OpenAI's STUNNING "GPT-based agents" for Businesses | Custom Models for Industries | AI Flywheels

Summary

OpenAI's shift towards its original mission of being the AI base layer is gaining momentum. The company is now focusing on fine-tuning its models for specific applications, enabling developers to build custom models that cater to various industries. This approach is set to revolutionize sectors like healthcare, law, and agriculture, by significantly reducing costs and improving efficiency. OpenAI's strategic partnerships and advancements in AI technology are poised to create a new wave of innovation and opportunities for businesses across the globe.

Takeaways

• 🌟 OpenAI's shift back to its original goal of being the AI base layer, allowing various industries to build on top of it for specific functionalities.
• 🚀 Introduction of improvements to the fine-tuning API and expansion of custom models program, giving developers more control and new ways to build domain-specific models.
• 🔍 Extension of model knowledge with techniques like retrieval augmented generation, enabling AI to search the internet and summarize information relevant to queries.
• 🎯 Custom models fine-tuned for specific tasks, such as customer service or medical applications, leading to higher quality results and reduced costs.
• 🌐 Partnerships with startups and companies outside of OpenAI to bring innovative features and applications based on the AI base layer.
• 🔧 New features for developers, including model checkpoints and a side-by-side playground UI for comparing model quality and performance.
• 🔗 Integration with third-party platforms like Weights & Biases for model tracking and evaluation, enhancing visibility into the model development process.
• 📈 Transition from traditional business operations to AI-driven processes, with a prediction that AI will significantly replace or improve a vast majority of tasks over the next decade.
• 💡 Showcase of real-world applications where custom-trained models have been successfully implemented, demonstrating the potential impact of AI across various sectors.
• 🌍 Empowerment of different communities, such as farmers and students, through AI solutions tailored to their specific needs and challenges.
• 💼 Opportunities for businesses and individuals to leverage AI for process automation, enhanced customer experiences, and the creation of new markets and services.

Q & A

What is the original mission of OpenAI according to the transcript?

The original mission of OpenAI is to be the AI base layer, providing foundational artificial intelligence like GPT, Sora, and others, on top of which various companies and apps can build specific functionalities for different fields such as science, chat applications, medical AI, etc.

How does OpenAI plan to move forward with its base layer strategy?

OpenAI plans to move forward by offering improvements to the fine-tuning API and expanding their custom models program, allowing developers more control over fine-tuning and new ways to build custom models with OpenAI. They aim to provide a foundation for others to build upon, leveraging their advanced technologies like Sora, Whisper, and data analytics capabilities.

What is fine-tuning in the context of AI models?

Fine-tuning is the process of adjusting a general AI model, like GPT 4, to perform better at specific tasks. For example, a model can be fine-tuned to excel in customer service or coding by training it with domain-specific data and objectives, thus creating a custom model tailored to those particular needs.

How can fine-tuning a model benefit businesses?

Fine-tuning a model can lead to higher quality results, reduced costs, and improved latency. By focusing the model on the specific needs of a business, it can operate more efficiently, potentially reducing the computational resources needed and providing more accurate and personalized outputs for tasks such as customer support or data analysis.

What is an example of a company leveraging OpenAI's fine-tuning capabilities?

Indeed, a global job matching and hiring platform, used fine-tuned GPT 3.5 Turbo to generate higher quality and more accurate recommendations for job seekers. By fine-tuning, they were able to cut their token costs by 80% and scale up their recommendations from less than 1 million to 20 million per month.

What new features is OpenAI introducing to aid developers?

OpenAI is introducing features such as model checkpoints to reduce retraining needs, a side-by-side playground UI for comparing model quality and performance, and integration with third-party platforms like Weights & Biases for better model tracking and evaluation.

How does the transcript suggest the future of AI implementation in various industries?

The transcript suggests that AI implementation will become ubiquitous, with custom models tailored to specific industries and use cases. It predicts a future where the majority of organizations will develop customized models personalized to their business, leading to widespread automation and efficiency improvements across all sectors.

What is the significance of the partnership between OpenAI and various startups and companies?

The partnership signifies OpenAI's commitment to fostering innovation and development across different sectors. By working with startups and companies, OpenAI can help bring new features and applications to life, leveraging the base layer AI capabilities they have built to meet diverse needs and drive progress in areas like legal, health insurance, education, and more.

How does the transcript view the role of AI in the future?

The transcript views AI as a transformative force set to revolutionize various aspects of life and business. It highlights AI's potential to automate tasks, improve efficiency, reduce costs, and create new opportunities for innovation and growth. The transcript suggests that AI will become an integral part of our daily lives and operations, leading to significant advancements and improvements across numerous fields.

What is the potential impact of AI on job roles and tasks?

The potential impact of AI on job roles and tasks, as outlined in the transcript, includes the automation of various administrative and repetitive tasks, allowing professionals to focus on higher-order tasks that require more critical thinking and human interaction. This shift could lead to increased productivity, reduced burnout, and the creation of new roles centered around AI management and optimization.

Outlines

00:00

🚀 Open AI's Strategic Shift and Base Layer AI

The paragraph discusses Open AI's return to its original mission of being the AI base layer, allowing various companies and apps to build on top of it. It mentions how Open AI initially encouraged others to build on its platform, but later developed its own applications, overshadowing startups that were filling gaps in its technology. Now, Open AI seems to be inviting startups again to build on its base layer, with improved functionalities like Sora, Whisper, advanced data analytics, and in-painting. The speaker shares news about Open AI's recent API improvements and its custom models program, aiming to give developers more control over fine-tuning and building custom models.

