3. OpenAI API Python - Earnings Call Summarization

Summary

This video script offers a tutorial on utilizing OpenAI's API with Python to perform tasks like summarizing earnings calls. It guides viewers through installing the OpenAI package, obtaining an API key, and crafting prompts for the AI model. The script demonstrates the process of summarization by breaking down a lengthy earnings call transcript into manageable chunks and using the API to generate concise summaries for each part. The tutorial emphasizes the importance of understanding basic functionalities to build more complex applications and hints at future videos exploring real-time speech transcription and text classification.

Takeaways

• 🧑‍💻 The power of OpenAI is fully realized when developers interact with it through computer programs using its API, allowing dynamic crafting of prompts and processing of outputs for unique tasks.
• 📚 To start with OpenAI's API in Python, one must first install the OpenAI Python package using the pip command 'pip install openai'.
• 🔑 Users need to sign up on OpenAI's website to obtain an API key, which is essential for accessing the API and managing credits for the services used.
• 💡 OpenAI provides free credits worth $18 to new users, which is a good starting point to learn and experiment with the API without incurring costs.
• 💰 The API key is necessary for billing purposes, as using OpenAI's services will eventually require payment when the free credits are exhausted.
• 📈 OpenAI's models, such as Ada and DaVinci, have different costs associated with them based on the complexity and capabilities of the model.
• 📝 The script demonstrates a basic task of using the OpenAI API to answer questions and generate summaries, starting with a simple Q&A prompt and moving to summarizing earnings calls.
• 🔍 The script also covers how to handle large text data by chunking it into smaller parts to comply with OpenAI's token limit for processing.
• 🔄 The process of summarizing long documents involves breaking down the text, sending chunks to OpenAI for summarization, and then combining the results into a cohesive summary.
• 🛠️ The script provides a foundation for building more complex applications, such as those that process audio, text, and video, and perform natural language processing tasks.
• 🔮 The tutorial encourages users to explore OpenAI's capabilities further and consider building applications that can leverage its advanced features for various use cases.

Q & A

What is the main purpose of the video script?

The main purpose of the video script is to demonstrate how to use OpenAI's API with Python to perform tasks such as summarizing earnings calls and to introduce viewers to the capabilities of AI for developers.

How can one start using OpenAI's API?

To start using OpenAI's API, one needs to install the OpenAI Python package using pip, obtain an API key from the OpenAI website, and then configure the API key in the Python code.

What is the significance of an API key in using OpenAI's services?

An API key is significant as it ties the usage of OpenAI's services to an individual account, allowing for tracking of usage and billing, ensuring that each user's credits are managed separately.

Why might OpenAI start charging for its services in the future?

OpenAI might start charging for its services in the future because running cloud infrastructure and making machine learning models available as a service incurs substantial costs.

What is the initial credit provided by OpenAI for new users?

OpenAI provides 18 dollars worth of credits for free to new users, allowing them to try out the service and learn how to use it.

How can the OpenAI API be used to summarize text?

The OpenAI API can be used to summarize text by sending a prompt with the text to be summarized appended with 'tldr' to the OpenAI completion endpoint, which will then return a summarized version of the text.

What is the limitation on the maximum content length for the OpenAI API?

The OpenAI API has a maximum content length limit of 4096 tokens, which means that very long texts need to be chunked into smaller parts before being sent for processing.

How can the text of an earnings call be obtained for summarization?

The text of an earnings call can be obtained by using the Python requests library to fetch the text from a URL where the transcript is available online.

What is the approach to handle long texts when the OpenAI API has a token limit?

The approach to handle long texts is to chunk the text into smaller parts, summarize each part individually using the OpenAI API, and then combine the summaries to form a complete summary of the original text.

What are some potential future applications of the OpenAI API as mentioned in the script?

Some potential future applications of the OpenAI API include real-time speech transcription, text classification for sentiment analysis, and building full-stack applications that process various types of data like sound, text, and video.

