What You Can Expect
- Invaluable takeaways from world-class experts assessing the emerging and longer-term future of AI’s impact on planning, forecasting, and employment
- Superb opportunities to interact with our speakers and network with your peers
- Ample session time for Q&A
- Complimentary copies of the new Foresight guidebook on AI
- A serene setting at one of North America’s finest hotels and conference centers— 2017 and 2018 TripAdvisor Certificate of Excellence recipient
Who Should Attend?
- Forecasters and planners
- Managers in all areas of operations, marketing, and finance
- Academics, researchers and data scientists addressing AI
- Anyone who wants to gain insight into AI’s effects on forecasting and planning
Machine Learning Science Manager
Amazon Web Services AI Labs
Neural Forecasting at Amazon: models, tools and use cases
Co Founder & Fellow
Using AI/ML to deliver a smarter supply chain
A good demand forecast is better than no forecast at all, and AI/ML methods can certainly provide a better forecast, but it’s what you do with that forecast that counts. In this session, learn how to apply AI/ML models for:
- Strategy and segmentation,
- Managing change, and
- Mitigating risks.
Each of these areas enable organizations to respond to supply chain variability more effectively to achieve business success.
Data Science Expert, SAP
Foresight Associate Editor
Author of Demand Forecasting for Managers with Enno Siemsen
Will Deep and Machine Learning Solve Our Forecasting Problems?
Deep Learning and other approaches in Machine Learning are having a tremendous impact on our life, from voice assistants to all kinds of image recognition tasks. Similarly, there have been more and more success stories of ML applied to forecasting tasks. Since there is no shortage of loudly proclaimed enthusiasm, I will draw attention to some aspects where DL/ML does not (yet) solve all our problems, as well as point out some issues where the ML hype actually helps “classical” forecasters learn new tricks. I anticipate vocal counterarguments and a lively, entertaining discussion.
Creator of the M-Competitions
Human Intelligence (HI) Versus Artificial Intelligence (AI) and Intelligence Augmentation (IA)
HI and AI, in its present form, are two dissimilar but complimentary forms of intelligence, with AI surpassing HI in games and images and approaching it in speech and text recognition while HI excels in most other tasks that involve situations/decisions where the rules are not known and the environment can change. The two critical questions are if AI and HI can coexist each contributing where they excel while avoiding their weaknesses? IA presents the possibility of substantial improvements in HI by exploiting progress in AI and advances in the related technologies of nanotechnology and neuroscience.
Distinguished Professor, Supply Chain Management
Humachine: The Enterprise of the Future
The Humachine represents the optimal human-machine partnership. Here, people and technology operate seamlessly. This hybrid workforce creates an unparalleled team to create industry leaders. It is an enterprise that takes advantage of technology to complement and augment human decisions. “Machines”– technologies such as AI – are stronger, better, faster, more precise. Humans are intuitive, creative, and understand context. Harnessing the strengths of both – and finding the right way for them to work together – is the recipe for success. This talk is about how an enterprise creates and manages synergies between technology and people to optimize decision-making, innovation, and performance, the roadmap for getting there, and what individuals can do to prepare themselves for work in the enterprise of the future. The future is already here.
Author of Supply Chain Systems
The Data Implications for AI/ML Projects
AI/ML brings up many data-related issues that tend to be glossed over. This presentation will address cover this neglected area. Among the questions we’ll discuss are:
1. Do we need specialized databases to support AI?
2. How much do Big Data and Data Lakes rely on the benefits of AI?
3. What are the issues with data availability and data development timelines for AI projects?
4. Are the data requirements for AI difference from those for statistical forecasting projects?
VP and Principal, First Analytics
Machine Augmented Demand Planning
Setting hype around machine learning and AI aside, these technologies are now finding success in assisting humans with forecasting and demand planning. We present a framework which classifies various applications for AI and closely related analytical tools. The framework is comprised of three broad application areas: data engineering; forecasting models; and planner decisions. Within each of these areas are sub-categories, and we lay out more than a dozen application cases. For a selection of these cases, we illustrate with real-world examples.
President, Technology Futures
Forecasting Artificial Intelligence
Artificial Intelligence will likely revolutionize our world, yet published forecasts on AI performance, adoption, and impacts are surprisingly weak given the high stakes. Here we report on TFI’’s work to correct this, including our current forecasts and work in progress. This includes quantitative forecasts of AI performance improvement and an analysis of the drivers and constraints for further AI adoption. We will also address the impacts and implications of AI for employment and our future. Finally, we discuss AI’s role in the future of forecasting, given the recent significant progress in that area.