The following webpage has detailed instructions for installing and configuring SonarQube on a RHEL/CentOS 7 Linux server (real or virtual) and it was one of the best guides I’ve seen (and I’ve reviewed half a dozen):
The webpage outlines how to update your Linux server, how to install MySQL (as a data repository) on it, and how to then install SonarQube software on the server.
Some things to note. First, this procedures has you using wget to get v6.0 of SonarQube:
Check out the page https://www.sonarqube.org/downloads/ and see the latest version of SonarQube (e.g. 6.4) and replace “sonarqube-6.0.zip” with the latest version (e.g. “sonarqub-6.4.zip”.)
One important thing to note: this procedure creates a userid and database called sonarqube.
Later in the process, the changes made to /opt/sonarqube/conf/sonar.properties needs to match this:
If the userid, password and database you created in MySQL do not match what it is the sonar.properties file, you will see cannot connect to the database errors in the /opt/sonarqube/logs/web.log file and SonarQube will not come up.
Once you enter: sudo ./sonar.sh start
Get the IP address of the SonarQube server and then go to a browser and enter:
…Then you want to go here and download and install the appropriate software for your Windows system: Security Essentials Download.
According to this, Microsoft has upgraded it’s security software to prevent similar attacks. That’s good. What’s not good is that you can be certain there will be a wave of copycat attacks coming. Get the software and install it today.
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