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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
  • AGI Is Not Around the Corner: Why Today’s LLMs Aren’t True . . .
    In reality, today’s AI systems are nowhere near AGI, and their incremental improvements don’t indicate exponential progress toward it This post takes a rationally skeptical look at the state of AI, explaining clearly why current LLMs are not AGI, what critical ingredients are missing, and why doomsday fears of an imminent superintelligent
  • What Apples controversial research paper really tells us . . .
    As the title reveals, the 30-page paper dives into whether large reasoning models (LRMs), such as OpenAI's o1 models, Anthropic's Claude 3 7 Sonnet Thinking (which is the reasoning version of the
  • Limits of Large Language Models: Why LLMs Fall Short of True AGI
    Part 1 of a two-part series examining the hype and limitations of large language models (LLMs) in the quest for artificial general intelligence (AGI) This deep dive unpacks the challenges of LLMs—lack of true understanding, reasoning flaws, and the illusion of progress—while questioning whether scaling alone can lead to AGI
  • 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
  • How Close is AGI Actually? Why LLMs Alone Will Not Get . . . - NJII
    In this post, we’ll explore why Large Language Models (LLMs) alone are unlikely to lead us to true AGI and what AGI would really entail Understanding LLMs: The Engine Behind ChatGPT and Claude To grasp why LLMs fall short of AGI, it’s crucial to understand how they work
  • Understanding LLMs’ Reasoning Limits Today . . . - Medium
    In its white paper, GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models (https: arxiv org abs 2410 05229), Apple highlighted how LLMs often fall short





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