You may be dazzled, spooked or annoyed by the ability of online retailers to predict which products you're interested in purchasing--but what if you could play the same game? Whether it's Amazon recommending soap or Netflix suggesting a movie, more companies are making personalized predictions using variations on a machine learning tactic called market basket analysis. This technique uses an algorithm that sorts through behavioral data to determine how frequently certain actions (purchases, views, etc.) are associated with other actions. The algorithm provides the statistical likelihood that if one action takes place, another desired action is likely to follow. Sophisticated algorithms aren't limited to shopping carts or movie recommendations, however.

Ever heard of AI for IT management (AIOps)? It's a burgeoning ecosystem of platforms and products that enhance IT operations by leveraging AI to analyze data collected from various tools and devices. OpsRamp is one of the startups leading the charge in what Research and Markets anticipates will be a $14.3 billion segment by 2025. Raju Chekuri and Varma Kunaparaju cofounded the San Jose, California-based company in 2014 with the goal of developing "service-centric" offerings for enterprises, and their investors evidently believe they've achieved that. Today OpsRamp announced the closing of a $37.5 million investment led by Morgan Stanley Expansion Capital, with new investor Hewlett Packard Enterprise.

From chatbots shopping recommendations -- AI has impacted every user's life today For most users, the first experience of artificial intelligence (AI) was through chatbots that responded to questions with preset responses. From Indian Railways' saree-clad chatbot Disha, to Amazon and Flipkart's shopping assistants, to Instagram's algorithm-backed advertisements, dynamic pricing on Uber and Ola to restaurant recommendations on Zomato and Swiggy -- are all examples of AI use-cases in digital marketing. While that may be the most basic way that AI and natural language processing is being used in India for marketing and sales, these days technology has grown beyond just bots on websites. According to Sudeshna Datta, cofounder of Absolutdata, marketing is largely about enhancing user experience whether traditional or digital. And these days, artificial intelligence is helping digital marketers provide the customer experience that directly boosts retention and increases brand loyalty measurably.

Will artificial intelligence (AI) create an environment where design thinking skills are more valuable than data science skills? Will AI alter how we define human intelligence? That sounds like questions one might expect from an episode of Rod Serling's TV series Twilight Zone. Instead of AI replacing humans, will AI actually make humans more human? Will characteristics such as empathy, compassion, and collaboration actually become the future high-value skills that are cherished by leading organizations?

RPA is most commonly thought of as a productivity and efficiency tool – employees simply don't WANT to waste time on the menial, repetitive tasks, and through leveraging automation tools they are afforded the opportunity to focus on the stuff that really matters. However as we enter a new year and a decade of possibilities, we're seeing the scope and application of Intelligent Automation (IA) technologies expand into previously unexplored territories. Tools that can automatically identify processes to automate, act upon this insight, and optimise deployments throughout to guarantee the best possible results. What a wealth of opportunities this could unlock. But what can what we can expect to see from Intelligent Automation in the next year?

Fujifilm India is eyeing double-digit growth every year from now, against the 9-10 per cent growth it has been clocking over the past few years, largely driven by an expansion of its medical products business. This would entail a shift in focus to offering medical solutions through Internet of Things (IoT)-enabled products. While the company has not invested in any manufacturing facility at the moment, it may consider setting up one here in the future for IoT- and artificial intelligence (AI)-enabled medical products, considering the huge potential in India, Haruto Iwata, Managing Director, Fujifilm India, told BusinessLine. Some of the company's IoT application software is already being developed in India, he added. Going forward, Fujifilm India's primary focus will be on offering healthcare solutions through AI and IoT, he said.

Recent years have seen the expansion of artificial intelligence into an array of industries with varying levels of disruption. Once a horizon-technology (perhaps similar to how we now view quantum computing) AI has officially breached everyday life, and informed opinions are no longer reserved for tech enthusiasts and elite data scientists. Now, stakeholders include executives, investors, managers, the government, and ultimately customers. While conversations regarding Explainable AI (xAI) date back decades, the concept emerged with renewed vigor in late 2019 when Google announced its new set of xAI tools for developers. The concept of xAI is relatively simple: historically, machine learning models have operated within a "black box," with outcomes determined by an astounding number of interwoven parameters so complex (in the millions) that explaining them proved impossible.

The danger of having artificially intelligent machines do our bidding is that we might not be careful enough about what we wish for. The lines of code that animate these machines will inevitably lack nuance, forget to spell out caveats, and end up giving AI systems goals and incentives that don't align with our true preferences. A now-classic thought experiment illustrating this problem was posed by the Oxford philosopher Nick Bostrom in 2003. Bostrom imagined a superintelligent robot, programmed with the seemingly innocuous goal of manufacturing paper clips. The robot eventually turns the whole world into a giant paper clip factory. Such a scenario can be dismissed as academic, a worry that might arise in some far-off future.

It took me an embarrassingly long time to realize that the "black mirror" of the popular anthology series Black Mirror was a screen, or rather, all the screens we surround ourselves with: phones, tablets, computers, TVs, and, increasingly, futuristic devices built by massive corporations that monitor our movements and preferences and words. We buy these black mirrors, welcoming them into our homes and lives and letting them -- true to their name -- reflect ourselves back to us. And as we know all too well, those reflections sometimes betray our darkest impulses. Unsettling reflections are not the black mirrors' fault. Gadgets are merely assemblages of wires and metal and glass. Devices don't have a point of view; they operate according to the input they receive, the algorithms and designs and patterns that power the software, written by humans and thus shaded and slanted by human biases.

As these teachers have come to understand, you don't have to be technically minded to introduce your students to the important concepts behind artificial intelligence. When computer science teacher Sharon Harrison wanted to introduce her eighth graders to the basic idea of artificial intelligence, she had them try out an online chatbot called Akinator, which asks the user questions to determine what historic or fictional character he or she is thinking of. In some instances, the students marveled at how quickly the program could figure out the answer. "Sometimes it would guess in three or four guesses, and we'd say, 'How on earth was it able to do that?'" From there, the discussion in this elective class at the University of Chicago Laboratory Schools examined how responses to the chatbot could be sabotaged -- by responding to the questions incorrectly, and thereby "damage the integrity of the program."