twitter-politely-asks-you-to-protect-its-targeted-ad-dollars-in-new-ios-14.5-prompt

Twitter politely asks you to protect its targeted ad dollars in new iOS 14.5 prompt

As part of iOS 14.5, Apple’s App Tracking Transparency forces developers to ask permission for something they used to be able to do for free: track iOS users. Today, Twitter is joining the ranks of other developers and adding a prompt that asks users to enable tracking on iOS (via MacRumors).

Twitter’s main justification for listening to its request is straightforward — having the feature enabled allows it to serve “better” ads. The company includes a link to settings so you can make those changes, but read Twitter’s explanation before you decide:

Keep ads relevant to you by allowing Twitter to track data from other companies on this device, like apps you use and websites you visit.

The company also includes a link to a support post in the Twitter help center which explains why it has to ask for permission, includes a link to its current App Privacy Policy, and goes over what enabling or disabling tracking does in iOS.

The new Twitter ad tracking prompt.

It’s a surprisingly low-key attempt to get users to allow Twitter to track them, considering the company highlighted Apple’s addition of App Tracking Transparency in iOS 14.5 as a potential risk in its recent earnings statement (PDF):

We continue to expect total revenue to grow faster than expenses in 2021, assuming the global pandemic continues to improve and that we see modest impact from the rollout of changes associated with iOS 14.5. How much faster will depend on various factors, including our execution on our direct response roadmap and macroeconomic factors.

Facebook and Instagram took a far more aggressive approach to convince users its use of ad tracking is on the up-and-up — even going as far as including a vague threat that enabling tracking will “help keep Facebook/Instagram free of charge.”

Companies like Twitter and Facebook rely on tracking users to support their separate, often very lucrative ad businesses. After all, it’s usually ad sales that pay for free social networks, and customer data helps to target those ads. As a company that’s more interested in selling hardware and subscription services, Apple doesn’t really have to worry about things like that, but brash changes like the new tracking permissions can leave developers scrambling.

App Tracking Transparency has proven popular, though — around 96 percent of US users are opting out of tracking according to some recent surveys. And with Google considering developing its own methods for blocking tracking on Android, we might just have to get used to apps coming to us and begging for free data.

google-and-seagate-are-using-ai-to-predict-hard-drive-failures

Google and Seagate Are Using AI to Predict Hard Drive Failures

(Image credit: Shutterstock)

Google Cloud and Seagate offered a peek at their efforts to use machine learning, a type of AI, to predict when data center hard disk drives (HDDs), which are responsible for storing many terabytes of data, might start to fail so they can plan around those disruptions to their systems.

Right now there’s no getting around the fact that HDDs fail. They’re less reliable than SSDs —assuming those drives aren’t being pushed to the limit while they mine Chia—but they also offer higher capacities at lower prices. That’s an important factor for companies like Google Cloud that need to be able to handle massive amounts of data, either in support of their own projects or on behalf of their customers.

”At Google Cloud, we know first-hand how critical it is to manage HDDs in operations and preemptively identify potential failures,” the company said in a recent blog post detailing those efforts. “We are responsible for running some of the largest data centers in the world—any misses in identifying these failures at the right time can potentially cause serious outages across our many products and services.”

The problem was that manually identifying a failing drive, which Google Cloud defined as an HDD “that fails or has experienced three or more problems in 30 days,” is a time-consuming process that requires physical access to the device. Google Cloud and Seagate wanted to use machine learning to reduce the amount of time engineers would have to spend testing drives to determine their risk of failure.

Google Cloud said that it has “millions of disks deployed in operation that generate terabytes (TBs) of raw telemetry data,” including “billions of rows of hourly SMART(Self-Monitoring, Analysis and Reporting Technology) data and host metadata, such as repair logs, Online Vendor Diagnostics (OVD) or Field Accessible Reliability Metrics (FARM) logs, and manufacturing data about each disk drive.”

That means the company has a staggering number of HDDs that all generate “hundreds of parameters and factors that must be tracked and monitored.” This being Google Cloud, however, the sheer amount of available information was also beneficial. Between Google Cloud, Seagate and Accenture, that data could be put to use in a machine learning model capable of predicting a drive‘s chances of failing.

The companies tested two models: One based on AutoML Tables and one that was custom-developed for this project. The former won out with “a precision of 98% with a recall of 35% compared to precision of 70-80% and recall of 20-25% from [the] custom ML model,” (which also means the experiment served the dual purpose of demonstrating the benefits of using AutoML instead of a custom solution).

Google Cloud said that it plans “to expand the system to support all Seagate drives—and we can’t wait to see how this will benefit our OEMs and our customers!“ More information about the project is available via the company’s blog post.