The Ai Element

Taha Jaffer: The AI Successes You’re Not Hearing About Guest: Taha Jaffer, Head of Wholesale Banking and Global Treasury AI at  Scotiabank   At a moment when most organizations are still in fairly early stages of their AI journey, Taha Jaffer, Scotiabank’s Head of Wholesale Banking and Global Treasury AI, gives us a preview of what to expect: it’s a transformative journey, and one that’s worth taking, even though elements like data, technology and governance all take time to develop. Taha talks about  Going from simple use cases to more sophisticated and value creating solutions;  An example use case using AI to optimize a system of international bank transfers; How his team’s governance focus has grown to ensuring data assets are in good shape early on, and how his team approaches model testing Why you don’t hear about many AI successes in banking; Plus: His 3 pieces advice for other AI leaders Further Reading AI Maturity Framework AI Maturity Survey How AI risk management is different The value of expla

This is a special, short episode with a summary of lessons complementing our full-length interview with Element AI’s CTO Jeremy Barnes on “The 4 Personas of AI Adoption”.  A lot of Jeremy’s work has him involved in the top level strategy of AI implementation for the Global 2000, and he’s recently synthesized “7 sins of Entreprise AI Strategies” based off of the common mistakes he has observed. From managing risk to accounting reforms to cultural enablement, these “sins” also come with suggestions for how boards and C-suites can best enable their AI strategies.   -------------   Les 7 péchés des stratégies d’IA d’entreprise   Voici un court épisode spécial avec un résumé des leçons complétant notre entretien complet avec Jeremy Barnes, directeur de la technologietechnique chez Element AI, sur « Les 4 personas de l’adoption de l’IA » Jeremy a beaucoup travaillé sur la stratégie de haut niveau de mise en œuvre de l’IA pour le Global 2000, et il a récemment synthétisé les « 7 péchés des stratégies d’IA d’entrepri

If you’re tight on time, there is a short complimentary episode with our guest where he summarizes the key takeaways in “The Seven Sins of Enterprise AI Strategies”. While business leaders may know AI needs a mindset shift to get the most out of the technology, communicating what exactly needs to change is challenging. Jeremy Barnes, Element AI’s CTO, has an incredible ability to find and make sense of the connecting thread between AI technology and business.  In this long-form interview, Jeremy talks about his initial role at Element AI as Chief Architect and helping to develop the company’s thesis, the 4 personas of AI adoption he’s observed in the market, and the importance of companies fostering a collaborative culture that will be able to experiment and change quickly around this new tech. If you’re curious how company leaders should think strategically about AI, this interview is for you.   -----------   Jeremy Barnes : Les 4 personas de l’adoption de l’IA Si vous êtes pressé par le temps, il y a un cou

This special episode was recorded on Monday, March 30th for the beta release of  the COVID-19 research platform that leverages technology from the Element AI Knowledge Scout product.  Guest: JF Gagne, CEO and Co-Founder of Element AI Research data and reports are being published at an unprecedented pace as organizations scale up their efforts to respond to COVID-19. In order to help clinical and scientific researchers, public health authorities and frontline workers navigate that wealth of information at a rapid pace, we’ve adapted our Element AI Knowledge Scout platform to run semantic search on over 45,000 scholarly articles in the COVID-19 Open Research Dataset (CORD-19) released by the Allen Institute for AI.  We are looking to rapidly add features and data sets that will be relevant for: Scientific researchers building models of the pandemic and its impacts. Public Health and Safety authorities sourcing the best practices from around the world.  Clinical researchers working on new therapies or vaccine

S2E7: Global Competition Policy and Japan’s Society 5.0   Guests: Philippe Aghion, Economist and Professor at College de France, The London School of Economics, and Harvard University Yuko Harayama,  Former Executive Member of Japan’s Council for Science, Technology and Innovation Policy This week on The AI Element we zoom out to look at how AI is impacting, and is being impacted by, global economic policies. Policy is hugely important for AI’s development and implementation. Though each application of AI differs from business to business and country to country, there are often similar patterns and concerns that arise, like the fear of automation replacing jobs and of increasing inequalities. Policy makers across the globe are trying to tackle these concerns to ensure they are creating a positive outcome for all.  This week we have two guests who are going to teach us about how AI is impacting growth and a radical new approach in Japan to guiding science and innovation. Our first guest Philippe Aghion is a w

