These are exciting times for data science professionals. With the rapid advances being made in analytics, specifically around machine learning, deep learning and AI, researchers and practitioners around the world have started adapting these technologies for deep, human-impacting applications. Social talk is replete with conversations that have shifted gears from self-driving cars to flying vehicles, smart machines to robots that are already collaborating quietly in our homes, and chatbots to conversational AI which is becoming omnipresent, contextual and indistinguishable from a human response. Many are proud of the fact that, as a race, humans have managed to create real-time intelligence, one that hasn't naturally evolved. With advances across disciplines, we have finally cracked several challenges in simulating human intelligence and are starting to surpass it in some areas.

These are exciting times for data science professionals. With the rapid advances being made in analytics, specifically around machine learning, deep learning and AI, researchers and practitioners around the world have started adapting these technologies for deep, human-impacting applications. Social talk is replete with conversations that have shifted gears from self-driving cars to flying vehicles, smart machines to robots that are already collaborating quietly in our homes, and chatbots to conversational AI which is becoming omnipresent, contextual & indistinguishable from human response. Many pride on the fact that, as a human race, we have managed to create real-time intelligence, one that hasn't naturally evolved. With advances across disciplines, we have finally cracked several challenges in simulating human intelligence and are starting to surpass it in some areas.