TRANSCRIÇÃOEnglish

Is There Any AI That Can Tune A Betaflight Drone? Could You Train One? - FPV Questions

12m 19s2,072 palavras299 segmentsEnglish

TRANSCRIÇÃO COMPLETA

0:01

Um, Clipsy asks, "Is there any AI that

0:03

can tune from Blackbox info?" I actually

0:07

addressed that

0:10

recently on the Joshua Bardwell live

0:13

stream clips

0:17

channel. Uh, that's where Blunty clips

0:21

out uh, segments from the live stream

0:24

and uploads them for you to enjoy.

0:27

And I've

0:32

recently, let's just search. Can chat

0:35

GBT tune your drone?

0:37

No, it

0:41

cannot. Well, if you want to go watch

0:43

that clip to get the details, you can.

0:45

It's on the Joshua Bardwell Live Stream

0:47

Clips channel. The short version is I I

0:50

think the answer is no

0:54

because I am pretty sure that in order

0:58

to do PID

1:01

tuning

1:03

you you have to be able to parse the

1:06

blackbox data in ways that I don't think

1:09

Chat GPT can

1:13

specifically in order to take the gyro

1:17

data and convert it into a frequency

1:19

plot. You have to do a fast Forier

1:21

transform which is a mathematical

1:23

function that does that thing. And chat

1:26

GPT can't do a fast Forier

1:29

transform. So when chat GPT tells you to

1:33

do something with your filters and then

1:35

it works for you, I think it's just

1:37

hallucinating and by chance it got the

1:39

answer right.

1:41

And I also don't think that chat GPT has

1:44

the ability to calculate like step

1:47

response like PID toolbox

1:50

does.

1:52

So if if you're handing chat GPT a

1:56

blackbox log, I don't think it knows how

1:58

to parse a blackbox

2:00

log. It's possible that if you give it a

2:03

CSV data, it will be able to parse the

2:06

CSV data. And you can convert a blackbox

2:08

log to CSV, but I still don't think it

2:11

actually has the core logic that it

2:14

needs to be able to interpret and make

2:16

recommendations on the data. And people,

2:18

it drives me crazy. And if I'm wrong

2:21

about this, I will happily admit that

2:23

I'm wrong. Chat TPT can do some cool

2:25

[ __ ] you know, but I just don't think

2:29

it

2:30

can parse blackbox data. Just to be

2:33

clear, you're also people in chat maybe

2:35

not, but you're using chat GPT as a

2:37

blanket term for AI LLM in general.

2:40

Yeah, LLMs in general. Is there any LLM

2:43

that can do a fast for transform? Can

2:46

any LLM? Well, I mean, any of them could

2:49

do it with code, right? Like if you're

2:51

in cursor and you prompted one, like you

2:53

could get a fast for

2:55

transform, you know what I mean? Like

2:57

through code. If you have an LLM, if you

3:00

had an LLM that could access a Python

3:04

function that could do a fast fora

3:05

transform. Yes. Yeah. I mean, it would

3:08

have to know that it needed to do that.

3:10

Like you could go and cursor prompt the

3:12

AI to get you something that could tune

3:14

a drone with the thing and explain. You

3:16

know what I mean? That's the sort of the

3:17

idea. But then you would have to work,

3:21

right? That's not what they do. So here,

3:22

for example, please write me a Python

3:24

function to calculate FFT.

3:27

Uh a fast for a um an AI could do that.

3:31

But what people are doing is

3:37

they're what they're doing is they're

3:39

just

3:41

saying I dumped a blackbox log. I can't

3:45

find I can't find an example. They're

3:47

just saying, "I dumped a blackbox log

3:49

into chat GPT." And then chat GPT goes,

3:51

"Cool. I will help you tune your

3:53

blackbox log. We will get maximum step

3:56

response and you know, properly tune

3:58

your filters." And it just says a bunch

4:00

of

4:01

[ __ ] And then it's like based on

4:03

what I see in your blackbox log, I

4:05

recommend that you increase your P gain.

4:08

And it's like, you know what? Like I'll

4:10

put on a white lab coat and a

4:12

stethoscope and hold a clipboard and

4:14

I'll say, "Cool. I've looked at your

4:16

I've looked at your blood test results

4:18

and it seems that your cholesterol is

4:21

132. I recommend that you get that

4:24

number up. A good cholesterol is between

4:27

187 and 221. Don't I sound

4:31

confident? I I'm completely just talking

4:34

talking out my ass. And I think that

4:37

when when chat GPT or any AI pretends to

4:40

be like it knows that terms like P gain,

4:44

D gain, filters, step response,

4:46

overshoot, oscillation. It knows that

4:49

these are terms that are associated with

4:51

blackbox logging and it makes sentences

4:54

that sound convincing and makes

4:57

recommendations which you then follow

4:59

and maybe they work, but it's not

5:02

because it actually understood what was

5:04

in your blackbox log. I don't think it

5:06

can possibly know

5:10

that. So

5:12

yeah, and this also isn't to mean that

5:14

somebody can't eventually train

5:15

something to do this specifically. Like

5:17

one of the things we're seeing now is

5:18

agentic. Somebody mentioned that in the

5:20

chat, agentic AI, where you have like

5:22

one I think that's how DeepC handled it.

