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  • Reasoning LLMs Deliver Value Today, So AGI Hype Doesnt . . .
    Reasoning LLMs are a relatively new and interesting twist on the genre They are demonstrably able to solve a whole bunch of problems that previous LLMs were unable to handle, hence why we've seen a rush of new models from OpenAI and Anthropic and Gemini and DeepSeek and Qwen and Mistral
  • AI flunks logic test: Multiple studies reveal illusion of . . .
    Bottom line: More and more AI companies say their models can reason Two recent studies say otherwise When asked to show their logic, most models flub the task – proving they're not reasoning
  • LLMs And The AGI Question - A Technical Analysis Of . . .
    This analysis examines the technical realities of current LLMs, the fundamental requirements of AGI, and whether we can bridge this considerable gap Understanding The Current LLM Architecture Today's Large Language Models operate on predictive mechanisms, using transformer architectures to calculate probabilities of token sequences
  • The AI Bubble: Advanced AI Faces A Complete Accuracy . . .
    These reasoning AI models will replace human scientists - and all future innovations will be created by AGI The Apple's Angle One major company that has been lagging in the development of Gen AI
  • The Limits of Logic: Are AI Reasoning Models Hitting a Wall?
    The hype around Large Language Models (LLMs) has reached a fever pitch, with specialized versions dubbed Large Reasoning Models (LRMs) like OpenAI’s o1 o3 and DeepSeek-R1 promising to not just parrot information, but actually think But beneath the surface of these apparent successes, a more nuanced picture is emerging
  • Cutting-edge AI models from OpenAI and DeepSeek undergo . . .
    Reasoning models, such as Meta's Claude, OpenAI's o3 and DeepSeek's R1, are specialized large language models (LLMs) that dedicate more time and computing power to produce more accurate responses
  • Apple Challenges AI Reasoning Hype: Exposes Limitations of LLMs
    Rethinking the Path to AGI: The research challenges the timeline and approach for achieving Artificial General Intelligence (AGI) If current reasoning models have fundamental scaling limitations, the path to AGI may require a more radical rethinking of AI architectures, as advocated by researchers like Yan LeCun





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