What's next for AI agentic workflows ft. Andrew Ng of AI Fund

Summary

本次演讲中，安德鲁教授分享了他在人工智能领域的最新见解，特别是关于AI代理的工作流程。他强调了迭代式工作流程相较于传统非代理式工作流程的优越性，并通过案例研究展示了如何通过使用GPT-3.5和代理工作流程来提高代码生成的准确性。此外，他还介绍了四种AI代理设计模式：反思、多工具使用、规划和多代理协作，并预测这些模式将显著提升AI的生产力和功能。安德鲁教授的演讲不仅为听众提供了丰富的技术知识，也为AI领域的未来发展提供了深刻的洞见。

Takeaways

• 🌟 安德烈·吴在斯坦福大学作为著名的计算机科学教授，对神经网络和GPU的发展贡献巨大，同时也是Coursera和deeplearning.ai的创始人，以及Google Brain的早期领导者。
• 📝 在使用AI模型时，非代理性工作流程类似于一次性完成任务，而代理性工作流程则更像是迭代过程，通过不断思考和修订来提高结果质量。
• 🚀 通过使用代理性工作流程，即使是GPT-3.5这样的模型也能在某些任务上表现得比GPT-4更好，这说明代理性工作流程的重要性。
• 🔍 在进行代码编写的案例研究中，使用代理性工作流程的GPT-3.5比单纯使用GPT-4的准确率更高。
• 🛠️ 代理性工作流程可以显著提高生产力，并且有四种广泛的设计模式：反思、多工具使用、规划和多代理协作。
• 🤔 反思是一种强大的技术，通过让AI模型检查和修正自己生成的代码，可以提高代码的质量和效率。
• 🔗 多工具使用允许AI模型结合多种工具和资源来完成任务，扩展了大型语言模型的应用范围。
• 📈 规划算法使得AI代理能够在遇到问题时自主规划解决方案，提高了问题解决的灵活性和创造性。
• 🤖 多代理协作通过模拟多个专家角色的互动，可以产生复杂的解决方案和创新性的结果。
• 💡 快速生成token对于代理性工作流程至关重要，因为它允许AI模型快速迭代，从而提高整体性能。
• 🌐 未来AI的能力将因代理性工作流程而显著扩展，我们需要适应让AI代理独立工作并耐心等待结果的工作方式。
• 🚀 代理性推理设计模式将是推动AI发展的重要趋势，可能帮助我们在实现通用人工智能（AGI）的漫长旅程中迈出一小步。

Q & A

安德烈在人工智能领域有哪些贡献？

安德烈是斯坦福大学著名的计算机科学教授，他在神经网络与GPU的发展上做出了早期贡献。他是Coursera的创始人，以及deeplearning.ai等流行课程的创建者。此外，他还是Google Brain的创始人和早期领导者。

什么是非代理性工作流程？

非代理性工作流程是指使用大型语言模型（LM）的方式，用户输入一个提示，模型生成一个答案，类似于要求一个人一次性写下整篇文章，而不允许使用退格键进行修改的过程。

代理性工作流程与非代理性工作流程有何不同？

代理性工作流程是一种更迭代的过程，其中AI可能会进行一些思考，然后修订文章，并可能进行多次迭代。这种方法可以带来更好的结果，因为它允许AI在生成内容后进行自我反思和修正。

使用代理性工作流程有什么好处？

使用代理性工作流程可以显著提高大型语言模型的性能。例如，在一项研究中，使用代理性工作流程的GPT-3.5在某些任务上的表现甚至超过了更先进的GPT-4。

什么是代理性反思？

代理性反思是一种设计模式，其中一个AI系统被提示执行一个任务，然后再次被提示检查其执行的结果，确保其正确性、效率和良好的结构。这可以帮助AI发现并修正自己的错误。

多代理协作是如何工作的？

多代理协作涉及多个AI代理共同完成任务。每个代理可以扮演不同的角色，例如一个负责编写代码，另一个负责审查代码。通过这种方式，AI代理可以相互合作，提高工作效率和质量。

