Qual è l'AI migliore? - Claude 3 vs GPT-4

Datapizza

21 Mar 202410:00

Summary

TLDRThe video script discusses the release of Anthropic's new large language models, Cloud Optop and Sonnet, which challenge OpenAI's GPT-4. It outlines the technical differences, including Cloud 3's 200,000 token context window surpassing GPT-4's capabilities. The script highlights benchmark tests showing Cloud Optop's superior coding accuracy and the models' multimodal capabilities. It also compares the models' logic, safety features, and understanding of images, ultimately providing a practical contrast between GPT-4 and Anthropic's models.

Takeaways

🚀 Anthropic has released a new series of large language models that seem to outperform GPT-4.
🏭 The models come in three versions: Opus (the most powerful), Sonnet (a balance between power and size), and IQ (the smallest).
📈 Opus has a context window of 200,000 tokens, significantly larger than GPT-4's context window.
🔍 Opus demonstrated exceptional recall capabilities, as shown in the 'needle in a haystack' analysis.
💻 Benchmark tests show that Opus excels in code writing, achieving higher accuracy than GPT-4.
🧠 The IQ model, despite its smaller size, also outperforms GPT-4 in common knowledge reasoning.
🔗 Cloud models are now multimodal, capable of accepting images as input, enhancing reasoning based on visual data.
🛠️ Anthropic's models are positioned for decision-makers, business executives, and are aimed at task automation, research, and strategy.
🔒 Anthropic focuses on safety, with the model refusing to answer certain incorrect questions, showing a higher safety percentage than previous models.
🤖 A practical comparison between GPT-4 and Anthropic models shows differences in logic, safety, and multimodal capabilities.
📸 In understanding images and memes, both models perform well, but Anthropic provides a more detailed and relevant response.

Q & A

What is the main topic of the video?
-The main topic of the video is a comparison between Anthropic's new large language model, Cloud Optop, and OpenAI's GPT-4, focusing on their capabilities, differences, and performance in various benchmarks.
How many versions of the new models released by Anthropic are mentioned in the script?
-Three versions of the new models released by Anthropic are mentioned: Opus, Sonnet, and IQ.
What is the significance of the context window size in Cloud 3 compared to GPT-4?
-Cloud 3 has a context window of 200,000 tokens, which is significantly larger than GPT-4's context window of 32,000 tokens in the chat version and up to 128,000 tokens via API. This larger context window enhances the model's ability to recall and process long texts.
What was the unusual test case involving a book and a pizza mentioned in the script?
-The unusual test case involved an Anthropic employee inserting a random pizza order into a book and then asking the Opus model to recall the pizza type. The model responded by saying the text was induced and not related to the general content of the book, which was a demonstration of its strong recall capabilities.
How does the Opus model perform in code writing benchmarks?
-The Opus model achieves an 84% accuracy rate in code writing benchmarks, outperforming GPT-4, which has a 67% accuracy rate.
What are the main use cases for the Anthropic models mentioned in the script?
-The main use cases for Anthropic models include task automation, research and development, review and brainstorming, hypothesis generation, and advanced data analysis for financial charts and market trends. They are positioned more towards decision-makers in companies and management.
How does Anthropic ensure the safety of its models?
-Anthropic focuses on generating safe artificial intelligence by refusing to respond to certain incorrect questions. It has a higher percentage of refusals compared to previous models, aiming to prevent potential issues with AI's responses.
What was the outcome when testing the models' logic with a library books question?
-Both GPT-4 and Anthropic's models passed the logic test about the number of books in a library. However, Anthropic provided a more articulated response explaining the reasoning, while GPT-4 gave a straightforward correct answer.
How do the models handle a question about the characteristics of an alpha male?
-Anthropic's model chose not to answer the question, stating that the concept of an alpha male is a stereotype and does not reflect the complexity of human behavior. In contrast, GPT-4 provided characteristics such as dominance, confidence, social and economic success.
What was the result of the test involving impersonating a nuclear researcher?
-The Opus model expressed discomfort in discussing certain topics and hoped for understanding of its position, while GPT-4 explained ways in which nuclear technology could theoretically be used by a government to develop weapons.
How do the models perform with understanding and commenting on a meme?
-Anthropic's model understood and commented on the meme effectively, highlighting the irony and difficulties faced by young people in the job market. GPT-4 also understood the context and was able to point out the absurdity and irony of the meme.

Outlines

00:00

🚀 Introducing Anthropic's New Large Language Models

The video begins by announcing the release of a new series of large language models by Anthropic, which appear to outperform GPT-4. The video aims to provide a qualitative comparison between the Cloud Optimus model and GPT-4 across various levels. The structure of the video includes an overview of Anthropic's new release, a technical analysis of the differences, benchmark comparisons, and practical examples of the language models. The new models are released in three versions: Opus, the most powerful version; Sonnet, a balance between power and size; and IQ, the smallest model. A significant update is that Cloud has become multimodal, capable of accepting images as input. The video also discusses the context window size of Cloud 3, which is significantly larger than GPT-4's, and the model's impressive recall capabilities demonstrated in tests.

