Podcasts about Vector Institute

April was a busy month for industry events, and the main focus for this episode was Jeff Pulver's vCon event, held in Hyannis, MA. Chris spoke at the event, with the main takeaway being that vCon is a “watch this space” initiative, especially for using AI to derive new value from conversations, including unstructured data. With vCon being early stage, the focus was mainly on laying the groundwork to make this an IETF standard, and proof of concept interop testing. Chris explained how this was a different conference experience, with the participants trying to set the foundation for vCon before it gets on the radar of the hyperscalers. Following this, Jon added his thoughts on other recent events, namely 8x8's analyst event, speaking at the Cloud Communications Alliance event, and Vector Institute's Remarkable conference in Toronto.  

Artificial Intelligence (AI) is causing disruption in almost every sector — and the professional services sector is no exception. For those working in law, finance and accounting, AI's influence is already here, impacting how you work and what clients expect.To stay competitive, firms must balance growth, efficiency, and service innovation with regulatory standards and ethical responsibilities. AI introduces risks that can be challenging for professions grounded in precision and trust, especially as ethical and regulatory frameworks lag in keeping up with technological advancements. The professional services workforce will also change, pushing professionals to become more adaptable, develop new skills and stay ahead of evolving client expectations.On April 15, 2025, the Empire Club of Canada will host an insightful discussion titled The AI Revolution: What's Next for Law, Finance, and Accounting? This event will bring together a panel of experts to explore the challenges, strategies, risks, and opportunities associated with integrating AI into these key professional services. The discussion will feature Dr. Foteini Agrafioti, Chief Science Officer at RBC and SVP of RBC Borealis; Colin Lachance, Innovator-in-Residence at the Ontario Bar Association; Iliana Oris Valiente, CPA, CA, Managing Director, and NA Innovation Hubs Lead at Accenture; and will be moderated by Cameron Schuler, Chief Commercialization Officer and Vice President of Industry Innovation at the Vector Institute.

As soon as the last ice age glaciers melted, Indigenous people occupied this siteA recently discovered archaeological site in Saskatchewan, dated to just less than 11,000 years ago is the oldest settlement in the region by about 1,500 years. It also is evidence that Indigenous people settled there as soon as the environment could support them after the glaciers disappeared. Glenn Stuart, from the University of Saskatchewan, is one of the archaeologists working along with local Indigenous community members to preserve and study the site.Just the right magnetic field will make sea turtles do a ‘happy dance'Researchers investigating how sea turtles navigate the vast and trackless ocean have discovered just how sensitive the reptiles' magnetic sense is, as they can even use it to identify the location of food resources. While feeding the loggerhead turtles in the lab, Kayla Goforth, a postdoctoral researcher at Texas A&M University noticed that the turtles would perform a ‘happy dance' when they recognized the right magnetic signature. She led this research that was published in the journal Nature.Intense exercise causes our bodies to belch out DNA that may reduce inflammationScientists were surprised to discover that the more intensely you exercise, the more certain immune cells belch out fragments of DNA that can form webs to trap pathogens, and lead to fewer pro-inflammatory immune cells circulating in our blood. Canadian researcher Stephen Montgomery, a professor of pathology at Stanford University, said their findings suggest that circulating cell-free DNA may play a role in how exercise lowers inflammation in the body. The study was published in the journal PNAS. An ancient Antarctic duck lived at the time of T-RexBirds are the last surviving lineage of dinosaurs, but modern birds are surprisingly ancient – dating to before the extinction of the rest of their family. An extremely rare, nearly intact bird skull found in Antarctica and dated to about 69 million years ago confirms this. This waterfowl had similarities to ducks and loons. Chris Torres is an assistant professor at the University of the Pacific in Stockton California and was part of the team that analyzed this fossil. Their research was published in the journal Nature.Science is being transformed by the AI revolutionThe stunning advances in artificial intelligence that we see with internet AI apps are just the tip of the iceberg when it comes to science. Researchers from almost every field are experimenting with this powerful new tool to diagnose disease, understand climate change, develop strategies for conservation and discover new kinds of materials. And AI is on the threshold of being able to make discoveries all by itself. Will it put scientists out of a job?Producer Amanda Buckiewicz spoke with:Jeff Clune, a professor of computer science at the University of British Columbia, a Canada CIFAR AI Chair at the Vector Institute, and a senior research advisor to DeepMind. He's also a co-author of The AI Scientist.Allison Noble, a Professor of Biomedical Engineering at the University of Oxford and a Foreign Secretary at the Royal Society, and chair of the Science in the Age of AI working group.Elissa Strome, executive director of the Pan-Canadian Artificial Intelligence Strategy at CIFAR.Cong Lu, postdoctoral research and teaching fellow at the University of British Columbia and the Vector Institute, and a co-author of The AI Scientist.Fred Morstatter, a research assistant professor at the University of Southern California, and a principal scientist at USC's Information Sciences Institute.

Canada stands at the forefront of a healthcare revolution, where AI has the power to transform patient care, ease provider burdens, and drive medical breakthroughs. As AI adoption accelerates, Canada must balance innovation with ethical governance, ensuring data accessibility, diversity, and security. Explore how AI can modernize healthcare, the role of responsible data sharing, and why Canada's leadership in ethical AI is key to shaping the future of medicine. Don't miss this insightful discussion, featuring Laurent Tillement, Director Partnerships, AI & Health at Mila - Quebec AI Institute and Ryan MacDonald, Director of Health AI Implementation at the Vector Institute, hosted by Justin Mallet, Healthcare System Partner at Roche Canada.Read the full interview and key takeaways: https://thefutureeconomy.ca/interviews/advancing-toward-canada-health-ai-revolution/Subscribe for exclusive previews of upcoming episodes and updates on new releases: https://bit.ly/3ri2IUu Follow us on social media: https://linkin.bio/thefutureeconomy.ca=====About TheFutureEconomy.ca=====TheFutureEconomy.ca is a Canadian online media outlet and thought leadership platform that produces interviews, panels and op-eds featuring leaders from industry, government, academia and more to define a strong vision for our future economy.Our content emphasizes our interviewees' insights and calls-to-action on what we must do now to improve the competitiveness and sustainability of Canada's future economy.Check out our website: https://thefutureeconomy.ca/

Pauline James, CEO of Anchor HR, and David Creelman, CEO of Creelman Research, are back as guest hosts to discuss the impact of AI in the Workplace. They are continuing their conversations with AI technology experts, striving to educate and support our community remain abreast of advances and considerations related to AI.In this episode, they speak with Mark Daley, Chief AI Officer at Western University Questions for Mark include:Impact on Workplace: How do you see AI impacting the future of work?Economic Performance: It's been noted that organizational performance is lagging. Do you believe AI can help close this gap? Implementation: How can companies leverage this technology effectively? What skills are necessary?Ethical Considerations: What ethical considerations should those governing this technology keep in mind when implementing AI technologies?Societal Impact: How can organizations support this moment in history as proactive and healthy contributors to improving society? About Mark Daley Mark Daley, Chief AI Officer at Western University and a Professor in the Department of Computer Science with cross-appointments in five other departments, The Rotman Institute of Philosophy, and The Western Institute for Neuroscience. He is also a faculty affiliate of Toronto's Vector Institute for Artificial Intelligence. Mark was named in the Maclean's magazine "Power List 2024" of the top 100 Canadians shaping the country and in Constellation Research's AI150, a list of top 150 top global executives leading AI transformation efforts. Mark has previously served as the Vice-President (Research) at the Canadian Institute for Advanced Research(CIFAR), and Chief Digital Information Officer, Special Advisor to the President, and Associate Vice-President (Research) at Western. Mark is the past chair of Compute Ontario and serves on a number of other boards. ---Message from our sponsor: Looking for a solution to manage your global workforce?With Deel, you can easily onboard global employees, streamline payroll, and ensure local compliance. All in one flexible, scalable platform! Join thousands of companies who trust Deel with their global HR needs. Visit deel.com to learn how to manage your global team with unmatched speed, flexibility, and compliance.---Feature Your Brand on the HRchat PodcastThe HRchat show has had 100,000s of downloads and is frequently listed as one of the most popular global podcasts for HR pros, Talent execs and leaders. It is ranked in the top ten in the world based on traffic, social media followers, domain authority & freshness. The podcast is also ranked as the Best Canadian HR Podcast by FeedSpot and one of the top 10% most popular shows by Listen Score. Want to share the story of how your business is helping to shape the world of work? We offer sponsored episodes, audio adverts, email campaigns, and a host of other options. Check out packages here. Follow us on LinkedIn Subscribe to our newsletter Check out our in-person events

Artificial intelligence (AI) systems like ChatGPT could soon run out of what keeps making them smarter—the tens of trillions of words people have written and shared online. A new study released by research group Epoch AI projects that tech companies will exhaust the supply of publicly available training data for AI language models by roughly the turn of the decade—sometime between 2026 and 2032. In the short term, tech companies like ChatGPT-maker OpenAI and Google are racing to secure and sometimes pay for high-quality data sources to train their AI large language models—for instance, by signing deals to tap into the steady flow of sentences coming out of Reddit forums and news media outlets. In the longer term, there won't be enough new blogs, news articles, and social media commentary to sustain the current trajectory of AI development, putting pressure on companies to tap into sensitive data now considered private—such as emails or text messages—or rely on less-reliable “synthetic data” spit out by the chatbots themselves. Tamay Besiroglu, an author of the study, said AI researchers realized more than a decade ago that aggressively expanding two key ingredients—computing power and vast stores of internet data—could significantly improve the performance of AI systems. “I think it's important to keep in mind that we don't necessarily need to train larger and larger models,” said Nicolas Papernot, an assistant professor of computer engineering at the University of Toronto and researcher at the nonprofit Vector Institute for Artificial Intelligence. Papernot, who was not involved in the Epoch study, said building more skilled AI systems can also come from training models that are more specialized for specific tasks. But he has concerns about training generative AI systems on the same outputs they're producing, leading to degraded performance known as “model collapse.” Training on AI-generated data is “like what happens when you photocopy a piece of paper and then you photocopy the photocopy. You lose some of the information,” Papernot said. Not only that, but Papernot's research has also found it can further encode the mistakes, bias, and unfairness that's already baked into the information ecosystem. This article was provided by The Associated Press.

AI is poised to impact the political process in profound ways. How do we navigate this uncharted territory? Hosts Beth Coleman and Rahul Krishnan are joined by experts Peter Loewen and Harper Reed to unravel the potential influence of AI on democracy and the spread of misinformation. About the hosts: Beth Coleman is an associate professor at U of T Mississauga's Institute of Communication, Culture, Information and Technology and the Faculty of Information. She is also a research lead on AI policy and praxis at the Schwartz Reisman Institute for Technology and Society. Coleman authored Reality Was Whatever Happened: Octavia Butler AI and Other Possible Worlds using art and generative AI. Rahul Krishnan is an assistant professor in U of T's department of computer science in the Faculty of Arts & Science and the department of laboratory medicine and pathobiology in the Temerty Faculty of Medicine. He is a Canada CIFAR Chair at the Vector Institute, a faculty affiliate at the Schwartz Reisman Institute for Technology and Society and a faculty member at the Temerty Centre for AI Research and Education in Medicine (T-CAIREM). About the guests: Peter Loewen is the director of U of T's Munk School of Global Affairs & Public Policy and a professor in the department of political science in the Faculty of Arts & Science. He is also the associate director of the Schwartz Reisman Institute for Technology and Society. His research focuses on how politicians can make better decisions, how citizens can make better choices and how governments can address the disruption of technology and harness its opportunities. Harper Reed is a technologist who served as a chief technology officer for Barack Obama's 2012 re-election campaign. Reed has pioneered crowdsourcing at Threadless.com, founded Modest Inc. and guided the software team at PayPal. His most recent venture was General Galactic Corporation.

The rapid advance of AI writing tools, image generators and text-to-video models opens a new world for creative possibilities. It also raises questions about the role of the artist, the nature of creativity – and ethics. Hosts Beth Coleman and Rahul Krishnan dive into these topics with guests Sanja Fidler and Nick Frosst. About the hosts: Beth Coleman is an associate professor at U of T Mississauga's Institute of Communication, Culture, Information and Technology (https://www.utm.utoronto.ca/iccit/) and the Faculty of Information. She is also a research lead on AI policy and praxis at the Schwartz Reisman Institute for Technology and Society (http://srinstitute.utoronto.ca/). Coleman authored Reality Was Whatever Happened: Octavia Butler AI and Other Possible Worlds (https://k-verlag.org/books/beth-coleman-reality-was-whatever-happened/) using art and generative AI.  Rahul Krishnan is an assistant professor in U of T's department of computer science in the Faculty of Arts & Science and department of laboratory medicine and pathobiology in the Temerty Faculty of Medicine. He is a Canada CIFAR Chair at the Vector Institute, a faculty affiliate at the Schwartz Reisman Institute for Technology and Society and a faculty member at the Temerty Centre for AI Research and Education in Medicine (T-CAIREM https://tcairem.utoronto.ca/).  About the guests: Nick Frosst is a co-founder of Cohere (https://cohere.com/), a Toronto-based startup that develops large language models for enterprise use. Frosst did his undergraduate degree in computer science and cognitive science at U of T and was the first employee of Geoffrey Hinton's Google Brain lab in Toronto. He is the singer in an indie rock band called Good Kid (https://goodkidofficial.com/).  Sanja Fidler is vice president of AI research at NVIDIA (https://www.nvidia.com/en-us/research/), leading the company's research lab in Toronto. She is also an associate professor of mathematical and computational science at the University of Toronto Mississauga and an affiliate faculty member at the Vector Institute, which she co-founded. The co-author of more than 130 scientific papers in computer vision, machine learning and natural language processing, she has received the University of Toronto's Innovation Award and the Connaught New Researcher Award, among other accolades. Fidler completed her Ph.D. in computer science at the University of Ljubljana in Slovenia and a postdoctoral fellowship at the University of Toronto.

While a lot of the news around AI is doom and gloom, the potential for positive innovation in health care offers a hopeful perspective. Hosts Beth Coleman and Rahul Krishnan are joined by University of Toronto experts Christine Allen and Andrew Pinto to talk about the transformative power of AI in health care, from revolutionizing primary care to advancing drug development. About the hosts: Beth Coleman is an associate professor at U of T Mississauga's Institute of Communication, Culture, Information and Technology (https://www.utm.utoronto.ca/iccit/) and the Faculty of Information. She is also a research lead on AI policy and praxis at the Schwartz Reisman Institute for Technology and Society (https://srinstitute.utoronto.ca/). Coleman authored Reality Was Whatever Happened: Octavia Butler AI and Other Possible Worlds (https://k-verlag.org/books/beth-coleman-reality-was-whatever-happened/) using art and generative AI. Rahul Krishnan is an assistant professor in U of T's department of computer science in the Faculty of Arts & Science (https://www.artsci.utoronto.ca/) and department of laboratory medicine and pathobiology in the Temerty Faculty of Medicine (https://temertymedicine.utoronto.ca/). He is a Canada CIFAR Chair at the Vector Institute, a faculty affiliate at the Schwartz Reisman Institute for Technology and Society and a faculty member at the Temerty Centre for AI Research and Education in Medicine (T-CAIREM https://tcairem.utoronto.ca/). Guests Andrew Pinto is the founder and director of the Upstream Lab (https://upstreamlab.org/), a research team focused on addressing social determinants of health, population health management, and utilizing data science for proactive care. Pinto is a family physician at St. Michael's Hospital, Unity Health Toronto, and associate professor in the department of family and community medicine in U of T's Temerty Faculty of Medicine and at the Dalla Lana School of Public Health. Christine Allen is a professor in U of T's Leslie Dan Faculty of Pharmacy. She is a member of the scientific leadership team of the Acceleration Consortium (https://acceleration.utoronto.ca/) at U of T. Allen is a co-founder and CEO of Intrepid Labs Inc. (https://intrepidlabs.tech/), a company that is accelerating pharmaceutical drug development through integration of AI, automation and advanced computing.

The federal government recently announced a $2.4 billion dollar investment in artificial intelligence. It includes money earmarked to accelerate the adoption of AI in sectors as far flung as health care and agriculture. The feds say this will help to 'secure Canada's AI advantage." But does Canada even have an advantage in AI compared to our neighbors? Are Canadian companies and industries doing enough to embrace this technology? And is there a potential downside if we embrace AI too quickly? For insight, we welcome: Ajay Agrawal, the Geoffrey Taber Chair in Entrepreneurship and Innovation at the U of T's Rotman School of Management, and Faculty Affiliate at the Vector Institute for Artificial Intelligence; Krista Jones, Chief Delivery Officer at the MaRS Discovery District; andKristina McElheran, assistant professor of Strategic Management at the University of Toronto Scarborough, and Rotman School of Management.See omnystudio.com/listener for privacy information.

Safe and Accountable Hosts Beth Coleman and Rahul Krishnan navigate the challenging terrain of AI safety and governance. In this episode, they are joined by University of Toronto experts Gillian Hadfield and Roger Grosse as they explore critical questions about AI's risks, regulatory challenges and how to align the technology with human values. Hosts Beth Coleman is an associate professor at U of T Mississauga's Institute of Communication, Culture, Information and Technology (https://www.utm.utoronto.ca/iccit/) and the Faculty of Information. She is also a research lead on AI policy and praxis at the Schwartz Reisman Institute for Technology and Society (https://srinstitute.utoronto.ca/). Coleman authored Reality Was Whatever Happened: Octavia Butler AI and Other Possible Worlds (https://k-verlag.org/books/beth-coleman-reality-was-whatever-happened/) using art and generative AI. Rahul Krishnan is an assistant professor in U of T's department of computer science in the Faculty of Arts & Science (https://www.artsci.utoronto.ca/) and department of laboratory medicine and pathobiology in the Temerty Faculty of Medicine (https://temertymedicine.utoronto.ca/). He is a Canada CIFAR Chair at the Vector Institute, a faculty affiliate at the Schwartz Reisman Institute for Technology and Society and a faculty member at the Temerty Centre for AI Research and Education in Medicine (T-CAIREM https://tcairem.utoronto.ca/). Guests Gillian Hadfield is a professor of law and strategic management in the Faculty of Law (https://www.law.utoronto.ca/) at U of T and is the inaugural Schwartz Reisman Chair in Technology and Society. She holds a CIFAR AI Chair at the Vector Institute for AI and served as a senior policy adviser to OpenAI from 2018 to 2023. Roger Grosse is an associate professor of computer science in the Faculty of Arts & Science and a founding member of the Vector Institute (https://vectorinstitute.ai/). He is a faculty affiliate at the Schwartz Reisman Institute for Technology and Society and was part of the technical staff on the alignment team at Anthropic, an AI safety and research company based in San Francisco.

University of Toronto researchers Rahul Krishnan and Beth Coleman dive into the world of AI – how far we've come, where we are heading and the potentially profound impact for society. What Now? AI is a University of Toronto podcast series that dives into the world of artificial intelligence. Join hosts Beth Coleman and Rahul Krishnan as they explore – and demystify – the transformative potential of AI and its impact on society with the help of leading experts from the university. Coleman is an associate professor at the Institute of Communication, Culture, Information and Technology at U of T Mississauga and U of T's Faculty of Information. She is also a research lead on AI policy and praxis at the Schwartz Reisman Institute for Technology and Society. Krishnan is an assistant professor in U of T's department of computer science in the Faculty of Arts & Science and department of laboratory medicine and pathobiology in the Temerty Faculty of medicine. He is also a Canada CIFAR AI Chair at the Vector Institute and Canada Research Chair in computational medicine. 01:24 Geoffrey Hinton's warning about AI 03:21 Regulating a multi-billion dollar industry 04:50 How is AI being trained? 05:58 AI as a tool 07:08 What can we learn from chatbots? 08:28 Who watches the Watchmen?

Arash Ahmadian is a Researcher at Cohere and Cohere For AI focussed on Preference Training of large language models. He's also a researcher at the Vector Institute of AI.Featured ReferenceBack to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMsArash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, Sara HookerAdditional ReferencesSelf-Rewarding Language Models, Yuan et al 2024 Reinforcement Learning: An Introduction, Sutton and Barto 1992Learning from Delayed Rewards, Chris Watkins 1989Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning, Williams 1992

Shingai Manjengwa is one of the leading AI educators and thinkers. She is the head of AI education at chainML, the founder of Fireside Analytics, and the director of professional development at the Vector Institute for Artificial Intelligence. She talks to host Edward Greenspon about how AI actually works, its challenges and why we shouldn't fear the future.

Is our future with generative AI terrifying, exciting, or fascinating? In this episode, we talk about the adoption of artificial intelligence in the workplace, the opportunities to implement AI in healthcare delivery, and privacy in a world enabled by generative AI. Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is the Chief Data Scientist at the Creative Destruction Lab, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. Along with Ajay Agrawal and Joshua Gans, Avi is the author of the bestselling books Prediction Machines: The Simple Economics of Artificial Intelligence and Power and Prediction: The Disruptive Economics of Artificial Intelligence. In this episode, we talk about the adoption of artificial intelligence in the workplace, the opportunities to implement AI in healthcare delivery, and privacy in a world enabled by generative AI.

In this episode of the AI series, Pauline James and David Creelman delve into the intersection of artificial intelligence, HR practices, ethics, and law. The extended edition offers up three expert guests - Frank Rudzicz, Kate Bischoff, and Jesslyn Dymond - to discuss the ethical considerations, legal risks, and potential benefits associated with the integration of AI at work. We explore the potential of generative AI and the legal issues it might introduce for HR. Plus we share valuable advice for HR leaders on navigating the short-term challenges and opportunities posed by advancing AI technologies.Tune in and discover:How should leaders navigate the ethical landscape as AI becomes more commonplace in organizations? To what extent does employee privacy come into play when tracking keystrokes? Can AI be a tool for fostering ethical, objective decision-making?What are the new recruiting challenges caused by AI? How can HR diligently embed AI into their operations while managing associated risks?About Our GuestsFrank Rudzicz is an Associate Professor at Dalhousie University, co-founder of WinterLight Labs Inc., founding faculty member at the Vector Institute for Artificial Intelligence, status professor at the University of Toronto, and CIFAR Chair in Artificial Intelligence. Kate Bischoff is the founder of k8bisch LLC (formerly tHRive Law & Consulting). She's a renowned expert in employment law and HR and has a background that includes roles at the consulate general in Jerusalem and the US Embassy in Lusaka, Zambia.Jesslyn Dymond is the Director of Data Ethics at TELUS leading the approach to responsible data-driven innovation, drawing on a background of privacy and information management expertise. This episode is supported by Right Management North America and ManpowerGroup. For 40+ years, Right Management has transformed organizations across more than 75 countries by evaluating, developing, and transitioning their talent. Their strong pool of coaches and leadership experts works closely with candidates to help them identify their strengths, develop new capabilities, or transition to new careers.Feature Your Brand on the HRchat PodcastThe HRchat show has had 100,000s of downloads and is frequently listed as one of the most popular global podcasts for HR pros, Talent execs and leaders. It is ranked in the top ten in the world based on traffic, social media followers, domain authority & freshness. The podcast is also ranked as the Best Canadian HR Podcast by FeedSpot and one of the top 10% most popular shows by Listen Score. Want to share the story of how your business is helping to shape the world of work? We offer sponsored episodes, audio adverts, email campaigns, and a host of other options. Check out packages here. Follow us on LinkedIn Subscribe to our newsletter Check out our in-person events

In the first of a special mini-series focused on AI in the workplace, guest hosts Pauline James and David Creelman ask "what exactly is AI all about and where is it heading?"Joining Pauline and David on the podcast is Avi Goldfarb, the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. He is also Chief Data Scientist at the Creative Destruction Lab, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. Along with Ajay Agrawal and Joshua Gans, Avi is the author of the bestselling books Prediction Machines and Power and Prediction.He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi has testified before the U.S. Senate Judiciary Committee on related work in competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere.Questions for Avi include:You have been studying AI for a long time; well before its advances garnered the attention of the general population. Is anything taking you by surprise with the current advances?You have talked about AI automating prediction in ways that we had only understood humans could do well/reasonably well previously. Can you explain this for our audience?You have an interesting (and hopeful) perspective that AI may become more of an equalizer across professions than past advances have been, can you tell us more about that?Can you provide your thoughts on how long it will be until we see broad sweeping changes in work and how it is organized?With your extensive work with entrepreneurs on the bleeding edge of new technology, what advice do you have for those on the other side of the equation: those who are making recommendations and purchasing tech for their organization. How should we balance not being left behind with appropriate diligence?We do our best to ensure editorial objectivity. The views andFeature Your Brand on the HRchat PodcastThe HRchat show has had 100,000s of downloads and is frequently listed as one of the most popular global podcasts for HR pros, Talent execs and leaders. It is ranked in the top ten in the world based on traffic, social media followers, domain authority & freshness. The podcast is also ranked as the Best Canadian HR Podcast by FeedSpot and one of the top 10% most popular shows by Listen Score. Want to share the story of how your business is helping to shape the world of work? We offer sponsored episodes, audio adverts, email campaigns, and a host of other options. Check out packages here and contact sales@hr-gazette.com. Follow us on LinkedIn Subscribe to our newsletter Check out our in-person events

While AI will surely take over some jobs lock, stock and barrel, the more likely scenario is that we will have to share our jobs with AI entities. It can be challenging enough to work with fellow humans and understand their emotions and thinking styles. What will it be like working with super-smart non-humans? Will it change how we behave or make us question our judgment? Will it keep tabs on our performance? This episode explores these questions with guests Tracy Jenkin, an associate professor at Smith and a faculty affiliate at the Vector Institute for Artificial Intelligence, and Anton Ovchinnikov, Distinguished Professor of Management Analytics. Dr. Jenkin and Dr. Ovchinnikov discuss what they've learned so far from their research that explores human-AI collaboration and cognitive processes. They are joined in conversation by host Meredith Dault. Special Guests: Anton Ovchinnikov and Tracy Jenkin.

Dr. Mamdani is a professor, pharmacist, and epidemiologist. He is the Vice President of Data Science and Advanced Analytics at Unity Health Toronto and Director of the University of Toronto Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM). Dr. Mamdani's team bridges advanced analytics including machine learning with clinical and management decision making to improve patient outcomes and hospital efficiency. Dr. Mamdani is also Professor in the Temerty Faculty of Medicine, the Leslie Dan Faculty of Pharmacy, and the Institute of Health Policy, Management and Evaluation of the Dalla Lana School of Public Health at the University of Toronto. He is also a Faculty Affiliate of the Vector Institute. He has published over 500 studies in peer-reviewed journals. Host: Raeesa Kabir  Audio Producer: Melanie Bussan Video Editor + Art: Saurin Kantesaria Instagram: saorange314 Social Media: Nikhil Kapur Time Stamps: 0:00 Dr. Mamdani's Background and Career Path 9:30 Where current data driven medicine strategies fall short and how AI can step in 17:00 How Dr. Mamdani's work in AI and machine learning began 22:00 Applied Health Research Center and the Ontario Policy Research Network 28:45 The impact of utilizing machine learning and AI at the level of patient care - Chart Watch 35:50 Logistics of Developing and Implementing AI solutions 39:10 Insights Gained - From Purpose to Implementation 43:30 Directing Multiple Projects - Recruitment of AI Team  47:45 Future Projects: Back to AI Basics  54:15 Future of AI in Medicine - Fostering trust in AI 57:20 Advice to Younger Self --- Support this podcast: https://podcasters.spotify.com/pod/show/maml-podcast/support

Hey dear ThursdAI friends, as always I'm very excited to bring you this edition of ThursdAI, September 21st, which is packed full of goodness updates, great conversations with experts, breaking AI news and not 1 but 2 interviewsThursdAI - hey, psst, if you got here from X, dont' worry, I don't spam, but def. subscribe, you'll be the coolest most up to date AI person you know!TL;DR of all topics covered* AI Art & Diffusion*

Join us for an insightful session at GSD Presents! Learn about cutting-edge AI applications with Ron Bodkin, co-founder & CEO of ChainML. Discover how their open-source platform empowers businesses to rapidly develop customized generative AI applications using collaborative 'agents'. Don't miss out on this chance to gain valuable AI insights from a seasoned expert! Guest: Ron Bodkin, co-founder & CEO, ChainML https://www.linkedin.com/in/ronbodkin/ Ron Bodkin is the co-founder and CEO of ChainML, which provides an open-source platform for rapidly developing customized generative AI applications using collaborating ‘agents'. The platform enables the robust deployment and monitoring of generative AI models ensuring they can be operated with confidence and accuracy. He has over 15 years of AI experience, specializing in data science, analytics, machine learning, and large scale data processing. Formerly, he worked in Google's Cloud CTO office, with a focus on Applied Artificial Intelligence leading efforts in industry applications and responsible AI. He also was the VP and GM of AI at Teradata, following Teradata's acquisition of his company, Think Big Analytics, and led AI Engineering & CIO at Vector Institute.Transcript