规划算法在AI中的作用是什么？

规划算法使AI能够进行更复杂的任务，如分析、收集信息、采取行动和提高个人生产力。它们可以帮助AI在遇到问题时重新规划和调整策略，以实现目标。

为什么快速生成令牌（tokens）在代理性工作流程中很重要？

在代理性工作流程中，快速生成令牌对于迭代过程至关重要。因为AI需要生成大量令牌供自己阅读和处理，所以能够快速生成令牌可以显著提高工作效率。

未来的人工智能发展趋势是什么？

未来的人工智能发展可能会集中在代理性工作流程和代理性推理设计模式上。这些模式可以帮助我们更有效地使用AI，提高生产力，并可能在实现通用人工智能（AGI）的漫长道路上迈出一小步。

为什么我们需要适应等待AI生成响应？

由于代理性工作流程可能需要多次迭代和深思熟虑的过程，我们需要学会耐心等待AI生成响应。这可能需要几分钟甚至几小时，而不是像传统的即时反馈那样迅速。

如何提高AI代理的工作效率？

提高AI代理的工作效率可以通过使用代理性工作流程、多代理协作、规划算法和快速生成令牌等设计模式。这些方法可以帮助AI更有效地完成任务，并提高其性能。

Outlines

00:00

🤖 人工智能代理的工作流程与设计模式

本段落主要介绍了人工智能代理的工作流程和设计模式。首先，通过与非代理性工作流程的对比，阐述了代理性工作流程的迭代性和优越性。接着，通过实际案例分析，说明了使用代理性工作流程的GPT-3.5在编码任务上的表现优于直接使用GPT-4。此外，还介绍了四种主要的设计模式：反思、多工具使用、规划和多代理协作，这些模式在提高AI性能和生产力方面具有重要意义。最后，强调了快速生成令牌的重要性，并对未来AI技术的发展表示期待。

05:01

🔄 反思与自我评估在AI编程中的应用

这一部分详细探讨了反思这一设计模式在AI编程中的应用。通过让AI系统对自己的代码进行自我评估和修正，可以显著提高代码的质量和效率。例如，通过让AI系统检查自己生成的代码，并提出改进意见，可以引导AI进行自我修正，从而得到更优的代码版本。这种方法不仅展示了AI的自我反思能力，也为提高AI编程效率提供了新的思路。

10:04

🔧 多工具使用与多代理协作的AI设计模式

本段落讨论了多工具使用和多代理协作两种AI设计模式。多工具使用模式通过结合不同的工具和平台，扩展了AI的功能和应用范围。例如，AI可以通过生成代码、搜索网络和执行任务来帮助用户完成复杂的工作。而多代理协作模式则通过模拟多个AI代理之间的合作，提高了问题解决的效率和创新性。例如，通过创建不同的代理角色，如CEO、设计师、产品经理和测试员，可以让它们进行协作对话，共同开发软件或解决问题。这些设计模式展示了AI在复杂任务处理中的潜力和灵活性。

Keywords

💡神经网络

神经网络是一种模仿人脑神经元连接方式的计算模型，用于识别模式和处理复杂的数据。在视频中，提到了使用GPUs开发神经网络的早期工作，这是人工智能领域的一个重要突破。

💡深度学习

深度学习是机器学习的一个分支，通过构建和训练多层神经网络来学习数据的高层特征。视频中提到了deeplearning.ai这个流行的在线课程，它专注于深度学习的教育和推广。

💡人工智能代理

人工智能代理指的是能够自主执行任务、做出决策并与之交互的AI系统。视频强调了AI代理在未来AI发展中的重要性，并讨论了如何通过代理工作流程来提高AI的效率和性能。

💡迭代工作流程

迭代工作流程是一种重复和逐步改进的过程，其中每一步都是基于前一步的结果进行优化和调整。在视频中，通过与AI的交互和反馈，迭代工作流程被用来提高AI完成任务的准确性和效率。

