CEO & Co-Founder
CEO & Co-Founder
Guillaume has an extensive background in technology companies. In the past six years, he has been a business consultant in strategy and innovation-driven growth, having consulted for large and medium-sized companies in various sectors. He is the Chairman of the Board of CTS Santé, the most active accelerator for medical technologies and devices in Canada and the Chairman of the Board of CM Labs, a Montreal high-tech software company offering simulators and simulation software for vehicles, the defense industry, robotics, construction and port operations around the globe. He is an active mentor and coach with tech start-ups at FounderFuel, TechStars, District 3, CTS Santé, and the Business Families Foundation. Previously, Mr. Hervé was a senior executive at CAE Inc. where he held several key positions. He was the founder and CEO of CAE Healthcare which leveraged simulation, haptics and virtual reality technologies and best practices in aviation simulation to offer surgical simulators, integrated simulation-based training solutions, and patient simulators to medical schools, hospitals and defence organizations worldwide. The company grew to $50M in revenues by year 3. He was also President & CEO of Presagis, specializing in delivering high fidelity, physics-based simulation and graphics software to defense and aeronautics organizations worldwide. Prior to this, he was an Executive VP in CAE Inc.’s core business. Some of his responsibilities included VP Global Operations and Technology for Commercial Aviation Training, VP Aviation for Americas and Asia, and Head of Engineering. Prior to CAE, Mr. Hervé was an officer in the Canadian Air Force. He completed an M.Sc. degree in business from the State University of New York, a Bachelor of Engineering in aerospace from the Royal Military College of Canada, and a Master degree in Program Management from the Canadian Forces School of Aerospace Studies.
Attributes and real-world examples of the next-generation of software for machine learning in enterprise: a better means to collaborate, work smarter and get buy-in for your AI projects.
Previous software tools first designed to develop AI in academic research contexts are less-than-ideal for developing applied AI solutions for enterprise today. Here we present core attributes required for the next generation of software tools needed to expedite the application of machine learning in business and industry. We begin with an overview of our research where we assessed the needs of hundreds of AI professionals as well as completed prototypes and simulations of various applied machine learning projects. These findings guided our development of a novel ‘software engine’ (or digital work environment) for advanced machine learning projects that is visual and less abstract. Our discussion will focus on why features of our software help better meet the needs of AI professionals in enterprise, focusing on issues related to:
- Better debugging of models and streamlined workflows;
- Facilitating collaboration and input from non-technical members within the business as well as oversight by domain experts such as clinicians for health-related projects;
- Strengthened abilities to simulate outputs and renderings from machine-learning models in order to better demonstrate AI innovations to clients and regulators.
Throughout the presentation, we will frame each topic within actual case studies and projects we completed with our enterprise clients in industries that include defense, transportation, biomedical research and more.
Stop making black-box algorithms and gain better means to showcase your work in AI. Rising beyond code, our software platform enables you to work with advanced data sets and build sophisticated machine learning algorithms in a visual 3D environment. A visual interface makes AI development less abstract and simplified, thus providing several benefits: 1) identify problems with data sets and models faster; 2) understand outcomes and optimize algorithms with ease; 3) gain abilities to collaborate with non-technical experts on AI projects, and; 4) master core concepts in AI without advanced knowledge of data science or python coding.