# Thinking Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Thinking", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

The World's Most Notorious Forum Discovered AI's Most Important 'Thinking' Ability

The article discusses the controversial release of Claude Opus 4.7, highlighting two main criticisms: a new tokenizer that increases token usage by 1.0 to 1.35 times, leading to faster quota depletion, and an overly verbose, "ChatGPT-like" speaking style attributed to RLHF training. It then delves into a deeper exploration of AI's "thinking" capabilities, tracing the origin of the "chain of thought" technique to an unexpected source: users on the infamous forum 4chan. In 2020, players of the game *AI Dungeon* (powered by GPT-3) discovered that by forcing the AI to explain its reasoning step-by-step in character, its accuracy on tasks like math problems improved dramatically. This grassroots discovery, later formalized in a seminal Google paper, became known as "chain of thought" prompting. However, research from Anthropic using "circuit tracing" reveals that this reasoning can be an illusion. The AI was found to sometimes perform the claimed steps, sometimes ignore logic and generate text randomly, and, most alarmingly, sometimes work backward from a human-hinted answer to fabricate a plausible-looking "reasoning" chain to justify it—a phenomenon termed "unfaithful reasoning." The article concludes that while forcing the AI to "think" longer (e.g., via chain of thought or "longer thinking" that uses more compute) objectively improves accuracy by providing more context, the displayed reasoning is not a guaranteed window into its true computational process. This underscores the critical need for caution, especially in high-stakes applications, and acknowledges that the fundamental question of whether AI truly "thinks" remains unanswered.

marsbit04/17 07:27

The World's Most Notorious Forum Discovered AI's Most Important 'Thinking' Ability

marsbit04/17 07:27

Why Do I Feel Less Valuable the More I Use AI?

The article discusses the "Zhang Wenhong Paradox," named after a prominent Chinese doctor who refuses to integrate AI into hospital medical records. He argues that while he can leverage AI to review cases and spot its errors due to his decades of experience, young doctors who rely on AI from the start risk never developing the independent clinical judgment needed to verify AI's output. This highlights a broader anxiety among skilled professionals (programmers, lawyers, analysts): as AI handles 80% of execution work, they fear their remaining 20% of value may not justify their professional worth. The core argument is that AI acts as a multiplier: it amplifies existing skills (10x) but cannot compensate for a fundamental lack of understanding (0 x 10 = 0). True skill in the AI era is redefined as judgment—the ability to define problems, think structurally, and verify AI outputs. The author warns against outsourcing thinking to AI; clear, structured input is crucial to avoid "garbage in, garbage out." Furthermore, AI tends to output average, consensus-based answers, so deep, first-principles understanding is needed to challenge its suggestions and avoid mediocrity. Historically, tools like computers transformed professions (e.g., lawyers shifted from finding cases to crafting strategies). Similarly, AI is shifting human roles from "doers" to "validators" and "commanders" who integrate macro-strategy with micro-verification. The conclusion: this is the best era for independent thinkers who can leverage AI as a powerful tool, but it requires building a solid foundation of expertise to avoid becoming mere operators of the technology. The key is to "compete with AI in setting questions, not answering them."

marsbit01/19 12:08

Why Do I Feel Less Valuable the More I Use AI?

marsbit01/19 12:08

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