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REVOLUTIONIZING NATURAL LANGUAGE PROCESSING WITH AUTO-GPT: THE STATE-OF-THE-ART LANGUAGE MODEL

MATT·APRIL 20, 2023

Natural language processing (NLP) has become increasingly popular in the field of artificial intelligence in recent years. GPT...


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MATT·APRIL 13, 2023

In this post, you can read all you need to start playing around with AutoGPT open-source library.  Features...

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MATT·APRIL 14, 2023

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MATT·APRIL 13, 2023

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PART - 2

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AutoGPT Tutorial - More Exciting Than ChatGPT


Transcript        

PART - 1 

0:00

so chat GPT is no longer the most

0:02

exciting thing in AI instead everyone's

0:05

freaking out about this new software

0:07

called Auto GPT which can essentially

0:09

automate way more than chat GPT was able

0:12

to this could make you a virtual

0:14

assistant this could be a wedding

0:15

planner this could be a nutritionist

0:17

this could be a lot of different things

0:19

and automate substantially larger tasks

0:21

than Chachi PT was able to now how is it

0:24

able to do this well we're going to talk

0:26

about that throughout this video and

0:27

this is a full tutorial showing you how

0:30

to set it up and how to actually use

0:31

that and I promise I will not be

0:33

skipping any steps at all so even an

0:36

absolute beginner who knows nothing

0:37

about coding can follow along type in

0:40

the letters I type and get to the exact

0:42

same place so that you can run your own

0:44

version of Auto GPT very easily on your

0:47

own computer and I can prove that right

0:49

here this laptop right here I have not

0:51

done this has nothing on there it says

0:53

no coding tools this has no python this

0:55

has nothing like that so we will

0:57

actually be setting this up from scratch

0:59

so we're not skipping any steps now like

1:01

I said Auto GPT is really really

1:04

impressive but why is it different from

1:06

chat GPT well chat GPT is great

1:09

obviously a large language model you're

1:11

able to ask your questions to run

1:12

commands and it can give you kind of one

1:15

response at a time sometimes those can

1:16

be complex responses but nonetheless one

1:19

thing at a time and in addition to that

1:21

you don't really have access to the

1:23

internet with that whereas Auto GPT is

1:26

able to aggregate many different what's

1:28

called apis essentially work with

1:30

different plugins so you can have a

1:32

Google API so it's able to search the

1:34

internet you can have a chat gpta API so

1:37

you're able to use chat GPT you have a

1:39

lot of different things in there it can

1:40

give you images it can output audio

1:43

that's actually like a simulated voice

1:45

it could be your simulated voice really

1:47

Endless Options here and really the big

1:49

difference is rather than just asking

1:51

for one command at a time you can give

1:54

it up to five different goals and then

1:57

it'll iterate on itself it'll run a

1:59

command it'll come up with an output

2:00

it'll give you a reason for that output

2:02

and then it'll come up with what its

2:04

next command is going to be and it can

2:06

iterate on that over and over until it's

2:08

able to get you the results so for

2:10

example if you say I want to plan a

2:12

wedding it could first say you know what

2:14

is the normal stuff for planning a

2:16

wedding and you can have a list of what

2:17

okay this is the results these are what

2:19

I think I need to do in order to plan a

2:21

wedding then the next step it'll say all

2:23

right now let's find local venues and it

2:25

can go and find local venues and then it

2:27

can look up the reviews of the venues

2:29

and then it can find the best reviewed

2:31

venue then it could go after that and

2:33

say all right let's find some catering

2:34

and so it can iterate and continue a

2:36

longer process that otherwise you'd need

2:38

a person to either work with just Google

2:41

or chat gbt to kind of work on this

2:43

themselves so I hope that kind of

2:45

explained it I think a lot of examples

2:47

throughout this video will be very

2:48

helpful but let's get over to my laptop

What do I need to run AutoGPT?