05:02

🎯 Fine-Tuning and Custom Models for Enhanced Performance

This paragraph delves into the concept of fine-tuning AI models for specific tasks, such as customer service or scientific research. It explains how fine-tuning can lead to higher quality results while reducing costs and latency. An example is given of how a global job matching platform fine-tuned GPT 3.5 to generate better recommendations, significantly reducing token costs. The speaker also discusses the effectiveness of smaller, fine-tuned models for specific tasks, predicting a trend of more such models being used in the future. Open AI is facilitating this by introducing new features for developers, such as model checkpoints and a side-by-side playground UI for model comparison.

10:03

🤖 Integration with Third-Party Platforms and AI Use Cases

The paragraph talks about Open AI's efforts to integrate with third-party platforms, starting with Weights and Biases, to track model versions and experiments. It also covers how Open AI is working with companies to create custom-trained models for specific domains. The speaker then transitions to discussing the broader impact of AI, predicting that in the next decade, AI will be applied to a vast majority of tasks, replacing or enhancing traditional methods. The paragraph highlights the potential of AI in various sectors, from legal to health insurance, and emphasizes the lucrative opportunities for developers and businesses in this space.

15:04

🌐 Custom AI Solutions Transforming Industries

This paragraph presents specific customer stories that illustrate the transformative power of custom AI solutions across different industries. It discusses how AI is being used to manage email inboxes, assist legal professionals with case law, streamline health insurance processes, and support weight loss applications. The speaker emphasizes the increased engagement and productivity brought about by these AI solutions, as well as the potential for AI agents to handle more complex tasks in the future. The paragraph also touches on the importance of training staff to understand the limitations and capabilities of AI.

20:06

🌱 AI Empowerment in Agriculture and Education

The paragraph highlights the use of AI in empowering farmers and improving education. It describes how AI is helping farmers in India and Kenya by providing knowledge on better farming practices and market information, significantly increasing their income. In the education sector, AI is making education data more accessible. The speaker also discusses the potential of AI in creating new industries, such as AI agents that could order food or book gym classes on behalf of users. The paragraph concludes with a call for excitement about the potential of AI to bring about positive change and opportunities in various sectors.

Keywords

💡Open AI

Open AI refers to an artificial intelligence research lab that aims to ensure artificial general intelligence (AGI) benefits all of humanity. In the context of the video, Open AI is associated with the development of AI technologies like GPT and other AI models that serve as foundational layers for various applications.

💡Base Layer AI

Base Layer AI refers to the foundational or底层 level of artificial intelligence systems that serve as a common platform upon which other applications and services can be built. The base layer provides essential capabilities such as language understanding and generation, which can then be customized for different industries and use cases.

💡Fine-Tuning

Fine-tuning is the process of adjusting a pre-trained AI model to better perform a specific task or improve its performance in a particular domain. This technique is used to adapt the model's parameters to the nuances of a specialized application area, such as customer service or medical diagnostics.

💡Custom Models

Custom models are AI models that have been specifically trained or modified to cater to the unique requirements of a certain domain, task, or application. They are built on top of a base layer AI, incorporating domain-specific knowledge and data to enhance their performance in that particular area.

💡AI Startups

AI startups refer to new businesses that focus on developing and implementing artificial intelligence technologies. These startups often aim to fill gaps in the market or create innovative solutions by leveraging the capabilities of AI in various sectors.

💡RAG Retrieval

RAG (Retrieval-Augmented Generation) is a technique used in AI models that combines the ability to generate text with the capability to retrieve and utilize information from a database or the internet. This enhances the model's ability to provide accurate and well-informed responses by incorporating relevant data into its outputs.

💡AI Integration

AI integration refers to the process of incorporating artificial intelligence technologies into existing systems, platforms, or workflows. This integration can lead to improved efficiency, automation of tasks, and enhanced user experiences by leveraging AI's capabilities to process data and make decisions.

💡AI Consultants

AI consultants are professionals who advise businesses on how to effectively implement and utilize artificial intelligence technologies. They help companies understand the potential of AI, design strategies for integration, and train staff to work with AI systems.

💡Industry-Specific AI

Industry-specific AI refers to AI models or solutions that are tailored to address the unique needs and challenges of a particular industry. These models are trained on data and knowledge relevant to that industry, allowing them to provide specialized insights and automate specific processes.

💡AI Automation

AI automation involves using artificial intelligence to perform tasks that were previously done by humans. This can include routine tasks, decision-making processes, and other activities across various industries, leading to increased efficiency, cost savings, and improved accuracy.

Highlights

SE Alman and the team behind OpenAI have been working on a new approach to AI, potentially signaling a shift back to their original goal of being the AI base layer.

The concept of the AI base layer involves using AI like GPT, Sora, and others as foundational tools upon which various companies and applications can be built.

OpenAI's strategy includes allowing developers to fine-tune their models for specific tasks, which can lead to improved performance, reduced latency, and cost-efficiency.

OpenAI has introduced improvements to the fine-tuning API and expanded their custom models program, giving developers more control and new ways to build custom models.

The company has partnered with several startups and established companies to bring innovative features and applications based on their AI technologies.

