The article presents Anthropic’s latest Generative AI large language model, Claude 3.5 Sonnet, which is highly proficient at arithmetic, reasoning, coding, and multilingual activities. It also covers its vision capabilities, real-world uses, security precautions, and prospects going forward with models like Haiku and Opus. The article emphasizes Claude 3.5 Sonnet’s important contribution to the development of AI.
In March 2024, Anthropic introduced its Claude 3 family of models setting a new standard for performance and cost-effectiveness. GPT-4o and Gemini 1.5 Pro surpassed Claude 3 within a few months in both arenas. Now, it’s time for Anthropic to make a comeback with its Claude 3.5 Sonnet which is the best model on both performance and cost-effectiveness.
As we can see from the above image, the Claude 3.5 Sonnet has the best quality and is less costly than the previously best-performing GPT-4o model.
It sets new benchmarks for most of the industry-standard metrics covering reasoning, reading comprehension, math, science, and coding.
Claude 3.5 Sonnet is the most powerful vision model on standard vision benchmarks. It excels in visual reasoning tasks, such as interpreting charts and graphs, and accurately transcribes text from imperfect images.
It can use external tools depending on the task at hand, and perform various tasks like returning API calls with natural language requests, extracting structured data, answering questions by searching databases, etc. We can even learn from Anthropic courses on GitHub itself about how to integrate tools.
Anthropic launched a new feature that revolutionizes user interaction with Claude. When users request content like code snippets, text documents, or website designs, these Artifacts now appear in a dedicated window alongside their conversation. This enhancement not only improves usability but also sets a new standard for interactive AI features.
Now let’s test the model’s vision capabilities with artifacts.
Here, we have given the ‘quality vs price’ chart taken from the above to the model and asked it “Which model is most cost-effective based on this chart?”
As we can see from the image, it answers the question correctly.
Then, we asked, “How can I make such a chart in Python?”. The model generated the code and displayed it on the side.
We can enable the artifact feature in ‘feature preview’ if it is not already enabled.
And Claude 3.5 Sonnet can also recognize that the chart is showing it is the best-performing model.
Claude 3.5 Sonnet is the default model in Claude.ai chat. In the free version, there are limits on the number of messages per day which can vary depending on the traffic. If we can upgrade to Pro, we can also get access to Claude 3 Haiku and Opus models.
We can also access the model through Anthropic API. It costs $3 / 1 Million tokens, and $15 / 1 Million tokens for input and output respectively.
All models undergo extensive testing to minimize misuse. Despite its leap in intelligence, Claude 3.5 Sonnet maintains an ASL-2 safety level, verified through rigorous red teaming assessments. All current LLMs appear to be ASL-2.
Claude 3.5 Sonnet was evaluated by the UK’s Artificial Intelligence Safety Institute, before deployment, with results shared with the US AI Safety Institute.
Feedback from policy experts and organizations like Thorn has been integrated to address emerging misuse trends. These insights have helped refine classifiers and improve model resilience against various abuses.
This model does not use user-submitted data for training generative models unless explicitly permitted by the user, ensuring robust protection of user privacy.
Like the Claude 3 family, Haiku and Opus models will be released soon. In addition to that features like memory, and new modalities are likely to be added. And of course, expect new models from OpenAI and Google as competition heats up.
A. It is Anthropic’s latest AI model, excelling in arithmetic, reasoning, coding, and multilingual tasks.
A. It leads in various metrics such as GPQA, MMLU, MATH, HumanEval, MGSM, DROP, BIG-Bench Hard, and GSM8K.
A. It Excels in visual reasoning, interpreting charts and graphs, and transcribing text from imperfect images.
I am working as an Associate Data Scientist at Analytics Vidhya, a platform dedicated to building the Data Science ecosystem. My interests lie in the fields of Deep Learning and Natural Language Processing (NLP).
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