No one is surprised to hear that Michael Jordan is at the top of his list. A recent study has deemed him to be the most important player in his chosen field... Okay, before you wonder if you went to the wrong site, you didn't. He is currently the Pehong Chen Distinguished Professor at the University of California, Berkeley. So what list are we talking about and how did he get to the top? Well, for those of you that have dipped into academics as a career, you already know that your reputation is established by the connections that are expressed in your published work.

During the last couple of weeks, I spent some time in San Francisco and Napa Valley. Next week, I will revert to the Napa Valley trip, but this Monday, I will focus on the visit to the world's technology centre and the start up environment there. Now, clearly it is a relatively new focus area for me, as you all know and therefore, I am still easily impressed. But, boy, when you are used to the toxic and money-starved startup environment in Europe, and you then experience the amazing energy and huge money floating around in Silicon Valley, it is really hard not to be impressed. Or rather, it is hard not to reconsider your priorities, and this is exactly what I intend to do.

I've written a few times recently about a number of projects that are using drone technology to monitor vast environments. As you can perhaps imagine, with such endeavors, there is a huge amount of data generated, and while it presents rich pickings from a scientific perspective, nonetheless raises challenges about how that data can be managed. A recent study tested the role automation could play in both easing the burden on research teams and making data analysis more effective. The paper revealed that when teams are looking for optimal speed and accuracy that an approach that combines both machine and human can be the best. The researchers used the analysis of aerial images taken by camera drones in the Kuzikus wildlife reserve as their testing ground. The drones were used to count the wildlife in the park, and generated a huge amount of images over the course of the study.

AILA, or Artificial Intelligence Lightweight Android, prepares to press switches on a panel it recognizes during a demonstration at the German Research Center for Artificial Intelligence GmbH (Deutsches Forschungszentrum fuer Kuenstliche Intelligenz GmbH) stand at the 2013 CeBIT technology trade fair on March 5, 2013 in Hanover, Germany. Artificial intelligence (AI) in fictional films is a thing of the past. In fact, the ever-evolving field of AI has become ubiquitous, making big strides in technological innovations. But as artificial intelligence revolutionizes healthcare, education, businesses and becomes a "meta-solution" to the world's biggest problems, its ubiquity spawns one major question: Will future AI wipe out humanity? Artificial intelligence has a more useful presence in humanity today.

VC investment in the space remains vibrant and the first few week of weeks of 2016 saw a flurry of announcements of big founding rounds for late stage Big Data startups: DataDog ( 94M), BloomReach ( 56M), Qubole ( 30M), PlaceIQ ( 25M), etc. Big Data startups received 6.64B in venture capital investment in 2015, 11% of total tech VC. In terms of fundamental trend, the action (meaning innovation, launch of new products and companies) has been gradually moving left to right, from the infrastructure layer (essentially the world of developers/engineers) to the analytics layer (the world of data scientists and analysts) to the application layer (the world of business users and consumers) where "Big Data native applications" have been emerging rapidly… The big trend over the last few months in Big Data analytics has been the increasing focus on artificial intelligence (in its various forms and flavors) to help analyze massive amounts of data and derive predictive insights. The recent resurrection of AI is very much a child of Big Data. The algorithms behind deep learning (the area of AI that gets the most attention these days) were for the most part created decades ago, but it wasn't until they could be applied to massive amounts of data cheaply and quickly enough that they lived up to their full potential… In turn, AI is now helping Big Data deliver on its promise.

High-tech entrepreneur Elon Musk has launched an open-source training "gym" for artificial-intelligence programmers. It's an interesting move for a man who in 2014 said artificial intelligence, or A.I., will pose a threat to the human race. "I think we should be very careful about artificial intelligence," Musk said about a year and a half ago during an MIT symposium. "If I were to guess at what our biggest existential threat is, it's probably that... with artificial intelligence, we are summoning the demon. In all those stories with the guy with the pentagram and the holy water, and he's sure he can control the demon. Today, Musk is moving to help programmers use A.I. and machine learning to build smart robots and smart devices. "We're releasing the public beta of OpenAI Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms," wrote Greg Brockman, OpenAI's CTO, and John Schulman, a scientist working with OpenAI, in a blog post . "We originally built OpenAI Gym as a tool to accelerate our own RL research.

San Francisco – April 28, 2016: Infosys (NYSE: INFY), a global leader in consulting, technology, and next-generation services, today announced the launch of Infosys Mana, a platform that brings machine learning together with the deep knowledge of an organization, to drive automation and innovation – enabling businesses to continuously reinvent their system landscapes. Mana, with the Infosys Aikido service offerings, dramatically lowers the cost of maintenance for both physical and digital assets; captures the knowledge and know-how of people, and fragmented and complex systems; simplifies the continuous renovation of core business processes; and enables businesses to bring new and delightful user experiences leveraging state of the art technology. Over the last 35 years, Infosys has maintained, operated and managed systems with global clients across every industry. Building on this deep experience, Infosys has recognized the need to bring artificial intelligence to the enterprise in a meaningful and purposeful way; in a way that leverages the power of automation for repetitive tasks and frees people to focus on the higher value work, and on breakthrough innovation. Today's AI technologies address part of this with learning and information; Infosys is now bringing this together in a fundamental way with knowledge and understanding of the business and the IT landscape – critical knowledge that is locked inside source code, application silos, maintenance logs, exception tickets and individual employees.

Do a google search for "all-inclusive beach holiday" and all you see for the next weeks are advertisements for all-inclusive holidays following the virtual you. Google knows you better than your family or friends- creepy but true! Whether or not we realize it, we have made the decision to donate our data to the virtual world. Can we put our data to better use – to improve healthcare on a scale unimaginable a decade or two ago? AI systems feed on big data. Big data is nothing but a massive amount of data, the sheer size of which makes data analysis a challenge.

By 2030, India's population of people age 60 or older is projected to grow by 64 percent. Also by 2030, India's urban areas are expected to more than double their current population levels. Additionally, factors such as an increase in income levels, increases in health care insurance penetration, increases in private and public health care expenditure and rising consumer awareness will shape the future of the health care sector in India for the coming decades. Total spending on health care has increased at double-digit rates and accounted for 4 percent of GDP in 2013. However, government spending still remains low at 1.3 percent of GDP, making private expenditures as high as 2.7 percent of GDP in 2013.

I had a recent meeting with a person who was introduced to Machine Learning for the first time. It was interesting to know how someone totally new to the field would interpret what Machine Learning would be. He could instantly connect the term learning with what most Data Scientists would call Reinforcement Learning. A machine could observe phenomena and refine itself by itself is what he thought. It is weird that the one branch of Artificial Intelligence a layman could best connect to is the one least studied.