5:24

You have one big LLM model, but it's

5:26

it's it's basically asking individual

5:28

agents that are good at certain jobs. So

5:32

it'll have a agent that's good at math.

5:33

And so if it has math in its thing,

5:35

it'll go ask the math agent to solve it

5:37

for it and bring it back. So the idea is

5:39

that you would have specific agents that

5:41

understand these pieces and it can't

5:42

really get lost as easy because it knows

5:44

to ask the thing who doesn't have as

5:46

much context like who doesn't have

5:48

contact. But but here's the problem with

5:50

that.

5:52

It ha there has the training set, the

5:56

training data has to include

6:00

examples that let the large language

6:03

model learn what right looks like. Does

6:08

that make sense? Of course. Well, the

6:12

that's how LLMs work. And obviously a a

6:14

treatise on how LLMs work is not I'm not

6:17

qualified to give it. But the short

6:19

version is that you feed the LLM a lot

6:21

of

6:22

data

6:24

that is

6:27

correct or that from which you want to

6:29

draw

6:30

inferences and then you ask the LLM

6:33

questions about the data and it tells

6:34

you and talks to you about the data but

6:36

garbage in garbage

6:39

out. Did any LLM get trained on blackbox

6:43

logs?

6:46

Can it even parse a blackbox log? If I

6:49

hand it a blackbox log from Betaflight,

6:52

it's just a bunch of ones and zeros.

6:54

Does it know how to interpret that? How

6:56

would it even know?

6:58

And if it Yeah, you build an interpreter

7:01

like that. You would have to build it. I

7:03

mean, that's you would have to do but no

7:04

one's done that. My point is no one's

7:07

done that.

7:08

Yeah. So you would have to number one

7:11

you would have to build an

7:13

interpreter so that it could in

7:15

basically you would have to build pit

7:17

toolbox into chat GPT or give it an API

7:21

that let it access PID toolbox so that

7:23

it could look at the blackbox log and go

7:25

okay your frequency response is X your

7:28

sorry you know your your step response

7:31

is Y here are your current PIDs and

7:34

based on that and then it's got to make

7:36

a recommendation. So again, you would

7:38

have to train it on when P is P goes up,

7:41

here's what happens. When P goes down,

7:43

here's what happens. And here is what

7:45

we're looking for. We're looking for

7:48

this. This is our end state that we

7:50

want. And no one's done that. No one's

7:53

done that.

7:55

I think the alternative would be, is

7:57

there enough data out there? If it comes

7:59

all the forums and all the things and if

8:01

it get transcripts of YouTube videos

8:03

like things like that, right? like is

8:05

there enough data out there to tell you

8:06

how to tune a drone or like you you were

8:09

saying you need to specifically tune

8:11

something and then like you said there's

8:12

also in a blackbox interpreter so you

8:14

need the data out of it. So uh you know

8:16

that's something they're doing more more

8:19

though is like now their image

DESBLOQUEAR MAIS

Registe-se gratuitamente para aceder a funcionalidades premium

VISUALIZADOR INTERATIVO

Assista ao vídeo com legendas sincronizadas, sobreposição ajustável e controlo total da reprodução.

REGISTE-SE GRATUITAMENTE PARA DESBLOQUEAR

RESUMO DE IA

Obtenha um resumo instantâneo gerado por IA do conteúdo do vídeo, pontos-chave e conclusões.

REGISTE-SE GRATUITAMENTE PARA DESBLOQUEAR

TRADUZIR

Traduza a transcrição para mais de 100 idiomas com um clique. Baixe em qualquer formato.

REGISTE-SE GRATUITAMENTE PARA DESBLOQUEAR

MAPA MENTAL

Visualize a transcrição como um mapa mental interativo. Entenda a estrutura rapidamente.

REGISTE-SE GRATUITAMENTE PARA DESBLOQUEAR

CONVERSAR COM A TRANSCRIÇÃO

Faça perguntas sobre o conteúdo do vídeo. Obtenha respostas com tecnologia de IA diretamente da transcrição.

REGISTE-SE GRATUITAMENTE PARA DESBLOQUEAR

APROVEITE MAIS DE SUAS TRANSCRIÇÕES

Inscreva-se gratuitamente e desbloqueie o visualizador interativo, resumos de IA, traduções, mapas mentais e muito mais. Não é necessário cartão de crédito.

    Is There Any AI T… - Transcrição Completa | YouTubeTranscript.dev