Customer preferences alter by the second. Getting in the buyer's good graces once does not guarantee lifelong loyalty. That's why retailers would do anything to lure shoppers. Even something as self-destructive as setting the lowest prices which they can't afford to maintain. Such tools as AI-backed pricing analytics software help businesses create pricing strategies which allow retaining customers and keeping margins at the same time. The thing with the lowest prices in the market does not work unless you are Walmart or Amazon enjoying excellent terms with vendors and pouring millions into marketing.

IN a world today that is increasingly looking like what was shown in the Jetsons cartoon series in the mid-1980s there are serious questions being asked about the future of the work and whether technology will destroy jobs. "Will robots steal our jobs; will my boss be an algorithm?" As ILO celebrates the completion of 100 years of its being it believes it will have to redefine what an employee and an employer may be in future; factor in new working time and figure out what a workplace may entail, to be able to draw new work policies, law and regulations. It will have to especially look at people that are doing gigs, by choice or otherwise. Gigs, for the uninitiated, mean a job for a specified, usually short, period of time -- like freelancers, consultants working on a project or even part-time hires. A recent study by online jobs marketplace Upwork found an estimated 4.1 million (or one-third of the workforce) Australians are freelancing.

In this paper we study k-means clustering in the online setting. In the offline setting the main parameters are number of centers, k, and size of the dataset, n. Performance guarantees are given as a function of these parameters. In the online setting new factors come into place: the ordering of the dataset and whether n is known in advance or not. One of the main results of this paper is the discovery that these new factors have dramatic effects on the quality of the clustering algorithms. For example, for constant k: (1) $\Omega(n)$ centers are needed if the order is arbitrary, (2) if the order is random and n is unknown in advance, the number of centers reduces to $\Theta(logn)$, and (3) if n is known, then the number of centers reduces to a constant. For different values of the new factors, we show upper and lower bounds that are exactly the same up to a constant, thus achieving optimal bounds.

Artificial Intelligence is on a rage! All of a sudden every one, whether understands or not, is talking about it. Understanding the latest advancements in artificial intelligence can seem overwhelming, but it really boils down to two very popular concepts Machine Learning and Deep Learning. But lately, Deep Learning is gaining much popularity due to it's supremacy in terms of accuracy when trained with huge amount of data. Machine Learning has become necessary in every sector as a way of making machines intelligent. In a simpler way, Machine Learning is set of algorithms that parse data, learn from them, and then apply what they've learned to make intelligent decisions.

Overnight on October 14, 2015, Tesla introduced an semi-autonomous driving system for owners of the Model S. Within a day, owners began uploading videos of themselves being driven around. You can see the fear wash over people's faces when cars slow down in front of them or the Model S automatically changes lanes. They brace themselves for impact. You can tell that part of them just wants to throw their hands on the steering wheel and take over. But slowly, as the car makes the right choices over and over, the human drivers slowly relax and enjoy the experience.