Real-world applications

1. Real-world applications

Let's explore some real-world applications of these powerful AI tools across various industries to understand the business opportunities and benefits of leveraging LLMs.

2. Business opportunities

Large language models have numerous applications and benefits across various industries, helping automate tasks, improve efficiency, create revenue streams, and enable new capabilities. The possibilities are endless. Businesses constantly seek new and innovative ways to improve their products and services using LLMs. We will examine how LLMs have transformed the finance, healthcare, and education industries.

3. Transforming finance industry

Let's start with the finance industry. Financial analysis of a company can be complex and may include processing unstructured text such as investment outlooks, annual reports, news articles, and social media posts. Unstructured data or text refers to data that lacks a pre-defined format and is typically presented in free-form text.

4. Transforming finance industry

LLMs can analyze such data

5. Transforming finance industry

to generate valuable insights into market trends, manage investments, and identify new investment opportunities.

6. Challenges in healthcare

Now let's look at the healthcare industry. Analyzing health records is important for giving personalized recommendations to provide quality healthcare. But, much of the information is in doctors' notes, which can be hard to understand because they use jargon and abbreviations. Furthermore, domain-specific knowledge and varying writing styles among practitioners add to the challenges of interpreting this critical information effectively. Processing such varied sources of text data and understanding complex acronyms makes it difficult to have a general system to describe any patient files.

7. Revolutionizing healthcare sector

But not anymore. LLMs have made it possible. LLMs can analyze large amounts of patient data, such as medical records, health check-up results, imaging reports, and more, to provide personalized treatment recommendations. Patient data is private and personal information, so anyone using LLMs this way must adhere to privacy laws and regulations.

8. Education

Our third and final example is the transformation in the education industry. Have you ever wished for a tutor who could personalize the coaching style based on the style and expectations of the learner? Well, here is some good news. Education companies and schools are integrating LLMs into their platforms to provide interactive learning experiences to learners. The learners can ask questions, receive guidance, and discuss their ideas with an AI-powered tutor. Moreover, the tutor can adapt its teaching style based on the learner's conceptual understanding.

9. Personalizing education: text generation

LLMs can be used for text generation, such as customized learning materials, which include explanations, examples, and exercises based on a learner's current knowledge and progress. We can observe in this example that the model can adapt the explanation of the same concept for a child and an astronomy expert. The response style and the level of detail show how an experienced professor might have approached the explanation.

10. Defining multimodal

Now that we understand how different industries can apply LLMs let's discuss the multimodal applications of LLMs. But what do we mean by multimodal? Multi means many, and modal means modes or types. These models can process and generate information across different data types, such as text, audio, video, and images, in contrast to non-multimodal, which work with only one of the modes, such as text only.

11. Visual question answering

Visual question answering is one such multimodal application. LLMs can generate meaningful and contextually relevant answers to questions about visual content, such as identifying objects, understanding relationships between them, and describing scenes. For example, we can see that the model recognizes the zebra image and also responds with additional information, such as making a joke. This multimodal application processed both image and text data to generate responses.

12. Let's practice!

Let's practice!

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