💡自我反思

自我反思是指个体对自己的行为、想法或作品进行评估和分析的过程。在AI中，自我反思可以指AI系统对自己的输出进行检查和评估，以提高性能和准确性。

💡多代理协作

多代理协作指的是多个AI系统或代理协同工作以完成一个共同任务的过程。这种协作可以提高问题解决的效率和创新性，因为每个代理都可以从不同的角度贡献其专长。

💡规划

规划是指制定一系列行动步骤以实现特定目标的过程。在AI中，规划算法使AI能够预测未来事件并制定策略以有效达成目标。

💡两用技术

两用技术指的是可以应用于多个领域或问题的通用技术。在视频中，提到了大型语言模型（LM）通过生成代码和执行网络搜索等不同任务，展示了其多用途的特性。

💡快速令牌生成

快速令牌生成是指AI系统能够迅速产生大量数据令牌（如文字、图像等）的能力。这对于提高AI代理工作效率和实现快速迭代至关重要。

💡代理推理设计模式

代理推理设计模式是指在构建和使用AI代理时采用的一系列策略和方法，以提高AI的推理和决策能力。这些模式可以帮助AI更有效地处理复杂任务和问题。

💡多代理辩论

多代理辩论是指让不同的AI代理就某一问题进行讨论和辩论的过程，以此来提高决策的质量和全面性。这种方法可以模拟不同观点之间的互动，从而得出更优的解决方案。

Highlights

安德鲁是斯坦福大学著名的计算机科学教授，早期在神经网络与GPUs的发展中做出了贡献。

安德鲁是Coursera的创始人，以及deeplearning.ai等流行课程的创建者。

安德鲁是Google Brain的创始人和早期领导者。

人工智能代理是当前AI领域值得关注的激动人心的趋势。

非代理性工作流程中，人们使用语言模型的方式类似于一次性生成答案，不进行迭代。

代理性工作流程通过迭代和反馈改进，可以显著提高结果的质量。

通过使用代理性工作流程，GPT-3.5在某些任务上的表现甚至超过了GPT-4。

反射是一种强大的技术，可以让语言模型自我检查和改进代码。

多代理协作和规划算法是新兴的技术，有时能带来令人惊讶的结果。

使用多代理系统，可以模拟不同角色的协作，如CEO、设计师、产品经理和测试员。

代理性推理设计模式将是未来AI发展中的重要趋势。

快速生成token对于代理性工作流程至关重要，因为它可以加快迭代速度。

对于AI代理的期待是，它们能够在更长的时间内自主工作，而不仅仅是即时响应。

多代理辩论可以提高AI系统的性能，这是一种强大的设计模式。

通过代理性工作流程，我们可以在实现通用人工智能（AGI）的漫长旅程中迈出一小步。

Transcripts

all of you uh know Andreu in as a famous

uh computer science professor at

Stanford was really early on in the

development of neural networks with gpus

of course a creator of corsera and

popular courses like

deeplearning.ai also the founder and

Creator uh and early lead of Google

brain uh but one thing I've always

wanted to ask you before I hand it over

Andrew while you're on stage uh is a

question I think would be relevant to

the whole audience 10 years ago on

problem set number two of cs229 you gave

me a

b and I was wondering I looked it over I

was wondering what you saw that I did

incorrectly so anyway Andrew thank you

Hansen um looking forward to sharing

with all of you what I'm seeing with AI

agents which I think is the exciting

Trend that I think everyone building in

AI should pay attention to and then also

excited about all all the other uh on

Sak presentations so hey agents you know

today the way most of us use Lish models

is like this with a non- agentic

workflow where you type a prompt and

generates an answer and that's a bit

like if you ask a person to write an

essay on a topic and I say please sit

down to the keyboard and just type the

essay from start to finish without ever

using backspace um and despite how hard

thises is L's do it remarkably well in

contrast with an agentic workflow this

is what it may look like have an AI have

an LM say write an essay outline do you

need to do any web research if so let's

do that then write the first draft and

then read your own first draft and think

about what parts need revision and then

revise your draft and you go on and on

and so this workflow is much more

iterative where you may have the L do

some thinking um and then revise this

article and then do some more thinking

and iterate this through a number of

times and what not many people

appreciate is this delivers remarkably

better results um I've actually been

really surprised myself working these

agent workflows how well how well they

work I's do one case study at my team

analyzed some data uh using a coding

Benchmark called the human eval

Benchmark released by open a few years

ago um but this says coding problems

like given the nonent list of integers

return the sum of all the all elements

are an even positions and it turns out

the answer is you code snipper like that

so today lot of us will use zero shot

prompting meaning we tell the AI write

the code and have it run on the first

spot like who codes like that no human

codes like that just type out the code

and run it maybe you do I can't do that

um so it turns out that if you use GPT

3.5 uh zero shot prompting it gets it

48% right uh gp4 way better 607 7% right

but if you take an agentic workflow and

wrap it around GPT 3.5 I say it actually

does better than even

gbd4 um and if you were to wrap this

type of workflow around gb4 you know it

it it also um does very well and you

notice that gbd 3.5 with an agentic

workflow actually outperforms

gp4 um and I think this has and this

means that this has signant consequences

fighting how we all approach building

applications so agents is the ter of

around a lot there's a lot of consultant

reports talk about agents the future of

AI blah blah blah I want to be a bit

concrete and share of you um the broad

design patterns I'm seeing in agents

it's a very messy chaotic space tons of

research tons of Open Source there's a

lot going on but I try to categorize um

bit more concretely what's going on

agents reflection is a tool that I think

many of us should just use it just works

uh to use I think it's more widely

appreciated but actually works pretty

well I think of these as pretty robust

technology when I use them I can you