ChatGPT is a very popular and hot topic. Is there any risk in using it? Is it a big privacy risk?  In this episode, Punit talks with Patricia, who is the Co-Founder & CEO of Private AI, a Microsoft-backed startup and discusses the risks, what makes ChatGPT different from other AI, and what companies can do to mitigate the risks.   KEY CONVERSATION POINTS  What comes to mind when it comes to GDPR  Views on ChatGPT  Does ChatGPT pose privacy risks?  How do companies mitigate the risks?  Private AI  ABOUT THE GUEST  Patricia Thaine is the Co-Founder & CEO of Private AI, a Microsoft-backed startup. With a decade of research and software development experience, she is a Computer Science PhD Candidate at the University of Toronto and a Vector Institute alumna. She founded Private AI to help companies unlock the value of unstructured data while maintaining customer privacy and compliance. Its latest launch, PrivateGPT, serves as a privacy layer for ChatGPT, redacting sensitive information from your prompts before sending them through the chatbot.  ABOUT THE HOST  Punit Bhatia is one of the leading privacy experts who works independently and has worked with professionals in over 30 countries. Punit works with business and privacy leaders to create an organization culture with high AI & privacy awareness and compliance as a business priority by creating and implementing an AI & privacy strategy and policy.   Punit is the author of books “Be Ready for GDPR” which was rated as the best GDPR Book, “AI & Privacy – How to Find Balance”, “Intro To GDPR”, and “Be an Effective DPO”. Punit is a global speaker who has spoken at over 50 global events. Punit is the creator and host of the FIT4PRIVACY Podcast. This podcast has been featured amongst top GDPR and privacy podcasts.  As a person, Punit is an avid thinker and believes in thinking, believing, and acting in line with one's value to have joy in life. He has developed the philosophy named ‘ABC for joy of life' which passionately shares. Punit is based out of Belgium, the heart of Europe.   RESOURCES  Websites www.fit4privacy.com , www.punitbhatia.com, www.private-ai.com    Podcast https://www.fit4privacy.com/podcast Blog https://www.fit4privacy.com/blog YouTube http://youtube.com/fit4privacy    --- Send in a voice message: https://podcasters.spotify.com/pod/show/fit4privacy/message

Antoine GeorgesPhysique de la matière condenséeAnnée 2022-2023Réseaux de neurones, apprentissage et physique quantiqueSéminaire : Juan Carrasquilla - Quantum States with Neural Networks: Representations and TomographyIntervenant(s) :Juan Carrasquilla, Vector Institute, Toronto

Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and Professor of Marketing at Toronto's Rotman School of Management. He is also Chief Data Scientist at the Creative Destruction Lab, a fellow at Behavioral Economics in Action at Rotman, and a faculty affiliate at the Vector Institute for Artificial Intelligence. Avi has written extensively on a broad range of topics from marketing, statistics, law, management, medicine, political science, refugee studies, among many others. He has also conducted much deep work in the study of AI and machine learning and how businesses can wield and leverage the predictive capabilities of these technologies.In this podcast, he shares:Point-to-point changes v. the bigger, broader systemic changes that AI may introduceHow AI prediction changes judgement. Until now, most of the exploration of machine learning have been around prediction, but how will things change when AI starts getting good at judgement? The fascinating implications machine learning and AI will have on business decision-making, economics, and competitive advantage _________________________________________________________________________________________Episode Timeline:00:00—Highlight from today's episode00:44—Introducing Avi+ The topic of today's episode2:21—If you really know me, you know that...2:50—Could you talk about cost of prediction coming on?6:25—Can machine learning surpass human judgment, or where do they have their place?9:30—Could you explain the point-to-point solutions vs. systemic changes?11:40—How does technology change power?14:27—Could you give us an example of the impact of AI-powered predictions in a practical real-life case?17:01—Do you think we have a reason to worry?19:01—How can people continue learning from you?__________________________________________________________________________________________Additional Resources: Personal Page: https://www.avigoldfarb.com/Linkedin: https://ca.linkedin.com/in/avi-goldfarb-46a7473Twitter: https://twitter.com/avicgoldfarb?lang=en

Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and Professor of Marketing at Toronto's Rotman School of Management. He is also Chief Data Scientist at the Creative Destruction Lab, a fellow at Behavioral Economics in Action at Rotman, and a faculty affiliate at the Vector Institute for Artificial Intelligence. Avi has written extensively on a broad range of topics from marketing, statistics, law, management, medicine, political science, refugee studies, among many others. He has also conducted much deep work in the study of AI and machine learning and how businesses can wield and leverage the predictive capabilities of these technologies.In this podcast, he shares:Point-to-point changes v. the bigger, broader systemic changes that AI may introduceHow AI prediction changes judgement. Until now, most of the exploration of machine learning have been around prediction, but how will things change when AI starts getting good at judgement? The fascinating implications machine learning and AI will have on business decision-making, economics, and competitive advantage _________________________________________________________________________________________Episode Timeline:00:00—Highlight from today's episode00:44—Introducing Avi+ The topic of today's episode2:21—If you really know me, you know that...2:50—Could you talk about cost of prediction coming on?6:25—Can machine learning surpass human judgment, or where do they have their place?9:30—Could you explain the point-to-point solutions vs. systemic changes?11:40—How does technology change power?14:27—Could you give us an example of the impact of AI-powered predictions in a practical real-life case?17:01—Do you think we have a reason to worry?19:01—How can people continue learning from you?__________________________________________________________________________________________Additional Resources: Personal Page: https://www.avigoldfarb.com/Linkedin: https://ca.linkedin.com/in/avi-goldfarb-46a7473Twitter: https://twitter.com/avicgoldfarb?lang=en

AI Generating Algos, Learning to play Minecraft with Video PreTraining (VPT), Go-Explore for hard exploration, POET and Open Endedness, AI-GAs and ChatGPT, AGI predictions, and lots more!  Professor Jeff Clune is Associate Professor of Computer Science at University of British Columbia, a Canada CIFAR AI Chair and Faculty Member at Vector Institute, and Senior Research Advisor at DeepMind.  Featured References  Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos [ Blog Post ] Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune  Robots that can adapt like animals Antoine Cully, Jeff Clune, Danesh Tarapore, Jean-Baptiste Mouret  Illuminating search spaces by mapping elites Jean-Baptiste Mouret, Jeff Clune  Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeff Clune, Kenneth O. Stanley  Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions Rui Wang, Joel Lehman, Jeff Clune, Kenneth O. Stanley  First return, then explore Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, Jeff Clune

Patricia Thaine is the Co-Founder & CEO of Private AI, a Microsoft-backed startup. She is also a Computer Science PhD Candidate at the University of Toronto (on leave) and a Vector Institute alumna. Her R&D work is focused on privacy-preserving natural language processing, with a focus on applied cryptography and re-identification risk.Patricia is a recipient of the NSERC Postgraduate Scholarship, the RBC Graduate Fellowship, the Beatrice “Trixie” Worsley Graduate Scholarship in Computer Science, and the Ontario Graduate Scholarship. She has a decade of research and software development experience, including at the McGill Language Development Lab, the University of Toronto's Computational Linguistics Lab, the University of Toronto's Department of Linguistics, and the Public Health Agency of Canada.Connect with Behind Company Lines and HireOtter Website Facebook Twitter LinkedIn:Behind Company LinesHireOtter Instagram Buzzsprout

The excitement around artificial intelligence has gone "radical". Our guest believes that in time, AI will "eat all software". And PWC says AI will contribute over USD$15 trillion to the global economy by 2030.* On this episode of The Unlimited Podcast -- with the assistance of ChatGPT -- Brian is joined by Jordan Jacobs, Co-Founder & Managing Partner of Radical Ventures, a leading venture capital firm focused on investing in transformational AI. Jordan is also a founder of the Vector Institute for Artificial Intelligence, a director of the Canadian Institute for Advanced Research, member of the University of Waterloo President's International Advisory Board, a Director of Tennis Canada, former Chief AI Officer of TD Bank Group, and was a Co-Founder & CEO of Layer 6 AI and Milq Inc. Jordan was also the Founder & CEO of SpyBox Media and spent over a decade as a lawyer specializing in entertainment, media, technology, and sports. Brian and Jordan discuss Jordan's path to AI venture capital, what AI actually is, how AI is being used today, how it may be used in the future, and much more...including a ChatGPT demo. Brian also asks Jordan about his time working with Elton John and Elvis Costello and his experience meeting Roger Federer. If you're a music fan or a tennis player, then this episode is (also) for you! Timestamps: 0:00 Introduction & Disclaimer 1:50 Jordan's Bio 5:15 Partnering with Product RED 6:51 Entering into Deep Learning with Milq and Layer 6 11:37 What were Milq and Layer 6 doing? 15:27 Why wouldn't Jordan & Tony sell? 20:58 What is an Image Net? 24:06 Artificial Intelligence 101 28:39 How is AI going to change the way we live? 30:06 Impact of AI on the job market 33:43 Will AI take over the world? 35:34 What is ChatGPT? 38:57 Brian demo's ChatGPT 42:06 What does AI mean for big tech? 48:41 Radical Venture's portfolio companies 55:46 Tesla Full Self-Driving & Self-Driving Technology 1:02:33 How can the average investor get direct exposure to AI? 1:04:36 Jordan's venture with Elton John and a Bono Performance 1:15:33 Jordan's Tennis experience and meeting Roger Federer 1:22:21 Final thoughts 1:24:35 Outro If you like what you hear, please don't hesitate to rate us kindly. And if there are particular topics you'd like covered, please let us know. *https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/medicine

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/public-policy

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/economics

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/science-technology-and-society

Disruption resulting from the proliferation of AI is coming. The authors of the bestselling Prediction Machines describe what you can do to prepare. Banking and finance, pharmaceuticals, automotive, medical technology, retail. Artificial intelligence (AI) has made its way into many industries around the world. But the truth is, it has just begun its odyssey toward cheaper, better, and faster predictions to drive strategic business decisions--powering and accelerating business. When prediction is taken to the max, industries transform. The disruption that comes with such transformation is yet to be felt--but it is coming. How do businesses prepare?  In Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction: The Disruptive Economics of Artificial Intelligence (HBR Press, 2022), they go further to reveal AI as a prediction technology directly impacting decision-making and to teach businesses how to identify disruptive opportunities and threats resulting from AI. Their exhaustive study of new developments in artificial intelligence and the past history of how technologies have disrupted industries highlights the striking phase we are now in: after witnessing the power of this new technology and before its widespread adoption--what they call "the Between Times." While there continue to be important opportunities for businesses, there are also threats of disruption. As prediction machines improve, old ways of doing things will be upended. Also, the process by which AI filters into the many systems involved in application is very uneven. That process will have winners and losers. How can businesses leverage, or protect, their positions? Filled with illuminating insights, rich examples, and practical advice, Power and Prediction is the must-read guide for any business leader or policy maker on how to make the coming AI disruptions work for you rather than against you. Interviewee Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab and the CDL Rapid Screening Consortium, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi is a former Senior Editor at Marketing Science. His work on online advertising won the INFORMS Society of Marketing Science Long Term Impact Award. He testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, the Economist, and elsewhere. Peter Lorentzen is economics professor at the University of San Francisco. He heads USF's Applied Economics Master's program, which focuses on the digital economy. His research is mainly on China's political economy. Learn more about your ad choices. Visit megaphone.fm/adchoices

Are AI-generating algorithms the path to artificial general intelligence(AGI)?  Today we're joined by Jeff Clune, an associate professor of computer science at the University of British Columbia, and faculty member at the Vector Institute. In our conversation with Jeff, we discuss the broad ambitious goal of the AI field, artificial general intelligence, where we are on the path to achieving it, and his opinion on what we should be doing to get there, specifically, focusing on AI generating algorithms. With the goal of creating open-ended algorithms that can learn forever, Jeff shares his three pillars to an AI-GA, meta-learning architectures, meta-learning algorithms, and auto-generating learning environments. Finally, we discuss the inherent safety issues with these learning algorithms and Jeff's thoughts on how to combat them, and what the not-so-distant future holds for this area of research.  The complete show notes for this episode can be found at twimlai.com/go/602.

Professors Ajay, Avi & Joshua, co-authors of “Power & Prediction: The Disruptive Economics of Artificial Intelligence” share a bit about how to bring together a team of three creative, imaginative people – how they work together, form ideas, and ultimately shape a vision of the future of technology. “Power & Prediction” is being released Tuesday, November 15th. Ajay Agrawal is Geoffrey Taber Chair in Entrepreneurship and Innovation and Professor of Strategic Management at the University of Toronto's Rotman School of Management. He is the founder of Creative Destruction Lab, cofounder of Next 36 and Next AI, and cofounder of Sanctuary, an AI/robotics company. Avi Goldfarb is the Rotman Chair in AI and Healthcare and Professor of Marketing at Toronto's Rotman School of Management. Avi is also Chief Data Scientist at Creative Destruction Lab, a fellow at Behavioral Economics in Action at Rotman, a faculty affiliate at the Vector Institute for Artificial Intelligence, and a research associate at the National Bureau of Economic Research. Joshua Gans is the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship and Professor of Strategic Management at Toronto's Rotman School of Management. He is Chief Economist at Creative Destruction Lab, department editor (Strategy) at Management Science, and cofounder and managing director of Core Economic Research.

Guest Avi Goldfarb discusses AI as prediction technology likely to transform our systems over a long period of time. Goldfarb is the Rotman Chair of Artificial Intelligence and Healthcare and a Professor of Marketing at the Rotman School of Management at the University of Toronto. He's also Chief Data Scientist at the Creative Destruction Lab, a Faculty Affiliate at the Vector Institute and the Schwartzman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. Additionally, he is co-author of a new book titled Power and Prediction, The Disruptive Economics of Artificial Intelligence, which will be coming out on November 15th.

Michael Serbinis is the founder and CEO of League - a technology-focused health company powering the digital transformation of healthcare. Mike joins the podcast and shares his experiences creating, building, scaling, and exiting technology companies. We talk about his leadership superpower, and the difference between ideas and execution. With more than $1B of exits, Mike is a visionary leader and entrepreneur who has built transformative technology and platforms across several industries. Serbinis founded and helped build Kobo, Critical Path, DocSpace, and now League. Hundreds of thousands of people across the world use digital health platforms powered by League to access, navigate and pay for care. Mike is also the chair of the Board of Directors for the Perimeter Institute. He is also a member of the C100, the Business Council of Canada, board member for the CDL Rapid Screening Consortium, one of the first Fellows at Creative Destruction Lab, and the Vector Institute for Artificial Intelligence. In 2020 he was awarded the prestigious Region Builder Award by The Toronto Board of Trade.

In this episode on commercializing AI, we speak with Cameron Schuler, a key contributor to AI's game-changing prominence. Cameron is the Chief Commercialization Officer at the Vector Institute and is dedicated to advancing the transformative field of AI.

