Artificial Intelligence

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Amazon’s $200 Billion Spending Plan Raises Stakes in A.I. Race

ai, AI (Artificial Intelligence), Amazon.com Inc, Anthropic AI LLC, Artificial Intelligence, Cloud Computing, Company Reports, Computers and the Internet, E-commerce, Jassy, Andrew R, Layoffs and Job Reductions

The company reported a strong holiday quarter on Thursday. But its spending, like that at other big technology companies, is starting to make investors nervous.

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AI companies want you to stop chatting with bots and start managing them

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI and work, AI assistants, AI benchmarks, AI coding, AI development tools, AI security, anthropic, Artificial Intelligence, Biz & IT, chatbots, Claude Code, Claude Opus, Claude Opus 4.6, code agents, Codex, Generative AI, GPT-5.3-Codex, Large Language Models, Machine Learning, OpenAI, Sam Altman

On Thursday, Anthropic and OpenAI shipped products built around the same idea: instead of chatting with a single AI assistant, users should be managing teams of AI agents that divide up work and run in parallel. The simultaneous releases are part of a gradual shift across the industry, from AI as a conversation partner to

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Anthropic Releases Claude Opus 4.6 With 1M Context, Agentic Coding, Adaptive Reasoning Controls, and Expanded Safety Tooling Capabilities

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, New Releases, Staff, Technology

Anthropic has launched Claude Opus 4.6, its most capable model to date, focused on long-context reasoning, agentic coding, and high-value knowledge work. The model builds on Claude Opus 4.5 and is now available on claude.ai, the Claude API, and major cloud providers under the ID claude-opus-4-6. Model focus: agentic work, not single answers Opus 4.6

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Soft Computing, Volume 30, Issue 1, January 2026

ai, AI (Artificial Intelligence), Artificial Intelligence, journals, Soft Computing

1) A novel framework approach for liver cancer prediction using Integrated Randomize Gated Layer Recurrent NeuroNet (IRGLRN)Author(s): M. Indumathi, M. Vanitha, V. PalanisamyPages: 1 – 13 2) An L-valued rough set model based on L-valued neighborhood systemsAuthor(s): Kamal El-Saady, Ayat A. TemrazPages: 15 – 31 3) Implications on function spaces: the extension constructionAuthor(s): Junsheng QiaoPages:

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