S2E6 - Histories of AI: Ancient Greek Myths and the Last AI Boom In 2020, and for our first episode in the new decade, we thought it would be good to continue to dig deeper into how AI has developed over time. Learning about the roots of AI, we are reminded that the north star of this field has always been what we tend to call artificial general intelligence today, intelligence that reflects the full breadth of human intelligence. This puts in context why the recent breakthroughs have been so significant, and at the same time there is still so far to go. On this week’s episode of The AI Element we are joined by two guests who share two very different histories of AI,  one of its ancient roots and the other of contemporary challenges in operationalizing it for mass use.  Adrienne Mayor is a historian and research scholar at Stanford University whose recent work focuses on the earliest imaginings of AI in ancient myths. She shares some insights from ancient Greek myths like Homer’s Iliad and writings by Aristot

In this bonus interview, historian, researcher and former TV star Jonnie Penn is joined by our Head of Public Policy and Government Relations Marc-Etienne Ouimette to talk about the history of AI. In the interview, Jonnie looks to the past to help answer a number of important questions about the future of AI. For instance, what parts of our social system do we not want to optimize? Who does this technological progress actually benefit? And how can more young people get involved in decision making processes surrounding tech?    1:19 - Jonnie Penn 1:25 - The Buried Life  1:54 - Berkman Klein Center 2:05 - MIT Media Lab 4:22 - Machines Who Think - Pamela McCorduck  23:21 - ‘Don’t Join a Union, Pop a Pill’ - Katrina Forrester  26:03 - The troubling case of the young japanese reporter who worked herself to death - Washington Post 27:14 - Germinal (novel) - Wikipedia   32:08 - The Cybernetic Brain - Andrew Pickering  42:52 - Marvin Minsky - Wikipedia  44:40 - Jonnie Penn - Twitter    Other links:  Jonnie Penn Pu

Bonus Episode - An Interview with Yohsua Bengio  Can AI be used to help solve Climate Change? If so, how?  In this bonus interview, world renowned machine learning researcher Yoshua Bengio joins host Alex Shee to talk about AI’s role in solving the climate crisis. Segments of this interview was featured on our episode about sustainability, in which he shared some examples of how AI is being used to mitigate and adapt to climate change. In the full-length interview he shares his personal motivations for getting involved in climate action and goes in depth about his cross-disciplinary work to solve what he thinks is one of the world’s largest existential risks. 2:03 - Mila - Yoshua Bengio’s Lab 2:04 - IVADO - The Institute for Data Valorization 3:11 - Montreal Declaration for Responsible AI  7:55 - Tackling Climate Change with Machine Learning - Rolnick et al., arXiv  11:19 - Mila - AI for Humanity  11:30 - Mila - Climate Change  13:31 - Visualizing the Consequences of Climate Change Using Cycle-Consistent Adve

This week we’re exploring if and how AI can help build a sustainable future. From solving climate change to improving health care, AI is being seen as a technology that can solve some of the world’s biggest problems. But can AI really save us? How can we be sure that AI for Good initiatives are actually helping the people they’re trying to reach? What role can, or should, AI practitioners play in finding solutions? Urvashi Vaneja explains why we should be skeptical of AI for Good initiatives that claim to be a cure-all and she shows how to start thinking constructively about how to do better. Sherif Elsayed-Ali shows how AI for Good can help scale the positive impact of human rights organizations and also why we need to expand our current understanding of human rights. Yoshua Bengio reflects on his recent research into different ways AI can be used to mitigate against and adapt to a changing climate. Yoshua also shares why he decided to use his machine learning expertise to try and solve climate change — and

Will AI take our jobs? AI’s main application is in the workplace and is being applied along all levels of the payscale. Critics are worried that this could lead to job loss but like any new technology application, it depends on how we implement it. How then can we create AI products that will enhance our capacity for work, not replace it? MIT Institute Professor Daron Acemoglu sheds light on AI’s impact on the job market and how AI could help both low skilled and high skilled workers alike. He breaks down how, if we implement AI properly, it could help expand the labour market and reorganize the way we work. Karthik Ramakrishnan, Head of Advisory at Element AI, talks about how we can successfully implement AI in organizations. The trick -- bring workers into the process.    Guests Daron Acemoglu, Institute professor MIT 1:07 - Daron Acemoglu - MIT 1:20 - Why Nations Fail by Daron Acemoglu  1:20 - Automation and New Tasks: How Technology Displaces and Reinstates Labor 1:39 - COMPUTER AND DYNAMO: THE MODERN PR

