Deep learning and large pools of data come together to form large language models, an AI-based algorithm. An LLM can generate text, translates languages, organizes content, and more, lending itself to AI chatbots like Bard and ChatGPT. At the same time, users should be aware of their shortcomings, such as bias, troubleshooting complexity, and the possibility of malicious attacks.
Humans need language to communicate. So it makes sense that AI does too a large language model or LLM is a type of AI algorithm based on deep learning and huge amounts of data that can understand, generate and predict language models aren’t new, the first AI language new content. model can be traced back to 1966. But large language models use a significantly larger pool of data for training, which means a significant increase in the capabilities of the AI model. One of the most common applications of LLMs right now is generating content using AI chatbots.
More and more popping up in the market as competitors look to differentiate themselves. Check out the link above or in the description below to see how two of the front runners chat GPT and Bard compare. And remember to subscribe to eye on tech for more videos on all things business tech. So just how large are large language models? Well, there’s no universally accepted figure for how large an LLM training dataset is, but it’s typically in the petabytes range. For context, a single petabyte is equivalent to 1 million gigabytes. The human brain is believed to store about two and a half petabytes of memory data. The LM training consists of multiple steps, usually starting with unsupervised learning, where the model starts to derive relationships between words and concepts, then fine tuned with supervised learning.
The training data then passes through a transformer which enables the LLM to recognize relationships and connections using a self attention mechanism. Once the LLM is trained, it can serve as the base for any AI uses. LLMs can generate text, translate languages summarize or rewrite content, organized content, analyze sentiment of content like humor or tone, and converse naturally with the user. Unlike older generations of AI chatbot technologies, LLM can be particularly useful as a foundation for customized uses for both businesses and individuals. They’re fast, accurate, flexible and easy to train. However user should heed caution too LLMs come with a number of challenges, like the cost of deployment and operation bias depending on what data it was trained on. Ai hallucinations where responses not based off of the training data, troubleshooting complexity and glitch tokens or words or inputs maliciously designed to make the LLM malfunction. How have you used LLM?
