How Do Different Businesses Use Applications Of Generative AI And Large Language Models?

Igor Izraylevych

4 min read

How Do Different Businesses Use Applications Of Generative AI And Large Language Models?

How do artificial intelligence (AI) technologies redefine industries and drive innovation? This article explores the concepts of Generative AI vs Large Language Models, their differences, and how businesses in different sectors, including healthcare and finance, use them to their advantage.

Concept Of Generative AI and Large Language Models

Generative AI refers to a class of artificial intelligence models that can create new data similar to the data they were trained on. These models generate images, text, audio, and even video content that is remarkably similar to human-created data. The most well-known example would probably be MidJourney, a generative AI producing images from verbal descriptions.

Large language models (LLMs), on the other hand, are a specific type of generative AI that focuses on processing and understanding natural language. They work in a slightly more complicated way and are trained on vast amounts of textual data to learn patterns, context, and semantics. It enables them to comprehend and generate human-like text. Examples include more than well-renowned GPT models and Bard. 

Difference Between Large Language Models And Generative AI

Mainly, the difference between generative AI and large language models lies in the broader range of applications of the former. Generative AI encompasses a more comprehensive choice of AI models that can create new content for various data types and is not just limited to text. Meanwhile, LLMs are a specific subset of generative AI models designed for text processing and generation. 

Large Language Models

LLMs are a specific type of artificial intelligence (AI) model designed to process and generate human-like language. These models are based on deep learning techniques, particularly transformer-based architectures like GPT-3.5, which is the architecture that powers ChatGPT.

The primary purpose of large language models is to understand and generate text based on the patterns and knowledge learned during their training phase. They excel in various natural language processing (NLP) tasks, such as text completion, language translation, question-answering, sentiment analysis, and more.

LLMs like GPT-3.5 have billions of parameters, allowing them to process vast amounts of data and generate coherent and contextually relevant responses. However, they are generally pre-trained on large datasets and then fine-tuned for specific tasks before they can be effectively used in real-world applications.

Generative AI

Generative AI is a broader category of artificial intelligence encompassing various models and algorithms capable of generating new content, not just text. While LLMs like GPT-3.5 are part of generative AI, other generative models are designed for different data types, such as images, music, and videos.

Generative AI models are engineered to understand patterns and structures within a given dataset and use that understanding to create new content that fits the learned distribution. For instance, in addition to language models that can generate text, generative adversarial networks (GANs) are used for image synthesis, variational autoencoders (VAEs) for image and audio generation, and similar architectures for other data types.

How Generative AI and Large Language Models Are Used in Healthcare

The healthcare industry is no stranger to technological advancements, and the integration of Generative AI and Large Language Models has opened up exciting possibilities in the realm of healthcare software development services.

Medical Imaging Analysis

In medical imaging analysis, generative AI is helpful with high-resolution images of organs, tissues, and even entire anatomies, aiding in accurate diagnostics and treatment planning. They can also help augment limited datasets, making them invaluable in training image recognition algorithms for identifying various conditions and anomalies.

Drug Discovery and Development

Large language models can analyse vast amounts of research papers, clinical trial data, and drug interactions to suggest potential drug candidates for specific diseases. This speeds up the drug development process significantly and increases the chances of finding effective treatments.

Personalised Treatment Plans

By analysing a patient’s medical history, symptoms, and genetic data, generative AI models can assist doctors in creating personalised treatment plans. These models can predict individual responses to specific treatments, optimising patient care and outcomes. In terms of practical results, apart from taking a load of work from demanded doctors, such an application increases the correct diagnosis rate, which ultimately results in a larger number of cured patients and saved lives.

Natural Language Processing in Patient Care

Using conversational chatbots and virtual assistants, large language models may provide patients with precise and timely information about their health, meds, and appointments. Additionally, they can help the medical team with administrative duties, freeing up vital time for more important patient care.

The Role Of Generative AI and Large Language Models In Finance and Banking Sector

Now let’s consider the use cases of generative AI vs large language models in another sector — finance and banking.

Fraud Detection and Risk Assessment

Generative AI models can analyse historical data on fraudulent transactions and learn patterns to identify potential fraud cases in real time. This enhances security measures and reduces financial losses for both customers and financial institutions. LLMs can aid in risk assessment by analysing vast amounts of textual data, including news articles, to provide valuable insights for investment decisions.

Customer Service and Support

LLMs are extensively used in developing advanced chatbots and virtual assistants in the finance sector. These language models can understand and respond to customer queries, provide personalised recommendations, and assist with various banking operations. This improves customer satisfaction and reduces the burden on human customer support teams.

Algorithmic Trading and Market Analysis

In algorithmic trading, where it can generate trading strategies based on both real-time and historical market data, generative AI comes in especially handy. By leveraging LLMs, traders and investors can better understand market sentiments and even predict future market trends using not only numerical but also text data (news, social media posts, etc.) to make more informed decisions.

Loan Underwriting and Credit Assessment

The finance industry heavily relies on risk analysis when providing loans and evaluating creditworthiness, which is a very precise job. LLMs can process vast amounts of financial documents, credit reports, and other textual data to assess credit risk more accurately: this capability unlocks fairer lending practices.

Conclusion: Generative AI vs. Large Language Models

Generative AI and Large Language Models are groundbreaking technologies that have opened up new business possibilities across various industries. For now, these technologies have drastically improved the medical sphere and the finance and banking sectors. As this technology continues to evolve, we can expect even more innovative applications that will shape the future of business and society at large, as well as a more significant AI impact within different industries.

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Igor Izraylevych