Controlling AI Agents: Lessons from Human Supervision
Rapid advances in artificial intelligence agents have prompted urgent discussions about security and control. A widely cited 2024 research paper by Yoshua Bengio, Geoffrey Hinton, and other AI pioneers warned of a future in which humans may irreversibly lose control over AI systems that pose threats
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17 Predictions for AI in 2026: Progress, Impact, and Market Dynamics
This is our comprehensive overview of the following year. The breakneck pace of 2025, defined by agentic breakthroughs, multitrillion-dollar investments, and the mainstreaming of AI, sets the stage for a pivotal 2026. It will be a year not of speculation, but of actual execution, when the potential of
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How to Start Building Your AI Career
In today’s rapidly evolving digital world, artificial intelligence (AI) is no longer a niche skill for data scientists alone. It is becoming a foundational layer across nearly every industry—healthcare, finance, marketing, education, logistics, law, and beyond. Whether you’re a software developer, business analyst, content strategist, or
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Top Skills That Will Make You AI-Ready
Artificial Intelligence (AI) has become a cornerstone of modern life, and professionals across multiple fields must develop core AI competencies if they wish to stay ahead in this increasingly AI-centric era. Machine learning engineers and data scientists may once have been responsible for becoming “AI ready.”
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Neural Network Architectures and Algorithms
Neural networks have revolutionized artificial intelligence, leading to breakthroughs across industries ranging from natural language processing and computer vision to bioinformatics and autonomous systems. Neural networks are computational models modelled after the human brain, consisting of interconnected nodes (neurons) organized into layers. The architecture of a
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Better Training Data, Better AI
Within AI research and deployment, longstanding emphasis on model architectures and computational scale has begun to bring diminishing returns. As AI models become more complex and require ever more resources to achieve marginal performance gains, the community has already started shifting toward a data-centric paradigm where
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Rethinking Training Data
Machine learning (ML) models rely heavily on accessing and providing high-quality training data. Due to the long deployment timeline and domain boundaries of such models, performance will inevitably decline due to a mismatch between training data and operational conditions. This paper highlights three situations where retraining
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