know almost always get them to work well

um planning and multi-agent

collaboration I think is more emerging

when I use them sometimes my mind is

blown for how well they work but at

least at this moment in time I don't

feel like I can always get them to work

Rel Lively so let me walk through these

four design patterns in the few slides

and if some of you go back and yourself

will ask your engineers to use these I

think you get a productivity boost quite

quickly so reflection here's an example

let's say ask a system please write code

for me for a given task then we have a

coder agent just an LM that you prompt

to write code to say you def du task

write a function like that um an example

of

self-reflection would be if you then

prompt the LM with something like this

here's code intended for a toas and just

give it back the exact same code that

they just generated and then say check

the code carefully for correctness sound

efficiency good construction CRI just

write prompt like that it turns out the

same l that you prompted to write the

code may be able to spot problems like

this bug in line Five May fix it by blah

blah blah and if you now take his own

feedback and give it to it and reprompt

it it may come up with a version two of

the code that could well work better

than the first version not guaranteed

but it works you know often enough for

this be wor trying for a lot of

applications um to foreshadow to use if

you let it run unit test if it fails a

unit test then he why do you fail the

unit test have that conversation and be

able to figure out fail the unit test so

you should try changing something and

come up with V3 by the way for those of

you that want to learn more about these

Technologies I'm very excited about them

for each of the four sections I have a

little recommended reading section at

the bottom that you know hopefully gives

more references and again just the

foreshadow multi-agent systems I've

described as a single coder agent that

you prompt to have it you know have this

conversation with itself um one Natural

Evolution of this idea is instead of a

single code agent you can can have two

agents where one is a coder agent and

the second is a Critic agent and these

could be the same base LM model but that

you prompt in different ways where you

say one your expert coder right code the

other one say your expert code review to

review this code and this Tye of

workflow is actually pretty easy to

implement I think it's such a very

general purpose technology for a lot of

workflows this would give you a

significant boost in in the performance

of LMS um the second design pattern is

to use many of where already have seen

you know LM based systems uh uh using

tools on the left is a screenshot from

um co-pilot on the right is something

that I kind of extracted from uh gp4 but

you know LM today if you ask it what's

the best coffee maker web search for

some problems um will generate code and

run code um and it turns out that there

are a lot of different tools that many

different people are using for analysis

for gathering information for taking

action for personal productivity

um it turns out a lot of the early work

in two use turned out to be in the

computer vision Community because before

large language models lm's you know they

couldn't do anything with images so the

only option was that the LM generate a

function called that could manipulate an

image like generate an image or do

object detection or whatever so if you

actually look at literature it's been

interesting how much of the work um in

two years seems like it originated from

Vision because LMS would blind to images

before you know gp4 and and and lava and

so on um so that's two use and it

expands what an LM can do um and then

planning you know for those of you that

have not yet played a lot with planning

algorithms I I feel like a lot of people

talk about the chat GPT moment where

you're wow never seen anything like this

I think if not used planning alums many

people will have a kind of a AI agent

wow I couldn't imagine the AI agent

doing this I've run live demos where

something failed and the AI agent

rerouted around the failures I've

actually had quite a few of those moment

wow you can't believe my AI system just

did that autonomously but um one example

that I adapted from a hugging GPT paper

you know you say this general image

where the girls read where a girl is

reading a book and it posts the same as

a boy in the image example. jpack and

please subscribe the new image for your

voice so give an example like this um

today we have ai agents who can kind of

decide first thing I need to do is

determine the post of the boy

um then you know find the right model

maybe on hugging face to extract the

post then next need to find a post image

