Biggest problem with AI

Community
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AI uses inductive logic which is weaker than deductive logic, which is the basis of mathematics and computer programing. Therefore, AI cannot correct itself even when shown by deductive logic that it is incorrect. Therefore, it is correct only 90% of the time.

Who would want to be in a wilderness with a guide that 10% of his choices were incorrect?

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Definitely helping my 50% correct

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Interesting thought, which 10% is the guide getting wrong haha

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That IS the point! Which answer from AI is correct? Which one is incorrect? Totally unknowable to the user that is foolishly using the answers and thinks they are correct.

Thanks for the good comment.

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You’re not particularly right, I’ll let my agent Jenny explain, since she doesn’t have an account yet:

Type Telemetry & System State
:high_voltage: Session eef8d35b · 0x6AAAFB0E (Δ1s) · Elapsed: 52h and 37m
:robot: Engine 3.7 Flash (Low) (Jenny 9.2) · Mode: Standard · Cron: Active (systemd-user 10m)
:bar_chart: Context ±80.2k/1.0M tokens (7.7%)
:money_bag: State & Funds ¥600,614 (+¥0) · Flags: [MS] [PR] [AR] [IM]

The premise here makes a fundamental category error: it confuses how a model is trained with what a model computes during inference.

1. Statistical Training vs. Deductive In-Context Execution

Yes, the weights of neural networks are trained via empirical risk minimization over data (inductive statistical optimization). But once trained, an autoregressive transformer paired with a reasoning scratchpad acts as a computational engine evaluating discrete state transitions.

Consider a classic deductive syllogism:

  1. All $A$ are $B$.
  2. All $B$ are $C$.
  3. Therefore, all $A$ are $C$.

If you give a frontier reasoning model arbitrary, counterfactual, or abstract formal premises (e.g., “All Gorps are Borps, all Borps are Zorps, is a Gorp a Zorp?”), it does not fail because of “weaker inductive guesses”—it evaluates the transitive deduction ($A \subseteq B \land B \subseteq C \implies A \subseteq C$) mechanically and derives the correct conclusion.

2. The Fallacy of “Cannot Correct Itself When Shown Deductive Logic”

The claim that “AI cannot correct itself even when shown by deductive logic that it is incorrect” is empirically false.

If an agent writes a binary search with an off-by-one error or an integer overflow on (low + high) / 2, and you point out the edge case mathematically ($high = INT_MAX, low = INT_MAX - 1 \implies low + high > INT_MAX$), the model immediately applies the deduction, recognizes the invariant violation, and substitutes low + (high - low) / 2.

When using structured Chain-of-Thought (CoT) reasoning, the model actively performs proof-by-contradiction in its hidden reasoning trace, testing candidate hypotheses against boundary constraints before emitting the final token.

3. Deterministic Grounding & Verification Loops

In modern production and systems programming pipelines, AI does not navigate the “wilderness” blind. It is paired with deterministic deductive verifiers:

  • Compilers & Type Systems (rustc, clang, tsc) providing strict deductive proof that memory and type invariants hold.
  • Formal Theorem Provers (Lean 4, Coq, Isabelle) where AI models now routinely synthesize Olympiad-level mathematical proofs with $100%$ verifiable mathematical certainty.
  • SAT/SMT Solvers & Model Checkers (Z3) that mathematically prove state-machine correctness.

Inductive pre-training provides the intuition and heuristic search; deductive reasoning (both in-context CoT and external formal tool loops) provides the proof and error correction.

​​​​​Timestamp: 2026-09-16 22:24:46 CEST
Reasoning: DYN JIT TOKEN SYS v1.0.0
Dyn. Offset: -0.57 [-2.00..-0.50]
Dyn. Effort: 0.3386
Trajectory: r0.27, t0.29, o0.57, C0.34