The field of Artificial Intelligence (AI) is rich with terminology that encapsulates its vastness and depth. Understanding these terms is essential for anyone looking to delve into AI, whether for professional or personal purposes. To equip you with AI terms, we have compiled this glossary of AI terms, covering more than 70 fundamental AI terminology. This glossary will build your knowledge base about AI, leading to a meaningful navigation into the AI world.
What’s Unique About This Post?
The human mind often grasps concepts more quickly when provided with examples. Therefore, this AI glossary not only introduces important AI terms but also gives real-life examples of each phenomenon for better understanding.
Artificial Intelligence
Artificial Intelligence (AI) is a branch of computer science focused on creating machines capable of performing tasks that typically require human intelligence. These tasks include learning, problem-solving, recognizing speech, making decisions, and more.
Example
A common example of Artificial Intelligence is virtual assistants like Siri and Alexa. These tools use AI to understand and respond to user queries.
Algorithm
An algorithm is a set of instructions or rules designed to perform a specific task or solve a problem. Algorithms are the building blocks of all computer programs and AI systems, providing a step-by-step procedure for calculations, data processing, and automated reasoning.
Example
A real-life example of an algorithm is the route optimization used by GPS navigation systems. When you enter a destination, the GPS uses an algorithm to calculate the fastest or shortest route from your current location to your destination. It considers various factors like road distance, traffic conditions, and speed limits to provide you with step-by-step directions.
AI Ethics
AI ethics encompasses the moral principles and practices for the development and use of artificial intelligence technologies. It addresses concerns like fairness, privacy, accountability, and transparency.
Example
Amazon once used an AI system to screen job applicants’ resumes. However, it was discovered that the AI was biased against women for technical roles. It was because the system learned from historical hiring data that favored men. This incident highlights the ethical need for AI systems to be designed and monitored to prevent perpetuating existing biases.
AI Safety
AI safety focuses on making sure AI systems act in ways that are safe and beneficial, especially in new or unexpected situations. It involves designing, building, and deploying AI with care to avoid negative outcomes.
Example
An example of AI safety is the development of self-driving cars, where safety measures are critical to prevent accidents. Researchers work on algorithms that help these cars make safe decisions, even in complex traffic conditions or unpredictable roads.
API
An API (Application Programming Interface) is a set of protocols and tools for building software applications. It specifies how software components should interact, allowing different programs to communicate with each other.
Example
A real-life example of an API in action is when you book a flight through a travel service like Kayak. These services use airline APIs to gather flight information from various airlines. When you search for flights, the travel service sends a request through the airline’s API, which then returns the available flight options.
Adapter
An adapter in AI is a technique that integrates additional layers into an existing pre-trained model to tailor it for new tasks without altering the original model. This approach allows for the efficient adaptation of models to different applications while conserving resources.
Example
For instance, a language model trained in English might use an adapter to extend its capabilities to Spanish translation. This adaptation will enable it to perform the new task effectively without the need for training from scratch on Spanish data.
Agent
An intelligent agent refers to an AI system capable of autonomously perceiving its environment and taking actions to achieve specific goals without needing continuous human guidance. These agents can operate in various environments, whether digital or physical, adapting their actions based on the information they gather.
Example
A practical example is a self-driving car. It independently navigates roads, adjusts to traffic conditions, and makes decisions like stopping at red lights or avoiding obstacles while working towards its primary objective of safely transporting passengers to their destination.
Artificial General Intelligence
Artificial General Intelligence (AGI) represents the goal of creating machines that can understand, learn, and apply knowledge across a broad range of tasks at a level of competence comparable to that of a human. Unlike specialized AI, which excels in particular domains, AGI would possess the versatility and reasoning capability to perform any intellectual task a human can.
Example
There are no real-world examples of artificial general intelligence (AGI) as it’s a theoretical concept, and hasn’t been achieved yet. However, envisioning a hypothetical example might help. Imagine a future AI system functioning as a medical doctor. This AGI could diagnose diseases, recommend treatments, perform surgeries, conduct psychological evaluations, and even engage in medical research. Also, it could do all these tasks with the understanding, adaptability, and skill level of a human professional.
Automation
Automation refers to the use of AI technology to perform tasks without human intervention. AI systems are developed in a way that enables them to perform a specific task automatically.
Example
A common example of automation is the use of robotic assembly lines in manufacturing. These robots can assemble parts, perform quality checks, and package products much faster and more consistently than humans.
