Generative AI: Moving Beyond The Hype & Hysteria

Summary

The video explores why generative AI is gaining significant attention. It compares generative AI's growth to the 'Big Data' trend, noting that generative AI goes beyond traditional machine learning by creating new data rather than classifying existing data. The speaker highlights real-world applications, like Wayfair's AI-powered room designs, and discusses how AI tools streamline creative processes without threatening jobs. The challenges of AI, such as hallucinations and legal concerns, are also mentioned. The speaker concludes by emphasizing AI's future, combining multiple models and applications for better, task-specific results.

Takeaways

• 📈 **Generative AI's Rise**: Generative AI is gaining popularity because it uses historical data to create new data instances, unlike traditional analytics that append information to existing data.
• 🚀 **Innovation Overload**: The ability to generate new data instances opens up new use cases that were not possible before, similar to how Big Data revolutionized the industry a decade ago.
• 🏠 **Practical Applications**: Companies like Wayfair use generative AI to allow customers to visualize room designs with actual purchasable products, streamlining the buying process.
• 🎨 **Creative Freedom**: Generative AI could free up designers to focus on more complex tasks by handling routine design mockups that customers might not purchase.
• 🤖 **Seamless Integration**: The technology is being integrated into tools like Adobe's, allowing users to interact with AI capabilities without needing to understand the underlying technology.
• 🤝 **Contrarian View on Fair Use**: The speaker argues that generative AI's use of training data is analogous to how artists and musicians learn from past works, suggesting AI should not be held to a higher standard.
• 📚 **Educational Parallel**: Just as students learn from existing works to create their own, generative AI learns from vast amounts of data to produce new creations.
• 🔍 **Hallucination Misconception**: Generative AI does not 'hallucinate' or match prompts to existing data; instead, it creates entirely new outputs based on mathematical probabilities.
• ⏲️ **Recency and Obscurity**: The accuracy of generative AI is influenced by the recency and obscurity of the information; well-documented recent events are more likely to be accurately represented.
• 📉 **Model Collapse Risk**: There's a risk of 'model collapse' where the AI's output becomes less diverse and converges towards the mean, reducing its effectiveness over time.
• 🛠️ **Ensemble Approaches**: The future of generative AI may involve using multiple models in an ensemble approach to increase accuracy and reliability.

Q & A

Why has generative AI gained so much attention recently?

Generative AI has gained attention because it creates new examples of data entirely, such as new images or strings of text, instead of simply classifying or predicting based on existing data. This ability opens up new possibilities that were not possible with traditional AI models.

How does generative AI differ from traditional machine learning models?

Traditional machine learning models classify or make predictions based on existing data, such as identifying if an image contains a cat or not. In contrast, generative AI creates new data by generating unique examples rather than analyzing existing ones.

What is an example of a business application of generative AI mentioned in the transcript?

Wayfair uses generative AI in a tool that allows customers to upload an image of their space and input design preferences. The AI generates a visual of the room with purchasable products, allowing customers to see what their space could look like and easily shop for items.

Does generative AI threaten jobs, such as interior designers?

The transcript suggests that generative AI doesn't necessarily threaten jobs like those of interior designers. Instead, it allows them to focus on more complex, creative tasks rather than routine tasks, such as creating multiple mockups for undecided customers.

How has the integration of analytics and AI changed over time?

In the past, analytics were more visible, and users knew they were interacting with algorithms. Today, AI functionalities are seamlessly embedded into products and services, so users often interact with AI without even realizing it, such as generating images or analyzing credit scores.

What is the 'contrarian view' on AI and copyright raised in the transcript?

The speaker argues that AI should not be held to a higher standard than humans when it comes to learning from existing content. Just as art or music students learn by studying past works, generative AI uses large datasets to create new works, and this should not inherently be seen as problematic.

What are AI 'hallucinations' and why do they occur?

AI hallucinations refer to situations where AI generates incorrect or made-up information. This happens because generative AI doesn't retrieve real-world documents or facts but creates responses based on mathematical probabilities derived from its training data.

What is one of the major limitations of current public AI models?

One major limitation is that public AI models, such as GPT, are trained on data up to a certain point (e.g., 2021). As a result, they may not be able to provide accurate information about recent events or conflicting documentation.

What risk does 'model collapse' pose for generative AI?

Model collapse occurs when AI models are trained on data that includes a large amount of generated content instead of human-created content. Over time, this can cause the model to produce less accurate or more generic outputs, which diminishes its usefulness.

