In the absence of an obvious copyright notice, I assume the code is 'public domain'.
This means: "no guarantee of success -- use at your own risk".
There is a slight but non-negligible chance that the generated code might implode your computer or wake up all the zombies in your area 😱
Chat GPT produces technical conversational pieces that you could dish out while standing around a bar after a few pints. No one is going to correct you at that point unless they are looking for an argument. The description of VFA and CFA is enough to tell you its way off the mark.
On the other hand, if you want to create some sort of sales or marketing narrative, it is a good tool. My son recently revamped my website to improve the SEO ranking as it had been sliding for years. To create verbiage that matched well with Googles requirements, he used this bot rather than my reserved articulation. Here is an an example of what it came up with that he used:-
"Are you passionate about music, audio and electronics? Do you enjoy building your own audio equipment? Then you've come to the right place! Hifisonix is dedicated to helping DIY enthusiasts create and learn about high-quality audio systems that deliver exceptional sound. Whether you're a beginner or an experienced audiophile, you'll find everything you need to take your audio setup to the next level."
and this one
"This website is the go-to destination for sound enthusiasts, audio electronics professionals, and DIY Hi-Fi builders. With an ever-growing collection of resources, this is the place to learn, share, and connect with others in the world of audio."
I told him it was over the top and too self-inflatorary (yes, I just invented that word), but he refused to remove it. My SEO ranking has improved and continues to improve, so I am not about to argue with him.
Please don't use this tool to get opinions on technical subjects - it just isn't there yet (maybe code, but not electronic design!).
On the other hand, if you want to create some sort of sales or marketing narrative, it is a good tool. My son recently revamped my website to improve the SEO ranking as it had been sliding for years. To create verbiage that matched well with Googles requirements, he used this bot rather than my reserved articulation. Here is an an example of what it came up with that he used:-
"Are you passionate about music, audio and electronics? Do you enjoy building your own audio equipment? Then you've come to the right place! Hifisonix is dedicated to helping DIY enthusiasts create and learn about high-quality audio systems that deliver exceptional sound. Whether you're a beginner or an experienced audiophile, you'll find everything you need to take your audio setup to the next level."
and this one
"This website is the go-to destination for sound enthusiasts, audio electronics professionals, and DIY Hi-Fi builders. With an ever-growing collection of resources, this is the place to learn, share, and connect with others in the world of audio."
I told him it was over the top and too self-inflatorary (yes, I just invented that word), but he refused to remove it. My SEO ranking has improved and continues to improve, so I am not about to argue with him.
Please don't use this tool to get opinions on technical subjects - it just isn't there yet (maybe code, but not electronic design!).
while the ability to sum our current level of knowledge and regurgitate it back in a friendly, well understood, concise manor is an advancement we will use it for the most shallow capitalist purposes until that is exhausted, marveling at our pseudo AI distraction and/or worshiping it as a god…if only the side effect was it ate carbon , now that would create some followers
"Yes, it is possible to create an AI that can learn beyond the limitations of human appreciation. Through the use of advanced machine learning techniques, such as deep learning and reinforcement learning, AI can be trained to identify patterns and make predictions that humans may not be able to discern. Additionally, AI can be designed to process and analyze large amounts of data at a much faster rate than humans, which can enable it to learn from a broader range of information. However, it is important to note that the development of such advanced AI systems can be complex and resource-intensive."
Chat GPT generated netlist haven't yet converted it to schematic, so I don't know whether its garbage. I haven't tried genetic sequences with chat gpt
"
*100W Class AB Amplifier Circuit with Quasi-Complementary Output Stage
Vin 1 0 AC 1
Rin 1 2 2k
LTP 2 0 3 NPN
R3 3 0 1k
Q1 4 5 6 NPN
Q2 7 8 9 PNP
R4 4 0 1k
R5 9 0 1k
R6 4 7 2.5k
R7 5 8 2.5k
C1 4 5 1uF
C2 7 8 1uF
L1 6 0 10mH
L2 9 0 10mH
Vcc 10 0 DC 40
Vee 11 0 DC -40
Q3 3 12 13 NPN
R8 12 0 1k
R9 13 14 5k
R10 14 0 5k
C3 12 14 100pF
.model NPN NPN ( BF=200 IS=10E-14 VAF=100 IKF=0.4 ISE=10E-14 NE=1.5 BR=4.0 NR=1.5 VAR=25 IKR=0.4 ISC=10E-14 NC=1.5 )
.model PNP PNP ( BF=200 IS=10E-14 VAF=100 IKF=0.4 ISE=10E-14 NE=1.5 BR=4.0 NR=1.5 VAR=25 IKR=0.4 ISC=10E-14 NC=1.5 )
.model LTP NPN ( BF=200 IS=10E-14 VAF=100 IKF=0.4 ISE=10E-14 NE=1.5 BR=4.0 NR=1.5 VAR=25 IKR=0.4 ISC=10E-14 NC=1.5 )
.tran 0.1m 1m 0 1u
.plot tran V(3) V(4) V(5) V(6) V(7) V(8) V(9)
.end
"
"
*100W Class AB Amplifier Circuit with Quasi-Complementary Output Stage
Vin 1 0 AC 1
Rin 1 2 2k
LTP 2 0 3 NPN
R3 3 0 1k
Q1 4 5 6 NPN
Q2 7 8 9 PNP
R4 4 0 1k
R5 9 0 1k
R6 4 7 2.5k
R7 5 8 2.5k
C1 4 5 1uF
C2 7 8 1uF
L1 6 0 10mH
L2 9 0 10mH
Vcc 10 0 DC 40
Vee 11 0 DC -40
Q3 3 12 13 NPN
R8 12 0 1k
R9 13 14 5k
R10 14 0 5k
C3 12 14 100pF
.model NPN NPN ( BF=200 IS=10E-14 VAF=100 IKF=0.4 ISE=10E-14 NE=1.5 BR=4.0 NR=1.5 VAR=25 IKR=0.4 ISC=10E-14 NC=1.5 )
.model PNP PNP ( BF=200 IS=10E-14 VAF=100 IKF=0.4 ISE=10E-14 NE=1.5 BR=4.0 NR=1.5 VAR=25 IKR=0.4 ISC=10E-14 NC=1.5 )
.model LTP NPN ( BF=200 IS=10E-14 VAF=100 IKF=0.4 ISE=10E-14 NE=1.5 BR=4.0 NR=1.5 VAR=25 IKR=0.4 ISC=10E-14 NC=1.5 )
.tran 0.1m 1m 0 1u
.plot tran V(3) V(4) V(5) V(6) V(7) V(8) V(9)
.end
"
Well, sometimes you just need a little push, when creating something; that chassis is hard to fold and all those holes to punch and drill. But with that "outline" put in place, it's much easier to let out the clutch on mounting up everything and connecting it all together.
