Stanford "Octopus v2" SUPER AGENT beats GPT-4 | Runs on Google Tech | Tiny Agent Function Calls
Summary
TLDRStanford University's breakthrough on-device language model, Octopus B2, outperforms GPT-4 in accuracy and speed. This compact model operates on various devices, enhancing privacy and reducing costs. It excels in automatic workflows and function calling, with potential applications in smartphones, cars, and more. The research emphasizes the viability of smaller AI agents that maintain high accuracy and efficiency without the need for large-scale cloud models.
Takeaways
- 🌟 Stanford University has developed Octopus B2, an on-device language model that outperforms GPT-4 in accuracy and latency.
- 📱 On-device models like Octopus B2 and Apple's RM can run on personal devices, addressing privacy and cost concerns associated with cloud-based AI models.
- 🚀 Octopus B2 is a small model with two billion parameters, offering fast function calling capabilities and high accuracy.
- 🔍 The model reduces context length by 95%, making it efficient for deployment across various edge devices.
- 📱 Examples of edge devices include smartphones, cars, thermostats, and VR headsets, where the AI can perform tasks like setting reminders, providing weather updates, and messaging.
- 📊 The research compares the performance of Octopus models with GPT-4, showing that smaller models can surpass larger ones in specific tasks.
- 🌐 The AI industry is moving towards on-device AI agents that are private, cost-effective, and can be deployed on personal devices.
- 🔧 The study uses Google's Gemma 2 billion model as a base and compares it with state-of-the-art models like GPT-4.
- 🏆 Octopus models demonstrated superior accuracy and latency in tests, with Octopus 2 being particularly notable.
- 📉 The research also explores the use of lower rank adaptation to reduce model size without significantly impacting performance.
- 🌐 The advancements in AI agents are rapid, and the industry is focusing on creating dependable software that empowers users through function calling and reasoning abilities.
Q & A
What is the significance of the development of the Octopus B2 model by Stanford University?
-The Octopus B2 model is significant because it is an on-device language model that surpasses the performance of GPT-4 in both accuracy and latency. This means it can run efficiently on personal devices like computers and phones, offering faster and more accurate function calling without the need for cloud-based services, which can have associated privacy and cost concerns.
How does the on-device model like Octopus B2 differ from cloud-based models in terms of privacy and cost?
-On-device models like Octopus B2 offer the advantage of processing data locally, which enhances privacy as the data doesn't have to be transmitted to external servers. Additionally, they eliminate the costs associated with cloud-based models where users might be charged per token or per million token usage fees.
What is the importance of reducing the context length by 95% in the development of on-device AI agents?
-Reducing the context length by 95% is crucial as it allows for the creation of more efficient and lightweight AI agents that can operate on a wider range of devices, from smartphones to smart home appliances. This reduction in data requirements makes the AI agents faster and more adaptable to various edge devices without compromising on performance.
How does the Octopus B2 model compare to Apple's on-device vision model in terms of size and functionality?
-While both Octopus B2 and Apple's on-device vision model are designed for efficient on-device processing, the Octopus B2 model is slightly larger in size but is optimized for language processing and function calling. Apple's vision model, on the other hand, is tinier and focuses on visual tasks, such as understanding text on screens.
What are some of the specific tasks that the Octopus B2 model can perform effectively?
-The Octopus B2 model can perform tasks such as creating calendar reminders, retrieving weather information, sending text messages about the weather, and searching YouTube for specific content, like a Taylor Swift concert. These tasks demonstrate its capability in understanding and executing function calls related to personal assistance and information retrieval.
How does the performance of the Octopus B2 model compare to GPT-4 in terms of accuracy and latency?
-The Octopus B2 model outperforms GPT-4 in both accuracy and latency. It has demonstrated higher accuracy rates in certain tasks and has significantly lower latency times, making it faster and more efficient for on-device applications.
What is the role of the RAG (Retrieval-Augmented Generation) technique in improving AI models?
-The RAG technique enhances AI models by providing them with a sort of 'cheat sheet' or database to reference when generating responses. This reduces the likelihood of 'hallucinations' or incorrect information being generated, thereby improving the accuracy and reliability of the AI's responses.
How does the development of smaller AI models like Octopus B2 impact the future of AI technology?
-The development of smaller AI models like Octopus B2 suggests that advancements in AI technology can be achieved not just by increasing model size but also by optimizing smaller models for specific tasks. This can lead to more efficient, cost-effective, and privacy-friendly AI solutions that can be deployed across a wide range of devices and applications.
