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Naveen Rao: 4D Computing, AI's Energy Wall & Beating Biology

0:00
Naveen Rao,非傳統人工智慧的聯合創始人兼首席執行官,這是一家人工智慧晶片初創公司。
Naveen Rao, co-founder and CEO of Unconventional AI, which is an AI chip startup.
0:06
以為深科技公司建造和銷售而聞名。Naveen在某種程度上是定義上的異類創始人。
Best known for building and selling to deep tech companies. Naveen is kind of definitionally outlier founder.
0:12
當我來到那裡時,我們的業務大約有2000萬美元,而我離開時是7億或8億美元。我認為,直到你能夠建造它,你才真正理解某件事。
When I came there, we had about a $20 million business and it was, you know, $700 or $800 million when I left. I don't think you really understand something until you can build it.
0:19
僅僅因為某件事被嘗試過並不意味著它是錯的。我是人工智慧悲觀者的對立面。我認為人工智慧是人類的下一次進化。
Just because something is tried does not mean it's wrong. I'm the opposite of an AI doomer. I think AI is the next evolution of humanity.
0:26
我們需要在硬體基礎上進行創新,才能真正建立智能。
We need innovation on the hardware substrate to actually build true intelligence.
0:31
請歡迎Naveen Rao。
Please welcome Naveen Rao.
0:44
嘿,大家好。很高興來到這裡。現在稍微轉向人工智慧,你們可能聽說過一些。
Hey, everyone. Great to be here. Switching gears a little bit to AI now, which you may have heard a little bit about.
0:52
能參加這個會議真是太令人興奮了,特別是因為
it's super exciting to be at this conference specifically because as
0:55
在介紹中提到的,我是完全相反的悲觀者,我
was said in the intro I'm the opposite of a doomer I
0:58
認為人工智慧是那些變革性技術之一,
think AI is one of those transformational technologies that
1:02
人類曾經創造過的,將使我們能夠達到下一個進化層次,我在這裡就是為了這個,這是一個反悲觀者的會議,所以讓我們開始吧
humanity's ever created and will enable us to get to that next level of evolution which I'm here for and this is sort of the anti-doomer conference so let's go
1:14
在我們開始之前,我想告訴你一些關於我自己的事情,你知道,我覺得有點奇怪,我真的在我一生中一直想要的地方。
so before we get going I'll tell you a little bit about myself you know I it's kind of weird I'm really right where I wanted to be my whole life.
1:21
這是我大約五或六歲的樣子,差不多是這樣。
This was me at about five or six years old, something like that.
1:25
我們很早就有了一台電腦,所以我可以追溯到1978年。我們買了一台電腦。
We had a computer very early on, so I'll date myself, but this was in 1978. We got a computer.
1:31
這大概是在80年代初。我小時候學會了編程。我覺得這就像是一個拼圖。
This is probably in the early 80s. I learned a program when I was a little kid. I just thought it was like a puzzle.
1:35
我成為了一名電氣工程師,真的,因為我喜歡科幻小說,總是想著如何製造一台智能機器。
I became an electrical engineer, really, because I enjoyed sci-fi and always wanted to think about how I could make an intelligent machine.
1:44
然後在從事計算機建設的職業生涯後,我回到學校獲得了神經科學的博士學位。
Then after a career in building computers, I went back to school and got a PhD in neuroscience.
1:53
我的想法是,讓我們回到那個問題。我們如何讓計算機變得智能?幸運的是,整個世界都朝著這個方向發展。
And the idea was like, let's go back to that thing. How do we make computers intelligent? And fortunately, the whole world kind of moves in this direction.
2:00
所以,你知道,作為一名技術專家,這現在算是一個夢想。
So, you know, as a technologist, it's sort of the dream right now.
2:05
關於我一些技術方面的背景,從公司創業的角度來看。
A little bit about me tech from a company entrepreneurship standpoint.
2:10
我實際上創立了第一家人工智慧晶片公司,叫做Nirvana Systems。這是在2014年。
Like I actually founded the first AI chip company called Nirvana Systems. So this was in 2014.
2:16
如果有人還記得那時候,根本沒有人工智慧,或者至少不在常用語言中。
If anyone remembers back then, there was no AI, or at least not in the common vernacular.
2:21
而且實際上說服人們這是重要的事情非常困難,更不用說圍繞它建立硬體了。
And it was really hard to actually convince people that this is important, much less to build hardware around it.
