Over the decades, ideas and concepts of fully functional artificial intelligence have been commonplace at the forefront of research and science fiction. Whether it’s computer development, Iron Man’s JARVIS assistant or Skynet, AI has always been a distant dream.
Recently, that dream has become a quickly approaching reality.
With innovations in computing power and cloud technology, AI and machine learning have made the leap into becoming functional assistants. From automated processes to digital art, the use cases for AI have shown early promise. While AI isn’t perfect (and has a lot of room for improvement), it’s becoming popular for tech companies and pioneers like IBM, Google and Apple to invest in AI as one of the next big tech frontiers.
Text generation – a time-consuming task – is one role AI has been able to take on thanks to advancements in specialized neural networks called Large Language Models.
What Are Large Language Models?
When you think of Artificial Intelligence, you might think of its ability to write content that could pass as written by a human. A core component that powers this is Large Language Models. LLMs are a class of computer models designed to understand and generate written text in a way a human would.
LLMs are built on the foundation of machine learning, a core part of AI and computer science. Machine learning focuses on using data and algorithms to train computers to imitate human learning, increasing accuracy and intelligence over time.
Deep learning also acts as an integral part of LLMs and AI. As a subset of machine learning, deep learning leverages neural networks with multiple layers and functions to simulate the human brain’s decision-making process. Think of it as the next level of computer intelligence – while machine learning uses several layers, deep learning will use thousands of layers to train a model.
LLM models leverage these two concepts to analyze a prompt and formulate a response.
- Natural Language Understanding: Gives computers the ability to analyze written text and spoken words and understand them as an informational sequence, rather than meaningless words.
- Natural Language Processing: Allows computers to take prompts and formulate a quality response using deep learning.
Basic tools like search engines and chatbots use NLU and NLP to register requests and provide answers but aren’t necessarily lifelike in their responses. On the other hand, more advanced Generative AI models leverage deeper neural network tech to complete intricate processes and communicate like a human.
Generative AI is a catch-all term referring to programs that can make new content, including audio, images, video, text, and even code for other programs. This technology leverages massive data sets (like the internet) to learn how to communicate and has the potential to improve productivity and assist creativity.
How do Large Language Models Work?
LLMs use neural networks called transformer networks to understand and process prompts. These networks are made up of several layers with various functions, all working together to puzzle out a request and effectively predict an appropriate answer based on a larger training data set. Transformer network layers can include:
- Self-Attention: A mechanism that provides context for a prompt.
- Feed Forward: Maintains forward momentum through a neural network’s layers and mechanisms, which allows for more complex patterns.
- Normalization: Ensures a consistent input distribution across the neural network. This reduces potential shift issues during training.
- Positional Encoding: Acts as an identifier telling the neural network where a word is in a sequence.
Rather than purely processing letters and words, transformer networks allow LLMs to analyze text, understand the context, and provide a relevant answer. Like the human mind, LLM understanding isn’t always sequential and can break down a request and process it in smaller pieces behind the scenes, allowing for greater detail in the response.
The process to train an LLM will also usually follow an established workflow that allows the model to function as intended. This workflow may change depending how an AI is developed and trained, but will usually follow this outline:
- Identify the goal/purpose: Laying out a use case for the LLM to fine-tune the training process.
- Pre-training: Gathering and refining a dataset to train with. This can include written content in bulk, depending on the use case.
- Tokenization: Breaking down text in a dataset into smaller units. This helps the LLM learn words and context.
- Infrastructure selection: Choosing how the LLM is hosted, and how it pulls its computing resources. Powerful computers or cloud servers are usually a good choice, but the power available can limit widespread LLM development.
- Training: Setting parameters for training and running data through the LLM’s neural network.
- Fine-Tuning: Adjusting training parameters to improve an LLM’s results. This is an ongoing process and key for improving usefulness!
After an LLM is developed and trained, it can be used for its intended purpose. Additional fine-tuning and development will also improve the network’s abilities over time.
What are Large Language Models Used For?