I imagine it'd be a great help for those having trouble kicking off a speech, report or "how it works description". Jan noticed it was wrong, but how much easier is that to (simply) correct then generate a similar text body from whole cloth?
I imagine it'd be a great help for those having trouble kicking off a speech, report or "how it works description". Jan noticed it was wrong, but how much easier is that to (simply) correct then generate a similar text body from whole cloth?
An orphan raised by a committee...
(If we suspend doubt for a moment and look at AI as a living being, then that's what it boils down to.)
LoL well .....people are still exploring what they feel about the new technology and where it all fits in in the restructuring
We are making the assumption that chat gpt is not well read, however it might just be teething and articulation problems because it has ingested the following media and many others
https://wiki.analog.com/university/courses/electronics/text/chapter-3
https://wiki.analog.com/university/courses/electronics/text/chapter-11
https://wiki.analog.com/university/courses/electronics/text/chapter-13
https://wiki.analog.com/university/courses/electronics/text/chapter-3
https://wiki.analog.com/university/courses/electronics/text/chapter-11
https://wiki.analog.com/university/courses/electronics/text/chapter-13
Exactly: mistakes are a function of what it learned from and topology. Give it large amounts of proper inputs, preferably pre-curated for accuracy and results should be better.
Even then, people will latch on mistakes here and there (no different from human posts in reality) and think this AI is a 'failure'.
Some even think that we still need to understand something in detail before we program it... Hasn't been the case since Machine Learning, and Deep Learning makes abstracting even easier since there's zero need for Factor Engineering as in ML, which is usually done by Humans.
A model is evolved by the network itself.
Even then, people will latch on mistakes here and there (no different from human posts in reality) and think this AI is a 'failure'.
Some even think that we still need to understand something in detail before we program it... Hasn't been the case since Machine Learning, and Deep Learning makes abstracting even easier since there's zero need for Factor Engineering as in ML, which is usually done by Humans.
A model is evolved by the network itself.
"
The choice of whether to use a no-feedback or a feedback amplifier for specific frequency ranges, such as the mid-range and high frequency, or the bass, depends on the specific design of the amplifier and the desired sound characteristics.
No-feedback amplifiers, also known as open-loop amplifiers, are known for their natural, warm and musical sound characteristics. They have a lower distortion and noise floor and a better transient response compared to feedback amplifiers. However, they can have a higher output impedance and a lower power efficiency. These characteristics make them well suited for the mid-range and high frequency sections of an audio system, where the natural and detailed sound reproduction is desired.
On the other hand, Feedback amplifiers, also known as closed-loop amplifiers, are known for their high power efficiency, low output impedance, and high stability. They can have a lower distortion and noise floor, but they can also have a higher phase shift and a slower transient response compared to no-feedback amplifiers. These characteristics make them well suited for the bass section of an audio system, where high power and low distortion are desired.
It's worth to mention that the choice of the amplifier type and its specific design will depend on the desired sound characteristics, the cost and complexity of the design, and the specific requirements of the application. Additionally, the choice of the amplifier type for each section of the audio system is not set in stone and can vary depending on the specific design and the desired sound characteristics.
"
The choice of whether to use a no-feedback or a feedback amplifier for specific frequency ranges, such as the mid-range and high frequency, or the bass, depends on the specific design of the amplifier and the desired sound characteristics.
No-feedback amplifiers, also known as open-loop amplifiers, are known for their natural, warm and musical sound characteristics. They have a lower distortion and noise floor and a better transient response compared to feedback amplifiers. However, they can have a higher output impedance and a lower power efficiency. These characteristics make them well suited for the mid-range and high frequency sections of an audio system, where the natural and detailed sound reproduction is desired.
On the other hand, Feedback amplifiers, also known as closed-loop amplifiers, are known for their high power efficiency, low output impedance, and high stability. They can have a lower distortion and noise floor, but they can also have a higher phase shift and a slower transient response compared to no-feedback amplifiers. These characteristics make them well suited for the bass section of an audio system, where high power and low distortion are desired.
It's worth to mention that the choice of the amplifier type and its specific design will depend on the desired sound characteristics, the cost and complexity of the design, and the specific requirements of the application. Additionally, the choice of the amplifier type for each section of the audio system is not set in stone and can vary depending on the specific design and the desired sound characteristics.
"
It may have algorithms to determine which strongly connected components are false and which strongly connected components are true, or which ones are in the grey, they maybe using tweaked graphsIt is well versed and well read, for sure. But it also read a lot of bullcrap in it's training phase