How can companies maximize the value of generative AI according to the transcript?

Companies can maximize the value of generative AI by building application layers that parse user prompts and direct parts of the task to the appropriate models, such as using a search engine for factual data and a math engine for calculations. This ensemble approach leverages AI’s strengths for different types of tasks.

Outlines

00:00

🚀 Generative AI's Impact and Applications

The speaker begins by discussing the surge in generative AI's popularity, drawing a parallel to the 'Big Data' trend of the past. They explain that big data's significance was not merely the volume of data but the introduction of new types of data, such as text and images, which enabled novel use cases. Similarly, generative AI stands out for its ability to create entirely new data instances, not just augment existing ones. The speaker illustrates this with an example of how Wayfair uses generative AI to help customers visualize room decorations, a process that was traditionally time-consuming and expensive. They also touch on the potential for generative AI to redefine roles like designers, allowing them to focus on more creative tasks.

05:02

🎨 The Art of Generative AI and Legal Considerations

The speaker offers a contrarian view on the legal disputes surrounding generative AI, particularly concerning fair use and training data. They compare the learning process of generative AI to how artists and musicians study the works of their predecessors to develop their own style. The speaker argues that generative AI, having 'studied' vast amounts of data, arguably 'steals' less from its training data than humans do from their learning experiences. They also address the misconception of AI 'hallucinations,' clarifying that generative AI creates outputs based on mathematical probabilities rather than matching existing data. The speaker highlights the importance of understanding the limitations and capabilities of generative AI when it comes to accuracy and consistency.

10:02

⏳ The Reliability and Risks of Generative AI

The speaker delves into the accuracy and reliability of generative AI, emphasizing the importance of recency and obscurity of information. They note that AI performs well on well-documented and recent events but may falter on obscure or more recent topics. The speaker shares personal anecdotes about AI inaccuracies, underscoring the need for caution when using AI-generated information. They also discuss the risk of 'model collapse,' where overexposure to AI-generated data can degrade the model's performance. The speaker suggests that using generative AI appropriately, avoiding tasks where exactness is critical, and combining it with other computational models can mitigate these risks.

15:05

🛠️ The Future of Generative AI: Ensemble Approaches and Custom Plugins

The speaker envisions the future of generative AI involving ensemble approaches, where multiple AI models are used to cross-verify information, and custom plugins that cater to specific tasks. They predict that underlying AI models will become commoditized, and the true value will lie in the application layers that parse prompts and direct them to the appropriate computational engines. The speaker suggests that companies will need to develop these application layers to leverage the strengths of generative AI while avoiding its pitfalls. They conclude by emphasizing the importance of thoughtful integration of AI into business processes to enhance efficiency and creativity.

Keywords

💡Generative AI

Generative AI refers to artificial intelligence systems that can create new content, such as images, text, or music, based on existing data. In the video, the speaker explains how generative AI differs from traditional AI by not just classifying or predicting data but by generating entirely new examples. It's highlighted as a significant development because it opens up new use cases and applications, much like the advent of Big Data did a decade ago.

💡Big Data

Big Data denotes the vast amounts of structured and unstructured data that are so large and complex that they are difficult to process using traditional data management tools. The speaker contrasts Big Data with generative AI, noting that while Big Data was about the volume and variety of data, generative AI is about creating new data types and applications, which is why it's gaining significant attention.

💡Machine Learning

Machine learning is a subset of AI that focuses on enabling machines to learn from data, identify patterns, and make decisions with minimal human intervention. The script mentions machine learning in the context of how traditional models would take data and append something to it, like a prediction or classification, unlike generative AI which creates new data.

💡Data Generation

Data generation in the context of the video refers to the process by which generative AI creates new data samples, such as images or text, from existing data. It's a key feature that distinguishes generative AI from other types of AI and is central to the new capabilities and applications discussed in the video.

💡AI Functionality

AI functionality in the video script refers to the capabilities and features of AI systems that can be embedded into various applications to enhance their performance. The speaker gives examples of how AI is being seamlessly integrated into tools like Adobe's product, where users can input prompts to generate images, illustrating the practical application of AI functionality.

💡Analytics

Analytics in the video script refers to the use of statistical and mathematical techniques to analyze data and extract meaningful insights. The speaker discusses how analytics, including traditional AI work and generative AI, are combined to create innovative solutions like room decoration tools that can generate images of room setups based on user inputs.