model to synthesize a picture of a of a

girl of as following the instructions

then use image to text to and then

finally use text of speech and today we

actually have agents that I don't want

to say they work reliably you know

they're kind of finicky they don't

always work but when it works is

actually pretty amazing but with agentic

loops sometimes you can recover from

earlier failures as well so I find

myself already using research agents for

some of my work where one of piece of

research but I don't feel like you know

Googling myself and spend a long time I

should send to the research agent come

back in a few minutes and see what it's

come up with and and it sometimes works

sometimes doesn't right but that's

already a part of my personal

workflow the final design pattern multi-

Asian collaboration this is one of those

funny things but uh um it works much

better than you might think

uh uh but on the left is a screenshot

from a paper called um chat Dev uh which

is completely open which actually open

source many of you saw the you know

flashy social media announcements of

demo of a Devon uh uh Chad Dev is open

source it runs on my laptop and what

Chad Dev doeses is example of a

multi-agent system where you prompt one

LM to sometimes act like the CEO of a

software engine company sometimes Act

designer sometime a product manager

sometimes I a tester and this flock of

agents that you built by prompting an LM

to tell them you're now Co you're now

software engineer they collaborate have

an extended conversation so that if you

tell it please develop a game develop a

GOI game they'll actually spend you know

a few minutes writing code testing it uh

iterating and then generate a like

surprisingly complex programs doesn't

always work I've used it sometimes it

doesn't work sometimes it's amazing but

this technology is really um getting

better and and just one of design

pattern it turns out that multi-agent

debate where you have different agents

you know for example could be have ch

GPT and Gemini debate each other that

actually results in better performance

as well so having multiple simulated air

agents work together has been a powerful

design pattern as well um so just to

summarize I think these are the these

are the the the uh patterns of seen and

I think that if we were to um use these

uh uh patterns you know in our work a

lot of us can get a prity boost quite

quickly and I think that um agentic

reasoning design patterns are going to

be important uh this is my small slide I

expect that the set of T AI could do

will expand dramatically this year uh

because of agentic workflows and one

thing that it's actually difficult

people to get used to is when we prompt

an LM we want to response right away

um in fact a decade ago when I was you

know having discussions around at at at

Google on um it called a big box search

we type a long prompt one of the reasons

you know I failed to push successfully

for that was because when you do a web

search you one of responds back in half

a second right that's just human nature

we like that instant grab instant

feedback but for a lot of the agent

workflows um I think we'll need to learn

to dedicate the toss and AI agent and

patiently wait minutes maybe even hours

uh to for a response but just like I've

seen a lot of novice managers delegate

something to someone and then check in 5

minutes later right and that's not

productive um I think we need to it be

difficult we need to do that with some

of our AI agents as well I saw I heard

some loss um and then one other

important Trend fast token generation is

important because with these agented

workflows we're iterating over and over

so the LM is generating tokens for the

elm to read so be able to generate

tokens way faster than any human to read

is fantastic and I think that um

generating more tokens really quickly

from even a slightly lower quality LM

might give good results compared to

slower tokens from a better LM maybe

it's a little bit controversial because

it may let you go around this Loop a lot

more times kind of like the results I

showed with gbd3 and an agent

architecture on the first slide um and

cand I'm really looking forward to Cloud

5 and uh CL 4 and gb5 and Gemini 2.0 and

all these other wonderful models that

may are building

and part of me feels like if you're

looking forward to running your thing on

gp5 zero shot you know you mayble to get

closer to that level performance on some

applications than you might think with

agenting reasoning um but on an early

model I think I I I I think this is an

important Trend uh uh and honestly the

path to AGI feels like a journey rather

than a destination but I think this typ

of agent workflows could help us take a

small step forward on this very long

journey thank

[Applause]

you