2079 lines
62 KiB
Plaintext
2079 lines
62 KiB
Plaintext
[by:whisper.cpp]
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[00:00.00]大家好,歡迎大家來到「Lit and Space Pockest」
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[00:02.50]我是Alessio,會員,和CTO在職業的職業會員
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[00:05.74]我是Makojo Swicks, founder of SmallAI
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[00:08.84]今天我們有David Luan, co-founder of ADEPT,在工作室,歡迎
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[00:12.98]謝謝你
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[00:14.10]一段時間在工作,我遇到你在VC的社交平台上
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[00:17.98]你也說了,你很興奮,我們終於能夠做到這件事了
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[00:21.88]對,很高興認識你
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[00:23.88]我們想介紹你的職業,然後再說一下你剛才說了什麼,在你的連結,什麼人應該知道你
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[00:32.02]你開始了一間公司,是第一次在實際視頻的視頻研究,例如DEXTRO,那是你的路,在你導致的AI,你開始了XON,然後你開始了30年,你開始了OpenAI?
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[00:47.06]對,30、35年,或是在那裡,或是在那裡,VP Avenge,兩年半,兩年半後,
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[00:53.48]我們在2022年開始了一個大型模式的創新
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[00:57.08]然後在2022年開始了一個大型模式的創新
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[01:00.32]所以那是一個短暫的CV
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[01:02.98]是否有其他東西?
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[01:03.98]對,是否有其他東西?
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[01:04.98]你覺得要做什麼?
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[01:05.98]或是人們應該知道更多?
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[01:07.98]我猜是一個比較大的故事
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[01:09.48]是加入OpenAI比較早期的
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[01:11.98]然後就做了兩、三個月的研究
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[01:15.48]那是很有趣的
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[01:16.48]第二或第三天的我的時間在OpenAI
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[01:18.98]Gregg and Ilya 找我住在房間,我們說要拿到我們的創新,我們會去…
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[01:23.98]我看過很多創新的工作
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[01:25.98]所以那是很有趣的
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[01:26.98]就在結合了一堆團隊
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[01:28.98]有幾個早期的領導人已經有了
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[01:30.98]公司的資料項目是很努力的
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[01:32.98]然後再多次地在大型研究中放大型的圖案
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[01:35.98]我們在做基本研究
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[01:36.98]所以我花了很多時間在做這個
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[01:37.98]然後我再加上Google的LM項目
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[01:39.98]但也加上Google的Brain
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[01:41.98]是一個Brain的領導人,更多次地
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[01:42.98]你知道,有幾個不同的領導人在AI的研究
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[01:46.98]我們在2012 before prehistory
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[01:48.98]很多人很討厭我
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[01:50.98]我跟你們三個最好的朋友
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[01:51.98]寫了一個研究的文件
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[01:53.98]從2012到2017
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[01:56.98]我覺得遊戲的改善在2017
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[01:58.98]然後很多學生都沒有發現
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[01:59.98]但是我們在OpenAI上真的做了
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[02:01.98]我想大部分的幫助是
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[02:02.98]Ilya的 constant beating of the drum
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[02:04.98]讓世界被遮蓋在data centers
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[02:06.98]還有其他人需要…
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[02:07.98]對,我覺得我們有確定在那裡
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[02:10.98]但沒有到我們開始看到
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[02:11.98]結果的結果,那是我們要去的
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[02:14.98]但也有一個部分
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[02:15.98]是在OpenAI上
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[02:16.98]我第一次加入
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[02:17.98]我認為一件事我必須要做
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[02:19.98]是如何告訴我們
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[02:20.98]我們是否有不同的觀點
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[02:22.98]比起我們是更小的GoogleBrain
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[02:25.98]或是我們在OpenAI上
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[02:26.98]只要生活在SF
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[02:27.98]然後不想接受Mountain View
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[02:28.98]或不想要生活在London
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[02:29.98]那是不足夠的
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[02:31.98]利用你的技術活動
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[02:33.98]所以我們真的…
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[02:34.98]我花了很多時間在推廣這個
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[02:36.98]就是我們要怎麼
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[02:37.98]要專注在
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[02:38.98]一個大學生的大學生
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[02:41.98]你從最底下的研究
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[02:44.98]變成了
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[02:45.98]如何讓你放棄這個環境
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[02:47.98]而讓你覺得
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[02:48.98]什麼是大學生的大學生
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[02:50.98]想要展現
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[02:51.98]然後你把他們解決
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[02:52.98]所有的財困
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[02:53.98]不管是否要在創意
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[02:54.98]創作什麼
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[02:55.98]這就變成了
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[02:56.98]大學生的大學生
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[02:57.98]對嗎
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[02:58.98]然後現在的改變
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[02:59.98]是我認為
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[03:00.98]第一次加入AiPrice
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[03:01.98]在下一幾年
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[03:02.98]會是最深的
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[03:03.98] co-design
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[03:04.98]和 co-evolution
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[03:05.98]產品和資料
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[03:07.98]和實際技術
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[03:08.98]而我認為
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[03:09.98]每個技術的技術
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[03:10.98]都會做得很好
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[03:11.98]那是一大部分
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[03:12.98]為何我開始深入
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[03:13.98]你提及Dota
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[03:14.98]哪些記憶在想
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[03:16.98]從RL 和 Transformers
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[03:18.98]在時間中
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[03:19.98]然後我認為
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[03:20.98]製造的工具
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[03:21.98]更加在LM 上
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[03:23.98]然後離開
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[03:24.98]更多的Agent Simulation
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[03:25.98]工作
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[03:26.98]像在移動的道路
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[03:27.98]我覺得Agent
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[03:28.98]是一個
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[03:29.98]完全正確的長途
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[03:30.98]你只要去找
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[03:31.98]AGI 是吧
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[03:32.98]你會說
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[03:33.98]首先
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[03:34.98]我其實不喜歡AGI
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[03:35.98]用人的改變
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[03:36.98]因為我真的不想
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[03:37.98]這樣會發生
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[03:38.98]我認為這個改變
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[03:39.98]AGI 是一些
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[03:40.98]人們表現的
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[03:41.98]非常值得的技術
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[03:43.98]是一個
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[03:44.98]極端的看法
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[03:45.98]和人的改變
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[03:46.98]我認為
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[03:47.98]我比較有興趣
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[03:48.98]AGI 的改變
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[03:49.98]就是
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[03:50.98]一個模式
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[03:51.98]可以做任何的
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[03:52.98]人能做的
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[03:53.98]如果你想到
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[03:54.98]超級有趣
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[03:55.98]Agent
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[03:56.98]是一種
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[03:57.98]自然的
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[03:58.98]改變
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[03:59.98]所以
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[04:00.98]所有的工作
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[04:01.98]我們在RL
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[04:02.98]這些技術
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[04:03.98]導致我們
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[04:04.98]有很清楚的
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[04:05.98]形容
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[04:06.98]你需要增加
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[04:07.98]你需要增加
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[04:08.98]對
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[04:09.98]而自然的LM
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[04:10.98]形容
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[04:11.98]沒有出現
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[04:12.98]我認為
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[04:13.98]我們
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[04:14.98]在這個場地
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[04:15.98]有很多想法
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[04:16.98]想想
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[04:17.98]我們如何解決
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[04:18.98]問題的問題
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[04:19.98]然後
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[04:20.98]我們忘記
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[04:21.98]我們在RL
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[04:22.98]是一個
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[04:23.98]很不容易的
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[04:24.98]方式
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[04:25.98]我們為何
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[04:26.98]我們在世界
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[04:27.98]找到所有的
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[04:28.98]知識
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[04:29.98]我們在一年
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[04:30.98]和一位
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[04:31.98]伯克里斯教授
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[04:32.98]教授
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[04:33.98]我們會拿到
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[04:34.98]AGI
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[04:35.98]他的觀點
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[04:36.98]對
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[04:37.98]他的理想
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[04:38.98]對
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[04:39.98]所以
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[04:40.98]我們都在
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[04:41.98]記錄
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[04:42.98]我們會
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[04:43.98]解決
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[04:44.98]我們已經解決
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[04:45.98]LM
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[04:46.98]我們已經解決
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[04:47.98]我們已經解決
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[04:48.98]我們已經解決
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[04:49.98]我們已經解決
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[04:50.98]我們已經解決
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[04:51.98]我們已經解決
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[04:52.98]我們已經解決
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[04:53.98]我們已經解決
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[04:54.98]我們已經解決
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[04:55.98]我們已經解決
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[04:56.98]我們已經解決
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[04:57.98]我們已經解決
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[04:58.98]我們已經解決
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[04:59.98]我們已經解決
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[05:00.98]我們已經解決
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[05:01.98]我們已經解決
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[05:02.98]每一句
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[05:03.98]文字
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[05:04.98]然後所有的圖案都會學習到模式
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[05:07.94]然後你能夠合作任何的組織
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[05:10.14]例如寫進、聲音、畫面、其他畫面、影片等等
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[05:14.42]這些都是圖案的圖案,可以學習到這類的動作
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[05:18.50]所以我希望我們能夠解決這件事
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[05:20.10]然後我們回到當時的歷史
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[05:22.74]我們如何跟我們一起學習這些圖案的學習
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[05:27.06]這就是我們要去進行的進步
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[05:28.62]我還要向大家提醒你多多的明年開放的故事
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[05:31.30]我們再回到大陸的故事
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[05:32.90]在你的個人網站,我愛的,因為是一個很好的個人的故事
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[05:37.38]故事的內容,像你的歷史
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[05:39.38]我需要更新,因為太老了
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[05:42.38]但是你提及GPC2,你忘記了GPC1嗎?我認為你忘記了,對吧?
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[05:46.18]我其實不太記得,我記得在那邊,我記得在那邊
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[05:50.70]對,《Canonical Story》是阿力的故事,他很擔心傳播者和傳播者
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[05:58.74]傳播者和傳播者和傳播者的訊息
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[06:01.38]對,你帶我們去… 拿我們傳播者和傳播者和傳播者的訊息
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[06:03.66]GPC的歷史,你也知道,對你來說
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[06:07.46]對我來說,歷史和GPC的歷史是一個很好的問題
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[06:10.02]所以我認為《Canonical Story》的故事,GPC的歷史是在谷歌上,對吧?
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[06:14.30]因為那是關於傳播者的故事
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[06:17.30]而我認為最驚訝的一件事,是…
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[06:21.26]這是一個成績,例如在谷歌設立,你跟你的最好的朋友寫文章,對吧?
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[06:26.26]好,所以在調查,我認為我的工作,當我當了學校的學長,是一個領導的領導人,對吧?
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[06:33.02]所以我真的有很好的朋友,我的工作是把人們的小數目和好幾個好意義,然後向他們進行完結的工作
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[06:41.10]我的工作不是在提供一百萬個意義,然後沒有任何股份的資料
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[06:45.54]然後當我的想法開始合作,然後我開始工作,我的工作是向他們扭動資料,向他們做好工作
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[06:52.50]然後開始將一些不正確的工作拆除,對吧?
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[06:56.06]那股股份並沒有存在在我的時間在谷歌上
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[06:59.34]如果他們有做好工作,他們會說:
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[07:02.06]"喂,你真棒,你懂這些東西的效果嗎?"
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[07:05.98]"這裡是所有的我們的TPUs,然後我認為他們會殺掉我們"
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[07:09.94]他肯定是想要的,他在2017年也說了一百萬公升的計劃
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[07:13.18]對,所以我認為這回合是在關於GPT的故事,對嗎?