Outlines

00:00

🚀 Getting Started with OpenAI's API

This paragraph introduces the capabilities of OpenAI's large language model, emphasizing the interactive experience provided by the Open AI playground and the powerful API available to developers. It outlines the process of installing the OpenAI Python package, obtaining an API key, and configuring the environment for API use. The speaker also discusses the cost implications of using the API, including the initial free credit and the potential future costs associated with extensive use, highlighting the balance between cost and the convenience of leveraging AI without training custom models.

05:01

📚 Exploring OpenAI's API Features and Prompts

The speaker delves into the various examples and prompts available within the OpenAI API, demonstrating how to interact with the API using Python to perform tasks like answering questions or summarizing text. The paragraph highlights the importance of crafting specialized prompts to achieve desired outcomes from the AI model. It also touches on the different models offered by OpenAI, such as Ada and DaVinci, and their respective costs, emphasizing the need to choose the right model based on the task's complexity.

10:03

🔍 Summarizing Earnings Calls with OpenAI

This section focuses on a practical application of the OpenAI API: summarizing earnings calls. The speaker describes the process of transcribing audio to text and then using the API to generate summaries with the 'TLDR' prompt. The paragraph discusses the limitations of the API in terms of content length and introduces a method for dividing the text into manageable chunks to overcome these limitations, providing a step-by-step approach to summarizing large documents effectively.

15:04

📈 Implementing Text Chunking for API Summarization

The speaker provides a detailed explanation of how to chunk long texts into smaller segments for summarization using the OpenAI API. This includes splitting the text into an array of words, dividing it into equal-sized chunks, and then joining these chunks back into sentences for summarization. The paragraph demonstrates the process of summarizing each chunk individually and then combining the summaries to form a comprehensive summary of the entire document.

20:06

🌟 Building Complex Applications with OpenAI API

In the final paragraph, the speaker envisions the potential for building more complex applications using the OpenAI API, starting from mastering simple tasks and gradually incorporating more sophisticated functionalities. The speaker expresses optimism about the future of AI applications and invites viewers to share ideas for specific use cases they would like to see demonstrated with OpenAI. The paragraph concludes with a teaser for upcoming videos that will explore real-time speech transcription and text classification, among other advanced topics.

Keywords

💡API

API stands for 'Application Programming Interface,' which is a set of rules and protocols that allows different software applications to communicate with each other. In the context of the video, the OpenAI API is highlighted as a powerful tool that enables developers to integrate AI capabilities, such as language models, into their own computer programs. The script discusses the process of using the OpenAI API with Python to perform tasks like summarizing text.

💡GPT (Generative Pre-trained Transformer)

GPT is an abbreviation for 'Generative Pre-trained Transformer,' which is a type of AI language model developed by OpenAI. The video script mentions experimenting with GPT through the OpenAI playground, showcasing its ability to generate creative and responsive text based on user prompts. The script also refers to various models like GPT-3 and DaVinci, indicating the evolution and capabilities of these models.

💡Language Model

A language model is an AI system that is trained to predict and generate human-like text based on the input it receives. The video emphasizes the capabilities of large language models like GPT to produce creative outputs and perform tasks such as summarizing earnings calls, which is a key demonstration in the script.

💡Summarization

Summarization refers to the process of condensing a large piece of text into a shorter version while retaining the main points. In the video script, the task of summarizing earnings call transcripts is used as an example of how the OpenAI API can be utilized to process and distill information efficiently, as demonstrated by appending 'TLDR' to a prompt for the AI to generate a summary.

💡Token

In the context of AI and language models, a token typically refers to a unit of text, which could be a word, a number, or a punctuation mark. The script mentions 'per thousand tokens' in relation to the cost of using certain models like Ada and DaVinci, indicating that the pricing is based on the amount of text processed by the AI.

