It is rare and rewarding to connect two vastly different areas of computer science. Fast randomized algorithms in model counting were discovered in the early 1980s, while the area of streaming algorithms did not take off in the theory community until the late 1990s. Only recently were these disparate areas connected in the accompanying paper, where it was observed that the algorithmic techniques developed in the two areas were strikingly similar. This connection has given us exciting streaming algorithms used in database design and in network monitoring, as well as a unified perspective on existing algorithms. What exactly is model counting?

When we talk of Customer Experience, the first thought probably coming to mind is about businesses reaching out to Gen Y and Gen Z. Nothing wrong in that, except that there's an equally important customer cohort that offers a huge opportunity for enterprises to enhance CX or Net Promoter Scores (NPS) – senior citizens! This demographic profile, comprising people 60 years and older, is what we refer to as senior citizens. In India, the number of senior citizens stands at approximately 140 million, and is projected to rise significantly over the next decade. With their global population projected to hit one billion by 2030 and spending power close to $15 trillion, rest assured this group will have huge impact on business going ahead. New vistas expanding CX for seniors Of late, we have begun to see remarkable changes in senior citizens.

Now, Google is preparing to launch an app that uses image recognition algorithms to provide more expert and personalized help. A brief demo at the company's developer conference last month showed the service suggesting several possible skin conditions based on uploaded photos. Machines have matched or outperformed expert dermatologists in studies in which algorithms and doctors scrutinize images from past patients. But there's little evidence from clinical trials deploying such technology, and no AI image analysis tools are approved for dermatologists to use in the US, says Roxana Daneshjou, a Stanford dermatologist and researcher in machine learning and health. "Many don't pan out in the real world setting," she says.

Robotic Process Automation (RPA) has helped businesses reduce the tedium of mundane tasks for people, giving them the opportunity to work on more rewarding tasks. The intelligent application of machine learning (ML) and artificial intelligence (AI) can take RPA to the next level. Software developers have been creating programs to automate tasks for many years, but RPA tools have democratized automation, bringing it into the reach of almost everyone. Drag and drop tools allow frontline workers to take everyday tasks and automate them. For example, a human resource (HR) officer might receive emails each day for leave requests.

Massive data is generated by sensors placed by billions of connected devices around the world. IoT is everywhere, consider the rise of smart watches that allow people to track their fitness, monitor their sleeping patterns, measure their heart rate, to smart sensors that go beyond the human reach in industrial maintenance activities, IoT is everywhere. Think of a future where self-driving cars will collect, process, and store driving data at the edge to make road travel safer and more enjoyable. IoT Techology is deployed by a handful of companies to keep track of their pest populations. For instance, Semios, uses sensors and machine vision technology to check the pest populations in vineyards, orchards and other agricultural settings.

As an emerging technology, AI faces and will continue to face its fair share of challenges. On the one hand, consumers remain wary about adopting new tech. Envisioning a world where humans are displaced by AI-empowered machines gone amuck may be haunting a few late adopters. On the other hand, companies express frustration that AI has yet to prove itself to be the magic pill that will streamline every business process and pave a path to bountiful profits. Here is tapping into the human side of AI.

Akshaya Asokan works as a Technology Journalist at Analytics India Magazine. She has previously worked with IDG Media and The New Indian Express. When not writing, she can be seen either reading or staring at a flower.

As companies begin to understand the vast potential of their data, the question they face is: How does our business make the most of it? The answer lies in getting real-time insights that enable better business decisions and accelerated product development. But what if the insights were used not just by humans, but by the systems themselves, leading to ongoing optimization at previously inconceivable speed and accuracy? That's the promise of adaptive intelligence (AI) and machine learning, which are already impacting consumer experience with personalized shopping, self-driving vehicles, online wealth management, and virtual assistants. Here, we'll look at how data is driving the coming AI revolution.

"For health care practitioners to remain relevant, it really means understanding data," said Dr. Mark Michalski, executive director of the Center for Clinical Data Science at Massachusetts General Hospital and Brigham and Women's Hospital. In fields like medical imaging, he says, "machine learning is going to be central to a lot of what we do." New technologies could improve the ability to detect and diagnose lung tumors or nodules, for instance. The challenge, he says, is incorporating emerging technology into health practice.

As companies begin to understand the vast potential of their data, the question they face is: How does our business make the most of it? The answer lies in getting real-time insights that enable better business decisions and accelerated product development. But what if the insights were used not just by humans, but by the systems themselves, leading to ongoing optimization at previously inconceivable speed and accuracy? That's the promise of adaptive intelligence (AI) and machine learning, which are already impacting consumer experience with personalized shopping, self-driving vehicles, online wealth management, and virtual assistants. Here, we'll look at how data is driving the coming AI revolution.