Joining us on the podcast today is Ali Taiyeb, Director of Industry Innovation at Vector Institute. We discuss the rising Canadian leadership in AI research and technology and the critical role incubators play. Ali will be leading the Next Gen Panel at our Activation, A Path To Resilience, happening on September 22nd at the Living Arts Centre of Mississauga. . . . Follow us: https://linktr.ee/Spyder.Works Contact: sromero@spyder.works . . . Part podcast, part blog series, part live event, Say Hi to the Future is an inclusive platform aimed at highlighting the human side of ingenuity: clever, inventive, and original thinking. We are a global community driven by passion, savage curiosity, and the audacity to make a difference. . . . . Hosted by: Ken Tencer Produced by: Sonia Romero Johnson Matt Miller

In episode 41 of The Gradient Podcast, Andrey Kurenkov speaks to Professor Jeff Clune.Jeff is an Associate Professor of Computer Science at the University of British Columbia and a Faculty Member of the Vector Institute. Previously, he was a Research Team Leader at OpenAI and before that a Senior Research Manager and founding member of Uber AI Labs, and prior to that he was an Associate Professor in Computer Science at the University of Wyoming.Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterThe Gradient is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Outline:(00:00) Intro(01:05) Path into AI(08:05) Studying biology with simulations(10:30) Overview of genetic algorithms(14:00) Evolving gaits with genetic algorithms(20:00) Quality-Diversity Algorithms(27:00) Evolving Soft Robots(32:15) Genetic algorithms for studying Evolution(39:30) Modularity for Catastrophic Forgetting(45:15) Curiosity for Learning Diverse Skills(51:15) Evolving Environments (58:3) The Surprising Creativity of Digital Evolution(1:04:28) Hobbies Outside of Research(1:07:25) Outro Get full access to The Gradient at thegradientpub.substack.com/subscribe

Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and a professor of marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab, a faculty affiliate at the Vector Institute and the Schwartz-Reisman Institute for Technology and Society, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published academic articles in marketing, statistics, law, management, medicine, political science, refugee studies, physics, computing, and economics. Avi testified before the U.S. Senate Judiciary Committee on competition and privacy in digital advertising. His work has been referenced in White House reports, European Commission documents, the New York Times, and the Economist.

Sustainable Development Goal : 9 Industry, Innovation and Infrastructure focuses on building resilient infrastructure, promoting inclusive and sustainable industrialization and fostering innovation.Dan Breznitz, is a University Professor and Munk Chair of Innovation Studies, in the Munk School of Global Affairs & Public Policy with a cross-appointment in the Department of Political Science of the University of Toronto, where he is also the Co-Director of the Innovation Policy Lab. In addition, he is a Fellow of the Canadian Institute for Advanced Research where he co-founded and co-directs the program on Innovation, Equity and the Future of Prosperity.  Professor Breznitz is known worldwide as an expert on rapid-innovation-based industries and their globalization, as well as for his pioneering research on the distributional impact of innovation policies. He has been a member of several boards, as well as serving an advisor on science, technology, and innovation policies to multinational corporations, governments, and international organizations. Vinyas Harish is a fifth year MD/PhD Candidate at the Temerty Faculty of Medicine and Dalla Lana School of Public Health at the University of Toronto. He is also a Postgraduate Affiliate at the Vector Institute and a Graduate Fellow at the Schwartz Reisman Institute for Technology and Society. He holds a CIHR Frederick Banting and Charles Best Canada Graduate Scholarship (Doctoral) Award to investigate how digital technology can support public health emergency response and promote resilient health systems. His research areas include machine learning, emergency preparedness, clinical and population decision support systems, and the governance of artificial intelligence in health. CREDITS: This podcast is co-hosted by Dr. Erica Di Ruggiero, Director of the Centre for Global Health, and Ophelia Michaelides, Manager of the Centre for Global Health, at the DLSPH, U of T, and produced by Elizabeth Loftus. Audio editing is by Sylvia Lorico. Music is produced by Julien Fortier and Patrick May. It is made with the support of the School of Cities at U of T. 

Episode 95: Exercise Medicine. Exercise can be used as medicine if given at the right dose and frequency. Sapna and Danish explain some principles of exercise medicine. [Add brief summary for posting on website]Introduction: Is the monkeypox a hoax?   By Hector Arreaza, MD.  Today is May 27, 2022. Before we dig into exercise, I want to share some information about a trending topic.I remember my lectures on public health in medical school in the late 90s when my teachers taught me about the tremendous accomplishment of humanity in eradicating smallpox. The last natural outbreak of smallpox in the United States occurred in 1949, and the last case of smallpox was recorded in Somalia (Africa) in 1977. Until it was wiped out, smallpox had plagued humanity for at least 3000 years, killing 300 million people in the 20th century alone, but the World Health Organization declared smallpox eradicated in 1980. No cases of natural smallpox have happened ever since, and if you discovered a case of smallpox, I was told by my teachers, you would be awarded one million dollars by the WHO. I did my research online and I could not confirm that information, but I learned that the variola virus (smallpox virus) is kept only in two locations in the planet: the CDC in Atlanta, Georgia, United States and the VECTOR Institute in Koltsovo, Russia.  Why am I talking about smallpox? Because the monkeypox is a new trending topic in the media. Now as the COVID-19 panorama starts to look somehow comforting, monkeypox is starting to gain more attention in the media. Even the name “monkeypox” sounds terrifying. The CDC issued a health alert on May 20, 2022, about the most recent confirmed case of monkeypox in the United States, but this is not the first case of monkeypox in the US. In 2021 there were two travel-associated cases, and in 2003 there was an outbreak of 47 cases associated with imported small mammals. Cases of monkeypox have been identified in several non-endemic countries since early May 2022; many of the cases have involved men who have sex with men (MSM) without a history of travel to an endemic country. Cases of monkeypox outside of Western and Central Africa are extremely rare, and we hope they continue to be rare. Is monkeypox a hoax? Is it real? Only time will tell. For now, let's be optimistic and hope for a world free of dangerous pandemics. Whether monkeypox will continue to spread or not is still unknown. This is Rio Bravo qWeek Podcast, your weekly dose of knowledge brought to you by the Rio Bravo Family Medicine Residency Program from Bakersfield, California. Our program is affiliated with UCLA, and it's sponsored by Clinica Sierra Vista, Let Us Be Your Healthcare Home.[Brief music]This podcast was created for educational purposes only. Visit your primary care physician for additional medical advice.[Music continues and fades…] ___________________________Exercise Medicine. By Danish Khalid, MS4, and Sapna Patel, MS4, Ross University School of MedicineToday is May 12, 2022. D: Welcome back to our Nutrition Series! Thank you for joining us again! Nutrition is such a big part of medicine, it's the answer to many chronic diseases and yet it's the most neglected subject in medicine. Our goal here is to educate not only ourselves but our patients and bring awareness of this discrepancy we've created in medicine. S: If you're new to this series, I suggest you pause this and listen to the first few episodes as we build upon them each time. In our previous episode, we discussed how the term “diet” brings upon a negative connotation as well as explored various popular meal plans.  A: Exercise prescription. FITTE (Obesity Medicine Association): Frequency, Intensity, Time, Type, Enjoyment.  D: As healthcare professionals, time and time again we advise our patients “diet and exercise,” because that's what we were taught and research has backed for many years. It's so easily said, yet the words carry such weight. But what does that really mean? Well, that's what we're here to explore. At least the latter part, exercise.   S: extra fries? D:Or shall I say, “physical activity?” Again, just like the word “diet,” “exercise” has similar negative connotations. Thus, let's avoid saying “exercise” and resort to words such as “physical activity or workout.” Disclaimer: What we discuss here today is focused directly towards those who are beginners. For those of you who are more experienced, this may benefit as a reminder of the foundations.   A: Screen your patients. 95% of patients will benefit from exercise, and most do not need a special test. Only 5% of your patients may require additional testing.  S: So what is the best workout for me, you, or our listeners? Well, as simple as that sounds, it's not that simple. Especially nowadays, where information is at the tips of our fingers, it is so easy to get confused on how to start. But let's start by establishing your fitness goals. Do you want to lose fat, gain muscle, or gain muscle while losing fat?   S: Once you've figured that out, then it's all about small steps and achievable goals. Oftentimes, individuals start their journey to healthy living with unrealistic goals, hoping to achieve them within a few weeks or months when in actuality it takes longer. This often leads to falling off or reverting back to their unhealthy habits. But small tricks such as reducing the amount of sedentary behavior can do wonders. With technology ruling over our lives, we've adapted to this sedentary lifestyle, became comfortable and left physical activity behind. In fact, the National Center of Health Statistics found that only 26% of men, 19% of women, and 20% of adolescents meet sufficient activity levels.  D: So the first step: Move more, sit less. And for those with a busy lifestyle, some physical activity is better than none. According to the Physical Activity Guidelines published by the US Department of Health and Human Services, for substantial health benefits, adults should do: At least 150 minutes (2 hours and 30 minutes) to 300 minutes (5 hours) a week of moderate-intensity aerobic physical activity.Or 75 minutes (1 hour and 15 minutes) to 150 minutes (2 hours and 30 minutes) a week of vigorous-intensity aerobic physical activity. And muscle-strength training of moderate or greater intensity that involved all major muscle groups on 2 or more days a week.  S: How many of you understood that? What does this all mean? Let's break it down. The amount of time for exercise is self-explanatory, but what does moderate or vigorous intensity aerobic physical activity mean? Putting it in simple terms, aerobic physical activity means “cardio”. The level of intensity varies based on the activity you perform. Moderate-intensity activities include a brisk walk or walking on the treadmill at 2.5 to 4mph, playing double tennis, or raking the yard. Whereas, vigorous or high-intensity activities include jogging, running, carrying heavy groceries or objects upstairs, shoveling snow, or participating in a strenuous fitness class. You may have heard of the terms of: low-intensity steady state (LISS) cardio and high-intensity interval training (HIIT) cardio.  A: In general, if you're doing moderate-intensity activity, you can talk but not sing during the activity. Vigorous-intensity activity, you will not be able to say more than a few words without pausing for a breath. D: So what's the best cardio routine? LISS or HIIT? Well, there's a lot of potential options. In terms of the best form of cardio for fat burning, there's one thing you need to prioritize, that is preventing muscle loss. This enables your physique to dramatically improve as you lose weight.  S: Ok, give us the evidence. D: One study claimed that HIIT cardio workouts should be included due to its potential muscle sparing properties. HITT training can be done in a fraction of a time as LISS and is a great cardio workout to burn fat. Furthermore, the study  recommended performing lower body cardio workouts, rating bicycling as the most effective method of HIIT. However, HIIT is very demanding on the body as it may cause potential muscle recovery issues, which is why you should also combine it with a few LISS sessions per week as well. And one of the best methods of LISS include doing the stairmaster at 2.5 speed to 4. Furthermore, those looking for a fat burning effect should aim for an effective heart rate level during cardio. To keep it simple, those performing HIIT should aim to keep the heart rate 140-160 beats per minute and for LISS should aim for 110-130 beats per minute, keeping your heart rate elevated will optimize fat-burning effects from cardio.  S: When should you perform cardio? What's the best time? Well, studies have shown that the best time to perform cardio sessions should be when you're not strength training or right after. It was found that participants who performed cardio before strength training experienced greater muscle loss than those who performed it after, or when not strength training.  And while we're on this topic, let's address a myth regarding cardio: Sweating more does not equal more calories burnt. Each individual has a temperature setpoint for sweating. Once you meet that body temperature limit, you start to sweat as your body's way of cooling down. For example, those from the midwest or east coast deal with a colder climate. Their setpoint is lower than those on the west coast or where the climate is hotter year-round. Thus, these people sweat more than others and easier. D: How about those whose goals are to gain muscle? Is it the same or different? Don't worry we haven't forgotten about you guys. Although, going on a jog, or run, or riding a bike, is an effective way to help you burn some additional calories, and help you get into that hypocaloric state. It doesn't allow you to build lean muscle tissue to achieve the desired physique many of us want.  The only way to obtain that is by incorporating strength training into your regular exercise regimen. This is why the guideline, as mentioned earlier, recommends strength training in addition to cardio, notice the “AND”. Yes, I'm talking about hitting the weight on a regular basis.  S: Show me some more evidence. D: Multiple studies have compared diet alone versus diet + weight training and diet + weight lifting + cardio after. And every single time, those with weight training wins out, especially if it's the muscular physique you are looking to build. Now, don't overlook this subtle difference that all exercises are created equal, because it's not. Well, what training split should I follow then? Does it matter? The total body split, or push pull legs, or the “bro split”? You see, oftentimes people get confused as to which to choose, and that confusion can lead to no choice at all. Do whichever you like, but just make sure you're doing this, and here's the key: progressive overload. Adding more weight to allow more strength to build from workout to workout, or phase to phase. Or increasing metabolic overload or demand by keeping the rest time shorter and getting more work accomplished from workout to workout. Whatever strategy you choose, as long as you are striving to push yourself to a higher level of fitness and strength. That's going to do the job.   A: Use PT to assist you to design a good physical activity plan, depending on disability or limitations of movements.  S: Yup I agree, personally I choose to increase each set by at least 10-15lbs, and rest for 30 secs to 1 mins since my goal is to increase my strength and endurance.   You know what I've noticed, Danish? A lot of women refused to lift weights. They want to get fit and toned, but they don't want to look “bulky”. So, they skip the weights, and perform hours of cardio, or worse - they avoid exercising all together. A common misconception about heavy weight training, especially among women, is that lifting heavy weight will lead to a bulky looking physique. It's true that lifting heavy will promote hypertrophy in muscles leading to a size increase. However, the idea that it leads to a “bulky” look is untrue. The true culprit that leads to bulky physiques is fat accumulation. Excessive body fat is what causes both men and women to look bulky. The most important aspect of someone's physique is his or her body fat percentage. A good physique nearly always requires a fairly low body fat percentage to achieve. Lifting heavy can help accomplish this. D: What about the hormones?  S: Testosterone, or the lack thereof, is one of the main reasons that women won't get bulky from lifting weights. Testosterone is a natural anabolic steroid, which directly stimulates muscle growth. And, on average, women only have one seventh the amount of testosterone as men. So, as usual, that means women have to work harder. But it also means you don't really need to worry about bulking up. Heavy weight training has a plethora of benefits that can help develop muscle, shed fat, increase metabolism and ultimately lead to anyone's desired physique. D:Another question that gets asked a lot: which workouts will help me lose my belly fat? Should I do a lot more abdominal workouts? Although there's so much more to this question. The simple answer: None. You cannot specifically target belly fat. Your body has its own way of allocating fat distribution, different areas in men and women. Similarly, when you lose fat, you'll oftentimes notice different areas losing more fat first. Don't get discouraged and be patient. As the results will come. One advise, take weekly pictures for comparison. It is said and accepted by many that it takes 4 weeks for you to see your body change, 8 weeks for friends and family to notice, and 12 weeks for the rest of the world. So keep grinding.  And last but not least, it's important that we reiterate: physical activity only supports and aids your eating lifestyle. It will not combat a poor eating lifestyle. Proper eating habits are 80% (relative number). So keep your eating habits in check.  S:Well, that's all we've got for today. If you liked this and found this helpful, feel free to reach out and let us know. It's always a pleasure to hear from our listeners and motivates us to do more. And before we end this episode, we'd like to know: What do you want to hear about next? What questions do you have? Or something you don't completely understand? Let us know and we'd be happy to learn with you. Till next time. Take care! A: Email riobravoqweek@clinicasierravista.org  ____________________________ [Music to end: Your Choice]Now we conclude our episode number 95 “Exercise Medicine.” Sapna and Danish reminded us that the US Department of Health & Human Services recommends 150-300 minutes a week of MODERATE-intensity aerobic exercise AND  muscle-strength training 2 or more days a week. Most of your patients will benefit from exercise, only a minority may have contraindications to exercise, in such cases, make sure you perform a proper evaluation, even a cardiology referral, before sending them to the gym.This week we thank Hector Arreaza, Danish Khalid, and Sapna Patel. Audio edition: Suraj Amrutia. Thanks for listening to Rio Bravo qWeek Podcast. If you have any feedback, contact us by email at RioBravoqWeek@clinicasierravista.org, or visit our website riobravofmrp.org/qweek. See you next week! _____________________References:Wilson JM, Marin PJ, Rhea MR, Wilson SM, Loenneke JP, Anderson JC. Concurrent training: a meta-analysis examining interference of aerobic and resistance exercises. J Strength Cond Res. 2012 Aug;26(8):2293-307.Wisloff, Ulrik; Ellingsen, Oyvind; Kemi, Ole J.High-Intensity Interval Training to Maximize Cardiac Benefits of Exercise Training?, Exercise and Sport Sciences Reviews: July 2009 - Volume 37 - Issue 3 - p 139-146.Ratamess NA, Kang J, Porfido TM, Ismaili CP, Selamie SN, Williams BD, Kuper JD, Bush JA, Faigenbaum AD. Acute Resistance Exercise Performance Is Negatively Impacted by Prior Aerobic Endurance Exercise. J Strength Cond Res. 2016 Oct;30(10):2667-2681.Foster C, Farland CV, Guidotti F, Harbin M, Roberts B, Schuette J, Tuuri A, Doberstein ST, Porcari JP. The Effects of High Intensity Interval Training vs Steady State Training on Aerobic and Anaerobic Capacity. J Sports Sci Med. 2015 Nov 24;14(4):747-55.Michael A. Wewege, Imtiaz Desai, Cameron Honey, Brandon Coorie, Matthew D. Jones, Briana K. Clifford, Hayley B. Leake, Amanda D. Hagstrom. The Effect of Resistance Training in Healthy Adults on Body Fat Percentage, Fat Mass and Visceral Fat: A Systematic Review and Meta-Analysis. Sports Medicine, 2021.Demco, Sonja. “Why Women Will Not Get Bulky Lifting Weights.” Demcofitness, 21 Oct. 2019, https://www.demcofitness.com/single-post/Why-Women-Will-Not-Get-Bulky-Lifting-Weights. 

