Like our brains, a new computer program created by Parham Aarabi of the University of Toronto can store and retrieve information strategically. An experimental tool that uses the novel algorithm to aid those with memory loss has also been developed by the associate professor in the Faculty of Applied Science & Engineering's Edward S. Rogers Sr. department of electrical and computer engineering. In the minds of most people, AI is more robotic than humans, according to Aarabi, whose approach is examined in a paper presented at the IEEE Engineering in Medicine and Biology Society Conference in Glasgow. Aarabi believes it should change. Computers have traditionally needed explicit instructions from their users on what data to save.

A new computer algorithm developed by the University of Toronto's Parham Aarabi can store and recall information strategically – just like our brains. The associate professor in the Edward S. Rogers Sr. department of electrical and computer engineering, in the Faculty of Applied Science & Engineering, has also created an experimental tool that leverages the new algorithm to help people with memory loss. "Most people think of AI as more robot than human," says Aarabi, whose framework is explored in a paper being presented this week at the IEEE Engineering in Medicine and Biology Society Conference in Glasgow. "I think that needs to change." In the past, computers have relied on their users to tell them exactly what information to store.

In some stores, sophisticated systems are tracking customers in almost every imaginable way, from recognizing their faces to gauging their age, their mood, and virtually gussying them up with makeup. The systems rarely ask for people's permission, and for the most part they don't have to. In our season 1 finale, we look at the explosion of AI and face recognition technologies in retail spaces, and what it means for the future of shopping. This episode was reported and produced by Jennifer Strong, Anthony Green, Tate Ryan-Mosley, Emma Cillekens and Karen Hao. Strong: Retailers have been using face recognition and AI tracking technologies for years. And what if you could know about the presence of violent criminals before they act? With Face First you can stop crime before it starts.] It detects faces, voices, objects and claims it can analyze behavior. But face recognition systems have a well-documented history of misidentifying women and people of color. And they're trying to sell it and impose it on the entirety of the country?] Strong: This is Representative Alexandria Ocasio-Cortez at a 2019 congressional hearing on facial recognition.

The days of hair coloring being characterized by failed henna experiments and leaps of faith at salons will soon be a thing of the past, thanks to AI. Episode 4 of our "I Am AI" docuseries introduces ModiFace, a Toronto-based company that's transforming the way people choose new hair colors and, along with it, the multi-billion dollar hair care industry. ModiFace has been bringing augmented reality to the beauty industry for the past decade. The company's founder and CEO, Parham Aarabi, had applied his work on using AI for face tracking and lip detection to enable consumers to see what beauty products would look like when applied. Already a leader in that market, ModiFace is now eyeing the much more ambitious task of letting people virtually try on hair colors. The company's iOS application does just that -- with startling effectiveness -- removing a lot of the inherent mystery that comes with deciding on a new hair color.

If Artificial Intelligence (AI) is increasingly able to recognise and classify faces, then perhaps the only way to counter this creeping surveillance is to use another AI to defeat it. We're in the early years of AI-powered image and face recognition but already researchers at the University of Toronto have come up with a way that this might be possible. The principal at the heart of this technique is adversarial training, in which a neural AI network's image recognition is disrupted by a second trained to understand how it works. This makes it possible to apply a filter to an image that alters only a few very specific pixels but makes it much harder for online AI to classify. The theory behind this sounds simple enough, explains the University of Toronto's professor Parham Aarabi: If the detection AI is looking for the corner of the eyes, for example, it adjusts the corner of the eyes so they're less noticeable.

A team of engineering researchers from the University of Toronto has created an algorithm to dynamically disrupt facial recognition systems. Led by professor Parham Aarabi and graduate student Avishek Bose, the team used a deep learning technique called "adversarial training", which pits two artificial intelligence algorithms against each other. Aarabi and Bose designed a set of two neural networks, the first one identifies faces and the other works on disrupting the facial recognition task of the first. The two constantly battle and learn from each other, setting up an ongoing AI arms race. "The disruptive AI can'attack' what the neural net for the face detection is looking for," Bose said in an interview.

A team of engineering researchers from the University of Toronto have created an algorithm to dynamically disrupt facial recognition systems. Led by professor Parham Aarabi and graduate student Avishek Bose, the team used a deep learning technique called "adversarial training", which pits two artificial intelligence algorithms against each other. Aarabi and Bose designed a set of two neural networks, the first one identifies faces and the other works on disrupting the facial recognition task of the first. The two constantly battle and learn from each other, setting up an ongoing AI arms race. "The disruptive AI can'attack' what the neural net for the face detection is looking for," Bose said in an interview with Eureka Alert.

Facial recognition systems are controversial, to say the least. Amazon made headlines last week over supplying law enforcement agencies with face-scanning tech. Schools in China are using facial recognition cameras to monitor students. And studies show that some facial recognition algorithms have built-in biases against certain ethnicities. Concerns about encroaching AI-powered surveillance systems motivated researchers in Toronto to develop a shield against them.