Artificial Intelligence.pptx power point
- 2. • What is artificial intelligence? • Artificial intelligence (AI) is the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing speech, making decisions, and identifying patterns. AI is an umbrella term that encompasses a wide variety of technologies, including machine learning, deep learning, and natural language processing (NLP).
- 3. • At the simplest level, machine learning uses algorithms trained on data sets to create machine learning models that allow computer systems to perform tasks like making song recommendations, identifying the fastest way to travel to a destination, or translating text from one language to another. Some of the most common examples of AI in use today include: • ChatGPT: Uses large language models (LLMs) to generate text in response to questions or comments posed to it. • Google Translate: Uses deep learning algorithms to translate text from one language to another. • Netflix: Uses machine learning algorithms to create personalized recommendation engines for users based on their previous viewing history. • Tesla: Uses computer vision to power self-driving features on their cars.
- 4. Types of artificial intelligence • Artificial intelligence can be organized in several ways, depending on stages of development or actions being performed. • For instance, four stages of AI development are commonly recognized. 1. Reactive machines: Limited AI that only reacts to different kinds of stimuli based on preprogrammed rules. Does not use memory and thus cannot learn with new data. IBM’s Deep Blue that beat chess champion Garry Kasparov in 1997 was an example of a reactive machine. 2. Limited memory: Most modern AI is considered to be limited memory. It can use memory to improve over time by being trained with new data, typically through an artificial neural network or other training model. Deep learning, a subset of machine learning, is considered limited memory artificial intelligence. 3. Theory of mind: Theory of mind AI does not currently exist, but research is ongoing into its possibilities. It describes AI that can emulate the human mind and has decision-making capabilities equal to that of a human, including recognizing and remembering emotions and reacting in social situations as a human would. 4. Self aware: A step above theory of mind AI, self- aware AI describes a mythical machine that is aware of its own existence and has the intellectual and emotional capabilities of a human. Like theory of mind AI, self-aware AI does not currently exist.
- 5. • What are AI Models? • AI programs primarily recognize patterns and provide results based on having previously reviewed examples of the patterns that deal with the topic (voice recognition, machine vision, etc.). AI models use neural network architectures to learn and produce results. AI models are complex mathematical and computational techniques to process vast amounts of data and extract meaningful insights. The term AI model encompasses a wide range of techniques and approaches used in artificial intelligence that include machine learning, deep learning , and neural networks. These models are trained on diverse dataset to learn from examples and derive patterns that enable them to perform specific tasks. • How Does AI Models Works? • AI models are like students who excel at finding patterns from information they are given. This information, the data, is the foundation of everything an AI model does. • There are two main types of data used in AI models: • Training data: This is the massive dataset the model is fed during the training process. It can include text, images, videos, numbers, or any other format relevant to the task the model is designed for. The quality and quantity of training data heavily influence the model's performance. • Input data: Once trained, the model is presented with new, unseen data. This data format should be similar to the training data. Based on the patterns learned during training, the model analyzes the input data and generates an output, such as a prediction or a decision.
- 6. • AI Model Training Process • The training process is where the AI model transforms from a blank slate into a pattern- recognition master. Here's a breakdown: 1.Data Preparation: The training data goes through a cleaning and pre-processing stage to ensure consistency and usability for the model. 2.Feeding the Model: The prepared data is fed into the AI model through a specific algorithm. Think of it like feeding problems and solutions to a student. 3.Pattern Recognition: The algorithm analyzes the data, searching for underlying patterns and relationships between different data points. Imagine the student noticing patterns in how to solve the problems. 4.Adjusting the Model: Based on the analysis, the model adjusts its internal parameters to better represent the discovered patterns. This is like the student refining their approach based on their understanding. 5.Iteration and Refinement: Steps 2-4 are repeated numerous times with different batches of training data. With each iteration, the model becomes more skilled at recognizing the patterns. This is similar to the student practicing and improving over time.
- 7. • Components of an AI Model • An AI model can be thought of as having three main components: • Algorithms: These are the mathematical formulas and rules that define the model's behavior and how it processes information. • Data: The training data provides the raw material for the model to learn from and build its predictive abilities. • Parameters: These are adjustable elements within the model that are fine-tuned during training to optimize its performance.
- 8. • Applications of AI Models • The applications of AI models are vast and ever-growing, impacting various aspects of our lives. Here are a few examples: • Image and video recognition: From unlocking your phone with your face to self-driving cars navigating the streets, AI models power image and video recognition. • Natural language processing (NLP): Powers chatbots that answer your questions, machine translation that breaks down language barriers, and sentiment analysis in social media. • Recommender systems: Whether it's suggesting movies you might enjoy or recommending products you might need, AI models power the personalized recommendations we encounter online and in stores. • Predictive maintenance: By analyzing sensor data, AI models can predict equipment failure in factories or power grids, preventing costly downtime. • Fraud detection: AI models can help identify suspicious financial transactions and protect against cybercrime by analyzing vast amounts of data in real-time.
- 9. • List of the Most Popular AI Models • List of the most popular AI Models are as follows: 1. Deep Neural Networks (DNNs) 2. Long Short-Term Memory (LSTM) 3. Generative Adversarial Networks (GANs) 4. Decision Trees 5. Support Vector Machines (SVMs) 6. K-Nearest Neighbors (KNN) 7. XGBoost