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[07:15.98]就是我正在跳舞歷史,對嗎?
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[07:18.38]但在GPT2之後,我們都很期待GPT2,我可以告訴你更多的故事
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[07:22.50]這是我最後的一篇文章,我甚至真的受到觸碍了,所以我變成了研究研究研究員
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[07:27.70]每天每天我們進行GPT3,我會醒來,然後感到緊張
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[07:32.38]我感到緊張,因為...你只要看看Fax,對嗎?
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[07:35.54]Google有所有的帖子,Google有所有的人 who invented all of these underlying technologies
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[07:40.74]有一個人叫Noam,他很聰明,他已經做了這個討論,他想要一百萬的計劃模式
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[07:46.54]我認為我們可能只是在做一些複雜的研究,對嗎?他有這個扣子,只有轉換模式,他可能會在我們之前進行的
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[07:54.66]我心想,拜託,讓這個模式結束,對嗎?
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[07:57.90]然後,整個時間都變成了他們沒有得到股票的資金
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[08:01.62]所以,我年紀中,我帶了Google的LM的活動,我當時是一名手機的,我變得很清楚為什麼,對嗎?
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[08:06.98]那時候,有一個東西叫做"Brain Credit Marketplace"
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[08:11.06]你記得Brain Credit Marketplace嗎?
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[08:13.26]沒有,我沒聽過這說法
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[08:14.30]其實,你會問任何Google,就像一件事,對嗎?
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[08:18.58]對,有限定資訊,你必須有一個市場的市場,對嗎?
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[08:23.06]你可能,有些時候是貧富,有些時候是政治欺負
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[08:27.34]你可能,所以,基本上,每個人都要給錢,對嗎?
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[08:30.10]如果你有錢,你必須買N-CHIPS,按照貿易和責任的方式
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[08:33.74]如果你想做一個大職業,你可能有19、20個朋友不願意去工作
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[08:38.86]如果這就是它們的效果
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[08:40.74]它們很難得獲得
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[08:42.14]當中的肺炎
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[08:43.86]去學習這些東西
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[08:44.98]而 Google 的團隊
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[08:45.86]正在打架
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[08:47.02]但我們只能打擊它們
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[08:48.22]因為我們拿了大大的肺炎
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[08:50.62]然後我們注射
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[08:51.42]然後我認為
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[08:52.30]這就像是一部分的故事
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[08:53.54]像是一部分的歷史
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[08:54.34]像是一部分的歷史
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[08:55.62]像是一部分的歷史
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[08:57.58]像是一部分的歷史
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[08:58.90]我認為同樣的
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[09:00.22]我認為一部分的
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[09:01.02]三部分會成為
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[09:01.90]一部分的歷史
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[09:03.22]因為是一部分的
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[09:04.18]一部分的成績
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[09:05.62]對
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[09:06.30]我覺得這部分的內容是如何的
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[09:07.70]和影片也有關的
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[09:09.02]在前一天的情況下
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[09:10.06]我認為可能
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[09:11.10]我認為是Jensen
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[09:11.90]不確定是誰
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[09:12.86]把最近的照片
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[09:13.90]給大家看過的
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[09:15.26]他在第一張DGX的照片中
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[09:17.66]我覺得Jensen 已經是
|
|
[09:19.06]一個完美的
|
|
[09:21.22]技術
|
|
[09:21.94]和精神的一切
|
|
[09:24.10]我對NVIDIA的尊敬有多大關注
|
|
[09:26.22]是不實際的
|
|
[09:26.94]但我會打開
|
|
[09:27.74]我給他們的需要
|
|
[09:29.46]讓他們構思一下
|
|
[09:30.30]或者
|
|
[09:31.34]你只要用任何NVIDIA給他們的東西
|
|
[09:33.70]所以我們很接近他們的工作
|
|
[09:35.38]我不確定能分享所有的故事
|
|
[09:37.62]但例子是我找到的
|
|
[09:39.42]特別有趣的
|
|
[09:40.14]所以 Scott Gray 是很棒的
|
|
[09:41.54]我很喜歡他
|
|
[09:42.22]他在我的隊伍中
|
|
[09:43.30]是一名超級電腦隊伍
|
|
[09:45.62]就是Chris Burner 做的
|
|
[09:46.74]Chris Burner 還做了很多東西
|
|
[09:48.82]結果
|
|
[09:49.70]我們有很接近NVIDIA的 ties
|
|
[09:52.62]其實我的 co-founder
|
|
[09:53.70]在Adept Eric Elson
|
|
[09:54.74]是一位以前的GPGPU人士
|
|
[09:56.78]所以他和Scott
|
|
[09:57.82]和Brian Kanzaro
|
|
[09:58.86]NVIDIA
|
|
[09:59.66] and Jonah
|
|
[10:00.26] and Ian at NVIDIA
|
|
[10:01.14]我覺得我們全都很接近
|
|
[10:02.54]我們是一部分的組織
|
|
[10:03.70]我們如何推動這些股票的限度
|
|
[10:05.82]我覺得那種組織
|
|
[10:07.42]幫助了我們
|
|
[10:08.38]我想有趣的部分
|
|
[10:09.50]是 knowing the A100 generation
|
|
[10:11.22]那個Quadsbar city
|
|
[10:12.26]會是一件事
|
|
[10:12.98]是我們想找到的
|
|
[10:14.50]來解決
|
|
[10:15.22]這是我們可以利用的
|
|
[10:16.50]模特兒訓練
|
|
[10:17.14] really what it boils down to
|
|
[10:18.50]是
|
|
[10:19.22]我認為更多人
|
|
[10:20.06]知道這件事
|
|
[10:21.26]6 年前
|
|
[10:22.34]甚至3 年前
|
|
[10:23.34]人們拒絕接受
|
|
[10:24.98]這個AI 是一件故事
|
|
[10:27.02]是一件故事
|
|
[10:27.62]如何讓你更能复入
|
|
[10:29.22]實際使用模特兒
|
|
[10:30.38]使用模特兒
|
|
[10:31.66]還有GPT 2 3 故事嗎
|
|
[10:35.78]你喜歡在外面
|
|
[10:37.78]我認為是
|
|
[10:38.78]很欣賞
|
|
[10:39.86]這個模特兒的作用
|
|
[10:41.66]有趣的GPT 2 故事
|
|
[10:43.66]我花了很長的時間
|
|
[10:45.86]幫Alex使用模特兒
|
|
[10:48.58]我記得
|
|
[10:49.82]最有趣的一刻
|
|
[10:52.22]是我們寫了模特兒
|
|
[10:54.70]我確定模特兒
|
|
[10:56.22]是一個最短的模特兒
|
|
[10:57.70]有任何ML
|
|
[10:58.70]像是最理想的
|
|
[10:59.90]ML 模特兒
|
|
[11:01.42]是三個模特兒
|
|
[11:03.18]這是一種模特兒
|
|
[11:04.54]Vanilla 模特兒
|
|
[11:05.58]只有轉換的模特兒
|
|
[11:06.38]這些特別的東西
|
|
[11:07.34]我記得是在《ParaGraph》裡
|
|
[11:08.58]我記得是在《ParaGraph》裡
|
|
[11:09.42]我們都在看這件事
|
|
[11:11.02]我認為是很難看的模特兒
|
|
[11:11.82]OGs 在廣場上
|
|
[11:13.02]會很討厭這個模特兒
|
|
[11:14.02]他們會說沒有創意
|
|
[11:15.50]為什麼你們要做這個作用
|
|
[11:16.94]現在是很有趣的
|
|
[11:18.02]在後期的看法是
|
|
[11:19.54]一件很刺激的作用
|
|
[11:20.82]但我覺得是一件很早的事
|
|
[11:22.54]我們完全遲到
|
|
[11:24.42]我們都要關心的問題是 AI 和不關的
|
|
[11:27.58]是否有四種不同的想法
|
|
[11:29.34]是否有一個很簡單的想法
|
|
[11:30.34]是否有一個很簡單的想法
|
|
[11:31.34]是否有一個很簡單的想法
|
|
[11:32.34]是否有一個很簡單的想法
|
|
[11:33.34]是否有一個很簡單的想法
|
|
[11:34.34]是否有一個很簡單的想法
|
|
[11:35.34]是否有一個很簡單的想法
|
|
[11:36.34]是否有一個很簡單的想法
|
|
[11:37.34]是否有一個很簡單的想法
|
|
[11:38.34]是否有一個很簡單的想法
|
|
[11:39.34]是否有一個很簡單的想法
|
|
[11:40.34]是否有一個很簡單的想法
|
|
[11:41.34]是否有一個很簡單的想法
|
|
[11:42.34]是否有一個很簡單的想法
|
|
[11:43.34]是否有一個很簡單的想法
|
|
[11:44.34]是否有一個很簡單的想法
|
|
[11:45.34]是否有一個很簡單的想法
|
|
[11:46.34]是否有一個很簡單的想法
|
|
[11:47.34]是否有一個很簡單的想法
|
|
[11:48.34]是否有一個很簡單的想法
|
|
[11:49.34]是否有一個很簡單的想法
|
|
[11:50.34]是否有一個很簡單的想法
|
|
[11:51.34]是否有一個很簡單的想法
|
|
[11:52.34]是否有一個很簡單的想法
|
|
[11:53.34]是否有一個很簡單的想法
|
|
[11:54.34]是否有一個很簡單的想法
|
|
[11:55.34]是否有一個很簡單的想法
|
|
[11:56.34]是否有一個很簡單的想法
|
|
[11:57.34]是否有一個很簡單的想法
|
|
[11:58.34]是否有一個很簡單的想法
|
|
[11:59.34]是否有一個很簡單的想法
|
|
[12:00.34]是否有一個很簡單的想法
|
|
[12:01.34]是否有一個很簡單的想法
|
|
[12:02.34]是否有一個很簡單的想法
|
|
[12:03.34]是否有一個很簡單的想法
|
|
[12:04.34]是否有一個很簡單的想法
|
|
[12:05.34]是否有一個很簡單的想法
|
|
[12:06.34]是否有一個很簡單的想法
|
|
[12:07.34]是否有一個很簡單的想法
|
|
[12:08.34]是否有一個很簡單的想法
|
|
[12:09.34]之前 Microsoft invested in OpenAI
|
|
[12:11.34]Sam Altman, myself, and our CFO
|
|
[12:13.34] flew up to Seattle
|
|
[12:14.34] to do the final pitch meeting
|
|
[12:16.34] and I'd been a founder before
|
|
[12:17.34] so I always had a tremendous amount of anxiety
|
|
[12:19.34] about partner meetings
|
|
[12:21.34] which this basis is what it was
|
|
[12:22.34] it was like Kevin Scott
|
|
[12:23.34] and Satya and Amy Hood
|
|
[12:25.34] and it was my job to give the technical slides
|
|
[12:27.34] about what's the path to AGI
|
|
[12:29.34] what's our research portfolio
|
|
[12:30.34] all of this stuff
|
|
[12:31.34] but it was also my job to give the GPT-2 demo
|
|
[12:34.34] we had a slightly bigger version of GPT-2
|
|
[12:36.34] that we had just cut
|
|
[12:38.34] maybe a day or two before this flight up
|
|
[12:40.34] and as we all know now
|
|
[12:42.34]Model behaviors you find predictable
|
|
[12:44.34] at one checkpoint
|
|
[12:45.34] are not predictable in another checkpoint
|
|
[12:46.34] and so like I spent all this time
|
|
[12:48.34] trying to figure out how to keep this thing on rails
|
|
[12:50.34] I had my canned demos
|
|
[12:51.34] but I knew I had to go
|
|
[12:52.34] turn it around over to Satya and Kevin
|
|
[12:54.34] and let them type anything in
|
|
[12:56.34] and that just that really kept me up all night
|
|
[12:58.34]Nice, yeah
|
|
[13:00.34]I mean that must have helped you
|
|
[13:01.34] talking about partners meeting
|
|
[13:03.34]You raised 420 million for ADAPT
|
|
[13:06.34]The last round was a $350 million series B
|
|
[13:09.34]So I'm sure you do great
|
|
[13:10.34]Pitching and painting
|
|
[13:12.34]Nice
|
|
[13:13.34]No, that's a high compliment coming from a VC
|
|
[13:15.34]Yeah, I mean you're doing great
|
|
[13:17.34]Let's talk about ADAPT
|
|
[13:19.34]and we were doing pre prep
|
|
[13:21.34]and you mentioned that maybe a lot of people
|
|
[13:22.34]don't understand what ADAPT is
|
|
[13:23.34]So usually we try and introduce the product
|
|
[13:26.34]and then have the founders fill in the blanks
|
|
[13:27.34]but maybe let's do the reverse
|
|
[13:28.34]Like what is ADAPT?
