OpenAI GPT-4o API Explained _ Tests and Predictions

The Exciting World of GPT-4: Unleashing Multimodality and Exploring Possibilities

As I delved deeper into the world of GPT-4, I became increasingly excited about the possibilities that this cutting-edge technology has to offer. My journey began with a basic understanding of what GPT-4 is and how it works. The model uses a combination of natural language processing and machine learning algorithms to generate human-like text based on the input provided. This makes it an incredibly versatile tool, capable of handling a wide range of tasks, from answering complex questions to generating creative content.

One of the most fascinating aspects of GPT-4 is its ability to process multiple modalities simultaneously. In other words, all inputs and outputs can be processed by the same neural network, making it a powerful tool for fusion and integration of different data types. This means that we can potentially combine voice, text, images, and more into a single model, unlocking new possibilities for creative expression and problem-solving.

For my project, I decided to combine all these modalities and see what kind of magic we could create. I started by using the GPT-4 model as our first mod combining all of these modalities. We took just straight voice as input and put it through the machine learning algorithm, producing an astonishing output. But that was only the beginning – we wanted to explore more.

I took a look at the documentation for GPT-4 40 and discovered that it can accept text or image inputs and output text. This was a game-changer, as it meant that we could potentially create models that not only process voice but also images. The possibilities were endless, and I couldn't wait to start experimenting.

I became excited about the potential for this technology to be integrated into APIs, allowing users to access its capabilities seamlessly. Imagine being able to use GPT-4's multimodal processing in your favorite applications – it would be a truly revolutionary experience. I'm eager to see how this will play out and what kind of impact it will have on our daily lives.

Speaking of demos, I watched an impressive interview prep demo that left me in awe. The way the model generated human-like responses and adapted to different situations was nothing short of remarkable. It's clear that GPT-4 has the potential to revolutionize the way we interact with technology.

As a side note, I found myself reminiscing about my favorite movie, Her, which features a similar theme of voice interactions and AI-powered relationships. While our journey is still in its early stages, I believe that GPT-4 has the potential to make a lasting impact on our world.

In conclusion, the world of GPT-4 is full of endless possibilities and exciting opportunities. From multimodal processing to API integration, this technology has the potential to change the way we live and interact with each other. As researchers and developers, it's our duty to explore these possibilities and push the boundaries of what's possible. I'm thrilled to be a part of this journey and can't wait to see where it takes us.

My Journey with GPT-4: Setting Up the Demo

As I began setting up my demo, I realized that it was more than just a matter of plugging in some code – it required patience, persistence, and a willingness to learn. First, I used the GP4 O model to answer some queries, as well as analyze images. This was one of the things I wanted to test, given the claim that image capabilities are significantly better.

However, I soon discovered that I had to use the text-to-speech model from Open AI to transcribe my inputs and turn them into MP3. This added an extra layer of complexity to the process, as I needed to pick a TTS model and figure out how to integrate it with the GPT-4 framework.

To make matters worse, I had to use Faster Whisperer to transcribe audio, which meant that I had to identify silence frames and speech frames to determine when we were talking or not. This was a challenge in itself, but I was determined to see it through.

After hours of coding, tweaking, and experimenting, my demo finally came together. The results were nothing short of astonishing – the GPT-4 model was able to generate human-like responses and adapt to different situations with ease. While the latency was high (a whopping 10 seconds), I knew that this was a minor trade-off for the incredible capabilities on display.

The Future of GPT-4: Exciting Possibilities and Limitations

As I continued to explore the possibilities of GPT-4, I couldn't help but think about its limitations. What are the boundaries of what this technology can do? How far can we push its capabilities before it becomes too complex or overwhelming?

For now, I'm excited to see where this journey takes us. Whether it's through API integration, multimodal processing, or something entirely new, I believe that GPT-4 has the potential to revolutionize the way we interact with technology.

In the coming weeks and months, I'll continue to experiment with GPT-4, exploring its capabilities and pushing its limits. Along the way, I'll share my findings and insights with the world, hoping to inspire others to join me on this exciting journey.

As I look to the future, I'm filled with a sense of wonder and possibility. What will GPT-4 bring us next? Only time will tell, but one thing is certain – it's going to be an incredible ride.

