Are ChatGPT Like LLMS 'Digital Gods' Or Just 'Imitating Monkeys' — Investor In Elon Musk's Grok Shares His Thoughts
Are ChatGPT Like LLMS 'Digital Gods' Or Just 'Imitating Monkeys' — Investor In Elon Musk's Grok Shares His Thoughts
Tesla bull and Grok investor Pierre Ferragu has shared his thoughts on the true nature of large language models (LLMs) amid ongoing debates in the AI space.
特斯拉的多頭和Grok投資者皮埃爾·費拉古分享了他對大型語言模型(LLMs)真實性質的看法,期間在人工智能領域引發了持續的討論。
What Happened: Over the weekend, Carlos E. Perez, co-founder of Intuition Machine, took to X, formerly Twitter, questioning the capabilities of LLMs.
發生了什麼:上週末,直覺機器的聯合創始人卡洛斯·E·佩雷斯在X平台(前身是Twitter)對LLMs的能力提出質疑。
In his post, Perez noted that while LLMs can tackle complex problems, they often falter on seemingly simple logical steps.
在他的發帖中,佩雷斯指出,雖然LLMs可以應對複雜問題,但它們在看似簡單的邏輯步驟上常常犯錯。
His post spotlighted a study titled "Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models," which found that LLMs' reasoning abilities are significantly influenced by programming code logic.
他的發帖重點介紹了一項名爲《預訓練中的程序知識驅動大型語言模型的推理》的研究,該研究發現LLMs的推理能力受到編程代碼邏輯的顯著影響。
The study used EK-FAC influence functions to identify the specific training data that most impact the model's output for a given query.
該研究使用Ek-FAC影響函數來識別對模型輸出特定查詢影響最大的訓練數據。
The research discovered a stark contrast in how LLMs handle factual and reasoning questions. LLMs often used a retrieval-based approach for factual questions.
研究發現,LLMs處理事實和推理問題時存在明顯差異。LLMs通常對事實問題採用基於檢索的方法。
However, for reasoning questions, LLMs consistently relied on documents demonstrating procedures—algorithms, formulas, and, importantly, code—for solving similar problems.
然而,針對推理問題,LLMs始終依賴於展示程序的文檔——算法、公式以及重要的代碼——來解決類似問題。
Sharing his post, Ferragu, an analyst at New Street Research, stated, "My left brain: LLM are digital gods. My right brain: LLM are glorified digital imitating monkeys. Time will tell and the truth is likely right in-between."
費拉古分享他的發帖時表示,"我左腦:LLM是數字神明。我的右腦:LLM是被美化的數字模仿猴。時間會證明,真相可能就在二者之間。"
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Why It Matters: The debate on LLMs' capabilities is not new. Earlier this year, a software engineer at Alphabet Inc.'s Google expressed concerns that OpenAI, the parent company of ChatGPT, had hindered the progress of AGI by 5 to 10 years.
爲什麼這很重要:關於LLM能力的爭論並不是新鮮事。今年早些時候,alphabet inc的谷歌一位軟件工程師表示擔憂,稱OpenAI(chatgpt母公司)將AGI的發展推遲了5到10年。
Salesforce CEO Marc Benioff also warned that the world was nearing the "upper limits" of LLMs like OpenAI's ChatGPT.
賽富時CEO Marc Benioff也警告說,世界正接近OpenAI的ChatGPt等LLM的「上限」。
He predicted that the future of AI would focus on autonomous agents capable of performing tasks independently, rather than relying on LLMs for advancements.
他預測,人工智能的未來將集中於能夠獨立執行任務的自主智能體,而不是依賴於LLM的進步。
Tony Fadell, the co-creator of the iPod, also expressed concerns about LLMs.
iPod的聯合創始人Tony Fadell也表達了對LLM的擔憂。
Previously, Nvidia CEO Jensen Huang said that humans will eventually work with AI agents and AI employees. Nvidia has also partnered with Accenture to deploy AI agents in businesses.
之前,英偉達CEO Jensen Huang表示,人類最終將與人工智能代理和人工智能員工合作。英偉達還與埃森哲合作,在企業中部署人工智能代理。
Microsoft Corporation also announced plans to let companies create their own autonomous agents, following Salesforce's launch of Agentforce in September 2024.
微軟-t也宣佈計劃讓公司創建自己的自主代理,跟隨賽富時於2024年9月推出的Agentforce。
OpenAI is also reportedly planning to launch a new AI agent, "Operator," in January.
OpenAI據報也計劃在1月推出一個新的人工智能代理「運營商-5g」。
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