Large language models (LLMs), advanced artificial intelligence (AI) models trained on vast amounts of text data, are designed to understand and generate human-like text using large-scale natural language processing (NLP) technologies like Generative Pre-trained Transformers (GPTs). LLMs have become a significant part of the AI landscape due to their ability to revolutionize various business processes, with ChatGPT spearheading the change in the tech landscape. Brainy Insights estimates that the generative AI market will reach $188.62 billion USD by 2032, with North America expected to have the largest market share in the generative AI market.
“Early foundation models like ChatGPT focus on the ability of generative AI to augment creative work, but by 2025, we expect more than 30% – up from zero today – of new drugs and materials to be systematically discovered using generative AI techniques,” says Brian Burke, Research VP for Technology Innovation at Gartner. ” And that is just one of numerous industry use cases.
By leveraging the power of LLMs and generative AI, businesses can automate tasks, improve decision-making and uncover valuable insights that lead to greater efficiency and competitive advantages. Despite their recent popularity, language models have been a part of the tech world for several years.
The biggest breakthrough since social media
LLMs never would have advanced so quickly without transformer models, a novel deep learning architecture introduced in 2017 in Transformer Model Architecture (by Ashish Vaswani and others) and which revolutionized NLP and the understanding of tasks. Transformers significantly improved NLP and enabled the development of more powerful and versatile AI models.
In 2023, with the “AI Arms Race,” as the media and industry calls it, in full swing, companies are racing to develop and implement AI-driven solutions. According to data gathered by Pitchbook, in the first quarter of 2023 (funding through March 16), venture capitals (VCs) invested $2.3 billion USD in generative AI projects. LLMs remain a crucial element of a shift coined ‘the most important technological advance since social media’.
Examples of Large Language Models
BERT: A LLM Google-developed model that revolutionized natural language understanding (NLU) tasks, particularly in search engine optimization (SEO) and question and answering systems.
GPT-3: A groundbreaking model created by OpenAI, it is a LLM capable of understanding and generating human-like text for various tasks.
GPT-4: The latest version of LLM released by OpenAI, it delivers improved performance according to professional and academic benchmarks – offering more reliability, creativity and nuanced understanding.
Bard AI: Powered by Google’s Language Model for Dialogue Applications (LaMDA), this is an experimental conversational AI service that provides high-quality responses by drawing information from the web, while promoting creativity and learning.
Notion AI: A versatile AI partner created by Notion Labs for tasks such as summarizing content, brainstorming ideas, drafting text, correcting spelling, improving grammar, and translating content across languages.
The Pathways Language Model (PaLM): A 540-billion parameter transformer model developed by Google Research that showcases advanced few-shot performance in language tasks. Utilizing the Pathways system for efficient distributed computation, PaLM achieves 57.8% hardware floating point operations per second (FLOPS) utilization, the highest for large language models at this scale.
Presently most companies embracing LLMs can be described as early adopters. It’s worth looking at real-world business use cases of LLM implementation and analyzing their outcomes.
LLM use case in the food processing industry
A company from the food processing industry Software Mind worked with wanted to eliminate the time-consuming task of analyzing a vast amount of research papers. A labor-intensive endeavor became solvable by turning to the GPT-3 LLM. After successfully fine-tuning the GPT-3 model, building an internal API and developing a user interface (UI) tool, the company’s scientists had seamless access to the model for research analysis. What were the direct results of leveraging the GTP-3 model?
Improved efficiency: By quickly accessing and examining research papers, the company’s scientists were able to make informed decisions promptly.
Reduced workload: The model’s ability to extract and summarize essential information from the research papers significantly reduced the manual effort required by the team.
Enhanced innovation: By streamlining access to research papers, the company’s scientists were able to identify new opportunities and trends, leading to the development of innovative products and processes.
Reduced costs: The company saved on resources and time previously spent on manual research analysis, which cut costs.
If you want to see more real-world use cases of LLM implementations and learn what’s next for LLMs, sign up for an on-demand video on How to enhance your business with AI and Large Language Models.
LLMs offer endless possibilities across industries, but which methods of implementation are the most effective for using AI in business?
Eight best practices for implementing large language models
In many cases, businesses must take advantage of advanced AI models. Here are eight essential steps to start that journey.
1. Identify the right use case: Assess your business operations to pinpoint areas where a LLM can add value, such as customer support, content generation, or data analysis. Identify tasks you can delegate to LLMs.
2. Select the appropriate model: Choose a suitable LLM based on your needs, taking into consideration task complexity, model capabilities and resource requirements.
3. Prepare and fine-tune the data: Gather and preprocess relevant data to fine-tune the chosen model, to ensure it aligns with your business context and produces accurate, domain-specific results.
4. Plan the integration with existing systems: Execute the integration of a LLM into your existing business processes and technology infrastructure to ensure seamless operation and minimal disruption.
5. Monitor and evaluate performance: Continuously track the performance of the implemented LLM, using metrics such as accuracy, response time and user satisfaction to identify areas for improvement.
6. Address ethical and privacy considerations: Be mindful of potential ethical and privacy concerns related to AI deployment, while ensuring compliance with data protection regulations and the responsible use of AI technologies.
7. Pay attention to scalability and maintenance: Prepare for the ongoing maintenance and potential scaling of your LLM implementation, keeping in mind aspects such as data storage, compute resources, and regular model updates.
8. Foster a culture of AI adoption: Encourage a company-wide understanding and acceptance of AI technologies by providing training and resources for employees to embrace and leverage LLMs.
Read also: What is meta-learning in machine learning?
Generative AI will lead the new age
Bill Gates named the model developed by OpenAI as the second demonstration of technology that struck him as revolutionary in his lifetime. The first was a graphical user interface he was introduced to in 1980. Bill Gates’ essay concludes with the words, “The Age of AI is filled with opportunities and responsibilities.”
LLM application will undoubtedly be crucial in data analysis, content creation, translation, human resources, finance, accounting, compliance and software development. But the AI model will go even further, as, according to Gartner, generative AI will be crucial in drug development, medical diagnostics, material science and chip design.
As with every new type of technology, the earlier you start implementing LLM, the better your results. Experts believe that companies only have a time frame of 3 to 5 years to adapt solutions offered by the new wave of AI, if they want to stay competitive in their market. LLMs will only get better and embracing them will give companies a needed edge. At the same time, not taking advantage of that limited window of opportunity will only mean being left behind, when like Nokia – unwilling to make the necessary shift – lost ground in the smartphone market after the introduction of the iPhone.
Implementing LLMs in any industry requires working with an experienced team who constantly keeps up to date with the latest developments regarding AI developments. If you’re interested in learning how LLMs can help your company, contact our experts using the form below.
About the authorDamian Mazurek
Chief Innovation Officer
A certified cloud architect with over 15 years’ experience in the software industry, Damian has spent the last 7 years as a cloud consultant. In his current role he oversees the technology strategy and operations, while working with clients to design and implement scalable and effective cloud solutions. In addition to his experience as a cloud consultant, Damian has data and machine learning expertise, which has enabled him to help numerous organizations leverage these technologies to improve operations and drive business growth.