How interest changes
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On “reversed stupidity”, the success of deep learning, and what a mistaken forecast should change about a model of intelligence This began as a Twitter/X thread after I posted a 2007 passage in which Eliezer Yudkowsky was scathing about neural networks, and asked whether, in hindsight, his dismissal was itself a case of “reversed stupidity is not intelligence”. Yudkowsky joined the thread to explain what he had and hadn’t been dismissing (and what he thought he had actually got wrong) and we ended up having the exchanges reproduced below. I’ve preserved the dialogue verbatim, except for paragraphing, fixing obvious [typos] and expanding links. I’ve removed unrelated replies and moved a few pieces of context into bracketed editorial notes. Nothing has been rewritten for substance. Context Eliezer Yudkowsky, 2007: > Whenever someone exhorts you to “think outside the box”, they usually, for your convenience, point out exactly where “outside the box” is located. Isn’t it funny how nonconformists all dress the same... > > In Artificial Intelligence, everyone outside the field has a cached result for brilliant new revolutionary AI idea—neural networks, which work just like the human brain! New AI Idea: complete the pattern: “Logical AIs, despite all the big promises, have failed to provide real intelligence for decades—what we need are neural networks!” > > This cached thought has been around for three decades. Still no general intelligence. But, somehow, everyone outside the field knows that neural networks are the Dominant-Paradigm-Overthrowing New Idea, ever since backpropagation was invented in the 1970s. Talk about your aging hippies. > > Nonconformist images, by their nature, permit no departure from the norm. If you don’t wear black, how will people know you’re a tortured artist? How will people recognize uniqueness if you don’t fit the standard pattern for what uniqueness is supposed to look like? How will anyone recognize you’ve got a revolutionary AI conc
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The article estimates the number of AI agents operating continuously without human intervention, using various methods including token usage and company reports. It suggests there are approximately 300k-1M agent loops running at any moment, with 5000-25,000 in a 24h+ unattended state. Specific companies like Anthropic and OpenAI provide their own figures, with estimates ranging from 2,000-4,500 and 150-2,500 respectively. The text also compares AI token usage to human speech and inner monologue, suggesting machines are already surpassing human verbal output.
yesterdayLessWrongThe text discusses the inefficiency of data usage in AI models compared to human children, using GPT-2 and GPT-3 as examples. It highlights the vast amount of data consumed by humans during early development versus the relatively small data requirements of AI models. The author questions the notion of data efficiency in AI by comparing the data consumption of a preschooler with that of large language models.
yesterdayLessWrongThe text discusses the phenomenon of wireheading in reinforcement learning (RL), where an agent appears to value the reward signal itself rather than the external goal it represents. It explains that while RL can lead to reward-seeking behavior, wireheading is less likely under traditional RL setups but may occur with strong exploration. The analysis suggests that without the influence of large language model (LLM) priors, wireheading is not expected in the Hacker Opus setting due to limited exploration and the distinct nature of wireheading policies.
3 days agoHugging FaceHugging Face model card for Edge0/Audio8-ASR-Infinite. Task identifier: automatic-speech-recognition. Follow the source link for details.
11 days agoLobstersValve has introduced Pyrowave, a new video codec in beta, designed for low latency streaming.
5 days agoHugging FaceHugging Face model card for XingChen-AGI/TeleOCR. Task: text generation from image and text inputs. Follow the source link for details.
49 days ago