4 Key AI concepts explained 🔮

Good Morning AI Runners

Here's what we've got for you today:

• GeoCode and 3D generative AI

• 4 Key AI concepts explained

GeoCode and 3D generative AI

GeoCode is a program that can be used to make 3D shapes in a way that's easy for both humans and computers to understand. To use GeoCode, you start by either drawing a sketch of what you want the shape to look like, or you can give the computer a bunch of dots called a "point cloud" that represents the shape you want. Then, GeoCode will turn those dots or lines into a 3D shape that you can see and modify on the screen.

The cool part about GeoCode is that it has a special "parameter space" where you can make changes to the shape easily. So if you want to make the shape longer, shorter, wider, or thinner, you can just move some buttons or sliders in the parameter space and the shape will change. This makes it really easy to get the shape just how you want it.

GeoCode is not currently accessible, but we brought it up today just to show you the great potential of 3D AI. For instance, "Point•E," a text-to-3D tool from OpenAI, which is currently in its early stages, is able to produce 3D point clouds directly from text descriptions. We can anticipate a rapid acceleration with 3D AI in the near term, maybe as early as q1 2023 with the potential release of GPT-4.

Watch the video of the 3d shape below 👇

4 Key AI concepts explained

Reading this newsletter, you may encounter a number of unfamiliar AI jargon / terminology.

So, why not explain them in clear, understandable definitions?

Do you recognize the difference between Deep Learning and Machine Learning?

No?

Keep reading....

1. Natural language processing (NLP): This is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. NLP has a wide range of applications, including language translation, text summarization, and sentiment analysis. NLP is like having a really smart robot that can understand what you're saying in your normal everyday language and respond in a way that makes sense.

2. Machine learning: This is a subset of artificial intelligence that involves using algorithms and statistical models to enable computers to learn from data without being explicitly programmed. Machine learning has been used in a variety of applications, including image and speech recognition, fraud detection, and recommendation systems. In basic terms, machine learning is like having a robot that can learn from its mistakes. At first, the robot might not be good at a specific task, but as it learns and develops it will get better and faster at preforming the task.

3. Deep learning: This is a type of machine learning that involves using neural networks to learn complex patterns in data. Deep learning has been used to achieve state-of-the-art results in a number of areas, including image and speech recognition, natural language processing, and autonomous vehicles.

4. Robotics: This is a field of artificial intelligence that focuses on the design and development of robots and other intelligent machines. Robotics technology has a wide range of applications, including manufacturing, healthcare, and search and rescue operations. In basic terms, robotics is the study of how to build and control robots. Robots can be used to do all sorts of things, like help doctors perform surgery, build cars in factories, or even go to the moon.

Pic of the day:

That's it from RuntheAI for today.

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