HALIFAX, CANADA - In recent years, artificial intelligence (AI) has been attracting more attention, money, and talent than ever in its short history. Yet much of the sudden hype is the result of myths and misconceptions being peddled by those outside the field.

For many years, the discipline was growing incrementally, with existing approaches performing around 1-2 percent better each year on standard benchmarks. Then a real breakthrough came in 2012, when computer scientist Geoffrey Hinton and his colleagues at Canada’s University of Toronto showed that their deep learning (DL) algorithms could beat state-of-the-art computer vision algorithms by a margin of 10.8 percentage points on the ImageNet Challenge (a benchmark dataset).

At the same time, AI researchers were benefiting from ever-more powerful tools, including cost-effective cloud computing, fast and cheap number-crunching hardware (GPUs), seamless data-sharing through the Internet, and advances in high-quality open-source software. Owing to these factors, machine learning (ML) - particularly DL - has taken over AI and created a groundswell of excitement. Investors have lined up to fund promising AI companies, and governments have poured hundreds of millions of dollars into AI research institutes.

While further progress in the field is inevitable, it will not necessarily be linear. Nonetheless, those hyping these technologies have seized on a number of compelling myths, starting with the notion that AI can solve any problem.

Hardly a week goes by without sensational stories about AIs outperforming humans: “intelligent machines are teaching themselves quantum physics” or “AI is better than humans at spotting lung cancer.” Such headlines are often true in only a narrow sense. For a general problem like spotting lung cancer, AI offers a solution only to a particular, simplified rendering of the problem by reducing the task to a matter