AI sits idle because nobody designed the handoffs between human and machine. What companies get instead is automated ...

AI has erased the old divide between customer experience and data infrastructure. Marketing and data engineering now function as one connected discipline. Historically, customer experience has ...

For the real deal on AI for business, where do we turn? This year on diginomica, we'll be hearing a strong take from Sage.

Abstract: This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning ...

Global industry is entering a high-stakes phase where artificial intelligence systems must become fully transparent, auditable and aligned with human oversight if the shift from Industry 4.0 to ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

Abstract: What if machine learning could predict inverter harmonics before prototyping? Conventional pulse width modulation (PWM) techniques in cascaded H-bridge (CHB) multilevel inverters (MLIs) ...

Machine identities—applications, services, and devices—now outnumber human identities by 17:1. They play a central role in automated workflows, cloud environments, and DevOps pipelines—and ...

Global spending on machine learning is expected to reach $503 billion by 2030. Investing in companies like Nvidia, Tesla, or Accenture offers exposure to machine learning benefits. Machine learning ...

Powerful and practical machine learning tools for machine vision applications are already available to everyone, even if you’re not a data scientist. It might come as a bit of a surprise, but machine ...