What's next for AI agentic workflows ft. Andrew Ng of AI Fund
Summary
TLDRThe transcript discusses the evolution of AI agents and their impact on computer science, highlighting the significance of iterative, agentic workflows in enhancing AI performance. It emphasizes the potential of design patterns like reflection, multi-agent collaboration, planning, and debate in boosting productivity and achieving remarkable results. The speaker also underscores the importance of adapting to these agentic workflows and the role of fast token generation in facilitating iterative processes.
Takeaways
- 🧠 The importance of neural networks and GPUs in AI development, with Andrew Ng's significant contributions through his work on Coursera, deeplearning.ai, and Google Brain.
- 📝 The contrast between non-agentic and agentic workflows in AI, where the latter involves iterative processes similar to human thought and revision.
- 🤖 The concept of AI agents and their potential to transform AI applications, emphasizing the shift from traditional AI usage to more interactive and collaborative models.
- 🔄 The iterative process of agentic workflows that involve planning, execution, revision, and testing, leading to improved outcomes over non-agentic approaches.
- 📈 The case study highlighting the effectiveness of agentic workflows with GPT-3.5, where incorporating agentic strategies improved performance over zero-shot prompting.
- 💡 The four broad design patterns observed in AI agents: reflection, multi-agent collaboration, planning, and multi-agent debate, each with varying degrees of maturity and reliability.
- 🔍 The use of self-reflection in AI coding agents to identify and correct errors in their own generated code, demonstrating a level of autonomy and self-awareness.
- 👥 The potential of multi-agent systems, where different AI agents can take on various roles and collaborate effectively, leading to complex problem-solving and decision-making.
- 🚀 The anticipation of rapid advancements in AI capabilities due to agentic workflows, suggesting a significant shift in how AI applications are designed and utilized.
- 🕒 The need for patience and dedication when working with AI agents, as the iterative process may require more time for the AI to deliver optimized results.
- 💬 The closing thoughts on the journey towards AGI (Artificial General Intelligence) and how agentic reasoning design patterns could contribute to this long-term goal.
Q & A
What is the main focus of the discussion in the transcript?
-The main focus of the discussion is the development and application of AI agents using various design patterns, particularly in the context of neural networks and large language models (LMs).
Who is Andrew and what is his notable contribution to the field of AI?
-Andrew is a renowned computer science professor at Stanford, known for his early work in developing neural networks with GPUs. He is also the creator of Coursera, popular courses like deeplearning.ai, and the founder and early lead of Google Brain.
What are the two different workflows for using LMs as mentioned in the transcript?
-The two workflows mentioned are non-agentic and agentic. The non-agentic workflow involves typing a prompt and generating an answer, similar to asking a person to write an essay without using backspace. The agentic workflow is more iterative, involving multiple interactions with the LM, such as writing an outline, conducting web research, drafting, revising, and iterating until the desired outcome is achieved.
How does the agentic workflow improve results compared to the non-agentic workflow?
-The agentic workflow delivers remarkably better results as it allows for a more iterative and reflective process. This includes the ability for the LM to self-evaluate its own code, revise it based on feedback, and continue to improve through several rounds of thinking and revising.
What is the significance of the study using the human eval benchmark?
-The study using the human eval benchmark demonstrated that an agentic workflow with GPT-3.5 outperformed even GPT-4 in certain coding tasks. This highlights the effectiveness of the agentic approach and its potential to enhance the performance of AI systems.
What are the four broad design patterns mentioned in the transcript?
-The four broad design patterns mentioned are reflection, planning, multi-agent collaboration, and two-use (using LMs for various tasks like analysis, information gathering, and action).
How does self-reflection work in the context of an LM coding agent?
-Self-reflection involves prompting the LM to review and evaluate the code it generated, identify any issues, and suggest improvements. This process can lead to the LM creating a better version of the code based on its own feedback.
What is the concept of multi-agent collaboration?
-Multi-agent collaboration involves using multiple LMs, each prompted to act in different roles (e.g., coder, critic, CEO, designer), to work together on a task. These agents can have extended conversations and collaborate to achieve complex outcomes, such as developing a software program.
Why is fast token generation important in agentic workflows?
