Sean Martin, Chief Scientist, Graph Technology at Altair and Gregory West, Lead Solutions Specialist at Altair presents at the 2024 ATCx AI for Engineers conference.

In this presentation, we explore the rationale behind integrating data with Knowledge Graphs, outline their fundamental concepts, and demonstrate practical implementation using Altair Graph Studio. Join us to get an introduction to the transformative potential of this technology.

Duration: 21 minutes

Please note that presentations are in English by default. The following languages are available by clicking on Audio Description AD)) on screen: ES, FR, JA, KO, PT, ZH-CN.

In this eGuide, we’ll provide a levelheaded, easy-to-understand overview of generative AI technology – what it is, where it came from, how it works, and what it can (and can’t) do. We’ll go over the considerations and responsibilities of any enterprise implementing generative AI, including how to access it, the risks and costs involved, and critically, the tasks at which generative AI excels. We’ll compare generative AI’s strongest use cases to problems that are still better suited to other kinds of machine learning algorithms and outline why it’s important to pick the right tool for the job. Finally, we’ll break down niche use cases industry by industry, highlighting opportunities in each field.

For developers of analytics and ML tools, free-to-use resources like Python, R, and SQL have redefined the landscape. And this isn't simply a matter of cost. These open-source languages offer greater productivity, flexibility, and their own specific sets of capabilities and benefits. But there's a catch. While it may no longer be the preferred choice for coders who have grown up with open source, the long-established, SAS language remains a staple of the data science ecosystem. As a result, many enterprises still rely on a host of business-critical applications that were built using SAS language.

Mabe wanted to leverage historical data from the field to gain new insights into how to optimize the frequency of compressor and air circulation fan operations in its refrigerators to save energy while also keeping food fresh based on how often the door is opened and how long it stays open. Together, Altair and Mabe built an AI workflow in Altair AI Studio that extracts, cleans, prepares, and transforms their data to predict consumer behavior based on the days of the week. Mabe’s engineering team deployed the workflow using Altair AI Hub. Overall, the Altair workflow will help Mabe predict consumer behavior to optimize their refrigerators’ performance and efficiency, ultimately helping customers keep their food fresher for longer.