Wendy's, a United States fast food chain, is looking to test having the prices of its menu items fluctuate throughout the day based on demand, a strategy that has already taken hold with ride-sharing companies and ticket sellers. During a conference call this month, Wendy's CEO Kirk Tanner said the Dublin, Ohio-based burger chain will start testing dynamic pricing, also known as surge pricing, as early as next year. "Beginning as early as 2025, we will begin testing more enhanced features like dynamic pricing and daypart offerings along with AI-enabled menu changes and suggestive selling," he said. "As we continue to show the benefit of this technology in our company-operated restaurants, franchisee interest in digital menu boards should increase, further supporting sales and profit growth across the system." Wendy's plans to invest about 20m to launch digital menu boards at all of its US company-run restaurants by the end of 2025.

More than ever, companies are able to tailor prices across people, places, and time. They do this to maximize profit, and sometimes simply to survive. We're in a new era of supercharged price discrimination, made possible by two major scientific and technological trends. First, AI algorithms -- often trained on highly detailed behavioral data -- enable organizations to infer what people are willing to pay with unprecedented precision. Second, recent developments in behavioral science -- often invoked with the tagline "nudge" -- provide organizations greater ability to influence their customers' behaviors.

On my first work trip to Jakarta 14 January 2016 for Grab, multiple terrorist bombs exploded a couple of miles from the GrabBike office where I had just arrived. People were fleeing cafes and restaurants around the attack site. My new colleagues were shaken, glad to be safe, looking to help. There was news of crowds on the streets trying to get away, confirmed by a spike in booking requests from the blocks around the explosion. My colleagues remembered the 2002 Bali bombings, and knew we should get people to spread out.

Within a decade, almost every major auto company, along with fleet operators such as Uber, have announced plans to put autonomous vehicles on the road. At the same time, electric vehicles are quickly emerging as a next-generation technology that is cost effective, in addition to offering the benefits of reducing the carbon footprint. The combination of a centrally managed fleet of driverless vehicles, along with the operating characteristics of electric vehicles, is creating a transformative new technology that offers significant cost savings with high service levels. This problem involves a dispatch problem for assigning riders to cars, a planning problem for deciding on the fleet size, and a surge pricing problem for deciding on the price per trip. In this work, we propose to use approximate dynamic programming to develop high-quality operational dispatch strategies to determine which car (given the battery level) is best for a particular trip (considering its length and destination), when a car should be recharged, and when it should be re-positioned to a different zone which offers a higher density of trips. We then discuss surge pricing using an adaptive learning approach to decide on the price for each trip. Finally, we discuss the fleet size problem which depends on the previous two problems.

It is almost certain that the sub-field of machine learning/artificial intelligence has progressively gained more fame in recent years. As Big Data is in fashion in the tech industry right now, machine learning is staggeringly effective to make predictions or computed recommendations with lot of information. Probably the most well-known cases of machine learning are Netflix or Amazon's algorithms. Machine learning is a type of artificial intelligence (AI) that enables programming applications to be exact in anticipating results without being explicitly modified. The fundamental preface of machine learning is to build algorithms that can get input information and utilize statistical analysis to predict an output value within a worthy range.

"Machine Learning – The Hot Technology Nurturing the Growth of Cool Products" If you keep yourself updated about technology news, you are probably seeing mentions about machine learning everywhere- from voice assistants to self-driving cars, and for good reasons. Everyday a new app, product or service unveils that it is using machine learning to get smarter and better. You've likely used machine learning on your way to work (Google Maps for suggesting Traffic Route, making an online purchase (on Amazon or Walmart), and for communicating with your friends online (Facebook). Not to mention, in the process of navigating to this blog page on your screen through Google Search, you almost certainly used Machine Learning. This post will try to give novice readers plenty of real world machine learning applications where the ML technology works like a charm.

Uber CEO Travis Kalanick resigned late Tuesday night from the company he cofounded in 2009. While he'll remain on the board of directors, Kalanick's departure comes after months, if not years, of reports of a toxic workplace culture, cutthroat business tactics, and the occasional public embarrassment. It's not clear who will replace Kalanick. But what is clear is that this person will have a lot to correct. Here's a timeline of many, many upheavals that led the $69-billion startup to this crisis point.

One of the theoretical advantages of software, artificial intelligence, algorithms, and robots is that they don't suffer many human foibles. They don't get sick or tired. They are polite -- or rude -- to everyone in equal measure. The reality, of course, is different. Technology is designed by humans in all their frailty. As a result, it is eminently capable of perfect human behavior.

A question I am often asked is, "Why did you get out of your comfort zone?" By any which yardstick, I ought to have stuck to honing my skills as a journalist and a writer. To which my simple answer is: "Because if I didn't, I'd be obsolete before I hail the next Uber." Uber is the new F-word. That if we don't take cognizance of entities like Uber and the forces that are shaping it, it is only a matter of time before a lot many of our livelihoods will be obsolete. To most people who've grown watching businesses evolve, this company makes no sense. I mean, come to think of it, what is it? An app that resides on my phone and allows me to hail taxis from wherever I am. And by all accounts, is one of the most unprofitable start-ups the world has ever seen. But for whatever strange reason, it is among the most valuable and sought after. But users like you and me are enamoured by it; traditional service providers don't know how to deal with it; policymakers have no clue what sense to make of this animal; and it is just but one metaphor for how the world is changing dramatically--India included. I touched upon how it is just one among the many things changing dramatically and how the ground beneath India's feet is shifting on Founding Fuel, the platform I co-founded. Why does it concern me?

By this point, frequent Uber users are probably familiar with Pool, the company's pseudo-carpooling service. UberPool works by finding riders who are heading along similar routes and grouping their trips together. A driver might pick up you, and then another passenger, and maybe even a third, before dropping you off at your destination. For this inconvenience and added human interaction, Uber promises Pool riders fixed fares, often at steep discounts to what it charges for private cars. But there's an even more important detail about Pool that's often overlooked: It doesn't show riders surge pricing.