I will dispense with any amenities and cut right to the chase. When it comes to corporate learning and training the numbers are truly staggering. Trust me, there's a lot more where this came from meaning there is no shortage of stats and research that speak to the benefits of e-Learning. In a piece last year for PC Magazine, Rob Marvin wrote something that of course struck a chord with me. I say of course because of me being the pop culture savant that I am.

The number of startups entering the healthcare AI space has increased in recent years, with over 50 companies raising their first equity rounds since January 2015. Deals to healthcare-focused AI startups went up from less than 20 in 2012 to nearly 70 in 2016. Last year also saw two new unicorns emerge in the space: China-based iCarbonX and oncology-focused Flatiron Health. "By 2025, AI systems could be involved in everything from population health management, to digital avatars capable of answering specific patient queries." We identified over 100 companies that are applying machine learning algorithms and predictive analytics to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images, among other things.

One of the most typical tasks in machine learning is classification tasks. It may seem that evaluating the effectiveness of such a model is easy. Let's assume that we have a model which, based on historical data, calculates if a client will pay back credit obligations. We evaluate 100 bank customers and our model correctly guesses in 93 instances. That may appear to be a good result – but is it really?

If you've ever had to call a customer help line or other call center, you probably experience a shiver of dread at the mere thought. Some customer help lines are so reviled that people would rather deal with the issues than try to get support. But all that may be changing as companies use advances in data collection, data analysis, and artificial intelligence (AI) to improve the call center experience for customers and provide valuable insights for companies. You've probably heard the recorded message that "your call may be recorded for training and quality purposes," but companies are starting to realize that those recordings are valuable for a great deal more. With the advent of natural language processing (NLP) technologies and the ability to understand more unstructured data, like phone call recordings, companies are sitting on a wealth of information every time they record a call. And because the field is expanding so rapidly, there are many different ways companies can put this data to use.

The past couple of years have been a real cringe-y time to be an AI researcher. Just imagine a whole bunch of famous technologists and top-serious science authorities all suddenly taking aim at your field of research as a clear and present threat to the very survival of the species. All you want to do is predict appropriate emoji use based on textual analyses and here's Elon Musk saying this thing he doesn't really seem to know much about is the actual apocalypse. It's not that computer scientists haven't argued against AI hype, but an academic you've never heard of (all of them?) pitching the headline "AI is hard" is at a disadvantage to the famous person whose job description largely centers around making big public pronouncements. This month that academic is Alan Bundy, a professor of automated reasoning at the University of Edinburgh in Scotland, who argues in the Communications of the ACM that there is a real AI threat, but it's not human-like machine intelligence gone amok.

You've been reading about artificial intelligence (AI) and its potential impact on the accounting profession – particularly as it relates to auditing. But, that's not the only place AI is having a huge impact. Marketing is also seeing a big uptick in AI efforts as the traditional sales model shifts. What exactly will it be doing for and in marketing? Trying to mimic human intelligence, which will allow marketers to increase actions based on past interactions between humans and their online activities.

Babak Hodjat believes humans are too emotional for the stock market. So he's started one of the first hedge funds run completely by artificial intelligence. "Humans have bias and sensitivities, conscious and unconscious," says Hodjat, a computer scientist who helped lay the groundwork for Apple's Siri. "It's well documented we humans make mistakes. For me, it's scarier to be relying on those human-based intuitions and justifications than relying on purely what the data and statistics are telling you."

With highly disruptive factors such as climate change creating risks for public, environmental and economic health, it's time to find a proactive response when it comes to managing our water-- and that's what EMAGIN aims to do. By combining artificial intelligence with water utilities and industries, EMAGIN wants to shift the paradigm from reacting manually to proactively controlling how water utilities are operated and managed. "Building an artificial intelligence company like EMAGIN has been an incredible and rewarding journey. What we've accomplished in such a short period of time has really come from our drive to bring change to the digital water landscapes, which is also a global issue," says Mohamad. "There hasbeen a growing interest from a number of industries and municipalities throughout North America. We're currently working with two Ontario municipalities that are using our AI to help operate their water and wastewater systems."

The field of Artificial Intelligence (AI) was first introduced at a Dartmouth college conference in 1956. At the time, AI founder, Herbert Simon, predicted, "machines will be capable, within twenty years, of doing any work a man can do." But what Simon and the other optimistic early AI experts failed to appreciate are the numerous challenges involved in relying solely on machines - entities that lack the emotion that informs the human decision making process. Despite the fact that the AI field was born more than half a century ago, we've only recently in the last few years made monumental strides in advancing the artificial intelligence game. More specifically, we've started to transition from a model whereby AI assists humans to one whereby humans assist AI.

If something happened in your business, like a drop in daily transactions or a shift in customer demographics, how long would it take you to find out about it? Here's another question: once you discover the incongruity in your data, how long would it take you to decide on how to resolve it, if necessary? We have finally reached the pinnacle where traditional marketing and data collection tapers, only to have discovered a mountain of potential insight far off in the distance. We now have the power to recognize, analyze, and solve complex business problems but to get to the next climb requires constructing a form of technology never explored before. Taking that next step requires combining dynamic algorithms with widely available hardware and other technical elements that can continue to grow and expand as it is used.