Category Archives: Random stuff

Spatial maps – the next valuable currency

Fascinating and thought provoking article here about iRobot’s reported plan to monetize the spatial maps created by Roombas. Time and time again in my career (including right now) there has been a need for accurate spatial maps. Once only accessible to high-end robots outfitted with Lidars, now almost anything that moves is capable of generating and refining spatial maps.

This fits very nicely with the idea that mixed reality glasses will become ubiquitous. Imagine walking into a new space and getting a spatial map automatically downloaded from the cloud. No need to ask where the restrooms are any more! This kind of capability would be of benefit to almost any enterprise. For example, check into a hotel and the spatial map with directions to your room gets downloaded to your glasses.

There are three parts to this puzzle – mapping, storage and delivery. Once all these become ubiquitous, not having access to this data or MR glasses will seem very odd indeed. Of course, selling data about private houses is not something that should be allowed without the owner’s explicit permission but making the data available to the owner would have tremendous value. There’s going to be a whole new type of specialist – the virtual interior designer. Unless you need to interact with something physically, why bother having the real object rather than a virtual version of it?

Of course there’s always the chance that some company gets the data and has some software that can detect if your floor plan has space for one of their products. In some kind of bizarre world the product could appear virtually in the space with a link to where you could buy it. This would be real/virtual product placement! What a ghastly prospect :-(.

Python Machine Learning – a really practical machine learning book

I am currently reading Python Machine Learning as I wanted to know more about scikit-learn, amongst other things. It’s a very practical guide with just enough theory to make sense of it all. A lot of machine learning books dive pretty deep into the theory, which is great if that’s what you want. On the other hand, if the idea is to get doing something fast, this book seems like a great place to start. It’s always easier to delve into theory when its relevance is clear and there’s nothing like actually writing and running code to get a feel for relevance.