In Stanley Kubrick's masterful film 2001: A Space Odyssey, the supercomputer HAL 9000 (heuristically programmed algorithmic computer) uses artificial intelligence to detect emotion and suffering, and controls all of a spaceship's systems, including its crew. The new labour monitoring practices we are seeing emerge today – with the stated aim of improving the working environment – appear just as outlandish. Take, for example, Canon's Beijing office, which has installed smart cameras that prevent any action from being performed (such as scheduling a meeting, accessing certain rooms, etc.) unless they detect a smile. In Europe, some companies are offering their employees the chance to participate in business-related trials which involve supplying them with glasses that establish emotion indicators. One example is the Shore app, developed by the Fraunhofer Institute for Integrated Circuits IIS in Germany, and which is used in Google's'smart glasses'.

Course Description: Machine learning has recently become the dominant field in AI research and constitutes the main part of the tools applied in industry-based AI positions. Business analysts, data scientists and AI engineers are required to know machine learning at different levels. The course (INF2179H -- Machine Learning with Applications in Python) will give a broad high-level overview of state-of-the-art machine learning methodologies. We shall focus on the application of these techniques to real-world data using the most advanced tools available for Python. The techniques will include: linear regression, basic techniques for classification, advanced regression and classification methods, and unsupervised learning.

Sociology professor Anton Oleinik argues that neural networks are structured in a way that limits the possibility that they will ever have true artificial creativity. Neural networks–a common type of artificial intelligence–are infiltrating every aspect of our lives, powering the internet-connected devices in our homes, the algorithms that dictate what we see online, and even the computational systems in our cars. But according to an article published in the peer-reviewed journal Big Data & Society by Anton Oleinik, a sociology professor at Memorial University of Newfoundland, there's one crucial area where neural networks do not outperform humans: creativity. Researchers have projected that automation may claim 800 million jobs around the world by 2030. Others suggest that as many as half of American jobs may be under threat from automation. But amid all the handwringing about robots taking people's jobs, Oleinik's analysis is further evidence that AI will likely only replace repetitive tasks that humans aren't particularly skilled at to begin with.

Article 22 regulating "automated individual decision-making" processes 28 29.