💡Training Data

Training data is the data used to train machine learning models to recognize patterns and make predictions. The video script touches on the debate over fair use and the use of training data in AI, suggesting that generative AI's use of vast amounts of training data is analogous to how artists and musicians learn from past works to create their own.

💡Model Collapse

Model collapse is a phenomenon where a generative model starts to produce generic or less varied outputs over time, often due to being trained on too much generated data. The speaker warns about the risk of model collapse in generative AI, emphasizing the need to maintain a balance between human-generated and AI-generated data to ensure the model's effectiveness.

💡Ensemble Approaches

Ensemble approaches in the video script refer to the use of multiple models or algorithms to solve a problem, with the idea that combining their outputs can improve accuracy or performance. The speaker predicts that we will see ensemble approaches in AI, where different models handle different parts of a task, to leverage the strengths of each and produce more reliable results.

💡Application Layers

Application layers in the context of the video script are additional software components or plugins that are built on top of foundational AI models to provide specific functionalities or improve performance for particular tasks. The speaker suggests that the real value for companies will come from creating these application layers that can parse prompts and direct different parts of them to the most appropriate AI models or engines.

Highlights

Generative AI is gaining significant attention due to its ability to create entirely new data examples, unlike traditional AI which appends predictions or classifications to existing data.

The emergence of Big Data 10-12 years ago was due to the availability of different types of data, such as text and images, which enabled new use cases.

Generative AI opens up new problem-solving possibilities that were not previously accessible, similar to how Big Data did a decade ago.

An example of generative AI in action is a Wayfair tool that allows users to upload images of their space and generate room layouts with different products.

Generative AI can revolutionize industries by providing instant design mockups, reducing the need for physical visits or manual designs.

Designers and data scientists can focus on more complex tasks as generative AI automates routine design mockups.

The seamless integration of AI and analytics into user interfaces allows for intuitive interactions without explicit awareness of the underlying technology.

Adobe's product demonstrates generative AI by allowing users to input prompts and generate images for presentations, enhancing creative workflows.

Generative AI's training on vast amounts of data means it has 'seen' more examples than a human, potentially leading to less 'stealing' from individual sources.

Copyright law should apply to AI similarly to how it applies to human learning and creation, according to the speaker's contrarian view on fair use.

Generative AI is not about finding existing data that matches a prompt but creating new, mathematically probable outputs based on the input.

The accuracy of generative AI depends on the recency and obscurity of the information; well-documented events are more likely to be correctly generated.

Generative AI can produce different but not necessarily incorrect answers to the same question, highlighting the probabilistic nature of its responses.

The speaker shares a personal anecdote where generative AI incorrectly 'awarded' their book a nomination, demonstrating the potential for inaccuracies.

Generative AI's ability to handle standardized tests with factual answers does not equate to general intelligence or sentience.

For tasks requiring exact answers, like inventory checks or flight availability, generative AI should not be used to provide probabilistic guesses.

Ensemble approaches that use multiple models and reconcile their answers can improve the accuracy and reliability of generative AI outputs.

The future of generative AI lies in building application layers that can parse prompts and direct different parts to the most suitable computation models.

Underlying generative AI models may become commodities, with value shifting towards companies that can create specialized plugins and application layers.

Transcripts

[Music]

