Imagine a future office in which the robot not only makes you tea but actually gets better at it over time. That is the very real future that awaits most workers. "You can imagine a world in five years' time when teams are made up of company employees, crowd specialists, and cognitive robots -- all creating and collaborating together", says Shamus Rae, the head of innovation and investments for KPMG in the UK. The future of robotic process automation also promises to bring more "human" intelligence to robots. "The robot would gain experience from its many interactions -- potentially thousands or more each day -- and gradually improve its responses", Rae explains in the KPMG report "The crowd in the cloud".

In the first blog post of this series we introduced the topic of machine learning and discussed why there is a lot of excitement around the topic. In this blog we explore different types of machine learning. Let's start with a simple example that everyone can relate to. You want to teach a three year old some basic discipline of keeping their toys in the right place. The room is full of interlocking blocks and soft toys.

Speaking to Anton was worrying, it seems that robots are already having better outcomes as psychologists and coaches. Which doesn't bode that well for me, time to consider what other skills I have that a robot doesn't. Artificial intelligence is already being used to deliver services that people once did, however, the surprising impact is that it is the professional services that are currently being hit the hardest. Professional services such as lawyers, doctors, and psychologists are the target of the first wave of the implementation of artificial intelligence. To give you an example, a chatbot lawyer recently overturned 160,000 in parking fines.

Previously I explored the value of eminence and thought leadership to consulting firms, and how unfortunately the power of inbound content marketing has a dark side that forms part of a three-pronged attack on the consulting industry. Meanwhile, the tireless invention and innovation efforts of research teams in companies around the world have helped to keep the pace of technological advancement in computer processing power at or above Moore's Law for several decades. This has given technology companies the ability to put more computing power than the entire Apollo space program into the pockets of more than a billion people around the world. It seems like everything has become digital, including music, books, and even movies. Increasingly intelligent digital technologies and mercurial customer expectations threaten both people and enterprise at every turn.

Previously I explored the value of eminence and thought leadership to consulting firms, and how unfortunately the power of inbound content marketing has a dark side that forms part of a three-pronged attack on the consulting industry. Meanwhile, the tireless invention and innovation efforts of research teams in companies around the world have helped to keep the pace of technological advancement in computer processing power at or above Moore's Law for several decades. This has given technology companies the ability to put more computing power than the entire Apollo space program into the pockets of more than a billion people around the world. It seems like everything has become digital, including music, books, and even movies. Increasingly intelligent digital technologies and mercurial customer expectations threaten both people and enterprise at every turn.

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 is the seventh in a special series of commissioned essays MIT Sloan Management Review will be publishing in Frontiers over the Spring and Summer of 2016. 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.

Assigning people the tedious task of manually reviewing and manipulating documents is a sure-fire way to miss errors and introduce inaccuracies as blurry-eyed employees move between programs and cut-and-paste data. Although this is standard practice at many companies, it's a problem that Robotic Process Automation (RPA) technology is now able to solve. RPA can mimic a human's ability to gather data and put data into Excel spreadsheets. It can access disparate systems (ERP, sales, tax, etc.), review tens of thousands of documents in an hour or two (instead of several days), do so without error, and put the data in a standardized format. But RPA's advantages go far beyond imitating keystrokes and avoiding errors; RPA's speed, accuracy and formatting abilities make it an essential foundational technology for a more automated, intelligent enterprise.

Artificial Intelligence is rapidly coming of age, as business leaders increasingly grasp the immense potential of "smart" machines and other innovations as catalysts for greater efficiency and competitiveness. The dollars tell a compelling story. By 2019, the global market for content analytics, discovery and cognitive systems software is projected to reach 9.2 billion, according to IDC, more than double that of 2014.* We see tremendous opportunities ahead for our clients. However, potential rewards will only become reality through substantial research and development, and we've got a lot of work ahead.

Hadoop distributor MapR has raised a new round of funding and may be preparing for an IPO next year, Salesforce acquires analytics startup BeyondCore, Coursera releases a new data analytics course together with PwC, and Apple CEO Tim Cook provided some illumination on how his company regards artificial intelligence (AI). We've got all the highlights in this Big Data Roundup for the week ending Aug. 21, 2016. Let's start with the news from Hadoop distributor MapR. The company recently announced that it has raised a round of equity financing worth 50 million, and provided a few select details about its financial performance. MapR is still a privately held company, so it can choose what to disclose and what not to disclose.

Succeeding in today's fast-paced business economy requires companies to harness data quickly and at scale. As the volume, velocity, and variety of data increase, it's becoming necessary to use machine learning and artificial intelligence (AI) to sift through all the incoming information, make sense of it, and accurately predict future business direction. It takes the right expertise, the right tools, and the right data to achieve the promise of machine learning. Even with all of those factors in place, it's still easy to get it wrong. "Machine learning gives us a very powerful set of techniques for making predictions, but it can also lead to disastrous results if you don't understand what your machine learning algorithm is doing," said Spencer Greenberg, a mathematician and founder of decision-making website ClearerThinking.org, in an interview.