While further progress in the development of artificial intelligence 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.
HALIFAX, NOVA SCOTIA – In recent years, artificial intelligence has been attracting more attention, money, and talent than ever in its short history. But much of the sudden hype is the result of myths and misconceptions being peddled by people outside of the field.
For many years, the field was growing incrementally, with existing approaches performing around 1-2% better each year on standard benchmarks. But there was a real breakthrough in 2012, when computer scientist Geoffrey Hinton and his colleagues at the University of Toronto showed that their “deep learning” 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, and particularly deep learning, has taken over AI and created a groundswell of excitement. Investors have been lining up to fund promising AI companies, and governments have been pouring hundreds of millions of dollars into AI research institutes.
HALIFAX, NOVA SCOTIA – In recent years, artificial intelligence has been attracting more attention, money, and talent than ever in its short history. But much of the sudden hype is the result of myths and misconceptions being peddled by people outside of the field.
For many years, the field was growing incrementally, with existing approaches performing around 1-2% better each year on standard benchmarks. But there was a real breakthrough in 2012, when computer scientist Geoffrey Hinton and his colleagues at the University of Toronto showed that their “deep learning” 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, and particularly deep learning, has taken over AI and created a groundswell of excitement. Investors have been lining up to fund promising AI companies, and governments have been pouring hundreds of millions of dollars into AI research institutes.