Once an LLM is trained, it’s ready to receive prompts. While Generative AI has recently been used to generate text on a larger scale, it can have dozens of applications. Most LLMs are developed to accomplish time-consuming, repetitive tasks. Summarization, content analysis, and basic text generation are all responsibilities that can be taken on by LLM-powered AI.
Thanks to years of research and development, LLMs have developed enough to function as a core part of Generative AI, which includes Open AI’s Chat-GPT, Google Gemini, IBM’s WatsonX, and many other AI models.
Here are a few other ways LLMs have streamlined processes across different industries.
- Content Generation
- Translation
- Research
- Customer Support
- Cybersecurity and Fraud Protection
It’s important to note that for the foreseeable future, content generated by AI and LLMs will likely require review from a human specialist. AI can generate a report based on any given topic, but the limited nature of the technology may result in low-quality writing or even plagiarism.
Large Language Model Limitations
LLMs are innovative, but they’re not flawless. Like any developing technology, AI faces challenges as it evolves. Major issues include reliance on limited datasets, which (as mentioned above) can result in the LLM copying content nearly word for word in some cases. As intelligent as AI can seem, these models can’t fully think for themselves. They still need prompts and coaching, like any computer, and will have limited reasoning and understanding for anything outside of its area of expertise.
Other limitations of AI include, but aren’t limited to:
- Context awareness: Due to the way AI is trained, LLMs are usually reliant on existing patterns in its training data. While AI understands text, it doesn’t comprehend language or subtext. Sarcasm, irony and slang may go beyond the AI’s understanding if it wasn’t trained on those things. This usually results in analytical text that isn’t as engaging to read, or responses that miss the point of the original prompt.
- Response Hallucination: If an LLM doesn’t have the information it needs to respond, it may make up an answer regardless of its correctness. This is usually due to a lack of boundaries that limit incorrect responses. While this issue can be fixed, hallucinations could cause misunderstandings and other serious issues.
- Prompt Perfection: Once again, due to an LLM’s inability to fully understand language and nuance, outputs are fully at the mercy of a good prompt. Prompt engineering can be a finicky process and requires some time and experience to get the best results from an AI.
- Mathematical Issues: Like a professional English Major at your local community college, LLMs can handle simple 2–3-digit mathematics. But as the complexity increases, the more an AI will struggle – even asking an LLM for a specific word count can be a challenge sometimes. The reason why? LLMs are experts at generating text, but they don’t understand the rules of math unless they are specifically trained for it.
- Cost Effectiveness: In addition to the upfront cost of research and development, LLMs also rack up a tab with every prompt they answer. For Chat GPT-4 (as of June 2024), costs are calculated based on the tokens used for input and output. If your prompt uses 100 tokens and the AI’s response is 200 tokens, the full interaction could cost about $0.15. As AI scales up and receives millions of requests per hour globally, you could expect it to cost a lot to operate.
How Safety Mojo Leverages LLMs
Managing safety at any scale in high-risk industries can be a challenge for any professional.
Here’s how Safety Mojo leverages the power of AI and LLMs to streamline workloads, improve safety culture and reduce risk for safety pros.
- Conversational Forms: Rather than filling out observations, injury reports, Job Safety Analysis, Pre-Task Plans, and many other kinds of essential documents by hand, Safety Mojo leverages LLMs to register voice input. If you see something unsafe on a job site, you can simply describe it. The onboard AI will then accurately classify the act, record all relevant details, and provide a preview for verification. Instead of taking hours to fill out reports, our AI will do it 80% faster than other manual methods.
- Ask Mojo: Our virtual assistant provides instant access to safety manuals, SOPs, JHAs, and more. Instead of searching through documents for answers, just ask Mojo a question. You’ll get a detailed summary based on available documentation.
- Goals and Controls: Want to encourage good behavior and help automate compliance across the job site? Goals and Controls tracks progress towards desired goals to show you who’s performing on the job site. Our AI will send automatic reminders and alerts to workers regularly, and alert you when goals are met.
- Dashboards: Track all your safety data in one place without needing to set up custom spreadsheets or reports. Mojo will automatically upload reports into your aggregated data set as they’re submitted, providing real-time results whenever you need them.
Want to see Safety Mojo in action? Check out our AI Features page to learn more today.