💡DaVinci Model

The DaVinci model is a specific version of the GPT language model, known for its advanced capabilities in generating human-like text. The video script discusses using the DaVinci model for tasks that require more sophistication, such as creative storytelling or advanced summarization, and notes that it comes at a higher cost due to its capabilities.

💡Earnings Call

An earnings call is a conference call that publicly traded companies typically hold to discuss their financial performance with investors and analysts. The script uses the task of summarizing an earnings call transcript as an example to demonstrate the practical application of the OpenAI API in processing and summarizing large amounts of financial and business-related text.

💡Python Package

A Python package is a collection of Python modules, libraries, or other packages that serve a specific purpose. The video script instructs viewers on installing the 'openai' Python package using the pip command, which allows developers to interact with the OpenAI API and utilize its features within their Python programs.

💡API Key

An API key is a unique code that identifies an application making requests to an API. In the script, the process of obtaining an API key from the OpenAI platform is described as a necessary step for developers to authenticate their applications and gain access to the API's capabilities, such as calling different models like GPT-3 or Codex.

💡Machine Learning Model

A machine learning model is an algorithm that learns patterns from data and makes decisions or predictions based on that learning. The video script refers to the costs associated with running these models as a service, indicating that while the initial use may be free, there are expenses involved in maintaining and providing access to these advanced AI models.

Highlights

Introduction to the power of AI and its creative output through the Open AI Playground.

The capability of developers to interact with OpenAI through a powerful API for dynamic tasks.

Tutorial on getting started with OpenAI's API using Python for summarizing earnings calls.

Installation of the OpenAI Python package using pip and importing it for use.

Explanation of obtaining and securing an API key from the OpenAI website.

The necessity of an API key for accessing the service and managing credits.

Details on the cost associated with different OpenAI models and their usage in applications.

Demonstration of using the OpenAI API to answer questions like 'Who is the best investor of all time?'

The process of crafting specialized prompts for the OpenAI API to perform unique tasks.

Experimentation with different parameters like 'temperature' to control randomness in responses.

Method of handling text length limitations by chunking data for processing by OpenAI.

Technique for summarizing long earnings call transcripts by breaking them into smaller pieces.

Example of summarizing the first 1/6th of an earnings call document using OpenAI.

Collating summaries from multiple text chunks to form a comprehensive summary.

Final summary of an earnings call highlighting key financial figures and business insights.

Discussion on the potential for building more complex applications using the OpenAI API.

Invitation for viewers to suggest specific applications or tasks for future OpenAI demonstrations.

