Key observations from the Edge Conference for Supply Chain, including the impact of generative AI and the gap in skills that is an outcome of this trend.
Our latest webinar examines the varying definitions of machine learning and artificial intelligence while discussing how they can be leveraged to improve your statistical forecast.
The adoption of machine learning and artificial intelligence is on the rise, but not at the pace of other transformations. Here are a few reasons why.
As more and more individuals utilize supply chain software, there is a need to simplify its usage. The next step in evolution can be Natural Language Processing (NLP) where the user expresses a desire in plain language, and the software translates it to queries in the background. This and other use cases such as the automation and analysis of content have made NLP an area of prominent growth.
As more and more data becomes available, more and more algorithms are being developed to analyze it. We see this happening in our day-to-day life where apps help us choose where to eat, sleep, dance and repeat. Based on some indicators such as age, friends, preferences and past activities, the new digital tools help plan