What does the comparison between the performance of the Octopus models and GPT-4 indicate about the potential of on-device AI agents?
-The comparison indicates that on-device AI agents can match or even surpass the performance of larger, cloud-based models like GPT-4 in terms of accuracy and latency. This suggests a promising future where AI agents can operate efficiently and effectively on personal devices without relying on cloud services.
How does the use of lower rank adaptation in the Octopus models affect their performance and deployment?
-Lower rank adaptation allows for the fine-tuning and simplification of models, reducing the number of parameters used while still maintaining similar results. This technique enables the deployment of AI agents that are robust and efficient, suitable for product use, while also reducing computational requirements and potentially lowering costs.
What are some of the emerging trends in the AI industry highlighted by the development of on-device models like Octopus B2?
-The development of on-device models like Octopus B2 highlights emerging trends such as the focus on creating AI agents that are highly efficient, lightweight, and capable of performing specific tasks with high accuracy. It also underscores the shift towards edge computing in AI, where the processing power is brought closer to the source of data, enhancing speed, reducing latency, and improving privacy.
Outlines
🤖 Introducing Octopus B2: A Compact On-Device Language Model
Stanford University has introduced a new on-device language model called Octopus B2, which is designed to surpass the performance of GPT-4 in both accuracy and latency. This compact model is capable of running on various devices, such as computers and smartphones, without the need for cloud-based processing. The model's efficiency is highlighted by its ability to decrease context length by 95% and its potential for deployment across a wide range of edge devices. The script discusses the importance of function calling in AI agents and the benefits of having on-device models that address concerns over privacy and cost, which are often associated with large-scale cloud models.
🏆 Benchmarking Octopus Models Against Industry Standards
The Octopus models, including Octopus 2, are benchmarked against industry standards like GPT-4 and GPT-3.5 to evaluate their performance. The models are tested on their ability to perform function calling and reduce hallucinations using the RAG (Retrieval-Augmented Generation) technique. The results show that while the performance of the models is close to 100%, the Octopus models outperform their counterparts, including GPT-4, in both accuracy and latency. The discussion also touches on the training methodologies, such as full model training and low-rank adaptation, and the impact of data set size on model performance. The significance of these findings lies in the potential for deploying highly efficient, cost-effective, and accurate AI agents on edge devices for tasks like creating reminders, fetching weather updates, and messaging.
📈 The Future of AI: Smaller is Smarter
The script concludes with a reflection on the future of AI, suggesting that advancements in the field are not limited to increasing model size. Instead, the development of smaller, more efficient models like Octopus B2 and Apple's on-device vision models demonstrate that compact AI agents can perform specific tasks with high accuracy and speed. This trend challenges the conventional belief that larger models are inherently better, highlighting the potential for smaller models to deliver robust performance while addressing concerns related to cost, efficiency, and privacy.
Mindmap
Keywords
💡Autonomous AI agent
💡On-device language model
💡Function calling
💡Latency
💡Privacy
💡Cost
💡Edge devices
💡Parameter
💡Latency reduction
💡AI advancements
💡Low-rank adaptation
Highlights
Stanford University introduces Octopus B2, an on-device language model for super agents that outperforms GPT-4 in accuracy and latency.
Octopus B2 is a small model that can run on various devices, such as computers and phones, offering faster and more accurate performance than GPT-4.
Apple has also developed an on-device model called RM, a vision model that is significantly smaller and more efficient than GPT-4 for visual tasks.
Language models like Octopus B2 have the potential to be highly effective in automatic workflows due to their ability to quickly call functions.
Large-scale language models in cloud environments, while high-performing, often raise concerns over privacy and cost.
Stanford's research presents a new method that empowers an on-device model with two billion parameters, which is smaller yet surpasses GPT-4 in performance.
The new on-device model decreases the context length by 95%, making it faster and more efficient for various tasks.
The AI agent's growing presence is rapid, with advancements leading to the development of more efficient and cost-effective on-device models.
Octopus models are capable of performing tasks like creating calendar reminders, getting weather forecasts, and texting, demonstrating their practical applications.
The research uses Google's Gemma 2 billion model as a basis for the development of the Octopus models, showcasing the potential of open-source models.
The Octopus models were tested against GPT-4 and other state-of-the-art models, with Octopus 2 showing superior accuracy and latency.
