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In recent years, the rapid advancement of artificial intelligence and machine learning (AI/ML) technologies has revolutionized various aspects of digital content creation. One particularly exciting development is the emergence of video generation capabilities, which offer unprecedented opportunities for companies across diverse industries. This technology allows for the creation of shortContinue Reading

This post was co-written with Herb Brittner from Netsertive. Netsertive is a leading digital marketing solutions provider for multi-location brands and franchises, helping businesses maximize local advertising, improve engagement, and gain deep customer insights. With a growing demand in providing more actionable insights from their customer call tracking data, NetsertiveContinue Reading

Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have developed a new, easily manufacturable solid-state thermoelectric refrigeration technology with nano-engineered materials that is twice as efficient as devices made with commercially available bulk thermoelectric materials. As global demand grows for more energy-efficient, reliable and compact coolingContinue Reading

This post is co-written with Taras Tsarenko, Vitalil Bozadzhy, and Vladyslav Horbatenko.  As organizations worldwide seek to use AI for social impact, the Danish humanitarian organization Bevar Ukraine has developed a comprehensive virtual generative AI-powered assistant called Victor, aimed at addressing the pressing needs of Ukrainian refugees integrating into DanishContinue Reading

Generative artificial intelligence (AI) applications are commonly built using a technique called Retrieval Augmented Generation (RAG) that provides foundation models (FMs) access to additional data they didn’t have during training. This data is used to enrich the generative AI prompt to deliver more context-specific and accurate responses without continuously retrainingContinue Reading

Foundation model (FM) training and inference has led to a significant increase in computational needs across the industry. These models require massive amounts of accelerated compute to train and operate effectively, pushing the boundaries of traditional computing infrastructure. They require efficient systems for distributing workloads across multiple GPU accelerated servers,Continue Reading

Time series forecasting is critical for decision-making across industries. From predicting traffic flow to sales forecasting, accurate predictions enable organizations to make informed decisions, mitigate risks, and allocate resources efficiently. However, traditional machine learning approaches often require extensive data-specific tuning and model customization, resulting in lengthy and resource-heavy development. EnterContinue Reading