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Claude 3.7 Sonnet was released on February 24, 2025. It is a pioneering hybrid AI reasoning model that allows users to choose between rapid responses and more thoughtful, step-by-step reasoning. This model integrates both capabilities into a single framework, eliminating the need for multiple models.
The models demonstrated significant improvements in capabilities across various benchmarks, with Claude 3 Opus notably outperforming leading models from OpenAI and Google. [56] In June 2024, Anthropic released Claude 3.5 Sonnet, which demonstrated improved performance compared to the larger Claude 3 Opus, particularly in areas such as coding ...
Benchmark tests show that V3 outperformed Llama 3.1 and Qwen 2.5 while matching GPT-4o and Claude 3.5 Sonnet. ... Accuracy reward was checking whether a boxed answer ...
Claude [32] December 2021: Anthropic: 52 [33] 400 billion tokens [33] beta Fine-tuned for desirable behavior in conversations. [34] GLaM (Generalist Language Model) December 2021: Google: 1200 [35] 1.6 trillion tokens [35] 5600 [35] Proprietary Sparse mixture of experts model, making it more expensive to train but cheaper to run inference ...
In 1960, AI pioneer Norbert Wiener described the AI alignment problem as follows: . If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere effectively ... we had better be quite sure that the purpose put into the machine is the purpose which we really desire.
The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]
In June 2024, Anthropic released Claude 3.5 Sonnet, which demonstrated improved performance compared to the larger Claude 3 Opus, particularly in areas such as coding, multistep workflows, and image analysis.
Knowledge representation and knowledge engineering [17] allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, [ 18 ] scene interpretation, [ 19 ] clinical decision support, [ 20 ] knowledge discovery (mining "interesting ...