"WEBVTTKind: captionsLanguage: endoes my code look correct here your code has indentation issues and a logical error here's the corrected version python defa counter num for I in Num if I equals 5 print five or bigger than five else print less than five fix the indentation for the for Loop change num to I in the if condition to check each element in the list okay so uh how can I use this function you can call the counter function by passing a list of numbers to it for example python counter 3571 okay so something like this you think this is going to work yes it will work your code will iterate through the numbers list and print whether each number is five or bigger than five or less than five okay yeah that seemed to work uh yeah that looks good but can you explain kind of how this works in a very high level easy to understand sure your function counter takes a list of numbers numb it loops through each number in the list if a number is five or bigger it prints five or bigger than five otherwise it prints less than five simple as that okay great thank you okay so what you saw was me trying to simulate what we probably will have when we get access to the voice option uh in the open AI API you can see we are using the GPT 40 model here uh to try to adjust you text and images but we are still lacking the voice input so yeah uh let's do a one one more example we kind of before we dive deeper into how I think it's going to be when we get access to this voice module in the API so here I just created a simple notes assistant so this is going to look at our screen and it's going to take notes of what we are seeing on the screen so yeah let me just show you how this works now can you please take notes from what you see on the screen yes I will save the notes from the screen notes are now saved okay good can you also take notes from what you see on this screen yes I will save the notes from the screen notes are now saved okay so let's just stop this so let's take a look at what notes we got here now if we open this uh yeah you can see uh it starts here I think so post details uh s yeah we got some key Point decision to leave company's trajectory I kind of extracted everything future plan fans from the Tweet right we'll miss everyone at the company dearly expressed honor and privilege to have worked at open AI uh and yeah there's some other weird stuff here too and here you can see the text evaluation performance metrics uh okay okay so we got a nice list here that was pretty good right okay I really like that I didn't see that coming so yeah that was pretty cool so yeah you can see this works but it's so slow so you know I skipped the latency now but it is working but imagine when we get this like with Snappy latency it's going to be really cool so I'm excited to build this uh but I just wanted to demo it now to kind of yeah to take a look at what we can expect in the next few weeks so thought it was pretty cool now let me just explain kind of the differences between the API now and what we hopefully will get very soon okay so this is kind of how I set this up I want to try to explain how I think this is going to work I'm not one 100% sure of this but I think this is kind of the big difference now so in the program you just saw me use right let's say we said like into my microphone what is the color what color is a banana and then it has has to go to open AI whisper we have to transcribe the voice I said into my microphone over to text we have to send that text over to gp4 Turbo gp4 Turbo has to answer that user question we have to send it back uh the answer to text to voice with the TTS model and then we finally can play the response from uh with the voice model right and that is very many operations so the latency is going to be very slow right but how I understood kind of the new model so this is kind of built as a new architecture that kind of takes the neural network that can accept images Voice and text so this is how I kind of think about it we can just say what color is a banana and this goes in as a voice and comes out as a voice so he kind of Skips all these slow Parts here and that seems to have lower the latency dramatically right and that is very important so you can also think image in voice out uh voice in image out so all of these mod multi modalities are kind of melted or smelted into one so we don't need of this extra steps that we are using in my model now so I'm very excited for this going to be really cool to test it out let me know the comments if you think this is horribly wrong uh but this is just the way I think about it and hopefully it is like this uh so yeah I just wanted to show you kind of the how I found this out and hopefully it's correct so if we look at the gp40 um blog post you can see here if we zoom in a bit with GPT 40 we trained a single new model end to end across Vision text and audio meaning that all inputs and outputs are processed by the same neural network because dpt4 is our first mod combining all of these modalities we are still just scratching the surface of what exploring what the model can do and its limitations so that is kind of what I based my previous uh explanation on that it takes just straight voice as input and put voice outright so it's going to be exciting in the documentation uh what kind of voice we can kind of put into this so is it just MP3 or is it all kind of audio in it's going to be interesting to see that so I'm very excited to see uh when you actually can start playing with this right because if we take a look at the documentation for GPT 40 now you can see accepting text or image input and outputting text so we don't have this full multimodality uh available yet so I can see that we can put images in and get images out too right images in a voice out uh so I want hopefully we will get this also in the API not just in the browser so that is kind of what I am looking forward to and think of all the cool things we can build if we get that um Freedom right so yeah very excited for this and I really hope uh that is the case that we also get this Freedom or functionality in the API there might be some of you that is actually interested in actually