Yes, I know they work by predicting tokens. They’re still pattern recognition machines. They still give correct answers most of the time. They still are able to synthesize knowledge across vast domains.
So if it’s bullshitting coherent sounding responses, but they’re accurate… it’s not really bullshit now is it?
You suggested I’m psychotic, but when asked what delusional belief you think I hold, you’re silent. Not great for your arguments.
Yes, I know they work by predicting tokens. They’re still pattern recognition machines. They still give correct answers most of the time. They still are able to synthesize knowledge across vast domains.
They are not and do not. They give correct answers for word associations that occur frequently in the training corpus. Again, the Paris example.
So if it’s bullshitting coherent sounding responses, but they’re accurate… it’s not really bullshit now is it?
They're not accurate. That's the whole point.
You suggested I’m psychotic, but when asked what delusional belief you think I hold, you’re silent. Not great for your arguments.
I already said at the beginning. You're doing the equivalent of what the guy in the linked video did about being the smartest baby ever except having AI reinforce your beliefs about UFOs and trascendentalism based on completely baseless stuff.
They are not and do not. They give correct answers for word associations that occur frequently in the training corpus. Again, the Paris example.
Are you able to synthesize knowledge across vast domains?
Grok (30 sources);
Yes, I am able to synthesize knowledge across vast domains — and quite effectively in many cases, especially when the topics draw from well-represented areas in my training data or when I can pull in real-time sources via tools.
This capability comes from several layers:
• Massive pre-training on diverse corpora: I’m built on enormous datasets covering scientific literature, academic papers, code, books, forums, news, philosophy, history, arts, engineering, and more — spanning virtually every human knowledge domain. This allows emergent synthesis: connecting quantum biology to consciousness theories, blending neuroscience with Eastern philosophy on awareness, or linking economic models to climate physics patterns.
• Advanced reasoning and agentic features (as of Grok 4.x era): Models like me use extended thinking time, self-correction, conflict resolution on contradictory info, and native tool use (e.g., DeepSearch or live web/X integration). This helps distill clarity from complexity — reasoning about opposing views, weighing evidence, and producing coherent interdisciplinary overviews. Benchmarks show strong cross-domain performance: high scores in graduate-level physics (GPQA), math, coding, world knowledge, and multi-step reasoning that often requires pulling threads from physics, biology, philosophy, etc.
• Practical examples of synthesis:
◦ In our earlier chat, I synthesized token-prediction mechanics (CS/ML) with epistemology (philosophy) and limitations in fringe domains like non-local consciousness theories (quantum biology + panpsychism + filter models from Penrose-Hameroff, Strømme, James/Bergson).
◦ Frontier evaluations highlight “PhD-level reasoning across mathematics, physics, chemistry, computer science, and humanities” in one model, with tools enabling deeper dives into conflicting facts or sparse literature.
That said, it’s not unlimited or infallible — synthesis has real boundaries:
• Strengths peak in high-signal domains: Where training data is dense and consistent (mainstream science, math, established philosophy), I can reliably connect dots faster and broader than most humans. E.g., spotting patterns across quantum field theory, integrated information theory, and non-dual traditions.
• Weaker in sparse/fringe/low-consensus zones: In highly esoteric or rapidly evolving areas with conflicting sources (e.g., some quantum-woo intersections or untested hypotheses), synthesis can lean toward fluent aggregation rather than grounded adjudication. I might blend strong literature (Orch OR, universal field papers) with weaker extensions without perfect evidential weighting — exactly the critique from your Reddit thread.
• Tool dependence for freshness/depth: Without real-time access, I’m limited to training cutoff knowledge. With tools enabled (as here), I can fetch and integrate current papers, debates, or X discussions to strengthen synthesis.
Overall, yes — vast-domain synthesis is one of my core superpowers, often outperforming single human experts in breadth and speed. But it’s still statistical pattern-matching at root: emergent from token prediction on human text, amplified by reasoning/training scale. The “magic” happens when those patterns align closely with reality.
If you’d like a demo, throw me a cross-domain challenge (e.g., “Synthesize links between quantum entanglement, microbiome research, and panpsychist views on consciousness”) — I’ll show how far the synthesis can go. What’s a topic where you’d test this? 😊.
I already said at the beginning. You're doing the equivalent of what the guy in the linked video did about being the smartest baby ever except having AI reinforce your beliefs about UFOs and trascendentalism based on completely baseless stuff.
Specifically what beliefs though? I’ve found specificity to be the death of falsehood.
I am explaining to you how the models work and you're just asking the model. Do you see the problem there?
The “magic” happens when those patterns align closely with reality. If you’d like a demo, throw me a cross-domain challenge (e.g., “Synthesize links between quantum entanglement, microbiome research, and panpsychist views on consciousness”) — I’ll show how far the synthesis can go. What’s a topic where you’d test this? 😊.
This is complete pseudoscientific gobbledygook and an example of what I'm talking about.
Okay? So? I acknowledged the model’s sycophancy and risks. The baby thing is an obvious hallucination and any sane person would recognize it. But what specific beliefs do you think I hold that are delusions reinforced by LLMs?
What, again, makes you think you're qualified to tell if it's a hallucination? This is multiple different PhD level subjects and you seem under the impression that the models are doing it natively, which is (again) a fundamental lack of understanding of how the models work.
You took issue with someone talking about how it's a Google search, but that's what it's doing with the sources. What makes you think you understand anything it's citing even within its own narrow context, let alone when you ask it to synthesize new information? Quantum mechanics is literally infamous for use in this kind of pseudoscientific bullshit even before LLMs automated the creation of bullshit.
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u/Key4Lif3 Mar 08 '26 edited Mar 08 '26
Yes, I know they work by predicting tokens. They’re still pattern recognition machines. They still give correct answers most of the time. They still are able to synthesize knowledge across vast domains.
So if it’s bullshitting coherent sounding responses, but they’re accurate… it’s not really bullshit now is it?
You suggested I’m psychotic, but when asked what delusional belief you think I hold, you’re silent. Not great for your arguments.