READ & LEARN Master the AI Era: 53 Tech Terms You Should Know
AI is transforming the world and the language of technology is evolving with it. Discover 53 essential terms you need to understand and work confidently in the age of artificial intelligence.
Artificial Intelligence (AI) is currently one of the most powerful terms in the world of technology. Whether you open your computer, browse the web, or use your smartphone, the digital world is now filled with intelligent systems that summarize content, answer questions, and even create new forms of media on our behalf.
From ChatGPT, which has become an intelligent personal assistant, to Google’s Search Generative Experience (SGE), which transforms how search results appear by displaying AI-generated summaries at the top, artificial intelligence is now seamlessly and powerfully embedded into our daily lives.
Yet, all of this represents only the tip of the iceberg.
Generative AI: The Transformative Power Behind the Scenes Generative AI, the type of artificial intelligence that can create new content on its own, is currently the most rapidly advancing field within AI. It can generate images, compose music, write code, and even summarize business reports. According to estimates by the McKinsey Global Institute, this technology could contribute up to 4.4 trillion US dollars to the global economy annually.
As AI continues to develop, new technical terms are emerging daily. These are not confined to the tech industry but have begun to influence business, marketing, and creative industries as well. AI terminology is quickly becoming a new language that professionals must understand in order to collaborate effectively with intelligent systems.
If you have ever wondered what LLM stands for, why people are discussing prompt engineering, or what AI hallucination really means, there is no need to worry. True Digital Park has compiled a list of 53 essential AI terms, based on definitions from CNET, to help you stay informed. In a world where AI is a game-changing tool, understanding its vocabulary is the first step to gaining a competitive edge.
1.Artificial General Intelligence (AGI)
This refers to artificial intelligence that possesses general cognitive abilities similar to those of a human brain. AGI can think, analyze, translate languages, plan, and solve new problems independently, without requiring step-by-step instruction. It differs from traditional AI systems, which are usually designed to excel in a single specific domain.
2.Agentive
This describes AI systems that act autonomously to achieve goals without waiting for human commands. For example, autonomous vehicles can analyze road conditions, stop, or make turns without human intervention, relying entirely on built-in decision-making capabilities.
3.AI Ethics
This refers to the set of ethical principles that guide the design and application of AI. The goal is to ensure that AI systems are safe, fair, and socially responsible. Key concerns include privacy protection, non-discrimination, and the avoidance of harmful biases in automated decision-making.
4.AI safety
A concept focused on the long-term safety of artificial intelligence, especially in cases where systems may become too intelligent to be controlled. This involves forecasting potential risks AI may pose in the future and developing strategies to mitigate them in advance.
5.Algorithm
A set of rules or instructions that enables AI to process information and make decisions. For example, if you often watch romantic movies, the system learns from your behavior and automatically recommends similar films.
6.Alignment
The process of ensuring that AI systems understand and align with human intentions. For instance, if a user requests unbiased information, the AI should provide straightforward, impartial answers without introducing bias or misinformation.
7.Anthropomorphism
The human tendency to attribute human characteristics to AI, such as assuming a chatbot "understands" you. In reality, the AI responds based on programmed data and learned patterns without conscious awareness or emotions.
8.Artificial Intelligence (AI)
Technology that enables machines or computers to perform tasks that typically require human intelligence, such as data analysis, customer service interactions, or automated image editing.
9.Autonomous agents
AI systems capable of performing entire tasks independently from start to finish without human intervention. For example, delivery robots that plan routes, avoid obstacles, and complete deliveries on their own.
10.Bias
A distortion or lack of fairness in AI decision-making, often caused by biased training data. For example, if an AI model is trained mostly on images of male doctors, it may incorrectly associate the profession exclusively with men.
11.Chatbot
A computer program designed to communicate with humans through text. Common applications include answering questions on websites, assisting app users, or processing commands.
12.ChatGPT
An advanced chatbot developed by OpenAI, capable of responding to a wide range of questions and understanding natural language. It exemplifies the capabilities of large-scale language models.
