This piece, Most engineers are white — and so are the faces they use to train software – Recode, implies that AI software doesn’t do a good job recognizing non-white faces because most engineers (i.e. software developers) are white. I’d argue that the AI does a poor job because of this: the developers aren’t very good.
Good software developers, in particular the lead developers, take an active role in ensuring they have good test data. The success of their software when it goes live is dependent on it. Anyone using training data (i.e. using test data) in AI projects that is not using a broad set of faces is doing a poor job. Period. Regardless of whether or not they are white.
If the AI is supposed to do something (i.e. recognize all faces) and it does not, then the AI sucks. Don’t blame it on anything but technical abilities.
I’ve recently added two repos to my github account:
The first one is some proof of concept code I wrote to demonstrate how to work with IBM Watson’s Tradeoff Analytics service using node.js
The second one is some sample code I have had for some time that does simple server monitoring of a Linux server.
There is no intellectual property involved in these repos: it is simple code based on documented code samples found in many places on the web.
For more details, see my Github landing page, here: blm849 (Bernie Michalik)
It’s only been out for a very short time, but already there’s at least one primer for it, here: Ten Things I Wish I Knew When I Started ‘Pokémon GO’ – Forbes. If you want to leapfrog others playing it, read this and then get going.
I have thought a lot about Waze since I started using it. Without a doubt, it has improved my life substantially. Here are some other thoughts I had as I used it.
- Waze is an example of how software will eat the world. In this case, the world of gPS devices. Waze is a GPS on steroids. Not only will Waze do all the things that a GPS will do, but it does so much more, as you can see from this other Waze post I wrote. If you have a GPS, after you use Waze for a bit, you’ll likely stop using it.
- Waze will change the way cities work. Cities are inefficient when it comes to transportation. Our work habits contribute to that, in that so many people commute at the same time, in the same direction, on the same routes, each work day. Waze and other new forms of adding intelligence to commuting will shape our work habits over time. Drivers being able to take advantage of unbusy streets to reduce congestion on major thoroughfares is just the start. City planners could work with Waze to better understand travel patterns and travel behaviour and incorporate changes into the city so that traffic flows better. It’s not that city planners don’t have such data, it’s that Waze likely has more data and better data than they currently have.
- Waze is a great example of how A.I. could work. I have no idea how much A.I. is built into Waze. It could be none, it could be alot. It does make intelligent recommendations to me, and that is all I care about. How it makes those intelligent recommendations is a black box. Developers of A.I. technologies should look at Waze as an example of how best to deploy A.I. Those A.I. developers should look at how best A.I. can solve a problem for the user and spend less time trying to make the A.I. seem human or overly intelligent. People don’t care about that. They care about practical applications of A.I. that make their lives better. Waze does that.
Posted in apps, IT, software
Tagged AI, apps, cities, commute, commuting, GPS, IT, planning, software, travel, Waze
If you were wondering why Python programs often have this: `if __name__ == “__main__”:` and then a call to a function, a good explanation is here.
In short, if your program is used as input to other programs, then you want to have that snippet of code in them. If your programs are standalone, you can get by without it.
If you hang around with or are involved in some way with IT people, you will come across individuals extolling the virtues of being a “Maker”. Making things (typically software or IT systems) is seen as a virtue, in some case one of the highest virtues, and the implication is that makers are virtuous people.
A well written critique of that is here: Why I Am Not a Maker – The Atlantic. If you consider yourself a maker or aspire to be considered one, you should read it. A key point is this:
When tech culture only celebrates creation, it risks ignoring those who teach, criticize, and take care of others.
This is true: tech culture sometimes places little or no value on other activities, such as the ones that the article mentions.
My main criticism of the article is that it has a blind spot for the middle ground. I know plenty of creative people whom I consider makers that also take care of others, teach, manage, administer…you name it. Often time the things they make are superior to those of people who devote themselves to being makers.
Being a maker is a virtuous thing, for the most part. But so is teaching, providing care, managing, cleaning, coaching and many other positive activities. Find the thing you are good at and contribute positively in your own way. If you can make some things along the way, all the better.