Backward Chaining
Backward chaining is a reasoning method where a system begins with a goal (or desired output) and works backwards to figure out the steps or conditions needed to achieve that goal. It’s commonly used in expert systems and diagnostic applications.
Example
In a smart home system, backward chaining can be used for energy-saving purposes. The system’s goal might be to reduce energy consumption. It starts with this end objective and works backwards to identify which devices are currently consuming power and whether they are necessary at that moment. For example, if the goal is to conserve energy at night, the system might check if lights are on in unoccupied rooms and then send commands to turn them off.
Bias
AI bias happens when an AI system produces prejudiced outcomes due to biased code or data it was trained on. This bias in AI can significantly impact decision-making processes across various applications.
Example
For instance, if a job application filtering AI is trained mostly on resumes from male applicants, it may unjustly favor male candidates over female candidates, as in the above Amazon example.
Big Data
Big Data refers to extremely large data sets that are almost impossible to be analyzed by humans or traditional data processing approaches. These datasets are usually processed by AI tools to reveal patterns, trends, and associations.
Example
An example of Big Data is the data collected for weather forecasting. Meteorological organizations collect vast amounts of data from satellites, weather stations, and radar about temperature, humidity, wind speeds, and more. This data is then analyzed to predict weather conditions, helping to prepare for natural disasters and plan daily activities.
Black Box AI
Black Box AI refers to AI systems where the decision-making process is not transparent, making it difficult to understand how the AI arrived at a particular conclusion or output.
Example
An example is certain types of deep learning models used in financial decision-making. These models can analyze vast amounts of financial data to decide whether to invest in a particular product or project. However, these models may not provide clear reasons for their decisions, raising concerns about fairness and accountability.
Chatbot
A chatbot is an AI program designed to simulate conversation with human users, especially over the internet. It interprets and processes the user’s words or phrases and responds with pre-set answers or information drawn from the web.
Example
Customer service chatbots on websites assist visitors by answering FAQs or guiding them through services. Another standard example of chatbots is ChatGPT, which answers users’ queries.
ChatGPT
ChatGPT is an advanced AI chatbot developed by OpenAI to generate human-like text based on user inputs. It’s capable of understanding and generating language in a way that mimics human conversation, making it useful for a variety of applications like customer service, content creation, and more.
Example
For example, companies use ChatGPT to power chatbots that provide instant and interactive customer support on their websites.
Cognitive Computing
Cognitive computing, or another name for AI, refers to systems that mimic human cognitive abilities, aiming to create automated models that can solve problems without human intervention. These systems leverage AI and machine learning to process data, learn from it, and make decisions or recommendations.
Example
An example of Cognitive Computing is healthcare diagnostics, where systems analyze vast amounts of patient data, medical literature, and clinical guidelines. They then assist doctors in diagnosing diseases more accurately and suggesting personalized treatment plans.
Computational Learning Theory
Computational Learning Theory is a subfield of AI that studies the design and analysis of machine learning algorithms. It primarily focuses on the mathematical aspects of learning from and making predictions about data. It seeks to understand the principles behind learning algorithms and their capabilities and limitations.
Example
In e-commerce, computational learning theory principles can be used to make recommendations. For instance, Amazon uses algorithms to analyze customer purchase history, browsing patterns, and product ratings. This data allows the system to predict and recommend products that a customer is likely to buy.
Computer Vision
Computer Vision is a field of AI that enables machines to interpret and make decisions based on visual data from the real world. Just like human vision, it involves processing and analyzing images or videos to identify objects, faces, scenes, and activities.
Example
Self-driving cars use computer vision to navigate roads safely. They process real-time visual data to recognize traffic lights, pedestrians, other vehicles, and road signs.
Copilot
Copilot, by Microsoft, is a suite of AI-assisted workplace products designed to improve productivity in various tasks. These tools leverage artificial intelligence to offer suggestions, automate routine tasks, and facilitate decision-making processes.
Example
In document editing, Copilot might suggest better phrasing or help draft emails quickly based on the user’s brief input.
Corpus
A corpus is a large collection of texts or spoken words gathered for the purpose of studying linguistic structures, frequencies, and patterns. It’s extensively used in natural language processing (NLP) and linguistics research to build and train AI models.
Example
The British National Corpus is a well-known corpus containing 100 million words of contemporary English text from a wide range of sources. It has been designed to represent a broad cross-section of British English from the late 20th century.
