In this episode of Coffee with a Googler, we meet with Christine Robson to talk all about Artificial Intelligence, Machine Learning and more. We look at project Magenta, and how it was used to create computer generated music. In addition to that we delve into how machine intelligence was used to award-winning effect in YouTube. And all of the technology that was used to build this is now available to you, open sourced.

Infosys is a global leader in consulting, information technology, outsourcing and next-generation services with clients in more than 50 countries. With 9.02 billion in Q2 FY16 revenues and more than 193,000 employees, the Indian multinational is helping enterprises redefine their present and future in a world where innovative solutions in mobility, sustainability, big data and cloud computing are required. Founded in 1981 by seven engineers with 250, Infosys is the second-largest Indian IT services company by 2016 revenues and was the fifth largest employer of H-1B visa professionals in the US in 2013. America is also home to its Global Head of Digital, Scott Sorokin, who has been a strategist and digital partner for senior-level executives at Fortune 100 companies for over 25 years. Formerly the the chief strategy officer at Publicis.Sapient/Razorfish and Rosetta, the New York-based Sorokin combines CXO-level business strategy, technology and marketing experience in a fast-changing global market to spur digital innovation at Infosys.

Artificial intelligence (AI) is poised to have a transformative effect on consumer, enterprise, and government markets around the world. An umbrella term that refers to information systems inspired by biological systems, AI encompasses multiple technologies including machine learning, deep learning, computer vision, natural language processing (NLP), machine reasoning, and strong AI. According to a new report from Tractica, these technologies have use cases and applications in almost every industry and promise to significantly change existing business models while simultaneously creating new ones. The market intelligence firm forecasts that annual worldwide AI revenue will grow from 643.7 million in 2016 to 36.8 billion by 2025. In sizing and forecasting the total global AI market, Tractica has identified 191 real-world use cases for AI, organized into 27 different industry sectors and corresponding with six major technology categories, plus multiple combinations of technologies.

In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They've mastered the ancient game of Go and thrashed the best human players. But there is a problem.

For millennia, our species has understood that what helps separate us from the animal kingdom is our ability to articulate abstract phenomena, like a fear of death in the absence of an immediate cause for alarm. But is our intelligence reducible to our biology? In the computer age, this question has assumed greater urgency since people such as Stephen Hawking have warned that unscrupulous research into artificial intelligence could pose a threat towards the human race. The idea of an untamed A.I. has energized the popular imagination for some time. In film, there are archetypes like HAL 9000 from "2001: A Space Odyssey" and the eponymous Terminator.

Imagine wanting a cup of coffee and suddenly finding it before you, freshly prepared to your exacting standards. In the not-so-distant future, this will be reality for Muggles, too. In fact, thanks to a surge in consumer data, brands and marketers can already make better inferences about consumer wants and needs, but as AI and machine learning are more deftly integrated, insights will only get better, as will the ability to anticipate consumer needs – and to even make decisions on behalf of consumers without any input from them whatsoever. Like, say, ordering a cup of coffee. As it stands, digital enables brands to customize offers for specific users rather than provide generic solutions.

Artificial intelligence and machine learning are at the core of the concept of a self-driving vehicle. By definition, such an automobile should not need human input, and must learn how to deal with the things it will face on a daily basis while driving on the open road. Powerful node hardware, sophisticated AI programming and a large, reliable backend are necessary for such an operation. Those same tools, however, could be used in a different sort of machine learning. Deep learning is a type of AI that seeks to imitate the human mind, and is found in projects like Google's Deep Dream.

While humans may be ahead of computers in the ability to create strategy today, we shouldn't be complacent about our dominance. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Editor's Note: This article is one of a special series of 14 commissioned essays MIT Sloan Management Review is publishing to celebrate the launch of our new Frontiers initiative. Each essay gives the author's response to this question: "Within the next five years, how will technology change the practice of management in a way we have not yet witnessed?" As a society, we are becoming increasingly comfortable with the idea that machines can make decisions and take actions on their own. We already have semi-autonomous vehicles, high-performing manufacturing robots, and automated decision making in insurance underwriting and bank credit.

The DeepMind unit, which Google bought in 2014 for roughly 533 million, develops supercomputers and artificial intelligence (AI). One of the AI programs DeepMind has developed is Wavenet, designed to mimic human speech. In blind tests human listeners indicated that Wavenet was the most natural sounding text-to-speech (TTS) program, after hearing samples from different programs in English and Mandarin Chinese. TTS programs continue to struggle to sound like natural speech, and Wavenet, while increasingly similar, does not yet sound just like actual human speech. Wavenet simulates certain brain functions by using what in artificial intelligence is called a "neural network."

Machine learning is a subfield of computer science, that grew out of the quest for artificial intelligence. It is so pervasive in today's world that you probably use it often in daily life, without even realising it. Machine learning has given us self-driving cars, effective web search, recommendations that you get when you visit web sites or social media sites, face detection in a digital photo album, stock trading etc. Machine learning enables computers to analyze vast amounts of data and automatically detect patterns and features, or make predictions regarding certain conditions. In a dynamic disease like cancer, gauging and diagnosing such a complex heterogeneity is the biggest challenge. After decades of cancer research, it has become increasingly clear that no two patients' cancers are exactly the same, and even within one person's tumor there is a wild diversity of cells. Accurate and quicker diagnosis is very crucial in rapidly progressing cancers.