Data drives our global digital ecosystem, and AI technologies reveal patterns in data. Smartphones, smart homes, and smart cities influence how we live and interact, and AI systems are increasingly involved in recruitment decisions, medical diagnoses, and judicial verdicts. Whether this scenario is utopian or dystopian depends on your perspective.
Despite their names, artificial intelligence technologies and their component systems, such as artificial neural networks, don’t have much to do with real brain science. I’m a professor of bioengineering and neurosciences interested in understanding how the brain works as a system – and how we can use that knowledge to design and engineer new machine learning models.
As a cryptocurrency, there is no physical form that gives Bitcoin value, so it is impossible to perform traditional fundamental analysis of the currency. Consequently, many investors track the so-called technical trading indicators (geometric patterns constructed from historical prices and trading volumes) in order to understand and predict Bitcoin’s future movement.
During the course of a day, robots might be expected to do everything from making a cup of tea to changing the bedding while holding a conversation. These are all challenging tasks that are more challenging when attempted together. No two homes will be the same, which will mean robots will have to learn fast and adapt to their environment. As anyone sharing a home will appreciate, the objects you ...[Read More]
A common question as these intelligent technologies infiltrate various industries is how work and labor will be affected. In this case, who – or what – will do journalism in this AI-enhanced and automated world, and how will they do it?
We might be on the right track to achieve a more comprehensive, human-level artificial intelligence. Applying this kind of learning to other tasks – perhaps applying it to signals...
In our lab at the University of Saskatchewan we are doing interesting deep learning research related to healthcare applications — and as a professor of electrical and computer engineering, I lead the research team. When it comes to health care, using AI or machine learning to make diagnoses is new, and there has been exciting and promising progress.
When artificial intelligence systems start getting creative, they can create great things – and scary ones. Take, for instance, an AI program that let web users compose music along with a virtual Johann Sebastian Bach by entering notes into a program that generates Bach-like harmonies to match them.
Emotion AI works on teaching robots how to feel empathy. Google AI stories are about how AI is helping people solve problems. Experts race to predict how we will be living with AI in the near future.
Artificial intelligence (AI) systems are becoming more like us. You can ask Google Home to switch off your bedroom lights, much as you might ask your human partner. When you text inquiries to Amazon online it’s sometimes unclear whether you’re being answered by a human or the company’s chatbot technology.
Some people suggest these tasks should be automated, as machines do not get bored, tired or distracted over time. However, computer vision algorithms tasked to recognize faces could also make mistakes. As my research has found, together, machines and humans could do much better.
Of all the fictional virtual assistants we know from pop culture, few stand up to the original and perhaps most famous: the HAL 9000 from the 1968 Stanley Kubrick film 2001: A Space Odyssey. We should probably be thankful for that. After all, Alexa may shut your lights off, but she won’t turn against you and wreak havoc on your life. Or will she?