The AI Element

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 Fits All Approach to Data Governance - Sylvie Delacroix and Neil Lawrence    Other Readings  Data trusts: reinforced data governance that empowers the public - Element AI  Data Trusts - Inverse Probability  Inverse Probability - Neil Lawrence Blog  Talking Machines Podcast - Neil Lawrence Podcast   ---------   Qu’est-ce que le féodalisme des données? Les machines doivent-elles s’adapter à nous ou devons-nous nous adapter aux machines? Comment pouvons-nous rétablir la capacité d’agir et le contrôle en ce qui concerne nos données personnelles?  Dans cet épisode bonus, Neil Lawrence, professeur d’apprentissage machine à l’Université de Cambridge, se joint au directeur des relations publiques et gouvernementales d’Element AI, Marc-Étienne Ouimette, pour répondre à ces questions et bien d’autres. Neil a participé à un épisode précédent du balado The AI Element intitulé « In Data We Trust? », dans lequel il a parlé des fiducies de données et de la protection des données. Dans cet entretien prolongé, il nous fait part de ses réflexions sur l’avenir de l’IA et sur la division croissante des données.    1:04 – L’Institut Alan Turing – Professeur Neil Lawrence  1:34 – Cambridge nomme le premier professeur d’apprentissage machine chez DeepMind  2:07 – Jonnie Penn  2:25 – Atelier L’IA pour le bien social  3:02 – Fondation Isaac Asimov – Wikipédia  12:24 – Les fiducies de données pourraient apaiser nos craintes en matière de vie privée – The Guardian  23:05 – Sylvie Delacroix – Twitter  23:09 – Fiducies de données ascendantes : Promouvoir l’approche uniformisée « One Size Fits All » pour la gouvernance des données – Sylvie Delacroix et Neil Lawrence   Autres lectures   Fiducies de données : une gouvernance renforcée des données qui habilite le public – Element AI  Fiducies de données – Probabilité inverse  Probabilité inverse – Blogue de Neil Lawrence  Balado Talking Machines – Balado de Neil Lawrence

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 and how it differs from data governance. Tanya O’Caroll and Alix Dunn tell us about the importance of governance in protecting human rights when building AI systems. Jesse McWaters shares his insights on AI’s impact on the financial sector and why a new form of governance will soon be necessary.    00:48 - How AI risk management is different and what to do about it - Element AI 05:07 - All the Ways Hiring Algorithms Can Introduce Bias - HBR 06:45 - The Why of Explainable AI - Element AI 07:37 - Amnesty Tech - Twitter 07:39 - Computer Says Maybe 07:52 - The Engine Room  10:18 - UN Guiding Principles on Business and Human Rights  14:18 - The Matthew Effect - Wikipedia 15:00 - Agile Ethics - Medium 17:48 - Human Rights Due Diligence 20:07 - The New Physics of Financial Services - World Economic Forum 24:30 - Consumer Financial Protection Bureau 29:00 - GDPR   Other Reading: Putting AI Ethics Guidelines to Work - Element AI  AI-Enabled Human Rights Monitoring - Element AI New Power Means New Responsibility: A Framework for AI Governance - JF Gagne Podcast: Opening the AI Black Box - Element AI     ---------   De la gouvernance des données à la gouvernance de l’IA   Richard Zuroff, directeur du conseil et de la mise en oeuvre de l'IA chez Element AI Tanya O'Carroll, directrice d'Amnesty Tech à Amnistie Internationale Alix Dunn, fondatrice et directrice de Computer Says Maybe Jesse McWaters, responsable de l'innovation financière au World Economic Forum   L’IA est un outil puissant et ce pouvoir s’accompagne d’une grande responsabilité. Comment pouvons-nous être sûrs de contrôler les systèmes d’IA? Et à quoi devrait ressembler la gouvernance?   La gouvernance des données est une pratique existante qui couvre beaucoup de bonnes choses en raison de la façon dont les données font partie intégrante du fonctionnement de l’IA. Cependant, la capacité de l’IA à apprendre et à évoluer au fil du temps signifie qu’elle s’adaptera aux changements de son environnement en fonction de son objectif donné. Cette relation dynamique entre l’environnement et le modèle rend les choses comme la conception du système et ses objectifs tout aussi intégrales que les données sur lesquelles le modèle fonctionne. La gestion des risques de ces nouveaux systèmes dynamiques a été largement qualifiée de « gouvernance de l’IA ».   Richard Zuroff analyse le concept de gouvernance de l’IA et en quoi il diffère de la gouvernance des données. Tanya O’Caroll et Alix Dunn nous parlent de l’importance de la gouvernance dans la protection des droits de la personne lors de l’élaboration de systèmes d’IA. Jesse McWaters nous fait part de son point de vue sur l’effet de l’IA dans le secteur financier et nous explique pourquoi une nouvelle forme de gouvernance sera bientôt nécessaire.    00:48 – En quoi la gestion du risque de l’IA est-elle différente et que faire à ce sujet – Element AI 05:07 – All the Ways Hiring Algorithms Can Introduce Bias - HBR 06:45 – Le «

