This is AI generated summarization, which may have errors. For context, always refer to the full article. AI. An AI sign is seen at the World Artificial Intelligence Conference (WAIC) in Shanghai, China July 6, 2023Smaller but more specialized language models , along with 'AI agents' or AI that has the ability to use digital tools rise in 2024From my vantage point, I observed significant developments in the field of AI language models in 2024, both in research and the industry.
Perhaps the most exciting of these are the capabilities of smaller language models, support for addressing AI hallucination, and frameworks for developing At the heart of commercially available generative AI products like ChatGPT are large language models, or LLMs, which are trained on vast amounts of text and produce convincing humanlike language. Their size is generally measured in parameters, which are the numerical values a model derives from its training data. The larger models like those from the major AI companies have hundreds of billions of parameters.First, organizations with the most computational resources experiment with and train increasingly larger and more powerful language models. Those yield new large language model capabilities, benchmarks, training sets and training or prompting tricks. In turn, those are used to make smaller language models — in the range of 3 billion parameters or less — which can be run on more affordable computer setups, require less energy and memory to train, and can be fine-tuned with less data. No surprise, then, that developers have released a host of powerful smaller language models – although the definition of small keeps changing: These smaller language models can be specialized for more specific tasks, such as rapidly summarizing a set of comments or fact-checking text against a specific reference. They can Increased access to highly capable language models large and small can be a mixed blessin
AI LANGUAGE MODELS SMALLER MODELS SPECIALIZATION AI DEVELOPMENT
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