let's start with why is generative AI

blowing up so much and I think there's

an important uh thing that's going on

here which is if you go back 10 years 12

years ago when Big Data hit and it feels

like it was a lot further than that it's

10 to 12 years I always said that big

data was a bad label for that Trend

because what made big data so big wasn't

that there was more data wasn't that it

was bigger data it was that it was

different data so we didn't just have a

bunch more transactions or inventory

data we had things for the first time

like text and images and web browsing

history and so forth and that opened up

entirely new use cases which is why Big

Data went big now ai's been trending for

years but why did it blow up so much in

the past year and it's because

historically every type of machine

learning or analytical model would take

a given row or a given example of data

and append something to it a typically a

prediction a forecast a

classification and the early AI even

though it was going hot and heavy for a

couple years was effectively doing the

same is this image a cat or is it not a

cat is the sentiment of this specific

piece of text good or is it bad and so

while there were definitely new problems

you could solve with that it was the

same type of problems with different

data whereas generative AI on the other

hand now is something totally different

where it uses all of the historical data

we have but then generates a new example

of that data in totality so a whole new

image not classifying an image a whole

new string of texts not classifying a

text and just like with big data then

there's all kinds of new problems we can

solve with that that weren't previously

uh possible and so I think that's a big

reason why it's gone so

big

and there's stuff happening very fast

this is a shot of one of my favorite uh

articles I've read in in recent months

this is Decor ay from Wayfair you know

back to companies morphine a classic

cataloger from the day that's that's

gone online and what they have if you

look at the top right corner there's an

image that's kind of cut down the middle

and there's little arrows you upload the

image of your space you enter in a

prompt what you're looking for what

color scheme and such it'll generate an

image with products in it so you can see

what your room would look like and if

you move that little arrow back and

forth it'll just give you more or less

of your room versus the new room and so

if you think about it historically if

you wanted to decorate a room you'd have

to go to a a store even if you're on an

online site maybe get a designer to

actually mock something like this up

which would take time you could only get

one or two examples uh and it was costly

for the the seller now you can go out

and with a just a few prompts I could

say you know I don't want white I want

blue and it'll instantly update and

what's even cooler is that all of those

products are actual products that are

purchasable so it's not just a

hypothetical here's what your room might

look like it is literally this is your

room if you want it you know you could

either buy the whole set or you could

click on on certain items and buy the

set and so this is an example of of so

many different types of analytics coming

together including some traditional AI

work some generative AI some traditional

uh propensity modeling and such but when

you think about it from a customer

perspective I can now go out and matter

of minutes have multiple options for

both style and product that I might put

in my room and buy it and be done and

the the retailer of course they're

getting business much faster I still

think though the designer if you're a

designer what you really want to do

probably much like data scientists you

want to work on the cool stuff those

designers don't get excited about having

to do 10 different quick room mockups

for 10 customers today nine of which

won't buy anything anyway they'd rather

be working with those customers that are

going to hire him to really do a full

classic design and I think that'll free

this up so I also don't know that it

threatens the jobs of designer so much

as it lets them focus on the cool

stuff and one of the things that's

happening in this isn't just with

generative AI but it's definitely going

up a notch is this idea of seamlessly

embedding analytics and AI functionality

so that the users aren't even aware

explicitly that they're using it so if

you think back even 5 10 years ago

everyone knew analytics was in use right

you go to a website you know those

offers are building off of whatever

history you had and such but you would

just see what they offered you you

wouldn't really interact with the

analytics per se and even with

traditional analytics this is changing

when you go to your credit card or bank

account and they say check your credit

score when you go to look at the credit

score they'll now all have little

simulators well you can simulate what

would happen if I increase the balance

on a card or opened a new account and

they'll simulate what your score would

be so you're actively interacting with a

model this is from adobe's um fir

product and much like PowerPoint and

Adobe for years have had type in a

keyword it'll give you real pictures

they've just embedded generative AI now

type in your prompt it'll generate

images you can insert them so you have

creative types now who don't know

anything about AI or modeling probably

don't care about how it works but

seamlessly in the same interface and

with the same style of interaction

they've always used are able to take

full advantage of the uh AI capabilities

generative

AI so I want to give you a bit of a

contrarian view this whole battle over

fair use and there's lawsuits all over

about training data and such and so my

contrarian view is that if I were to go

and study art or music as an example

what's a huge part of what you learn

they make you go out and study past

artists past musicians learn about their

style and why they did what they did and

how they did it and then over time

you're supposed to synthesize what you

learn about that art or that music and

then you create your own style which

which is by necessity based off of

everything that you've ever seen of

those artists and musicians now we can't

go too far there's cases you know the

early days of sampling I remember it was

uh Vanilla Ice was one of the first

people got sued because he sampled too

