Wild salmon are essential to the ecological, economic, and cultural sustainability of the North Pacific Rim. Y et climate variability, habitat loss, and data limitations in remote ecosystems that lack basic infrastructure support pose significant challenges to effective fisheries management. This project explores the integration of multimodal foundation AI and expert-in-the-loop frameworks to enhance wild salmon monitoring and sustainable fisheries management in Indigenous rivers across Pacific Northwest. By leveraging video and sonar-based monitoring, we develop AI-powered tools for automated species identification, counting, and length measurement, reducing manual effort, expediting delivery of results, and improving decision-making accuracy. Expert validation and active learning frameworks ensure ecological relevance while reducing annotation burdens. To address unique technical and societal challenges, we bring together a cross-domain, interdisciplinary team of university researchers, fisheries biologists, Indigenous stewardship practitioners, government agencies, and conservation organizations. Through these collaborations, our research fosters ethical AI co-development, open data sharing, and culturally informed fisheries management.

Artificial intelligence is suddenly everywhere. Fueled by huge technological advances in recent years and gobs of venture capitalist money, AI has become one of the hottest corporate buzzwords. Roughly 1 in 7 public companies mentioned "artificial intelligence" in their annual filings last year, according to a Washington Post analysis. But the term is fuzzy. "AI is purposefully ill-defined from a marketing perspective," said Alex Hanna, director of research at Distributed AI Research Institute.

I teach managing technological innovation in Simon Fraser University's Management of Technology MBA program. No matter our industry or field, we should regularly review our tools and workflows. New tools, like AI, are excellent triggers for this assessment. Sorting out how best to adjust our work, as per the values and existing norms of different fields, takes a systematic approach. My research examines how companies can adjust how they use talent, technology and technique to hit work targets and stay aligned with the times -- what I've called thinking in 5T.

AI is being applied to whale research, especially to understand what whales are trying to communicate in the audible sounds they make to each other in the ocean. For example, marine biologist Shane Gero has worked to match clicks coming from whales around the Caribbean island nation of Dominica, to behavior he hopes will reveal the meanings of the sounds they make. Gero is a behavioral ecologist affiliated with the Marine Bioacoustics Lab at Aarhus University in Denmark, and the Department of Biology of Dalhousie University of Halifax, Nova Scotia. Gero works with a team from Project CETI, a nonprofit that aims to apply advanced machine learning and state-of-the-art robotics to listen to and translate the communication of whales. Project CETI has recently announced a five-year effort to build on Gero's work with a research project to try to decipher what sperm whales are saying to each other, according to a recent account in National Geographic.

Researchers are aiming to "teach" a computer to recognize the sounds of resident killer whales in order to develop a warning system for preventing ships from fatally striking endangered orcas off British Columbia's coast. Steven Bergner, a computing science research associate at Simon Fraser University's Big Data Hub, said he is collecting and managing a database of sounds picked up 24 hours a day by a network of hydrophones in the Salish Sea. Marine biologists will identify the sounds of different species of whales, including humpbacks and transients, and differentiate the acoustics from other noise such as waves and boats, he said. Machine learning or artificial intelligence would help detect the presence of orcas through patterns in the data. "That (information) goes through another system that then decides whether there should be a warning that ultimately reaches the vessel pilots," Bergner said.

The team is working with citizen scientists and the Orcasound project to provide several terabytes of whale call datasets, being collected by Steven Bergner, a computing science research associate at SFU's Big Data Hub. Bergner says the acoustic data will be used to'teach' the computer to recognize which call belongs to each type of cetacean. The project requires interdisciplinary expertise and brings together experts from fields such as biology, statistics and machine learning. "In the end, we are developing a system that will be a collaboration between human experts and algorithms," he says. Orcas or killer whales that are seen along the West Coast are divided into four distinct populations: the salmon-eating southern and northern residents, the transients, which prey on seals or other whales, and offshore, which mostly prey on sharks.

This 1-day workshop will not only equip artists to understand the implications and opportunities of artificial intelligence but also imagine the appropriate artistic and political responses to world that will be significantly altered by the introduction of these technologies. We need artists not only using these tools, but informing the conversation about how these tools will be deployed, and to whose benefit. This workshop is designed for ALL levels of technical expertise.

Engineers have created a new deep-learning software capable of assessing complex audience reactions to movies using the viewer's facial expressions. Developed by Disney Research in collaboration with Yisong Yue of Caltech and colleagues at Simon Fraser University, the software relies on a new algorithm known as factorized variational autoencoders (FVAEs). Variational autoencoders use deep learning to automatically translate images of complex objects, like faces, into sets of numerical data, also known as a latent representation or encoding. The contribution of Yue and his colleagues was to train the autoencoders to incorporate metadata (pertinent information about the data being analyzed). In the parlance of the field, they used the metadata to define an encoding space that can be factorized. In this case, the factorized variational autoencoder takes images of the faces of people watching movies and breaks them down into a series of numbers representing specific features: one number for how much a face is smiling, another for how wide open the eyes are, etc. Metadata then allow the algorithm to connect those numbers with other relevant bits of data--for example, with other images of the same face taken at different points in time, or of other faces at the same point in time.

DiPaola's and Yalcin's extensive research on empathy in AI is also addressing issues in a variety of industries, including e-health. In a collaborative project with the national AGE-WELL initiative, a helper AI conversational bot is being developed to assist the elderly in staying independent at home. Other applications are geared to the entertainment industry. After premiering at the NeuroIPS conference, the AI Empathic Painter system will travel to Europe to be showcased in Florence in May 2020. Formerly from Stanford University, DiPaola lead SFU's Interactive Visualization Lab (iVizLab), which strives to make computational systems bend more to the human experience by incorporating biological, cognitive and behavior knowledge models.

Called LOGAN, the deep neural network, i.e., a machine of sorts, can learn to transform the shapes of two different objects, for example, a chair and a table, in a natural way, without seeing any paired transforms between the shapes. All the machine had seen was a bunch of tables and a bunch of chairs, and it could automatically translate shapes between the two unpaired domains. LOGAN can also automatically perform both content and style transfers between two different types of shapes without any changes to its network architecture. The team of researchers behind LOGAN, from Simon Fraser University, Shenzhen University, and Tel Aviv University, are set to present their work at ACM SIGGRAPH Asia held Nov. 17 to 20 in Brisbane, Australia. SIGGRAPH Asia, now in its 12th year, attracts the most respected technical and creative people from around the world in computer graphics, animation, interactivity, gaming, and emerging technologies. "Shape transform is one of the most fundamental and frequently encountered problems in computer graphics and geometric modeling," says senior coauthor of the work, Hao (Richard) Zhang, professor of computing science at Simon Fraser University.