Introduction to AI Systems

The process of gathering data from various sources such as sensors, databases, or user interactions.

Cleaning, organizing, and normalizing data to ensure consistency and accuracy.

AI learns from labeled data where inputs and corresponding outputs are provided (e.g., teaching an AI to recognize cats).

AI analyzes unlabeled data to find patterns or groupings (e.g., identifying customer segments).

AI learns through interacting with an environment and receiving rewards or penalties based on its actions (e.g., a robot navigating a maze).

The phase where the AI processes data through mathematical models to adjust its internal parameters.

Inspired by the human brain, these networks consist of layers of interconnected nodes (neurons) that process information.

A technique used to fine-tune the model by minimizing errors through iterative adjustments.

A subset of Machine Learning that uses neural networks to identify complex patterns in data. Think of it like a powerful brain that can learn from experience.

The ability of machines to understand and generate human language. It's how computers can read, write, and speak like us.

A field of AI that focuses on processing visual data like images and videos. It's how machines 'see' the world.

Combines AI with physical systems to perform tasks in the real world. Think of robots working in factories, or self-driving cars.

A standard way for AI models to communicate with applications over the internet. Think of it as a common language for sharing AI information.

A fast and efficient way for AI systems to communicate with each other. Think of it as a speedier way for AI to talk.

Used for real-time data streaming between devices and AI models. Think of it as a live feed for AI.

A lightweight way to transmit structured data between AI services. Think of it as a simple way to share information between AI systems.

A standard format for exchanging trained deep learning models between various frameworks like TensorFlow, PyTorch, and MXNet.

A protocol for sharing trained machine learning models across platforms, ensuring compatibility between different systems.

A framework for developing robotics applications, including AI-powered perception and control.

A lightweight protocol for AI-enabled IoT devices, enabling communication in resource-constrained environments.

Protocols for deploying and serving trained machine learning models efficiently within the TensorFlow ecosystem.

Standards for AI ethics, safety, and risk management, aiming to ensure systems are fair, transparent, and secure.

A protocol for distributed training of AI models across multiple GPUs and nodes, facilitating parallel training.

Protocols allowing AI models to perform computations on encrypted data without decrypting it, safeguarding privacy.

AI systems are designed to simulate human creativity, but lack true emotional understanding and are limited by their programmed parameters. This can lead to AI-generated content that lacks the depth and authenticity of human storytelling.

Misuse or exposure of sensitive data by AI systems raises ethical and security concerns. This can involve data breaches or the misuse of personal information in surveillance.

Overreliance on AI can reduce self-reliance and critical thinking skills. For example, depending on GPS navigation may lead to a decrease in spatial awareness.

Ethical concerns arise from the use of AI in applications like autonomous weapons, surveillance, and decision-making systems. For instance, ethical questions emerge regarding the decisions of self-driving cars in accident scenarios.

The complex nature of AI systems can lead to unpredictable outcomes due to their learning processes. This may result in AI models producing misleading or harmful content.

Training and deploying AI models require significant computational resources, contributing to environmental concerns. Energy-intensive data centers for training deep learning models highlight the energy consumption associated with AI.

AI is used in healthcare for disease diagnosis, medical image analysis, and personalized treatment plans. Examples include IBM Watson for oncology and AI-powered radiology tools for detecting anomalies.

AI plays a significant role in finance, assisting with fraud detection, algorithmic trading, credit scoring, and customer support. Examples include fraud detection systems and robo-advisors for investment management.

DTN networks are designed to operate in environments where traditional communication channels are unreliable or disconnected.

A type of routing strategy for DTNs where data is spread across multiple paths to increase delivery chances. Like a viral message, the data is shared among connecting nodes.

A routing approach for DTNs that prioritizes data delivery based on the expected delay in reaching the destination. It predicts the best path and transmits data accordingly.

DTNs can provide connectivity even where traditional communication channels are unreliable. They are useful for scenarios like remote areas with limited infrastructure.

DTN routing protocols must consider factors like node mobility, network connectivity, and packet lifetime. This ensures an efficient and reliable data delivery.

The Turing Test, proposed by Alan Turing in 1950, aims to determine if a machine can exhibit intelligent behavior indistinguishable from a human. It involves a conversation between a human and an unknown entity, with the goal of determining if the entity is a machine or another human. The test is used as a benchmark for AI systems, evaluating their ability to mimic human-like interactions.

The Dartmouth Conference in 1956 is widely recognized as the birthplace of Artificial Intelligence. It brought together pioneers like John McCarthy, Marvin Minsky, Claude Shannon, and others, who formally defined AI as the study of creating machines capable of "thinking" and problem-solving like humans. This meeting set the stage for the formal development of AI as a distinct field of study.

Hebbian learning, introduced by Donald Hebb in 1949, is a biological learning principle, used in neural networks. This principle states that when two neurons fire together, they become more closely associated. In other words, if neurons are frequently activated concurrently, their connection strengthens, playing a crucial role in learning and memory formation within neural networks.

Perceptrons, introduced by Frank Rosenblatt in 1958, are the foundation of early neural networks. These simple, binary classification models consist of a single layer of neurons, each processing inputs with a fixed activation function. They are capable of learning basic classification patterns by adjusting their weights based on training data. While limited in complexity, perceptrons paved the way for more sophisticated neural network architectures.

Backpropagation, a key technique in deep learning, allows neural networks to learn more efficiently. It works by iteratively adjusting the weights within the network based on the error between the predicted output and the desired output. This adjustment process, like fine-tuning instruments, helps to optimize the network's performance by minimizing errors and improving accuracy.

The innovation of the transformer architecture in 2017 revolutionized Natural Language Processing (NLP). Transformers allow for parallel processing of text data, significantly enhancing performance in tasks like translation and summarization. This breakthrough arises from their ability to capture long-range dependencies within text, enabling them to learn the relationships between words and phrases in a more comprehensive way.

The Generative Pre-trained Transformer (GPT) models, developed by OpenAI since 2018 are large language models trained on massive datasets. These models exhibit impressive capabilities in various NLP tasks, such as text generation, translation, and question answering, due to their extensive training on diverse text data. GPT models demonstrate the power of large-scale language models in generating human-like text and understanding natural language.

Bidirectional Encoder Representations from Transformers (BERT), developed by Google in 2018, is a transformer-based language model that achieved state-of-the-art results in NLP tasks. BERT's key innovation lies in its training method, which considers the context of words in both directions, resulting in a deeper understanding of language. This bidirectional approach allows BERT to capture the nuances of word meaning based on its surrounding words, making it a highly effective language model.