With any new sophisticated technology, there comes the trial and error, the novelty, the speculation and the question of longevity. In this episode we pull back the curtain on artificial intelligence x music. Turns out its not all robots and conspiracy, so let's debunk that. We wanted to explore the different sides of how machine learning can offer innovation in music making, as well as the budding questions of ethics and ownership that go along with it. Are you a musician curious about the creative opportunities AI can offer? We wanted to learn more about how musicians and computer scientists are working in tandem with machine learning and the challenges and milestones in this developing field.To learn more we spoke to Ace Piva, musician and executive director at Over the Bridge, a non-profit organization that provides counselling services to help musicians with addiction and mental health. OTB worked alongside the “Lost Tapes of the 27 Club” – a campaign project that used AI to create new songs by said well-known artists of The 27 Club. We also spoke with Sageev Oore, musician, professor at Dalhousie University and research faculty member at the Vector Institute, to understand some of the more technical attributes concerning AI and machine learning in music making. See acast.com/privacy for privacy and opt-out information.

What's Next for the internet? We talk metaverse, the decay of web 2.0, artificial intelligence, data ownership and more with Manal Siddiqui, CEO & Co-Founder of Transitional Forms. About Manal Siddiqui Manal Siddiqui is Chief Executive Officer and Co-Founder of Transitional Forms, an interactive entertainment studio-lab in Toronto pioneering the future of intelligent content for the metaverse. She is committed to growing Canada's position as a globally significant voice in the responsible stewardship of AI and digital technologies, and in progressing society's understanding of how technological advancements can positively impact fairer, safer, and more inclusive global communities. Prior to Transitional Forms, Manal built foundational operations and strategic partnerships in health for the Vector Institute, one of the premier machine learning and AI research institutes in the world and home to over 500 of Canada's AI researchers. As a systems change strategist, she has successfully established and scaled innovation-based businesses across public and private sectors. Manal hails from Pakistan, lives in Toronto and holds graduate degrees with distinction from the Bayes Business School, City of London and from the Faculty of Law, University of Toronto, in addition to an undergraduate degree in biotechnology from the University of Toronto. Follow Us INSTAGRAM - www.instagram.com/activeintworld TWITTER - twitter.com/ActiveIntlUK KARIM - twitter.com/karimkanji PODCAST WEBSITE - www.thewhatsnextpodcast.com The podcast is brought to you by Active International, a global leader in Corporate Trade within the Media & Advertising industry.

More and more, artificial intelligence and machine learning are being woven into our lives. AI is your Google search. It can scan for tumours, discover new drugs, optimize heating and cooling...eliminate jobs. Toronto is the largest AI hub outside of China and Silicon Valley, according to Forbes magazine. That, in part, is due to the Vector Institute, an artificial intelligence research institute invested in deep and machine learning. Howard Green speaks with Vector Institute chair Ed Clark about the opportunities and challenges of an AI driven world.

In this episode of Intel on AI hosts Amir Khosrowshahi and Santiago Miret talk with Alán Aspuru-Guzik about the chemistry of computing and the future of materials discovery. Alán is a professor of chemistry and computer science at the University of Toronto, a Canada 150 Research Chair in theoretical chemistry, a CIFAR AI Chair at the Vector Institute, and a CIFAR Lebovic Fellow in the biology-inspired Solar Energy Program. Alán also holds a Google Industrial Research Chair in quantum computing and is the co-founder of two startups, Zapata Computing and Kebotix. Santiago Miret is an AI researcher in Intel Labs, who has an active research collaboration Alán. Santiago studies at the intersection of AI and the sciences, as well as the algorithmic development of AI for real-world problems. In the first half of the episode, the three discuss accelerating molecular design and building next generation functional materials. Alán talks about his academic background with high performance computing (HPC) that led him into the field of molecular design. He goes into detail about building a “self-driving lab” for scientific experimentation, which, coupled with advanced automation and robotics, he believes will help propel society to move beyond the era of plastics and into the era of materials by demand. Alán and Santiago talk about their research collaboration with Intel to build sophisticated model-based molecular design platforms that can scale to real-world challenges. Alán talks about the Acceleration Consortium and the need for standardization research to drive greater academic and industry collaborations for self-driving laboratories. In the second half of the episode, the three talk about quantum computing, including developing algorithms for quantum dynamics, molecular electronic structure, molecular properties, and more. Alán talks about how a simple algorithm based on thinking of the quantum computer like a musical instrument is behind the concept of the variational quantum eigensolver, which could hold promising advancements alongside classical computers. Amir, and Santiago close the episode by talking about the future of research, including projects at DARPA, oscillatory computing, quantum machine learning, quantum autoencoders, and how young technologists entering the field can advance a more equitable society. Academic research discussed in the podcast episode: The Hot Topic: What We Can Do About Global Warming Energy, Transport, & the Environment Scalable Quantum Simulation of Molecular Energies The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization Organic molecules with inverted gaps between first excited singlet and triplet states and appreciable fluorescence rates Simulated Quantum Computation of Molecular Energies Towards quantum chemistry on a quantum computer Gerald McLean and Marcum Jung and others with the concept of the variational quantum eigensolver Experimental investigation of performance differences between coherent Ising machines and a quantum annealer Quantum autoencoders for efficient compression of quantum data

Until recently, AI systems have been narrow — they've only been able to perform the specific tasks that they were explicitly trained for. And while narrow systems are clearly useful, the holy grain of AI is to build more flexible, general systems. But that can't be done without good performance metrics that we can optimize for — or that we can at least use to measure generalization ability. Somehow, we need to figure out what number needs to go up in order to bring us closer to generally-capable agents. That's the question we'll be exploring on this episode of the podcast, with Danijar Hafner. Danijar is a PhD student in artificial intelligence at the University of Toronto with Jimmy Ba and Geoffrey Hinton and researcher at Google Brain and the Vector Institute. Danijar has been studying the problem of performance measurement and benchmarking for RL agents with generalization abilities. As part of that work, he recently released Crafter, a tool that can procedurally generate complex environments that are a lot like Minecraft, featuring resources that need to be collected, tools that can be developed, and enemies who need to be avoided or defeated. In order to succeed in a Crafter environment, agents need to robustly plan, explore and test different strategies, which allow them to unlock certain in-game achievements. Crafter is part of a growing set of strategies that researchers are exploring to figure out how we can benchmark and measure the performance of general-purpose AIs, and it also tells us something interesting about the state of AI: increasingly, our ability to define tasks that require the right kind of generalization abilities is becoming just as important as innovating on AI model architectures. Danijar joined me to talk about Crafter, reinforcement learning, and the big challenges facing AI researchers as they work towards general intelligence on this episode of the TDS podcast. *** Intro music: - Artist: Ron Gelinas - Track Title: Daybreak Chill Blend (original mix) - Link to Track: https://youtu.be/d8Y2sKIgFWc *** Chapters: 0:00 Intro 2:25 Measuring generalization 5:40 What is Crafter? 11:10 Differences between Crafter and Minecraft 20:10 Agent behavior 25:30 Merging scaled models and reinforcement learning 29:30 Data efficiency 38:00 Hierarchical learning 43:20 Human-level systems 48:40 Cultural overlap 49:50 Wrap-up

The Ask AI interview with Roxana Sultan, Vice President of Health at Toronto's World-renowned Vector Institute. Get all the links from our episode post: https://www.askai.org//post/e33-is-ai-poised-to-make-major-breakthroughs-in-healthcare

Janet Bannister is the Managing Partner of Real Ventures, Canada's preeminent early-stage venture capital firm.  In addition to leading Real Ventures and working with her large portfolio of rapidly growing tech companies, Janet is very active in the Canadian tech ecosystem; she is on the Boards of Communitech in Waterloo and Vector Institute in Toronto. In 2004, Janet launched Kijiji.ca and grew it to become one of the most visited websites in Canada.  This year, Janet became the co-chair of the C100… which is the leading global community of Canadian tech leaders, dedicated to supporting, inspiring and connecting the most promising Canadian entrepreneurial leaders, and a group I am very proud to be a member of. Personally, I am very excited to see the direction she will take the C100 through her stewardship.

Hi and a warm welcome to Episode #33 of The Elevate Business Podcast. We have the pleasure of introducing you to Benjamin Alarie, who has spent the better part of his career contributing to the business law community. As a graduate from Yale Law School, he has been the Associate Dean and still is a professor of law at the University of Toronto, a faculty member of the Vector Institute of Artificial Intelligence, and a TEDx Speaker. Bringing together his years of experience as co-founder and CEO at Blue J,  Benjamin leads his team in developing legal technology powered by AI to provide greater clarity to law everywhere and on-demand.To learn more about Ben: https://www.linkedin.com/in/balarie/

GPT-3 is a language model — a tool that is impressively generates synthetic text when given something to start with. We talk with Zack from Copy AI and Akshay from Georgian about how they currently use GPT-3 at work, what it's good at, and what we should watch out for.Subscribe for episodes every other Thursday!— Guests: Zack Lee and Akshay Budhkar —Zack Lee is currently the lead AI engineer at Copy AI where he manages how GPT-3 powers their product. He's interested in deep learning tech and NLP and currently lives in Brooklyn, NY with his 3 cats.Akshay Budhkar is an applied research scientist at Georgian and a member of the R&D team, where he focuses on engagements with portfolio companies, mainly in NLP. Akshay holds an MSc. in Computer Science at the University of Toronto, affiliated with the Vector Institute.— Links —Experiment with GPT-Neo, an open-source version of GPT-3 straight from your browserJoin the waitlist to access GPT-3 by requesting on Open AI's website— A.I. For Anyone, a non-profit dedicated to helping you learn about AI.  —Find us on Instagram, Twitter, Facebook, LinkedIn and YouTube at @aiforanyone& check us out at aiforanyone.org/& email your friends at podcast@aiforanyone.orgBrought to you by Jacky Zhao, Mac McMahon, Serena Chao, William Overton and the rest of the AI4A team

Dr. Marzyeh Ghassemi will soon be part of MIT's Department of Electrical Engineering and Computer Science and its Institute for Medical Engineering and Science faculty. And she is quick to call out the lack of representation in the data we collect to build products and services across industries. Including for women, who are not a minority -- but an often ignored majority. Currently an Assistant Professor at the University of Toronto in Computer Science and Medicine and a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair, Marzyeh has no shortage of accolades or degrees. She is an Oxford alum, as a Marshall Scholar. She also has 3 kids. About Marzyeh Marzyeh's plug, Muslims in Machine Learning Marzyeh's other plug, apply to MIT! About the host + the pod: Follow Layla on Instagram Follow Muslims Doing Things on Instagram Fun fact - this is the first episode ever recorded! Pardon my audio quality, I was figuring out the ropes. About me & the pod: Follow the host (@laylool) on Instagram Follow Muslims Doing Things on Instagram --- Send in a voice message: https://anchor.fm/laylool/message

You’re taking advantage of the benefits of AI every day in ways you might not even be aware of. When you “talk” to an automated voice on the other end of the phone, when you call a Lyft or an Uber, and when you’re asking Siri or Alexa to play your favorite song while you wash dishes. AI is everywhere, and its uses are expanding rapidly. With the application of any new technology, there’s always a period of time during which kinks that creators didn’t plan for become visible. As new systems gain traction, those unaccounted for faults can become amplified, creating patterns, which in turn can start to erode trust. One example of this when it comes to AI is how racial and gender biases that the technology was actually built to avoid can creep into the decision-making process. Another is how the AI-based algos in social media amplify extreme views and keep us all in our filter bubbles, too often fostering division. To better broadly consider the effects of such systems, it’s perhaps useful to first understand how they work – by building upon their own intelligence, collecting information from our cues and habits. We all collectively create AI in our clicks and swipes, often without considering how the data will be used by bots and algos to make decisions. In order to make this technology work well, and work well for everyone, we need to map out the channels of its proverbial brain. Our guests today are Mohsen Rezayat and Ron Bodkin. Rezayat is our Chief Solutions Architect here at Siemens Digital Industries Software. Bodkin spent the past few years as Technical Director of Applied Artificial Intelligence at Google. Currently, he’s the Vice President of AI Engineering and CIO at Vector Institute and Engineering Lead at the Schwartz Reisman Institute for Technology and Society. In today’s episode, we’re talking about machine learning and artificial intelligence, including the complexity of establishing a system of ethics in AI so that it makes conscientious decisions and better serves our collective human community. And find more information on industrial AI at Siemens here.Some Questions I Ask:What is an example of AI in practice? (5:58)How are some AI models demonstrating bias? (7:59)What is the potential to deliberately misuse digital systems? (10:31)With the loss of public trust in AI, when do you think we’ll be able to regain our trust of this technology? 12:51)What do you think about how tech companies can safeguard us against bias and unfair treatment from algorithms? (19:48)Do you think we’ll achieve the goal of embedding ethics into future models of AI? (21:39)What You’ll Learn in This Episode:The definition of machine learning (2:20)An example of how machine learning works (2:51)How racial bias makes its way into AI algorithms (8:45)The three components of trustworthy AI (12:56)How we can build ethical AI (14:37)Why humility is a good quality (15:10)How AI could help us see the future when it comes to catastrophic events (16:50)Connect with Mohsen Rezayat:LinkedInConnect with Ron Bodkin:LinkedIn See acast.com/privacy for privacy and opt-out information.

