Accelerate Digital Advantage for Business with AI
Artificial Intelligence (AI) is a buzz word surfacing for many years now, but its implementation and full potential still remains in early stages. The goal for every brand and company should be to use AI to interpret and analyze data (both quantitative and qualitative) to offer solutions within their eco-systems. Using AI to support human capabilities across e-commerce, lifestyle, gaming, chatbots, marketing and more will be areas of discussion. We hope to bring forward case studies on the use of AI across marketing, personalized shopping, content creation, voice assistants, robotics, fraud prevention and more.
How AI and advanced analytics are re-shaping R&D. Nestle's case
The presentation will provide an in-depth look at AI’s potential to transform multinational corporations in Nestle's case. We will focus on how AI/ML is driving progress toward the corporate’s key strategic objectives and unlocking massive opportunities across all areas of manufacturing, procurement and administration.
Overview of main AI/ML use cases in CPG impacting revenue, costs and risks
How the product development lifecycle works today
How it will work in the future with AI/ML, from suggesting new product concepts to improving the effectiveness of product trials to detecting early-warning signals for food safety
AI has been identified as a key enabler of sustainability, and it is important to focus on AI ethics and governance for social impact.
Towards Responsible AI: Issues and Challenges.
Ethical AI & Governance - Successful AI Implementation.
Data governance: Protecting privacy in an AI-driven world.
Going through ethical AI and governance practices: success stories and lessons learned.
Robust AI Systems
Practical decision systems require much more than end-to-end learned models. This talk will focus on research and engineering questions on machine learning robustness that executives should be aware of.
- Usable/practical decision systems (especially for high-stakes decisions) have to be both
- The major practical challenge in designing and implementing robust decision systems: it is easy to describe the desired correct behavior, but specifying correctly all consequences for failing to meet the terms of the contract, are extremely hard to specify.
- Practical decision systems are required to decide and act in the face of unknowns
- To do that, ALL learned models are required to output not only a prediction/estimate, but also a self-competence estimate
- Other methods for achieving robustness: multifaceted understanding, and causal models.