|
|
[13:30.34]Yeah, so I think ADAPT
|
|
[13:31.34]is the least understood company
|
|
[13:34.34]in the broader space of foundation models
|
|
[13:36.34]plus agents
|
|
[13:37.34]So I'll give some color
|
|
[13:39.34]and I'll explain what it is
|
|
[13:40.34]and I'll explain also
|
|
[13:41.34]why it's actually pretty different
|
|
[13:43.34]from what people would have guessed
|
|
[13:44.34]So the goal for ADAPT
|
|
[13:46.34]is we basically want to build an AI agent
|
|
[13:48.34]that can do
|
|
[13:49.34]that can basically help humans
|
|
[13:50.34]do anything a human does on a computer
|
|
[13:51.34]and so what that really means is
|
|
[13:53.34]we want this thing to be super good
|
|
[13:55.34]at turning natural language
|
|
[13:56.34]like goal specifications
|
|
[13:58.34]right into the correct set of end steps
|
|
[14:00.34]and then also have all the correct sensors
|
|
[14:02.34]and actuators
|
|
[14:03.34]to go get that thing done for you
|
|
[14:04.34]across any software tool
|
|
[14:05.34]that you already use
|
|
[14:06.34]and so the end vision of this
|
|
[14:07.34]is effectively like
|
|
[14:08.34]I think in a couple years
|
|
[14:09.34]everyone's going to have access
|
|
[14:10.34]to an AI teammate
|
|
[14:11.34]that they can delegate arbitrary tasks to
|
|
[14:14.34]and then also be able to use it
|
|
[14:16.34]to a sounding board
|
|
[14:17.34]and just be way, way, way more productive
|
|
[14:19.34]right and just changes the shape
|
|
[14:21.34]of every job
|
|
[14:22.34]from something where you're mostly
|
|
[14:23.34]doing execution
|
|
[14:24.34]to something where you're mostly
|
|
[14:25.34]actually doing these core liberal arts skills
|
|
[14:26.34]of what should I be doing and why
|
|
[14:28.34]right and
|
|
[14:29.34]I find this like really exciting
|
|
[14:31.34]motivating because
|
|
[14:32.34]I think it's actually
|
|
[14:33.34]pretty different vision
|
|
[14:34.34]for how AI will play out
|
|
[14:36.34]I think systems like ADAPT
|
|
[14:37.34]are the most likely systems
|
|
[14:38.34]to be proto-AGI's
|
|
[14:40.34]but I think the ways in which
|
|
[14:41.34]we are really counterintuitive
|
|
[14:42.34]to everybody
|
|
[14:43.34]is that
|
|
[14:44.34]we've actually been really quiet
|
|
[14:45.34]because we are
|
|
[14:46.34]not a developer company
|
|
[14:47.34]we don't sell APIs
|
|
[14:48.34]we don't sell open source models
|
|
[14:50.34]we also don't sell bottom-up products
|
|
[14:52.34]we're not a thing
|
|
[14:53.34]that you go and click
|
|
[14:54.34]and download the extension
|
|
[14:55.34]and like we want more users
|
|
[14:56.34]signing up for that thing
|
|
[14:57.34]we're actually an enterprise company
|
|
[14:58.34]so what we do is
|
|
[14:59.34]we work with a range
|
|
[15:00.34]of different companies
|
|
[15:01.34]some like late-stage
|
|
[15:02.34]multi-thousand people start-ups
|
|
[15:04.34]some Fortune 500s etc
|
|
[15:06.34]and what we do for them
|
|
[15:07.34]is we basically give them
|
|
[15:09.34]an out-of-the-box solution
|
|
[15:11.34]where big complex workflows
|
|
[15:12.34]that their employees
|
|
[15:13.34]do every day
|
|
[15:14.34]could be delegated to the model
|
|
[15:15.34]and so we look a little
|
|
[15:16.34]different from other companies
|
|
[15:17.34]in that in order
|
|
[15:18.34]to go build this
|
|
[15:19.34]full agent thing
|
|
[15:20.34]the most important thing
|
|
[15:21.34]you gotta get right
|
|
[15:22.34]is reliability
|
|
[15:23.34]so initially zooming
|
|
[15:24.34]way back when
|
|
[15:25.34]one of the first things
|
|
[15:26.34]debt did was we released
|
|
[15:27.34]this demo called Act 1
|
|
[15:28.34]act 1 was like pretty cool
|
|
[15:30.34]it's kind of become
|
|
[15:31.34]a hello world thing
|
|
[15:32.34]for people to show
|
|
[15:33.34]agent demos
|
|
[15:34.34]by going to redfin
|
|
[15:35.34]and asking to buy a house
|
|
[15:36.34]somewhere
|
|
[15:37.34]because like we did that
|
|
[15:38.34]in the original Act 1 demo
|
|
[15:39.34]and like showed that
|
|
[15:40.34]showed like Google Sheets
|
|
[15:41.34]all this other stuff
|
|
[15:42.34]over the last like year
|
|
[15:44.34]since that has come out
|
|
[15:45.34]there's been a lot
|
|
[15:46.34]of really cool demos
|
|
[15:47.34]and you go play with them
|
|
[15:48.34]and you realize
|
|
[15:49.34]they work 60% of the time
|
|
[15:50.34]but since we've always
|
|
[15:51.34]been focused on
|
|
[15:52.34]how do we build
|
|
[15:53.34]an amazing enterprise product
|
|
[15:54.34]enterprises can't use
|
|
[15:55.34]anything
|
|
[15:56.34]the reliability
|
|
[15:57.34]and so we've
|
|
[15:58.34]actually had to go down
|
|
[15:59.34]a slightly different
|
|
[16:00.34]tech tree than what you
|
|
[16:01.34]might find in the
|
|
[16:02.34]prompt engineering
|
|
[16:03.34]sort of plays in
|
|
[16:04.34]the agent space
|
|
[16:05.34]to get that reliability
|
|
[16:06.34]and we've decided
|
|
[16:07.34]to prioritize reliability
|
|
[16:08.34]over all else
|
|
[16:09.34]so like one of our use
|
|
[16:10.34]cases is crazy enough
|
|
[16:11.34]that it actually ends
|
|
[16:12.34]with a physical truck
|
|
[16:13.34]being sentto a place
|
|
[16:15.34]as the result
|
|
[16:16.34]of the agent workflow
|
|
[16:17.34]and if you're like
|
|
[16:18.34]if that works like 60%
|
|
[16:19.34]of the time
|
|
[16:20.34]you're just blowing money
|
|
[16:21.34]and poor truck drivers
|
|
[16:22.34]going places
|
|
[16:23.34]interesting
|
|
[16:24.34]one of the
|
|
[16:25.34]common teams
|
|
[16:26.34]has this idea of services
|
|
[16:27.34]as software
|
|
[16:28.34]I'm actually giving a talk
|
|
[16:29.34]at nvidia gtc
|
|
[16:30.34]about this
|
|
[16:31.34]but basically
|
|
[16:32.34]software as a service
|
|
[16:33.34]you're wrapping
|
|
[16:34.34]user productivity
|
|
[16:35.34]in software
|
|
[16:36.34]with agents
|
|
[16:37.34]and services as software
|
|
[16:38.34]is replacing things
|
|
[16:39.34]that you know
|
|
[16:40.34]you would ask somebody
|
|
[16:41.34]to do
|
|
[16:42.34]and the software
|
|
[16:43.34]just does it for you
|
|
[16:44.34]when you think
|
|
[16:45.34]about these usecases
|
|
[16:46.34]do the users
|
|
[16:47.34]still go in
|
|
[16:48.34]and look at the agent
|
|
[16:49.34]kindof like
|
|
[16:50.34]doing the things
|
|
[16:51.34]and can intervene
|
|
[16:52.34]or likeare they slowly
|
|
[16:53.34]remove from them
|
|
[16:54.34]are there people
|
|
[16:55.34]in the middle
|
|
[16:56.34]checking in
|
|
[16:57.34]I think there's two current flaws
|
|
[16:58.34]in the framing
|
|
[16:59.34]for services
|
|
[17:00.34]as software
|
|
[17:01.34]or I think what you just said
|
|
[17:02.34]I think that one of them
|
|
[17:03.34]is likein our experience
|
|
[17:04.34]as we've been rolling
|
|
[17:05.34]out adept
|
|
[17:06.34]the people who actually
|
|
[17:07.34]do the jobs
|
|
[17:08.34]are the most excited
|
|
[17:09.34]about it
|
|
[17:10.34]because they don't go from
|
|
[17:11.34]I do this job
|
|
[17:12.34]to I don't do this job
|
|
[17:13.34]they go from
|
|
[17:14.34]I do this job
|
|
[17:15.34]for everything
|
|
[17:16.34]including the shitty
|
|
[17:17.34]wrote stuff
|
|
[17:18.34]to I'm a supervisor
|
|
[17:19.34]and I literally
|
|
[17:20.34]likeit's pretty magical
|
|
[17:21.34]when you watch the thing
|
|
[17:22.34]being used
|
|
[17:23.34]sequentially by hand
|
|
[17:24.34]as a human
|
|
[17:25.34]and you can just click
|
|
[17:26.34]in any one of them
|
|
[17:27.34]be like hey I want to watch
|
|
[17:28.34]the trajectory
|
|
[17:29.34]the agent went through
|
|
[17:30.34]to go solve this
|
|
[17:31.34]and the nice thing
|
|
[17:32.34]about agent execution
|
|
[17:33.34]as opposed to
|
|
[17:34.34]like LLM generations
|
|
[17:35.34]is that
|
|
[17:36.34]a good chunk of the time
|
|
[17:37.34]when the agent
|
|
[17:38.34]fails to execute
|
|
[17:39.34]it doesn't give you
|
|
[17:40.34]the wrong result
