Much of the tech talk as of late in the swirling landscape of artificial intelligence (AI) has to do with two concepts: large language models vs generative AI. The two are often confused or conflated, yet impacting everything from customer service and operations to entirely new forms of content creation. This blog is an attempt to delaminate the technologies by exploring the functionalities, differences, and impacts on the industries.
Table of Contents
Understanding Large Language Models (LLMs)
What Are Large Language Models?
Large language models form a subset of artificial intelligence especially created to be in a position of comprehending and generating human-like text based on the input given. The models are trained on large datasets inclusive of written text from books, articles, and websites. The challenge is to build an artificial intelligence system that can understand contexts, give coherent responses, and try to mimic the conversational style of humans in the best way possible.
How LLMs Work
These models are centered around machine learning, using a technique referred to as transformers. Such models predict the probability that a sequence of words should follow a given word or phrase and then proceed to create paragraphs of text that are unexpectedly coherent and contextually relevant.
Exploring Generative AI
Definition of Generative AI
Speaking of generative AI, it would mean an algorithm capable of creating any form of content: text, pictures, music, or whatever else one can think of. It must be, as the same principle should apply, for it is not text-generative but for everything AI does, learning from the dataset from which it creates new content.
Applications of Generative AI:
There are many applications of generative AI, which span across a wide spectrum of activities: it could be used to develop new images and make an edit on the old ones, at least not to distinguish human-made art. In the music sector, such models could come up with new tunes imbued with human emotions. In the video gaming area, generative AI is applied to create a dynamic environment that changes with player actions.
What are the main differences between large language models and generative AI?
The basic difference between big language models and generative AI is, from a superficial view, they look quite similar, designed in a vastly different manner, and for an entirely dissimilar purpose.
Purpose and Function
Big Language Models exist purely to understand and generate text in a very human-like way. Generative AI is a far larger class, among which content generation from text to literally anything is in the subset.
Training and Data Use
Huge language models are designed explicitly for text data and hence demand really large datasets. On the other hand, generative AI can be trained by using various datasets pictures, audio files, or videos, depending on what kind of content it is supposed to generate.
Impact on Industries
These two technologies are highly impactful in industries but in different ways. The most transformative industries with LLM are industries that rely heavily on text, such as the legal industries, journalism, and customer support. Generative AI has, on the other hand, applications in wider applicability; for example, in creative, entertainment, and design industries.
Industrial Application and Examples
Large Language Models in Action:
- Customer Service: Chatbots with prompt and precise automated responses.
- Content Generation: Help journalists and writers by writing the first draft for them or suggesting some edits.
Generative AI at Work
- Art and Design: Produce visual content for ad campaigns minus humans present in them.
- Entertainment: Writing music scores for motion pictures or developing storylines for video games.
FAQs on Large language models vs generative AI
Q1: Can large language models generate images or videos?
A1: No, large language models are developed to understand and generate text. You would have to look towards specific types of generative AI models for image or video generation.
Q2: Are these technologies safe?
A2: As shown by LLMs and generative AI, these technologies provide meaningful upside while raising important ethical and safety considerations. Some of the key areas in urgent need of control and regulation include aspects related to the misuse of AI-generated content and data privacy and the necessity for transparent AI operations.
Q3. How do you envision the future for all of these AI technologies?
A3: Future developments will move towards the efficiency, ethicality, and capability of these models to handle very complex tasks within a wide array of domains.
Therefore, while large language models and generative AI may overlap in some of the capabilities of producing human-like text, their functionalities and application impacts are very different. The differentiator in this understanding lies in the ability to harness and make good use of such capabilities across industries most effectively and ethically possible. As AI continues to permeate all domains of life, better implementations and more innovative solutions in the tech landscape are drawn from differentiating these technologies. If you want to send feedback about our post feel free to contact us here or at our facebook page.