The development of on-device AI agents like Octopus B2 and Apple's RM indicates a trend towards smaller, faster, and more efficient models for specific tasks.
The Octopus models demonstrate that AI can be improved by making it smaller, challenging the notion that bigger models are always better.
The research suggests that for certain tasks, tiny AI agents can outperform larger models, offering a new direction for AI development.
The Octopus models' performance indicates that there is potential for deploying AI agents on edge devices without the need for massive models or extensive resources.
The study highlights the importance of function calling in AI agents and the potential for on-device models to excel in this area.
The development and testing of the Octopus models demonstrate a significant step forward in the creation of efficient, on-device AI agents.
Transcripts
the autonomous AI agent space is heating
up Stanford University drops this gem
octopus B2 on device language model for
super agent a small model that surpasses
the performance of GPT 4 in both
accuracy and latency how fast it is and
what it is it's an on device language
model for super agent so on device
meaning it can run on your computer on
your phone on whatever and we've seen
something very similar from Apple
recently they have an on device model
they calling it RM and it basically is
kind of like a vision model that is Tiny
compared to gbt 4 for example something
like 8,000th of a% the size of gpc4 can
run on device and for certain visual
tasks for understanding what's written
on your computer screen on your phone
screen it exceeds GPT 4's capabilities
and this is kind of in the same vein so
this is octopus 2 they're saying that
language models these llms they're
potentially effective in automatic
workflows they possess The crucial
ability to call functions now really
fast let's talk about what what calling
functions means just so everybody's on
the same page most people probably have
heard me talk about it but just really
fast so for example if you're dealing
with an Android phone and the same thing
can be said for Apple phones or your
computer or your thermostat or your car
or pretty much anything nowadays it has
certain functions that you can call that
Define what it can do so for example one
of them if we're talking about the
Android phone for example or any Android
system it can take take a photo and you
can specify which camera to use like the
back camera and what resolution to take
the photo at right and here are the
parameters you can use you can say the
camera is in the front or the back right
depending on what phone you're using
resolution Etc another function is get
trending news right in the US region in
English Give Me Five results that are
the top results or get the weather
forecast or send an email or search
YouTube videos etc etc all right but
that's function calling right that's
that's what functions are an example of
a few function and they're essential in
creating AI agents they saying despite
the high performance of large scale
language models in Cloud environments
they are often associated with concerns
over privacy and cost so certainly I saw
a number of applications with Claude or
GPT for that were kind of cool but boy
they cost a lot cuz you're paying you
know open AI or anthropic to run their
model in the cloud it's not on your
device right it's not local so
everything's going through their
services you're paying some per token or
per million token fee that you're paying
them so the more complicated this gets
the more you pay and also of course
privacy right they can see exactly what
you're doing so the solution to that
would be something that just runs on
your device right but our current on
device models for function calling they
have issues with latency right how fast
they're able to run and accuracy how
good they are at actually calling the
right function and they're saying our
research presents a new method that
empowers an onice model with two billion
parameters which is rather small it's
not as Tiny as some of the smaller Rome
models the Apple Vision models but it's
definitely on the really smaller side
but these on device models they surpass
the performance of GPT 4 in both
accuracy and latency and they decrease
the context length by 95% then they dunk
on Zuckerberg for a bit and they're
saying this thing is fast enough to
deploy across a variety of edge devices
so think you know in your phone in your
car in your fridge in your thermostat
some examples are creating calendar
reminders getting the weather and text
messaging you know either the user or
somebody else about the weather and
searching YouTube for a Taylor Swift
concert and of course those things are
completed and the agent successfully
does the thing it is asked and so
they're saying that the AI agent's
growing presence is very rapid the
advancement in agents is rapid right so
you have ai assistant tools like
multi-on so we've covered that here I
was pretty surprised about how good it
was like that's where I was like well
we're we're certainly farther ahead than
I than I realized adep day I so I've
heard quite a bit about them and then
there's of course rabbit R1 the Humane
AI pin and there's a number of other
ones including open- sourced ones and
they're talking various things various
research that went into that like
prompting techniques Chain of Thought
reasoning and the rise of multi-agent
systems but this is a kind of a new
trend in this industry showcasing the
use of language models right so these
gpts and clouds and whatnot and Geminis
to develop a Dependable software that
empowers users we use API calling you
know function calling and reasoning
