SAN FRANCISCO – Soon, you could get fewer familiar ads following you around the internet -- or at least on Facebook. Facebook is launching a long-promised tool that lets you limit what the social network can gather about you on outside websites and apps. The company said Tuesday that it is adding a section where you can see the activity that Facebook tracks outside its service via its "like" buttons and other means. You can choose to turn off the tracking; otherwise, tracking will continue the same way it has been. Formerly known as "clear history," the tool will now go by the slightly clunkier moniker "off-Facebook activity."

USA TODAY Sports' Gabe Lacques breaks down how MLB is trying computer generated strike zones in the Atlantic League. An automated strike zone that converts the home-plate umpire from arbiter to mere messenger is right far more often than it is wrong. A ban on mound visits and relief specialists undeniably speeds the game's pace. And rules changes aimed to encourage balls in play and runners in motion – Thou shalt not shift defensively, but you may "steal" first base – gives hitters options beyond launching balls over a vexing alignment of fielders. Yet as its experiment with a "robotic" strike zone and other nuances enters its second month, the formal partnership between MLB and the Atlantic League illustrates the upsides and consequences of optimization.

When humans learned to extract metals from their ores and mix them into alloys such as bronze, brass and steel, technology took great leaps forward. Now researchers are turning to artificial intelligence to find the next generation of alloys. Scientists are already finding new alloys with increased strength and other improved features. A research team based in China have now published such discoveries in the journal Acta Materialia. Explaining the origins of their work, researcher Yanjing Su of the Beijing Advanced Innovation Center for Materials Genome Engineering cites as his inspiration the success of machine learning in mastering the strategy game Go.

Everyone's talking about the fast.ai Massive Open Online Course (MOOC) so I decided to have a go at their 2019 deep learning course Practical Deep Learning for Coders, v3. I've always known some deep learning concepts/ideas (I've been in this field for about a year now, dealing mostly with computer vision), but never really understood some intuitions or explanations. I also understand that Jeremy Howard, Rachel Thomas and Sylvain Gugger (follow them on Twitter!) are influential people in the deep learning sphere (Jeremy has a lot of experience with Kaggle competitions), so I hope to gain new insights and intuitions, and some tips and tricks for model training from them. I have so much to learn from these folks.

In this blog, we are going to classify images using Convolutional Neural Network (CNN), and for deployment, you can use Colab, Kaggle or even use your local machine since the dataset size is not very large. At the end of this, you will be able to build your own image classifier that detects males and females or anything that is tangible. Let's see a bit of theory about deep learning and CNNs. It is a machine learning algorithm, which is built on the principle of the organization and functioning of biological neural networks. This concept arose in an attempt to simulate the processes occurring in the brain by Warren McCulloch and Walter Pitts in 1943.

As the name suggests, outliers are datapoint which differs significantly from the rest of your observations. In other words, they are far away from the average path of your data. In statistics and Machine Learning, detecting outliers is a pivotal step, since they might affect the performance of your model. Namely, imagine you want to predict the return of your company based on the amount of sold units. Nice, but what if, among your data, there was an outlier?

Last week, Eko Devices announced a new service that matches ECG and heart sound recordings with clinical data to help pinpoint novel drug-data combinations. The Silicon Valley startup is pitching the platform, called Eko Home, as a resource for clinical trials targeting new therapies. The new platform is already seeing some action. According to the company, an ongoing Mayo Clinic study exploring how carvedilol-based cardiovascular therapies could reduce heart failure or other heart function declines among breast cancer patients undergoing chemotherapy is using the Eko Home platform to drive insights. Eko -- which is best known for its Eko Duo device, a smart remote monitor that's part stethoscope, part ECG -- also said in its announcement that it "expects to offer the drug-data combinations with other life science partners by the end of the year with additional plans to offer its SDK to hospitals and healthcare providers that wish to build the platform directly into their applications."

Researchers have come up with a mobile-sensing system that can track and rate the performance of workers. This research team used a mobile-sensing system to track 750 U.S. workers for one year and through this discovery, the system was able to tell the difference between high performers and low performers with 80% accuracy. You're probably wondering, how this is tracked? The mobile-sensing system has a few distinct pieces. A smartphone tracks physical activity, location, phone use and ambient light.

Natural Language Processing is fast becoming Artificial Intelligence's new frontier which we all are using on daily basis – Siri, Google search, chatbots, automatic translation are just some examples. NLP can offer much more within your organization. We can combine together with your existing business applications tailor-made solution to analyze text, understand the conclusions without human effort, turn unstructured data into tabular data and much more.

Big-data company Databricks Inc. is hoping to empower so-called citizen data scientists to create their own machine learning models with new "Automated Machine Learning" capabilities in its Unified Analytics platform. The AutoML capabilities announced today rely on machine learning too, and are designed to help untrained workers muddle their way through the key steps involved in creating and training machine learning models. Machine learning models are mathematical representations of real-world processes that are used to make predictions, and are created by providing training data for an algorithm to learn from. Creating machine learning models is no easy task, however. It's normally done by highly trained data scientists and requires extensive preparation of the training data that's going to be used.