- "Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals."
The simulation of human intelligence by computer systems. Artificial Intelligence is an essential element of creating intelligent virtual agents.
Machine Learning: This involves the study of algorithms and statistical models that allow computer systems to improve their performance on a specific task based on experience.
Neural Networks: These are a type of machine learning algorithm that is modeled after the human brain, with multiple layers of interconnected nodes.
Natural Language Processing (NLP): This is a subfield of AI that deals with machines understanding, interpreting, and generating human language text and speech.
Robotics: This refers to the study of machines that can perceive and interact with the world around them, often with the goal of automating tasks that are dangerous or difficult for humans.
Computer Vision: This is the ability of computers to perceive, interpret, and understand visual information from the world around them, with applications ranging from automated surveillance to self-driving cars.
Data Analytics: This involves the use of statistical and computational techniques to extract insights from large and complex data sets, allowing businesses and researchers to make more informed decisions.
Deep Learning: This is a subset of machine learning that uses neural networks with multiple layers, allowing for more complex representations and better performance on certain tasks.
Cybersecurity: As AI becomes more prevalent in our daily lives, protecting against cyberattacks and ensuring the security of data is becoming increasingly important.
Ethics and Governance: As AI technology becomes more powerful and interconnected, it raises important ethical and governance questions around issues such as data privacy, algorithmic bias, and the role of humans in decision-making.
Augmented Reality: This involves overlaying digital information onto the real world, often using wearable devices such as smart glasses or headsets.
Virtual Assistants and Chatbots: These are computer programs that use natural language processing to understand and respond to user requests, typically in the form of text or voice-based interactions.
Cognitive Computing: This involves the use of AI systems that can reason and learn from data in ways that mimic human cognition, allowing for more complex decision-making and problem-solving.
Reinforcement Learning: This is a type of machine learning where an agent learns to interact with an environment in order to achieve a specific goal, receiving rewards or punishments based on its actions.
Expert Systems: These are computer systems that are designed to mimic the decision-making process of a human expert in a specific domain, often used to provide advice or support for complex tasks.
Ontologies and Knowledge Graphs: These are methods for organizing and representing knowledge in a way that is easy for machines to understand, often used in natural language processing and other AI applications.
Reactive Machines: These are the most basic form of AI that can only respond to specific pre-programmed or predefined instructions or scenarios. They cannot learn or adapt to new situations or data. Examples include Deep Blue (chess) and Alpha Go (Go).
Limited Memory Machines: As the name suggests, these AI systems can learn and operate based on prior experience or historical data, but they still can't think ahead or project beyond what they already know. They can react to new data to some extent but can't generalize beyond their memory.
Theory of Mind AI: These AI systems can recognize and understand human emotions and intentions by analyzing social cues, gestures, and verbal communication. They can also predict how people might act or react based on their mental states.
Self-Aware AI: This is the most advanced form of AI, which not only has cognitive abilities to process complex data and make decisions but can also recognize its own existence and learn from itself. It can also adapt and adjust its own model and behavior based on new data, feedback, or changing goals.
Machine Learning AI: This is a subset of AI that uses statistical techniques and algorithms to identify patterns in data and learn from them. Machine learning can be supervised, unsupervised or reinforcement. In supervised learning, the AI is given input and output data and learns to predict the output from various inputs. In the unsupervised method, the input data is not paired with output data and the AI detects patterns by itself. The third type: Reinforcement learning - is a method where the AI learns from responses from a contextual environment to repeated actions it takes.
Natural Language Processing (NLP): NLP is a type of AI that deals with human language, which can sometimes be ambiguous, complex, or context-dependent. NLP systems can help parse, translate, and generate human language, as well as recognize intent and sentiment behind words.
Computer Vision: Computer vision can help machines accurately "see" and interpret visual data like images and videos, as humans do. This AI subfield applies deep learning techniques to help machines perceive and recognize objects, faces, and scenes, and to extract useful information from visual data.
Robotics/Intelligent Agents: These are special applications of AI that deal with physical interactions and movements in the real world. Intelligent agents can act autonomously or semi-autonomously in complex systems or environments, like drones, self-driving cars, and industrial robots.
- "It is also the field of study in computer science that develops and studies intelligent machines."
- "AI technology is widely used throughout industry, government, and science."
- "Some high-profile applications are: advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), and competing at the highest level in strategic games (such as chess and Go)."
- "Artificial intelligence was founded as an academic discipline in 1956."
- "After 2012, when deep learning surpassed all previous AI techniques, there was a vast increase in funding and interest."
- "The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics."
- "General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals."
- "AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics."
- "AI also draws upon psychology, linguistics, philosophy, neuroscience, and many other fields."
- "Understanding human speech (such as Siri and Alexa)."
- "When deep learning surpassed all previous AI techniques."
- "Generative or creative tools (ChatGPT and AI art)."
- "Self-driving cars (e.g., Waymo)."
- "Advanced web search engines (e.g., Google Search)."
- "The various sub-fields of AI research are centered around particular goals and the use of particular tools."
- "It is the intelligence of machines or software."
- "AI technology is widely used throughout industry, government, and science."
- "The field went through multiple cycles of optimism followed by disappointment and loss of funding."
- "AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics."