Systems like these rely on a technology infrastructure that can import, analyse and interpret huge volumes of data and take appropriate action without the need for human intervention. While this is already a reality for the types of recommendations referenced above, the next step for AI or machine learning technologies is to form an established part of customer service and broader business operations, says Neil Sholay, Head of Oracle Digital, EMEA.

Artificial Intelligence and Machine Learning will be eating the world. Don't take my word for it -- in the roundup of venture capital predictions for 2017, I found it to be the top recurring theme. Since AI and ML startups cut across verticals (analytics, fintech, health, adtech, security, etc), it's easier to group them under the "machine intelligence" umbrella, coined by Shivon Zillis, a partner at Bloomberg Beta. In 2016 alone, 300 "machine intelligence" (AI ML) startups in Europe raised over €1.4 Billion in VC funding. While the terms AI and ML get thrown around readily, the companies that truly apply Artificial Intelligence, Machine Learning, Computer Vision and Deep Learning have the potential to address problems that were unsolvable before.

Microsoft CEO Satya Nadella spoke at a public event in India on Monday, stressing upon the immense potential of artificial intelligence (AI), calling it the "ultimate breakthrough" in technology. SEE ALSO: Here's why those tech billionaires are throwing millions at ethical AI "Because for all the advances in computer interface, there is nothing to beat language [the ability to do human-level speech recognition]," he said during a fireside chat with Nandan Nilekani -- India's premier technocrat and the brain behind the Aadhaar identification system. The chat was streamed live on the Microsoft Developer page on Facebook. Nadella and Nilekani were later joined by Binny Bansal, CEO of Flipkart, India's largest e-commerce company that announced a cloud partnership with Microsoft's Azure. Calling AI "the third run time", Nadella said, "If the operating system was the first run time, the second run time you could say was the browser, and the third run time can actually be the agent. Because in some sense, the agent knows you, your work context, and knows the work. And that's how we are building Cortana. We are giving it a really natural language understanding."

"Whether you know it or not, as an executive in a company, your company is doing machine learning and artificial intelligence," says Stephen Pratt. "It's just in hidden pockets within your company." Pratt is the founder and CEO of Noodle.ai, In this talk clip from Strata Hadoop World 2016 in New York, he describes a framework for determining where your company stands on its technological leadership, how your company's technology can contribute to shareholder value, and the importance of developing a meaningful plan for implementing artificial intelligence in the enterprise. For high-level data strategy, join the Strata Business Summit, March 14-16, 2017.

As companies talk seriously about the workplace role of adaptive intelligence, machine learning, artificial intelligence, and natural language understanding, it's time to talk about the fundamental shift in user experience (UX) these emerging technologies could also bring about. The goal is to deliver more personalized and contextual user experiences based on where users are and what they're doing. Any form of intelligence, whether machine learning or AI, is there to anticipate your needs. This extends beyond computers--a butler, for example, is there to do what you ask and anticipate your needs. From a UX perspective, we can look at any transaction.

As machine learning becomes more accessible through avenues such as Intel Corp.'s BigDL and IBM Corp. opening Watson's core machine learning components up to businesses, some developers and industry insiders are cautioning against getting too dazzled by the potential without considering the human role. However much data those programs can process, in the end, "what you do with the results of algorithms is key," said Jean-Francois Puget, Ph.D. (pictured), distinguished engineer, machine learning and optimization, IBM Analytics, at IBM. Puget spoke with Dave Vellante (@dvellante) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media's mobile live streaming studio, at the IBM Machine Learning Launch Event in New York, NY. He offered his perspective on machine learning and its applications. "For most people, machine learning equals machine learning algorithms," Puget said. "When you look at newspapers or blogs, social media, it's all about algorithms. Our view [is] that sure, you need algorithms for machine learning, but you need steps before you run algorithms, and after."

Emerging technologies such as Artificial Intelligence (AI) must be harnessed strategically in national building to provide equal access to opportunities to all, Nadan Nilekani, the former chairman of India's Unique ID Authority, said during a conference in Bengaluru on Monday. Failure to do so will invite a backlash, Nilekani warned, as large numbers of people see themselves being left out with the fruits of technological advancement benefiting only a few. "How do we use all this stuff that you guys have built and flip it around to benefit large numbers of people, and I think that is very important," Nilekani said in a discussion with Satya Nadella, CEO of Microsoft Corp, at a conference on Artificial Intelligence organised by the company. "If we don't do that, I think the backlash, as we've seen in the US, is going to be quite a lot." Nilekani wasn't available for a follow-on discussion, but he was probably referring to many more Americans voting for Donald Trump -- helping Trump become their president -- than any pundits had anticipated, as they feared that his opponent Hillary Clinton represented the elite.

Many machine learning applications require extensive preprocessing on the data for state-of-the-art performance. A large number of preprocessing procedures have to be fused with a learner to create a wrapped learner, which assures that the same preprocessing is used in training and prediction. This creates a preprocessing chain which can be added to any learner with a single command to construct a configurable pipeline. The R programming language already offers a general purpose package for piping function output to new functions, magrittr. This is heavily utilized by the package dplyr for data manipulation, but mostly consists of basic low level functionality, e.g., mutation, aggregation, selection, filtering etc.

Potentially describing how general artificial intelligence will look like. Since scientists started building and training neural networks, Transfer Learning has been the main bottleneck. Transfer Learning is the ability of an AI to learn from different tasks and apply its pre-learned knowledge to a completely new task. It is implicit that with this precedent knowledge, the AI will perform better and train faster than de novo neural networks on the new task. DeepMind is on the path of solving this with PathNet.

Google's ongoing experiments into machine learning are always fascinating, fundamentally reconfiguring the way things are programmed and implying possible sea changes in, well, pretty much any field that involves computers. Still, we are in the early days of this technology, and its impression of humanity is noticeably off. This is particularly true when it attempts art. We have marveled together over a neural network's stomach-churning attempt at writing a Christmas carol--never forget the line "I've always been there for the rest of our lives"--and now Google has released something called the A.I. Duet. As coder and musician Yotam Mann describes above, the technology has absorbed vast quantities of music to create an idea of what relationships of notes sound like together.