The dialogue touches on how media frames shape public perception and the critical importance of equitable media representation and privacy protection in fostering a fairer society.

Birago shares the origins of Pienso, starting from research projects at MIT focused on mitigating cyberbullying, to building a full-fledged AI company. He discusses the core challenges enterprises face, such as GPU costs, data residency, and privacy issues, and explains how Pienzo adapts to the customer’s preferred environment.

Salim and our hosts delve into the differentiation of Cohere in the large language model (LLM) market, focusing on enterprise needs and cybersecurity. They also discuss the criteria for funding AI-inventing companies, emphasizing the importance of technical strength, product vision, and go-to-market skills.

Key Points From This Episode:

• The breadth of experience and field of expertise of today’s guest, Sheetal Patole.
• Sheetal explains the purpose of AI in the context of an enterprise, which is commonly misunderstood.
• Examples of the different ways that AI is used in different industries.
• Benefits of the autonomous trucks that are being used on mines.
• Sheetal shares how she and her team used AI to solve a transportation challenge.
• How to determine whether a business challenge can be solved using AI.
• Dos and don’ts when it comes to putting together a strategy for solving a business problem.
• The importance of having a coherent and mature strategy across an entire organization before implementing AI at scale.
• How to cultivate a level of maturity in an organization.
• Sheetal’s recommendation for navigating inter-team dynamics in an organization.
• A challenge that the AI industry is currently facing, and Sheetal’s thoughts on how it can be combated.
• AI governance in real-time; Sheetal shares her thoughts on why this is necessary, and how it should be implemented.
• What Sheetal wishes she could do differently, advice to her younger self, and an element of the AI realm that she overestimated.

Tweetables:

“Using data intelligently and moving away from a system of record to a system of intelligence that allows your organization to learn from this information and data sets to drive change.” — Sheetal Patole [0:03:18]

“[AI] has never been something just purely to make money, it’s been to transform the organization from where it is, and it’s always gone to somewhere better.” — Sheetal Patole [0:08:36]

“If you want to do data analytics and AI at scale, you need to build a level of maturity in your organization to be able to do it. The way you build that maturity is first to centralize the team.” — Sheetal Patole [0:27:57]

“AI is not a mythical creature or black box, it requires human beings.” — Sheetal Patole [0:38:35]

Links Mentioned in Today’s Episode:

Sheetal Patole on LinkedIn

Barclays UK

Key Points From This Episode:

• Phil shares the exciting career journey that led him to where he is currently.
• The changes that have taken place in the AI world since Phil entered it.
• Why a large percentage of AI in enterprise still fails.
• A major difference between the academics researching AI and those applying that research.
• Phil’s thoughts on how to bridge the gap between academia and enterprise.
• An explanation of one of the lesser-known threats of AI.
• The inspiration behind Phil’s app, Waverly, and how it works.
• Why companies are hesitant to change the way their recommendation engines are built.
• Challenges of balancing company optimization with the best interests of the user.
• Problems that Phil sees with the implementation of policies and regulations around AI technology.
• The empathy component of AI technology that Phil thinks we should be focusing on.
• Advice for researchers and practitioners for dealing with the unintended consequences of working in the AI field.

Tweetables:

“[Waverly] is my take on how to build better AI systems, and for me, better means AI systems that care more about the user they are trying to help than we are used to seeing.” — @PhilBeaudoin [0:02:43]

“Vision has improved a lot, natural language understanding, speech recognition, our ability to find patterns, all of that has improved a lot. It’s not AGI but it has improved quite a bit.” — @PhilBeaudoin [0:04:46]

“Bring an open mind, bring a lot of respect, and try to see the importance of the other party.” — @PhilBeaudoin [0:13:05]

“Most people have aspirations, most people have a direction they want to grow into, and when they go about their everyday life they are super happy to have assistance, but this assistance should be at the service of our aspirations.” — @PhilBeaudoin [0:18:50]

Links Mentioned in Today’s Episode:

Phil Beaudoin

Element AI

Waverly

Key Points From This Episode:

• Introducing Dr. Gillian Hadfield and what drew her to the space of law and globalization.
• How the challenges of AI align with the challenges of economics.
• The need for people in social sciences and humanities to engage in design and building.
• The objective of the Schwartz Reisman Institute for Technology and Society.
• Defining AI governance to address the alignment problem.
• Comparing AI with conventional programming and the difficulties with test sets.
• The difference between AI explainability and justifiability.
• Talking about the GDPR and what they are really looking for.
• A legal analogy on incorporating transparency into justifiable systems.
• Discussing the chicken-and-egg confusion that regulators are feeling.
• Why we haven't seen the growth of AI we would expect.
• How regulatory regimes haven't kept up with the speed of globalization and digitization.
• The balance of having the right kind of regulation.
• A walk through the current landscape of AI regulation.
• What regulatory technologies look like.
• The focus on fairness and algorithmic bias and AI's capacity in all domains.
• Dr. Hadfield's advice for people who are looking for AI integration in their practices.

Tweetables:

“There's no one solution to how you align AI.” — @ghadfield [0:10:19]

“We have the alignment problem everywhere. How do you get a corporation to do what you want it to do, how do you get governments to do what you want them to do?” — @ghadfield [0:23:52]

“AI is a general-purpose technology, it's a way of solving problems, it's a way of coming up with new ideas. It's going to be everywhere. I prefer to think of the regulatory challenge as, how is AI changing your capacity to achieve your regulatory goals, in any domain?” — @ghadfield [0:37:41]

“We need way more people who are not engineers, deeply engaged in the process of building our systems.” — @ghadfield [0:42:13]

Links Mentioned in Today’s Episode:

Gillian Hadfield on LinkedIn

Gillian Hadfield on Twitter

The Vector Institute

Schwartz Reisman Institute for Technology and Society 

GDPR

Mila

Key Points From This Episode:

• An introduction to today’s esteemed guest, Yoshua Bengio.
• Yoshua briefly shares his thoughts on the crisis that is currently taking place in Ukraine. 
• Origins of Yoshua’s journey in the AI world.
• The research field that Yoshua is most excited about at the moment. 
• Yosuha explains the concept of Consciousness Prior.
• System one versus system two thinking. 
• How neural networks solved the problem of ‘search’ in AI. 
• Ways of mitigating the risks of AI, and some of the organizations that are working on this. 
• How our inductive biases can be problematic.
• Lack of regulation in the computing industry, and why this needs to change.
• The global threats that AI has the potential to solve. 
• The importance of knowledge sharing.
• Advice from Yoshua for all scientific researchers.
• Yoshua’s favorite AI movie. 

Tweetables:

“The idea that there would be general principles that could explain intelligence, both ours, the intelligence of animals, and would allow us to build intelligent machines, I found that so exciting, and I’ve been riding that wave since then.” — Yoshua Bengio 

“I really believe in the importance of a diversity of research paths and research directions.” — Yoshua Bengio 

“The system one, system two division is a path towards making more moral machines.” — Yoshua Bengio 

“The area of healthcare is one where AI has the greatest potential of touching human beings positively in the coming years, and really saving a lot of lives.” — Yoshua Bengio

Links Mentioned in Today’s Episode:

Yoshua Bengio

Yoshua Bengio on LinkedIn

The Consciousness Prior

Mila

University of Montreal

A.M. Turing Award

2001: A Space Odyssey