|
|
[17:41.34]it just fails to execute
|
|
[17:42.34]and the whole trajectory
|
|
[17:43.34]is just broken and dead
|
|
[17:44.34]and the agent knows it
|
|
[17:45.34]right so then
|
|
[17:46.34]those are the ones
|
|
[17:47.34]that the human
|
|
[17:48.34]then goes and solves
|
|
[17:49.34]and so then they become
|
|
[17:50.34]a troubleshooter
|
|
[17:51.34]they work on the more
|
|
[17:52.34]present piece
|
|
[17:53.34]of it
|
|
[17:54.34]that we found
|
|
[17:55.34]is our strategy
|
|
[17:56.34]as a company
|
|
[17:57.34]is to always be
|
|
[17:58.34]an augmentation company
|
|
[17:59.34]and I think
|
|
[18:01.34]one out of principle
|
|
[18:02.34]that's something
|
|
[18:03.34]we really care about
|
|
[18:04.34]but two
|
|
[18:05.34]actually if you're
|
|
[18:06.34]framing yourself
|
|
[18:07.34]as an augmentation
|
|
[18:08.34]company
|
|
[18:09.34]you're always going to
|
|
[18:10.34]live in the world
|
|
[18:11.34]where you're solving
|
|
[18:12.34]tasks that are a little
|
|
[18:13.34]too hard for what
|
|
[18:14.34]the model can do today
|
|
[18:15.34]and still needs a human
|
|
[18:16.34]to provide oversight
|
|
[18:17.34]provide clarifications
|
|
[18:18.34]provide human feedback
|
|
[18:19.34]and that's how you
|
|
[18:20.34]build a data flywheel
|
|
[18:21.34]smart as humans
|
|
[18:22.34]how to solve
|
|
[18:23.34]things models
|
|
[18:24.34]can't do today
|
|
[18:25.34]and so I actually
|
|
[18:26.34]think that
|
|
[18:27.34]being an augmentation
|
|
[18:28.34]company
|
|
[18:29.34]forces you to go
|
|
[18:30.34]develop your core
|
|
[18:31.34]AI capabilities
|
|
[18:32.34]faster than someone
|
|
[18:33.34]who's saying
|
|
[18:34.34]ah okay
|
|
[18:35.34]my job's like
|
|
[18:36.34]deliver you
|
|
[18:37.34]a lights off
|
|
[18:38.34]solution for X
|
|
[18:39.34]it's interesting
|
|
[18:40.34]because we've seen
|
|
[18:41.34]two parts
|
|
[18:42.34]of the market
|
|
[18:43.34]one is
|
|
[18:44.34]we have one company
|
|
[18:45.34]that does
|
|
[18:46.34]agents for
|
|
[18:47.34]sock analysts
|
|
[18:48.34]people just
|
|
[18:49.34]don't have them
|
|
[18:50.34]which is
|
|
[18:51.34]the augmentation product
|
|
[18:52.34]and then you have
|
|
[18:53.34]sweep.dev
|
|
[18:54.34]any of these products
|
|
[18:55.34]which they just
|
|
[18:56.34]do the whole thing
|
|
[18:57.34]I'm really curious
|
|
[18:58.34]to see how that evolves
|
|
[18:59.34]I agree that today
|
|
[19:00.34]the reliability is
|
|
[19:01.34]so important
|
|
[19:02.34]in the enterprise
|
|
[19:03.34]that they just
|
|
[19:04.34]don't use
|
|
[19:05.34]most of them
|
|
[19:06.34]that's cool
|
|
[19:07.34]but it's great
|
|
[19:08.34]to hear the story
|
|
[19:09.34]because I think
|
|
[19:10.34]from the outside
|
|
[19:11.34]people are like
|
|
[19:12.34]oh that
|
|
[19:13.34]they do act one
|
|
[19:14.34]they do person on
|
|
[19:15.34]they do foo you
|
|
[19:16.34]they do all these
|
|
[19:17.34]it's just the public stuff
|
|
[19:18.34]it's just the public stuff
|
|
[19:19.34]我們想要更多的客人來領導
|
|
[19:22.20]所以我們想要更多的客人來領導
|
|
[19:26.08]但我們希望我們會更多的客人來領導
|
|
[19:29.32]我們想要更多的客人來領導
|
|
[19:31.48]我們想要更多的客人來領導
|
|
[19:33.68]所以這次我們想要更多的客人來領導
|
|
[19:36.70]為什麼你變得更多的客人?
|
|
[19:38.78]如果整個推動...
|
|
[19:40.12]你已經領導了你的公司
|
|
[19:41.82]但是你也會更加努力去領導更多的客人來領導
|
|
[19:46.20]我覺得我們剛剛領導過那一步
|
|
[19:48.14]因為我最近還沒有領導過那一步
|
|
[19:49.14]這是一個好問題
|
|
[19:50.14]我認為這兩件事其實是很重要的
|
|
[19:51.14]一件事我認為是...
|
|
[19:53.14]坦白說,大部分是公共的歷史
|
|
[19:56.14]在公司中的公司中的歷史是最重要的
|
|
[19:58.14]我非常高興這件事發生
|
|
[20:00.14]因為當我們開始公司在2022年代
|
|
[20:03.14]大家都在社會中知道歷史的歷史
|
|
[20:06.14]但公司中的歷史沒有任何意義
|
|
[20:08.14]他們還會把所有的歷史都放在桌上
|
|
[20:11.14]所以我認為現在
|
|
[20:13.14]我真的要注意的是
|
|
[20:15.14]當人們認為歷史
|
|
[20:16.14]他們會認為是對的
|
|
[20:17.14]對,所有各種各樣的東西都會被引起
|
|
[20:19.14]會被引起的電話電話電話電話
|
|
[20:20.14]會被引起的東西都會被引起的東西
|
|
[20:21.14]或是被引起的電話電話電話
|
|
[20:22.14]我認為電話電話電話
|
|
[20:23.14]是一個可以給你一個目標
|
|
[20:25.14]再次進行的工作
|
|
[20:27.14]並且在最少數個步驟中
|
|
[20:28.14]所以這就是一個大部分的原因
|
|
[20:30.14]我認為其中一個部分
|
|
[20:31.14]是因為我認為更好讓人們
|
|
[20:33.14]更加 aware of the depth
|
|
[20:34.14]他們想要做的事情
|
|
[20:35.14]他們的生意
|
|
[20:36.14]這塊地是在世界中
|
|
[20:38.14]在於在更多的利益
|
|
[20:40.14]我認為大量的利益
|
|
[20:43.14]會發生從
|
|
[20:44.14]你使用的研究模式
|
|
[20:46.14]作為大量學童的學童
|
|
[20:49.14]去解決這些事
|
|
[20:50.14]我認為那些人
|
|
[20:51.14]想要做的研究
|
|
[20:52.14]應該有所改善
|
|
[20:53.14]當你提到
|
|
[20:54.14]研究已經變成
|
|
[20:55.14]更多的一部分
|
|
[20:56.14]有什麼特別的東西
|
|
[20:57.14]你會問我嗎
|
|
[20:58.14]我會給你一個名字
|
|
[20:59.14] Bill Gates 在 his blog post
|
|
[21:00.14]提及「Agent of the Future」
|
|
[21:02.14]我是那個人 who made OSs
|
|
[21:04.14]我認為「Agent of the Next Thing」
|
|
[21:05.14]所以 Bill Gates
|
|
[21:07.14]我會叫他出來
|
|
[21:08.14]然後 Sam Altman 也會說
|
|
[21:09.14]「Agent of the Future for Open AI」
|
|
[21:10.14]我認為之前
|
|
[21:11.14]我認為
|
|
[21:12.14]有些人在《紐約 Times》
|
|
[21:13.14]Kade Metz 也在《紐約 Times》
|
|
[21:15.14]對於現在
|
|
[21:16.14]在一些不同的
|
|
[21:17.14]我看過 AI 開始的
|
|
[21:18.14]使用的研究模式
|
|
[21:19.14]是 AI 公司
|
|
[21:20.14]現在的 AI 公司
|
|
[21:21.14]是 AI 公司
|
|
[21:22.14]只是我認為
|
|
[21:23.14]是一段時間
|
|
[21:24.14]從 VC 開始
|
|
[21:25.14]是有點混合
|
|
[21:26.14]是嗎
|
|
[21:27.14]我認為有很多 VC
|
|
[21:28.14]會說我不會
|
|
[21:29.14]觸碰 any agent start-ups
|
|
[21:30.14]因為
|
|
[21:31.14]為什麼
|
|
[21:32.14]你告訴我
|
|
[21:33.14]我認為有很多 VC
|
|
[21:35.14]比較少技術
|
|
[21:37.14]不懂得
|
|
[21:38.14]限制的東西
|
|
[21:39.14]不不不
|
|
[21:40.14]你會這樣嗎
|
|
[21:41.14]不不
|
|
[21:42.14]我認為
|
|
[21:43.14]今天的可能性
|
|
[21:44.14]是否適用
|
|
[21:46.14]我認為
|
|
[21:47.14]人們會看你
|
|
[21:48.14]然後說
|
|
[21:49.14]這傢伙
|
|
[21:50.14]需要 400 億元
|
|
[21:51.14]去做
|
|
[21:52.14]所以有很多 VC
|
|
[21:53.14]都會說
|
|
[21:54.14]我會再加上
|
|
[21:55.14]有些東西
|
|
[21:56.14]協助 AI
|
|
[21:57.14]有些東西
|
|
[21:58.14]是比較容易
|
|
[21:59.14]進行
|
|
[22:00.14]進行的
|
|
[22:01.14]但我還驚訝
|
|
[22:02.14]有些 funders
|
|
[22:03.14]不想做 agent
|
|
[22:04.14]不只是 funding
|
|
[22:05.14]有時候
|
|
[22:06.14]我們在看
|
|
[22:07.14]為什麼沒有人
|
|
[22:08.14]做 agent for acts
|
|
[22:09.14]那是好
|
|
[22:10.14]其實
|
|
[22:11.14]我從沒知道
|
|
[22:12.14]我的觀點
|
|
[22:13.14]是
|
|
[22:14.14]有新的 agent company
|
|
[22:16.14]在進行
|
|
[22:17.14]所以可能
|
|
[22:18.14]他們也有
|
|
[22:19.14]但我提供人員
|
|
[22:20.14]去取消 agent
|
|
[22:21.14]他們的名字
|
|
[22:22.14]是因為
|
|
[22:23.14]他們的名字
|
|
[22:24.14]他們的名字
|
|
[22:25.14]所以
|
|
[22:26.14]他們不等待
|
|
[22:27.14]對
|
|
[22:28.14]那是好處
|
|
[22:29.14]你的 portfolio allocator
|
|
[22:31.14]有些人
|
|
[22:32.14]知道 about persimmon
|
|
[22:33.14]一些人知道
|
|
[22:34.14]for you and for you heavy
|
|
[22:35.14]你覺得
|
|
[22:36.14]怎麼想
|
|
[22:37.14]那個 evolution of that
|
|
[22:38.14]什麼人
|
|
[22:39.14]想想
|
|
[22:40.14]那是
|
|
[22:41.14]a depth
|
|
[22:42.14]搜尋個案
|
|
[22:43.14] kind of take us
|
|
[22:44.14]through the stuff
|
|
[22:45.14]you should recently
|
|
[22:46.14]and how people
|
|
[22:47.14]should think about
|
|
[22:48.14]the trajectory
|
|
[22:49.14]what you're doing
|
|
[22:50.14]the critical path
|
|
[22:51.14]for adept
|
|
[22:52.14]is we want to build
|
|
[22:53.14]agents that can do
|
|
[22:54.14]a higher and higher
|
|
[22:55.14]level of abstraction
|
|
[22:56.14]things over time
|
|
[22:57.14]all while keeping
|
|
[22:58.14]insanely
|
|
[22:59.14]high reliability standard
|
|
[23:00.14]because that's
|
|
[23:01.14]what turns this from
|
|
[23:02.14]research into something
|
|
[23:03.14]that customers want
|
|
[23:04.14]and if you build
|
|
[23:05.14]agents with really
|
|
[23:06.14]high reliability standard
|
|
[23:07.14]your users
|
|
[23:08.14]how to get that
|
|
[23:09.14]next level of
|
|
[23:10.14]straction faster
|
|
[23:11.14]so that's how
|
|
[23:12.14]you actually build
|
|
[23:13.14]the data level
|
|
[23:14.14]that's the critical path
|
|
[23:15.14]for the company
|
|
[23:16.14]everything we do
|
|
[23:17.14]is in service of that
|
|
[23:18.14]so you go zoom
|
|
[23:19.14]way way back to
|
|
[23:20.14]act one days right
|
|
[23:21.14]like the core thing
|
|
[23:22.14]behind act one
|
|
[23:23.14]is can we teach
|
|
[23:24.14]large model basically
