The 5 (most well-known) Subsets of AI

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 👋 Hey there!

Happy Monday! One of the goals of this newsletter is to help break down the world of AI.

It’s quite vast, and it expands outside of ChatGPT (as we currently know it). In today’s newsletter, I’m going to explain the different subsets of AI and where applications like ChatGPT fall.

I’m closing in on 1k words this edition, so it’s a bit longer. It’s worth the morning read!

No more wasting time, let’s dive into it.

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Curated links to anything AI related

🚀 Streamline digital asset proofing and the approval process with your team quickly and easily with Zipboard.

🌪️ Amy McGovern is looking to establish a center/institute focused on AI for climate resilience, weather, and risk.

🧑‍💻 Taipy is a data scientist’s best friend: your Data and AI Full-Stack App Builder.

👷 OpenAI is looking for a Front End Engineer, Evals and Understanding.

📱 Let AI understand and organize your screenshots with Camp 2.0.

🌎️ AI In Browsers: Features and Opportunities

 

 

Over the last ~year or so, we’ve been attributing “AI” to one specific kind of AI. We often attribute, unintentionally, AI to be exclusively ChatGPT.

In today’s deep dive, I want to show you that AI is more than just ChatGPT. I won’t be providing tools, but rather a high-level overview of a few of the branches within AI.

» A quick note before starting: as AI development advances, effective collaboration becomes increasingly crucial. zipBoard steps in to support this need, offering a platform that emphasizes real-time communication and streamlined project management. It's particularly adept at enhancing project transparency and efficiency through its visual feedback and intuitive bug tracking capabilities, simplifying the complexities of AI project workflows.

AI, in short, is an umbrella term that refers to anything that allows a machine to do something that normally would require human intelligence.

Machine learning, on the other hand, is a type of AI that allows a machine to learn by itself by analyzing training data.

1. Machine Learning

As mentioned above, a large part of AI is machine learning. Machine learning allows the computer to learn and improve itself from existing data, without explicitly being programmed.

This kind of AI also encapsulates other kinds of AI (which will be discussed below) and their associated algorithms. These algorithms are generally very fancy and advanced statistics; anticipate this in a future edition.

2. Natural Language Processing

Natural Language Processing, or NLP, involves the development of algorithms that can understand, interpret, and respond to human languages.

NLP is a blend between computational linguistics with statistical, machine learning, and deep learning models.

This branch of AI includes applications such as ChatGPT and Siri. In fact, Google searches use this to show you a better results.

3. Robotics

While a lot of AI is software, there are significant applications in the robotics industry.

Robotics has been around for several years, but we’re now seeing an explosion in comparison to previous years due to the growth of the AI industry as a whole.

There are several different kinds of robotics (which I won’t dive into here), but think of this subset of AI a “hands-on” application of AI.

In fact, I think 2024 is going to be the year for robotics. Stay tuned.

4. Neural Networks

Neural networks are modeled after the neural network in our brains; this type of AI is designed to learn and make decisions like humans.

The biggest advantages of a neural network compared to other kinds of AI is that it can be trained to recognize patterns in data that are too complex for a traditional algorithm.

Since they’re able to recognize complex patterns in data, it allows us to apply it to a variety of advanced applications and fields, such as image and video recognition, predictive analysis, and even other kinds of machine learning.

5. Deep Learning

Deep learning is a subset of neural networks and has a primary application in computer vision, self-driving vehicles, and speech recognition.

A deep learning network generally consists of many layers of processing for classification or predictions.

Deep learning can be scaled across many industries and applications, and is very widely used in today’s market to solve some of the most complex problems we face.

And that’s a wrap for this edition. While there are many more subsets, these 5 are some of the more well-known. If you want to learn more about AI and how you can use it, be sure to check out the MIT Sloan Executive Education Short Course.

 

 

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