-Fast token generation is crucial in agentic workflows because it allows for quicker iterations. The LM can generate tokens at a pace much faster than a human can read, which facilitates the rapid exchange and refinement of ideas, leading to more efficient and potentially higher-quality outcomes.
What is the significance of the trend towards AGI (Artificial General Intelligence) as mentioned in the transcript?
-The path towards AGI is viewed as a journey rather than a destination. The use of agentic workflows and design patterns is seen as a way to make progress towards achieving AGI, with the potential to improve AI systems' ability to perform a wide range of tasks autonomously and effectively.
What is the advice given for effectively using AI agents in one's workflow?
-The advice given is to be patient and allow AI agents time to process and respond, even if it takes minutes or hours. Just like delegating tasks to a team member and checking in later, it's important to give AI agents the time they need to provide the best possible outcomes.
Outlines
🤖 Introduction to AI Agents and Their Impact
The speaker begins by acknowledging Andreu's contributions to computer science, particularly in the realm of neural networks and AI. He introduces the concept of AI agents, emphasizing their potential as a significant trend in AI development. The speaker shares his experience with problem sets from a Stanford course, highlighting the importance of iterative learning and refinement. He contrasts non-agentic workflows, where tasks are completed in one go without revision, to agentic workflows that involve multiple iterations and refinements, leading to better outcomes. The speaker also discusses the effectiveness of using agentic workflows with GPT-3.5 and the potential for improved performance over newer models like GPT-4.
📚 Design Patterns in AI Agents
The speaker delves into the design patterns observed in AI agents, noting the chaotic yet promising landscape of AI research and development. He outlines four key design patterns: reflection, multi-agent collaboration, planning, and multi-agent debate. Reflection involves self-assessment and improvement of code, while multi-agent collaboration introduces the concept of having different agents perform specialized tasks. Planning algorithms are highlighted for their ability to adapt and overcome failures, and multi-agent debate showcases the power of diverse perspectives leading to enhanced outcomes. The speaker emphasizes the importance of these patterns in boosting productivity and the potential for AI to expand its capabilities.
🚀 Future Trends and the Path to AGI
In the final paragraph, the speaker discusses the future trends in AI, predicting a significant expansion of AI capabilities due to agentic workflows. He challenges the conventional expectation of immediate responses from AI, advocating for patience and the understanding that AI agents may require more time to deliver high-quality outcomes. The speaker also emphasizes the importance of fast token generation for iterative processes in agentic workflows. He expresses excitement for upcoming AI models and believes that these advancements could bring us closer to achieving AGI, viewing it as a journey rather than a destination. The speaker concludes with a hopeful outlook on the role of agent workflows in propelling AI forward.
Mindmap
Keywords
💡Neural Networks with GPUs
💡Coursera
💡Deeplearning.ai
💡Google Brain
💡AI Agents
💡Iterative Workflow
💡Human Evaluation Benchmark
💡Planning Algorithms
💡Multi-Agent Collaboration
💡Agentic Reasoning
💡Fast Token Generation
Highlights
Andreu's early contributions to neural networks with GPUs and his role in creating Coursera and deeplearning.ai.
The importance of iterative processes in AI workflows, compared to non-agentic, one-shot methods.
The surprising effectiveness of agentic workflows in improving AI performance, even surpassing newer models like GPT-4.
The concept of reflection as a powerful tool in AI design patterns, allowing systems to self-evaluate and improve their output.
The potential of multi-agent collaboration, where different AI agents can work together, each playing different roles.
The use of two-use systems in expanding the capabilities of language models, especially in areas like computer vision.
The impact of planning algorithms on AI's ability to autonomously handle failures and reroute processes.
The increasing role of AI agents in personal workflows, such as research, where they can assist in gathering and analyzing information.
The significance of fast token generation in agentic workflows, allowing for quicker iterations and potentially better results.
The anticipation of future AI advancements due to agentic reasoning design patterns.
The need for patience and dedication when working with AI agents, as they may require more time to process and respond effectively.
The potential of lower quality models with faster token generation to outperform slightly higher quality models with slower token generation.
The ongoing journey towards AGI and how agent workflows might contribute to this progression.
The excitement around upcoming AI models like Cloud T5, CL 4, GPT-5, and Gemini 2.0.
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
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