|
|
[23:25.14]how to even
|
|
[23:26.14]actuate your computer
|
|
[23:27.14]and I think we're
|
|
[23:28.14]one of the first places
|
|
[23:29.14]to have solved that
|
|
[23:30.14]and shown it
|
|
[23:31.14]and shown the generalization
|
|
[23:32.14]that you get when you
|
|
[23:33.14]give it various different
|
|
[23:34.14]workflows and texts
|
|
[23:35.14]but I think from
|
|
[23:36.14]these models
|
|
[23:37.14]to be able to
|
|
[23:38.14]get a lot better
|
|
[23:39.14]at having some
|
|
[23:40.14]specificationof some
|
|
[23:41.14]guardrails for what it
|
|
[23:42.14]actually should be doing
|
|
[23:43.14]and I think in conjunction
|
|
[23:44.14]with that a giant thing
|
|
[23:45.14]that was really
|
|
[23:46.14]necessaryis really
|
|
[23:47.14]fast multimodal models
|
|
[23:48.14]that are really good
|
|
[23:49.14]at understanding
|
|
[23:50.14]knowledge work
|
|
[23:51.14]and really good
|
|
[23:52.14]at understanding screens
|
|
[23:53.14]and that needs to
|
|
[23:54.14]kind of be the base
|
|
[23:55.14]for some of these
|
|
[23:56.14]agentsback then
|
|
[23:57.14]we had to do a ton
|
|
[23:58.14]ofresearchbasically
|
|
[23:59.14]on how do we
|
|
[24:00.14]actually make that
|
|
[24:01.14]possiblewell first off
|
|
[24:02.14]back in
|
|
[24:03.14]free at exact
|
|
[24:04.14]one month of 23
|
|
[24:05.14]and then
|
|
[24:06.14]we had to
|
|
[24:07.14]get a lot better
|
|
[24:08.14]at the first place
|
|
[24:09.14]and then
|
|
[24:10.14]we had to
|
|
[24:11.14]get a lot better
|
|
[24:12.14]at the first place
|
|
[24:13.14]and then
|
|
[24:14.14]we had to
|
|
[24:15.14]get a lot better
|
|
[24:16.14]at the first place
|
|
[24:17.14]and then
|
|
[24:18.14]we had to
|
|
[24:19.14]get a lot better
|
|
[24:20.14]at the first place
|
|
[24:21.14]and then
|
|
[24:22.14]we had to
|
|
[24:23.14]get a lot better
|
|
[24:24.14]at the first place
|
|
[24:25.14]and then
|
|
[24:26.14]we had to
|
|
[24:27.14]get a lot better
|
|
[24:28.14]at the first place
|
|
[24:29.14]and then
|
|
[24:30.14]we had to
|
|
[24:31.14]get a lot better
|
|
[24:32.14]at the first place
|
|
[24:33.14]and then
|
|
[24:34.14]we had to
|
|
[24:35.14]get a lot better
|
|
[24:36.14]at the first place
|
|
[24:37.14]and then
|
|
[24:38.14]we had to
|
|
[24:39.14]get a lot better
|
|
[24:40.14]at the first place
|
|
[24:41.14]and then
|
|
[24:42.14]we had to
|
|
[24:43.14]get a lot better
|
|
[24:44.14]at the first place
|
|
[24:45.14]and then
|
|
[24:46.14]we had to
|
|
[24:47.14]get a lot better
|
|
[24:48.14]at the first place
|
|
[24:49.14]and then
|
|
[24:50.14]we had to
|
|
[24:51.14]get a lot better
|
|
[24:52.14]at the first place
|
|
[24:53.14]and then
|
|
[24:54.14]we had to
|
|
[24:55.14]get a lot better
|
|
[24:56.14]at the first place
|
|
[24:57.14]and then
|
|
[24:58.14]we had to
|
|
[24:59.14]get a lot better
|
|
[25:00.14]at the first place
|
|
[25:01.14]and then
|
|
[25:02.14]we had to
|
|
[25:03.14]get a lot better
|
|
[25:04.12]at the first place
|
|
[25:05.12]and then
|
|
[25:06.12]we had to
|
|
[25:07.12]get a lot better
|
|
[25:08.12]at the first place
|
|
[25:09.12]and then
|
|
[25:10.12]we had to
|
|
[25:11.12]get a lot better
|
|
[25:12.12]at the first place
|
|
[25:13.12]and then
|
|
[25:14.12]we had to
|
|
[25:15.12]get a lot better
|
|
[25:16.12]at the first place
|
|
[25:17.12]and then
|
|
[25:18.12]we had to
|
|
[25:19.12]get a lot better
|
|
[25:20.12]at the first place
|
|
[25:21.12]and then
|
|
[25:22.12]we had to
|
|
[25:23.12]get a lot better
|
|
[25:24.12]at the first place
|
|
[25:25.12]and then
|
|
[25:26.12]we had to
|
|
[25:27.12]get a lot better
|
|
[25:28.12]at the first place
|
|
[25:29.12]and then
|
|
[25:30.12]we had to
|
|
[25:31.12]get a lot better
|
|
[25:32.12]at the first place
|
|
[25:33.12]and then
|
|
[25:34.12]we had to
|
|
[25:35.12]get a lot better
|
|
[25:36.12]at the first place
|
|
[25:37.12]and then
|
|
[25:38.12]we had to
|
|
[25:39.12]get a lot better
|
|
[25:40.12]at the first place
|
|
[25:41.12]and then
|
|
[25:42.12]we had to
|
|
[25:43.12]get a lot better
|
|
[25:44.12]at the first place
|
|
[25:45.12]and then
|
|
[25:46.12]we had to
|
|
[25:47.12]get a lot better
|
|
[25:48.12]at the first place
|
|
[25:49.12]and then
|
|
[25:50.12]we had to
|
|
[25:51.12]get a lot better
|
|
[25:52.12]at the first place
|
|
[25:53.12]and then
|
|
[25:54.12]we had to
|
|
[25:55.12]get a lot better
|
|
[25:56.12]at the first place
|
|
[25:57.12]and then
|
|
[25:58.12]we had to
|
|
[25:59.12]get a lot better
|
|
[26:00.12]at the first place
|
|
[26:01.12]and then
|
|
[26:02.12]we had to
|
|
[26:03.12]get a lot better
|
|
[26:04.12]at the first place
|
|
[26:05.12]and then
|
|
[26:06.12]we had to
|
|
[26:07.12]get a lot better
|
|
[26:08.12]at the first place
|
|
[26:09.12]and then
|
|
[26:10.12]we had to
|
|
[26:11.12]get a lot better
|
|
[26:12.12]at the first place
|
|
[26:13.12]and then
|
|
[26:14.12]we had to
|
|
[26:15.12]get a lot better
|
|
[26:16.12]at the first place
|
|
[26:17.12]and then
|
|
[26:18.12]we had to
|
|
[26:19.12]get a lot better
|
|
[26:20.12]at the first place
|
|
[26:21.12]and then
|
|
[26:22.12]we had to
|
|
[26:23.12]get a lot better
|
|
[26:24.12]at the first place
|
|
[26:25.12]and then
|
|
[26:26.12]we had to
|
|
[26:27.12]get a lot better
|
|
[26:28.12]at the first place
|
|
[26:29.12]and then
|
|
[26:30.12]we had to
|
|
[26:31.12]get a lot better
|
|
[26:32.12]at the browser level
|
|
[26:33.12]I really want
|
|
[26:34.12]at your papers
|
|
[26:35.12]you have like a different representation
|
|
[26:36.12]kind of like
|
|
[26:37.12]you don't just take the dome
|
|
[26:38.12]and act on it
|
|
[26:39.12]you do a lot more stuff
|
|
[26:40.12]how do you think about
|
|
[26:41.12]the best way
|
|
[26:42.12]the models will interact
|
|
[26:43.12]with the software
|
|
[26:44.12]and like how
|
|
[26:45.12]the development of products
|
|
[26:46.12]is going to change
|
|
[26:47.12]with that in mind
|
|
[26:48.12]as more and more
|
|
[26:49.12]the work is done by agents
|
|
[26:50.12]instead of people
|
|
[26:51.12]this is
|
|
[26:52.12]there's so much surface area here
|
|
[26:53.12]and it's actually one of the things
|
|
[26:54.12]I'm really excited about
|
|
[26:55.12]and it's funny because
|
|
[26:56.12]I've spent most of my time
|
|
[26:57.12]doing research stuff
|
|
[26:58.12]but this is like a whole
|
|
[26:59.12]new ball game that I've been
|
|
[27:00.12]doing about
|
|
[27:01.12]and I find it
|
|
[27:02.12]really cool
|
|
[27:03.12]so I would say
|
|
[27:04.12]the best analogy
|
|
[27:05.12]I have to
|
|
[27:06.12]why ADAPT
|
|
[27:07.12]is pursuing a path
|
|
[27:08.12]of being able to
|
|
[27:09.12]use your computer
|
|
[27:10.12]like a human
|
|
[27:11.12]plus of course
|
|
[27:12.12]being able to call
|
|
[27:13.12]APIs
|
|
[27:14.12]being able to call
|
|
[27:15.12]APIs is the easy part
|
|
[27:16.12]like being able to
|
|
[27:17.12]use your gear like humans
|
|
[27:18.12]is a hard part
|
|
[27:19.12]it's in the same way
|
|
[27:20.12]why people are excited
|
|
[27:21.12]about humanoid robotics
|
|
[27:22.12]right
|
|
[27:23.12]in a world where
|
|
[27:24.12]you had t=infinity
|
|
[27:25.12]right you're probably
|
|
[27:26.12]gonna have various
|
|
[27:27.12]different form factors
|
|
[27:28.12]that robots
|
|
[27:29.12]do
|
|
[27:30.12]without changing
|
|
[27:31.12]everything along the way
|
|
[27:32.12]it's the same thing
|
|
[27:33.12]for software
|
|
[27:34.12]right
|
|
[27:35.12]if you go itemize out
|
|
[27:36.12]the number of things
|
|
[27:37.12]you wanna do on your computer
|
|
[27:38.12]for which every step
|
|
[27:39.12]has an api
|
|
[27:40.12]those numbers
|
|
[27:41.12]will workflows add up
|
|
[27:42.12]pretty close to zero
|
|
[27:43.12]and so then many
|
|
[27:44.12]points along the way
|
|
[27:45.12]you need the ability
|
|
[27:46.12]to actually control
|
|