2:50

and start off actually setting up Auto

2:53

GPT like I said it's very simple very

2:55

straightforward you just have to follow

2:57

along and do what I do now before we get

2:59

into the first step I want to point out

3:01

that I will have links down below like

3:03

not affiliate links just regular links

3:05

because it's free to use and this will

3:07

be a link to the GitHub which is the

3:08

repository of where the source code is

3:11

actually stored so we will be

3:12

downloading that and I'll go through

3:14

that link in a second I also have a link

3:16

down there to install python which we

3:18

will need I use Anaconda that's how I've

3:20

always operated with python and we will

3:22

have a link to visual code if you want

3:24

to use that that's not absolutely

3:25

necessary but it does make it so much

3:28

easier to view things like markdown and

3:30

different types of files that we will

3:32

have in the repository and then the

3:34

fourth thing down there is a link to our

3:36

free newsletter so we are launching a

3:38

brand new newsletter because AI is

3:39

moving so quickly so just when you

3:41

thought you got a hold on chat GPT Auto

3:43

GPT came out and there's going to be

3:45

more iterations and more changes and

3:47

more Improvement in this field and so if

3:50

you don't end up keeping up with this

3:51

space you're going to fall behind and

3:53

eventually somebody else is going to

3:55

make the equivalent of Auto gbt to

3:58

replace your job and so really really in

4:00

our opinion the best way to prevent this

4:02

the best way to keep up to date on

4:04

everything is to know what's going on in

4:06

AI so that you're on the Leading Edge so

4:08

you're not the one getting replaced

4:09

you're the one innovating and the most

4:12

valuable person at your company so step

Getting started

4:14

number one is to copy and paste the link

4:15

in the description it goes to github.com

4:17

and this just has latest release so it's

4:20

going to change when you actually go to

4:21

this so right now the latest is V

4:24

0.2.2 and this does of course change as

4:26

they iterate depending on when you're

4:28

watching this video don't worry if it's

4:30

a later version I will you know cover

4:31

everything that might change in this

4:33

video as well as how to see what the

4:35

changes are and if anything changes how

4:37

to actually utilize that so that'll

4:39

bring us to GitHub which if you already

4:41

have done any coding you definitely know

4:42

what this is it's a very very common

4:44

place for any coders to save their

4:47

source code so other people can read it

4:49

they can Fork it make different versions

4:50

of it it's super popular and really a

4:52

powerful tool in the space so going down

4:55

here you can see that auto GPT version

4:57

0.2.2 they have a couple things that

4:59

they change change from the last version

5:01

and down near the bottom we should see

5:03

download source code ZIP right now by

5:06

the way I'm using a Mac this will change

5:07

almost nothing in this tutorial I'll

5:09

point out the things that it does change

5:11

if using Windows essentially what I'm

5:13

saying is you can still follow along and

5:15

it's going to be essentially identical

5:17

so I just made a new folder called Auto

5:19

GPT and I want to make sure that I'm

5:20

downloading this ZIP file to Auto GPT it

5:24

downloaded to my downloads folder so I'm

5:25

just going to copy and paste it over to

5:27

this folder and we do want to make sure

5:28

it is extracted because that's a zipped

5:30

file so once we're extracted on Mac you

5:33

just double click that on Windows you'll

5:34

click on it and at the top you'll see a

5:36

little menu that says extract all just

5:38

click on that and you'll have it all

5:39

extracted and you should have all these

5:41

files here now it might look a little

5:42

different when you're whenever you're

5:43

downloading that that's the first step

Downloading Python and VisualStudio Code

5:45

we want to do the second step is to

5:47

download python so if we just go to

5:48

anaconda.com that'll bring you to this

5:51

right here and we can download so right

5:53

now it knows that I'm on a Mac so I can

5:54

download for Mac I'm going to click on

5:56

that once again that will download to my

5:58

downloads folder and this is is going to

6:00

give us access to python which is

6:02

incredibly powerful it's free to use and

6:04

is really going to be essential in what

6:06

we're doing in this video so you'll see

6:08

this package is downloading when it's

6:10

done I'm just going to click on that

6:11

it's going to run me through the

6:12

installation

6:13

so I'm going to say continue I'm just

6:15

going to keep continuing and agreeing

6:17

I've already read all this stuff before

6:18

and then when you're done you can click

6:20

on close we're just going to move that

6:21

to trash so what we want to do is

6:23

download visual studio code so then this

6:25

is the next thing so we can go to

6:27

code.visualstudio.com and right here we

6:29

can have download Mac universal that's

6:31

what we want depending on if you're on

6:32

Windows it'll just say windows obviously

6:34

and you'll have that version but it

6:36

should be essentially the same so this

6:37

is going to download I'm going to click

6:39

on it so that when it's done downloading

6:40

it'll open that and we can install it so

6:42

we're going to click on it and open it

6:43

and that'll bring us to

6:45

this page right here so there we go we

6:47

have Visual Studio code now we're going

6:48

to add this to our path so it's easy to

6:50

access later on just kind of saves us

6:52

some steps so hitting command shift p on

6:54

Mac that'll bring up this we can type in

6:57

shell and you'll see shell command

6:58

install code command in path that's

7:01

exactly what we want to do I'm just

7:02

going to say okay I'm going to type in

7:04

my my password for my laptop and now it

Configuring in Terminal

7:07

is successfully installed so we can

7:08

actually close out of this all right so

7:10

here we are now we're ready to actually

7:11

get started if you're on a Mac you can

7:13

hit the magnifying glass or command

7:15

space and type in terminal we're going

7:17

to open Terminal if you're on Windows

7:19

you can simply do this with uh

7:22

Powershell and before anybody gets mad

7:25

at me I'm going to switch over to dark

7:26

mode I had it in light mode because

7:27

other tutorials it was just like easier

7:28

to see but anyway here we go so we're in

7:30

dark mode I'll make this a little bit

7:32

bigger as well the first thing we're

7:34

going to do is set up our anacon or our

7:36

python environments we're going to type

7:37

in conda create dash n Auto Dash GPT

7:41

python equals 3.8 you can use other

7:44

versions depending on when your watching

7:46

this video but we're going to do 3.8

7:47

right now I'm going to hit enter

7:51

and wants to know if we're going to

7:52

proceed so letter Y and then enter means

7:55

yes so it's going to proceed

7:58

now a couple little housekeeping items

7:59

we want to navigate into out of the

8:02

folder that we have so this folder right

8:04

here Auto GPT I'm going to unmack you

8:07

just you know right click down here and

8:09

you can say copy the path name on

8:10

Windows it'll be on the top so you can

8:12

just click on the bar that has all the

8:13

path name listed copy that and then down

8:16

here we can say CD space and then paste