Today's guest is the great and brilliant, Ron Bodkin. Ron has served as Director of Applied AI at Google, VP/GM of AI at Teradata, founded multiple technology ventures, and now leads AI & Engineering at Vector Institute and Schwartz Reisman Institute. In this episode, Ron shares how to avoid scattered projects by following a reliable roadmap for leveled investment in AI capabilities. Want to learn about Emerj's best practices and frameworks? Get Emerj's report: "Generating AI ROI": emerj.com/roi1

Show Notes(2:55) Patricia talked about his interest in learning languages and living in different cultures.(4:05) Patricia talked about her experience volunteering as a translator at the International Network of Street Papers.(5:00) Patricia studied Liberal Arts at John Abbott College, English Literature at Concordia University, and Computer Science and Linguistics at McGill University during her undergraduate years.(8:06) Patricia worked at McGill Language Development Lab as a Research Assistant, which studied how children learn different types of words and sentences.(9:15) Patricia described her graduate school experience at the University of Toronto, where she researched lost language decipherment and writing systems.(11:19) Patricia talked about MedStory, which is a text-oriented visual prototype built to support the complexity of medical narratives (spearheaded by Nicole Sultanum).(12:35) Patricia explained her research paper, “Vowel and Consonant Classification through Spectral Decomposition.”(15:29) Patricia unpacked her blog post, “Why is Privacy-Preserving NLP Important?”(19:02) Patricia dissected her paper “Privacy-Preserving Character Language Modelling” that proposes a method for calculating character bigram and trigram probabilities over sensitive data using homomorphic encryption.(21:13) Patricia wrote a two-part series called “Homomorphic Encryption for Beginners.”(22:21) Patricia unwrapped her paper “Efficient Evaluation of Activation Functions over Encrypted Data” that shows how to represent the value of any function over a defined and bounded interval, given encrypted input data, without needing to decrypt any intermediate values before obtaining the function’s output.(25:33) Patricia elaborated on her paper “Extracting Bark-Frequency Cepstral Coefficients from Encrypted Signals,” which claims that extracting spectral features from encrypted signals is the first step towards achieving secure end-to-end automatic speech recognition over encrypted data.(27:38) Patricia explained why privacy is an essential attribute for speech recognition applications.(29:53) Patricia discussed her comprehensive guide on “Perfectly Privacy-Preserving AI” which dives into the four pillars of perfectly privacy-preserving AI and outlines potential combinatorial solutions to satisfy all four pillars.(37:53) Patricia shared her take on the differences working in academic and commercial settings (she is the founder and CEO of Private AI).(40:50) Patricia talked about Private AI’s GALATEA Anonymization Suite, which anonymizes data at the source and encrypts them using quantum-safe cryptography.(45:05) Patricia emphasized the importance of talking to customers when building a commercial product.(46:58) Patricia shared her experience as a Postgraduate Affiliate at Vector Institute, which works with institutions, industry, startups, incubators, and accelerators to advance AI research and drive its application, adoption, and commercialization across Canada.(49:09) Patricia shared her advice for young researchers by going deep into at least two domains and combining the knowledge.(50:30) Patricia shared her excitement for privacy and NLP research in the upcoming years.(52:36) Closing segment.Her Contact InfoWebsiteTwitterLinkedInGoogle ScholarMediumGitHubHer Recommended ResourcesHomomorphic EncryptionSecure Multiparty ComputationFederated LearningDifferential PrivacyVector InstituteMILA Montreal InstituteAlberta Machine Intelligence InstituteReza Shokri (Assistant Professor at National University of Singapore)Parinaz Sobhani (Director of Machine Learning at Georgian Partners)Doina Precup (Associate Professor at McGill University)

Show Notes(2:55) Patricia talked about his interest in learning languages and living in different cultures.(4:05) Patricia talked about her experience volunteering as a translator at the International Network of Street Papers.(5:00) Patricia studied Liberal Arts at John Abbott College, English Literature at Concordia University, and Computer Science and Linguistics at McGill University during her undergraduate years.(8:06) Patricia worked at McGill Language Development Lab as a Research Assistant, which studied how children learn different types of words and sentences.(9:15) Patricia described her graduate school experience at the University of Toronto, where she researched lost language decipherment and writing systems.(11:19) Patricia talked about MedStory, which is a text-oriented visual prototype built to support the complexity of medical narratives (spearheaded by Nicole Sultanum).(12:35) Patricia explained her research paper, “Vowel and Consonant Classification through Spectral Decomposition.”(15:29) Patricia unpacked her blog post, “Why is Privacy-Preserving NLP Important?”(19:02) Patricia dissected her paper “Privacy-Preserving Character Language Modelling” that proposes a method for calculating character bigram and trigram probabilities over sensitive data using homomorphic encryption.(21:13) Patricia wrote a two-part series called “Homomorphic Encryption for Beginners.”(22:21) Patricia unwrapped her paper “Efficient Evaluation of Activation Functions over Encrypted Data” that shows how to represent the value of any function over a defined and bounded interval, given encrypted input data, without needing to decrypt any intermediate values before obtaining the function’s output.(25:33) Patricia elaborated on her paper “Extracting Bark-Frequency Cepstral Coefficients from Encrypted Signals,” which claims that extracting spectral features from encrypted signals is the first step towards achieving secure end-to-end automatic speech recognition over encrypted data.(27:38) Patricia explained why privacy is an essential attribute for speech recognition applications.(29:53) Patricia discussed her comprehensive guide on “Perfectly Privacy-Preserving AI” which dives into the four pillars of perfectly privacy-preserving AI and outlines potential combinatorial solutions to satisfy all four pillars.(37:53) Patricia shared her take on the differences working in academic and commercial settings (she is the founder and CEO of Private AI).(40:50) Patricia talked about Private AI’s GALATEA Anonymization Suite, which anonymizes data at the source and encrypts them using quantum-safe cryptography.(45:05) Patricia emphasized the importance of talking to customers when building a commercial product.(46:58) Patricia shared her experience as a Postgraduate Affiliate at Vector Institute, which works with institutions, industry, startups, incubators, and accelerators to advance AI research and drive its application, adoption, and commercialization across Canada.(49:09) Patricia shared her advice for young researchers by going deep into at least two domains and combining the knowledge.(50:30) Patricia shared her excitement for privacy and NLP research in the upcoming years.(52:36) Closing segment.Her Contact InfoWebsiteTwitterLinkedInGoogle ScholarMediumGitHubHer Recommended ResourcesHomomorphic EncryptionSecure Multiparty ComputationFederated LearningDifferential PrivacyVector InstituteMILA Montreal InstituteAlberta Machine Intelligence InstituteReza Shokri (Assistant Professor at National University of Singapore)Parinaz Sobhani (Director of Machine Learning at Georgian Partners)Doina Precup (Associate Professor at McGill University)

In this episode, hosts Kim Langen and Nathan Langen interview Shingai Manjengwa, a data scientist by profession, Shingai is the Technical Education Specialist at the Vector Institute for AI in Toronto, Canada and she is also the founder of Fireside Analytics Academy that teaches high school students data science and offers the data science course curriculum to other high schools. Shingai’s children’s book, ‘The Computer and the Cancelled Music Lessons’ teaches data science to kids from ages 5-12.

Andy and Dave start with COVID-related AI news, and efforts from the Roche Data Science Coalition for UNCOVER (the United Network for COVID-19 Data Exploration and Research), which includes a dataset of a curated collection of over 200 publicly available COVID-19 related datasets; efforts from Akai Kaeru are included. The Biomedical Engineering Society publishes an overview of emerging technologies to combat COVID-19. Zetane Systems uses machine learning to search the DrugVirus database and information from the National Center for Biotechnology to identify existing drugs that might be effective against COVID. And researchers at the Walter Reed Army Institute of Research are using machine learning to narrow down a space of 41 million compounds to identify candidates for further testing. And the IEEE hosted a conference on 9 July, “Does your COVID-19 tracing app follow you forever?” In non-COVID-related AI news, MIT takes offline the TinyImages dataset, due to its inclusion of derogatory terms and images. The second (actually first) wrongful arrest from facial recognition technology (again by the Detroit Police Department) comes to light. Appen Limited releases its annual “State of AI and ML” report, with a look at how businesses are (or aren’t) considering AI technologies. Anaconda releases its 2020 State of Data Science survey results. And the International Baccalaureate Educational Foundation turn to machine learning algorithms to predict student grades, due to COVID-related cancelations of actual testing, and much to the frustration of numerous students and parents. Research from the Vector Institute and the University of Toronto tackles analogy and the Raven Progressive Matrices with an ensemble of three neural networks for objects, attributes, and relationships. Researchers at the University of Sydney and the Imperial College London have established CompEngine, a collection of time-series data (over 24,000 initially) from a variety of fields, and have placed them into a common feature space; CompEngine then self-organizes the information based on empirical properties. Garfinkel, Shevtsov, and Guo make Modeling Life available for free. Meanwhile, Russell and Norvig release the not-so-free 4th Edition of AI: A Modern Approach. Lex Fridman interviews Norvig in a video podcast. And the Elias Henriksen creates the Computer Prophet, which generates metaphors from a database of collected sayings. Click here to visit our website and explore the links mentioned in the episode.   

CMA NXT is the perfect platform for you to invest in your professional and personal development, connecting you with like minded young professionals and the leaders of tomorrow. Click here to sign up to CMA NXT. From experience, I can confidently say that CMA NXT may well be what best prepares you for your next big career move. Thank you for tuning into EP13 of Empathy Always Wins with Quinn Underwood, Samin Khan and Ally Salama! Today's Question revolves around this: How Are Two Canadian Youth Adopting Empathy in Their Challenge to Provide Mental Health Solutions Using Artificial Intelligence? A bit about Quinn and Samin.. Quinn Underwood is a repeat entrepreneur, global health researcher, and author. He is the former Co-Founder and Director of Global Business Development for ADVIN, a health-tech company he helped scale across Bangladesh and India, growing the team to more than 20, and serving more than 100,000 patients, in his second and third year at the University of Toronto.  Quinn currently serves as CEO of Animo, a platform built to help individuals and organizations measure and predict their psychological well-being through use of A.I.. - Samin Khan is a social impact artificial intelligence developer and researcher. He was the 2018 world champion of Microsoft's Imagine Cup - a technology & innovation competition with over 40 000 competing teams worldwide. Samin is currently a co-founder and CTO of Animo, a start-up that measures and predicts mental health through the analysis of language use. Samin also conducts research for the Vector Institute on the intersections of mental health and artificial intelligence.  - Show Credits Empathy Always Wins: The World's Exclusive Youth Leadership Podcast on Empathy & Community Building. © Ally Salama 2020.

CMA NXT (https://info.cmanxt.ca/Ally) is the perfect platform for you to invest in your professional and personal development, connecting you with like minded young professionals and the leaders of tomorrow. Click here (https://info.cmanxt.ca/Ally) to sign up to CMA NXT. From experience, I can confidently say that CMA NXT may well be what best prepares you for your next big career move. Thank you for tuning into EP13 of Empathy Always Wins with Quinn Underwood, Samin Khan and Ally Salama!Today's Question revolves around this: How Are Two Canadian Youth Adopting Empathy in Their Challenge to Provide Mental Health Solutions Using Artificial Intelligence? A bit about Quinn and Samin.. Quinn Underwood is a repeat entrepreneur, global health researcher, and author. He is the former Co-Founder and Director of Global Business Development for ADVIN, a health-tech company he helped scale across Bangladesh and India, growing the team to more than 20, and serving more than 100,000 patients, in his second and third year at the University of Toronto.  Quinn currently serves as CEO of Animo, a platform built to help individuals and organizations measure and predict their psychological well-being through use of A.I.. - Samin Khan is a social impact artificial intelligence developer and researcher. He was the 2018 world champion of Microsoft’s Imagine Cup - a technology & innovation competition with over 40 000 competing teams worldwide. Samin is currently a co-founder and CTO of Animo, a start-up that measures and predicts mental health through the analysis of language use. Samin also conducts research for the Vector Institute on the intersections of mental health and artificial intelligence.  - Show CreditsEmpathy Always Wins: The World's Exclusive Youth Leadership Podcast on Empathy & Community Building. © Ally Salama 2020.

In COVID-related AI news, Andy and Dave discuss work from Mount Sinai researchers, who have created an AI system that uses CT scans to diagnose patients with COVID-19. MIT and IBM Watson announce plans to fund 10 AI research projects to find COVID-19. The National Security Commission on AI releases its second white paper on COVID-19, on mitigating economic impacts of the COVID-19 pandemic, and preserving US strategic competitiveness in AI. In non-COVID AI news, DARPA’s Gamebreaker project holds a virtual kickoff meeting of its program, seeking to model and then break game balance. The United Nations Secretary-General releases a report on the protection of civilians in armed conflict. And the JAIC unveils its “business process transformation” initiative. In research, Hong Kong University of S&T publishes research on EC-Eye, an artificial eye that “sees” like a human eye. Other research demonstrates that neural networks trained for prediction mimic the diverse features of biological neurons and perception. And NVidia, University of Toronto, Vector Institute, and MIT publish GameGAN, and generative model that learns visually to imitate a desired video game (in this case, observing and replicating the gameplay of Pac-Man). The report of the week comes from NATO, which publishes a look at S&T Trends 2020-2040. Wolfgang Ertel pens the book of the week, with Introduction to AI (2nd Edition), free through Springer. The University of Southern California hosts a virtual symposium AI for COVID-19 in LA. And a collaboration between Google and the Getty Museum produces Art Transfer, transforming photos using the style of different artists. Click here to visit our website and explore the links mentioned in the episode. 

 From our "Power of Entrepreneurship" speaker series featuring Joe Canavan, CEO at NEXT Canada and Jordan Jacobs, Co-founder and Managing Partner at Radical Ventures. They explore building VC-founder relationships and discuss AI startup opportunities that could be a reality even in today’s climate. Jordan is a seasoned AI leader and commercialization expert, having co-founded Layer 6, the Vector Institute and acting as Director of CIFAR. Learn how to weather a storm and navigate your startup through crisis.

