, Amazon Echo, Machine Learning, Artificial Intelligence, Amazon

IMAGE: Modified from Jose Miguel S. — Pixabay (CC0)

Using a smart home assistant such as Amazon Echo or Google Home can quickly become repetitive: one tends to ask for the same things again and again: maybe some general knowledge questions, playing a particular song or album, turning the lights on and off, doing the shopping list, setting alarms, etc. In short, we’re probably only using a tiny amount of the device’s capacities, and rarely take the time to discover their many possibilities.

Some functions, like answering questions or playing music, are probably part of the original features that led us to install the device in the first place. Other functions come from additional devices: if you buy a camera, an irrigation system, a TV remote or light bulbs, you will look for the “works with Alexa” sticker, and as soon as you install the device in question, you will incorporate commands into your routine. For the manufacturers of many devices, the idea of making them operable from a home assistant of this type is an increasingly important attribute for certain market segments.

But what about additional possibilities? In general, the algorithms that allow these assistants to do their job are installed by the company that makes them (Amazon, Google, Apple, Baidu, etc.) and are unidirectional manner. Some users do create their own commands and instructions, known as routines: for instance, when I go to bed, I want all the lights on a floor turned off and those in the bedroom turned on. Basically, putting together a bunch of predefined commands, and that’s about as sophisticated as it gets.

That, however, could start to change: a note published by Amazon a few weeks ago says its devices could soon be trained by their users to acquire new functionalities derived from bi-directional processes. We could now “explain” to our device what a certain configuration involves: if you tell it to put the lights in dinner mode, the device would ask you to define what you mean by dinner mode, and you would explain that you want it to dim all the lights in the room except for those on the table so you can see your food. The device would store that user-defined setting and use it later.

The idea of a “teachable Alexa” with algorithms we can interact with or define is an interesting one, and is a further step in the customization of these types of device. Households that adopt smart assistants are seeing how, starting from very basic functions, they become more sophisticated as more devices are installed: I have had several Alexas at home for some time now, and I also have several Ring cameras, but I realized only yesterday that, because I have an Amazon Fire TV plugged int the television set, I could ask it to show me the camera at the front door on the screen before I answered it, or cut to the camera in the the garden. If Alexa and all its growing ecosystem of surrounding devices can learn some of my requests or allow me to define them, its potential increases, and so does the value proposition.

At the same time, it is interesting to see how our brain evolves when we interact on a regular basis with artificial intelligence: some people argue that such devices make us lazier, because we ask it to turn the lights on or off instead of getting up to do it, when the reality is that it teaches you how to formulate your requests, to think of ways to access information, or even to write some commands in a simple way. In practice, we’re rewiring our brain to prepare us for a world in which these types of assistants or task automation will undoubtedly be much more common. Despite their apparent simplicity, initially people tend to make mistakes such as talking to these devices as though they were human, instead of simply using the activation command at the beginning of each sentence, rather providing meaningless or confusing requests.

The idea of bidirectionality, that these algorithms can be trained by us, offers huge potential and could be the basis to develop new uses that increase their value proposal, and a further step in our interaction with these types of devices, which in most cases is simply request — response. If we start to include dialogues in phases, such as those needed to explain what we mean by something so that the device learns it for next time, we can son add many new possibilities. Improving these devices’ algorithms can quickly créate a valuable ecosystem. It’s just a matter of pushing the boundaries of your imagination…