Leader Brief: Where does Generative AI fit in the AI landscape?

Introduction

Artificial Intelligence (AI) is a rapidly evolving field of computer science focused on mimicking human-like cognitive abilities in machines. The foundational concept of AI is representing the real world within a computer’s memory so that it can understand and draw inferences about our environment.

Modeling the Real World

To illustrate, imagine a scenario where we represent a map of a town in a computer. This map includes various components: your house, a restaurant where friends are meeting, and routes between them. We can also represent different modes of transportation – such as walking, cycling, and driving – along with their average speeds. With this information, the computer can compute the time it would take to reach the restaurant using a chosen mode, thus allowing for better planning in real life.

Knowledge Representation in AI

The first step in AI involves knowledge representation, which can be accomplished in two significant ways:

Symbolic Representation: In this method, information is represented as symbols that can be manipulated to make inferences about the world. For instance, your home and the restaurant can be designated as symbols within a computational framework, allowing algorithms to compute travel times based on symbolic connections.
Data Representation: In contrast, this approach comprises raw data, like pixels that make up a digital representation of a map. Here, your house, the restaurant, and roads are depicted as clusters of pixels, equivalent to a more complex model called a neural network. In this case, the computer processes input data to yield useful information on travel time based on mode of transportation.

Historically, researchers were confident that symbolic representation was superior for achieving true artificial intelligence. However, advances in computing power have preferentially spotlighted deep learning models, which can represent intricate world concepts using fundamental data points. This approach has laid the groundwork for deep learning research.

The Role of Generative AI

Now, the question arises: where does generative AI, such as ChatGPT, fit into this intricate AI landscape? AI can represent the real world via two primary routes: using symbols or employing data, particularly through machine learning. In machine learning, varying models symbolize data, ranging from simple equations to complex deep neural networks.

Deep learning, a subset of machine learning, utilizes structures like:

Convolutional Neural Networks (CNNs): Used primarily for image recognition, mimicking how humans perceive visual information.
Recurrent Neural Networks (RNNs): Ideal for processing temporal patterns, such as transforming voice into text.
Transformers: Designed to comprehend relationships among words, essential for language processing.

Transformers, in particular, enable the development of foundational models. These models learn from vast datasets filled with text, often drawn from the internet. After establishing a strong foundational model, it can be further trained for specific applications, such as building chat-based question-and-answer systems like ChatGPT. Alternatively, they can be tailored for tasks such as code generation or medical diagnosis, representing specialized versions of foundational models.

In summary, generative AI fits seamlessly into the broader AI landscape by leveraging foundational models to perform various complex functions effectively.

Keywords

Artificial Intelligence (AI)
Cognitive Abilities
Knowledge Representation
Symbolic Representation
Data Representation
Deep Learning
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Transformers
Foundational Models
Generative AI
ChatGPT

FAQ

What is AI?
AI, or Artificial Intelligence, is a field of computer science focusing on creating systems that can mimic human-like cognitive abilities.

How does AI represent the real world?
AI can represent the real world through symbolic representation, using symbols and manipulating them to infer and plan, or through raw data like pixels, processed through neural networks.

What is deep learning?
Deep learning is a subset of machine learning that employs deep neural networks to represent complex data structures, allowing for intricate data modeling and inference tasks.

What are different types of neural networks?
Notable types include Convolutional Neural Networks (CNNs) for image recognition, Recurrent Neural Networks (RNNs) for processing sequential data, and Transformers for language processing.

How does generative AI differ from other AI models?
Generative AI uses foundational models, such as those built on Transformer architectures, to generate human-like text or responses based on extensive training data. Examples include systems like ChatGPT, which can be fine-tuned for various applications.