abilities and while this works well they
want to create something that is on
device something that can be run
privately and not cost too much and they
want to be able to deploy these agents
in these models on edge devices like
smartphones cars VR headsets and
personal computers I'll link the paper
if you want to go through the
methodology there's quite a bit here we
will just highlight the most important
parts looks like they've used Google's
Gemma 2 billion model so this is the
small model that Google has made open
source and after training this model
they will be comparing it to kind of the
state of the art models specifically
here they're going to test it against
GPT 4 the uh January 25th checkpoint or
sort of that update right cuz they have
multiple GPT 4 models so this is the
Chabot Arena leaderboard so you can see
here this is the 0125 January 25 January
25th which is uh you know one of the
better ones it looks like the 1106
November 06 is more highly rated but I
mean they're also close it's pretty much
the same that could be just a small
variation there and not significant and
they also test against GPT 3.5
and also they're going to test the rag
technique so this ability to for it to
sort of check against a database or you
know you can think of it like if you
have a like a cheat sheet during to
which you can look to see which
potential functions are available which
is going to reduce hallucinations so
they talk about llama 7 billion with rag
with that sort of retrieval augmented
generation so it seems like it didn't do
too well so the performance was modest
even though they gave it you know few
shot learning so they gave it examples
of how to do it it was slow and had a
68% accuracy so so llama kind of gets a
thumbs down GPT 3.5 with you know
retrieval with it's little cheat sheet
where can look up answers that one did
pretty well it has an impressive
accuracy of 98.0 N5 and the latency was
significantly improved only 1.97 seconds
so here's kind of the chart uh that you
can see comparing all of them this is
llama boy it did not do well but the
thing to notice here is that obviously
all of these other ones well I mean
they're all pretty close and they're all
pretty close to 100 % right none of them
are at 100% but they're 98 97 98 99 99
99 and then 98 with the
99s with the three 99s being octopus 0
octopus 1 and octopus 2 they beat all of
the other ones including GPT 4 which got
a
98.57% models so octopus I guess zero
this would be zero so they used so they
either used full model training or they
used Laura which is uh low rank
adaptation so we we did a video a long
time ago about it basically it's a way
to kind of fine-tune and simplify models
to use less parameters while keeping
similar results so I guess you can like
think of it kind of like a you know how
sometimes you can make image files
smaller without necessarily losing
detail kind of think of it like that I
guess and then they train uh some of
them on 1,000 data points so sort of
their data size was their data set size
was 1,000 and they've also tried 500 for
octopus 3 and then 1004 octopus 4 so it
looks like that's why there was an
actually a drop off for octopus 3
perhaps maybe because of the smaller
data set and then when they were testing
GPT 4 interestingly so of course it
exhibited Superior accuracy at 98.5 and
even lower latency than GPT 3.5 even
though is a bigger model and they're
saying GPT 4's enhanced performance
suggests open AI could be allocating
more GPU resources to it or that
experiences less demand compared to GPT
3.5 that's interesting and this is sort
of like the latency or you know how long
it takes for it to run so obviously the
higher the worse it is the lower the
better so this is in seconds so as you
can see again llama oh my God 13 plus
seconds right then you have gbt 4 at
just over 1 second but the octopus
models they're at like a third of a
second for most of them so 38
3736 so lower the low rank uh adaptation
so switching to lower training results
in a minor accuracy decrease but it's
still high enough that it's sufficiently
robust for product deployment but the
point of all this is is what does this
all mean what is what is the importance
of all this and that is simply that and
we we've seen the same thing with
Apple's research and now this is
Stanford it looks like these on device
AI agents the archit texture behind them
doesn't have to be these massive models
like GPT 4 with their 1.7 trillion
parameters or whatever that exact number
is they can be tiny and they can be very
fast and they can be very inexpensive
while still maintaining a lot of their
accuracy it's interesting to think about
because right now a lot of companies are
betting that all the kind of forward
progress will come from building up more
chips right bigger power plants more
parameters just more more more bigger
meanwhile these two papers from Apple
and Stanford are showing that tiny
models tiny agents can be extremely
effective at certain specific tasks you
want an agent that does function calling
well here's a tiny one that does better
than GPT 4 you want something that can
read your screen understand like all the
words on your screen so it knows what to
click on well here's a microscopic one
right from Apple if I recall correctly
it was like
250 million parameters was the smallest
one then it goes up to 3 billion for the
biggest one right compare that to 1.7
trillion GPT 4 right these outperform
the massive one it's interesting because
we can make the AI better by making it
bigger we can make it better by making
it smaller there doesn't seem to be a
limit to where it can go
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