Tianchu Gao is an IPilogue Writer and a 1L JD Candidate at Osgoode Hall Law School.

Rising to the challenges and opportunities posed by the disruptive technology of artificial intelligence, 91��ɫ’s Artificial Intelligence and Society Task Force leveraged collegial expertise from a wide range of disciplines to explore the possibility of research, development, and innovation of artificial intelligence and its impacts on humanity and society. The Task Force, Co-Chaired by Prof. Pina D’Agostino and Prof. James Elder, hosted a virtual symposium “” on Nov. 16. The summarizes current AI research, teaching, and learning activities at 91��ɫ and offers advice on further expansion of AI-related initiatives in the near future. After welcoming remarks from 91��ɫ’s President & Vice-Chancellor Rhonda Lenton, VPRI Amir Asif, and Mayor Frank Scarpitti of Markham, the symposium featured a keynote speech by , Head of JP Morgan Chase AI Research and Herbert A. Simon University Professor in the School of Computer Science at Carnegie Mellon University. It was followed by a panel discussion led by Prof. D’Agostino and Prof. Elder. The panel featured Dr. Veloso, Neetika Sathe (Vice President, Alectra’s GRE&T Centre), James Goel (Director of Engineering Technical Standards, Qualcomm) and Androu Waheeb (JD Candidate 2023, Osgoode Hall Law School). The livestream of the complete symposium is now available on .

Artificial intelligence is “.” Professor Veloso began her speech by tracing the origin of AI to a ten-man workshop that lasted two months at Dartmouth College in 1956. The workshop proceeded based on the conjecture that The workshop’s goal to simulate human perception and cognition on machines continues to challenge scientists today. Despite the revolutionary breakthroughs in the past decades, artificial intelligence is still a young science that has a long journey to advance ahead.

Professor Veloso shared in the speech a few projects she and her team have developed for JP Morgan Chase when she worked as the head of its AI Research department since 2018. One example is the use of image-based decision making and prediction in stock market analysis. Machines have learned to recognize and classify objects; we see the implementation of this technology in self-driving cars, as the car needs to “see” the environment—identify roads, cars, and pedestrians—to navigate in the traffic safely. Similarly, this technology is used to analyze the stock market as pure images. The researchers labeled the images with “buy” and “no buy” decisions made by humans from historical data, and they can use visual signals to predict transactions in the stock market with 95% accuracy.

Other important tasks that AI can tackle for the financial market include the generation of synthetic data for development and exploration. Synthetic data is easier to access and process than real data. It allows banks to explore the impacts of the decisions they made and develop new approaches to problems like fraud and money laundering. Professor Veloso’s team is also working on the automated standardization of financial data represented in different formats. It is an AI-driven task because the computer program needs to recognize visual and semantic cues in order to understand the meaning of a document and convert it into the standard form. These are a few examples of the various tasks that AI can accomplish and continuously refine for the financial market and beyond.

The challenges AI poses for us do not concern technology alone. As panelist James Goel pointed out, the tremendous investments (nearly a trillion dollars) that AI-related markets will attract in the next five to ten years will generate transformative impacts on society at large. The question remains: what could academia and government policy do in response to this social challenge?

91��ɫ is taking the initiative to foster an interdisciplinary approach to explore AI as a key area of accelerated research and its social implications. The report by the Task Force is a first step of the journey; it studies the existing AI terrain in 91��ɫ in terms of research, teaching, and learning activities and offers guidance on how to grow AI initiatives in the university. 91��ɫ’s new Markham campus will further provide greater resources for AI-related research and innovation. Substantial activities suggest that 91��ɫ has a great potential to be a great center of teaching and research in AI.

The post Recap of “Fostering the Future of Artificial Intelligence: Report from the 91��ɫ Task Force on AI & Society” appeared first on IPOsgoode.

Photo Credit: Ashley Moniz

Ali Mesbahian is an IPilogue Writer and a 2L JD Candidate at Osgoode Hall Law School.

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On June 28, 2021, IP Osgoode hosted a panel discussion in their Bracing for Impact Webinar Series titled AI’s Dirty Footprint. Organized in collaboration with the Harry Radzyner Law School at IDC Herzliya in Israel, Microsoft Canada, and Alectra's GRE&T Centre, the central question of this webinar was: in what way can we use artificial intelligence (AI) to ensure that the negative impacts of its energy consumption do not exceed its beneficial effects for environmental sustainability?