much of a David Bo song as is and didn't

credit him and he lost a big lawsuit and

had to pay royalties and such and such

so the point is there's already

copyright law and I don't think that AI

should be held to a higher standard

though than we are held to so if I'm an

artart an art student I could learn

let's say tens of thousands of pieces of

art maybe if I'm a musician tens of

thousands of songs maybe on the Outer

Edge O of which I would then generate my

output these gener of AI algorithms have

looked at millions of photos tens of

millions hundreds of millions every song

ever created so I would argue that in a

way a generative AI song or piece of art

steals a little less quote unquote from

all of its training data than you or I

would steal from from all of our

training data because they have a whole

lot more training data so I think

there's a lot to figure out here but I

do think that this idea that just

because AI looked at an image or

ingested a song and then generated

something that inherently that's a you

know a big problem but again this this

will be in the courts for

years so the biggest misnomer I think

that happens around generative AI is

this idea of hallucinations I'm sure

everybody here has heard of

hallucinations show P everybody

everybody's heard of it so here's the

key to understand and this is where you

folks in the room I'll bet a higher

percentage of you know this by far than

the general population but when you go

to Google you type in keywords and what

Google's going to attempt to do is to

match your keywords to real websites

real documents real tags on real images

whatever the case is but the idea is

matching what you've asked for to real

specific items that exist now it might

do a better or worse job of that

sometimes the links won't be as relevant

but it'll always be a real document a

real link

Etc the thing with generative AI it's in

the name generative generative AI is not

attempting to match anything to your

prompt what it's doing is reducing your

prompt to a mathematical representation

passing it through an algorithm to then

spit out a

mathematically uh probable answer it's

effectively making up every single

answer that it gives and so rather than

being concerned about the hallucinations

you should actually be amazed that while

generative AI is literally making it up

word by word every time you ask it a

question it gets a lot of things right

because well documented things

probabilistically speaking is the right

the right answer is what the facts

are but there's some nuances here I went

in and asked the same question tell me

the history of the world in 50 words as

soon as the first answer came back I

resubmitted the exact same question

question if you read that neither one of

them I would argue is wrong although

there could be some actually wrong facts

in those if you really look carefully

but they're also not the

same and the interesting thing after I

post about this on LinkedIn a friend of

mine then went and did the same

experiment and within say chat GPT and

similar tools within the context of a

single session it retains some some

memory of it and that's why those two

answers are very similar this other

gentleman submitted the same question

and what he got similar two similar but

different answers but in my case it's

really a Humanity Centric uh history of

the world about civilization both of his

answers started about you know 13

billion years ago the Earth formed and

then there were dinosaurs and then you

know at the end it says there's people

and so between he and I we got four

answers none of which were wrong per se

all of which were different and his

versus mine would have given a

completely different impression of the

history of the world so you just have to

be aware and be cautious when you use

the generative AI that not only could it

give you wrong information but it might

not even give you the same

information a minute from now let alone

if one of your you know colleagues goes

to replicate it a week from

now so what will it get right and wrong

this is actually uh discernible first

thing any recency is important because

the current models that are public I

think it was the end of

2021 uh they were cut off so you asked

something before that it might do a

pretty good job but you ask say about

the Ukraine war there's two problems

with that one it didn't exist in 2021

and two there's a lot of

conflicting uh documentation of the war

in Ukraine and so probabilistically who

knows what you're going to

get but equally important is obscurity

so if I ask about World War II and I ask

about Pearl Harbor or Winston

Churchill the most probable answer is

probably pretty much the right answer

because Pearl Harbor's highly documented

and very consistently documented Winston

Churchill but if I go and ask about an

obscure Colonel and an obscure battle in

the middle of Germany I mean who knows

what you're going to get it's probably

half made up it might be all you know

all bogus so I've asked gone and asked

it asked it questions about myself and

it came back uh I I remember this one

really threw me it said that my first

book was nominated for an award by

informs and I I was like I don't

remember this and I said well maybe

because I got nominated and didn't win

and no one ever told me I I just didn't

know I went and poked around I could

find no record of it and so even with

myself and I know my history it it made

up a very believable award by a very

real organization that would have had

exactly that award but then you know my

book never had so anyway I didn't update

my bio with that award

nomination so the point is if it can

fool me about myself you got to remember

that it can fool you very easily on

other

things if if you want facts or

subjectivity people have I think wrongly

said that this is you know general

intelligence sentient because it can

pass an SAT or GRE and so forth with a

high level but think about those exams

they ask a very specific question with a

factual answer and then there's four

options that you choose from and so

actually probabilistically speaking if I

say you know who won the case of Brown

versus Board of Education in 1970

whatever it was or 1960 whatever the

point is that chat gbd should do a good

job of something like that because it's

a Well documented case the outcome is

known and probabilistically it'll

probably get the right answer and so a

lot of those standardized tests are