Fine-tuning can be particularly effective for specific tasks, such as customer service or scientific research, leading to high-quality results at a lower cost and faster speeds.

OpenAI's developments suggest a future where most organizations will develop customized models tailored to their industry, business, or use case.

The company has published examples of successful custom model implementations across various industries, including legal, health insurance, and education.

AI technology is projected to automate or significantly improve a vast range of tasks, moving from near 0% to potentially 100% market penetration over the next decade.

The potential applications of AI are vast and varied, from improving email management to assisting with legal case law and enhancing educational data accessibility.

OpenAI's advancements are expected to create lucrative opportunities for developers and businesses that can successfully integrate AI into their operations.

AI's role in various sectors, such as agriculture and healthcare, is highlighted by the ability to increase efficiency and reduce costs, making it a valuable tool for societal improvement.

The AI landscape, despite being overhyped, is also underestimated in terms of its potential impact and transformative power.

Custom AI models are being developed to handle complex tasks with greater accuracy and nuance, leading to better outcomes in fields like law and medicine.

The future of AI includes the development of autonomous agents that could manage various aspects of our lives, from scheduling to personal assistance.

OpenAI's initiatives reflect a shift towards making AI more accessible and customizable, paving the way for widespread adoption and integration into various industries and tasks.

The potential for AI to revolutionize industries and create new markets is evident in the diverse range of applications and custom models being developed.

AI's impact on global challenges, such as agricultural practices and income disparities, demonstrates its potential to empower and improve conditions on a large scale.

The narrative of AI as a transformative tool is reinforced by the real-world applications and success stories showcased by OpenAI's developments.