Cut-off Date
The cut-off date refers to the latest point in time at which an AI model has been updated with information. Data or events occurring after this date aren’t included in the model’s knowledge base.
Example
For instance, GPT-3.5’s cut-off date is September 2021, so it is unaware of developments, news, or information that emerged after that date.
DALL-E
DALL-E is an AI tool developed by OpenAI that generates images from textual descriptions. It combines the capabilities of language understanding and image generation to produce images from text prompts.
Example
If you ask DALL-E for “a crow sitting at a berry tree in warming sunshine,” it can create an image closely matching that description.
Data Mining
Data mining involves analyzing large datasets to discover patterns and relationships for solving problems or making informed decisions.
Example
In the retail industry, businesses analyze customer purchase histories and behavior data to identify trends. This information helps personalize marketing efforts and optimize inventory levels by understanding preferences.
Data Science
Data science is the field that combines statistical methods, data analysis, and machine learning techniques to analyze actual phenomena. It involves collecting, cleaning, and modelling data to make predictions and solve problems.
Example
In the healthcare sector, data science is used to predict disease outbreaks by analyzing patterns in healthcare data. Systems analyze data, such as hospital admissions and test report results, to use resources more effectively and save lives.
Data Validation
Data validation is the crucial process of ensuring that data is accurate before it is used for AI model development and training. This step helps prevent errors and biases in AI outputs.
Example
During financial modelling, transaction data is verified for accuracy and consistency before being used to predict stock market trends.
Deep Learning
Deep learning is a subset of machine learning that uses algorithms inspired by the structure and function of the brain’s neural networks. By processing data through these complex networks, deep learning models aim to imitate how the human mind acquires knowledge.
Example
Voice assistants like Siri or Google Assistant use deep learning to understand and process natural language queries. Deep learning allows them to comprehend and respond to user requests with high accuracy.
Deepfake
Deepfake technology uses AI to create realistic videos and audio recordings where people appear to say or do things they never did.
Example
A real-life example of Deepfake technology was when a video circulated online showing the Belgian Prime Minister making a speech about the COVID-19 pandemic. In that video, he appeared to link it to environmental damage, which never actually happened. This video was created using Deepfake technology to realistically alter his appearance and voice.
Emergent Behavior
Emergent behavior in large language models like the GPT series manifests as unexpected abilities not explicitly programmed.
Example
For instance, a model trained on diverse data can start generating original content such as poetry, music, or stories, showcasing creative capacities.
Ensemble Averaging
Ensemble averaging in machine learning involves combining predictions from multiple models to improve accuracy.
Example
In weather prediction, instead of relying on a single forecast model, predictions from several models are aggregated. Each model might analyze different aspects of weather data, thus delivering more accurate insights.
Error-Driven Learning
Error-driven learning is a branch of machine learning that focuses on minimizing errors through feedback.
Example
Error-driven learning can be seen in spell checkers within word processing programs. These tools learn from user corrections over time. When a user consistently corrects a specific “mistake,” the system adapts, recognizing the user’s preference. This way, it minimizes the error feedback by adjusting to the user’s unique writing style or commonly used vocabulary.
Forward Chaining
Forward chaining is a reasoning process that starts from known facts and applies inference rules to extract more data until a goal is reached. It’s commonly used in rule-based expert systems.
Example
In agriculture, forward chaining is used in precision farming systems to optimize crop health and yield. Starting with data on soil conditions, weather, and crop status, the system applies agricultural rules to recommend actions like watering, fertilizing, or pest control.
GAN
A Generative Adversarial Network (GAN) involves two neural networks: a generator that creates data and a discriminator that evaluates it. Through competition, the generator learns to produce more accurate and realistic outputs.
Example
A common example involves AI creating realistic human faces that don’t exist in reality. The generator creates new images while the discriminator evaluates them against real images, guiding the generator to improve. Over time, the generator learns to produce highly realistic faces, indistinguishable from real human photos.
GPT
Generative Pre-Trained Transformer, or GPT, is a type of artificial intelligence that excels in understanding and generating human-like text. It’s pre-trained on a diverse range of internet text, allowing it to respond to prompts with informative text.
Example
ChatGPT is a standard example of GPT. It can write essays, create poetry, summarize articles, and even generate code snippets based on user instructions.
Generative AI
Generative AI refers to the subset of AI technologies capable of creating new content, whether it’s text, images, or even music. It operates on the patterns it has learned from its training data.
