Among the numerous articles dedicated to autonomous cars today, one particular thought experiment resurfaces often: the trolley problem. Adapted to self-driving cars, it is usually presented as follows: an autonomous car, with a person onboard, sees five pedestrians suddenly appear in its path. The only way to avoid them is for the car to send itself off the road, crashing and therefore causing its passenger's death. Faced with this Cornelian dilemma, what choice does the Artificial Intelligence control software have to make? This thought experiment is anything but science-fiction.

Syngenta and the AI for Good Foundation have partnered to launch the Syngenta AI Challenge, a new international competition focused on leveraging Artificial Intelligence (AI) tools for use in seed breeding. The competition is accepting submissions from applicants who are ready to put their programming skills to the test for the chance to win $7,500. "This new competition will give entrants the chance to use their talents to take on the extraordinary complexity of seed genetic data," said Joseph Byrum, Ph.D., MBA, PMP and senior R&D strategic marketing executive with Syngenta. "In the face of a rising global population, we need to grow plants that can adapt and thrive in changing conditions – especially as vital resources like water and land are finite. The Syngenta AI Challenge is about creating models that can help solve this puzzle and ensure world food security."

They often have to navigate, with limited resources, a stormy market made of customers, competitors, and regulators, and the interactions between all these actors make finding answers to business questions a complex process. But recently, machines have demonstrated their abilities to help shine some light on this chaos and provide, if not direct answers, context clues that help guide executives in using AI to to handle business problems. In this article, we delve into examples of how natural language processing (NLP) business applications can be applied at scale to address 5 pressing business questions. NLP is used by computers to manipulate human language, whether to extract meaning, generate text, or for any other purpose. The interaction computer-language is categorized according to the task that needs to be accomplished: summarizing a long document, translating between two human languages, or detecting spam email are all examples of tasks that today can be decently accomplished by a machine.

The information security industry's rush to adopt machine learning will help businesses burn US$96 billion on big data, intelligence, and analytics by 2021, says research house ABI . The report by lead number cruncher Dimitrios Pavlakis claims User and Entity Behavior Analytics (UEBA) and "deep learning algorithm designs" will be widely adopted by security companies as they collectively put big data to work detecting threats. The former machine learning technology, UEBA, is correlation on steroids, capable of detecting anomalies that can indicate if staff logins have been compromised and are being tested across the enterprise network. It can learn the activities and services most typical of a user to generate alerts when something anomalous occurs, like login attempts to odd network shares. Vendors are buying up across the space including Splunk's buy of Caspida, and Arksight selling Securonix.

Machine learning and cognitive computing have emerged as two of the hottest topics in digital marketing during this year, and look set to dominate much of the discussion in 2017. Yet many marketers are only just coming to an appreciation of technologies that have been under development for more than a decade. In large measure, that is because practical applications for cognitive computing through platforms like IBM's Watson have emerged only in recent years. These issues were covered in detail at a recent series of senior executive round tables hosted by IBM and ADMA in Sydney and Melbourne. In simple terms, think of Watson as a very large computer system that sits in the cloud and has advanced cognitive capabilities -- including the ability to ingest data, learn from that data and then provide answers by interacting in a variety of ways with people and systems.

The history of Artificial Intelligence is long, but it's only been recently that technology companies and markets have begun to get excited about it… Why? After a few decades of exploration of symbolic AI methods, the field shifted toward statistical approaches, that have as of late started working in a broad array of tasks due to the explosion of data and computing power, this in turn has led to machine learning and, most importantly, enabled deep learning. This is great news for the tech industry. The downside is that there aren't enough data scientists that understand deep learning. For those who do, there is a huge demand for their services.

The world is always moving in different directions towards an innovative future. IBM worked with Daybreaker, "an early morning dance movement in 15 cities around the world," to plan a special dance party. The genius super computer, Watson, that took down two all-star Jeopardy champs in 2011 was used to plan the first "cognitive dance party" in San Francisco. The computer is widely used for daily tasks in lung cancer treatment, financial advising and it even assists veterans return to civilian life. To understand the artificial intelligence even more, check out the video at the bottom of the article made by IBM. Watson is truly something amazing and the technology will surely advance the world.

The artificial intelligence research behind bots_alive promises to usher in lifelike robots at affordable price points. Artificial intelligence is all the rage in robotics these days, and for good reason: Properly implemented, it has the potential to program'bots on the fly. But those toys and others react in predictable ways to changing contexts and situations. One startup, though, purports to have developed an algorithm capable of generating entirely new behaviors dynamically. It's called bots_alive, and it's the brainchild of Brad Knox.

It is often talked about as the ultimate prize of artificial intelligence: a machine that can think like a human. But human minds are only one example of the kinds of minds on earth. So what are those other minds like? How do they work and how can we understand them? Suppose we do create human level cognition in artificial intelligence (AI), does that widen the'space of possible minds' to include AI alongside humans and animals?

Researchers have developed an Artificial Intelligence (AI) bot that beat professional players in a 20-day poker tournament. The bot, named Libratus, beat four of the World's best Heads-Up No-Limit Texas Hold'Em poker players at a casino in Pittsburgh, Pennsylvania. Libratus won the tournament after 120,000 hands, winning with a lead of $1.7 million in virtual poker chips. Professor of computer science Tuomas Sandholm said: 'The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans' The AI was developed at Carnegie Mellon University (CMU) by Professor Tuomas Sandholm and his PhD student Noam Brown. They said that the AI's victory wasn't just a matter of luck - it was statistically significant.