Transcripts

SE Alman and the team behind open AI

have been kind of quiet for a while a

calm before the storm if you will but in

the last few days we've been getting

some glimpses into what potentially is

coming next let's take a look at what

they've been cooking up interestingly

enough this seems to be a nod back to

their original goal their original state

mission of being the AI base layer

here's I think is a great illustration

of what they meant by that so imagine

this sort of big block here that's layer

one that's the base layer AI That's the

GPT that's the Sora that's the do and on

top of that we build the various

companies various apps various software

that takes advantage of this base layer

of this AI to build specific

functionality for example for science

for chat applications for medical AI etc

etc now when Chad BT exploded onto the

scene opening eye did say that this was

kind of the plan for the future they

were going to be the base layer and

people could build on top of it tons of

people in apps did build on top of it

but then opening eye was like I am death

and just killed them all by building

their own sort of versions of it their

own applications now that's probably not

100% Fair because a lot of these

startups they were trying to build

technology to fill in the gaps that

opening ey was missing for example

things like whisper things that were

similar to code interpreter or Advanced

data analytics as it's called in other

words a lot of these startups they were

building kind of these obvious apps that

openi itself probably had in the works

and they kind of got rolled over by open

AI by the base layer if you will but it

seems that now the game is changed now

open AI seemingly now that it has all

the pieces in place it has the Sora The

Whisper the voice engine it has advanced

data analytics it has do with in

painting and editing and all that stuff

like it's built all of the different

pieces of the base layer their full

functionality you can talk back and

forth to Chad PT it's able to understand

your speech it's able to talk back to

you and now you know the base layer a

lot of it is complete all the companies

that try to plug missing pieces of the

Bas layer are dead and now seemingly

Open the Eyes saying okay guys we've got

the foundation we've got the base layer

build stuff on top of it now that's my

opinion but let me show you a few key

pieces of news that came out just in the

last few days that I think support the

Viewpoint as you look at this ask

yourself this question is open AI

rolling out the red carpet for the layer

to startups to build on top of it on top

of open ai's uh base layer let's get

started so this drop today introducing

improvements to the fine-tuning API and

expanding our custom models program

developers get more control over

fine-tuning and new ways to build custom

models with open AI So It Begins there

are a variety of techniques that

Developers can use to increase model

performance in an effort to reduce

latency improve accuracy reduce costs

with it's extending model knowledge with

rag retrieval augmented generation so

basically having that model you know as

example would be to search the internet

for information like perplexity it

returns to you the various links it

finds and then kind of summarize them or

if you've ever done the chat with a PDF

thing right the PDF is your sort of

database extra data extra info that you

have in there so the model instead of

kind of making up an answer it retrieves

information from that PDF and then

answers with that information in mind

you know various custom models

fine-tuning custom train models with new

domain specific knowledge and they're

launching new features to give

developers more control over fine-tuning

and they're introducing more ways to

work with our team of AI experts and

researchers to build custom models and

they're not kidding as you'll see in a

second they've partnered with quite a

number of startups of different

companies that are outside of openai to

bring some pretty interesting features

we'll cover that right after this so

they start by talking about some

fine-tuning of API features or rather

fine-tuning of these models using an API

for those that are not familiar fine

tuning is basically if you think of the

GPT 4 model as this kind of Big Blob

that can do anything but maybe it

doesn't do everything well right what we

can do is let's say we wanted to do

exclusively coding or customer service

with it we can fine-tune this model to

do customer service I'm going to say

support because that's easier to Rite we

can take this big huge model and find

tuna to be great at support a customer

service to answer questions that's

specific to our company our business

train how to answer questions correctly

in the style that we want now of course

there's going to be tons of other areas

where it's going to get worse because

this support model is going to be almost

a brand new model right it's a

fine-tuned GPT 4 it's a custom model for

support and chances are it might get a

lot worse in various other things like

it might not be that good at coding

anymore it might get bad at poetry but

it gets good at support so again there

sort of like little uh demonstration

here this little representation here

right the base layer right that's let's

say GPT 4 right we take that we we pull

it out and we find tun it to be a

science gp4 or a medical gp4 all the

stuff that gp4 already has comes with it

right so it understands language it can

reason right it has certain knowledge it

has certain skills we're just taking it

and shaping it into this specific task

and the other really big thing about

fine-tuning is you can achieve higher

quality results while reducing cost and

latency since we're pulling out just

what we need for our customer support

agent for example there's tons of things

that are still in this big GPT model

that we don't need that we kind of leave

behind so this model could be cheaper to

run much faster Etc as an example they

give indeed a global job matching and

hiring platform wanted to send

personalized recommendations to job

Seekers highlighting relevant jobs based

on skills experience Etc and they were

able to fine-tune GPT 3.5 turbo to

generate higher quality and more

accurate recommendations they were able

to cut their token costs by 80% so was

able to improve cost and latency how

fast it was doing it cutting the number

of tokens like the words that were run

through the model by 80% this allowed

him to scale from less than 1 million

recommendations to job Seekers per month

to roughly 20 million by the way as a

quick aside so on this Channel we cover

a lot of research from companies like

apple Microsoft meta openi Google deep

mine Etc and I'm beginning to notice a

pattern here that usually we see the

research papers come out you know let's

say 3 to 6 months before sort of the

things that are talked about in those

papers start hitting the market the Orca

2 paper from Microsoft research kind of

showed that teaching small language

models how to reason that this was

extremely effective or could two models

match or surpass all other mod models

including models 5 to 10 times larger so

smaller fine-tune models built for

specific tasks are incredibly effective

incredibly fast and Incredibly cheap

we've covered uh Apple's Research into

Vision models you could say we covered

this a few days ago so they had this

model that was 80 million parameters

that was in some cases beating GPT 4

getting close in other cases but 80

million parameters is a tiny tiny model

GPT 4 is G gantu clocking in at we think

1.7 trillion or you know that's what

people are guessing it's probably

somewhere in the low trillions so here's

a model that is if I did my math

correctly this is like a thousandth of a

percent the size of GPT 4 that is

getting close to it uh for performance

for certain specific tasks not for

everything obviously but for a specific

task this microscopic model is as good

as GPT 4 and so if I had to guess I

would guess that we're going to be

seeing a lot more things like this where

