Decoding AI & Generative AI

What is the difference between AI & Generative AI?

You hear the terms AI and Generative AI in the media all the time, but what do they each really mean? They’re both concepts within the broad field of computer science and machine learning, but they serve different purposes and have distinct characteristics. Let’s walk through both of them.

Artificial Intelligence, AKA AI

AI is a large field encompassing any technique that enables computers to mimic human intelligence. The main goal of AI is to create machines that can perform tasks that would normally require human intellectual agility. These tasks can range from simple ones like calculations to more complex activities like understanding natural language, recognizing objects in images, or playing strategic games like chess.

“The Imitation Game” is a 2014 movie based on Alan Turing & the team that broke Nazi secret codes. The movie received 8 Oscar nominations & 5 Golden Globe nominations.

Subfields of AI

Machine Learning (ML): This is a subset of AI where machines learn from data. Instead of being explicitly programmed to perform a task, they use algorithms to learn patterns and make decisions based on historical data. Supervised learning, unsupervised learning, and reinforcement learning are common approaches in ML.

Natural Language Processing (NLP): This deals with the interaction between computers and human languages. The goal is to enable machines to understand, interpret, and generate human language. Applications include language translation, sentiment analysis, and chatbots.

Robotics: This involves creating robots that can perform tasks in the physical world. It integrates AI with mechanical engineering to develop autonomous systems.

Computer Vision: This area focuses on enabling machines to interpret and make decisions based on visual data from the world. It includes tasks like image recognition, object detection, and facial recognition.

Nathaniel Rochester was the engineering manager for the first mass-produced scientific computer.

Generative AI

Generative AI is a subset of AI, specifically focusing on creating new content. Instead of merely analyzing or interpreting existing data, generative AI models can produce texts, images, or music. These models are typically powered by deep learning techniques, particularly neural networks. Generative AI is making quite a stir in Hollywood, where new programs are able to produce high quality videos and avatars that are hard to detect from reality.

Subfields of Generative AI

Content Creation: Generative AI can produce new, original content. For instance, it can write essays, generate realistic images from text prompts, compose music, or even create 3D models. This is in contrast to other AI models that primarily classify or analyze existing data.

Generative Models: The core of generative AI lies in models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

GANs: Consist of two neural networks, a generator and a discriminator, that are trained together. The generator creates content, and the discriminator evaluates it for authenticity. This adversarial process continues until the generator produces content that is indistinguishable from real data.

VAEs: These models encode data into a latent space and then decode it to generate new data. They are particularly useful for tasks where a more controlled generation of content is needed.

Applications: Generative AI has numerous applications. In the entertainment industry, it can create realistic special effects, generate artwork, or produce new music (and more). In the field of natural language, models like OpenAI's GPT series can write articles, answer questions, chat with users, and even assist in programming by generating code snippets.

Pushing the Boundaries of Innovation

AI uses various techniques tailored for different tasks to make improvements across myriad industries. Generative AI’s applications tend to be more focused on creative domains and content creation. Both are pushing the boundaries of artificial innovation.

Authored by Melissa E. Daley, CMO

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Sources:

https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/

https://redirect.cs.umbc.edu/courses/471/papers/turing.pdf

https://www.pbs.org/newshour/science/8-things-didnt-know-alan-turing

https://www.forbes.com/sites/gilpress/2016/12/30/a-very-short-history-of-artificial-intelligence-ai/

https://www.imdb.com/title/tt2084970/?ref_=tt_mv_close

https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/1904

https://hai.stanford.edu/sites/default/files/2020-09/AI-Definitions-HAI.pdf

https://history.computer.org/pioneers/rochester.html