A large language model (LLM) is essentially a machine-learning algorithm that processes and understands natural language (or what you and I would call “human language”). It is a type of foundation model (a general-purpose language model) trained on enormous amounts of data to perform an infinite number of tasks, including text generation, machine translation, summary writing, and even machine coding.
LLMs use deep-learning methodologies to correctly interpret and analyze the complex linguistic relationships between the semantics and syntax of language to perform specific actions.
Large language models and artificial intelligence
LLMs represent a significant breakthrough in artificial intelligence and natural language processing (NLP). They are the reason we can use interfaces like ChatGPT and other generative AI models without needing coding knowledge. Think about the last time you used an AI tool: Did you write “normally” or as if you were speaking to a human?
In a nutshell, large language models are designed to understand and generate text the way a real human would. Additionally, they are constantly learning and regularly processing huge amounts of data to infer from context, summarize text, and answer questions.
In the most advanced generative AI models, LLMs are trained to assist in writing creative content or academic papers. (It must be noted that the accuracy and quality of these produced assets are not entirely up to par, but the mere fact that AI can now do this is impressive).
Nevertheless, the opportunities presented by LLMs are astounding. It is not far-fetched to imagine countless improvements in various fields, from chatbots to virtual assistants to language translation. Even in the IT field, which paradoxically is resistant to generative AI to help with code generation or cybersecurity training, for example, LLMs are poised to reshape and influence how we interact with technology and access information.
In fact, the latest McKinsey research found that 65% of organizations regularly use generative AI in at least one business function. This number is predicted to increase in the coming years.
How large language models work
LLMs leverage deep learning techniques and textual data. Typically, these models consist of multiple layers of neural networks—each with parameters that can be further improved and enhanced during training.
During training, the large language model is taught how to predict the next work in a sentence based on context. To do this, it applies mathematical models to calculate a probability score of the recurrence of words that have been tokenized (smaller sequences of characters). The tokens are then translated into numeric representations of the context.
This is the first part. LLMs are then rigorously trained with massive amounts of text (literally billions upon billions of pages) to help them learn grammar, including the relationship between semantics and syntax. This ensures the accuracy of the numeric context or whether the generated sentence “makes sense”.
Once trained, large language models can generate text by predicting the next work based on the input they receive and then learning patterns for how sentences are formed.
As with any predictive and generative tool, continuous fine-tuning is required, using methods like reinforcement learning with human feedback (RLHF). In RLHF, LLMs learn more “human” aspects of language and speech. For example, creating an algorithm to define what is “funny” is difficult. Mathematically, it’s almost impossible to do—but human feedback can rate jokes, which, in turn, teaches the LLM the concept of humor. Thus, human feedback helps LLMs learn holistically through trial and error— with the model highly motivated tp succeed through strong incentives.
LLMs and their use in endpoint management
You may think that large language models and endpoint management are completely distinct concepts. As an MSP leader, you may wonder why NinjaOne would discuss LLMs at all.
However, LLMs are increasingly becoming intertwined with modern-day enterprises. Their ability to process and generate human-like text offers significant potential for enhancing your endpoint management experience.
On one hand, LLMs can automate routine tasks such as patch management, software updates, and incident response. By analyzing vast amounts of data from endpoints, LLMs can identify patterns, predict issues, and suggest optimal solutions. This frees your IT team to focus on more high-level strategic projects.
Conversely, endpoint management provides critical data for LLMs to operate effectively. Comprehensive endpoint data, including software inventory and hardware specification, allows LLMs to create more informed recommendations for your company.
This convergence of technologies promises to make endpoint management more efficient, reduce costs, and improve overall IT security.
LLM use cases
LLMs have proven versatility across numerous use cases in various industries. Let’s look at some of them.
- Text generation. LLMs provide the most benefits to companies that require language generation abilities, such as writing emails, blogs, or other written content that can be easily generated in response to prompts.
- Content summarization. You can summarize long articles and highly technical articles into more digestible assets.
- AI assistance. LLMs contribute to chatbot development, where your users can interact with an automated machine as part of a self-service customer care solution.
- Code generation. LLMs may assist IT developers in building applications and finding errors in codes.
That said, there are multiple other use cases for which you can use LLMs. For endpoint management, for example, you can use LLMs to help you develop better cybersecurity training models to identify phishing emails and detect ransomware attacks.
Large language models for endpoint management
Large language models can offer numerous benefits for better endpoint management. In the ever-evolving IT landscape, MSPs and IT enterprises need to remain agile, leveraging various tools to simplify work and automate mundane tasks.
LLMs can personalize the user experience by providing intelligent support and troubleshooting recommendations. As endpoint environments become increasingly complex, LLMs are set to become indispensable tools for managing and optimizing critical IT assets.
