Remove Computer Vision Remove Data Analysis Remove NLP
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Hunyuan-Large and the MoE Revolution: How AI Models Are Growing Smarter and Faster

Unite.AI

Built using the Transformer architecture, which has already proven successful in a range of Natural Language Processing (NLP) tasks, this model is prominent due to its use of the MoE model. By activating only the relevant experts, MoE models can handle massive datasets without increasing computational resources for every operation.

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The Top 10 AI Research Papers of 2024: Key Takeaways and How You Can Apply Them

Towards AI

From breakthroughs in large language models to revolutionary approaches in computer vision and AI safety, the research community has outdone itself. Vision Mamba Summary: Vision Mamba introduces the application of state-space models (SSMs) to computer vision tasks. And lets be real what a year it has been!

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Supervised vs Unsupervised Learning for Computer Vision (2024 Guide)

Viso.ai

In the field of computer vision, supervised learning and unsupervised learning are two of the most important concepts. In this guide, we will explore the differences and when to use supervised or unsupervised learning for computer vision tasks. We will also discuss which approach is best for specific applications.

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The most valuable AI use cases for business

IBM Journey to AI blog

Voice-based queries use natural language processing (NLP) and sentiment analysis for speech recognition so their conversations can begin immediately. With text to speech and NLP, AI can respond immediately to texted queries and instructions. Humanize HR AI can attract, develop and retain a skills-first workforce.

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Five machine learning types to know

IBM Journey to AI blog

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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Agentic AI: The Foundations Based on Perception Layer, Knowledge Representation and Memory Systems

Marktechpost

The consistent theme in these use cases is an AI-driven entity that moves beyond passive data analysis to dynamically and continuously sense, think, and act. Yet, before a system can take meaningful action, it must capture and interpret the data from which it forms its understanding.

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AI and Blockchain Integration for Preserving Privacy

Unite.AI

Artificial Intelligence is a very vast branch in itself with numerous subfields including deep learning, computer vision , natural language processing , and more. NLP in particular has been a subfield that has been focussed heavily in the past few years that has resulted in the development of some top-notch LLMs like GPT and BERT.