Exploring Different Types of AI and How They Work
Link to Colab Notebook: https://colab.research.google.com/dri...
This video explores different types of AI architectures beyond Large Language Models (LLMs), emphasizing the importance of understanding the underlying mathematics.
Here's a breakdown of the key aspects:
Hyperdimensional Computing (HDC): This approach uses XOR functions across thousands of dimensions to classify or optimize tasks [03:16]. It encodes concepts into binary hypervectors and trains on XOR math [04:15]. HDC relies on the principles of sparse spaces and orthogonal binary functions [06:28].
Swarm Algorithms (specifically Particle Swarm Optimization - PSO): Inspired by the swarming behavior of birds or fish, PSO uses multiple agents to find optimal solutions in complex search spaces [10:29]. These agents have memory functions and update their positions based on personal and global bests [11:00].
Large Concept Models (LCM): Developed by Yan LeCun, LCMs utilize concept spaces and knowledge graphs to overcome the mathematical limitations of LLMs [14:08]. They build logical relationships and treat problems as environmental challenges for the model to solve [15:14].
The presenter highlights that each architecture has its strengths and weaknesses, and the best choice depends on the specific problem [17:45]. The video includes code implementations and examples to illustrate how each type of AI works [07:39], [12:30], [15:10].