Bryce Goodman

Members in the One Option Chat Room have been making very decent gains on trades! Congrats to all.

If we do not use the Nonconformist Bee Strategy we will never reach AGI. Here is why. The epsilon function in AI, specifically in the epsilon-greedy strategy used in reinforcement learning, balances exploration and exploitation. I will get a bit technical but please go in to it slowly. You can understand it and it is important for you to know. Epsilon (ε) sets the probability of random actions to explore new possibilities versus exploiting known rewards, starting high (e.g., 0.9) and decaying (e.g., to 0.01) as learning progresses. This method suits structured environments like games but struggles to uncover true novelty or fringe advancements. It fails to capture radical breakthroughs because exploration is shallow, limited to predefined action spaces, and biased toward existing data distributions. AI prioritizes efficiency, converging on safe, incremental solutions rather than high-risk, paradigm-shifting ideas often sparked by serendipity or interdisciplinary leaps in human contexts, like penicillin’s discovery. Studies note AI’s tendency to consolidate rather than disrupt, with 86% of R&D cases favoring augmentation over novelty due to cost and benchmark pressures. AI lacks human-like intuition or the unconstrained persistence of lone inventors, further limiting its reach into fringe innovation. This problem intensifies when AI trains on conformist sources like Wikipedia and Reddit, which enforce status quo biases that stifle fringe perspectives. Wikipedia’s editor consensus rules create a “debunker gaming system” bias, retaining existing content unless broad agreement favors change, leading to systemic underrepresentation of non-mainstream views and higher exit rates among pro-fringe editors. Agenda-driven “keepers” weaponize this for ideological control, replicating paid science publication biases in sourcing and marginalizing diverse or disruptive narratives. Reddit’s karma system, an intermittent reinforcement loop, rewards conformity through upvotes for popular opinions while punishing dissent via downvotes, fostering echo chambers where unpopular ideas tank karma and restrict posting. Moderators, often biased, amplify this by removing non-conformist content, turning subreddits into hiveminds that conflate popularity with truth. Training AI on these datasets—Wikipedia comprising up to 38% of GPT-3’s tokens and Reddit-linked web text 72%—embeds their flaws, creating a catastrophe of amplified biases and ideological distortions propagate into AI outputs, hallucinating stereotypes (a grifter, a quack, crazy) and suppressing novelty. This feedback loop risks a “doom spiral” for reliable knowledge, as AI-generated junk floods sources, eroding trust and innovation while fringe advancements drown in curated conformity. Historically, lone or fringe inventors drove 50–70% of major U.S. inventions pre-1900 (e.g., telephone), but now contribute 30–65% of granted patents annually (~10,000–45,000). They remain overrepresented in high-impact breakthroughs (~80% of disruptive patents), despite teams dominating 85–90% of output. Modern patents average 3.2 inventors (up from 1.7 in 1976), reflecting a shift to collaborative, less risky innovation. Fringe inventors face barriers like funding, with only 0.2% of people inventing but potential for 4x growth if barriers drop, especially for underrepresented groups (e.g., 17% of global inventors are women). If AI continues to use the very flawed epsilon function we will not see the very basis that has driven humanity forward. If AI continues to site sources as “facts” and then takes on the “debunker” role learned by Wikipedia and Reddit, there will be no innovations. This is not a guess, I have tested it in a small scale on my garage AI models. It is one reason I wrote about the Nonconformist Bees in the article attached below. It is important for you to know, and not just a few math majors that only see this as a calculation. AI must be the Nonconformist Bee.