What is data feudalism? Should machines adapt to us or should we adapt to machines?  How can we reinstate agency and control when it comes to our personal data?  In this bonus episode, Neil Lawrence, Professor of Machine Learning at the University of Cambridge, joins Element AI’s Head of Government and Public Policy Marc Etienne Ouimette to answer these questions and many more. Neil was featured in a previous episode of The AI Element, “In Data We Trust?”, in which he spoke about data trusts and data protection. In this extended interview he shares more of his thoughts on the future of AI and the growing data divide.  1:04 - The Alan Turing Institute - Professor Neil Lawrence  1:34 - Cambridge appoints first Deepmind professor of machine learning  2:07 - Jonnie Penn  2:25 - AI for social good workshop  3:02 - Isaac Asimov’s Foundation - Wikipedia  12:24 - Data Trusts could allay our privacy fears - The Guardian  23:05 - Sylvie Delacroix - Twitter  23:09 - Bottom-Up Data Trusts: Distributing the ‘One Size

Guests  Richard Zuroff, Director of AI Advisory and Enablement at Element AI Tanya O'Carroll, Director of Amnesty Tech at Amnesty International Alix Dunn, Founder and Director of Computer Says Maybe Jesse McWaters, Financial Innovation Lead at World Economic Forum   AI is a powerful tool and with that power comes a great deal of responsibility. How can we be sure that we’re in control of AI systems? And what should the governance look like?   Data governance is an existing practice that covers a lot of good ground because of how integral data is to AI’s functioning. However, AI’s ability to learn and evolve over time means it will adapt to changes in its environment based on its given objective. That dynamic relationship between environment and model makes things like the design of the system and its objectives just as integral as the data the model runs on. Managing the risks of these new, dynamic systems has been widely branded as “AI Governance”.   Richard Zuroff breaks down the concept of AI governance an

We don’t have enough control over our data—how it is collected, by whom, what it’s used for. We’re used to hitting “accept” to whatever agreement we need to use the online platforms, mobile apps and other digital services that run our daily lives. Yet public awareness is growing about the importance of privacy and data control. Major data breaches and scandals about the misuse of data have shown the failures of the private sector when it comes to self-regulation.  Now, governments and policymakers are stepping in with efforts to address the power imbalance between consumers and big companies when it comes to data. It’s about time — the impact of artificial intelligence could exacerbate that power imbalance, and help the data-rich get richer. Element AI’s Marc-Etienne Ouimette spoke with some of those leading the charge around taking back control of our data and the notion of data trusts — think a union, but for your data.   Guests  Ed Santow, Australia’s Human Rights Commissioner  Christina Colclough, Direc

“Explainability” is a big buzzword in AI right now. AI decision-making is beginning to change the world, and explainability is about the ability of an AI model to explain the reasons behind its decisions. The challenge for AI is that unlike previous technologies, how and why the models work isn’t always obvious — and that has big implications for trust, engagement and adoption. Nicole Rigillo breaks down the definition of explainability and other key ideas including interpretability and trust. Cynthia Rudin talks about her work on explainable models, improving the parole-calculating models used in some U.S. jurisdictions and assessing seizure risk in medical patients. Benjamin Thelonious Fels says humans learn by observation, and that any explainability techniques need to take human nature into account.  Guests Nicole Rigillo, Berggruen Research Fellow at Element AI  Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University Benjamin Theloniou

Many have dystopian projections of what our future with AI will look like, but professionals working in AI see things differently. For some, our future with AI may simply mean more free time and cheaper access to quality services.   We check in with Jordan Fisher, Daniel Gross, Natacha Mainville and JF Gagné who together paint a picture of what a not-so-distant future might look, especially in retail and insurance. They may not know exactly what the year 2050 will look like, but they are hopeful. Featured in this episode: Daniel Gross, Partner at Y Combinator and Head of the AI Track Natacha Mainville, Chief Innovation Officer at TandemLaunch Jordan Fisher, CEO at Standard Cognition JF Gagné, CEO of Element AI Mentioned in the episode: Standard Cognition, AI-powered checkout Investing in the Future of Retail with Standard Cognition (Further reading) 1920s - What The Future Will Look Like (Video)

