Artificial intelligence (AI) has become a driving force behind innovative technologies in today’s digital age. The AI landscape can be overwhelming with its jargon and concepts. After conversing or working on my next presentation, I often ponder some of these terms. Sometimes I find it beneficial to walk through some of these terms myself.
Large Language Model (LLM):
Large Language Models are advanced AI models that can understand and generate human-like text. These models, such as OpenAI’s GPT-3, are trained on vast amounts of data from the internet and excel at various natural language processing tasks. For instance, given a prompt, an LLM can generate coherent and contextually relevant responses, translate text, summarize documents, and much more.
Example: Imagine an LLM trained on a large corpus of books. When given the prompt, “Write a short story about a detective solving a mysterious crime,” the model can generate a compelling story with characters, plot twists, and suspense.
Natural Language Processing (NLP):
Natural Language Processing is a branch of AI that focuses on enabling computers to understand, interpret, and manipulate human language. NLP algorithms and techniques empower machines to process and analyze text, speech, and other forms of natural language data. NLP plays a crucial role in developing language models like LLMs.
Example: An NLP application could be sentiment analysis, where a model analyzes social media posts to determine if they express positive, negative, or neutral sentiments. This helps companies gauge public opinion about their products or services.
Generative Data Models:
Generative Data Models are AI models that can create new data instances that resemble the training data they were exposed to. These models learn patterns from existing data and generate new samples based on that learned information. Generative data models have applications in various fields, including image generation, text generation, and music composition.
Example: One example of a generative data model is a deep learning-based image generator. Given a dataset of plant images, the model can generate realistic new plant images that look similar to the training examples but are not identical.
Grounded and Not Grounded Data:
Grounded Data refers to data directly connected to or aligned with real-world observations, experiences, or measurements. It has a clear and explicit relationship with the physical or tangible aspects of the world. Not Grounded Data, on the other hand, lacks a direct connection to real-world observations and is more abstract or conceptual.
Example: Grounded data could be a dataset of weather measurements, including temperature, humidity, and wind speed, collected from various weather stations. This data is directly tied to real-world atmospheric conditions. In contrast, not grounded data could be a dataset of movie reviews where the text contains subjective opinions and sentiments rather than objective measurements. Data could also be “dated,” and results are limited to that date.
We’ve taken a significant step toward understanding AI fundamentals by exploring the concepts of LLM, NLP, generative data models, and grounded and not grounded data. LLMs like GPT-3 demonstrate the power of language models, while NLP enables machines to comprehend and process human language. Generative data models can produce new data instances, and distinguishing between grounded and not grounded data helps us understand the relationship between data and real-world observations. As AI advances, grasping these concepts will prove valuable in navigating the ever-evolving AI landscape.