8:19

that and that'll bring you into that now

8:21

if you're not familiar with terminal or

8:23

Powershell essentially what you can do

8:25

CD is going to navigate around different

8:27

folders so if you want to go back a

8:29

folder you could say CD space dot dot

8:31

and that'll bring you back to the

8:32

previous folder and if you don't know

8:34

what's in that folder you can say LS

8:36

that'll list out what is in that folder

8:38

so we're going to list it out I only

8:40

have one thing in this folder you can

8:41

see right there and so if you want to go

8:43

into that you can say CD and then we can

8:45

type in Auto Dash and you can hit tab to

8:48

auto complete that once you have some of

8:50

it typed and hit enter and that's how

8:52

we're going to end up in that folder so

8:54

this is exactly where we want to be

8:56

so now that we're okay so now that we're

8:58

in the right folder if we type LS you'll

8:59

see these are all the files this is

9:01

exactly what we want now we can say code

9:03

space Dot and that'll open up a visual

9:05

studio code as you can see right here

9:07

um so I'm going to trust my own parent

9:09

folder here and you can see on the left

Reading the ReadMe markdown file

9:11

these are all the files now the reason I

9:13

told you to download visual studio code

9:15

is because we have files in here for

9:16

example markdown which is really hard to

9:19

read when you just look at it like this

9:20

but if you right click on it you can go

9:23

and say preview open preview and it'll

9:25

open it up in this really nice looking

9:27

format with pictures and it just looks

9:29

like it's so much easier to read

9:31

something like this now this is the

9:33

readme file which is going to

9:34

essentially be our guide to use to use

9:37

Auto GPT so the reason I wanted to show

9:40

you this I mean first of all we're going

9:41

to follow along in this video but just

9:43

in case anything changes this is where

9:46

you would go to actually find that so

9:47

I'm trying to make this video as future

9:49

proof as possible so that future

9:51

iterations of this if anything subtle

9:53

changes you will still be able to use

9:55

this but I'm confident that for the most

9:57

part everything we're doing in this

9:58

video will be exactly correct no matter

10:01

when you're doing that so right now

10:02

we're at this stage right here so we

10:04

want to we already downloaded that we

10:06

want to install the requirements so

10:08

there's a file over here called

10:09

requirements uh down here requirements

10:12

and we want to install all of the

10:13

necessary packages that we'll need for

10:16

this Auto GPT so I'm just going to copy

10:18

this and we're going to go back to our

10:20

terminal right here our Command terminal

10:22

and we can paste it down there this will

10:24

be pip install Dash R requirements.txt

10:27

hit enter it's going to install a bunch

10:30

of little things down there all right so

10:31

while that's installing going back to

10:32

the readme the next thing we want to do

10:34

is configure Auto GPT now luckily

10:36

there's really not much we have to do

10:38

here we just have to add in our personal

10:41

API key and I'll show you exactly how to

10:43

do that and change one other thing if

Adding your OpenAI API key

10:45

you're using a Mac there's other apis we

10:47

can enable and you can mess around this

10:49

later but just getting it set up for

10:51

like the absolute Basics let's start off

10:53

with adding your open API keys so we

10:55

want to do is go back to terminal and we

10:57

can type in

10:59

cp.env.template space dot EnV so

11:02

essentially what we're doing is we're

11:03

making a copy of env.template and just

11:06

making it dot EnV so I'm going to hit

11:07

enter and that should do that over here

11:09

we can go back to visual studio code and

11:11

see that we now have a file called dot

11:13

EnV and everything is green which means

11:16

it's commented out which means it's not

11:18

going to be run as code but if there's

11:20

anything that you do want to run as code

11:22

so if we go down here let me just search

11:24

for Mac it's somewhere down here there

11:26

we go so because we're using Mac right

11:28

here the thing that is single commented

11:30

I'm just going to delete that little

11:31

hash and now we are using Mac and it's

11:34

set to false I'm going to change that to

11:37

true so we're going to set that to true

11:38

now the only other thing on here that

11:40

I'm going to change initially like I

11:42

said there's other things we can add we

11:43

can add an API for image output or Voice

11:46

output or different things like that but

11:48

the only thing that you actually need to

11:50

add to run this is your own API key

11:53

which you can see right here so I'm

11:54

going to delete that right now and we're

11:56

going to go over to chat GPT or open


PART - 2 

12:00

II's website rather and we're going to

12:01

get our API key our secret key that is

12:04

personal for us that we can use right

12:06

here

12:07

so what I'm going to do is from my

12:09

browser go to

12:10

platform.openai.com account slash API

12:13

Dash Keys again I have a link in the

12:15

description so you can follow along

12:17

there I'm going to hit enter and from

12:19

here I'm just gonna have to sign in so

12:20

you should already have a chat GPT

12:22

account if you don't uh you can sign up

12:24

right now and I highly recommend you

12:26

watch my full video about chat GPT where

12:28

I go through the 10 different major

12:30

commands and modifiers that you can use

12:32

to really optimize your Effectiveness on

12:35

chat GPT to become better in life at

12:38

work whatever you're doing whatever

12:39

you're using it for highly recommend

12:41

that video right now it has 1.4 million

12:43

views and a lot of people are really

12:45

finding it helpful but regardless

12:46

assuming you already watched that video

12:48

the next thing is to log in so that

12:50

should take you to a page that looks

12:52

just like this you probably won't have

12:53

these two right here I actually don't

12:56

even need those so I can delete those as

12:57

well so we're going to revoke that key

12:59

and we're just going to click on create

13:00

new secret key I'm going to do this in

13:02

this video I'm going to delete it so you

13:04

guys don't use my credits here but if we

13:06

clicks create new secret key we can call

13:08

this whatever we want so I'm going to

13:10

call it auto GPT I can copy that that

13:13

secret key we're going to say done and

13:16

then going back to visual studio code we

13:18

have Visual Studio code right here I can

13:20

paste it right in there now you could

13:22

add this with quotes on either side I

13:24

don't believe you need to because they

13:26

didn't originally I did last time this

13:28

time I'm going to not do that and it

13:30

should work either way that's just a

13:32

string of text there so then when you're

13:33

done with this we can save it so we're

13:35

going to hit Ctrl or command s control s

13:37

depending on what you're using and of

13:38

course I do also want to save this

13:40

workspace so we're going to go to file

13:42

save workspace As and we're going to

13:44

call it that looks fine to me and it's

13:46

in this folder so we're going to say

13:47

save and now there is one more thing we

13:50

have to do on open airi's website and we

13:52