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, Director of Platform and Agency Workers, Digitalisation and Trade at UNI Global Union Neil Lawrence, Professor of Machine Learning at the University of Sheffield  Show Notes  03:40 - NSW police may be investigated for ‘secret blacklist’ used to target children - The Guardian 07:52 - 94% of Australians do not read all privacy policies that apply to them – and that’s rational behaviour - The Conversation  08:02 - Click to agree with what? No one reads terms of service, studies confirm - The Guardian  10:38 - Up for Parole? Better Hope You’re First on the Docket - The New York Times 14:45 - The GDPR Covers Employee/HR Data and It's Tricky - Dickson Wright 18:08 - Companies are trying to test if they can make employees wear fitness trackers - Business Insider  20:11 - Silicon Valley & the Netherlands: Drivers of the future of automation - Netherlands in the USA 23:34 - Data Trusts - Neil Lawrence, inverseprobability.com  23:48 - Data trusts could allay our privacy fears - The Guardian  32:32 - Data trusts: reinforced data governance that empowers the public - Element AI   Further Reading  What is a data trust? - Open Data Institute Data trusts: reinforced data governance that empowers the public - Element AI Uncertainty and the Governance Dilemma for Artificial Intelligence - Dan Munro Governing AI: Navigating Risks, Rewards and Uncertainty - Public Policy Forum Anticipatory regulation - Nesta  Disturbing the ‘One Size Fits All’ Approach to Data Governance: Bottom-Up Data Trusts - Sylvie Delacroix & Neil Lawrence  Human Rights and Technology Issues - Australian Human Rights Commission The Civic Trust - Sean McDonald & Keith Porcaro Facebook’s privacy policy is longer than the US Constitution - The Next Web   Follow Us  Element AI Twitter Element AI Facebook  Element AI Instagram  Alex Shee’s Twitter Alex Shee’s LinkedIn     ---------   Nous n’avons pas suffisamment de contrôle sur nos données : comment elles sont recueillies, par qui et à quoi elles servent. Nous sommes habitués à « accepter » tout accord dont nous avons besoin pour utiliser les plateformes en ligne, les applications mobiles et autres services numériques qui gèrent notre vie quotidienne. Pourtant, le public est de plus en plus conscient de l’importance de la protection de la vie privée et du contrôle des données. D’importantes atteintes à la protection des données et des scandales concernant l’utilisation abusive des données ont montré les échecs du secteur privé en matière d’autoréglementation.  Aujourd’hui, les gouvernements et les décideurs s’efforcent de remédier au déséquilibre de pouvoir entre les consommateurs et les grandes entreprises lorsqu’il s’agit de données. Et ce n’est pas trop tôt! En effet, l’incidence de l’intelligence artificielle pourrait exacerber ce déséquilibre de pouvoir et aider les personnes riches en données à s’enrichir. Marc-Étienne Ouimette d’Element AI s’est entretenu avec certains des principaux responsables de la pr

“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 Thelonious Fels, founder of AI healthcare startup macro-eyes Show Notes  01:11 - Facebook Chief AI Scientist Yann LeCun says rigorous testing can provide explainability01:58 - Berggruen Institute, Transformation of the Human Program05:34 - Judging Machines. Philosophical Aspects of Deep Learning - Arno Schubbach 06:31 - Do People Trust Algorithms More Than Companies Realize? - Harvard Business Review 08:25 - Introducing Activation Atlases - OpenAI10:52 - Learning certifiably optimal rule lists for categorical data (CORELS) - YouTube11:00 - CORELS: Learning Certifiably Optimal RulE ListS 11:45 - Stop Gambling with Black Box and Explainable Models on High-Stakes Decisions 16:52 - Transparent Machine Learning Models for Predicting Seizures in ICU Patients - Informs Magazine Podcast19:49 - The Last Mile: Challenges of deployment - StartupFest Talk24:41 - Developing predictive supply-chains using machine learning for improved immunization coverage - macro-eyes with UNICEF and the Bill and Melinda Gates Foundation Further Reading  A missing ingredient for mass adoption of AI: trust - Element AI Breaking down AI’s trustability challenges - Element AI The Why of Explainable AI - Element AI  Follow Us  Element AI Twitter Element AI Facebook  Element AI Instagram  Alex Shee’s Twitter Alex Shee’s LinkedIn   --  L’« Explicabilité » est un grand mot à la mode en IA en ce moment. La prise de décision en matière d’IA commence à changer le monde, et l’explicabilité concerne la capacité d’un modèle d’IA à expliquer les raisons qui sous-tendent ses décisions. Le défi pour l’intelligence artificielle est que, contrairement aux technologies précédentes, la façon dont les modèles fonctionnent et les raisons pour lesquelles ils fonctionnent ne sont pas toujours évidentes — et cela a de grandes répercussions sur la confiance, l’engagement et l’adoption. Nicole Rigillo décompose la définition de l’explicabilité et d’autres idées clés, y compris l’interprétabilité et la confiance. Cynthia Rudin parle de son travail sur les modèles explicables, l’amélioration des modèles de calcul des libérations conditionnelles utilisés dans certaines juridictions américaines et l’évaluation du risque de crise chez les patients médicaux. Benjamin Thelonious Fels estime que les humains apprennent par l’observation et que toute technique d’explication doit tenir compte de la nature humaine.  Invités Nicole Rigillo, chercheuse de l’Institut Berggruen chez Element AI Cynthia Rudin, professeure d’informatique, de génie électrique et informatique, et de sciences statistiques à l’Université Duke Benjamin Thelonious Fels, fondateur de l’entreprise en démarrage macro-eyes œuvrant en IA dans le domaine de la santé Afficher les notes  01:11 – Yann LeCun, scientifique en chef de l’intelligence artificielle sur Facebook affirme que des tests rigoureux peuvent fournir des explications.01:58 – Institut Berggruen, Transformation du programme humain05:34 – Machines de

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 Foundation, Understanding the Public Interest Implications of Artificial Intelligence Toward Ethical, Transparent and Fair AI/ML: A Critical Reading List (Further reading)

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

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)

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 networks (Wikipedia)

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

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)