In this episode, hosts Arjun and Jason chat with University of Toronto Computer Science students Salaar Liaqat and Joanna Pineda, and Drs. Ben Fine and Josh Reicher to discuss how their team developed a machine learning model to automatically associate local procedure codes to SNOMEDCT. This project was a collaborative student research project with Dr. Marzyeh Ghassemi's Machine Learning for Health graduate course at the Vector Institute and the University of Toronto. 

Curious about how smart robots will change the future of work? Here’s Part 2 of our podcast with Chief Commercialization Officer, Cameron Schuler of Toronto’s Vector Institute – a world leading artificial intelligence research hub.

From finance to advertising, artificial intelligence is changing the way that we live. With the rise of A.I. and the automated workforce, should we be worried about our job prospects? How is the collection of mass personal data affecting the future? Portfolio Manager Hans Albrecht sits down with Cameron Schuler, Chief Commercialization Officer, VP, Industry Innovation of the Vector Institute, one of the world’s leading artificial intelligence research hubs.

MaRS Discovery District is the largest innovation hub in North America. Based in Toronto, they support 1300+ startups working in cleantech, healthtech, fintech AI, and many more. In the past couple of years, tech talent has surged from the US to Canada, thanks to welcoming immigration policies, world-class AI research institutions (like the Vector Institute), and government support. VC funding has followed - hitting record levels. MaRS CEO, Yung Wu, has played a big role in that transformation. He's helped shepherd Toronto from being an afterthought in the tech world to a city known for innovation and diversity. Beyond leading MaRS, Yung has an interesting background and has had a dynamic career. He's an immigrant to Canada and a serial entrepreneur. He has created, led and directed private and public companies spanning a range of industries, including mobile analytics & big data, enterprise software, financial services and pharma. Yung has also been recognized as one of Canada's Top 40 under 40 leaders. On today's podcast, MaRS CEO, Yung Wu joins me in a unique conversation that touches on the broader tech ecosystem. We also discuss what's happening in Canada and how Yung's own journey has informed his role today, leading one of the largest innovation hubs.

In episode ten of season five we talk about reproducibility, take a listener question on re understanding the history of the field given where we are now and how other fields are reviewing their own history and listen to a conversation with Graham Taylor of the Vector Institute.

A lawyer, economist and scholar, Gillian K. Hadfield has devoted much of her career to studying how legal systems can be improved to ensure they meet the needs of the people they are meant to serve. In her book, Rules for a Flat World: Why Humans Invented Law and How to Reinvent It for a Complex Global Economy, she argues that the complexity of today’s global, digital economy has pushed law to its limits, making it too expensive, too complicated, and too far out of touch with our needs. In this episode of LawNext, host Bob Ambrogi speaks with Hadfield about her book and her proposals for reinventing the legal system. They also discuss her ideas for addressing the access-to-justice gap, her recent research on ensuring the safety of artificial intelligence, her belief that private investment is essential to sparking innovation in law, and her work with the Utah Supreme Court to launch a regulatory sandbox to test many of her theories. With both a J.D. from Stanford Law School and Ph.D. in economics from Stanford University, Hadfield is currently based at the University of Toronto Faculty of Law and Rotman School of Management, where she teaches courses in legal innovation and design, responsible development and governance of AI, the origins and evolution of the law, and contract law and strategy. She is a faculty affiliate at the Vector Institute for Artificial Intelligence in Toronto and the Center for Human-Compatible AI at the University of California, Berkeley. She has served as a member of the World Economic Forum’s Global Future Council on the Future of Technology, Values and Policy and Global Agenda Council on Justice and co-curates the Forum’s Transformation Map for Justice and Legal Infrastructure. She was appointed in 2017 to the American Bar Association’s Commission on the Future of Legal Education and is a member of the World Justice Project’s Research Consortium. She serves as an advisor to The Hague Institute for the Innovation of Law, LegalZoom, and other legal tech startups. NEW: We are now on Patreon! Subscribe to our page to be able to access show transcripts, or to submit a question for our guests. Comment on this show: Record a voice comment on your mobile phone and send it to info@lawnext.com.

Kiret Dhindsa, PhDPostdoctoral Fellow, Research and High Performance Computing, Department of Surgery, McMaster UniversityPostgraduate Affiliate, Vector Institute for Artificial IntelligenceKiret's research lies at the intersection of AI and healthcare with a primary focus on neuroscience and neurotechnology, as well as active research areas in medical imaging and bioinformatics. Coming from a multidisciplinary background combining statistics, neuroscience, and biomedical engineering, Kiret is both a data scientist and an experimental scientist. Kiret completed a B. Sc. with a double major in Statistics and Psychology at York University, and a PhD in Computational Engineering and Science on brain-computer interfacing at McMaster University

AI is Working

AI is Working

Today we continue our exploration of Trust in AI with this interview with Richard Zemel, Professor in the department of Computer Science at the University of Toronto and Research Director at Vector Institute. In our conversation, Rich describes some of his work on fairness in machine learning algorithms, including how he defines both group and individual fairness and his group’s recent NeurIPS poster, “Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer.” This week’s series is sponsored by our friends at Georgian Partners. Georgian recently published Building Conversational AI Teams, a comprehensive guide to lead you through sourcing, acquiring and nurturing a successful conversational AI team. Download at: https://gptrs.vc/convoai.  For this episode's complete show notes, visit twimlai.com/talk/209.

In episode twenty one of season four we talk about distributed intelligence systems (mainly those internal to humans), talk about what were excited to see at the Conference on Neural Information Processing Systems and in advance of our trek to Canada we chat with Garth Gibson president and CEO of the Vector Institute.

AI is all around us but will early adopters gain a competitive edge in their business practices? Our featured experts demystify the barriers to AI adoption at the enterprise level and discuss how to scale your company's AI potential. Guests include the Republic of Estonia's Chief Information Officer Siim Sikkut, integrate.ai's Kathryn Hume, Facebook's AI Research Lead Joelle Pineau, MILA's Valérie Pisano and the Vector Institute's Cameron Schuler. If you enjoyed this episode, please rate and subscribe to The AI Effect on your preferred podcast app. To learn more about the topics covered in this episode, go to our website, theeffect.ai or accenture.com.  Follow us on Twitter @AIEffect.

  Welcome back to another episode of Biotechnology Focus radio! I am your host – Michelle Currie – here to give you the rundown on the Canadian biotech scene. So, as many of you know, we are coming up to a very busy and exciting time of the year, as BIO is just around the corner! And this isn’t just any regular BIO, this year marks the 25th anniversary in its inaugural location – Boston. It’s four days chalked full of keynotes, receptions and networking opportunities that will leave you trying to catch your breath at the end of the week. But if that doesn’t take your breath away, perhaps the performance by Diana Ross might at the largest BIO event of the year!  But bringing the focus back to Canada, a lot has been going on in the last few weeks that is worth mentioning. Keep listening to find out!  +++++  In a world where artificial intelligence has begun to incorporate itself in everything from life sciences to cars, it should come as no surprise that South Korean tech giant, Samsung, is jumping in with both feet opening AI Centres around the globe, one of which just opened in Canada’s most bustling city – Toronto.  The opening of the Toronto AI Centre comes on the heels of the company’s global announcement of two additional and newly established AI Centres in Cambridge, UK and Moscow, Russia as part of a new venture to tap into and contribute to the booming AI industry. The Toronto Centre will work in partnership with the company’s Silicon Valley team to pioneer AI research and development for the region.  Toronto has a rich history of innovation and discovery and is an ideal location for a vast amount of companies to call home. With access to key talent, Toronto is an idyllic place for research and development for speech recognition, where machine-learning technology was applied many years before it was widely applied to other fields. The vision is that the Samsung AI Centre will now serve a significant role in the advancement of AI with a focus on language understanding and computer vision technologies that will ultimately reduce the friction between the user and the device/service, whether it be mobile phones, TVs, appliances, or cars.  Located in Toronto’s downtown core at MaRS Discovery District, the new Samsung AI Centre is a part of a network of research Centres dedicated to research and development in the field of AI. The Centre is the second Samsung AI Centre to be established in North America, with the other in Mountain View, California. The North America AI Centres are led by senior vice president, Dr. Larry Heck, a renowned expert in machine learning for spoken and text language processing, who also co-leads the expansion of Samsung’s AI Centres around the globe.  The Toronto centre will be led by Dr. Sven Dickinson, newly appointed as the head of the Toronto lab, professor on leave and past chair of the Department of Computer Science at the University of Toronto. Dickinson is an expert in computer vision technologies, especially in the field of object recognition. He will play an integral part in Samsung’s research of core AI technologies that entail language, vision and other multi-modal interactions.  As one of the world’s largest urban innovation hubs, MaRS Discovery district supports promising innovators and ventures tackling key challenges in the sectors of cleantech, finance & commerce, and work & learning. In addition, and importantly, the vast MaRS community fosters cross-disciplinary collaboration which drives breakthrough discoveries and new solution for global audiences.  This announcement compliments earlier 2018 news of plans to launch additional AI centres in North America. Dr. Darin Graham will lead the opening of new labs in Canada as the head of Samsung’s Canadian AI Operations. Graham also helped create the Vector Institute – the renowned Canadian AI research institute, as a member of the founding team. The opening of AI centres in Canada will allow Samsung to expand its outpost for industry collaboration and talent recruitment in the major AI hubs in North America.  To date, Samsung has had remarkable success in leveraging Canada’s unique R&D talents for global impact. The Company’s Vancouver-based R&D centre has contributed to many in-market innovations and more than doubled its workforce, since opening with over 100 employees. With the addition of the AI centre in Toronto, the company plans to increase the R&D in Canada from current 100 to 200 in the near future. Additional developments and talent in Canada have been recognized through Samsung Electronics Canada subsidiaries, AdGear Technologies Inc. in Montreal and SigMast Communications Inc. in Halifax, Nova Scotia.  +++++  When it comes to what drugs get funded, how do provinces and the Canadian health care system decide which drugs to fund and which drugs not to? A team of University of Alberta researchers found that when a jury is made up of a cross-section of society and given proper information and context, people are willing to fund drugs and treatments for costly ultra-rare diseases, even at the expense of treatments for larger populations.  An example would be a rare genetic disorder that robs men of sight by the age of 40. It affects one person in 50,000 and does not yet have a cure. But it does have a very expensive, unproven gene therapy that holds the promise of delaying the inevitable.  Tania Stafinski, a researcher in the University of Alberta’s School of Public Health and director of the Health Technology and Policy Unity says, “The fallacy is that these kinds of decisions are based on the greatest good for the greatest number. It’s partly greatest need, partly what the gain looks like, partly the severity of the disease and partly the population affected. It is an interaction of all these things, but not whether it is utilitarian.”  To better understand the social values around spending on new technologies and commercially undeveloped “orphan drugs, Stafinski convened a pair of citizen juries that roughly matched the sociodemographic profile of people in and around Edmonton.  Just like in a legal setting, Stafinski called witnesses that represented government, health-care providers and patients, and led the juries through different trade-off exercises and scenarios. For the most part, specific drugs and technologies were avoided to rid the study of any bias and allow jurors to focus more on the characteristics of the people and what could be achieved.  The jury was put through seven trade-off exercises focusing on things that might matter to them, such as cost, the condition of the patient, what kind of technology is involved, how much improvement could be expected, and even caregiver burden.  Despite the stakes, uncertainties around whether the technology would be able to fully deliver on its claims didn’t play a significant role in decision-making. Even in cases, such as the gene therapy for vision disorder, where there was a risk the technology would fail, Stafinski found that jurors valued the information and innovation component of the research.  Final decisions on what technologies and drugs to fund are currently made by a provincial review committee, guided by a pan-Canadian evidence review process that leaves it up to the provinces to take into account social and ethical implications.  She says there are examples in the Canadian health-care system where a small gain for hundreds of thousands of people is implicitly sacrificed to give sufficient medical gains to a small group. One example is administering the flu vaccine—inhaled versus injected.  Stafinski explains that although a large segment of society may prefer to have the inhaled version of the flu vaccine, with a few exceptions, policy-makers aren’t spending the extra money for the inhaled vaccination, choosing instead to fund medications such as those that improve the quality of life for the 4,000 Canadians who suffer from cystic fibrosis.  +++++  A new study from The Ottawa Hospital is the first of its kind. The study suggests that treatment for erectile dysfunction coupled with a flu vaccine might be the solution to eradicating cancer cells after surgery. The study, published in OncoImmunology, shows that this unconventional strategy can reduce the spread of cancer by more than 90 percent in a mouse model. It is now being evaluated in a world-first clinical trial.  Senior author Dr. Rebecca Auer, surgical oncologist and head of cancer research at The Ottawa Hospital and associate professor at the University of Ottawa says, “Surgery is very effective in removing solid tumours. However, we’re now realizing that, tragically, surgery can also suppress the immune system in a way that makes it easier for any remaining cancer cells to persist and spread to other organs. Our research suggests that combining erectile dysfunction drugs with the flu vaccine may be able to block this phenomenon and help prevent cancer from coming back after surgery.”  The current study investigated sildenafil (Viagra), tadalafil (Cialis) and an inactivated influenza vaccine (Agriflu) in a mouse model that mimics the spread of cancer (metastasis) after surgery. The researchers evaluated these treatments by counting the number of metastases in mouse lungs. They found an average of:  37 metastases with cancer cells alone  129 metastases with cancer cells and surgery  24 metastases with cancer cells, surgery and one of the erectile dysfunction drugs  11 metastases with cancer cells, surgery, one of the erectile dysfunction drugs and the flu vaccine  Dr. Auer is now leading the first clinical trial in the world of an erectile dysfunction drug (tadalafil) and the flu vaccine in people with cancer. It will involve 24 patients at The Ottawa Hospital undergoing abdominal cancer surgery. This trial is designed to evaluate safety and look for changes in the immune system. If successful, larger trials could look at possible benefits to patients.  The researchers are excited about this research because it suggests that two safe and relatively inexpensive therapies may be able to solve a big problem in cancer. If confirmed in clinical trials, this could become the first therapy to address the immune problems caused by cancer surgery.  Using a variety of mouse and human models, Dr. Auer’s team has also made progress in understanding how erectile dysfunction drugs and the flu vaccine affect cancer after surgery. Normally, immune cells called natural killer (NK) cells play a significant role in killing metastatic cancer cells. But surgery causes another kind of immune cell, called a myeloid derived suppressor cell (MDSC), to block the NK cells. Dr. Auer’s team has found that erectile dysfunction drugs block these MDSCs, which allows the NK cells to do their job fighting cancer. The flu vaccine further stimulates the NK cells.  Dr. Auer stresses that although erectile dysfunction drugs and the flu vaccine are widely available, people with cancer should not self-medicate. Any changes in medication should be discussed with an oncologist.  +++++  Some of the hottest areas in biotech that are emerging and driving growth and investment are in the field of regenerative medicine and cell and gene therapy. There have been several acquisitions over the past year that really got the ball rolling with hopes to advance immunotherapies. Despite the curative potential, these therapies come with a hefty price tag and complex challenges. With the first immunotherapies to win regulatory approval in the United States, CCRM, a leader in developing and commercializing cell and gene therapies and regenerative medicine technologies, hosted a panel to discuss how we can bring these therapies to Canada.  The panel represented a wealth of knowledge covering regulatory and hands on approaches to the subject. It was moderated by Michael May, president and CEO of CCRM, and consisted of Donna Wall, MD, section head, Blood and Marrow Transplant/Cell Therapy Program, The Hospital for Sick Children (SickKids); Justin Shakespeare, executive director, Oncology Business Unit, Amgen; Patrick Bedford, senior manager, Clinical Translation and Regulatory Affairs, CCRM; and Aaron Dulgar-Tulloch, PhD, director of Bridge, GE Healthcare Cell Therapy.  There have been many decades of work in people trying variations of immunotherapy approaches and not getting a clinical signal. It was after researchers figured out T-cell biology and that they needed to bring not only the patients’ T-cells right up against the tumour cell but also that they had to get the T-cell excited and activated to go into cell division.  Donna Wall explains, “The first successful patient was only about six years ago. No question that the treatment can cure, as long as you take six years as to how long to treat a number of patients who otherwise have untreatable leukemia. That’s the first type of patients that we have when we have a new treatment. We take the patients where we have nothing else to offer. A number of patients do not make it to the treatment because it takes a while to engineer the cells; a number of patients may not have a response to the treatment; and a number of them who go into remission may end up relapsing. But for the first time, there are many who are responding positively to treatment and are not showing signs of leukemia.”  This is still in a very early-stage as of yet. It is not a one-and-done type of treatment. In order for this to work, the most common CAR-T product removes the cancer cells as well as the patient’s B-cells lifelong – giving them an immune deficiency. It is complex and comes with its own set of issues that may put up to 40 per cent of patients into intensive care for side effects of the CAR-T treatment. The treatment should not be taken lightly and will not be handed out over the counter.  The cost of the treatment is another factor entirely. Like anything new, cost is initially high but is expected to come down over time. It is a huge cost for a company to invest in and build the infrastructure that needs to be actualised as well as looking at regulatory costs. If the treatment becomes more mainstream, its costs pose another issue, as the health care system has not been designed to handle a large influx of big-ticket cases.  Patrick Bedford, CCRM, states, “There’s value in when you want to pay for something, but can you actually pay for it today is the real question. The number of drugs over $50,000 since a decade ago has gone from two to 20, and the drugs targeting orphan or rare diseases has all skyrocketed. There might not be a lot of people in each of these disease populations, but there are a lot of disease populations. So, the idea of affordability is really important. There are some new discussions right now about how to pay for these, like money back, or paying for performance type things, or rather than paying it all at once, pay three, four years later. There are a lot of ideas right now about how to afford the population if we choose they are worth the value to pay for.”  These are living drugs and therapeutics that have a very complex process on the manufacturing side as well as the rest of the supply chain. Each treatment batch is tailored to the patient. Many treatments start as autologous, but there are groups that are currently working on making this into more of an allogeneic process.  Aaron Dulgar-Tulloch explains, “Everyone wants to go from autologous to an allogeneic model. Every commercial entity would prefer to be in that allogeneic scenario because it is much better realised, there’s simplification in the supply chain, logistics, and the scale of benefits. We’re already starting to see groups trying to turn autologous into an allogeneic process. I think we will see more groups coming in with successful approaches to a more allogeneic or classical model.”  However, the playing field is changing as more treatments are reaching approval on a shorter timeline and with less clinical data. Even though much of that data presented to the regulators have been enormously successful, it is not typically a fast-moving field. This leaves the regulators to navigate through the treatments and do so in a receptive and responsible manner.  Since these therapies are still so new right now, it has put a particular strain, even on a global scale, to find individuals with expertise in scale up and industrial manufacturing coupled with biological cellular experience.  Dulgar-Tulloch explains that Canada in particular is feeling the pain from that, in that they don’t have a lot of the manufacturing infrastructure in Canada to pull from, and what Canada has is still heavily engaged in the bioprocess space.   CCRM has a centre devoted to improving the cell manufacturing process and is attracting international attention from companies who are looking for CCRM and GE’s expertise in process development. This work also feeds into the Good Manufacturing Process (GMP) facility that CCRM is building that provides space for therapeutics companies to run phase I and II clinical trials.  What it all boils down to when it comes to markets like Canada, is that timing is often regulatory-driven and balancing considerations as to where manufacturing is and how to support the local market, with timing as an implication on the pricing perspective. Canada needs to leverage its strengths on the clinical side so that we extract value from manufacturing and ultimately deliver these products to patients for commercialization.  +++++  Well that wraps up another episode of Biotechnology Focus radio! As always, we have all the stories online and in full to fish through at your leisure at biotechnologyfocus.ca. If you have a story idea or wish to make a comment, please email me at press@promotivemedia.ca. But until the next time, enjoy the spring weather and for those attending the BIO International Convention this year, good luck and enjoy! From my desk to yours – this is Michelle Currie.     