have to add a billing method so you just

13:54

go down to billing and it'll prompt you

13:56

to add a payment method otherwise you'll

13:59

just click on payment methods you can

14:00

add a new one there but essentially it's

14:03

not free to use but don't worry it's not

14:04

like you're not paying anywhere near

14:06

dollars if we go to billing history for

14:08

me it was like a couple cents or uh was

14:10

it usage so if I go to usage limits I

14:12

was messing around this yesterday I

14:14

spent 15 cents on this after a couple

14:16

dozen commands on this account in

14:19

particular so like I said it's really

14:21

not going to cost you basically anything

14:23

at all but I highly recommend still go

14:24

to usage limits and add your own limit

14:26

on there hard limit's going to be like

14:28

do not spend more than this amount just

14:30

in case you send it on like full

14:32

autonomous mode and iterates like a

14:34

million times you don't want to end up

14:36

spending more than you expected and the

14:38

soft limit is going to email you when

14:39

you get to that limit just so you have

14:41

an idea of where you are so you don't

14:42

accidentally terminate like some big you

14:45

know Loop that you don't want to

14:46

terminate so that's nice to have the

14:48

soft limit as well but right now like I

Running AutoGPT

14:50

said I'm only 15 cents in so it's going

14:52

to take a while to get anywhere near

14:53

those limits all right so now let's go

14:55

back to terminal and we want to activate

14:57

that environment we made a while ago

14:58

remember we did the conda activate so

15:00

I'm just going to type in conda activate

15:03

Auto GPT so I'm going to hit enter

15:05

that'll activate that and now in the

15:07

beginning instead of saying base it'll

15:08

say Auto GPT so we're in this new

15:10

environment and now there's one last

15:12

thing we need to do before we can start

15:13

using Auto GPT and this is agree to the

15:16

licenses so in order to do that you

15:18

simply need to type sudo Space X code

15:22

build space Dash license and hit enter

15:25

and it's going to ask for your your

15:27

password that is the password for your

15:28

computer so I'm just going to type that

15:30

in and now it's going to ask us to press

15:32

the return key to view the license

15:34

agreement and we're going to keep

15:35

pressing this until we press spacebar

15:37

until we get to the bottom so you can

15:39

read through all this this is the

15:40

license agreement for what you're doing

15:42

and at the end you can type in agree

15:45

type in agree and we'll be ready to go

15:47

so now we're able to run auto GPS so

15:49

we're ready to go now if you go back to

15:51

the readme it tells you to run uh to say

15:54

dot slash run dot sh space start you

15:58

probably don't actually want to do that

15:59

instead you could just type in like

16:01

forget the word start and just run it

16:03

like this and it should be running

16:04

chanchi or it should be running Auto GPT

16:07

you have your API if everything goes

16:09

according to plan this will be running

16:11

and it'll prompt you in just a minute to

16:13

ask a couple fundamental questions like

16:15

what is the name and what are the goals

16:17

of your auto GPT alright and there we go

16:19

once you see this color text you'll know

16:21

that you are ready to go we're using

16:23

Auto GPT now and so it wants to know

16:25

first of all what the name is I'm just

16:27

going to call this one micbot but again

16:29

you can use this for many different

16:30

things you can search other examples of

16:33

people that used it to make an assistant

16:34

to to plan a wedding to do things like

16:36

that so let's do this I'm going to

16:38

create a new document we're going to say

16:39

file save as so I'm just going to save

16:41

this called diet and we want this to be

16:43

plain text Dot so dot txt let's save it

16:46

and

16:48

we're going to

16:51

say okay now we have just regular

16:53

document called diet it's a plain text

16:54

file so in Auto GPT we can say the AI

16:57

name this is going to be Mike Mike's

17:00

nutritionist Mike's diet or nutritionist

17:05

now it's going to ask us to Define it so

17:07

that describe the role we're going to

17:08

say create a meal plan

17:12

for this week

17:14

and so I'm just going to say let's

17:16

create a 70 meal plan we want to write

17:18

the recipes for each one and at the end

17:20

we want to just save to the file and

17:22

then stop and if we only have three

17:23

goals instead of five you just hit enter

17:25

again and it'll start running this is

17:27

using local memory by the way you could

17:29

pay and use pine cone or some of the

17:30

other ones to not need local memory but

17:33

obviously for something like this local

17:34

memory is just fine

17:36

now the way this works is it'll run

17:38

through one prompt at a time so it'll

17:40

tell you that first of all this is what

17:42

it thinks it's supposed to do it should

17:43

start looking up healthy meal plans for

17:44

the week the reasoning it's going to

17:46

tell you why it's doing this the

17:47

criticism maybe what it doesn't want to

17:49

do and then it'll tell you what it's

17:51

going to do next and we could just say

17:53

why for yes to mean to go ahead and do

17:55

it n is going to be no meaning don't do

17:57

it it's going to exit the program or you

17:59

can add your own feedback in there as

18:01

well I'm just going to say why and let

18:02

it run and right now you can see right

18:04

there Google returns so it's actually

18:06

searching on Google right now to find

18:07

some results from the internet so the

18:09

first thing it wants to know what

18:10

dietary restrictions or preferences they

18:12

have so I'm going to say I have no

18:16

dietary restrictions

18:19

I love Mexican and Asian food as well as

18:25

American

18:28

food I have

18:31

plenty of vegetables

18:33

okay so there we go just give it some

18:35

feedback that's what he was asking so it

18:37

said like before we proceed he needs to

18:38

know any dietary restrictions or what it

18:40

wants for the meal plan or what I want

18:42

for the meal plan

18:43

it's gonna ask me about each meal as we

18:45

go along and so yeah stir fry sounds

18:47

good I love some stir fry for Tuesday

18:49

how about make some tacos we can use

18:51

ground turkey let's say let's use

18:54

shredded

18:56

chicken so you could say like yeah I

18:58

like tacos but let's change that I don't

19:00

actually want ground turkey uh let's do

19:02

something else so you don't always have

19:03

to say why or n for yes or no on these

19:05

things you can give some other feedback

19:07

and it'll kind of iterate and keep

19:09

making your your diet plan around that

19:11

so let's see great we can use shredded

19:13

chicken yeah no problem so now we're

19:15

gonna go on to the next thing press Y

19:16

and it's going to kind of keep iterating

19:18

on this and it should hopefully make my

19:20

diet by the end

19:21

now there now if you get tired of

19:24

pressing y every time or giving it

19:26

feedback like this there are also fully

19:29

autonomous ways to do this so if you go

19:31

into the readme so let me just open it

19:33

up real quick while it's running you can

19:35

go to down down near the bottom just

19:37

look for like a little skull emoji

19:38

because they tell you it's dangerous but

19:40

I'm still interested in doing it and

19:42

[Music]

