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Continual Learning in AI: How It Works & Why AI Needs It

Like humans, machines need to continually learn from non-stationary information streams. While this is a natural skill for humans, it’s challenging for neural networks-based AI machines. One inherent problem in artificial neural networks is the phenomenon of catastrophic forgetting. Deep learning researchers are working extensively to solve this problem in their pursuit of AI agents that can continually learn like humans.

How Computer Vision is Revolutionizing Industries

Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs - and take actions or make recommendations based on that information. At a high level, computer vision involves processing visual data using algorithms and deep learning models to mimic human vision. The computer analyzes patterns and features in visual data to identify objects, faces, scenes, and actions.

Max Pagel, SensorFlow: Amplifying Advancement with AI

SensorFlow’s Co-Founder and CTO on using technology to do more with less and why the platform approach will always win Like many entrepreneurs, Pagel’s step into business was borne out of a desire to do things differently. His founder journey began in 2016 in Singapore — a place he still calls home today. While working as a research associate at a university, he became frustrated with the science ecosystem and how it all worked.

Mastering Azure OpenAI Costs and Capacity: Strategies for Efficient Cloud Management

In the rapidly evolving world of cloud computing, Azure OpenAI has emerged as a cornerstone for businesses seeking to leverage advanced artificial intelligence (AI) capabilities. Developed through a collaboration between Microsoft and OpenAI, this managed service has transformed how organizations build and deploy large language models (LLMs), integrating seamlessly with Microsoft services such as GitHub Copilot, Power BI, Designer, and Office 365.

From MLOps to LLMOps: The evolution of automation for AI-powered applications

Machine learning operations (MLOps) has become the backbone of efficient artificial intelligence (AI) development. Blending ML with development and operations best practices, MLOps streamlines deploying ML models via continuous testing, updating, and monitoring. But as ML and AI use cases continue to expand, a need arises for specialized tools and best practices to handle the particular conditions of complex AI apps — like those using large language models (LLMs).

Democratizing AI Research: How Llama's Accessibility Changes the Game

Artificial Intelligence (AI) is on the brink of unlocking a new era in nearly every industry, from healthcare to manufacturing. However, until recently, conducting AI research remained exclusive to well-funded labs and academics with extensive computational resources and domain knowledge. The emergence of accessible AI research tools like Llama 2 is changing this paradigm, empowering individuals and small teams to contribute to the AI revolution. Here's how Llama's accessibility transforms AI research and why every aspiring AI enthusiast should take note.

Maximize IT efficiency leveraging alert management with Elastic AI Assistant for Observability

Manage and correlate signals and alerts in Elastic Observability As organizations embrace increasingly complex and interconnected IT systems, the sheer volume of alerts generated by diverse monitoring tools has given rise to a critical challenge — how do we efficiently sift through the noise to identify and respond to the most crucial issues? Event management and correlation are two indispensable pillars in the realm of IT service management.