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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."
Ever wondered how video-streaming services such as YouTube and Netflix fetch videos that you like? Or how Google and Facebook find stories that are interesting to you? This is because these services are powered by Artificial Intelligence (AI) and Machine Learning (ML) algorithms – These algorithms are coded using a programming language in such a way that they can analyze your behavior at a granular level to find out your interests and preferences. This article focuses on Python programming language and explains why it is the most effective AI and ML language. AI and ML are seeping into nearly every aspect of our lives, helping us in ways that augment our abilities and make us better at what we do.
Quillionz is the world's first AI-powered platform that automatically generates questions from your text content. Using artificial intelligence, Quillionz helps you create questions on your teaching content, quickly and efficiently. Quillionz works with any textual content--a chapter in the textbook, an article off the internet, or even a PDF, and automatically creates factual questions in a matter of seconds. A tool that works best with informative and factual content (as most educational content tends to be), Quillionz is valuable addition to any teacher's arsenal.
I see two main points of interest personally. The first is adversarial examples. There have been adversarially robust generative models developed, but it seems to me that there is more to be understood here. Obviously the'adversarial examples are features, not bugs' paper lays out a convincing argument around the theoretical meaning of the problem, but... is there some overarching pattern that can help distinguish useful features from brittle features? The main area I'm personally interested in though (nowhere near knowledgable enough to be caught up with current research, but it's what I'm working towards at the moment) is unsupervised model based reinforcement learning.
Over the past several years, commercial use of biometric data has become increasingly prevalent. In response, several states have adopted biometric data privacy legislation. Consequently, companies that rely on biometric data face new regulatory risks, in addition to increased legal exposure to individual and class action lawsuits. In fact, the Ninth Circuit Court of Appeals recently affirmed certification of a class action alleging Facebook's face-scanning practices violate Illinois' biometric privacy law, finding that the class alleged sufficiently concrete injuries based on Facebook's alleged use of facial recognition technology without users' consent to establish standing. Insurance policies currently available on the market, including cyber insurance policies, may not adequately cover these risks.
A former manager of mine once told me, "If I am going down, you are going down with me." That was in relation to having a bad sales quarter. From that point on, there was no need to pay attention to her. It becomes difficult to continue to feel passion for the job once you see a lapse in integrity in your boss. It only got worse from there, so I knew it was time to start planning my exit strategy.
If we're going to map the world, we're not going to do it with ever-greater volumes of elbow grease. There's just too much work to do. AI and computer vision are helpful assistants in this task, however, as a Facebook effort has shown, laying down hundreds of thousands of miles of previously unmapped roads in Thailand and other less well-covered countries. The problem is simply that there's a whole lot of Earth and only a handful of people actually making maps of it. Sure, Google and Apple have dueling products -- but their focus is on businesses in cities and accurate navigation, not including every dirt path and gravel road.