In 2017, over half of senior artificial intelligence (AI) professionals stated that a lack of qualified personnel in their field is the single biggest barrier to AI implementation across businesses. As more and more companies choose to explore AI, deep learning and machine learning (ML), this knowledge gap begins to cause some serious problems. The solution may be in de-humanising ML functions by using automatic (also known as augmented or assisted) ML techniques. AI experts are costly, with a reported average annual salary of $314,000. But before you can even worry about affording an AI expert, you have to find one.

There is much more to a successful technology product than its code. Companies seeking to exploit artificial intelligence need employees who understand how machine learning works and how it can be applied in business. But people who can do both are hard to find. Smith School of Business in Toronto is trying to fill that gap with North America's -- and it believes the world's -- first master of management in artificial intelligence (MMAI). This month, 40 students are beginning the programme, studying topics such as how to apply AI in finance and the ethical implications of the technology, intertwined with hands-on training in natural language processing and deep learning (the use of artificial neural networks in advanced pattern recognition).

BOSTON--(BUSINESS WIRE)--O'Reilly, the premier source for insight-driven learning on technology and business, today announced the results of its 2018 Artificial Intelligence (AI) survey, "How Companies Are Putting AI to Work Through Deep Learning." Focused on deep learning, a technique used primarily for supervised machine learning, the survey explores the adoption of tools and techniques to build AI applications and the barriers that hinder business adoption. Findings suggest that the democratization of AI and deep learning applications will continue, as development tools and libraries improve. However, the shortage of AI-trained engineers and developers will persist. For example, while 54% of respondents indicated AI will play a big role (35%) or essential role (19%) in their organization's future projects, lack of skilled people was the number one bottleneck reported.