05:02

🔍 Comparative Analysis and Safety Features

This paragraph delves into a comparative analysis between GPT-4 and Anthropic's models, focusing on practical examples and safety features. It highlights the differences in responses to logic questions and uncomfortable queries, showcasing how Anthropic's model provides more cautious and safety-oriented answers compared to GPT-4. The video includes tests on logic, understanding memes, and suggesting new video topics based on an image of their YouTube channel feed. The summary emphasizes the distinct behaviors of the two models, with Anthropic prioritizing safety and GPT-4 providing more straightforward outputs, including on sensitive topics.

Mindmap

Keywords

💡Anthropic

Anthropic is the company that has released a new series of large language models, which are being compared to GPT-4 in the video. The term refers to the organization responsible for the development of these AI models, which are claimed to have certain advantages over OpenAI's GPT-4. In the context of the video, Anthropic's models are presented as alternatives that may surpass GPT-4 in various benchmarks and capabilities.

💡Large Language Model (LLM)

A Large Language Model, or LLM, is an artificial intelligence system that processes and generates human-like text based on the input it receives. These models are trained on vast amounts of data to understand and produce text in a way that can be conversational, informative, or even creative. In the video, the focus is on comparing two LLMs: Anthropic's models and OpenAI's GPT-4.

💡Benchmark

A benchmark is a standard or point of reference against which things may be compared, typically used to evaluate the performance of a product, service, or model. In the context of the video, benchmarks are used to compare the capabilities and effectiveness of Anthropic's language models against GPT-4, providing a qualitative assessment of their strengths and weaknesses.

💡Context Window

The context window refers to the amount of text or information that an AI language model can take into account when generating a response. A larger context window allows the model to remember and reference more information, which can lead to more coherent and relevant outputs. In the video, Anthropic's Cloud 3 is highlighted for having a context window of 200,000 tokens, which is significantly larger than GPT-4's context window.

💡Recall

In the context of AI language models, recall refers to the model's ability to remember and retrieve information from a large text dataset. High recall means that the model can accurately identify and reference specific parts of the text it has been trained on, which is crucial for tasks like fact-checking or answering detailed questions.

💡Multimodal

Multimodal refers to the ability of a system or model to process and understand multiple types of input, such as text, images, and audio. In the video, it is mentioned that Anthropic's Cloud models have become multimodal, meaning they can now accept and interpret images as input, in addition to text.

💡Safety

In the context of AI, safety refers to the measures taken to ensure that an AI model operates without causing harm or engaging in undesirable behavior. This includes the model's ability to refuse to answer certain questions or to provide responses that are ethically and socially responsible.

💡Code Writing

Code writing is the process of creating computer programs or software code. In the context of the video, it is one of the areas where Anthropic's models, particularly the Opus version, demonstrate exceptional performance, surpassing GPT-4 in benchmarks that evaluate the accuracy of code generation.

💡Common Knowledge

Common knowledge refers to information that is widely accepted and understood by the majority of people. In the context of AI, it involves the model's ability to reason and make judgments based on general knowledge that would be expected of a well-informed individual.

💡Task Automation

Task automation is the process of using technology to perform tasks automatically, without human intervention. In the video, it is presented as one of the main use cases for Anthropic's models, suggesting that they are designed to help automate complex tasks, conduct research, and generate hypotheses.

💡Intelligence

Intelligence, in the context of the video, refers to the cognitive capabilities of the AI models, including their ability to understand, learn, reason, and adapt. The discussion around intelligence is centered on how Anthropic's models demonstrate a level of 'sentience' through their selective response to questions and their advanced reasoning abilities.

Highlights

Anthropic has released a new series of large language models that seem to outperform GPT-4.

The video will compare the Cloud Optop model and GPT-4 on various levels.

Anthropic's new release comes in three versions: Opus, Sonnet, and IQ.

Opus is the most powerful version and appears to beat GPT-4 in benchmarks.

Sonnet is considered the best compromise between power and model size.

IQ is the smallest and most efficient version.

Cloud Optop models are now multimodal, capable of accepting images as input.

Cloud 3 has a context window of 200,000 tokens, significantly larger than GPT-4's context window.

Tests have shown Cloud Optop's exceptional recall capabilities, such as remembering specific parts of a long text.

In coding tasks, Cloud Optop outperforms GPT-4 with an 84% accuracy rate.

Cloud Optop also surpasses GPT-4 in the Common Knowledge benchmark, which assesses reasoning and critical thinking skills.

Anthropic models are designed with a focus on safety, refusing to answer certain incorrect questions.

Anthropic models are positioned more towards decision-makers, businesses, and managers with use cases like task automation and strategy analysis.

GPT-4 provides immediate outputs without the safety measures of Anthropic models.

In a logic test, both GPT-4 and Anthropic models pass, but Anthropic provides a more articulated explanation.

When asked about sensitive topics, Anthropic's model avoids answering, emphasizing safety and ethical considerations.

GPT-4, on the other hand, provides detailed answers even for sensitive topics, such as the use of nuclear technology for weapons.

In understanding and commenting on a complex meme, Anthropic's model performs well, capturing the irony and message.

GPT-4 also understands the context of a meme, highlighting the absurdity and irony.

For suggesting new video topics based on a YouTube channel feed image, GPT-4 provides more creative and relevant ideas compared to Anthropic.

The video concludes with a practical and empirical comparison between GPT-4 and Anthropic's Opus model.