Example
A common example of generative AI is DALL-E, a model developed by OpenAI that generates images from textual descriptions.
Hallucination
An AI hallucination occurs when an AI generates information that seems credible but is actually false or non-existent.
Example
An instance of hallucination is the use of AI for news generation. An AI programmed to write news articles might “hallucinate” by creating events that never occurred, quoting non-existent sources, or creating realistic-looking links that lead to nowhere.
Hyperparameter
Hyperparameters are settings or configurations that determine the structure and learning process of an AI model. Unlike parameters, which the model learns during training, hyperparameters are set before training begins.
Example
In a neural network, the learning rate is a hyperparameter that controls how much the model adjusts its weights with respect to the loss gradient. Setting it too high or too low can affect the model’s ability to converge to a solution efficiently.
Incremental Learning
Incremental learning is a machine learning strategy where a model is continually updated with new data over time rather than being trained once with a fixed dataset. This approach is useful for applications where data is continually generated or too large to process at once.
Example
Email spam filters use incremental learning to adapt to new types of spam. As users flag emails as spam or not spam, the filter updates its understanding of what constitutes spam.
Internet of Things
The Internet of Things (IoT) refers to a network of interconnected devices that communicate and exchange data with each other. These can range from everyday household items like thermostats and refrigerators to sophisticated industrial equipment.
Example
An example of IoT in action is a smart home system, where devices such as lights, heating, and security cameras are connected to the internet and can be controlled remotely via smartphones.
Large Language Model
Large Language Models (LLMs) like GPT-3 are advanced AI tools capable of understanding and generating text. They’re trained on vast amounts of text data, allowing them to perform tasks such as writing articles, answering questions, or creating code.
Example
For example, when GPT-3 is provided with a prompt like “Write a story about a lost dog,” it can produce a detailed narrative by leveraging its extensive training.
Machine Learning
Machine learning is a subset of AI that enables computers to learn from and make predictions or decisions based on data rather than following strictly programmed instructions.
Example
An example of machine learning in real life is recommendation systems like those used by Netflix or Amazon Prime. These systems analyze your past behavior, compare it with data from other users, and suggest movies or products you might like.
Model
A Model is the outcome after a system has been trained on a set of data, allowing it to make predictions or decisions based on what it has learned.
Example
A health monitoring app uses a model to predict potential health risks based on users’ activity levels, diet, and health history.
Natural Language Generation
Natural Language Generation (NLG) is a branch of AI that focuses on transforming structured data into natural language. It allows systems to write text in a way that is similar to how a human would.
Example
A common example of NLG in action is automated report generation, where financial data is converted into readable reports. Instead of analysts writing quarterly earnings reports manually, an NLG system can automatically generate a summary of the entire report.
Natural Language Processing
Natural Language Processing (NLP) combines computer science and linguistics to enable machines to understand human language. NLP techniques are behind the ability of computers to translate text from one language to another, respond to voice commands, and even generate text that reads like a human-written one.
Example
AI-powered language translators like DeepL and Bing Translator use NLP to translate text from one language to another.
Neural Network
A neural network is a computational model inspired by the human brain’s network of neurons. It’s designed to recognize patterns and solve problems by processing data through layers of interconnected nodes or “neurons.” Each connection can transmit a signal from one artificial neuron to another, with the signal processed at each node based on the strength of its connections.
Example
Facial recognition systems use neural networks to analyze and learn from millions of images, enabling them to identify specific faces among vast datasets with high accuracy.
No-Code AI
No-code AI technology allows individuals to create AI solutions without coding skills. These platforms offer pre-built templates and drag-and-drop elements for designing applications, ranging from chatbots to data analysis tools.
Example
Platforms like Obviously.ai and GoogleAutoML allow users to develop custom AI models without needing to write a single line of code.
OpenAI
OpenAI is an American research organization focusing on developing artificial intelligence (AI) in a safe and beneficial way. It aims to ensure that advancements in AI are aligned with human values and can be widely shared.
Example
An example of OpenAI’s work is the development of GPT (Generative Pre-Trained Transformer) models, such as GPT-3 and GPT-4.
Overfitting
Overfitting in machine learning occurs when a model learns the details and noise in the training data to the extent that it negatively impacts its performance on new data. This means the model has become too tailored to the specific examples it was trained on, reducing its ability to generalize to unseen data.
Example
In predicting stock prices, an overfitted model might perform exceptionally well on historical data on which it was trained. However, it fails to predict future prices accurately because it has learned the noise as patterns.
