This study uses wide-scale single-neuronal recordings combined with natural language processing models to identify fine-grained linguistic representations in the human frontotemporal cortex during speech production. Researchers found that specific neurons represent grammatical relationships, parts of speech, higher-order syntactic structures, and sentence sequences. These cells encode both semantic properties and dynamic sentence contexts at highly granular levels. The findings reveal that language encoding is left-lateralized and varies across cortical regions, defining the cellular building blocks of language from a micro to macro scale.
* Neurons track hierarchical syntactic structures and phrase transitions.
* Linguistic information is encoded combinationally with sentence context.
* Encoding patterns are distributed broadly but show left-hemisphere lateralization.
* Microscale neuronal activity differs significantly from wider field potential patterns.
* Neurons track hierarchical syntactic structures and phrase transitions.
* Linguistic information is encoded combinationally with sentence context.
* Encoding patterns are distributed broadly but show left-hemisphere lateralization.
* Microscale neuronal activity differs significantly from wider field potential patterns.
This guide offers an accessible overview of SHRDLU, a landmark AI program from the late 1960s and early 1970s developed by Terry Winograd. It explains how the system processes English conversations about colored blocks by integrating parsing, semantic interpretation, and logical reasoning into a single coherent architecture.
Key points include:
- The historical significance of SHRDLU in AI research at MIT
- A breakdown of its modular subsystems from raw input to natural language response
- How the PROGRAMMAR metalanguage allows grammar rules to act as executable programs
- The function of the Micro-Planner engine for theorem proving within Lisp
- The integration of syntactic, semantic, and geometric world knowledge into a unified system
Key points include:
- The historical significance of SHRDLU in AI research at MIT
- A breakdown of its modular subsystems from raw input to natural language response
- How the PROGRAMMAR metalanguage allows grammar rules to act as executable programs
- The function of the Micro-Planner engine for theorem proving within Lisp
- The integration of syntactic, semantic, and geometric world knowledge into a unified system
Learn how to build a simple semantic search engine using sentence embeddings and nearest neighbors, focusing on the limitations of keyword-based search and leveraging large language models for semantic understanding.
This tutorial explores how to use LLM embeddings as features in time series forecasting models. It covers generating embeddings from time series descriptions, preparing data, and evaluating the performance of models with and without LLM embeddings.
This article explores how to represent sentences as graphs, moving beyond traditional semantic modeling to a more natural-language oriented approach using reification and context graphs. It demonstrates how to translate sentences into RDF, Turtle, Open Cypher, and JSON-LD, highlighting the benefits of reification for capturing nuanced information and creating cleaner, more intuitive knowledge representations.
This tutorial demonstrates how to perform document clustering using LLM embeddings with scikit-learn. It covers generating embeddings with Sentence Transformers, reducing dimensionality with PCA, and applying KMeans clustering to group similar documents.
This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems.
This article discusses how Large Language Models (LLMs) are changing the way we approach reading research papers, offering strategies to leverage them for efficient comprehension and critical evaluation. It covers techniques like summarizing, explaining complex concepts, identifying limitations, and using LLMs to generate questions for deeper understanding.
This paper proposes a zero-shot grammar competency estimation framework leveraging unlabeled data and Large Language Models (LLMs) to generate pseudo-labels for training a transformer-based model. It addresses the challenges of limited labeled data in grammar assessment, particularly for spoken language.
By mid-2025 China had become a global leader in open-source large language models (LLMs). According to Chinese state media, by July 2025 China accounted for 1,509 of the world’s ~3,755 publicly released LLMs, far more than any other country. This explosion reflects heavy state and industry investment in domestic AI, open licensing (often Apache- or MIT-style), and a strategic pivot by Chinese tech giants and startups toward publicly shared models. The result is a "revival" of open-source AI, with dozens of Chinese LLMs now available for download or use via Hugging Face, GitHub, or cloud APIs. These range from general-purpose foundation models dozens of billions of parameters in size to specialized chatbots and domain experts, many built on Mixture-of-Experts (MoE) architectures.