19:46

there we go continuous mode so right

19:47

here you can actually run in continuous

19:49

mode here it'll iterate continuously

19:51

until you press Ctrl C so obviously like

19:54

this said it could get stuck in Loops it

19:56

could do things that you don't want it

19:57

to do so definitely be careful with this

20:00

um but again you don't always have to

20:02

press y if you set up continuous mode

20:04

I'm not recommending you do that but

20:06

I might end up doing that just it's

20:08

interesting I don't know and by the way

20:10

in case you ever get stuck in a loop on

20:12

here or if it's running something and

20:14

it's stuck it it's not you have some

20:15

kind of error and it doesn't actually

20:16

stop you can terminate any of these by

20:18

saying Ctrl C this is on Mac if you're

20:22

on Windows I don't think it's

20:23

controlling against alt but it's a

20:24

control C and it's going to quit we can

20:26

hit any key and we're back to where we

20:28

were and if you want to run it again you

20:30

can just go press the up key to the most

20:31

recent command which was dot slash

20:33

run.sh hit enter and it's going to run

20:36

it again and we'll be able to start off

20:37

again with a brand new auto GPT all you

20:40

have to do down here it's going to ask

20:41

if you want to continue you just press

20:43

the letter n and you'll go from scratch

20:45

so now it found a diet it's going to

20:47

write that to the document sometimes it

20:49

takes a minute to think it's a little

20:51

bit slower if a lot of people are on it

20:52

you can go and check the uptime and you

20:54

can check basically the status of open

20:55

ai's chat gbt there's a nice website for

20:58

that as well if it's ever not working

21:00

maybe that would be a reason otherwise

21:02

if you ever have any bugs in this if

21:04

anything ever doesn't work you should be

21:07

getting like I said you could hit Ctrl C

21:08

that'll terminate this if it gets stuck

21:10

on thinking for too long and you could

21:12

debug it using chat GPT it's one of the

21:14

greatest tools out there especially if

21:16

you don't know what you're doing you

21:18

just copy the error code onto chat GPT

21:20

tell it what you're trying to do ask why

21:23

it's not working and it can walk you

21:24

through things surprisingly well in

21:26

plain English I found this works

21:28

tremendously well on many different

21:30

programs out there that you find on

21:31

GitHub that maybe have a bug or you know

21:33

something's not right with them so I'm

21:35

editing the video right now and I

21:36

realized I used a really simple example

21:38

here so you might be wondering how this

21:40

is even different from regular chat GPT

21:42

like I could ask Chad gbt to make me a

21:45

diet plan and it could do something

21:46

pretty similar to this so really the

21:48

benefit of Auto GPT is that it connects

21:50

and integrates with different apis

21:52

different things as well so you could

21:54

add into this use my voice like you can

21:57

use a plugin and use my voice to speak

21:59

it to me you could translate videos you

22:01

could output images there's a lot of

22:04

different things you can connect with

22:05

this so that's really where the power of

22:06

Auto GPT is and so as you start to like

22:09

this is a great first example and I

22:10

recommend you do this just so you get

22:13

Auto GPT running and start getting used

22:15

to it and mess around with little you

22:16

know some little commands like this but

22:18

eventually what you want to do is start

22:19

using those Integrations so you can

22:22

really get this to the next level in

22:23

addition this can also do things like

22:25

write to text files do you know do a lot

22:27

more that auto that chat GPT cannot so

22:30

chat GPT is purely on the Internet it's

22:32

stuck there like what you're doing on

22:34

that interface you're just going to get

22:35

text output whereas this like I said can

22:37

change files can do things on your

22:39

computer it can output different files

22:41

as well whether they're audio files that

22:43

are recordings of a like an AI simulated

22:46

version of your voice or images there's

22:49

a lot more you can do with this this is

22:51

really only the beginning but I mean I

22:54

just wanted to clarify that in case

22:55

you're watching this video and wondering

22:57

how this was even different from chat

22:59

GPT and of course it's able to access

23:01

the internet while also accessing chat

23:03

GPT so it's kind of a nice bridge

23:05

between chat GP T and the internet so

23:07

you can find more relevant and more

23:09

recent information so that is my

23:12

tutorial on how to use Auto GPT it's

23:14

really just the tip of the iceberg here

23:16

this is a massive software and you can

23:18

do a lot of automations so I highly

23:19

encourage you start messing around with

23:21

that and please leave a comment Down

23:22

Below on this video and let me know what

23:25

you would use Auto GPT for I really I'm

23:27

always interested are you using it for a

23:29

personal assistant are you using it to

23:31

emulate your voice and you know open

23:34

images and do different things there's

23:35

really Endless Options that you could do

23:37

so I would love to hear what your

23:39

thoughts are on the best uses for auto

23:40

GPT and once again if you guys want to

23:42

keep up with the latest here anything

23:44

that changes with auto GPT we will be

23:46

covering in that newsletter which is

23:48

linked down below if you enjoyed the

23:50

video consider liking and subscribing

23:52

I'm Mike O'Brien thanks for watching and

23:54

we'll see you in the next one


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In this video, I show you how to use the latest breakthrough in AI innovation, AutoGPT. This is a step by step tutorial.

🧠 The world's leading AI newsletter: https://neuralfrontier.beehiiv.com/su...


AutoGPT Github: https://github.com/Significant-Gravit...


Download Python: https://www.anaconda.com/


Download VisualStudio Code: https://code.visualstudio.com/


Find your OpenAI API Keys: https://platform.openai.com/account/a...


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Summary of steps: 

Install anaconda

Install visual code

Open terminal

Download stable not master from github 

In command line, navigate to the folder, then type “conda create -n auto-gpt python=3.8”


Then cd into the auto gpt master folder which was downloaded from GITHUB


Open visual studio code, cmnd+n to open new, then cmnd+shift+p for command palette


Type “shell” then select install to path


Type “code .” in terminal to open VS code

Save workspace as


This will install your environment, then you can activate it with “conda activate auto-gpt”

In terminal, type “pip install -r requirements.txt”


Open readme.md then right click tab and open preview


Type cp .env.template .env

Then type code .


Open .env in VScode

Copy and paste your API from https://platform.openai.com/account/a...

Remove hash for Use Mac

Save it


In Terminal, type “sudo xcodebuild -license”

Enter your pw

agree


Add billing to https://platform.openai.com/account/b... 


In Terminal, type ./run.sh 


Customize your auto-gpt!


Some Terminal commands you may want to know:

Cd [navigates to the folder path you type next]

Cd .. [goes back one]

Ls [lists everything in the current folder]

Python [opens python]

quit() [quits python]

Clear [deletes everything on the screen]



TIMESTAMPS:

0:00 What is AutoGPT?

2:50 What do I need to run AutoGPT?

4:13 Getting started

5:45 Downloading Python and VisualStudio Code

7:05 Configuring in Terminal

9:10 Reading the ReadMe markdown file

10:45 Adding your OpenAI API key

14:50 Running AutoGPT




DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This helps support the channel and allows us to continue to make videos like this. Thank you for the support!  Everything in this video is based on information we learned from online resources, our own experience, and books we have read.  Please do your own research before making any important decisions.  You and only you are responsible for any and all digital marketing decisions you make.  Thank you for watching!


ChatGPT API in JavaScript for Beginners - AI Chatbot Tutorial



Ultimate Guide To Auto-GPTs in a Browser


Ultimate Guide To Auto-GPTs in a Browser



AI-driven virtual assistant with ASR, ChatGPT, TTS, Audio2face & Metahuman.


AI-driven virtual assistant with ASR, ChatGPT, TTS, Audio2face & Metahuman.


MASTER Auto-GPT

MASTER Auto-GPT in under 60 MINUTES | Ultimate Guide


AutoGPT: Turn GPT-4 Into A Powerful Self Learning AI


Auto-GPT: An Autonomous GPT-4 Experiment


Torantulino Update README.md

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2 days ago

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Added ability to output news/announcements on startup

2 days ago

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updated contributing docs

4 days ago

CONTRIBUTING.md

docs: add warning for non-essential contributions (#2359)

2 days ago

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2 days ago

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Create LICENSE

3 weeks ago

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Update README.md

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docker-compose.yml

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5 days ago

main.py

Convert to python module named autogpt.

last week

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Plugins initial

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pyproject.toml

isort implemented

3 days ago

requirements.txt

Fix all commands and cleanup

13 hours ago

run.bat

Fix run.bat to use the new module

5 days ago

run.sh

Add run scripts for shell

5 days ago

run_continuous.bat

spelling

5 days ago

run_continuous.sh

Fix: Remove quotes around $@ in run_continuous.sh

4 days ago

tests.py

isort implemented

3 days ago


Auto-GPT: An Autonomous GPT-4 Experiment


🔴 🔴 🔴 Urgent: USE stable not master 🔴 🔴 🔴

Download the latest stable release from here: https://github.com/Significant-Gravitas/Auto-GPT/releases/latest. 


The master branch may often be in a broken state.


Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set. As one of the first examples of GPT-4 running fully autonomously, Auto-GPT pushes the boundaries of what is possible with AI.

Demo April 16th 2023


Auto-GPT

Comparing changes


Comparing changes

Choose two branches to see what’s changed or to start a new pull request. If you need to, you can also compare across forks.

base: v0.2.1

 

...

 

compare: v0.2.2

 65 contributors

 Commits 240 Files changed 103

Commits on Apr 15, 2023

Commits on Apr 16, 2023

Commits on Apr 16, 2023

Commits on Apr 17, 2023

Commits on Apr 17, 2023

Commits on Apr 18, 2023

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Commits on Apr 19, 2023

Commits on Apr 20, 2023


ALL 5 STAR AI.IO PAGE STUDY

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This website is a collection of Artificial Intelligence (AI) tools and services that can be used to create and improve websites. It includes tools for creating interactive visuals, animations, and videos, as well as tools for SEO, marketing, and web development. It also includes tools for creating and editing text, images, and audio. The website is intended to provide users with a comprehensive list of AI-based tools to help them create and improve their websites.



אתר זה הוא אוסף של כלים ושירותים של בינה מלאכותית (AI) שניתן להשתמש בהם כדי ליצור ולשפר אתרים. הוא כולל כלים ליצירת ויזואליה אינטראקטיבית, אנימציות וסרטונים, כמו גם כלים לקידום אתרים, שיווק ופיתוח אתרים. הוא כולל גם כלים ליצירה ועריכה של טקסט, תמונות ואודיו. האתר נועד לספק למשתמשים רשימה מקיפה של כלים מבוססי AI שיסייעו להם ליצור ולשפר את אתרי האינטרנט שלהם.

