Every day we hear about Machine Learning and Big Data Analytics... 'United Parcel Service saves 39 million gallons of fuel after using Big Data Analytics to optimise fleet operations'; 'PayPal uses Machine Learning on Customer, Financial and Network data to combat fraud'; 'Amazon uses Machine Learning to discover'lowest price' for over 20 million products'... Machine learning, a subset of Artificial Intelligence (AI) is a method of data analysis that uses algorithms to iteratively learn from data and derive insights without being explicitly programmed. We can find examples of how Machine Learning is already a part of our daily lives -- like Google Maps, using location data from smartphones, analyses the speed of movement of traffic at any given time. Or like Amazon makes recommendations for products -- "customers who bought this item also bought". Behind all these lie complex algorithms that are continuously learning new data and refining outcomes. In banking for example, using client's financial data, risk preferences and desired target return, 'Robo-Advisers' provide personalised, algorithm driven portfolio management services without human supervision.

The AI (artificial intelligence) revolution is well and truly upon us, and we are at a significant watershed moment in our lives where AI could become the new electricity – pervasive and touching every aspect of our life. While many industries including healthcare, education, retail and banks have already started adopting AI in key business aspects, there are also new business models which are predicated on AI. With the global market of AI expected to grow at 36% annually, reaching a valuation of $3 trillion by 2025 from $126 bn in 2015, new age disruption is not only redefining the way traditional businesses are run, but is also unfolding as a new'factor of production'. However, the fear of what might happen once AI evolves into artificial general intelligence – which can perform any intellectual task that a human can do – has now taken centre stage with the ongoing debate between two tech titans – Elon Musk and Mark Zuckerberg. Similarly, Microsoft co-founder Bill Gates had also voiced his views that in a few years, AI would have evolved enough to warrant wide attention, while Facebook has ended up shutting down one of its AI projects as chatbots had developed their own language (unintelligible to humans) to communicate.

Elon Musk's recent remark on Twitter that artificial intelligence (AI) is more dangerous than North Korea is based on his bedrock belief in the power of thought. But this philosophy has a dark side. If you believe that a good idea can take over the world and if you conjecture that computers can or will have ideas, then you have to consider the possibility that computers may one day take over the world. This logic has taken root in Musk's mind and, as someone who turns ideas into action for a living, he wants to make sure you get on board too. But he's wrong, and you shouldn't believe his apocalyptic warnings. Here's the story Musk wants you to know but hasn't been able to boil down to a single tweet.

A Chrysler Pacifica hubrid minivan, decked out in Waymo's colors and self-driving technology. Fiat Chrysler Automobiles is joining a partnership that includes German automaker BMW and U.S. tech giant Intel to develop self-driving car technology. The budding alliance offers the Italian-American automaker a clear route to putting self-driving vehicles on the road amid signs that global collaboration is increasingly key to the technology's future. Until now, the company's highest-profile involvement in self-driving vehicles has been to provide Chrysler Pacifica hybrid minivans to former Google self-driving car project Waymo. But facing the expensive demands of developing autonomous vehicle technology, the BMW-Intel alliance is appealing to Fiat Chrysler, which does not necessarily have the financial girth to justify developing its own self-driving cars from scratch.

Big data has been the unavoidable buzzword of the Internet for the past few years, but it's actually the fuel behind the next big thing: data mining using machine learning. The recent explosion of big data is what made data mining using machine learning possible. Machine-learning algorithms are the heart of various studies across industries, from mapping genomes to improving car safety. So, what exactly is machine-learning? It's basically algorithms constructed by researchers and data scientists that can learn from and make predictions based on data.

On August 21, 2017, there's going to be a total eclipse of the Sun visible on a line across the US. But when exactly will the solar eclipse occur at a given location? Being able to predict astronomical events has historically been one of the great triumphs of exact science. But in 2017, how well can it actually be done? Stephen Wolfram is a computer scientist, physicist, and businessman. Sign up to get Backchannel's weekly newsletter. The answer, I think, is well enough that even though the edge of totality moves at just over 1000 miles per hour it should be possible to predict when it will arrive at a given location to within perhaps a second. And as a demonstration of this, we've created a website to let anyone enter their geo location (or address) and then immediately compute when the eclipse will reach them--as well as generate many pages of other information. These days it's easy to find out when the next solar eclipse will be; indeed built right into the Wolfram Language there's ...

When Benchmark Capital distributed a letter this week about its lawsuit against former Uber CEO Travis Kalanick, it was addressed to Uber's employees. But make no mistake: It wasn't written primarily for them. The letter quickly became a scavenger hunt for analysts, journalists, and investors trying to decode its meaning. Buried within it were clues as to how Uber's future might play out. Is there more to the Holder Report?

Last year, Intel partnered with Lady Gaga on the Super Bowl Halftime Show to showcase its latest aerial technology called "Shooting Star." Intel did a reprise performance of its Shooting Star technology for Singapore's 52nd birthday this past week. Instead of fireworks, the tech-savvy country celebrated its National Day Parade with a swarm of 300 LED drones animating the night sky with shapes, logos, and even a map of the country. Intel's global drone chief, Anil Nanduri, explained, "There's considerably more operational complexity in handling a 300 drone fleet, compared with 100 drones in a show. You may be able to juggle three, but if you juggle nine, you may have to throw them higher and faster to get more time."

About this course: This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of instructor-led presentations, demonstrations, and hands-on labs, students learn how to carry out no-ops data warehousing, analysis and pipeline processing. Prerequisites: • Google Cloud Platform Big Data and Machine Learning Fundamentals • Experience using a SQL-like query language to analyze data • Knowledge of either Python or Java Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google is currently blocked in China).

The day before a fully automated grocery store opened its doors in 1939, the inventor Clarence Saunders took out a full page advertisement in the Memphis Press-Scimitar warning "old duds" with "cobwebby brains" to keep away. The Keedoozle, with its glass cases of merchandise and high-tech system of circuitry and conveyer belts, was cutting edge for the era and only those "of spirit, of understanding" should dare enter. Inside the gleaming Tennessee store, shoppers inserted a key into a slot below their chosen items, producing a ticker tape list that, when fed into a machine, sent the goods traveling down a conveyer belt and into the hands of the customer. "People could just get what they want – boom, it comes out – and move on," recalled Jim Riot, 75, who visited the store as a child. "It felt like it was The Jetsons." Despite Saunders' best efforts, the Keedoozle's circuits frequently failed and the store closed for good by 1949.