small fast models gbt 3.5 turbo and the

like get fine-tuned to do pretty

incredible things in specific domains

for pennies for for very cheap and

they'll do it very very fast and the

people that will be building this for

businesses those people will be printing

money that's my guess and open AI is

Building Services to make that easier

now they're going to be working with

some companies on their own so it's

going to be an open AI plus this is the

company partnership but I know a

percentage of you listening right now

are builders in the space are developers

I mean these B2B Solutions if you're

looking to make money seem like a very

very lucrative uh thing to approach and

so opening eyes is launching new

features to give developers even more

control over their fine-tuning jobs by

the way I've said this before but just

let me reiterate because I I don't think

I've said it recently for the next let's

say 5 years to a decade one place where

people are going to make a lot of money

and one place where we're going to see a

lot of progress is this idea of shoving

llms into everything in in other words

for any digital task that that's running

there's probably some solution where you

take a custom fine-tuned large language

model or maybe some custom small model

something like Orca 2 which Microsoft by

the way was kind enough to open source

so you're able to see exactly how they

built it and then apply to something

like this think about how much indeed a

global job matching firm how much were

they spending on these customized

personalizations how important it is to

their business model it probably has a

very high importance and probably had a

high cost right so if you're able to

build this custom model that cuts down

how much it costs them to do and how

fast it's done by

80% how many millions are you saving

them per year certainly you can probably

charge a pretty penny for doing

something like that and I would guess

the the talent pool for people that know

how to do this isn't massive like it's

not like I doubt that we have an

overwhelming amount of people that are

highly skilled in something like this

and in just a second I'll show you

examples of how this is applied but

here's the new features that openi is

rolling out so it's adding a uh sort of

model checkpoints so during each

training Epoch to reduce the need for

you know various retraining Etc so kind

of like a Version Control side by side

playground UI for comparing model

quality and performance apparently

somebody made two alms fight each other

in uh Street Fighter too so they trained

them to Output you know what it should

do Fireball kick move closer Etc to see

which which LM is the best Street

Fighter 2 player so that's kind of what

I'm imagining here I doubt it's that

cool and it also supports integration

with thirdparty platforms starting with

weights and biases so that's this

platform where you're able to track

experiments evaluate model performance

Etc where you have the model registry

the added openi so openi uses weights

and bias models to track all their model

versions across 2,000 plus projects

millions of experiments and hundreds of

team members so you can have visibility

into model development process with just

a few lines of code you know you're able

to answer questions like what exact

version of the data set was this model

trained on now I'm going to skip some of

the parts I will leave a link so if

you're really interested in this if

you're in the space I highly encourage

to read this obviously but for the rest

of us I think it might be more

interesting to quickly go over the kind

of the big points and then let's dive

into exactly what this looks like what

are the actual final outputs of this

open

as a Bas layer thing that's happening so

first of all they're talking about

something called assistant fine-tuning

so this is where openi helps train

models for a specific domain right in

partnership with a dedicated group of

openai researchers since then we've met

with dozens of customers to assess their

custom model needs and evolve their

program to further maximize performance

and they've published this so we're

going to take a look at that next but

before we look into those specific

examples here's kind of the end of this

block post where opening ey saying well

here's what's next for model

customizations they're saying that we

believe that in the future the vast

majority of organizations will develop

customized models that are personalized

to their industry business or use case I

think this is very important especially

if you're you know if you're in this for

the money now not everybody listening to

this channel is approaching this from

kind of a business Centric perspective

some of us are just interested in the

research and kind of personal use of AI

how can we take advantage of it for work

for our careers so I understand that

this might not apply to everybody but I

still think this is very important to

understand because this gives you a

glimpse into what's going to happen over

the next decade so here's 0% and here's

100% this is a line right these are all

the tasks that can be either automated

or significantly improved or maybe made

Cheaper by applying AI right so from

taking either a biological neural net

like a human being right an employee or

even taking you know code like a lot of

stuff runs on code right on something

that a smart software developer sat down

and typed out on their keyboard and now

that code runs to complete a certain

task like in accounting or stocks or I

mean nowadays pretty much everything

right now not that long ago close to

zero of those business operations ran on

neural Nets so neural Nets that's the AI

that we're talking about that's chbt

that's Sora that's all the AI music all

the AI images that you're seeing a lot

of Google stuff is machine learning you

know how they run their ad serving

platform content recommendations stuff

like that but you know you go back a few

years and maybe the number of those

things were like 1% right that could be

automated neural Nets or improved the

neural Nets in some way that was like 1%

since chbt came out and all that stuff

started coming out in the last few years

maybe we're at like I don't know 1 and a

half% 2% I don't no this might be even

like a horrible overestimation now that

I think about it it's probably some

fraction of 1% over the next decade plus

all of those processes will be replaced

by AI by neural Nets some of them will

be a one model to rule them all for

example some people would just use Chad

GPT for whatever task they need right

they would just use the base layer kind

of like that non-custom non- fine-tuned

version of GPT 4 to ask questions one

interesting thing about perplexity for

example that's having incredible success

and progress It's not really doing that

much on top of the sort of the base

layer so it's taking all the language

models that already exist right so you

have your GPT 4 Turbo clot 3 Sonet Cloud

3 Opus mistal large they have a few that

you can play around in the um playground

with and then you have kind of their own

fast ones that they've kind of

fine-tuned but for the pro users they're

just using like the base layer right

whether that's claw 3 or GPT 4 with you

know heaps of software on top of it

right so they're taking gp4 they're

building a little perplexity little

search engine on top of it and they're

adding heaps of software to you know

search the internet pull back answers so

literally in this image you know let's

say this is GPT 4 this is you know

perplexity and the heaps of software is

the features of that uh perplexity

search engine and they're valued at

billions of dollars Bezos is investing

in it tons of other people are investing

in it and rumor is Apple is in talks of

potentially buying them so that's one

example where literally you know

billions of dollars are created just

with this but these large language

models or other things like Vision

models and speech models and image

generation models they're coming for

everything they're going to be in your

