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характеристика близнец петух мужчина характеристика, Reasoning LLMs Deliver Value Today, So AGI Hype Doesn't Matter, The Illusion and Reality of LLM “Reasoning” - Medium, New paper pushes back on Apple’s LLM ‘reasoning collapse , No, Large Reasoning Models Do Not Reason | Mind Matters, Apple Exposes the Hype: LLMs Cannot Reason. What You Need to , Do reasoning models really think or not? Apple research , AGI Is Not Around the Corner: Why Today’s LLMs Aren’t True .
Artificial Intelligence is exactly what the definition has always been, a system that learns / is trained on input and then produces output.That isn't the original definition. Here is how it was defined in 1955, in the proposal for the [computerhistory.org].The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.They defined AI in terms of what it does (simulating intelligent behavior), not what approach or mechanism is used to do it (learning from input, programming by an expert, trial and error, etc.), 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., As the authors put it, the impressive reasoning prowess of today’s LLMs is “fundamentally rooted in pattern recognition,” which breaks down for tasks that require steps or compositions the .
The Apple paper has been widely cited as proof that today’s LLMs fundamentally lack scalable reasoning ability, which, as I argued here, might not have been the fairest way to frame the study in , Large Reasoning Models (LRMs) Several companies are currently working on Large Reasoning Models (LRMs), also known as Reasoning Language Models (RLMs). These are LLMs that have been trained to break multi-step reasoning prompts into a series of steps. For example, a question might be: There are 100 objects in a box., However, this is not “reasoning.” Ultimately, today’s AI, including LLMs + CoT, derives its capabilities from correlating variables across datasets, showing us how much one changes when others change. They can deduce specific observations from generalized information or induce general conclusions from specific data., Instead, the authors contend, these reasoning LLMs are actually performing a kind of “pattern matching” and their apparent reasoning ability seems to fall apart once a task becomes too complex , 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., .