[27:47.12]your computer like a human
|
|
[27:48.12]it also lets you learn
|
|
[27:49.12]from human usage
|
|
[27:50.12]of computers
|
|
[27:51.12]as a source of training
|
|
[27:52.12]data that you don't get
|
|
[27:53.12]if you have to somehow
|
|
[27:54.12]figure out how every
|
|
[27:55.12]particular step needs to be
|
|
[27:56.12]some particular custom
|
|
[27:57.12]private api thing
|
|
[27:58.12]it's the most practical path
|
|
[27:59.12]i think a lot of
|
|
[28:00.12]success will come
|
|
[28:01.12]from going down
|
|
[28:02.12]this path
|
|
[28:03.12]i kinda think about this
|
|
[28:04.12]early days of the agent
|
|
[28:05.12]interaction layer
|
|
[28:06.12]level is a little bit
|
|
[28:07.12]like do y'all remember
|
|
[28:08.12]windows 3.1
|
|
[28:10.12]like those days
|
|
[28:11.12]this might be
|
|
[28:12.12]i might be too old
|
|
[28:13.12]for you guys on this
|
|
[28:14.12]but back in the day
|
|
[28:15.12]windows 3.1
|
|
[28:16.12]we had this transition period
|
|
[28:17.12]between pure command line
|
|
[28:18.12]right
|
|
[28:19.12]being the default
|
|
[28:20.12]into this new world
|
|
[28:21.12]with the gui is the default
|
|
[28:22.12]and then you drop into the
|
|
[28:23.12]command line for like
|
|
[28:24.12]programmer things
|
|
[28:25.12]the old way was
|
|
[28:26.12]you booted your computer up
|
|
[28:27.12]and then it would
|
|
[28:28.12]give you the c colon
|
|
[28:29.12]slash thing
|
|
[28:30.12]and you typed windows
|
|
[28:31.12]and you hit enter
|
|
[28:32.12]and then you got
|
|
[28:33.12]put into windows
|
|
[28:34.12]and then the gui
|
|
[28:35.12]kind of became a layer
|
|
[28:36.12]above the command line
|
|
[28:37.12]the same thing
|
|
[28:38.12]is gonna happen
|
|
[28:39.12]with agent interfaces
|
|
[28:40.12]is like today
|
|
[28:41.12]what we have in the gui
|
|
[28:42.12]is like the base layer
|
|
[28:44.12]and then the agent
|
|
[28:45.12]just controls
|
|
[28:46.12]the current gui
|
|
[28:47.12]layer plus apis
|
|
[28:48.12]and in the future
|
|
[28:50.12]as more and more
|
|
[28:51.12]trust is built towards
|
|
[28:52.12]agents and more and more
|
|
[28:53.12]things can be done by
|
|
[28:54.12]agents and more UIs
|
|
[28:55.12]for agents are actually
|
|
[28:56.12]users
|
|
[28:57.12]then that just becomes
|
|
[28:58.12]a standard
|
|
[28:59.12]interaction layer
|
|
[29:00.12]and if that becomes
|
|
[29:01.12]a standard
|
|
[29:02.12]interaction layer
|
|
[29:03.12]what changes for
|
|
[29:04.12]software is that
|
|
[29:05.12]a lot of software
|
|
[29:06.12]is gonna be
|
|
[29:07.12]either systems
|
|
[29:08.12]or record
|
|
[29:09.12]or like certain
|
|
[29:10.12]customized
|
|
[29:11.12]workflow
|
|
[29:12.12]execution engines
|
|
[29:13.12]and a lot of
|
|
[29:14.12]how you actually
|
|
[29:15.12]do stuff will be
|
|
[29:16.12]controlled at the
|
|
[29:17.12]agent layer
|
|
[29:18.12]and you think the
|
|
[29:19.12]rabbit interface
|
|
[29:20.12]is more like
|
|
[29:21.12]it would like
|
|
[29:22.12]you're not actually
|
|
[29:23.12]seeing the app
|
|
[29:24.12]that the model
|
|
[29:25.12]I can see that
|
|
[29:26.12]being a model
|
|
[29:27.12]I think
|
|
[29:28.12]I don't know
|
|
[29:29.12]enough about
|
|
[29:30.12]what using
|
|
[29:31.12]rabbit in real life
|
|
[29:32.12]will actually be like
|
|
[29:33.12]to comment on
|
|
[29:34.12]that particular
|
|
[29:35.12]thing but I think
|
|
[29:36.12]the broader idea
|
|
[29:37.12]that you know
|
|
[29:38.12]you have a goal
|
|
[29:39.12]the agent knows
|
|
[29:40.12]how to break
|
|
[29:41.12]your goal down into steps
|
|
[29:42.12]the agent knows
|
|
[29:43.12]how to use
|
|
[29:44.12]the underlying
|
|
[29:45.12]software
|
|
[29:46.12]and systems
|
|
[29:47.12]or record
|
|
[29:48.12]to achieve
|
|
[29:49.12]that goal for you
|
|
[29:50.12]the agent may presents
|
|
[29:51.12]you information
|
|
[29:52.12]in a custom way
|
|
[29:53.12]that's only
|
|
[29:54.12]you're a power
|
|
[29:55.12]user
|
|
[29:56.12]for some niche thing
|
|
[29:57.12]general question
|
|
[29:58.12]so first of all
|
|
[29:59.12]I think like
|
|
[30:00.12]the sort of input
|
|
[30:01.12]mode conversation
|
|
[30:02.12]I wonder if you have
|
|
[30:03.12]any analogies
|
|
[30:04.12]that you like
|
|
[30:05.12]with self-driving
|
|
[30:06.12]because I do think
|
|
[30:07.12]there's a little bit
|
|
[30:08.12]of how the model
|
|
[30:09.12]should perceive the world
|
|
[30:10.12]and you know
|
|
[30:11.12]the primary split
|
|
[30:12.12]in self-driving
|
|
[30:13.12]is LiDAR
|
|
[30:14.12]versus camera
|
|
[30:15.12]and I feel like
|
|
[30:16.12]most agent companies
|
|
[30:17.12]that I'm tracking
|
|
[30:18.12]are all moving towards
|
|
[30:19.12]camera approach
|
|
[30:20.12]which is like
|
|
[30:21.12]the multimodal approach
|
|
[30:22.12]that we're doing
|
|
[30:23.12]you're
|
|
[30:24.12]focusing on that
|
|
[30:25.12]including charts
|
|
[30:26.12]and tables
|
|
[30:27.12]and do you find
|
|
[30:28.12]inspiration there
|
|
[30:29.12]from the self-driving
|
|
[30:30.12]world?
|
|
[30:31.12]that's a good question
|
|
[30:32.12]I think sometimes
|
|
[30:33.12]the most useful
|
|
[30:34.12]inspiration I've found
|
|
[30:35.12]from self-driving
|
|
[30:36.12]is the levels analogy
|
|
[30:37.12]I think that's awesome
|
|
[30:38.12]but I think that
|
|
[30:39.12]our number one
|
|
[30:40.12]goals for agents
|
|
[30:41.12]not to look like
|
|
[30:42.12]self-driving
|
|
[30:43.12]we want to minimize
|
|
[30:44.12]the chances
|
|
[30:45.12]that agents are sort
|
|
[30:46.12]of a thing
|
|
[30:47.12]that you just
|
|
[30:48.12]have to bang
|
|
[30:49.12]your head at
|
|
[30:50.12]for a long time
|
|
[30:51.12]to get to like
|
|
[30:52.12]completely
|
|
[30:53.12]and that takes you
|
|
[30:54.12]all the way
|
|
[30:55.12]up to the top
|
|
[30:56.12]but similarly
|
|
[30:57.12]I mean
|
|
[30:58.12]compared to self-driving
|
|
[30:59.12]like two things
|
|
[31:00.12]that people really
|
|
[31:01.12]undervalue
|
|
[31:02.12]that's like really
|
|
[31:03.12]easy to driving
|
|
[31:04.12]a car down
|
|
[31:05.12]highway 101
|
|
[31:06.12]in a sunny day
|
|
[31:07.12]demo
|
|
[31:08.12]that actually
|
|
[31:09.12]doesn't prove anything
|
|
[31:10.12]anymore
|
|
[31:11.12]and I think
|
|
[31:12.12]the second thing
|
|
[31:13.12]is that
|
|
[31:14.12]as a non-self-driving
|
|
[31:15.12]expert
|
|
[31:16.12]I think one of the things
|
|
[31:17.12]that we believe
|
|
[31:18.12]really strongly
|
|
[31:19.12]is that
|
|
[31:20.12]everyone under
|
|
[31:21.12]get a lot
|
|
[31:22.12]of reliability
|
|
[31:23.12]is a really
|
|
[31:24.12]strong focus on
|
|
[31:25.12]actually why
|
|
[31:26.12]does the model
|
|
[31:27.12]not do this thing
|
|
[31:28.12]and the non-trivial amount
|
|
[31:29.12]of time
|
|
[31:30.12]the time the model
|
|
[31:31.12]doesn't actually
|
|
[31:32.12]do the thing
|
|
[31:33.12]is because if
|
|
[31:34.12]you're a wizard
|
|
[31:35.12]of ozing it yourself
|
|
[31:36.12]or if you have
|
|
[31:37.12]unreliable actuators
|
|
[31:38.12]you can't do the thing
|
|
[31:39.12]and so we've
|
|
[31:40.12]had to fix
|
|
[31:41.12]a lot of those problems
|
|
[31:42.12]I was slightly
|
|
[31:43.12]surprised just because
|
|
[31:44.12]I do generally
|
|
[31:45.12]consider the way
|
|
[31:46.12]most that we see
|
|
[31:47.12]all around San Francisco
|
|
[31:48.12]as the most
|
|
[31:49.12]I guess real case
|
|
[31:50.12]it's a big
|
|
[31:51.12]job but it has taken
|
|
[31:52.12]a long time
|
|
[31:53.12]for self-driving
|
|
[31:54.12]temperature from
|
|
[31:55.12]when it entered
|
|
[31:56.12]the consciousness
|
|
[31:57.12]and the driving down
|
|
[31:58.12]when it went on a sunny
|
|
[31:59.12]day moment
|
|
[32:00.12]happened to now.
|
|
[32:01.12]so I want to see
|
|
[32:02.12]the more compressed
|
|
[32:03.12]cruise, you know,
|
|
[32:04.12]R.I.P.
|
|
[32:05.12]recently.