Transcripts

over the past several weeks millions of

people have been experimenting with chat

GPT and the open AI playground by typing

in a specialized text prompt into a text

area and clicking submit users are able

to witness the power of AI right in

front of their faces and see the very

creative output that can be produced by

this large language model but the real

power of open AI comes when you are a

developer who is able to interact with

openai from a computer program openai

provides a very powerful API that allows

us to call gpt3 codex and Dolly from our

computer programs allowing us to

dynamically craft specialized prompts

and process that output in order to

perform very unique and interesting

tasks and build applications that do

whatever we want to do with AI without

training our own models in this tutorial

I'm going to show you how to get started

with openai's API and Python and we're

going to start with a very simple task

which is summarizing earnings calls from

there we'll be able to build more and

more complex things but I want to start

with a very basic task and get people up

and running with open AI first and then

we'll begin to build more and more

complex applications if you've never

written code to interact with the open

AI API I've included a Google collab

notebook down below where you can follow

along exactly with what you see here

execute the code and see how all this

works so to get started with the open AI

API from python the first thing we need

to do is install the open AI python

package and to do this you just run this

pip command pip install openai and

that's going to use the python package

manager to install open AI once you see

that the package is installed right

there you see the check mark we need to

import the open AI package very easy

right the next thing you need to do is

if you don't have an account on open AI

already you need to visit this API page

here and sign up and when you do that

you'll be able to log in with your

account so you sign up there I just used

my Google account so it'll just log me

right in and and what you're going to

need to do is visit this view API Keys

page right here and what you can do is

create a new secret key so I'm going to

click that and I'm going to delete this

key right afterwards but you'll get this

key and you can copy it right there to

your clipboard and then you can paste it

in down below and you can delete these

if you're worried that someone gets it

you can just delete them I'm going to

delete that one right after this

tutorial so what I'm doing here is

configuring open AI to use this API key

and so you can either set that as a

string right here but since I want to

show a an API key on here without

showing it to you I realize I already

did but what you can do is you can just

import this Library called get pass if

you run this it'll actually prompt it

prompt you for it and I'm going to paste

a new one right there that you've never

seen before right I paste it in and I

click enter it should be done and now

open AI should have access to this API

key now why do you need an API key what

do you mean I've been using chat gbt and

typing stuff in the Box and it's been

free no once uh mentioned anything about

an API key well I don't expect this free

stuff to last forever when you sign up

for open AI they give you 18 worth of

credits free and this is enough to

actually use it quite a bit you can

probably run thousands and thousands of

requests on this thing and learn how to

use it and all of that but actually

running all this Cloud infrastructure

and making this machine learning model

available as a service I think costs a

substantial amount of money for them so

I expect that over time that they'll

begin to charge money for a lot of this

stuff now is that worth it for you for

me it personally is I have plenty of

money and I'm willing to pay for things

in order to accomplish tasks and to be

able to train these models on my own

would take so much time and buying like

Nvidia gpus and all the things I would

need to do this and running this service

myself would cost a lot of money so if I

can spend a few bucks a month and run

all this these cool summaries and build

an entire startup on top of it or do

something that helps me financially then

I would love to to use this and I'm

happy to pay for it and I've actually

already done that and I'm spending a few

bucks at this point but I was able to

get by I think you can get by for a

month on just this 18 and learn this so

definitely a sign up and it's definitely

worth it and the reason there's an API

key is so it can tie it to you and so

you have your own credits and you'll get

this uh billing screen if you haven't

used it yet but there's this December

usage and you can see this isn't my main

account but it'll show the number of

times you call it and there's a whole

section where it shows how much it costs

to call each one of these machine

learning models so you can see

generating all these images cost a

couple cents and using these language

models if you use this smaller model

called Ada it's .0004 cents per thousand

tokens and if you use the most powerful

DaVinci model it's two cents per

thousand tokens and so you can send it

tons and tons of text and it'll charge

fractions of a Cent and then over time

you think if you built some startup

that's worth millions of dollars on top

of this and called it thousands of times

this would add up over time right and so

you know to come up with your your use

case for it and decide if this thing is

going to be worth it for you but for now

it's absolutely free and you can try it

out uh with no cost so I recommend

trying it out and learning about this

