Artificial Intelligence (AI) is the ability of a computer or computer-controlled products to perform tasks that only intelligent beings are able to do. The most common use for this term is to categorize projects, systems, or products that are able to develop intellectual processes normally performed by humans, such as reasoning, analyzing, generalizing, learning from previous experiences, etc. Business analysts expect that the AI industry invade business processes around the world, estimating that the AI market will grow at an annual rate of 20%. In the past four years, there has been a 270% increase in organizations that have recently implemented AI products. Additionally, analysts estimate that 80% of emerging technologies will have an AI component. The recent disruption of this sector has also reached the intellectual property sector.

Advanced analytics, artificial intelligence and machine learning are arguably the most powerful general-purpose technologies invented since the dawn of modern computing. Extracting value from these is an imperative for business and society. It requires a deeper understanding and self-reflection among leaders of human strengths and frailties in contrast to that of modern, software-based machines and algorithms, writes Ravi Bapna in this opinion piece. Bapna is a professor of business analytics and information systems at the University of Minnesota's Carlson School of Management. Companies and societies are at the precipice of rebuilding their foundations to compete in an age of advanced analytics, artificial intelligence (AI) and machine learning (ML).

Research forecasts that by 2025, machines will perform more current work tasks than humans. Murat Sonmez, member of the managing board, and Head of the Centre for the WEF Fourth Industrial Revolution Network, expands on the role humans might play. The Fourth Industrial Revolution (4IR) is at the center of the current economic frontier. In reality, is Africa prepared for such changes? Moving quickly and being agile are key principles of success in the 4IR.

Revised text of talk I gave for the Harvard Library Leadership in a Digital Age program. The description of this course promises that "you will identify fundamental changes occurring in the field of knowledge management and consider their implications for libraries, information services, and library leadership." I think my session maybe breaks the rules a bit (which is my first leadership tip for you: when it feels like the right thing to do, break the damn rules!). One of the things I think is important for library leaders is that we look at fundamental changes outside of knowledge management and consider their implications for libraries and the work we do. I think looking outside of changes in our own field is essential if we want to be active, effective leaders who don't merely respond to change, but who create and shape the change we believe is needed in libraries and archives.

Like it or not, data-driven artificial intelligence algorithms and other high-tech robotic applications are coming to fill our jobs. An analysis by PwC estimated that up to 38 percent of current American jobs could be taken over by machines within the next 15 years. Even white-collar jobs aren't safe, since algorithms are capable of governing sophisticated tasks for machines in ways that previously were unthinkable, such as writing or distributing pharmaceuticals. The transition has given rise to fears. On a small scale, individuals in potentially obsolete positions are worried they won't be able to support their families.

Like it or not, data-driven artificial intelligence algorithms and other high-tech robotic applications are coming to fill our jobs. An analysis by PwC estimated that up to 38 percent of current American jobs could be taken over by machines within the next 15 years. Even white-collar jobs aren't safe, since algorithms are capable of governing sophisticated tasks for machines in ways that previously were unthinkable, such as writing or distributing pharmaceuticals. The transition has given rise to fears. On a small scale, individuals in potentially obsolete positions are worried they won't be able to support their families.

Neural networks are machines and algorithms developed to behave like the human brain--but a development from Google Translate shows that (once again) AI can outperform humans in a big way. Google's AI can now translate language pairs it has not been trained for. To be clear, this means that it can translate between languages that it wasn't taught to translate. This works if the AI first translates both of the languages into a common language that it knows. The development is detailed in a paper published on Cornell University's arXiv.