2:27
現在,你聽到Jensen在這裡說,世界上最大的公司是一家因為人工智慧而成為硬體公司的公司。所以我們當時是早期的參與者。
Now, you heard from Jensen up here, like the largest company in the world, a hardware company because of AI. So we were early on.
2:33
我想我把公司賣給英特爾的時候太早了。但我創立並運營了英特爾的人工智慧團隊。
I think I sold the company way too early to Intel. But I ran, I started and ran the AI group at Intel.
2:40
在2020年我完成那件事後,我開始思考下一個問題,就是我們如何建立更大的模型,像今天我們談論的大型語言模型?
After I was done with that in 2020, I actually started thinking about the next problem, which was how do we build bigger models like the large language models we talk about today?
2:49
這是關於我如何建立基礎設施來構建這些模型的問題。
And it was about how do I build the infrastructure to build those models?
2:53
於是我們開始將GPU平台化,使其能夠擴展,並讓其他人使用起來更方便。
And so we started platformizing GPUs and enabling it to scale and making that easy to use for other people.
2:59
在2022年ChatGPT出現後,我們成為了人們開始建立自己模型的最佳選擇。
And after ChatGPT happened in 2022, we were kind of the best game in town for people to start building their own models.
3:06
這發展得非常快。我們決定與Databricks聯手。
So it took off really fast. We decided to actually join forces with Databricks.
3:10
那是在2023年,實際上這已經佔據了Databricks今天總收入的四分之一。
That was in 2023, and actually that's a quarter of the total revenue of Databricks today.
3:17
與Allie和Databricks團隊一起做這整件事非常有趣。
So a lot of fun doing that whole thing with Allie and team at Databricks.
3:22
現在我想告訴你們關於非常規AI的事情,這是重新思考計算機運作基礎的問題。
And now I want to tell you about unconventional AI, which is rethinking the foundations of how a computer works.
3:27
所以我們回到第一原則,真正嘗試建立一台新機器。
So we're going back to first principles here to really trying to build a new machine.
3:33
計算機已經以某種方式運作了很長時間。我們想重新思考這一點,目的是為了使某些東西非常節能。
Computers have worked a certain way for a long time. We want to rethink that for the singular purpose of making something very power efficient.
3:40
目標是在五年內達到一千倍的能效。
And the goal has been within, it was initially within five years to get to a thousand X power efficiency.
3:46
我實際上將這個目標修訂為三年半,因為事情的進展比我們預期的要快。
I've actually revised this to three and a half years because things have gone faster than we anticipated.
3:50
有趣的是,我們實際上因為AI更快地解決了非常深奧的科學問題。
We've actually solved very deep scientific problems quicker because of AI, interestingly enough.
3:56
那麼,我們是如何組織的呢?我們真的是一個從上到下的公司。我們從理論家開始。
So just a little bit, how are we organized? Like we're truly a top to bottom company. We start with theorists.
4:03
這些人擁有數學博士學位,還有理論神經科學等背景。
These are people with like math PhDs and you know, theoretical neuroscience, that kind of thing.
4:08
他們提出了我們認為能有效提高能效的概念,從減少信息傳遞的角度來看。
They come up with, you know, concepts that we think would effectively give us more power efficiency from the perspective of moving less information around.
4:15
然後我們將這些概念轉化為能做實際事情的模型,這些模型是在真實數據上訓練的,並根據真實標準進行評估。
And we then translate that into models that do real things, trained on real data and evaluated against real criteria.
4:23
這就像是這些概念的實際應用。然後最終我們必須實際構建一些物理的東西。
So it's kind of the rubber hitting the road of these concepts. Then eventually we have to actually build something physical.
4:29
這些人設計物理電路,實際設計這些電路,建模並檢查它是否真的有效。
So these are people who architect a physical circuit, actually design those circuits, model them, and see if it actually works.
4:36
所以我們嘗試將整個堆疊連接在一起。然後最終我們必須構建一個系統和一個電路板,還有所有這些東西,並製作一個產品。
So we try to connect this whole stack together. Then eventually we have to build a system and a board and all that kind of stuff and build a product.
4:43
那麼,能源真的是一個問題嗎?
So is energy really a problem?
4:48
我不確定在座的每個人對此思考了多少,但有趣的是,我會給你一些數據點。這是一家公司,僅僅是Google。
I'm not sure how much everyone in this audience has thought about this, but interestingly enough, I'll give you some data points here. This is one company. This is just Google.
4:54
我使用Google是因為Google實際上公開談論過這個問題。他們每月處理超過3.2萬兆個標記。
I'm using Google because Google has actually talked about this publicly. Per month, they cross 3.2 quadrillion tokens.

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