The future won't be made by either humans or machines alone – but by both, working together. Technologies modeled on how human brains work are already augmenting people's abilities, and will only get more influential as society gets used to these increasingly capable machines. Technology optimists have envisioned a world with rising human productivity and quality of life as artificial intelligence systems take over life's drudgery and administrivia, benefiting everyone. Pessimists, on the other hand, have warned that these advances could come at great cost in lost jobs and disrupted lives. And fearmongers worry that AI might eventually make human beings obsolete.

Analytics India Magazine brings you the annual list of the top data scientists in India, for the fourth year in a row. We have been identifying the ingenious minds in the world of data science and analytics who are also driving the innovations across various industries in India. From building analytics team to bringing newer processes in the working, the data scientists listed here have been instrumental in changing the face of the organisation. We have considered data scientists working with an organisation or independently, irrespective of size and nature of work. Here's the list of top 10 data scientists, to draw some inspiration and motivation from.

Machine learning is slowly changing the world -- helping cars to "see" the world around them and virtual assistant to understand our questions and commands. Driving forward machine-learning research are companies like Facebook, Google and Baidu -- each of which are identifying new applications for the technology. But how is the field of machine learning changing and what factors are shaping its future direction? Yangqing Jia, director of engineering for Facebook's AI platform team, spoke about the changing nature of the field at the recent AI Conference presented by O'Reilly and Intel AI in London. In supervised learning, the system learns by example, typically by analyzing labelled data, for example, photos annotated to indicate whether they contain a cat.

The future won't be made by either humans or machines alone – but by both, working together. Technologies modeled on how human brains work are already augmenting people's abilities, and will only get more influential as society gets used to these increasingly capable machines. Technology optimists have envisioned a world with rising human productivity and quality of life as artificial intelligence systems take over life's drudgery and administrivia, benefiting everyone. Pessimists, on the other hand, have warned that these advances could come at great cost in lost jobs and disrupted lives. And fearmongers worry that AI might eventually make human beings obsolete.

Technologies modeled on how human brains work are already augmenting people's abilities, and will only get more influential as society gets used to these increasingly capable machines. Technology optimists have envisioned a world with rising human productivity and quality of life as artificial intelligence systems take over life's drudgery and administrivia, benefiting everyone. Pessimists, on the other hand, have warned that these advances could come at great cost in lost jobs and disrupted lives. And fearmongers worry that AI might eventually make human beings obsolete. However, people are not very good at imagining the future.

Synechron Inc., a New York-based technology consulting and outsourcing firm, has started deploying data science and artificial intelligence-based applications for banking, financial services and insurance companies. These applications, or accelerators, will help financial services companies solve complex business problems by discovering relationships between events that impact one another and cause a future event to happen, the company said in a press release. Faisal Husain, co-founder and chief executive of Synechron, said the data science accelerators provide a powerful causation platform and business-driven use cases for four complex financial services challenges. Synechron said its s AI data science experts have developed a set of accelerators that allow financial services companies to address business challenges related to investment research generation, predicting the next best action for a wealth management client, high-priority customer complaints, and predicting risk on mortgage. The accelerators combine Natural Language Processing, Deep Learning algorithms and data science to solve problems.

If there is one true indicator to measure the disruptiveness of a new technology, it's certainly the public outpouring of fear and suspicion. If we use societal angst as a measure, the current renaissance of artificial intelligence (AI) is a good candidate for groundbreaking technological disruption. AI will change life as we know it, as Elon Musk, Bill Gates, Stephen Hawking and other great minds have told us. The widespread anxiety about the harmful consequences of AI applications is not an unparalleled reaction to technological change but rather an expression of the societal unease that commonly precedes the changes associated with new technologies and the vast potential that comes with them. We're looking beyond today's IoT, toward a future where smart connected devices not only talk with each other but where they use AI to interact with each other on our behalf.

It was seven minutes to ten o'clock in the morning, and it was the only good thing that had happened." If you get the feeling that these sentences could have been better structured, it's simply because these seemingly disparate, literary threads have been stitched into a novel by an algorithm. That's also why the human author of this novel, Ross Godwin, calls himself'writer of writers'. He is an artist and creative technologist at Google, and also a former Obama administration ghostwriter. In March 2017, Godwin fitted a Cadillac car with a surveillance camera, global positioning system (GPS) unit, microphone and clock, and connected these devices to a portable artificial intelligence (AI) writing machine that fed on these input data in real-time.

All around us, our perception of learning and intellect is being challenged daily with the advent of new and emerging technologies. From self-driving cars, playing Go and Chess, to computers being able to beat humans at classic Atari games, the advent of a group of technologies we colloquially call Machine Learning have come to dominate a new era in technological growth – a new era of growth that has been compared with the same importance as the discovery of electricity and has already been categorized as the next human technological age. Games and simulations are no stranger to AI technologies and there are numerous assets available to the Unity developer in order to provide simulated machine intelligence. These technologies include content like Behavior Trees, Finite State Machine, navigation meshes, A*, and other heuristic ways game developers use to simulate intelligence. The reason is due in large part to the OpenAI initiative, an initiative that encourages research across academia and the industry to share ideas and research on AI and ML.

The various cutting-edge technologies that are under the umbrella of artificial intelligence are getting a lot of attention lately. As the amount of data we generate continues to grow to mind-boggling levels, our AI maturity and the potential problems AI can help solve grows right along with it. This data and the amazing computing power that's now available for a reasonable cost is what fuels the tremendous growth in AI technologies and makes deep learning and reinforcement learning possible. With the rapid changes in the AI industry, it can be challenging to keep up with the latest cutting-edge technologies. In this post, I want to provide easy-to-understand definitions of deep learning and reinforcement learning so that you can understand the difference.