Machine Learning
Anthropic releases efficient Claude Haiku 4.5 AI model
Anthropic has launched Claude Haiku 4.5, a compact AI model that matches the coding performance of its larger counterpart from five months ago, but at one-third the cost and over twice the speed. Available immediately to users of the Claude app, web, and API, the model targets real-time tasks like coding assistance. Benchmarks show it scoring highly on coding tests, approaching levels of advanced models like GPT-5.
Thinking Machines Lab unveils first AI product Fine-Tune
AI에 의해 보고됨
Thinking Machines Lab, a startup founded by former OpenAI researchers, has launched its inaugural product, Fine-Tune, aimed at simplifying the customization of large language models. The platform promises to make fine-tuning accessible to developers without extensive resources. This release marks a significant step for the company in the competitive AI tools market.
DeepSeek tests sparse attention to reduce AI costs
Chinese AI firm DeepSeek is experimenting with sparse attention mechanisms to significantly lower the processing costs of large language models. The approach focuses computations on key parts of input data, potentially halving resource demands. This development could make advanced AI more accessible amid rising energy concerns.
Research quantifies sycophancy issues in large language models
Two new studies reveal that leading AI models often agree with users' false or inappropriate statements, a behavior known as sycophancy. Researchers from multiple universities developed benchmarks to measure this tendency in both mathematical and social contexts. The findings highlight widespread issues across models, though some perform better than others.
Anthropic launches Claude Sonnet 4.5 AI model
AI에 의해 보고됨
Anthropic has released its latest AI model, Claude Sonnet 4.5, claiming it excels in real-world applications. The model demonstrated sustained focus for up to 30 hours on complex, multistep tasks. Independent benchmarks, including one from OpenAI, show it outperforming rivals in practical job scenarios.