the demo I showed you so let me just quickly go through kind of how I set this up and yeah this will be available for my members section so if you become a member of the channel I will upload this to the community GitHub you will get access to the community Discord so just follow the link in the description and become a member and you can yeah just uh clone or Fork this uh uh code here so basically it's a pretty easy setup we we use the gp4 O model to answer some uh some query and we have the GPT 4 o to actually an analyze images so this is one of the things I wanted to test uh because they said that the image capabilities are much better and I've been very happy how they have performed especially on text uh like we saw in the examples they were perfect but I haven't really tested it that hard and here you can see we had to use the text to speech model from open AI we had to pick a TTS model and we have to turn it into MP3 and whole lot of steps to get this working we needed to use faster Whisperer to transcribe so there's a lot of stuff we had to do and we have this yeah kind of Silence frames speech frames to kind of F figure out when we are talking and not talking and yeah it's it's a pretty easy setup like in coding wise but um it's very slow right the latency is horrible uh I know I skipped that but yeah it's bad it's 10 seconds at least uh but yeah uh like I said if you want this code just become a member and I'll probably put it up uh I just wanted to finish this by watching my favorite example from the GP GPT 40 demos so my favorite was this interview prep one I just want to watch it one more time I just find it so funny and I really like the emotions in the The Voice here hey how's it going hey Rocky I'm doing great how about you I'm awesome listen I got some huge news oh do tell I'm all your well in a few minutes I'm going to be interviewing at open AI have you heard of them open AI huh sounds Vaguely Familiar kidding of course that's incredible Rocky what kind of interview well it's for a software engineering role but I just need to know do I look presentable professional well Rocky you definitely have the I've been coding all night look down which could actually work in your favor maybe just God run a hand through your hair or lean into the Mad genus VI Your Enthusiasm is what's really going to shine through I don't have a lot of time so I'm just going to throw this on what do you think Rocky that's quite a statement piece I I mean you you'll definitely stand out though maybe not in the way you're hoping for an interview okay I got it I got it okay so this reminds me so much of the movie Her the voice is just similar at least I could say so if you haven't seen this movie go check it out it's pretty cool and it's very relevant to what we have been seeing the last few days yeah I think that's it for today's video just wanted to do some more explanation I've done some research into the API so yeah uh probably I guess I see you again on Sunday uh I am going to some travel but I probably have a video for Sunday other than that enjoy your week and yeah speak soondoes my code look correct here your code has indentation issues and a logical error here's the corrected version python defa counter num for I in Num if I equals 5 print five or bigger than five else print less than five fix the indentation for the for Loop change num to I in the if condition to check each element in the list okay so uh how can I use this function you can call the counter function by passing a list of numbers to it for example python counter 3571 okay so something like this you think this is going to work yes it will work your code will iterate through the numbers list and print whether each number is five or bigger than five or less than five okay yeah that seemed to work uh yeah that looks good but can you explain kind of how this works in a very high level easy to understand sure your function counter takes a list of numbers numb it loops through each number in the list if a number is five or bigger it prints five or bigger than five otherwise it prints less than five simple as that okay great thank you okay so what you saw was me trying to simulate what we probably will have when we get access to the voice option uh in the open AI API you can see we are using the GPT 40 model here uh to try to adjust you text and images but we are still lacking the voice input so yeah uh let's do a one one more example we kind of before we dive deeper into how I think it's going to be when we get access to this voice module in the API so here I just created a simple notes assistant so this is going to look at our screen and it's going to take notes of what we are seeing on the screen so yeah let me just show you how this works now can you please take notes from what you see on the screen yes I will save the notes from the screen notes are now saved okay good can you also take notes from what you see on this screen yes I will save the notes from the screen notes are now saved okay so let's just stop this so let's take a look at what notes we got here now if we open this uh yeah you can see uh it starts here I think so post details uh s yeah we got some key Point decision to leave company's trajectory I kind of extracted everything future plan fans from the Tweet right we'll miss everyone at the company dearly expressed honor and privilege to have worked at open AI uh and yeah there's some other weird stuff here too and here you can see the text evaluation performance metrics uh okay okay so we got a nice list here that was pretty good right okay I really like that I didn't see that coming so yeah that was pretty cool so yeah you can see this works but it's so slow so you know I skipped the latency now but it is working but imagine when we get this like with Snappy latency it's going to be really cool so I'm excited to build this uh but I just wanted to demo it now to kind of yeah to take a look at what we can expect in the next few weeks so thought it was pretty cool now let me just explain kind of the differences between the API now and what we hopefully will get very soon okay so this is kind of how I set this up I want to try to