stuff and I think ultimately

um there will be more and more versions

of this maybe not run by open AI but but

by someone else for instance stable

diffusion I can run it on my MacBook

right here and run it for absolutely

free and it doesn't go through an API I

can download it and run it right here

and open AI whisper which I ran in the

in the last video if you watch that that

does live speech transcription that's

absolutely free and we're able to just

run it on our own machine but uh this

latest DaVinci 3 Model right here I need

to access it through the API and using

chat GPT cost a lot of resources and so

that's why there's an API key here so

once we have the API key configured then

we just need to call openai with one of

these prompts and so I recommend if you

haven't done so already I assume a lot

of people here have already done this

but if you click around they have these

examples here and can look at some of

the powerful prompts that can be sent

here so they have like a q a where you

can ask it a question they have a a text

command they have natural language to

open AI API there's a SQL translate

where you can send in a database

structure we'll play with that later so

send it a database structure and have a

query and maybe build some kind of a

stock screener on top of that there's

parsing unstructured data python to

natural language so this takes code

translates it in a to natural language

so it tells you what the code does and

so the first thing we can try is just

this q a and it shows you what the

prompt looks like and you can say open

in the playground here and type this

prompt directly in and so I can ask of

you know who is the best investor right

and as you probably know you can't just

ask it anything especially about Finance

it doesn't know like real-time price

date or anything and so I don't want

anyone to be misled into thinking you

can ask in any Financial question that's

going to give some great answer for you

it turns down out a lot of those

questions but it doesn't mean it's not

extremely useful for financial tests so

if I say who is the best investor of all

time right it probably has an answer for

that

right if I click that it just says

Warren Buffett just like that and or I

can say who are the best investors of

all time so who are the best investors

of all time let's see if it understands

multiple right George Warren Buffett

George Torres Carl icon Ray dalio

generally considered some of the best

investors of all time so there's no real

absolute there and it has some knowledge

it's trained on all this text and knows

you know these guys are investors that

are highly acclaimed or widely praised

right and so what we can do how do we

call this from the open AI API this is

one interface for openai but the

powerful power as I mentioned is

creating our own unique interfaces that

allow the user to interact with and

enter these prompts in interesting ways

and so what we want to do is take this

exact prompt right and send it over uh

using python code and so we can do is

using this open AI python package we can

send this prompt and so same way we had

this I'm highly intelligent question

answering box ask me a question blah

blah blah I can say who is the the

greatest investor of all time and notice

I have these Carriage returns in here

back just and backslash n right and

that's just a character chart so when I

hit enter that's actually a backslash n

so that's how I encode that in a string

if you're a programmer you know this

stuff already but I'm just beating it to

death here and so uh prompt so there's

our prompt and now we just need to pass

that prompt to this open AI completion

create and so that's a parameter so the

prompt equals this prompt up here and we

need to tell it an engine and so I

mentioned all these models have a

different cost associated with them

right Ada is this cheap one DaVinci and

DaVinci 3 is the latest one this came

out like last week this stuff is

changing so fast so new stuff is coming

out like all the time so we start for

free with 18 in free credit pay as you

go uh choose your own model and so you

can look at this and see how much those

things cost and depending on the

complexity of your task you might just

need this most basic model so you should

use just the cheap one but if you want

something very Advanced you need to

summarize or tell a very creative story

maybe you'll use DaVinci and it'll come

up with a better better answer so I'm

using text DaVinci 3 right here but I

might not have to so you can experiment

with the different engines so you can do

a text Babbage 001 for instance and so

you can change these variables

experiment with different prompts and I

encourage you to play with the

playground for a while figure out what

this thing is capable of and how you

might craft these prompts in order to

come up with unique things right and so

there's a variety of parameters here

this temperature parameter you can

experiment with and this controls the

randomness of the response so you can

have it give a real exact answer or you

can have it be a little more creative a

little bit more random the same with

these uh parameters down here you can

also control the maximum number of

tokens so if you want the response to be

longer or shorter right and so there's a

variety of parameters that are all

documented and we'll talk about these in

more and more detail as we build more

applications and so let's run this now

we're going to send in this string as

input and get a response from open Ai

and see what it tells us so I'm going to

run this right and I ask it the greatest

investor of all time and you can see the