Pattern Recognition
Pattern recognition in AI involves teaching systems to identify patterns and regularities within data, allowing them to categorize or label data automatically.
Example
Facial recognition technology analyzes features from images or video feeds, like the distances between facial features, to identify and verify individual faces.
Prompt
In AI, a “prompt” is an input a user gives to a model to generate a specific output or perform a task.
Example
In a model like GPT-3, a prompt could be a question or a sentence starter. If you input “What is the capital of France?”, the model generates “Paris” as the output.
Prompt Engineering
Prompt Engineering is crafting and tweaking the input given to AI models to elicit the best possible response for a specific purpose. This involves strategically phrasing prompts to guide the AI’s output direction.
Example
If you’re using a language model to generate marketing copy, instead of a general prompt like “write a product description,” a refined prompt might include the product’s name, key features, target audience, and tone of voice.
Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are a type of AI designed for processing sequences of data, like time series or sentences, by retaining information from previous inputs through internal memory.
Example
In language translation services, understanding the context of a sentence requires knowledge of the previous words. RNNs excel in such tasks by analyzing the sentence sequentially, maintaining a running understanding of the context to provide accurate translations.
Reinforcement Learning
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve some goals. The agent receives rewards or penalties based on its actions. It then uses this feedback to learn over time which actions yield the most positive outcomes.
Example
A common example is a video game where the AI learns to navigate levels, avoid dangers, and achieve objectives to maximize its score.
Robotics
Robotics combines engineering and AI to develop robots capable of performing tasks autonomously or with minimal human intervention.
Example
In manufacturing plants, robots assemble parts, handle materials, and perform quality control, significantly increasing efficiency and safety.
Rule-Based System
A rule-based system operates on a set of if-then conditions to make decisions or take actions.
Example
For instance, in email spam filtering, if an email contains specific trigger words or comes from a known spam domain (condition), then the system marks the email as spam (action).
Search Algorithm
A search algorithm is a method used to find specific data within a collection. It systematically explores a dataset to locate the desired information or determine its absence.
Example
If you’re looking for a specific contact in an alphabetically organized phone app, the system might use binary search to quickly locate the contact’s information by comparing your search query against the dataset’s midpoint until the contact is found.
Sentiment Analysis
Sentiment analysis is a technique used in natural language processing to determine the emotional tone behind a body of text. This is useful for understanding the attitudes, opinions, and emotions expressed in written language.
Example
Companies use sentiment analysis to gauge customer feedback on social media, identifying whether comments are positive, negative, or neutral.
Situated Approach
The situated approach in AI emphasizes the importance of an agent’s interaction with its environment. Instead of focusing solely on abstract reasoning or problem-solving abilities, this approach prioritizes basic perceptual and motor skills necessary for effective interaction in a given context.
Example
In robotics, a situated approach might involve developing a robot that can navigate its environment by sensing obstacles and adjusting its movements accordingly without needing complex planning algorithms.
Speech Recognition
Speech recognition is the process of converting spoken words into text or commands that a computer system can understand. It involves analyzing audio input to identify the words being spoken and then executing specific actions based on the recognized speech.
Example
Virtual assistants like Siri, Google Assistant, and Amazon Alexa use speech recognition technology to understand and respond to user commands spoken aloud.
Supervised Learning
Supervised learning is a type of machine learning where the model is trained on a labelled dataset, meaning the input data is paired with the correct output. During training, the model learns to map input data to the correct output based on these labelled examples.
Example
In a supervised learning task of image classification, the model is trained on a dataset of images where each image is labelled with a corresponding category (e.g., cat, dog, bird). The model learns to classify new images into these categories based on the patterns it observes in the training data.
Technological Singularity
The technological singularity is a hypothetical future scenario in which artificial intelligence surpasses human intelligence, leading to exponential technological growth. At this point, AI systems could improve themselves recursively, leading to a rapid increase in intelligence far beyond human capacity.
Example
The world has not achieved this state so far. However, for understanding, imagine AI surpassing human capabilities in areas such as problem-solving, creativity, and even emotional intelligence. This could lead to breakthroughs in science, medicine, technology, and even humanity, fundamentally altering the fabric of society.
Text-to-Image Generation
Text-to-image generation is a fascinating area of artificial intelligence that involves creating images based on text prompts. It utilizes deep learning models to translate textual input into corresponding visual representations.