testing your ability to recall some

general facts more than it is that

you're really having to Think Through

complicated uh

topics and then last you don't want it

to do math or any kind of computation

because remember it's not a calculator

if you say what is 1+ one and it says

two it has not gone and calculated that

1 + 1 equals 2 it is probabilistically

saying if I see the string 1 + 1 what is

the most likely next answer and there's

enough 1 plus 1 equals 2 out there it's

probably going to get two almost every

time but you can go out and find

examples of even late Elementary School

math it'll mess it up left and right

it'll do the wrong order of operation in

a complicated math probably it'll put an

extra parenthesis and it might even get

80 90% of it right but then that one

thing it gets wrong makes the entire

answer wrong

but this isn't to say it's not at all

useful so here's a a prototype I saw

that Amazon's about to roll out you know

when I go to a product and I'm looking

at reviews I get um stressed because I

feel obligated to look at a bunch of

reviews the positive ones and negative

ones and I try and figure out what's the

general pattern here and is the things I

care about going to be affected what

they're doing here is they're taking

those thousands of reviews passing it

through and saying give us a paragraph

summary and then see if you can tag any

of a number of important things like

performance ease of use that there that

this appears to be positive or negative

on that and so the beauty of this is we

don't care it doesn't have to be the

exact same answer like those examples I

gave you earlier as long as it's

generally uh the right direction that's

what we need and in this case they

probably freeze this and it'll show it

to everybody who asks for the sumary

reviews up until at some point they'll

refresh it and give it to everyone so

it's not like I'll get a different

answer the next minute the next person

gets a different answer and so these are

these these nuances where this would be

an incredibly powerful approach that

will leverage all the strengths of these

models without actually the negatives

whoops thing just did a double click so

one of the big risks we have is this

idea of model collapse and so any kind

of model as you know it kind of

regresses to the mean you tend to trim

off more and more outliers each pass so

on the top is a real distribution and as

you move to the right that's what

happened over many iterations first the

distribution changed then it converged

on a point

down on the bottom the classic uh hand

handwritten numbers as you take a bunch

of the original human generated data and

pollute it with more and more algorithm

generated data and retrain it it

eventually converges to a bunch of like

Blurry zeros and ones and so the the

power of the current models is that it's

almost all human generated content

training them to predict human generated

output we're soon going to have more

images and more text than was human

generated it might almost be true today

but we're talking very short time and if

we don't filter it properly will

basically have generative AI become

completely useless because we won't be

able to have a good model anymore

because we polluted so much generated

stuff that comes down to the mean so we

have to figure this one out uh pretty

quickly and then this gets back you just

don't just don't force generative AI

where it doesn't belong you got to you

got to be thinking about what's the task

you've asked for if you want to know how

much inventory is left you don't want it

to generate a probabilistic answer you

want it to to actually go look up that

answer if I call to know is there a seat

available on that flight and how much

does it cost I don't want a

probabilistic guess I want an actual

answer if on the other hand I want to

know are these reviews generally good or

bad can you give me the main themes that

someone's talked about or here's a draft

I just wrote can you double check this

for some grammar and such it makes a lot

of

sense and so what we're going to start

to see and it's already happening is

this idea of Ensemble approaches using

multiple types of models multiple types

of things and you have to think about

chat GPT everyone talks about that's the

app on top of the underl model GPT 4

these underl models are already

commoditized right you can use Microsoft

open AI all of these you don't need to

build your own but you want to build

layers on top and so back to the math

problem instead of

asking do math have the prompt parsed

and realize here's a math question and

pass it to a math engine that's what

wolfrom has now it'll extract the parts

that are asking a math question do a

computation with a computation engine

and spit it back and so what I see over

time will be first of all you'll submit

your prompt to multiple large language

models and then reconcile say three or

four answers together in an ensemble

approach and this engine over here might

have made something up and this engine

over here might have made something up

but hopefully that will be the parts

that aren't consistent and the

consistent Parts across the four will be

will be mostly the accurate pieces so I

think we'll have plugins for physics for

chemistry for certain types of of

physics chemistry math and then last not

Le you with this is where I think the

real value of this if you want to

differentiate as a company is build

these application layers that are going

to parse out the prompt identify what

part of this prompt needs what type of

information computation input and maybe

part of it goes to a a traditional

Google search to actually look up some

documents while another part goes and

does some math another part looks up

something about the chemistry and the

last part crafts some kind of paragraph

summary of the information that was

found but I think this is where this

value of all of this is going to play

out those underlying models will be a

commodity there will be companies

generating um let's say generic add-ons

and then as an organization what you all

are going to need to do is generate your

own little add-ons and plugins that you

can put on top and you'll get your most

value so with that I think I'm right at

about 20 and I'll say uh thank you for

coming I'll be around for an hour or

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two

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oh