The past decade has witnessed tremendous influx of technology across all aspects of business operations. Organizations are increasingly turning to automation, artificial intelligence and robotics to bring about a transformation in their revenue generation capability, reduction in costs and increase in efficiency. Indian banking leaders like ICICI and HDFC have piloted using humanoids/robots to bring about operational efficiency by intelligently automating many of the backend processes and customer facing areas. On a similar note, global banks like Mizuho and Sberbank use virtual assistants to enhance customer experience. Technological advances are increasingly being viewed as game changers in the ever evolving business landscape.

Significant advances in artificial intelligence over the past few years have broadened AI's reach into industries such as healthcare, finance and even retail. Businesses and consumers alike are benefiting from the rise of big data and the growth of AI techniques like deep learning and natural language processing. But we're still only scratching the surface of what is possible with AI, and the full impact of the technology may be years away. In the near-future, however, AI advances will give rise to increasingly powerful applications like personal assistants with more robust utility in the workplace and in our personal lives. These assistants could provide personalized information, help us make more informed decisions, and perhaps even provide physical assistance.

It's hard to believe the first personal computer was introduced only 40 years ago. Captivated by the implications of this intelligent machine, the world began to speculate on the next big thing for IT. And while the idea of self-driving cars or the "smart" home seemed like science fiction back then, the future is now and these innovations are real. Fast forward to 2017 – technology is directly shaping how people live, work, and even think. State-of-the-art technology allows visions to become printed realities.

Machine learning-powered social analysis enables you to pinpoint exactly what you're looking for (here, the brand Tide) by using example posts to train the algorithm to recognize the patterns of language that indicate that a post is about Tide the brand, instead of something else. Machine learning's ability to interpret the nuances of language and return the most relevant results reaches far beyond identifying brand mentions.

No billion-dollar machine could replace a doctor. But a $25 machine can tell you when you need one. In 1996, the ER at Cook County Hospital of Chicago used an algorithm to determine when a patient with chest pain was in danger of having a heart attack and was thus worth one of its scarce hospital beds. Using a systematic, flowchart-based approach of basic tests, the algorithm proved not only to be quick and efficient, but accurate: It sorted 70 percent more patients into the low-risk category, but caught a higher percentage of heart attacks (95 percent) than human doctors (75-89 percent). And this was before any deep computing was involved.

Not all bots are created equal. To find one that can help you meet business goals, you're going to need one that can do much more than obey a few simple commands or respond humorously to questions. If your criteria for success goes beyond entertainment and engagement, and you're looking to achieve tangible business benefits -- such as reducing call volumes to the contact center or offsetting online shopping cart abandonment over the holiday season -- then understanding the difference between chatbots is critical. If you've ever engaged a virtual assistant like Alexa (or Google Assistant, Siri, or Cortana), then you probably don't have a lot of confidence in their ability to help improve your business in a serious way. And you're correct to be skeptical.

Microsoft Corp. is trying again in health care, betting its prowess in cloud services and artificial-intelligence can help it expand in a market that's been notoriously hard for technology companies. A new initiative called Healthcare NExT will combine work from existing industry players and Microsoft's Research and AI units to help doctors reduce data entry tasks, triage sick patients more efficiently and ease outpatient care. "I want to bring our research capabilities and our hyper-scale cloud to bear so our partners can have huge success in the health-care world," said Peter Lee, a Microsoft Research vice president who heads Healthcare NExT. Microsoft has tried to expand in health care before, with mixed results. It had a Health Solutions Group for many years, but combined that into a joint venture with General Electric Co.

In Investment Management, we are just getting a grasp on this social media thing, marketing automation, and trying to simplify to an investor what Alts are –and I want you to concern yourself with something out, on the horizon? We are typically late to the party with bleeding edge marketing capabilities –a blessing and a curse. We let other industries test and learn while warming Compliance up to fancy newish "toys." We are pardoned time of these means to engage with investors and/or Financial Advisors but when leadership or sales partners get hip to these channels, we play catch-up with aggressive deadlines. Not to mention the impact to our brand with not having parity with our friends in other categories.

Artificial intelligence boosters predict a brave new world of flying cars and cancer cures. Detractors worry about a future where humans are enslaved to an evil race of robot overlords. Veteran AI scientist Eric Horvitz and Doomsday Clock guru Lawrence Krauss, seeking a middle ground, gathered a group of experts in the Arizona desert to discuss the worst that could possibly happen -- and how to stop it. Their workshop took place last weekend at Arizona State University with funding from Tesla Inc. co-founder Elon Musk and Skype co-founder Jaan Tallinn. Officially dubbed "Envisioning and Addressing Adverse AI Outcomes," it was a kind of AI doomsday games that organized some 40 scientists, cyber-security experts and policy wonks into groups of attackers -- the red team -- and defenders -- blue team -- playing out AI-gone-very-wrong scenarios, ranging from stock-market manipulation to global warfare.

Niederhoffer will be sitting on the panel Artificial Intelligence – should we unplug man from the machine? In his view, whilst it is clear that in some domains machine learning and artificial intelligence is starting to make a big difference, the key is understanding which domains are appropriate and which domains are potentially problematic. Some domains, like object and speech recognition, linguistic analysis, and credit analysis are perfect for machine learning and particularly, deep learning algorithms. But in Niederhoffer's experience, making short term market predictions using machine learning is perilous, though possible.