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  • Reasoning LLMs Deliver Value Today, So AGI Hype Doesnt . . .
    So now we're getting daily or more stories questioning the value of machine learning, discounting the importance of machine learning, declaiming the feasibility of AGI, downplaying the impact of models on employment, criticizing the hype around it all, predicting crashes in investments, etc
  • Apple AI boffins puncture AGI hype as reasoning models flail . . .
    The authors' findings, described in a paper titled, "The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity," indicate that the intellectual potential of such models is so far quite limited Large reasoning models (LRMs), such as OpenAI’s o1 o3, DeepSeek-R1, Claude 3 7
  • Apple’s Latest AI Study Strikes at the Heart of “Reasoning . . .
    AGI isn’t just a scaling problem: Throwing more parameters, tokens, or training data at the problem won’t produce general intelligence Hybrid systems may be essential: Apple’s results support the growing push for models that combine neural networks with symbolic reasoning, long-term memory, and structured world models
  • Apple Exposes the Hype: LLMs Cannot Reason. What You Need to . . .
    And, in turn, the application of algorithmic “reasoning” that can provide new reasoning superpowers to humans, and organizations allike This is why, in part, the 2025 Gartner AI Hype Cycle predicted that Causal AI would become a “high impact” technology in the 2-5 year timeframe, stating:
  • Cutting-edge AI models from OpenAI and DeepSeek undergo . . .
    Reasoning models, such as Anthropic'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
  • Apple Just Pulled the Plug on the AI Hype. Here’s What Their . . .
    The “reasoning” we’re seeing in today’s most advanced AI models is not a budding form of general intelligence It is an incredibly sophisticated form of pattern matching, so advanced that it can mimic the output of human reasoning for a narrow band of problems But when tested in a controlled way, its fragility is exposed
  • AGI Is Not Around the Corner: Why Today’s LLMs Aren’t True . . .
    Today’s LLMs like GPT-4 and Claude are impressive pattern-recognition tools, but they’re not anywhere near true intelligence Despite the hype, they lack core AGI traits like reasoning, autonomy, and real-world understanding This article cuts through the noise, explaining why fears of imminent AGI are wildly premature





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