|
|
[32:06.12]and then one more thing
|
|
[32:07.12]on just like
|
|
[32:08.12]just going back on
|
|
[32:09.12]this reliability
|
|
[32:10.12]thing, something
|
|
[32:11.12]I have been holding
|
|
[32:12.12]in my head
|
|
[32:13.12]that I'm curious
|
|
[32:14.12]to get your commentary on
|
|
[32:15.12]is I think there's a
|
|
[32:16.12]treatup between
|
|
[32:17.12]reliability and generality
|
|
[32:18.12]or I want to broaden
|
|
[32:19.12]because you have
|
|
[32:20.12]reliability also have
|
|
[32:21.12]cost of speed
|
|
[32:22.12]speed is a huge emphasis
|
|
[32:23.12]for a debt
|
|
[32:24.12]the tendency or the
|
|
[32:25.12]attemptation is to reduce
|
|
[32:26.12]generalityto improve
|
|
[32:27.12]reliability
|
|
[32:28.12]and to improve
|
|
[32:29.12]cost improve speed
|
|
[32:30.12]do you perceive a tradeoff
|
|
[32:31.12]do you have any
|
|
[32:32.12]insights that
|
|
[32:33.12]solve those tradeoffs
|
|
[32:34.12]for you guys
|
|
[32:35.12]there's definitely a tradeoff
|
|
[32:36.12]if you're at
|
|
[32:37.12]the predo frontier
|
|
[32:38.12]I think a lot of folks
|
|
[32:39.12]aren't actually
|
|
[32:40.12]at the predo frontier
|
|
[32:41.12]I think the way you get
|
|
[32:42.12]there is basically
|
|
[32:43.12]how do you frame
|
|
[32:44.12]the fundamental
|
|
[32:45.12]agent problem in a way
|
|
[32:46.12]that just continues
|
|
[32:47.12]to benefit from data
|
|
[32:48.12]I think one of
|
|
[32:49.12]the main ways
|
|
[32:50.12]of being able to solve
|
|
[32:51.12]that particular tradeoff
|
|
[32:52.12]is you basically
|
|
[32:53.12]just want to formulate
|
|
[32:54.12]the problem such that
|
|
[32:55.12]every particular use
|
|
[32:56.12]case just looks like
|
|
[32:57.12]you collecting more
|
|
[32:58.12]data to go make
|
|
[32:59.12]that use case possible
|
|
[33:00.12]I think that's how
|
|
[33:01.12]you really solve it
|
|
[33:02.12]then you get into the
|
|
[33:03.12]other problems like
|
|
[33:04.12]are you overfitting
|
|
[33:05.12]on these end use cases
|
|
[33:06.12]right but like you're
|
|
[33:07.12]not doing a thing
|
|
[33:08.12]where you're like
|
|
[33:09.12]being super prescriptive
|
|
[33:10.12]for the end steps
|
|
[33:11.12]that the model can
|
|
[33:12.12]only do for example
|
|
[33:13.12]then the question becomes
|
|
[33:14.12]kind of do you have
|
|
[33:15.12]one sort of house model
|
|
[33:16.12]they customize
|
|
[33:17.12]the customer's
|
|
[33:18.12]specific use case
|
|
[33:19.12]we're not sharing
|
|
[33:20.12]we're not sharing
|
|
[33:21.12]it's tempting
|
|
[33:22.12]but that doesn't
|
|
[33:23.12]look like AGI to me
|
|
[33:24.12]you know what I mean
|
|
[33:25.12]that is just
|
|
[33:26.12]you have a good
|
|
[33:27.12]base model
|
|
[33:28.12]and then
|
|
[33:29.12]you fine tune it
|
|
[33:30.12]for what it's worth
|
|
[33:31.12]I think there's
|
|
[33:32.12]two paths
|
|
[33:33.12]to a lot more
|
|
[33:34.12]capability coming out
|
|
[33:35.12]of the models
|
|
[33:36.12]that we
|
|
[33:37.12]all are training
|
|
[33:38.12]these days
|
|
[33:39.12]one path
|
|
[33:40.12]is you figure out
|
|
[33:41.12]how to spend
|
|
[33:42.12]compute and turn
|
|
[33:43.12]into data
|
|
[33:44.12]and so in that
|
|
[33:45.12]path I consider
|
|
[33:46.12]off play
|
|
[33:47.12]all that stuff
|
|
[33:48.12]the second path
|
|
[33:49.12]is how do you
|
|
[33:50.12]get super
|
|
[33:52.12]competent
|
|
[33:53.12]high intelligence
|
|
[33:54.12]demonstrations
|
|
[33:55.12]from humans
|
|
[33:56.12]and I think
|
|
[33:57.12]the right way
|
|
[33:58.12]to move forward
|
|
[33:59.12]is you kind of
|
|
[34:00.12]want to combine the two
|
|
[34:01.12]the first one
|
|
[34:02.12]gives you maximum
|
|
[34:03.12]sample efficiency
|
|
[34:04.12]for the second
|
|
[34:05.12]but I think
|
|
[34:06.12]that is going to be
|
|
[34:07.12]hard to be running
|
|
[34:08.12]at max speed
|
|
[34:09.12]towards AGI
|
|
[34:10.12]without actually
|
|
[34:11.12]solving a bit of both
|
|
[34:12.12]you haven't talked
|
|
[34:13.12]much about synthetic
|
|
[34:14.12]data as far as I can
|
|
[34:15.12]any insights
|
|
[34:16.12]on using synthetic
|
|
[34:17.12]data to augment
|
|
[34:18.12]the expensive
|
|
[34:19.12]human data
|
|
[34:20.12]the best part
|
|
[34:21.12]about framing AGI
|
|
[34:22.12]is being able
|
|
[34:23.12]to help people do
|
|
[34:24.12]things on computers
|
|
[34:25.12]is you have an environment
|
|
[34:26.12]yes
|
|
[34:27.12]so you can
|
|
[34:28.12]simulate all of it
|
|
[34:29.12]you can do a lot
|
|
[34:30.12]of stuff
|
|
[34:31.12]when you have an environment
|
|
[34:32.12]we were having dinner
|
|
[34:33.12]for our one year
|
|
[34:34.12]anniversary
|
|
[34:35.12]the other round
|
|
[34:36.12]thank you
|
|
[34:37.12]Raza from human
|
|
[34:38.12]loop was there
|
|
[34:39.12]and we mentioned
|
|
[34:40.12]you were coming on
|
|
[34:41.12]the pod
|
|
[34:42.12]this is our first
|
|
[34:43.12]so he submitted a question
|
|
[34:44.12]now you had
|
|
[34:45.12]gbd4 vision
|
|
[34:46.12]and help you
|
|
[34:47.12]building a lot
|
|
[34:48.12]of those things
|
|
[34:49.12]how do you think
|
|
[34:50.12]about the things
|
|
[34:51.12]that are unique to you
|
|
[34:52.12]as a depth
|
|
[34:53.12]and like going back
|
|
[34:54.12]to like the maybe
|
|
[34:55.12]research direction
|
|
[34:56.12]that you want to take
|
|
[34:57.12]the team and what you
|
|
[34:58.12]want people to come
|
|
[34:59.12]work on at a depth
|
|
[35:00.12]versus what is maybe
|
|
[35:01.12]not become commoditized
|
|
[35:02.12]that you didn't expect
|
|
[35:03.12]everybody would
|
|
[35:04.12]have access to
|
|
[35:05.12]yeah that's
|
|
[35:06.12]a really good question
|
|
[35:07.12]I think implicit
|
|
[35:08.12]in that question
|
|
[35:09.12]and I wish he were
|
|
[35:10.12]tier two so he can
|
|
[35:11.12]push back on my
|
|
[35:12.12]assumption about his
|
|
[35:13.12]questionbut I think
|
|
[35:14.04]is calculus of where
|
|
[35:16.04]does advantage a crew
|
|
[35:18.04]in the overall
|
|
[35:19.04]ML stack
|
|
[35:20.04]and maybe part
|
|
[35:21.04]of the assumption
|
|
[35:22.04]is that advantage
|
|
[35:23.04]a crew is solely
|
|
[35:24.04]to base model scaling
|
|
[35:25.04]but I actually
|
|
[35:26.04]believe pretty strongly
|
|
[35:27.04]that the way
|
|
[35:28.04]that you really
|
|
[35:29.04]win is that you
|
|
[35:30.04]have to go build
|
|
[35:31.04]an agent stack
|
|
[35:32.04]that is much more
|
|
[35:33.04]than that
|
|
[35:34.04]of the base model itself
|
|
[35:35.04]and so I think
|
|
[35:36.04]like that is
|
|
[35:37.04]always going to be
|
|
[35:38.04]a giant advantage
|
|
[35:39.04]of vertical integration
|
|
[35:40.04]I think like
|
|
[35:41.04]it lets us do things
|
|
[35:42.04]like have a really
|
|
[35:43.04]bad cat and dog
|
|
[35:44.04]photo
|
|
[35:45.04]it's pretty good
|
|
[35:46.04]at cat and dog
|
|
[35:47.04]photo
|
|
[35:48.04]it's not like
|
|
[35:49.04]soda at cat
|
|
[35:50.04]and dogphoto
|
|
[35:51.04]so like we're allocating
|
|
[35:52.04]our capacity wisely
|
|
[35:53.04]is like one thing
|
|
[35:54.04]that you
|
|
[35:55.04]really get to do
|
|
[35:56.04]I also think that
|
|
[35:57.04]the other thing
|
|
[35:58.04]that is pretty
|
|
[35:59.04]important now
|
|
[36:00.04]in the broader
|
|
[36:01.04]foundation modeling
|
|
[36:02.04]space is
|
|
[36:03.04]I feel despite any
|
|
[36:04.04]potential concerns
|
|
[36:05.04]about how good
|
|
[36:06.04]is agents as
|
|
[36:07.04]like a startup area
|
|
[36:08.04]like we were talking
|
|
[36:09.04]about earlier
|
|
[36:10.04]I feel super good
|
|
[36:11.04]that we're
|
|
[36:12.04]cap just flowing
|
|
[36:13.04]from can we make
|
|
[36:14.04]a better agent
|
|
[36:15.04]because right now
|
|
[36:16.04]I think we all see
|
|
[36:17.04]that you know
|
|
[36:18.04]if you're training
|
|
[36:19.04]on publicly available
|
|
[36:20.04]web data
|
|
[36:21.04]you put in the
|
|
[36:22.04]flops and you do
|
|
[36:23.04]reasonable things
|
|
[36:24.04]then you get
|
|
[36:25.04]decent results
|
|
[36:26.04]and if you just
|
|
[36:27.04]double the amount
|
|
[36:28.04]of compute
|
|
[36:29.04]then you get
|
|
[36:30.04]predictably
|
|
[36:31.04]better results
|
|
[36:32.04]and so I think
|
|
[36:33.04]pure play foundation
|
|
[36:34.04]model companies
|
|
[36:35.04]are just going to be
|
|
[36:36.04]pinched by how
|
|
[36:37.04]good the next couple
|
|
[36:38.04]lamas are going to be
|
|
[36:39.04]and the next
|
|
[36:40.04]what good open source
|
|
[36:41.04]on these base foundation
|
|
[36:42.04]models I think it's
|
|
[36:43.04]gonna commoditize a lot
|
|
[36:44.04]of the regular llms
|
|
[36:45.04]and soon regular
|
|
[36:46.04]multimodal models
|
|
[36:47.04]so I feel really good
|
|
[36:48.04]that we're just focused
|
|
[36:49.04]on agents so you
|
|
[36:50.04]don't consider yourself
|
|
[36:51.04]a pure play foundation
|
|
[36:52.04]model company no
|
|
[36:53.04]because if we were pure
|
|
[36:54.04]play foundation model
|
|
[36:55.04]company we would be
|
|
[36:56.04]traininggeneral foundation
|
|
[36:57.04]models that do
|
|
[36:58.04]summarization and
|
|
[36:59.04]all this dedicated
|
|
[37:00.04]towards the agent
|
|
[37:01.04]yeah and our business
|
|
[37:02.04]is an agent business
|
|
[37:03.04]we're not here to
|
|
[37:04.04]sell you tokens right
|
|
[37:05.04]and I think like
|
|
[37:06.04]selling tokens unless
|
|
[37:07.04]there's like yeah I
|
|
[37:08.04]love it there's like
|
|
[37:09.04]if you have a particular
|
|
[37:10.04]area of specialty
|
|
[37:11.04]right then you won't
|
|
[37:13.04]get caught in the fact
|
|
[37:14.04]that everyone's just
|
|
[37:15.04]scaling to ridiculous
|
|
[37:16.04]levels of compute
|
|
[37:17.04]but if you don't have a
|
|
[37:18.04]specialty I find that
|
|
[37:19.04]I think it's gonna be
|
|
[37:20.04]a little tougher
|
|
[37:21.04]interesting are you
|
|
[37:22.04]interested in robotics at
|
|
[37:23.04]all just a personally
|
|
[37:24.04]fascinated by robotics
|
|
[37:25.04]have always loved robotics
|
|
[37:26.04]embodied agents as a
|
|
[37:27.04]business you know figure
|
|
[37:28.04]is like a big also
|
|
[37:29.04]so the open ai
|
|
[37:30.04]affiliated company
|
|
[37:31.04]that raises a lot of
|
|
[37:32.04]money I think it's
|
|
[37:33.04]cool I think I mean
|
|
[37:34.04]I don't know exactly
|
|
[37:35.04]what they're exactly
|
|
[37:36.04]what they're doing but
|
|
[37:37.04]robots yeah yeah
|
|
[37:38.04]well I mean that's
|
|
[37:39.04]well Christian
|
|
[37:40.04]would you ask
|
|
[37:41.04]like if we
