TECHNOLOGY IN AI

Technology in AI (Artificial Intelligence) :

Neural Networks: Show intricate layers of interconnected nodes or neurons, resembling the structure of a brain. These layers are connected with lines, symbolizing the flow of data through the model.

Machine Learning: Depict algorithms (in the form of code or graphs) that represent the training process. Show a feedback loop where data enters the model, predictions are made, and then corrections are applied to improve accuracy.

Natural Language Processing (NLP): Include a human interacting with a digital assistant or a robot with speech bubbles showing text being processed and translated into meaningful information.

Computer Vision: Illustrate a camera or eyes scanning the environment, recognizing objects like faces, cars, or buildings, with data points floating above each recognized object.

Robotics: Add a robot, either interacting with humans or moving autonomously through a space. It could be shown using data from sensors (such as images or environmental data) to make decisions in real-time.

Self-driving Cars: A self-driving car could be shown navigating through a city, with data from the vehicle’s sensors being processed to avoid obstacles and make decisions.

Data Flow: Show lines or light beams running through the entire structure, connecting the components, symbolizing the constant exchange of data, computations, and learning.

Generative AI: A screen generating new images or text from prompts, with a "creative spark" symbolizing the generation of content.

INFORMATION TO AI:

Data Input: AI systems, especially machine learning models, need data to learn and make decisions. This data can be in the form of text, images, numbers, or even sound, depending on the type of AI (e.g., natural language processing, computer vision, etc.).

Data Preprocessing: Before the data can be used by the AI, it often undergoes preprocessing. This can involve cleaning the data (removing errors, missing values), normalizing (scaling data), or transforming it into a format the AI can understand.

Training: In machine learning, the AI "learns" from this data by finding patterns or structures. For example, a neural network might adjust its parameters during training to minimize the difference between its predictions and the actual values.

Modeling: AI models are trained using various algorithms to make decisions or predictions. The model uses the information it has learned to make inferences when new data is presented.

Feedback & Refinement: Once the AI makes a prediction, it can be evaluated based on accuracy. Feedback is often provided, and adjustments are made to improve its performance, leading to more refined outputs.

https://youtu.be/dv9q7Ema40k?si=39gk29vIA-mVkCjf&t=1 in link to touch and watch the video.