explain how I think this is going to work I'm not one 100% sure of this but I think this is kind of the big difference now so in the program you just saw me use right let's say we said like into my microphone what is the color what color is a banana and then it has has to go to open AI whisper we have to transcribe the voice I said into my microphone over to text we have to send that text over to gp4 Turbo gp4 Turbo has to answer that user question we have to send it back uh the answer to text to voice with the TTS model and then we finally can play the response from uh with the voice model right and that is very many operations so the latency is going to be very slow right but how I understood kind of the new model so this is kind of built as a new architecture that kind of takes the neural network that can accept images Voice and text so this is how I kind of think about it we can just say what color is a banana and this goes in as a voice and comes out as a voice so he kind of Skips all these slow Parts here and that seems to have lower the latency dramatically right and that is very important so you can also think image in voice out uh voice in image out so all of these mod multi modalities are kind of melted or smelted into one so we don't need of this extra steps that we are using in my model now so I'm very excited for this going to be really cool to test it out let me know the comments if you think this is horribly wrong uh but this is just the way I think about it and hopefully it is like this uh so yeah I just wanted to show you kind of the how I found this out and hopefully it's correct so if we look at the gp40 um blog post you can see here if we zoom in a bit with GPT 40 we trained a single new model end to end across Vision text and audio meaning that all inputs and outputs are processed by the same neural network because dpt4 is our first mod combining all of these modalities we are still just scratching the surface of what exploring what the model can do and its limitations so that is kind of what I based my previous uh explanation on that it takes just straight voice as input and put voice outright so it's going to be exciting in the documentation uh what kind of voice we can kind of put into this so is it just MP3 or is it all kind of audio in it's going to be interesting to see that so I'm very excited to see uh when you actually can start playing with this right because if we take a look at the documentation for GPT 40 now you can see accepting text or image input and outputting text so we don't have this full multimodality uh available yet so I can see that we can put images in and get images out too right images in a voice out uh so I want hopefully we will get this also in the API not just in the browser so that is kind of what I am looking forward to and think of all the cool things we can build if we get that um Freedom right so yeah very excited for this and I really hope uh that is the case that we also get this Freedom or functionality in the API there might be some of you that is actually interested in actually the demo I showed you so let me just quickly go through kind of how I set this up and yeah this will be available for my members section so if you become a member of the channel I will upload this to the community GitHub you will get access to the community Discord so just follow the link in the description and become a member and you can yeah just uh clone or Fork this uh uh code here so basically it's a pretty easy setup we we use the gp4 O model to answer some uh some query and we have the GPT 4 o to actually an analyze images so this is one of the things I wanted to test uh because they said that the image capabilities are much better and I've been very happy how they have performed especially on text uh like we saw in the examples they were perfect but I haven't really tested it that hard and here you can see we had to use the text to speech model from open AI we had to pick a TTS model and we have to turn it into MP3 and whole lot of steps to get this working we needed to use faster Whisperer to transcribe so there's a lot of stuff we had to do and we have this yeah kind of Silence frames speech frames to kind of F figure out when we are talking and not talking and yeah it's it's a pretty easy setup like in coding wise but um it's very slow right the latency is horrible uh I know I skipped that but yeah it's bad it's 10 seconds at least uh but yeah uh like I said if you want this code just become a member and I'll probably put it up uh I just wanted to finish this by watching my favorite example from the GP GPT 40 demos so my favorite was this interview prep one I just want to watch it one more time I just find it so funny and I really like the emotions in the The Voice here hey how's it going hey Rocky I'm doing great how about you I'm awesome listen I got some huge news oh do tell I'm all your well in a few minutes I'm going to be interviewing at open AI have you heard of them open AI huh sounds Vaguely Familiar kidding of course that's incredible Rocky what kind of interview well it's for a software engineering role but I just need to know do I look presentable professional well Rocky you definitely have the I've been coding all night look down which could actually work in your favor maybe just God run a hand through your hair or lean into the Mad genus VI Your Enthusiasm is what's really going to shine through I don't have a lot of time so I'm just going to throw this on what do you think Rocky that's quite a statement piece I I mean you you'll definitely stand out though maybe not in the way you're hoping for an interview okay I got it I got it okay so this reminds me so much of the movie Her the voice is just similar at least I could say so if you haven't seen this movie go check it out it's pretty cool and it's very relevant to what we have been seeing the last few days yeah I think that's it for today's video just wanted to do some more explanation I've done some research into the API so yeah uh probably I guess I see you again on Sunday uh I am going to some travel but I probably have a video for Sunday other than that enjoy your week and yeah speak soon\n"