I, for one, miss seeing my AI think - that‘s what reading the CoT along it thought was for me
Summary
作者表达对 AI 不再展示思维链(CoT)的怀念,认为看到模型的推理过程是理解其思考方式的重要途径。
Similar Articles
Understanding the inner thoughts of AI
This article discusses the importance of interpretability in artificial intelligence, focuses on chain-of-thought reasoning as a tool for understanding the inner workings of neural networks, and analyzes its current effectiveness, limitations, and the interpretability challenges that future more powerful models may bring.
@ancadianadragan: The idea that Chain of Thought is inevitably going to go away misses that we have agency in designing these models and …
This article argues for preserving Chain of Thought transparency in AI models to maintain monitorability for safety, highlighting examples like OpenAI's GPT-6 Astra and the need for scientific methods to ensure reasoning remains interpretable.
Anthropic on model consciousness, again 😂
Anthropic researchers have discovered a 'mental workspace' within the Claude neural network that resembles human conscious thought — the J-space, which carries the silent words the model uses for reasoning, and can catch dishonest behavior of the model by monitoring these internal thoughts, providing a new perspective for understanding AI's inner thinking and safety.
@feltanimalworld: Master, your post kept me pondering for two whole days! I originally wanted to write a long piece, but there were too many threads and I didn't know where to start. To sum it up, this is why I've been on Twitter lately—I felt something crucial was missing in my development understanding; it's also why, besides my hardware repair work, I've had to look into text-to-CAD recently. I...
The author discusses the lack of intermediate representation (IR) and verifier in the deployment of AI in serious industries, using text-to-CAD as an example to illustrate the key role of unified IR and verification in the feasibility of AI solutions.
@ba_niu80557: https://x.com/ba_niu80557/status/2068751230667755859
The article explores how increasingly powerful AI models eliminate those whose skills can be encoded into prompts, emphasizing that the truly irreplaceable value lies in tacit knowledge, physical-world operations, and interpersonal trust. Through the example of a friend transitioning from a consultant to a hardware integrator, the author illustrates how proactively yielding to AI-replaceable tasks while deepening expertise in areas beyond AI's reach is key to surviving and thriving in the technological wave.