LLMs Are Not Reasoning—They’re Just Really Good at Planning

Large language models (LLMs) like OpenAI’s o3, Google’s Gemini 2.0, and DeepSeek’s R1 have shown remarkable progress in tackling complex problems, generating human-like text, and even writing code with precision. These advanced LLMs are often referred as “reasoning models” for their remarkable abilities to analyze and solve complex problems. But do these models actually reason,…

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AI is the Perfect Teaching Assistant for Any Educator

Schools, universities, and other educational institutions around the world are facing a crisis. There simply aren’t enough teachers to meet the educational needs of a growing student population. UNESCO’s recent global report on teachers reveals that 44 million additional teachers are needed globally to provide sufficient primary and secondary education by 2030. In England, for…

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StealthGPT Review: Can It Really Fool AI Detectors?

Have you ever spent hours carefully writing something, only to have an AI detector flag it as machine-generated? It can be frustrating. With AI writing tools becoming more advanced, so are the detection systems trying to spot them. That’s where StealthGPT comes in! StealthGPT is an AI tool that not only generates high-quality content but…

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Like human brains, large language models reason about diverse data in a general way

While early language models could only process text, contemporary large language models now perform highly diverse tasks on different types of data. For instance, LLMs can understand many languages, generate computer code, solve math problems, or answer questions about images and audio.    MIT researchers probed the inner workings of LLMs to better understand how they…

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Beyond Manual Labeling: How ProVision Enhances Multimodal AI with Automated Data Synthesis

Artificial Intelligence (AI) has transformed industries, making processes more intelligent, faster, and efficient. The data quality used to train AI is critical to its success. For this data to be useful, it must be labelled accurately, which has traditionally been done manually. Manual labelling, however, is often slow, error-prone, and expensive. The need for precise…

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