response right here is this dictionary

where choices usage tokens the model

that's used and that's the text

completion it says the greater greatest

investor of all time is widely

considered to be Warren Buffett and so

the magic here is that we can vary this

prompt and build it dynamically based on

some variables based on a text file

based on audio and we can generate this

cool string whatever we want to send it

and then parse this response and present

it in a unique way in order to build a

sophisticated applications right and so

it responds to us that Warren Buffett is

the greatest investor of all time and

that's great and I've included a link

here if you want to check out check out

the difference between the different

models and all of the different

parameters and they're all in the

documentation right there I just want to

show you you know you could learn all

this from the documentation but a lot of

people like this channel because I

showed an action and kind of explain it

from the ground up give you the code and

walk through it and that helps a lot of

people see it visually so now that we've

done the most basic thing you know we've

successfully used the open AI API let's

try to get it to do something a little

bit more sophisticated so let's try a

summarization task and so what I've done

here in the last video we transcribed

the audio to text so we transcribed the

FED speech now in this video I'm going

to use the text transcript of an

earnings call and so you could build

this huge data set of different earnings

calls and extract information out of

these earnings calls and so I've linked

an earnings call that I've already done

so we don't need to do that again and

transcribe it from audio but this is the

text of an earnings call and you can see

it's quite long right quite long and if

I click the raw thing you can see all

the text right and where they say on

Reddit whenever someone posts this big

long wall of text and we don't have time

for it too long didn't read they post

tldr and it turns out you can send this

exact prompt that's one of the examples

in the open AI examples here there's

this too long didn't read right and so

if you look for summarization there's

TLD are a summarization so you can add

that to the end of The Prompt and so

what we can do is do a an audio

transcription a two text then take the

text and then append a couple Carriage

returns and tldr to it and then send

that to the open AI API and instead of

telling us a story or answering a

question it will do a tldr summary now

how am I going to get this earnings

transcript into python well this one is

on the internet here and so I'm just

going to use the python request library

to a request that URL and just get the

text transcript right here and so if I

import that I can retrieve that and here

you see I have the plain text and so now

I'll be able to get that plain text and

append tldr to it and some Carriage

returns and craft a special response and

tell it to summarize this earnings call

and so what I have here is a prompt

variable and I'm going to pass it this

transcript variable so this transcript

is going to be inside I put a couple

character terms and this tldr colon

right there and this forms my prompt and

so if I run this you'll see my prompt

right there and if I were to expand this

out you'll see the entire Big Blob of

text here and this little TR tldr right

here at the end so I know that seems

pretty basic but these very basic

building blocks are what are going to

enable us to build more and more complex

things so we need to master these very

simple tasks and we'll be able to put

them together to build these full stack

applications in uh following videos and

so we'll be able to build as some type

of crawler that gets tons of audio and

extracts a tons of text and builds a

queue and sends it through open AI build

summaries extracts does natural language

processing uh pulls named entity

recognition pulls company names and

different numbers out of them and we'll

be able to perform more and more unique

tasks as we go

so let's first see if we can summarize

this text so we have the prompt now and

we can just call open AI again with the

same completion endpoint and send us

this new send in this new prompt now if

I run that what you're going to see is

the multiple the model's maximum content

length is 4097 tokens and so this

returns an error it doesn't work we send

out this huge wallet text and open an AI

says no not going to do it and so we're

going to need to learn how to take all

of our different inputs and massage them

and do different things with them in

order to make it acceptable to open AI

so it'll process them and so the first

thing we're going to do here is chunk up

our data so we can't send it a giant

like thousands of pages of text and tell

it to summarize Wikipedia all at once

right that's too complex of a task to

get an instant response like this and so

we're going to take this long earnings

call and chunk it up into pieces and

this is the approach we're going to take

so we have this long text transcript in

a string and so since there's spaces

that separate all the work words right

here what I'm going to do is just divide

this long blob of text into an array of

words and so if I run this and split it

on Spaces we'll get an array of words so

good afternoon my name is Emma and so we

have a python list I'm just going to

show the first 20 words just to show

what that looks like now what I can do

here is since openai has this length

limit here what I'm going to do is just

divide up our text into different chunks

here and so numpy has this array split

function right here and so I'm going to

send it this this giant list of