thermostat they're going to be in the

food photography right some sort of

enhancement you know this is magnific AI

or magnific AI right that upscaler i' I

featured it on the channel some people

use it to upscale their food photography

right look at that thing you can see

every little grain of pesto every pine

nut I'm guessing this is cracked black

pepper I mean that looks delicious I

mean take a look at this burger like

they're going to use this in product

photography the left is sort of the unup

scaled the right is the upscaled I mean

most product images will use this in one

way or another we've covered Google

shopping AI with their new thing they're

rolling out where you're basically able

to use AI to create product visuals to

put them into different environments to

create virtual sort of those models

right that kind of showcase your product

it's able to translate everything into

different languages to you can basically

have an international store selling to

the entire world in their own native

language you don't have to do product

photography right you can pull out your

phone if you have a prototype take a

picture of it and the AI will create 3D

images and variations and put them in

the hands of models and just do all of

that for you as you'll see in a second

uh opening eyes partnering up with

people in the legal field to create

custom train models for them another

company that does health insurance

another company that does education jet

brains for coding email assistance

Salesforce health and weight loss

productivity clinical trials creativity

for Farmers to increase their income

personalized fitness and health coaching

content creation like I can sit here all

day and just named use cases but I think

this kind of chart I hope represents a

little bit better right we're at close

to zero right now of uh penetration of

this technology in the market that's

going to change everything it's going to

reduce costs improve automations create

new use cases that we haven't even

thought of right everybody's going to

want it everybody's going to need it and

over the next you know however many

years 5 10 20 it's going to get rolled

out from almost zero to you know

eventually approaching 100 and for every

model that gets rolled out people will

pay money and this money it won't be an

issue it won't be hard to sell these

Services because they're going to be

saving money money they're going to be

making things faster it's it's hot it's

sexy everybody wants it from your local

mom and pop store that's local that's

brick and mortar that's selling I don't

know quilts I've been doing e-commerce

for the last 10 years and I'm telling

you there's like off the top of my head

I can think of a number of places where

if I could get a custom trained finetune

model to just do that task for me it

would probably be able to do it faster

better cheaper than I could from

inventory management to customer service

to a lot of aspects of marketing so what

I'm saying is this may be a good

opportunity for those of you who like

money so opening I continues saying with

a variety of techniques available to

build a custom model organizations of

all sizes and this is important this is

kind of like the big thing too of all

sizes because with Enterprise level

software like software that you sell to

hospitals and government organizations

right you know it was hard to build you

know you had to have a massive team to

even approach projects like that right

you know there's know such thing as like

Artisan software right you know how you

can go to a farmers market and buy some

Artisan loaves of bread that's really

delicious I mean with software you

wanted a certain amount of scale because

you had to build that software so the

bigger of a customer size that it served

the better the economics of that

software would be right right it's a

little bit different here you know we

covered this blog post from semi

analysis a while back so semi analysis

is this I mean it's a website that talks

about all the news and events and and

does analysis of semiconductor and AI

Industries so sort of the the hardware

the chips behind AI like all that stuff

Nvidia and tsmc and all of that right

and they dropped this little line that I

thought was fascinating they're saying

we're training a CNN so CNN is a

convolutional neural network to

accelerate the frequent satellite

imaging of data centers to expand our

tracking to every data center across

every country right so this is kind of

like a a a publisher right so they have

a website a news newletter they sell

their reports and they have this like

really specific use case right they have

access to satellite images right I'm

sure a lot of that stuff is is public

right they track 1100 data centers and

their deployments with publicly

available information including but not

limited to property records power usage

this is Freedom of Information Act

requests and satellite images right so

they have these very specific use cases

where it would help them to have a

neural net a CNN in this case so it's

not a large language model it's not a

Transformer based architecture a

convolutional neural network in this

case it kind of like labels and uh is

strained on on images but they're

building their own custom AI model to

you know help them analyze images of

these data centers we looking at the

satellite images and figure out like

what's happening there they might do the

same thing for these other things for

property records power usage right all

this stuff how many other people in the

world might be interested in doing

something like this to analyze satellite

images for this specific AI accelerator

data centers probably not that many

maybe maybe a handful the point being

that there's going to be a great demand

for these custom small AI models for

very specific use cases for

organizations of all sizes they can

develop personalized models to realize

more meaningful specific impact from

their AI implementations the key is to

clearly scope the use case design and

Implement evaluation systems choose the

right techniques and be prepared to

iterate over time for the model to reach

Optimal Performance again the people

doing this will be making lots of money

with opene most organizations can see

meaningful results quickly with the

self- serve fine-tuning API for any

organization that needs to more deeply

fine-tune their models or imbue new

domain specific knowledge into the model

our custom model programs can help so

one this is where they have their own

in-house AI people two is where open AI

helps them develop it probably for

bigger companies with specific use cases

they can afford to pay for that but I

think there's another obvious sort of

category and that's people they're

outside of a corporation right like a

third party that just goes to businesses

and says hey I can create this for you

and charge them a certain amount of

money a consultant or whatever AI

consultant automation consultant

whatever you want to call that and again

we're at close to 0% now and it's

heading to 100 and so the early movers

will probably have a massive Advantage

but you might be saying okay but like

what are some of the use cases that make

sense is all this just hype is all this

just like uh marketing hype people are

getting over excited for Nothing by the

way Demi saabi said an interesting thing

but here's AI breakfast saying in the

words of Demi saabi the AI landscape is

overhyped yes it is overhyped but

underestimated it's funny how both can

be true but it makes sense there's a lot

of hype but people aren't fully grasping

the massiveness of everything that's

happening here because what we're

talking about is just a tiny sliver of

it like these fine-tune models for

specific business cases that's just a

tiny tiny part of it but let's see what

these use cases are so these are

customer stories from open eii so this

is kind of what they''re been talking

about where they work together with

companies to create custom fine-tuned

models for their specific Solutions

here's one that kind of jumped out of me

