Denis Rothman graduated from Sorbonne University and Paris Diderot University, designing one of the very first word2matrix patented embedding
and vectorizing systems. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated
language teacher for Moët et Chandon and other companies. He has authored an AI resource optimizer for IBM and apparel producers and an advanced planning
and scheduling (APS) solution used worldwide. Author of Artificial by Example, 2nd edition(2020), Explainable AI(XAI) with Python(2020) and Transformers for NLP(2021).
May 28, 2021 11:05 AM PT
Recommender systems for e-business have been expanding during the past years. At the same time, the COVID pandemic showed the importance of efficient Supply Chains. Global actors must master the whole chain: selling a product online, producing a product, storing a product in an optimized warehouse, and delivering the product on time. The consequences, as we have seen, can be disastrous for global actors who do not deliver on time. Managing huge amounts of data, constraints, and micro-decisions in a large supply chain has now become impossible without artificial intelligence. Artificial intelligence itself had to progress to predict sequences of actions and events. Artificial intelligence was not meeting the challenge up to the arrival of the Transformer model first designed by Google in 2017.
The old, obsolete, 1980 architecture of Recurrent Neural Networks(RNNs) including the LSTMs were simply not producing good results anymore. In less than two years, transformer models wiped RNNs off the map and even outperformed human baselines for many tasks.
This presentation goes to the core of recommender-based Transformers applied to the supply chain. In a world of complexity, only AI-driven recommenders will be able to learn the behavior and constraints of a market. The presentation will begin with the supply chain paradigm major corporations are facing. Then, a recommender-based transformer will show how AI can predict hundreds of micro-decisions both managers and users make. Finally, the presentation shows how the world has evolved into a new micro-decision real-time era with AI-driven recommenders.