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AAAI 2020 | A Turning Point for Deep Learning?

AAAI 2020 | A Turning Point for Deep Learning?

Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by AAAI 2020.
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Bridging the Gap: Induction-Head Ngram Models for Efficient, Interpretable Language Modeling

Bridging the Gap: Induction-Head Ngram Models for Efficient, Interpretable Language Modeling

Recent large language models (LLMs) have shown impressive performance across a diverse array of tasks. However, their use in high-stakes or…
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Self-Evolving Prompts: Redefining AI Alignment with DeepMind & Chicago U’s eva Framework

Self-Evolving Prompts: Redefining AI Alignment with DeepMind & Chicago U’s eva Framework

For artificial intelligence to thrive in a complex, constantly evolving world, it must overcome significant challenges: limited data…
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Unlocking Turing Completeness: How Large Language Models Achieve Universal Computation Without…

Unlocking Turing Completeness: How Large Language Models Achieve Universal Computation Without…

The rise of large language models (LLMs) has sparked questions about their computational abilities compared to traditional models. While…
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From OCR to Multi-Image Insight: Apple’s MM1.5

From OCR to Multi-Image Insight: Apple’s MM1.5

Multimodal Large Language Models (MLLMs) have rapidly become a focal point in AI research. Closed-source models like GPT-4o, GPT-4V…
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AI Self-Evolution: How Long-Term Memory Drives the Next Era of Intelligent Models

AI Self-Evolution: How Long-Term Memory Drives the Next Era of Intelligent Models

Large language models (LLMs) like GPTs, developed from extensive datasets, have shown remarkable abilities in understanding language…
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Breaking Barriers in Cellular Automata with CAX: Faster, Scalable, and Open for All

Breaking Barriers in Cellular Automata with CAX: Faster, Scalable, and Open for All

Cellular automata (CA) have become essential for exploring complex phenomena like emergence and self-organization across fields such as…
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LLMs as Code Architects: Meta’s New Approach to Precise Code Transformations

LLMs as Code Architects: Meta’s New Approach to Precise Code Transformations

Tools designed for rewriting, refactoring, and optimizing code should prioritize both speed and accuracy. Large language models (LLMs)…
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Thinking Fast and Slow: Google DeepMind’s Dual-Agent Architecture for Smarter AI

Thinking Fast and Slow: Google DeepMind’s Dual-Agent Architecture for Smarter AI

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From Dense to Dynamic: NVIDIA’s Innovations in Upcycling LLMs to Sparse MoE

From Dense to Dynamic: NVIDIA’s Innovations in Upcycling LLMs to Sparse MoE

Sparse Mixture of Experts (MoE) models are gaining traction due to their ability to enhance accuracy without proportionally increasing…
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