so this is superhuman that's the company

name and they're introducing a new era

of email with open AI they're building a

suite of Next Generation AI email

products that are saving users time

driving value and increasing engagement

now we've all struggled with the

overflowing inbox right there's too many

things too many appointments to too many

meeting minutes to keep uh up on and so

superhuman is reming how that could be

using AI to help you write emails to

rewrite certain ideas in your voice or

for example just say it out loud kind of

dictate it and have the AI kind of fill

the gaps write it in your voice make

sure everything's correct and then send

it out of summarize a quick summary of

each email that's arriving instant reply

allows you to reply from certain

contextual options similar to how a a

text message you you have those little

options to reply now it's important to

understand that yes so GPT Chad GPT can

do a lot of this but this we're talking

about custom Solutions so that's

somebody sitting there and specifically

trying to make this as good as possible

for the specific use cases but okay so

maybe this isn't as uh sexy as I made it

sound out to be but here's what caught

my attention they're saying here okay so

what's next so superh humanist company

imagines the world where GPT based

agents will soon be able to filter Tre G

and respond to email automatically

scheduling meetings and appointments and

taking basic actions online and in the

real world so again I mean they're

talking about building a email agent or

a scheduling agent so kind of like an

executive assistant for you that has

access to your emails to your calendar

and you can kind of like manage and stay

on top of it based on what you tell it

to do so the idea of opening up an email

inbox and then reading you know the

first email and the second email the

third email or you know doing triage

kind of thinking okay like what can I

get away with not answering today let me

see what what's the most important

things like the idea of you doing that

yourself with your brain and your eyes

and you clicking the mouse or whatever

like in 5 years that's going to be

laughable that's going to be on par with

manually adjusting the the heat in your

house every 10 minutes instead of you

know using a thermostat how many people

have that now close to zero right maybe

some fraction of 1% of the people in the

world are using that in their inbox

right it it's not 1% it's less for sure

if you look at the entire world even in

the United States it's less in 5 years

it'll be 100% or or close right

somewhere getting closer to 100% next we

have Harvey so it's a custom train model

for legal professionals now of course

lawyers and Technology the combination

of the two is hilarious right the I am

not a cat your honor video is perhaps

the funniest video I've ever seen in my

life I think the first time I I heard

myself laughing I'm not even

exaggerating then of course you have the

lawyers that use Chad BT to I don't know

what they were trying to do but it

basically made up a bunch of court cases

to cite their arguments and it got them

into a lot of trouble now at the time a

lot of the clueless reporters that that

cover AI wrote articles saying you know

the conclusion was AI bad the reality

was they shouldn't have used gpc4 kind

of that that that base model right they

needed something that was custom trained

that had some architecture that had some

rag that retrieval augmented generation

right so as you can see here this is the

model that uh was trained so it's custom

trained and as you can see here it's

giving you little citations of the case

law so for everything that it writes it

supports it by giving you a little okay

so I'm referencing this specific uh case

law right stone versus writer or

whatever right to support its claims

it's not guessing it's not hallucinating

it's not taking it the best shot at it

it's using ACT ual databases to support

those claims it's custom trained not to

you know take a stab at it or whatever

it's custom train to answer just as

accurately as possible so the company

that's doing it they're called Harvey

and they've worked with 10 of the

largest law firms to test this model and

they were surprised by how strong the

reaction was from those law firms 97% of

the time the lawyers prefer the output

from the case law model from this custom

trained model because it was longer more

complete answer went into the Nuance of

what the question was asking and covered

more relevant case law hallucination

reduction was one of Harvey's

motivations for building a custom model

so again the the problem wasn't quote

unquote AI the problem was it just

didn't have the correct architecture

needed to provide the correct answers

and the people using it didn't have the

correct training to understand what it

was good at where it could fail again

that's another part of this whole thing

is training the staff the people using

it to kind of understand its limitations

Etc right having Chad gbt represent you

in a legal case is shockingly not a good

idea and so where are they seeing this

whole thing going next well they're

saying this don't build for the current

capabilities of these models today CU

remember when you do that what happens

opening ey comes in it's like I am death

and it just like reaps everybody right

build for where the models are going to

be tackle more complex versions of

problems so that when better versions of

the model come out they aren't solved as

a side effect and what's Harvey working

on next let's see one of their main

focuses is Agents again surprise

surprise AI autonomous agents are once

again the next Frontier here's yet

another company that's like well that's

the very next step it's in this case how

to combine multiple model calls together

in a single working output this would

simplify the user experience and reduce

the amount of prompt engineering and

typing users needed to do another one is

Oscar bringing AI to health insurance

the open AI models together with Oscar

help ensure hippoc compliance doubling

productivity automated documentation and

claims processing medical care workers

cut their time spent you know

documenting various Medical Care

conversations stuff like that by 40% so

that's your doctor that's your nurse

almost half of the time instead of

thinking like what your needs are

they're sitting there filling out this

paperwork draining their mental energy

their physical energy think about how

many millions of dollars we spend having

them do that instead of something like a

large language models taking care of it

for you know pennies this reduces

burnout and allows nurses and clinicians

to focus on higher order tasks how many

lawyers and doctors and nurses use

various tools like this to help them in

their work right now probably close to

zero right we're we're definitely not

near 1% right it's some tiny fraction of

1% how many of them will be using it in

5 to 10 years probably a lot closer to

100% right next they're also talking

about you know claims improving accuracy

automating 408,000 tickets by the end of

the year all right so this is what a

claim looks like right you click this

little magic button help me with this

inquiry and it fills it out I assume

next they're saying creating an AI

flywheel a flywheel in in business

usually refers to kind of like a virtual

cycle where something almost kind of

like Builds on itself to keep improving

that business or that habit or whatever

the more users a social network has the

more value there is for existing users

and the more users it will attract and

so this is kind of like a direct Network

effect they're saying we don't want to

just nibble around the edges of

administrative use case simplification

right because this is what we're talking

about right is just simplifying

administrative use cases right important

thing very valuable thing will save a

lot of money will save the mental

energies of doctors and nurses that need

to focus on their patients so it's it's

it's it's important it's critical but

they're saying that's just the beginning

