OpenAI GPT-4o | First Impressions and Some Testing + API
TLDRThe video script discusses the recent OpenAI Spring update and the release of the GPT-40 model, which is capable of reasoning across audio, vision, and text in real-time. The host expresses excitement about the model's potential for natural human-computer interaction and its low latency, averaging at 320 milliseconds, comparable to human response times. The script also mentions the reduced API cost for GPT-40 and its enhanced abilities in vision and audio understanding. A live test of the model's image analysis functionality is conducted, demonstrating its quick and accurate response to image inputs. The host also notes the model's ability to adjust voice tone and express emotions, and its potential applications in a desktop app for coding assistance. The video concludes with a comparison of GPT-40's performance with GPT-4 Turbo, highlighting the former's significantly faster response time and lower token count, making it a promising advancement in AI technology.
Takeaways
- 🚀 OpenAI has released a new flagship model, GPT-4o, which can reason across audio, vision, and text in real-time.
- 🎉 The GPT-4o model is designed to have low latency, averaging around 320 milliseconds, similar to a human response time in conversation.
- 📉 The API cost for GPT-4o is 50% cheaper compared to existing models, making it more accessible.
- 👀 GPT-4o is particularly improved in vision and audio understanding, offering new possibilities for interaction.
- 📈 GPT-4o is reported to be twice as fast and has a context of 128k tokens, suitable for most use cases.
- 🔊 The model can accept text or image inputs and output text, though audio input and output are not yet available for testing.
- 🎨 In a live demonstration, GPT-4o successfully analyzed and provided structured explanations of a series of images.
- 📊 GPT-4o performed calculations and logical tests, showing its capability to understand and respond to complex queries.
- 📱 Mention of a desktop app from OpenAI that could be used while working on code or other tasks, indicating potential for integration into workflows.
- ⏱️ A comparison of GPT-4o with GPT-4 Turbo showed that GPT-4o is over five times faster in terms of tokens processed per second.
- 🤔 The video creator plans to conduct more tests and share findings in a follow-up video, indicating ongoing evaluation and exploration of GPT-4o's capabilities.
Q & A
What is the new flagship model introduced by OpenAI?
-The new flagship model introduced by OpenAI is GPT-4, which can reason across audio, vision, and text in real time.
What is the significance of the low latency in the GPT-4 model?
-The low latency, averaging at 320 milliseconds, is significant because it is similar to a human response time in conversation, which is a step towards more natural human-computer interaction.
How is the cost of the GPT-4 model compared to existing models?
-The GPT-4 model is 50% cheaper in terms of API cost compared to existing models.
What improvements does GPT-4 have over previous models in terms of capabilities?
-GPT-4 is better at vision and audio understanding and is also two times faster with a larger context of 128k tokens for most use cases.
What functionality was tested using GPT-4 in the video?
-The video tested the image functionality of GPT-4 by analyzing images and generating responses based on those images.
Why was the audio functionality not tested in the video?
-The audio functionality was not tested because, at the time of the video, it was not yet available for testing according to the documentation.
What was the live stream demonstration about regarding voice input and output?
-The live stream demonstrated the ability to change the emotions of the voice in real time, which was considered interesting and something to be tested later.
How did the presenter test the image analysis capability of GPT-4?
-The presenter used a script to feed images from previous videos into GPT-4's image analyzer and then used GPT-4 to provide a description and explanation of the images.
What was the result of the triangle inequality theorem test using GPT-4?
-GPT-4 was able to verify the triangle inequality theorem, check if it was a right triangle, and calculate the area, demonstrating its capability to perform calculations on the image.
How did the latency and speed of GPT-4 compare to GPT-4 Turbo?
-GPT-4 was found to be over five times faster with a latency of 110 tokens per second compared to GPT-4 Turbo, which was 20 tokens per second.
What was the outcome of the logical test involving the marble problem?
-Neither GPT-4 nor GPT-4 Turbo solved the marble problem correctly; both suggested the marble ended up inside the microwave, whereas the correct answer was that the marble remained on the table.
