Machine learning is the process of enabling computers to tackle different kinds of tasks that have been carried out by people until now. Machine learning algorithms are built in such a way that it helps automate self-driving cars, translate speech and execute many other tasks. Machine learning technology is driving an explosion in the field of artificial intelligence. Let us see what exactly is machine learning. Machine learning is a type of artificial intelligence that allows software applications to become accurate at predicting outcomes without being explicitly programmed.

This article was published as a part of the Data Science Blogathon. When asked about a business case challenge at an interview for a Machine learning engineer, Data scientist, or other comparable position, it is typical to become nervous. Top firms like FAANG like to integrate business case problems in their screening process these days. This approach is followed by a few other leading companies, like Uber and Twitter. Most case studies are open-minded and technical.

Microsoft is rolling out a revamped personalized news service called Start. The feed will be baked into the Windows 10 taskbar and the Windows 11 widgets section. You can also access it via the web, iOS and Android apps and the new tab page in Microsoft Edge. Start draws from Microsoft's artificial intelligence and machine learning expertise (as well as human curation) to create a news feed featuring up-to-date info tailored to your interests. The more you use Start and indicate what you're interested in, the closer the feed will hew to the things you prefer to read about.

The Cambridge Analytica scandal would kick off Facebook's largest publicity crisis ever. It compounded fears that the algorithms that determine what people see on the platform were amplifying fake news and hate speech, and that Russian hackers had weaponized them to try to sway the election in Trump's favor. Millions began deleting the app; employees left in protest; the company's market capitalization plunged by more than $100 billion after its July earnings call. In the ensuing months, Mark Zuckerberg began his own apologizing. He apologized for not taking "a broad enough view" of Facebook's responsibilities, and for his mistakes as a CEO. Internally, Sheryl Sandberg, the chief operating officer, kicked off a two-year civil rights audit to recommend ways the company could prevent the use of its platform to undermine democracy. Finally, Mike Schroepfer, Facebook's chief technology officer, asked Quiñonero to start a team with a directive that was a little vague: to examine the societal impact of the company's algorithms. The group named itself the Society and AI Lab (SAIL); last year it combined with another team working on issues of data privacy to form Responsible AI. Quiñonero was a natural pick for the job. He, as much as anybody, was the one responsible for Facebook's position as an AI powerhouse. In his six years at Facebook, he'd created some of the first algorithms for targeting users with content precisely tailored to their interests, and then he'd diffused those algorithms across the company. Now his mandate would be to make them less harmful. Facebook has consistently pointed to the efforts by Quiñonero and others as it seeks to repair its reputation. It regularly trots out various leaders to speak to the media about the ongoing reforms.

"Be aware of how the balance of control is shifting" NB: This article was first published in 2012 and was copied here in 2020. For as long as humans have been inventing new technologies, we have tried to use these technologies to create automatons in our image. In other words, robots that we can talk to. Our oldest myths include robots made from clay. With the advent of metalworking, Hephaestus, blacksmith to the ancient greek gods, built handmaidens out of gold.

Mashable's series Algorithms explores the mysterious lines of code that increasingly control our lives -- and our futures. An algorithm is a simple concept that, today, has many complex manifestations. Algorithms' central and opaque position at the heart of social networks like Facebook cause some to view algorithms in general with a sort of mystical reverence. Algorithms have become synonymous with something highly technical and difficult to understand, that is either an arbiter of objective truth, or, on the other end of the spectrum, something wholly untrustworthy. But when people refer to "the algorithm" -- whether Facebook's or another tech company's recommendation algorithm, or just "algorithms" in general -- do they really know what it means?

It's a truism that we live in a "digital age". It would be more accurate to say that we live in an algorithmically curated era – that is, a period when many of our choices and perceptions are shaped by machine-learning algorithms that nudge us in directions favoured by those who employ the programmers who write the necessary code. A good way of describing them would be as recommender engines. They monitor your digital trail and note what interests you – as evidenced by what you've browsed or purchased online. Amazon, for example, regularly offers me suggestions for items that are "based on your browsing history".

Every time you open Instagram, there are some new ads for you. All these ads relate to what you search for. You move to the explore section and can find thousands of related posts that interest you. But, how is that possible? This is not just the story of Instagram but pretty-much every social media platform today.

Facebook has updated its Messenger app to prioritise Stories and kill off the business and ad-focused'Discover' tab. The update to both iOS and Android gives greater prominence to Stories – a feature copied from Instagram – at the expense of Discover, which was designed to provide users with easy access to businesses, chatbots and games. Facebook announced plans to phase out the Discover tab back in August 2019. 'Simply put, we want to make it more seamless for people to reach out to businesses on Messenger in places where they're already looking to connect,' it said in a blog post at the time. 'Businesses will continue to appear in the app through the search feature and advertising surfaces, making it easy for people to connect with them.'

Every aspect of life can be guided by artificial intelligence algorithms – from choosing what route to take for your morning commute, to deciding whom to take on a date, to complex legal and judicial matters such as predictive policing. Big tech companies like Google and Facebook use AI to obtain insights on their gargantuan trove of detailed customer data. This allows them monetize users' collective preferences through practices such as micro-targeting, a strategy used by advertisers to narrowly target specific sets of users. In parallel, many people now trust platforms and algorithms more than their own governments and civic society. An October 2018 study suggested that people demonstrate "algorithm appreciation," to the extent that they would rely on advice more when they think it is from an algorithm than from a human.