they're looking to bring down the cost

of seeing Physicians and being in the

hospital by a factor of 10 in the next 3

to 5 years another customer was Zelma

who's using GPT 4 to make Education data

accessible another customer success

story is healthify an app that helps

with weight loss now this is for a

specific population it looks like

they're really focusing on uh India it

seems um and specifically classifying

traditional Indian foods so focusing on

certain custom populations can be a

really good idea so here they're saying

snap achieved around 80% accuracy for

single IND Indian foods right so here's

where it gets a little bit interesting

if you're looking at it from a a

business perspective like how important

is it for businesses to have something

like this well increased engagement

because of the finetune models the AI

the engagement increases people are more

interested people tend to use it more

users track 50% more often with these

new models users engage more for

nutrition and fitness coaching clients

engage with AI supported coaches 18%

more and and this is kind of interesting

with users permission the agents will

even be able to order food or book Gym

classes a brand new industry that's

going to emerge that we we haven't yet

seen but a few people are beginning to

mention it in the future when we have

agents that control our lives that

sounds harsh that help us run our lives

they're kind of like our assistants but

they do connect us to information to

services to everything they're going to

recommend certain products right they're

going to order food for us book Gym

classes Etc so now a lot of marketing

dollars are spent on marketing to the

person right Billboards ads it's focused

on you right it's focused on trying to

get you the human to buy something

slowly over time we're going to see more

and more money going into marketing to

AIS to these agents what is that going

to mean well I mean the simplest way is

spending ad dollars to get the company

Behind These agents to recommend your

products over the competitor that's the

most obvious one but there might be

things like with search engine

optimization right so it's how you

optimize your web page the links that

are pointing to your web page that

determines how high you appear in Google

search could there be an SEO but for

autonomous AI agents an a if you will

autonomous AI agents optimization it'll

be interesting to see how that unfolds

but you know companies will be spending

money trying to figure out how to get

autonomous agents to recommend them over

their comp competition like that's going

to happen 100% chance of that and gen

isn't just for us soft first worlders

only digital green uses open ey to

increase farmer income in countries

including India and Kenya it's important

for these Farmers to be able to teach

each other best practice for growing new

crops share various local weather

conditions and there's a lot of problems

with that right not just connecting them

but also having a lot of different

languages that are being spoken right

they need to connect with their

suppliers and provide market and pricing

information so it's like Eve online but

for Farmers farmer to Farmer training

videos increase farmer income by an

average of 24% this was interesting so

we I mentioned this in yesterday's video

so there's some things that governments

and various well-funded government like

entities can do that may not be

necessarily something that you know

large corporations are are willing to do

where they can use their resources to

build something that's useful for the

whole country right so for example a

database for all the countries and that

cultural AI to be trained on right

Microsoft probably won't do that Google

won't do that but maybe something like

uh DARPA might consider it uh I don't

know if that's exactly in their

wheelhouse or not but certainly it's

something that would help everybody in

that country that's in AI to be able to

do something like this here India's

Ministry of Agriculture validates old

documents in the knowledge based to

ensure accuracy and reliability and then

with GPT 4 they're using rag retrieval

augmented generation to pull the needed

stuff from there various crop research

fact sheets Etc and the cost of this

farmer chat these extension services

they went from $35 per farmer to 35

cents per farmer again massive massive

leap and again right now this technolog

is available to close you know to 0% of

the world's population of farmers with

those needs right and over the next

decade hopefully it'll start approaching

100% And there's like 20 of these little

um customer profiles each one has its

own mindblowing application like there's

not one use case here that isn't kind of

revolutionary in its own way where it

provides more more features more use

cases it it drops the price of doing

something it frees up the humans the

experts to focus on what they're

supposed to be doing instead of being

bed down in the details where this

Medical Group believed they would need

specialized medical models to get good

results and they were shocked to find

that GPT 4 outperformed a team of highly

trained human experts they said it not I

they were shocked I'm sensing that the

title of this video is going to have the

word shocking in it what do you think am

I am I am I correct is my intuition

accurate so I'll link this page down

below so this is the open.com so this is

not their blog which I think a lot of

people are familiar with this is/

custom- stories and I think it just got

populated with all of this stuff there's

a whole bunch of them here and most of

them aren't like fluffy they're not just

whatever nonsense they're impactful and

they showcase how powerful these custom

train models can be so I'll leave it off

there I think I've made my point back

when software was rolling out that

created a lot of wealth a lot of change

in the world software was eating the

world they said and now this is the next

step where AI is doing that thing we

probably shouldn't say AI is eating the

world we should say AI is helping the

world I think that's the a16z motto AI

is going to save the world and I agree

for a lot of people that are scared of

AI I don't know becoming the Terminator

and turning us into paper clipse or

whatever that scenario is I hope they

take a look at this it has the ability

to make our lives easier to eliminate

busy work to help people understand

various medical procedures better to

deal with email to to understand legal

cases better to make Education data more

accessible so more people understand

what decisions are being made in our

school system systems right how people

in farming communities they're

struggling to a little bit better this

is where AI is right now this is what it

can do right now and over the next 5

years 10 years it's going to be rolling

out from zero to 100% of you know

whatever use cases we can apply it to

the world will change some people will

make a lot of money and it's going to be

a heck of a wild ride so I don't know

about you but I'm pretty excited about

what's coming I'm excited about the

empowerment that AI is going to bring to

education to business to creating music

to creating movies with things like

sunno Ai and soraa is going to empower

people to do more and we'll have some

bumps in the road I'm sure but wherever

you are in life I would say get excited

if you're in a position to become this

AI automation consultant and help people

incorporate neural networks into various

parts of their business to create these

fine-tuned custom models for them I

think that's going to be a millionaire

making industry probably for some people

if you're running a business or or want

to think about where things like that

could help you in the business where

they could help you do more or do more

with less and if you're wondering why I

keep showing you this wolf from p and

boots I honestly don't know it's just a

great character he just cracks me up

this is the guy that plays them by way

if you weren't aware anyways my name is

Wes Roth and thank you for watching