Every touchpoint with a prospect is an opportunity to nurture that relationship, but also a potential entry point for hackers. Given that cybercriminals are more resourceful than ever, cybersecurity experts need to be just as sharp. Oren Falkowitz is combining past experience at the NSA and US Cyber Command with AI to combat phishing attacks worldwide. In this episode, Alex Shee speaks to him and cybersecurity expert Frederic Michaud about how AI is currently being used to make businesses more safe. Featured in this episode: Frederic Michaud, Director at Element AI Oren Falkowitz, CEO of Area 1 Security Mentioned in the episode: Area 1 Security, performance-based cybersecurity company Hackers, Computer Outlaws: Segment about the history of phreaking (Video) John Draper AKA Cap’n Crunch, phone phreaker extraordinaire

Societal hype around AI is a byproduct of a few recent scientific breakthroughs — speech recognition, computer vision, natural language processing — in short, a computer’s ability to acquire human senses and mimic the human brain.   Yoshua Bengio (world-renowned professor and head of the Montreal Institute for Learning Algorithms) has been at the front lines of the Deep Learning Revolution that has enabled this kind of innovation. In this episode, he gives an overview of where the tech is actually at: how close is it to mirroring human senses? Featured in this episode: Yoshua Bengio, Head of the Montreal Institute for Learning Algorithms (MILA) Daniel Gross, Partner at Y Combinator and Head of the AI Track Mentioned in the episode: The Rise of Artificial Intelligence through Deep Learning (video), Yoshua Bengio at TEDxMontreal NIPS, the Annual Conference on Neural Information Processing Systems CIFAR, Canadian Institute for Advanced Research Asimov’s Three Laws of Robotics (Wikipedia) Artificial neural n

Successful adopters of AI develop an AI-first strategy supporting all functional areas of the business: marketing, product development, customer support, sales, and beyond. What does this look like in practice? Naomi Goldapple of Element AI, who consults with execs about AI strategy on the regular, provides some insight. Alex also talks to Chris Benson who was hired at Honeywell to inject AI into the traditional-but-transforming manufacturing and logistics space. He shares some case studies of AI transformation and touches on the pervasive fear of job loss. Featured in this episode: Naomi Goldapple, Program Director at Element AI Chris Benson, Chief Scientist for Artificial Intelligence & Machine Learning at Honeywell Safety & Productivity Solutions Mentioned in the episode: Element AI, AI solutions provider Honeywell, manufacturing and logistics conglomerate company Crash Destroys F-22 Test Model (Eric Schmitt for the New York Times, 1992) Atlanta Deep Learning Meetup (Meetup.com)

As AI seeps into every industry, businesses are being forced to adapt. Old school industries may not be as lean or quick to pivot as startups, but they have access to a motherlode of funding and data. Still, red tape and outdated infrastructure may block them from the timely AI transformation they need to stay afloat tomorrow. Alex Shee speaks with serial AI entrepreneur JF Gagné about this tension between startups and more corporate environments. Then, 15-year veteran of the insurance industry Natacha Mainville shares some real-world examples of how AI is flipping the industry on its head, forcing incumbents to keep up. Featured in this episode: JF Gagné, CEO of Element AI Natacha Mainville, Chief Innovation Officer at TandemLaunch Mentioned in the episode: JDA Software, retail and supply chain solutions Element AI, AI solutions provider Convolutional neural networks (Wikipedia) Lemonade Renters & Home Insurance, insurance startup

Charles C Onu is using AI to detect birth asphyxia in babies. His story is inspiring because of its impact on society and the field of healthcare (in 2016, 1,000,000 babies died from asphyxia), but also because of his humble beginnings. In this episode, Charles shows us that a passion for solving problems can help you overcome many obstacles. Host Alex Shee also sits down with Rediet Abebe, co-founder of Black in AI, to expand on how others are using AI to change not just their industry, but the world. Featured in the episode: Charles C Onu, Founder and AI Research Lead at Ubenwa Rediet Abebe, PhD candidate at Cornell, researching AI applications for social good Mentioned in the episode: Ubenwa, birth asphyxia detection system This Nigerian AI Health Startup Wants to Save Thousands of Babies’ Lives with a Simple App (Further reading) Supervised learning (Wikipedia) MOOC, Massive Open Online Courses (MOOCs) Black in AI (GitHub) Women in Machine Learning Mechanism Design for Social Good MacArthur Foundatio