Hello and welcome to our new site that shares with you the most powerful web platforms and tools available on the web today

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The platforms, websites and the tool's  are the best  for growing your business in 2022/3

שלום וברוכים הבאים לאתר החדש שלנו המשתף אתכם בפלטפורמות האינטרנט והכלים החזקים ביותר הקיימים היום ברשת. כל הפלטפורמות, האתרים והכלים הם בעלי בינה מלאכותית AI ובעלי דירוג של 5 כוכבים. כל הפלטפורמות, האתרים והכלים חינמיים ומקצועיים בתשלום הפלטפורמות, האתרים והכלים באתר זה הם הטובים ביותר  והמועילים ביותר להצמחת ולהגדלת העסק שלך ב-2022/3 

A Guide for AI-Enhancing Your Existing Business Application


A guide to improving your existing business application of artificial intelligence

מדריך לשיפור היישום העסקי הקיים שלך בינה מלאכותית

What is Artificial Intelligence and how does it work? What are the 3 types of AI?

What is Artificial Intelligence and how does it work? What are the 3 types of AI? The 3 types of AI are: General AI: AI that can perform all of the intellectual tasks a human can. Currently, no form of AI can think abstractly or develop creative ideas in the same ways as humans.  Narrow AI: Narrow AI commonly includes visual recognition and natural language processing (NLP) technologies. It is a powerful tool for completing routine jobs based on common knowledge, such as playing music on demand via a voice-enabled device.  Broad AI: Broad AI typically relies on exclusive data sets associated with the business in question. It is generally considered the most useful AI category for a business. Business leaders will integrate a broad AI solution with a specific business process where enterprise-specific knowledge is required.  How can artificial intelligence be used in business? AI is providing new ways for humans to engage with machines, transitioning personnel from pure digital experiences to human-like natural interactions. This is called cognitive engagement.  AI is augmenting and improving how humans absorb and process information, often in real-time. This is called cognitive insights and knowledge management. Beyond process automation, AI is facilitating knowledge-intensive business decisions, mimicking complex human intelligence. This is called cognitive automation.  What are the different artificial intelligence technologies in business? Machine learning, deep learning, robotics, computer vision, cognitive computing, artificial general intelligence, natural language processing, and knowledge reasoning are some of the most common business applications of AI.  What is the difference between artificial intelligence and machine learning and deep learning? Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions.  Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.  Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.  What are the current and future capabilities of artificial intelligence? Current capabilities of AI include examples such as personal assistants (Siri, Alexa, Google Home), smart cars (Tesla), behavioral adaptation to improve the emotional intelligence of customer support representatives, using machine learning and predictive algorithms to improve the customer’s experience, transactional AI like that of Amazon, personalized content recommendations (Netflix), voice control, and learning thermostats.  Future capabilities of AI might probably include fully autonomous cars, precision farming, future air traffic controllers, future classrooms with ambient informatics, urban systems, smart cities and so on.  To know more about the scope of artificial intelligence in your business, please connect with our expert.

מהי בינה מלאכותית וכיצד היא פועלת? מהם 3 סוגי הבינה המלאכותית?

מהי בינה מלאכותית וכיצד היא פועלת? מהם 3 סוגי הבינה המלאכותית? שלושת סוגי הבינה המלאכותית הם: בינה מלאכותית כללית: בינה מלאכותית שיכולה לבצע את כל המשימות האינטלקטואליות שאדם יכול. נכון לעכשיו, שום צורה של AI לא יכולה לחשוב בצורה מופשטת או לפתח רעיונות יצירתיים באותן דרכים כמו בני אדם. בינה מלאכותית צרה: בינה מלאכותית צרה כוללת בדרך כלל טכנולוגיות זיהוי חזותי ועיבוד שפה טבעית (NLP). זהו כלי רב עוצמה להשלמת עבודות שגרתיות המבוססות על ידע נפוץ, כגון השמעת מוזיקה לפי דרישה באמצעות מכשיר התומך בקול. בינה מלאכותית רחבה: בינה מלאכותית רחבה מסתמכת בדרך כלל על מערכי נתונים בלעדיים הקשורים לעסק המדובר. זה נחשב בדרך כלל לקטגוריית הבינה המלאכותית השימושית ביותר עבור עסק. מנהיגים עסקיים ישלבו פתרון AI רחב עם תהליך עסקי ספציפי שבו נדרש ידע ספציפי לארגון. כיצד ניתן להשתמש בבינה מלאכותית בעסק? AI מספקת דרכים חדשות לבני אדם לעסוק במכונות, ומעבירה את הצוות מחוויות דיגיטליות טהורות לאינטראקציות טבעיות דמויות אדם. זה נקרא מעורבות קוגניטיבית. בינה מלאכותית מגדילה ומשפרת את האופן שבו בני אדם קולטים ומעבדים מידע, לעתים קרובות בזמן אמת. זה נקרא תובנות קוגניטיביות וניהול ידע. מעבר לאוטומציה של תהליכים, AI מאפשר החלטות עסקיות עתירות ידע, תוך חיקוי אינטליגנציה אנושית מורכבת. זה נקרא אוטומציה קוגניטיבית. מהן טכנולוגיות הבינה המלאכותית השונות בעסק? למידת מכונה, למידה עמוקה, רובוטיקה, ראייה ממוחשבת, מחשוב קוגניטיבי, בינה כללית מלאכותית, עיבוד שפה טבעית וחשיבת ידע הם חלק מהיישומים העסקיים הנפוצים ביותר של AI. מה ההבדל בין בינה מלאכותית ולמידת מכונה ולמידה עמוקה? בינה מלאכותית (AI) מיישמת ניתוח מתקדמות וטכניקות מבוססות לוגיקה, כולל למידת מכונה, כדי לפרש אירועים, לתמוך ולהפוך החלטות לאוטומטיות ולנקוט פעולות. למידת מכונה היא יישום של בינה מלאכותית (AI) המספק למערכות את היכולת ללמוד ולהשתפר מניסיון באופן אוטומטי מבלי להיות מתוכנתים במפורש. למידה עמוקה היא תת-קבוצה של למידת מכונה בבינה מלאכותית (AI) שיש לה רשתות המסוגלות ללמוד ללא פיקוח מנתונים שאינם מובנים או ללא תווית. מהן היכולות הנוכחיות והעתידיות של בינה מלאכותית? היכולות הנוכחיות של AI כוללות דוגמאות כמו עוזרים אישיים (Siri, Alexa, Google Home), מכוניות חכמות (Tesla), התאמה התנהגותית לשיפור האינטליגנציה הרגשית של נציגי תמיכת לקוחות, שימוש בלמידת מכונה ואלגוריתמים חזויים כדי לשפר את חווית הלקוח, עסקאות בינה מלאכותית כמו זו של אמזון, המלצות תוכן מותאמות אישית (Netflix), שליטה קולית ותרמוסטטים ללמידה. יכולות עתידיות של AI עשויות לכלול כנראה מכוניות אוטונומיות מלאות, חקלאות מדויקת, בקרי תעבורה אוויריים עתידיים, כיתות עתידיות עם אינפורמטיקה סביבתית, מערכות עירוניות, ערים חכמות וכן הלאה. כדי לדעת יותר על היקף הבינה המלאכותית בעסק שלך, אנא צור קשר עם המומחה שלנו.