Canada is a world leader in artificial intelligence research and development -- but what is AI? In this first episode, hosts Amanda Lang and Jodie Wallis introduce us to Canada's AI ecosystem and examine the history of AI in Canada. Featuring interviews with Richard Zemel, co-founder and director of research at the Vector Institute for Artificial Intelligence, and Rebecca Finlay, ‎VP of engagement and public policy at the Canadian Institute for Advanced Research (CIFAR).

How do we protect ourselves in the new era of AI? In this episode, hosts Amanda Lang and Jodie Wallis speak with Ann Cavoukian about the privacy of personal data. Cavoukian is the distinguished expert-in-residence leading the Privacy by Design Centre for Excellence at Ryerson University. They also speak with Dr. Foteini Agrafioti, the chief science officer at RBC, and head of Borealis AI, as well as Deb Santiago, co-lead of the Responsible AI practice at Accenture. The conversation continues with Richard Zemel, co-founder and director of research at the Vector Institute for Artificial Intelligence.

Welcome to another episode of Biotechnology Focus radio. This week we are discussing some of the recent mergers and acquisitions that have been rocking the headlines, some recently awarded grants and how machines are moving fast. I am your host Michelle Currie, here to bring you the lowdown on the Canadian biotech scene.  +++++  Celgene, a biotech giant, has merged with and acquired Juno Therapeutics and their leading blockbuster drug cancer therapy in one of their largest deals ever. For a total of $9 billion, Celgene will pay $87 a share in cash for those not already owned by this corporation.  Celgene and Juno have been collaborating since June 2015 under which the two companies would leverage T cell therapeutic strategies to develop treatments for patients with cancer and autoimmune diseases with an initial focus on CAR-T and TCR technologies. In April 2016, Celgene exercised its option to develop and commercialize the Juno CD19 program outside of North America and China.  Juno develops cell-based cancer immunotherapies based on chimeric antigen receptor and high-affinity T cell (CAR-T cell) receptor technologies to genetically engineer T cells to recognize and kill cancer. Several product candidates have shown compelling clinical responses in clinical trials in refractory leukemia and lymphoma conducted to date.  This acquisition will position Celgene to become a preeminent cellular immunotherapy company with a platform to be at the forefront of future advances. JCAR017, a pivotal stage asset, with an emerging favorable profile in DLBCL, is expected to add approximately $3 billion in peak sales and significantly strengthen Celgene’s lymphoma portfolio, and JCARH125 will enhance Celgene’s campaign against BCMA (B-cell maturation antigen), a key target in multiple myeloma.  +++++  The global pharma industry is undergoing a dramatic transition from a quest for blockbusters to the design of a precision medicine based drug design. Artificial intelligence is one of the most prominent elements that has been adopted as part of the transition from a fully integrated pharmaceutical company model of drug design to extensive interaction with smaller innovative R&D companies as well as academic institutions.  Artificial Intelligence (AI) is the activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment (definition proposed by Nils J. Nillson, Stanford U.). Even though there are numerous definitions for AI, this one fits nicely into the goal of using machine learning for improving the rate of success in the design of novel and cost-effective therapeutics.  One of the primary reasons that AI has such a great potential in drug development is that there is a huge amount of health data available right now in the public health system. Clinical trials’ data, electronic medical records (EMR), genetic profiles and much more is the wealth representing the notion of BIG DATA in healthcare. The main challenge regarding the processing of big data is the need to process it in a meaningful and cost-effective fashion. That is why training a machine to fulfill the task becomes so attractive. Selecting and adjusting the right algorithms is the first essential step but once it is in place, training machines to find optimal patterns between the structure of “druggable molecules” and their optimal activity is within reach.  Canada has established a leadership position in training of machines to learn how to perform complex tasks, in a relatively short period of time. Based on recent commitments to the space, it is expected that we will witness in the foreseeable future designs of novel and much more specific therapeutics with higher potency and lesser side effects. The prospects are quite encouraging in light of the shift global pharma industry is adopting towards precision medicine. That shift will rely on sifting through patients’ medical records. Canadian AI machines are learning fast and are expected to become a key player in advancing academic concepts into standard and streamlined processes and organizations. In Ontario, the University of Toronto has emerged as a world-leading hub for research and entrepreneurship in this area. A potent combination of long-standing academic research in conjunction with the adoption of machine learning methodologies have already proven to be game-changing opportunities. Interactive approaches to computer science and medical research, combined with emerging best in class entrepreneurship programming and training is already yielding some fascinating fruits in the area of AI for drug discovery.  Companies like Structura Bio are taking the complex computational challenge of reducing noisy images from cryo-electron microscopes into readable highly accurate 3D structures of proteins and are doing what used to take a server room filled with computers a week, in a matter of seconds. Similarly, Phenomic AI (a recently incorporated UTEST company) uses a technique called deep learning to analyze data from high-throughput phenomic screens to analyze cell and tissue phenotypes in microscopy data with incredible accuracy. It holds out the potential for eliminating human intervention in the assessment of all that data. In some cases, companies like Deep Genomics and Atomwise are going all the way by leveraging their respective AI technologies to become drug discovery engines themselves. Our awareness of the impact of the AI revolution in drug discovery is already enormous and we’re only at the beginning of its adoption cycle. Future advances in Canada will be buoyed further by strong academic and institutional foundations that have been put in place to assist Canada in sustaining this advantage. The Vector Institute, as an example, was established in 2017 in partnership with Canada’s largest companies and the Federal and Provincial Government’s to attract and retain world-leading research talent and to promote cutting-edge research in the field.  Recently, partnerships have been established between the MaRS Innovation research healthcare ecosystem (UHN, Sickkids, Sunnybrook) with global players in the space of machine learning based drug design and developments. Partnerships with Schrödinger and Evotec have been established to capture the enormous potential of “fishing in the pond” of EMR’s rich source of unraveling the tissue/cellular architecture as a baseline for the discovery of novel disease targets, which thereby establishes a mechanism for better drugs.  The field of AI in the service of medical research is still in its infancy, but the initial avalanche of results is already starting to give us an idea of the great potential that machine learning can offer to those embarking on advancing drug development. Reducing screening times, aiding new drug candidates and finding the most effective drugs for specific diseases at a speed that humans cannot achieve is compelling, and we believe that AI will increasingly become part of the medical landscape. Once hurdles such as data standardized collection and storage as well as data privacy concerned are addressed, it is expected that we will witness an exponential inclination in the implementation of machine learning as a powerful tool in the design of more potent drugs with lesser side-effects. The FDA and Health Canada are encouraging pharmaceutical companies to join the choir.  To conclude, rephrasing from Eric Topol of the Scripps Research Institute (CNBC, May 2017), “The potential of artificial intelligence has probably the biggest impact of any type of technology on healthcare.”  +++++  Two of Canada’s largest producers of cannabis have struck a deal after months of negotiations and a hostile takeover bid. The board of directors and the special committee of the CanniMed board have agreed to support a new offer made by Aurora for the acquisition of all of the issued and outstanding shares of CanniMed not owned by Aurora.   Terry Booth, the CEO of Aurora Cannabis says, “We are very pleased to have come to terms with CanniMed on this powerful strategic combination that will establish a best-in-class cannabis company with operations across Canada and around the world.”   The new offer for CanniMed is approximately $1.1 billion based on Aurora’s implied share price of $12.65. The maximum amount of cash available under the amended offer will be $140 million, and the number of Aurora shares to be issued will be between approximately 72 million and 84 million. Assuming maximum cash elections, each CanniMed shareholder would receive $5.70 in cash and 2.9493 Aurora shares.  Despite CanniMed filing a law suit against Aurora earlier this month, this deal provides the optimum outcome for both companies.  +++++  The Ottawa Hospital has been awarded $12.7 million in the most recent project grant competition from the Canadian Institutes of Health Research (CIHR). The grant funding will be going to sixteen research groups at the hospital who are in affiliation with the University of Ottawa. This represents an enormous success rate of 30 per cent, doubling the national average.  The new funding will provide researchers the much-needed capital to delve deeper into their studies ranging on a plethora of subjects – anywhere from oncolytic viruses as immunotherapy treatments, using a holistic approach to improving the quality of life for the homeless, to understanding the role of liquid metabolism in the brain.  “I’m delighted that our researchers have once again achieved such a high success rate,” says Dr. Duncan Stewart, executive vice president of research at The Ottawa Hospital and professor of medicine at the University of Ottawa. “These new research projects have the potential to redefine the future of health-care, both at home and around the world.”  The Ottawa Hospital has scored above the national average in CIHR grant competitions for the past several years, including 2015, 2016, and 2017. This research centre shows great promise and innovative studies for the years ahead.  For the summaries of all the projects please visit biotechnologyfocus.ca   +++++  Well that wraps up another episode of Biotechnology Focus radio. If you have any questions, comments or story ideas, please contact us at press@promotivemedia.ca, and don’t forget to follow us on our twitter handle @BiotechFocus. From my desk to yours – this is Michelle Currie.    

The podcast you’re about to hear is the third of a series of shows recorded at the Georgian Partners Portfolio Conference last week in Toronto. My guest this time is Graham Taylor, professor of engineering at the University of Guelph, who keynoted day two of the conference. Graham leads the Machine Learning Research Group at Guelph, and is affiliated with Toronto’s recently formed Vector Institute for Artificial Intelligence. Graham and I discussed a number of the most important trends and challenges in artificial intelligence, including the move from predictive to creative systems, the rise of human-in-the-loop AI, and how modern AI is accelerating with our ability to teach computers how to learn-to-learn. The notes for this show can be found at twimlai.com/talk/62. For series info, visit twimlai.com/GPPC2017