The evolution of Large Language Models (LLMs) in artificial intelligence has spawned several sub-groups, including Multi-Modal LLMs, Open-Source LLMs, Domain-specific LLMs, LLM Agents, Smaller LLMs, and Non-Transformer LLMs.
Multi-Modal LLMs, such as OpenAI's Sora, Google's Gemini, and LLaVA, consolidate various types of input like images, videos, and text to perform more sophisticated tasks. OpenAI's Sora…
The creation and implementation of effective AI agents have become a vital point of interest in the Language Learning Model (LLM) field. AI company, Anthropic, recently spotlighted several successful design patterns being employed in practical applications. Discussed in relation to Claude's models, these patterns offer transferable insights for other LLMs. Five key design patterns examined…
As the use of AI, specifically linguistically-minded model (LLM) agents, increases in our world, companies are striving to create more efficient design patterns to optimize their AI resources. Recently, a company called Anthropic has introduced several patterns that are notably successful in practical applications. These patterns include Delegation, Parallelization, Specialization, Debate, and Tool Suite Experts,…
Self-supervised learning (SSL) has broadened the application of speech technology by minimizing the requirement for labeled data. However, the current models only support approximately 100-150 of the over 7,000 languages in the world. This is primarily due to the lack of transcribed speech and the fact that only about half of these languages have formal…
Large language models (LLMs) are known for their ability to contain vast amounts of factual information, leading to their effective use in factual question-answering tasks. However, these models often create appropriate but incorrect responses due to issues related to retrieval and application of their stored knowledge. This undermines their dependability and hinders their wide adoption…
Generating synthetic data is becoming an essential part of machine learning as it allows researchers to create large datasets where real-world data is scarce or expensive to obtain. The created data often display specific characteristics that benefit machine learning models' learning processes, helping to improve performance across various applications. However, the usage of synthetic data…
Large Language Models (LLMs) have proven highly competent in generating and understanding natural language, thanks to the vast amounts of data they're trained on. Predominantly, these models are used with general-purpose corpora, like Wikipedia or CommonCrawl, which feature a broad array of text. However, they sometimes struggle to be effective in specialized domains, owing to…
Large Language Models (LLMs) are typically trained on large swaths of data and demonstrate effective natural language understanding and generation. Unfortunately, they can often fail to perform well in specialized domains due to shifts in vocabulary and context. Seeing this deficit, researchers from NASA and IBM have collaborated to develop a model that covers multidisciplinary…
Training large language models (LLMs) hinges on the availability of diverse and abundant datasets, which can be created through synthetic data generation. The conventional methods of creating synthetic data - instance-driven and key-point-driven - have limitations in diversity and scalability, making them insufficient for training advanced LLMs.
Addressing these shortcomings, researchers at Tencent AI Lab have…
In the quick-paced field of artificial intelligence (AI), GPT4All 3.0, a milestone project by Nomic, is revolutionizing how large language models (LLMs) are accessed and controlled. As corporate control over AI intensifies, there emerges a higher demand for locally-run, open-source alternatives that prioritize user privacy and control. Addressing this demand, GPT4All 3.0 provides a comprehensive…
In a significant reveal that has shaken the world of technology, Kyutai introduced Moshi, a pioneering real-time native multimodal foundation model. This new AI model emulates and exceeds some functionalities previously demonstrated by OpenAI’s GPT-4o. Moshi understands and delivers emotions in various accents, including French, and can simultaneously handle two audio streams, allowing it to…