Glossary of Terms


Application Programming Interface(API):

An API, or application programming interface, is a set of rules and protocols that allows different software programs to communicate and exchange information with each other. It acts as a kind of intermediary, enabling different programs to interact and work together, even if they are not built using the same programming languages or technologies. API's provide a way for different software programs to talk to each other and share data, helping to create a more interconnected and seamless user experience.

Artificial Intelligence(AI):

the intelligence displayed by machines in performing tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and language understanding. AI is achieved by developing algorithms and systems that can process, analyze, and understand large amounts of data and make decisions based on that data.

Compute Unified Device Architecture(CUDA):

CUDA is a way that computers can work on really hard and big problems by breaking them down into smaller pieces and solving them all at the same time. It helps the computer work faster and better by using special parts inside it called GPUs. It's like when you have lots of friends help you do a puzzle - it goes much faster than if you try to do it all by yourself.

The term "CUDA" is a trademark of NVIDIA Corporation, which developed and popularized the technology.

Data Processing:

The process of preparing raw data for use in a machine learning model, including tasks such as cleaning, transforming, and normalizing the data.

Deep Learning(DL):

A subfield of machine learning that uses deep neural networks with many layers to learn complex patterns from data.

Feature Engineering:

The process of selecting and creating new features from the raw data that can be used to improve the performance of a machine learning model.

Freemium:

You might see the term "Freemium" used often on this site. It simply means that the specific tool that you're looking at has both free and paid options. Typically there is very minimal, but unlimited, usage of the tool at a free tier with more access and features introduced in paid tiers.

Generative Art:

Generative art is a form of art that is created using a computer program or algorithm to generate visual or audio output. It often involves the use of randomness or mathematical rules to create unique, unpredictable, and sometimes chaotic results.

Generative Pre-trained Transformer(GPT):

GPT stands for Generative Pretrained Transformer. It is a type of large language model developed by OpenAI.

GitHub:

GitHub is a platform for hosting and collaborating on software projects


Google Colab:

Google Colab is an online platform that allows users to share and run Python scripts in the cloud

Graphics Processing Unit(GPU):

A GPU, or graphics processing unit, is a special type of computer chip that is designed to handle the complex calculations needed to display images and video on a computer or other device. It's like the brain of your computer's graphics system, and it's really good at doing lots of math really fast. GPUs are used in many different types of devices, including computers, phones, and gaming consoles. They are especially useful for tasks that require a lot of processing power, like playing video games, rendering 3D graphics, or running machine learning algorithms.

Large Language Model(LLM):

A type of machine learning model that is trained on a very large amount of text data and is able to generate natural-sounding text.

Machine Learning(ML):

A method of teaching computers to learn from data, without being explicitly programmed.

Natural Language Processing(NLP):

A subfield of AI that focuses on teaching machines to understand, process, and generate human language

Neural Networks:

A type of machine learning algorithm modeled on the structure and function of the brain.

Neural Radiance Fields(NeRF):

Neural Radiance Fields are a type of deep learning model that can be used for a variety of tasks, including image generation, object detection, and segmentation. NeRFs are inspired by the idea of using a neural network to model the radiance of an image, which is a measure of the amount of light that is emitted or reflected by an object.

OpenAI:

OpenAI is a research institute focused on developing and promoting artificial intelligence technologies that are safe, transparent, and beneficial to society

Overfitting:

A common problem in machine learning, in which the model performs well on the training data but poorly on new, unseen data. It occurs when the model is too complex and has learned too many details from the training data, so it doesn't generalize well.

Prompt:

A prompt is a piece of text that is used to prime a large language model and guide its generation

Python:

Python is a popular, high-level programming language known for its simplicity, readability, and flexibility (many AI tools use it)

Reinforcement Learning:

A type of machine learning in which the model learns by trial and error, receiving rewards or punishments for its actions and adjusting its behavior accordingly.

Spatial Computing:

Spatial computing is the use of technology to add digital information and experiences to the physical world. This can include things like augmented reality, where digital information is added to what you see in the real world, or virtual reality, where you can fully immerse yourself in a digital environment. It has many different uses, such as in education, entertainment, and design, and can change how we interact with the world and with each other.

Stable Diffusion:

Stable Diffusion generates complex artistic images based on text prompts. It’s an open source image synthesis AI model available to everyone. Stable Diffusion can be installed locally using code found on GitHub or there are several online user interfaces that also leverage Stable Diffusion models.

Supervised Learning:

A type of machine learning in which the training data is labeled and the model is trained to make predictions based on the relationships between the input data and the corresponding labels.

Unsupervised Learning:

A type of machine learning in which the training data is not labeled, and the model is trained to find patterns and relationships in the data on its own.

Webhook:

A webhook is a way for one computer program to send a message or data to another program over the internet in real-time. It works by sending the message or data to a specific URL, which belongs to the other program. Webhooks are often used to automate processes and make it easier for different programs to communicate and work together. They are a useful tool for developers who want to build custom applications or create integrations between different software systems.