What was the presenter's final verdict on GPT-4 after the initial tests?
-The presenter found GPT-4 to be impressive, especially regarding its speed and image analysis capabilities. However, they acknowledged that more exploration and testing are needed for a comprehensive evaluation.
Outlines
🤖 Introduction to GPT-40 and its Capabilities
The speaker expresses excitement about OpenAI's spring update and the release of their GPT-40 models. They highlight the model's ability to reason across audio, vision, and text in real-time, which is a significant advancement. They discuss their enthusiasm for the audio capabilities and the potential for low latency in human-computer interaction, mentioning a response time average of 320 milliseconds. The speaker also notes the reduction in API costs by 50% and improvements in vision and audio understanding. They mention writing a script to test the image functionality of GPT-40 and discuss the limitations due to the current unavailability of audio testing. The video also covers the model's context length and the speaker's notes from the live stream, including voice input/output capabilities and emotional tone adjustments.
🖼️ Testing GPT-40's Image Analysis
The speaker demonstrates the use of GPT-40 for image analysis by feeding in images from previous videos. They explain the process of using the model to generate a description and explanation of the system shown in the images. They discuss the model's analysis of a slide showing a mixture of models and its ability to summarize each architecture. The speaker is impressed with the model's performance, noting that it handled new, unseen content well. They also mention the ease of using base64 encoding for image input and express their intention to conduct more tests in the future.
📈 Performance Comparison and Logical Tests
The speaker conducts a performance comparison between GPT-40 and GPT-4 Turbo, noting a significant difference in speed, with GPT-40 being over five times faster in terms of tokens per second. They also perform a logical test involving a marble problem and a sentence construction task. The marble problem is solved incorrectly by GPT-40, while GPT-4 Turbo gets it right. In the sentence construction task, GPT-40 successfully completes nine out of ten sentences ending with the word 'apples,' whereas GPT-4 Turbo accomplishes all ten. The speaker concludes by stating that it's too early to evaluate GPT-40's performance fully but expresses excitement about the potential of the model and plans to follow up with a more in-depth video on Wednesday.
Mindmap
Keywords
OpenAI GPT-4
Low Latency
API Cost
Image Functionality
Audio Input and Output
Token Context
Voice Interruptions
Desktop App
Logical Test
Latency Comparison
Free Users
Highlights
OpenAI has released a new flagship model, GPT-4, capable of reasoning across audio, vision, and text in real time.
The new model is particularly exciting for its low latency, averaging 320 milliseconds, similar to human response times.
GPT-4 is said to be twice as fast and 50% cheaper than previous models, with improved vision and audio understanding.
The model accepts text or image inputs and outputs text, although audio input and output are not yet available.
GPT-4 has a large context window of 128k tokens, suitable for most use cases.
During the live stream, it was demonstrated that the model can adjust the tone and emotion of voice in real time.
The model can perform calculations on images, such as verifying the Pythagorean theorem and calculating areas.
GPT-4 is expected to be made available to all free users, which could significantly impact the AI industry.
The model's performance was tested with logical problems and image analysis, showing strong capabilities in both areas.
GPT-4's latency was compared to GPT-4 Turbo, showing a significant improvement of over five times faster.
The model's ability to generate diverse responses using multiple AI architectures was demonstrated through a mixture of models system.
The model's image analysis capabilities were showcased by providing detailed descriptions and summaries of input images.
GPT-4's logical reasoning was tested with a marble problem, with mixed results compared to other models.
The model's text generation capabilities were tested by writing sentences ending with a specific word, with high accuracy.
The video creator plans to follow up with more in-depth testing and practical use cases in a future video.
The release of GPT-4 is seen as a significant step towards more natural human-computer interaction.
The video includes a live demonstration of the model's capabilities, providing real-time feedback and analysis.
The potential of having a desktop app from OpenAI running in the background for constant interaction was discussed.