No matter your role on the L&D team or the makeup of your organization, it's important to consistently review and evaluate new technologies, tools, and trends to see if they make sense for your organization. Technology changes rapidly, and if you fail to keep up, you will be left behind. Learning technologies (LT) do not just include new software and emerging tech. It also includes the LT ecosystem--a collection of people, processes and tools that deliver, integrate, and support the L&D function across your organization--and that requires knowledge in assessing, defining, and articulating requirements. Ensuring that the latest advancements benefit both the learner and the organization means understanding of the learners' needs and overall experience.

The technology environment in which businesses and society operate is becoming increasingly complex. Learn how organizations can use artificial Intelligence to secure our digital society moving forward.

Increasingly, the data that is relevant for companies' machine learning efforts will be not just some data, but all of it; anything less risks missing what could conceivably be the critical insight down the road, the answer to questions as yet Chief information officers of companies have a strange predicament in an age of AI: They are meant to solve problems for companies by marshaling the relevant data on customers and transactions, but the data itself is going to raise new, unexpected questions. Increasingly, the data that is relevant for companies' machine learning efforts will be not just some data, but all of it; anything less risks missing what could conceivably be the critical insight down the road, the answer to questions as yet unasked. Until recently, the era of "big data," as it's called, has been about providing only the requisite information to answer some straightforward question, where the "known unknowns" are all that matters. For example, if you're a retailer, you might want to know how many of your customers would be likely to return items they've bought based on patterns of purchases. In fact, a group from Indian online apparel retailer Myntra this summer showed off a machine learning model for just such an application.

Increasingly, the data that is relevant for companies' machine learning efforts will be not just ... [ ] some data, but all of it; anything less risks missing what could conceivably be the critical insight down the road, the answer to questions as yet unasked. Chief information officers of companies have a strange predicament in an age of AI: They are meant to solve problems for companies by marshaling the relevant data on customers and transactions, but the data itself is going to raise new, unexpected questions. Increasingly, the data that is relevant for companies' machine learning efforts will be not just some data, but all of it; anything less risks missing what could conceivably be the critical insight down the road, the answer to questions as yet unasked. Until recently, the era of "big data," as it's called, has been about providing only the requisite information to answer some straightforward question, where the "known unknowns" are all that matters. For example, if you're a retailer, you might want to know how many of your customers would be likely to return items they've bought based on patterns of purchases.

There is no doubt that artificial intelligence is revolutionising the world we live in today. In particular when it comes to makes big decisions in terms of life and death. With the help of artificial intelligence (AI), even the medical industry can take advantage of the technology and improve the health care system which is very intriguing and promising. There is huge interest in improving and developing machine learning technology for the medical industry in regards to improving machine errors and reducing human oversight. To look a bit closer to the advantages of such technology, here are some examples.

Fear continues to saturate our lives: fear of nuclear destruction, fear of climate change, fear of the subversive, and fear of foreigners. But a recent Rolling Stone article about our "age of fear" notes that most Americans are living "in the safest place at the safest time in human history." Around the globe, household wealth, longevity and education are on the rise, while violent crime and extreme poverty are down. In the U.S., life expectancy is higher than ever, our air is the cleanest it's been in a decade and, despite a slight uptick last year, violent crime has been trending down since 1991. Emerging technology and media could play a role.

The world of cyber security is becoming increasingly more difficult for businesses looking to protect their assets. Attacks are becoming more sophisticated, with cyber criminals making use of ever advancing technologies. Even with modern machine driven security systems, it is becoming increasingly complex for businesses to differentiate between a genuine visitor and criminals attempting to breach or bring down their systems. With news that MIT has developed Artificial Intelligence (AI) capable of detecting 85% of cyber attacks - and still learning - does the future of cyber security lie with robots? Built by MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx, this artificial intelligence platform known as AI2 will spark an interesting debate about the role of AI in protecting an organization from cyber attacks.

Darwin's theory of evolution is a simple but powerful framework that explains how complexity can come from simplicity: how everything biological around us - from the microbial biofilms on your teeth to the majestic redwood trees - emerged from the very simplest of beginnings. How exactly this happened is, of course, a matter of intense research. Each species is finely adapted to thrive in its environment, which in turn has shaped that species' evolutionary history. But those environmental forces exerted on a species occurred over a very long period of time, in the often very distant past. How can we understand which environmental features were responsible for which adaptations we see today?