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DeepSeek vs. OpenAI: The Battle of Open Reasoning Models

Unite.AI

This shift has increased competition among major AI companies, including DeepSeek, OpenAI, Google DeepMind , and Anthropic. Each brings unique benefits to the AI domain. DeepSeek focuses on modular and explainable AI, making it ideal for healthcare and finance industries where precision and transparency are vital.

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The Evolution of AI Agent Infrastructure: Exploring the Rise and Impact of Autonomous Agent Projects in Software Engineering and Beyond

Marktechpost

Notable AI Agent Projects Several innovative projects exemplify the capabilities and potential of AI agents: SWE-Agent: Developed by researchers at Princeton University, SWE-Agent transforms large models (like GPT-4) into software engineering agents capable of resolving issues in real GitHub repositories.

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Transforming Software Development with AI Agents

ODSC - Open Data Science

Robert Brennan, co-founder and CEO of All Hands AI, is at the forefront of this movement. His open-source project, OpenHands , aims to empower developers by automating tedious tasks, enhancing productivity, and reimagining the role of the software engineer. Robert is quick to reassure that this is far from the truth.

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The ODSC East 2025 Schedule: 150+ AI & Data Science Sessions, Keynotes, & More

ODSC - Open Data Science

Sessions: Keynotes: Eric Xing, PhD, Professor at CMU and President of MBZUAI: Toward Public and Reproducible Foundation Models Beyond Lingual Intelligence Book Signings: Sinan Ozdemir: Quick Start Guide to Large LanguageModels Matt Harrison: Effective Pandas: Patterns for Data Manipulation Workshops: Adaptive RAG Systems with Knowledge Graphs: Building (..)

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

Machine learning engineers can specialize in natural language processing and computer vision, become software engineers focused on machine learning and more. to learn more) In other words, you get the ability to operationalize data science models on any cloud while instilling trust in AI outcomes.

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Steven Hillion, SVP of Data and AI at Astronomer – Interview Series

Unite.AI

Deep learning is great for some applications — large language models are brilliant for summarizing documents, for example — but sometimes a simple regression model is more appropriate and easier to explain. What are some future trends in AI and data science that you are excited about, and how is Astronomer preparing for them?

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MLOps and the evolution of data science

IBM Journey to AI blog

MLOps fosters greater collaboration between data scientists, software engineers and IT staff. The goal is to create a scalable process that provides greater value through efficiency and accuracy. Creating an MLOps process builds in oversight and data validation to provide good governance, accountability and accuracy of data collection.