thousands and thousands of words I'm

going to say divide this up into

pieces of equal size so I'm going to get

six lists here and so you can come up

with different rules on how you want to

divide your text but for this particular

one I just hard coded six and I said

divide this into a six pieces you could

divide it into paragraphs and do other

things like that as well and so I call

it numpy dot array split and I'm going

to split this list of words into six

smaller arrays and I'm calling this

chunks and so if I run this and you see

the chunks here here you see the first 1

6 of the earning calls the next one six

and so forth so this is a list of arrays

here right and so now we have this

nicely chunked up and then now I can

join one of these so I'm going to take

the first array here so the first one

six this list of words I'm going to join

this back on spaces and just turn it

back into the sentences and you'll see

this is just it in paragraph format so

this is the first 1 6 of sentences in

that giant document right and so now

let's see how one of these looks like

let's summarize just the first 1 6 of

the document so I'm going to get the

sentences here and tldr and send it

again and you'll see a summary of the

first 1 6 of the document and so we can

take the same technique and chunk up

this into six pieces then summarize all

six pieces and call open AI for each

chunk of text and then we can collate

and combine all these different chunks

into a single final summary of the

entire earnings call right and so if I

go here and I run it you see the summary

of that first one six and you'll see

Simona welcomed everyone to the earnings

call Colette Cress Executive Vice

President and CFO reported revenue of

5.93 billion down 12 successionally

sequently data center a revenue of 3.83

billion and so just a few sentences here

we summarized that first uh that first

section of the earnings call so that

looks pretty good so how do we summarize

all of it what we can do here since I've

divided this into six chunks right we

access the first chunk with uh chunks

zero what I'm going to do is create a

list of all the different summary

responses and we're going to store all

those summaries in this list so we're

going to Loop through all the chunks

we're going to take the sentences from

each chunk we're going to tldr and call

open AI then we're going to build this

list of summary responses then we're

going to join all those summary

responses together and print out the

full summary of the earnings call and so

this is calling open AI six times with

six different chunks of text we're going

to get the summary put it all together

and we'll have an earnings call summary

just like that and that's our full

Earnest call summaries and so we got

that back and so I'm going to copy this

into a text editor and let's look at it

and see if it makes sense see how well

open AI gpt3 understands the language of

those many pages of text and so if you

look at this gpt3 is very aware of all

the different people that are talking in

this earnings transcript so I have

Simona Jankowski collect Cress and

Jensen the CEO here we see a summary of

the earnings right here just the the

basic numbers we talk we see some

information about the sell-through rate

in gaming and then there's some comments

on AI so the dynamic Computing

environment so AI focus on productivity

and the AI Enterprise software stack and

uh Cloud gpus which I actually bought

some Nvidia about a couple months ago a

month and a half ago and so I'm kind of

optimistic on the demand for a GPU use

and their use in machine learning and so

you can see some questions about

hyperscale Cloud purchases and demand

for these gpus in the future there's

also some brief notes here that

blockchain is not expected to be a major

part of the company's business in the

future because of the ethereum change to

the proof of stake and so these miners

aren't needing these gpus as much but

you can see there's a huge demand coming

in for gpus for AI machine learning

purposes so there you go just like that

we have a quick understanding of how to

use the open AI API for a very specific

task I'll be at a fairly simple one but

this lays a very solid Baseline so that

we can get started building more and

more complex application some folks had

said in comments in the previous video

they couldn't even get the basic example

off the open AI web page going so I'm

hoping this helps those people get

started and now we can continue to build

on this and start building apps that

accept sound text video URLs process

them and perform perform more and more

complex task and let our creativity run

wild and so yeah I have a lot of good

ideas in the pipeline and also let me

know if there's anything specific you

want to see with open Ai and I'll try to

take that into account I already have

like 10 to 12 other video ideas in my

head right now and also a few people I

just posted the open AI Whisperer video

and a couple people are already asking

me for like real time uh real-time uh

Speech analysis of the FED speech and

executing trades based on that which is

completely expected because we have a

bunch of Trader Types on this channel

and so I'll show how to do a real-time

speech transcription as well using

whisper and talk about how we might

classify text and maybe classify

sentences as hawkish or dovish and

things like that so text classification

is something we need to do as well image

classification all those types of things

so thanks a lot for watching hope this

was helpful for getting started with

open ai's API and stay tuned got a lot

in store for you thanks a lot for

watching see you next time bye