מילון מונחים


ממשק תכנות יישומים (API): API, או ממשק תכנות יישומים, הוא קבוצה של כללים ופרוטוקולים המאפשרים לתוכנות שונות לתקשר ולהחליף מידע ביניהן. הוא פועל כמעין מתווך, המאפשר לתוכניות שונות לקיים אינטראקציה ולעבוד יחד, גם אם הן אינן בנויות באמצעות אותן שפות תכנות או טכנולוגיות. ממשקי API מספקים דרך לתוכנות שונות לדבר ביניהן ולשתף נתונים, ועוזרות ליצור חווית משתמש מקושרת יותר וחלקה יותר. בינה מלאכותית (AI): האינטליגנציה שמוצגת על ידי מכונות בביצוע משימות הדורשות בדרך כלל אינטליגנציה אנושית, כגון למידה, פתרון בעיות, קבלת החלטות והבנת שפה. AI מושגת על ידי פיתוח אלגוריתמים ומערכות שיכולים לעבד, לנתח ולהבין כמויות גדולות של נתונים ולקבל החלטות על סמך הנתונים הללו. Compute Unified Device Architecture (CUDA): CUDA היא דרך שבה מחשבים יכולים לעבוד על בעיות קשות וגדולות באמת על ידי פירוקן לחתיכות קטנות יותר ופתרון כולן בו זמנית. זה עוזר למחשב לעבוד מהר יותר וטוב יותר על ידי שימוש בחלקים מיוחדים בתוכו הנקראים GPUs. זה כמו כשיש לך הרבה חברים שעוזרים לך לעשות פאזל - זה הולך הרבה יותר מהר מאשר אם אתה מנסה לעשות את זה לבד. המונח "CUDA" הוא סימן מסחרי של NVIDIA Corporation, אשר פיתחה והפכה את הטכנולוגיה לפופולרית. עיבוד נתונים: תהליך הכנת נתונים גולמיים לשימוש במודל למידת מכונה, כולל משימות כמו ניקוי, שינוי ונימול של הנתונים. למידה עמוקה (DL): תת-תחום של למידת מכונה המשתמש ברשתות עצביות עמוקות עם רבדים רבים כדי ללמוד דפוסים מורכבים מנתונים. הנדסת תכונות: תהליך הבחירה והיצירה של תכונות חדשות מהנתונים הגולמיים שניתן להשתמש בהם כדי לשפר את הביצועים של מודל למידת מכונה. Freemium: ייתכן שתראה את המונח "Freemium" בשימוש לעתים קרובות באתר זה. זה פשוט אומר שלכלי הספציפי שאתה מסתכל עליו יש אפשרויות חינמיות וגם בתשלום. בדרך כלל יש שימוש מינימלי מאוד, אך בלתי מוגבל, בכלי בשכבה חינמית עם יותר גישה ותכונות שהוצגו בשכבות בתשלום. אמנות גנרטיבית: אמנות גנרטיבית היא צורה של אמנות שנוצרת באמצעות תוכנת מחשב או אלגוריתם ליצירת פלט חזותי או אודיו. לרוב זה כרוך בשימוש באקראיות או בכללים מתמטיים כדי ליצור תוצאות ייחודיות, בלתי צפויות ולעיתים כאוטיות. Generative Pre-trained Transformer(GPT): GPT ראשי תיבות של Generative Pre-trained Transformer. זהו סוג של מודל שפה גדול שפותח על ידי OpenAI. GitHub: GitHub היא פלטפורמה לאירוח ושיתוף פעולה בפרויקטי תוכנה

Google Colab: Google Colab היא פלטפורמה מקוונת המאפשרת למשתמשים לשתף ולהריץ סקריפטים של Python בענן Graphics Processing Unit(GPU): GPU, או יחידת עיבוד גרפית, הוא סוג מיוחד של שבב מחשב שנועד להתמודד עם המורכבות חישובים הדרושים להצגת תמונות ווידאו במחשב או במכשיר אחר. זה כמו המוח של המערכת הגרפית של המחשב שלך, והוא ממש טוב לעשות הרבה מתמטיקה ממש מהר. GPUs משמשים סוגים רבים ושונים של מכשירים, כולל מחשבים, טלפונים וקונסולות משחקים. הם שימושיים במיוחד למשימות הדורשות כוח עיבוד רב, כמו משחקי וידאו, עיבוד גרפיקה תלת-ממדית או הפעלת אלגוריתמים של למידת מכונה. מודל שפה גדול (LLM): סוג של מודל למידת מכונה שאומן על כמות גדולה מאוד של נתוני טקסט ומסוגל ליצור טקסט בעל צליל טבעי. Machine Learning (ML): שיטה ללמד מחשבים ללמוד מנתונים, מבלי להיות מתוכנתים במפורש. עיבוד שפה טבעית (NLP): תת-תחום של AI המתמקד בהוראת מכונות להבין, לעבד וליצור שפה אנושית רשתות עצביות: סוג של אלגוריתם למידת מכונה המבוססת על המבנה והתפקוד של המוח. שדות קרינה עצביים (NeRF): שדות קרינה עצביים הם סוג של מודל למידה עמוקה שיכול לשמש למגוון משימות, כולל יצירת תמונה, זיהוי אובייקטים ופילוח. NeRFs שואבים השראה מהרעיון של שימוש ברשת עצבית למודל של זוהר תמונה, שהוא מדד לכמות האור שנפלט או מוחזר על ידי אובייקט. OpenAI: OpenAI הוא מכון מחקר המתמקד בפיתוח וקידום טכנולוגיות בינה מלאכותית שהן בטוחות, שקופות ומועילות לחברה. Overfitting: בעיה נפוצה בלמידת מכונה, שבה המודל מתפקד היטב בנתוני האימון אך גרועים בחדשים, בלתי נראים. נתונים. זה מתרחש כאשר המודל מורכב מדי ולמד יותר מדי פרטים מנתוני האימון, כך שהוא לא מכליל היטב. הנחיה: הנחיה היא פיסת טקסט המשמשת לתכנון מודל שפה גדול ולהנחות את הדור שלו Python: Python היא שפת תכנות פופולרית ברמה גבוהה הידועה בפשטות, בקריאות ובגמישות שלה (כלי AI רבים משתמשים בה) למידת חיזוק: סוג של למידת מכונה שבה המודל לומד על ידי ניסוי וטעייה, מקבל תגמולים או עונשים על מעשיו ומתאים את התנהגותו בהתאם. מחשוב מרחבי: מחשוב מרחבי הוא השימוש בטכנולוגיה כדי להוסיף מידע וחוויות דיגיטליות לעולם הפיזי. זה יכול לכלול דברים כמו מציאות רבודה, שבה מידע דיגיטלי מתווסף למה שאתה רואה בעולם האמיתי, או מציאות מדומה, שבה אתה יכול לשקוע במלואו בסביבה דיגיטלית. יש לו שימושים רבים ושונים, כמו בחינוך, בידור ועיצוב, והוא יכול לשנות את האופן שבו אנו מתקשרים עם העולם ואחד עם השני. דיפוזיה יציבה: דיפוזיה יציבה מייצרת תמונות אמנותיות מורכבות המבוססות על הנחיות טקסט. זהו מודל AI של סינתזת תמונות בקוד פתוח הזמין לכולם. ניתן להתקין את ה-Stable Diffusion באופן מקומי באמצעות קוד שנמצא ב-GitHub או שישנם מספר ממשקי משתמש מקוונים הממנפים גם מודלים של Stable Diffusion. למידה מפוקחת: סוג של למידת מכונה שבה נתוני האימון מסומנים והמודל מאומן לבצע תחזיות על סמך היחסים בין נתוני הקלט והתוויות המתאימות. למידה ללא פיקוח: סוג של למידת מכונה שבה נתוני האימון אינם מסומנים, והמודל מאומן למצוא דפוסים ויחסים בנתונים בעצמו. Webhook: Webhook הוא דרך של תוכנת מחשב אחת לשלוח הודעה או נתונים לתוכנית אחרת דרך האינטרנט בזמן אמת. זה עובד על ידי שליחת ההודעה או הנתונים לכתובת URL ספציפית, השייכת לתוכנית האחרת. Webhooks משמשים לעתים קרובות כדי להפוך תהליכים לאוטומטיים ולהקל על תוכניות שונות לתקשר ולעבוד יחד. הם כלי שימושי למפתחים שרוצים לבנות יישומים מותאמים אישית או ליצור אינטגרציות בין מערכות תוכנה שונות.

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