The 4 stories you should read today: From layoffs to recommendation systems

I’m keeping it light again for the last issue after a slow news week. Here are four (actually five) things I think were really interesting or newsworthy, but about which I just don’t have a lot to say.

China’s Baidu being probed after CEO tests driverless car on public roads (Reuters): Apparently authorities are investigating whether Baidu CEO Robin Li conducted part of his keynote at Baidu’s big conference Wednesday sitting inside an unlicensed driverless car. Any actual rule-breaking with regard to permission is probably a very small deal, but suggestions that the car wasn’t following rules of the road might be noteworthy. I’m sure that’s easily fixed in production vehicles but, you know, the whole point of driverless cars is that they’re supposed to be better than humans.

Microsoft to layoff 3K sales employees, will focus on cloud (Fortune): That’s a lot of jobs, which is both sad and another result of the shift to cloud services. Theoretically, cloud computing should help streamline the sales process on both sides of the equation, which is not a good situation for salespeople. I’m not certain it’s a sign of anything wrong with Microsoft, but rather that Microsoft is gearing up for a tough international fight for cloud business. That will require some different sales skills faster cycles.

There’s a fight brewing between the NYPD and Silicon Valley’s Palantir (BuzzFeed): Palantir is involved in multiple legal situations at the moment and, frankly, things do not look that good from the outside. But Palantir’s business problems aside, there’s a bigger technological story here about the potential pitfalls of proprietary systems. The root of the showdown is that NYPD moved to a system comprised of IBM tools and its own home-built software, which it claims give the department better features and more control. Now NYPD wants Palantir to hand over its data stored on Palantir’s system, in a format the NYPD can use, and there’s disagreement over whether that happened.

Recommendations systems are an unsung hero of the web: Seriously, we interact with them so frequently, and they’ve driven God-only-knows how many sales on Amazon, views on Netflix and clicks on BuzzFeed. With that in mind, here’s a good breakdown of several popular methods (including deep learning) for building them (from Stats and Bots), and here’s a look at Amazon’s evolving and diverse approach through the years, from collaborative filtering to machine learning (from IEEE).