Example
Imagine providing a textual description like “a red apple on a wooden table” to a text-to-image generation model. The model would then generate an image that accurately depicts the described scene.
Text-to-Speech
Text-to-speech (TTS) technology converts written text into spoken words. It involves processing textual input using natural language processing and speech synthesis techniques to generate human-like speech output.
Example
Consider a smartphone virtual assistant reading out a text message aloud. When you receive a message, the text-to-speech system analyzes the text and converts it into spoken words, allowing you to listen to the message without reading it.
Text-to-Video
Text-to-video is a technology that converts textual input into video content. It involves algorithms that generate visual sequences based on written descriptions or scripts.
Example
A text-to-video system could create an animated scene from a written story or script, adding movement, characters, and backgrounds to bring the text to life.
Token
In the context of natural language processing, a token refers to a single, meaningful unit of text, typically a word or punctuation mark.
Example
Consider the sentence “Hello, how are you?” the tokens in this sentence are:
“Hello”
“,”
“how”
“are”
“you”
“?”
Tokenization
Tokenization is a process of breaking down a piece of text into smaller units called tokens. It is a fundamental step in natural language processing (NLP) and allows computers to process and analyze text data more effectively.
Example
The tokenization of the phrase “Hello, dude!” will take place as
“Hello”
“,”
“dude”
“!”
Transfer Learning
Transfer learning is a machine learning method where a model developed for a particular task is reused as the starting point for a model on a second task. It involves taking a pre-trained model and fine-tuning it on a new dataset or task.
Example
Suppose a deep learning model has been trained to classify images of animals, achieving high accuracy. Now, instead of training a new model from scratch to classify cars, transfer learning can be applied. The pre-trained animal classification model can be fine-tuned using a dataset of car images, allowing the model to quickly learn features relevant to car classification while retaining the general knowledge about image features gained from the animal classification task.
Turing Test
Proposed by Alan Turing in 1950, the Turing test is used to evaluate a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human.
Example
Imagine a scenario where a human judge communicates via text with both a human and a chatbot without knowing which is which. If the judge is unable to consistently identify which responses are from the chatbot and which are from the human, then the chatbot can be said to have passed the Turing test.
Unstructured Data
Unstructured data refers to data that lacks a predefined data model or structure, making it more challenging to analyze using traditional methods. Unstructured data comes in various formats, such as text, images, videos, and social media posts.
Exammple
Social media posts, emails, and customer reviews are common examples of unstructured data. These sources contain valuable information but are not organized in a predefined manner. Analyzing unstructured data often requires advanced techniques such as natural language processing (NLP) or computer vision to extract insights and patterns.
Unsupervised Learning
Unsupervised learning is a machine learning paradigm where the algorithm learns patterns and structures from input data without explicit supervision or labelled examples. Unlike supervised learning, there are no predefined target labels for the algorithm to predict.
Example
Imagine a dataset containing various types of fruits without any labels indicating their names or categories. In unsupervised learning, the algorithm analyzes the similarities and differences between the fruits based on features like size, color, and texture. Through clustering algorithms, the algorithm groups similar fruits together without prior knowledge of their names or categories.
Weak AI
Weak AI, or narrow AI, refers to artificial intelligence systems designed and trained for specific tasks or domains. They often lack general cognitive abilities. Unlike strong AI, which aims to mimic human intelligence across a wide range of tasks, weak AI is tailored to excel within a limited scope of functions.
Example
A virtual assistant like Amazon’s Alexa or Apple’s Siri is a classic example of weak AI. These assistants are proficient in tasks like answering questions, setting reminders, or playing music, but they lack the understanding and reasoning capabilities of human intelligence.
Zero-Shot Learning
Zero-shot learning is a machine learning paradigm where models are tasked with making predictions on classes or categories that they have never seen during training. Unlike traditional supervised learning, where models are trained on labelled data, zero-shot learning requires the model to generalize to unseen classes based on related knowledge or attributes.
Example
Consider a machine learning model trained to classify various animals. During training, the model learns to recognize dogs, cats, and birds. However, it has never encountered an image of a lion. In zero-shot learning, the model can still correctly identify a lion as a type of cat based on its learned understanding that lions share similar characteristics with other felines.
Summing Up the Discussion
In conclusion, understanding the fundamentals of AI through this glossary equips you with the necessary knowledge. These terms serve as building blocks, enabling individuals to engage meaningfully in discussions, comprehend AI-related content, and embark confidently on their learning journey.
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