IP Osgoode’s own Professor Giuseppina (Pina) D’Agostino opened the panel by setting the stage for the discussion and introducing the speakers.��

In his opening remarks, Dr. Amir Asif, Vice President of Research and Innovation at 91��ɫ, noted that AI remains a “key strategic area” for research at 91��ɫ. Emphasizing the need for an interdisciplinary approach, Dr. Asif also stated that exploring AI’s ethical and legal implications will require collaboration between researchers in the AI community, social sciences, and the humanities.

Indeed, “collaboration” was one common thread among all the speakers. In his first formal address since he took office, the Hon. David Piccini—Ontario Minister of the Environment, Conservation and Parks—turned not only to universities for ideas in using AI to improve the climate situation, but also to youth as part of the government’s broader environmental policy plan. The youth, he noted, must be “unapologetically engaged”. Given the , the Minister’s words are welcome if they signal any change.

The panel then proceeded to a discussion moderated by Dr. Aviv Gaon, professor at IDC Herzliya. In his introduction, Gaon brought attention to that outlines 17 internationally-agreed-upon sustainable development goals for 2030, spread across 169 targets. With respect to the environment, the study shows that AI’s potential to enable the environmental targets of these 17 goals outweighs its inhibitory effects.

The first panelist was Andrea Roszell, Director of Energy, Sustainability and Infrastructure at Guidehouse. Her discussion was centred on AI’s capabilities to increase efficiency in the energy and utility sector. In particular, she pointed to the “energy cloud”, a concept developed at Guidehouse that moves away from a “one-way flow” of power from energy centers to consumers, to a more networked, interconnected “multi-flow” dynamic. This requires an infrastructure—a neural grid—that utilizes artificial intelligence in technology, such as sensors software and monitoring systems, to create large “data sets” for utilities to access. Despite requiring increased energy consumption, Roszell stated that these data sets are a net benefit to the environment due to the new efficiency gained in management of greenhouse gases and predictive maintenance models that ultimately lead to a more sustainable and reliant energy infrastructure.

The second panelist was Dr. Audrey Lee, Senior Director of Energy Strategy at Microsoft. She started by pointing to Among other goals, Dr. Lee highlighted Microsoft’s plan to offset all of its electricity usage with renewable energy by 2025 and to be carbon negative by 2050. Lee noted, however, that the first step in achieving any such goal is to establish a proficient “measurement infrastructure” that can enable us to quantify our environmental footprint with sufficient precision—for example, data analytics that detail how and to what extent a particular utility uses electricity at each hour.

The panel then continued to its third speaker, Kapil Singhal, Co-Founder & CEO of Vyntelligence. At the very outset of his discussion, he too emphasized the need for collaboration. In particular, Singhal noted how Vyntelligence has made possible a new form collaboration between artificial intelligence and human brain power. Utilizing short videos of workflow in the field, artificial intelligence can augment workers’ awareness of a given project by revealing further areas of risk and benefit. This, when combined with human cognitive and decision-making power (which Singhal noted far exceeds what AI can learn), will yield more efficient outcomes. One such outcome is enhancing the infrastructure that allows for remote work (the importance of which is vividly felt in times of COVID-19), reducing thereby the carbon footprint of work-related travel.��

Finally, the panel featured Neetika Sathe, Vice President of the GRE&T Centre at Alectra Inc. First, she noted that as more and more people gain access to the internet, global energy consumption is bound to increase. Thus, she emphasized the need for international collaboration beyond local efforts. She further mentioned that about half of the energy used at datacentres is used to cool their servers, which brings attention to the need for more efficient infrastructures.

In closing, it is important to address that, as the panelists mentioned, data centres account for only 1-2% of global energy consumption. However, as I mentioned in , AI’s “dirty footprint” is not confined to the energy it consumes, but extends to its ability to offer services for resource extraction which, for example, is enabled by the connection and collaboration between the tech and fossil fuel industries. Any meaningful policy directed at reducing AI’s negative environmental impacts must also account for this broader perspective.

A link to watch a recording of the event can be found on IP Osgoode's page.

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