|
|
[37:42.04]had them on like
|
|
[37:43.04]what would you ask them
|
|
[37:44.04]oh I just want to
|
|
[37:45.04]understand what their
|
|
[37:46.04]overall strategy is
|
|
[37:47.04]gonna be between now
|
|
[37:48.04]and when there's reliable
|
|
[37:49.04]stuff to be deployed
|
|
[37:50.04]but honestly
|
|
[37:51.04]I just don't know
|
|
[37:52.04]enough about it
|
|
[37:53.04]and if I told you
|
|
[37:54.04]hey fire your entire
|
|
[37:55.04]warehouse workforce
|
|
[37:56.04]and you know
|
|
[37:57.04]put robots in there
|
|
[37:58.04]isn't that a strategy
|
|
[37:59.04]oh yeah yeah sorry
|
|
[38:00.04]I'm not questioning
|
|
[38:01.04]whether
|
|
[38:02.04]they're doing smart
|
|
[38:03.04]things I genuinely
|
|
[38:04.04]don't know what
|
|
[38:05.04]they're doing as much
|
|
[38:06.04]but I think there's
|
|
[38:07.04]two things one
|
|
[38:08.04]it's just
|
|
[38:09.04]I think it's
|
|
[38:10.04]just gonna work
|
|
[38:11.04]like I will die
|
|
[38:12.04]on this hill
|
|
[38:13.04]like I mean
|
|
[38:14.04]like again this whole
|
|
[38:15.04]this whole time
|
|
[38:16.04]like we've been on this
|
|
[38:17.04]podcast it's just
|
|
[38:18.04]gonna continually saying
|
|
[38:19.04]these models
|
|
[38:20.04]are basically behavioral
|
|
[38:21.04]cloners right
|
|
[38:22.04]so let's go behavioral
|
|
[38:23.04]clone all this like
|
|
[38:24.04]robot behavior right
|
|
[38:25.04]and then
|
|
[38:26.04]now you figure out
|
|
[38:27.04]everything else
|
|
[38:28.04]you have to do in order
|
|
[38:29.04]to teach you how to
|
|
[38:30.04]solve new problem
|
|
[38:31.04]that's gonna work
|
|
[38:32.04]I'm super stoked for that
|
|
[38:33.04]I think unlike
|
|
[38:34.04]what we're doing with
|
|
[38:35.04]helping humans with
|
|
[38:36.04]knowledge work
|
|
[38:37.04]and I'm personally
|
|
[38:38.04]less excited about that
|
|
[38:39.04]we had a
|
|
[38:40.04]canjun from imbu
|
|
[38:41.04]on the podcast
|
|
[38:42.04]we asked her
|
|
[38:43.04]why people should
|
|
[38:44.04]go work there
|
|
[38:45.04]and not at adept
|
|
[38:46.04]so I wanna
|
|
[38:47.04]well she said
|
|
[38:48.04]you know
|
|
[38:49.04]there's space for everybody
|
|
[38:50.04]in this market
|
|
[38:51.04]we're all doing
|
|
[38:52.04]interesting work
|
|
[38:53.04]and she said
|
|
[38:54.04]they're really excited
|
|
[38:55.04]about building
|
|
[38:56.04]an operating system
|
|
[38:57.04]for agent
|
|
[38:58.04]and for her
|
|
[38:59.04]the biggest research
|
|
[39:00.04]thing was like
|
|
[39:01.04]getting models
|
|
[39:02.04]better reasoning
|
|
[39:03.04]and planning
|
|
[39:04.04]for these agents
|
|
[39:05.04]the reverse question
|
|
[39:06.04]I'm excited to
|
|
[39:07.04]come work at adept
|
|
[39:08.04]instead of imbu
|
|
[39:09.04]and maybe
|
|
[39:10.04]what are like
|
|
[39:11.04]the core research
|
|
[39:12.04]questions
|
|
[39:13.04]that people should
|
|
[39:14.04]be passionate about
|
|
[39:15.04]to have fun at adept
|
|
[39:16.04]yeah first off
|
|
[39:17.04]I think that
|
|
[39:18.04]I'm sure you guys
|
|
[39:19.04]believe this too
|
|
[39:20.04]the AI space
|
|
[39:21.04]to the center
|
|
[39:22.04]there's an AI space
|
|
[39:23.04]and the AI agent
|
|
[39:24.04]space are both
|
|
[39:25.04]exactly as
|
|
[39:26.04]she likely said
|
|
[39:27.04]I think colossal
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[39:28.04]opportunities
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[39:29.04]and people are just
|
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[39:30.04]going to end up
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[39:31.04]winning in different
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[39:32.04]areas and a lot
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[39:33.04]of companies are
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[39:34.04]going to do well
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[39:35.04]to be at
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[39:36.04]adept
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[39:37.04]I think there's
|
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[39:38.04]two huge reasons
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[39:39.04]to be at adept
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[39:40.04]I think one of them
|
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[39:41.04]is everything we do
|
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[39:42.04]is in the service
|
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[39:43.04]of like useful agents
|
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[39:44.04]we're not a
|
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[39:45.04]research lab
|
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[39:46.04]we do a lot of research
|
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[39:47.04]in service of that goal
|
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[39:48.04]but we don't
|
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[39:49.04]think about ourselves
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[39:50.04]as like a classic
|
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[39:51.04]research lab at all
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[39:52.04]and I think the second
|
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[39:53.04]reason at work at
|
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[39:54.04]adeptis
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[39:55.04]if you believe that
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[39:56.04]actually having customers
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[39:57.04]and a reward signal
|
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[39:58.04]from customers
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[39:59.04]lets you build
|
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[40:00.04]AGI faster
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[40:01.04]which we really believe
|
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[40:02.04]then you should come here
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[40:03.04]and I think the examples
|
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[40:04.04]are evaluations
|
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[40:05.04]they're not
|
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[40:06.04]academic evals
|
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[40:07.04]they're not simulator
|
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[40:08.04]evals
|
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[40:09.04]they're like
|
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[40:10.04]okay we have a
|
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[40:11.04]customer that
|
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[40:12.04]really needs us to do
|
|
[40:13.04]these particular things
|
|
[40:14.04]we can do some
|
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[40:15.04]of them
|
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[40:16.04]these other ones
|
|
[40:17.04]they want us to
|
|
[40:18.04]we can't do them at
|
|
[40:19.04]all we've turned
|
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[40:20.04]those into evals
|
|
[40:21.04]solve it
|
|
[40:22.04]I think that's
|
|
[40:23.04]really cool
|
|
[40:24.04]like everybody knows
|
|
[40:25.04]a lot of these evals
|
|
[40:26.04]are like
|
|
[40:27.04]pretty saturated
|
|
[40:28.04]and the new ones
|
|
[40:29.04]that even are
|
|
[40:30.04]not saturated you look
|
|
[40:31.04]at someone and you're
|
|
[40:32.04]like is this actually
|
|
[40:33.04]and all of this stuff
|
|
[40:34.04]but they're very grounded
|
|
[40:35.04]and actual needs
|
|
[40:36.04]right now
|
|
[40:37.04]which is really cool
|
|
[40:38.04]yeah this has been
|
|
[40:39.04]wonderful dive
|
|
[40:40.04]I wish we had more time
|
|
[40:41.04]but I'll just leave it
|
|
[40:42.04]kind of open to you
|
|
[40:43.04]I think you have broad thoughts
|
|
[40:44.04]you know just about
|
|
[40:45.04]the agent space
|
|
[40:46.04]but also just general AI
|
|
[40:47.04]space any sort of rants
|
|
[40:48.04]or things that
|
|
[40:49.04]they're just helping
|
|
[40:50.04]might for you right now
|
|
[40:51.04]any rants
|
|
[40:52.04]minding you
|
|
[40:53.04]for just general
|
|
[40:54.04]wow okay
|
|
[40:55.04]so Amelia's already
|
|
[40:56.04]made the rant better
|
|
[40:57.04]than I have
|
|
[40:58.04]but not just
|
|
[40:59.04]not just chatbots
|
|
[41:00.04]is like kind of rant one
|
|
[41:01.04]but the rant two
|
|
[41:02.04]is AI's really been
|
|
[41:03.04]the story of compute
|
|
[41:04.04]and compute plus data
|
|
[41:06.04]and ways in which
|
|
[41:07.04]you could change one
|
|
[41:08.04]for the other
|
|
[41:09.04]and I think as much as
|
|
[41:10.04]our research community
|
|
[41:11.04]is really smart
|
|
[41:12.04]we have made many
|
|
[41:13.04]many advancements
|
|
[41:14.04]and that's going to
|
|
[41:15.04]continue to be important
|
|
[41:16.04]but now I think
|
|
[41:17.04]the game is
|
|
[41:18.04]increasingly changing
|
|
[41:19.04]and the rapid
|
|
[41:20.04]industrialization
|
|
[41:21.04]error has begun
|
|
[41:22.04]and I think
|
|
[41:23.04]we unfortunately
|
|
[41:24.04]have to embrace it
|
|
[41:25.04]excellent awesome David
|
|
[41:26.04]thank you so much
|
|
[41:27.04]for your time
|
|
[41:28.04]cool yeah thanks guys
|
|
[41:29.04]this was fun
|
|
[41:30.04]thank you
|
|
[41:31.04]thank you
|
|
[41:32.04]thank you
|
|
[41:32.04]thank you
|
|
[41:33.04]thank you
|
|
[41:34.04]thank you
|
|
[41:35.04]thank you
|
|
[41:36.04]thank you
|
|
[41:37.04]thank you
|
|
[41:38.04]thank you
|
|
[41:39.04]thank you
|
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[41:40.04]thank you
|
|
[41:41.04]thank you
|
|
[41:42.04]thank you
|
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[41:43.04]thank you
|
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[41:44.04]thank you
|
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[41:45.04]thank you
|
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[41:46.04]thank you
|
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[41:47.04]字幕by索兰娅
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[41:49.04]字幕:J Chong
|
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[41:50.04]请不吝点赞 订阅 转发 打赏 打赏
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