13.Cognitive computing
An alternative term for AI that emphasizes simulating human thought processes, such as reasoning, memory, and learning, to help computers understand and make decisions more like humans.
14.Data augmentation
A method used to enhance training data diversity to improve AI learning. This can involve flipping images, changing colors, adding noise, or rotating visuals to help the model learn from varied representations.
15.Dataset
A collection of information used to train AI systems, including large volumes of images, text, or audio recordings. These datasets serve as the foundation for machine learning.
16.Deep learning
A branch of AI involving neural networks with multiple layers, designed to analyze complex data. This technique allows AI to perform tasks such as facial recognition or speech interpretation.
17.Diffusion
A technique that trains AI to generate images by starting with a noisy or distorted version and progressively refining it until it becomes a clear and realistic visual.
18.Emergent behavior
Unexpected capabilities that AI systems develop, even if not explicitly programmed. For example, the ability to tell jokes or translate rare languages without direct training.
19.End-to-end learning
A training approach where all steps of a task are handled within a single model, from data input to output generation. This enables comprehensive learning without dividing the process into separate stages.
20.Ethical consideration
Moral and social questions that must be addressed when designing or using AI systems, such as data ownership, safety risks, and potential societal impacts.
21.Foom
A term derived from "Fast Takeoff," referring to a hypothetical scenario where AI rapidly evolves to surpass human intelligence in a short period, raising concerns about loss of control.
22.Generative Adversarial Networks (GANs)
An AI training technique where two systems work in opposition: one generates synthetic content, while the other evaluates its authenticity. This process helps produce highly realistic outputs, such as images or audio.
23.Generative AI
Artificial intelligence that can independently create new content, including articles, images, music, or code, based on prior learning from existing data.
24.Google Gemini
An AI system developed by Google, comparable to ChatGPT, with the added advantage of integrating seamlessly with Google services such as Maps, Gmail, and Docs.
25.Guardrails
Predefined constraints built into AI systems to prevent misuse, such as restricting dangerous responses or avoiding the generation of harmful content.
26.Hallucination
A phenomenon where AI provides confident but incorrect information, often fabricating details without credible sources—a common issue in large language models.
27.Inference
The application of previously acquired knowledge by AI to new, unseen situations. For instance, after learning the rules of chess, the AI can play new matches without retraining.
28.Large Language Model (LLM)
A type of AI trained on massive amounts of text data, enabling it to understand language, engage in conversation, and answer questions effectively. Examples include ChatGPT, Claude, and Gemini.
29.Latency
This refers to the time delay required for an AI system to process a request and provide a response. For example, after a user inputs a question, they may need to wait two seconds for the answer to appear.
30.Machine Learning (ML)
A method that enables computers to learn from data without being explicitly programmed for every step. For instance, feeding the system numerous images of cats allows it to eventually recognize what a cat is.
31.Microsoft
Bing A search engine developed by Microsoft that incorporates AI to provide intelligent, summarized responses directly within the search interface, similar to ChatGPT.
32.Multimodal AI
AI systems that can process and integrate multiple types of information simultaneously, such as audio, images, and text, to gain a more comprehensive understanding of user input.
33.Natural Language Processing (NLP)
A field of AI focused on enabling computers to understand and use human language. Applications include reading news, summarizing articles, and engaging in conversations.
34.Neural Network
A structure that mimics the human brain, composed of multiple layers of artificial neurons, enabling the system to learn and analyze complex data more effectively.
35.Overfitting
A common issue where AI memorizes the training data too precisely, leading to poor performance on new, unseen data, as it lacks the ability to generalize.
36.Paperclips
A thought experiment illustrating the potential risks of poorly defined AI objectives. If an AI is told to maximize paperclip production, it might go to extreme lengths, even attempting world domination, to achieve its goal. This underscores the importance of setting goals responsibly.
37.Parameters
The internal values within an AI model that determine how it behaves, such as how polite it should respond or how it connects information. A greater number of parameters generally enhances the AI's capabilities.
38.Perplexity
An AI system that combines the functionality of a chatbot with a search engine, capable of retrieving information and citing reliable sources.
39.Prompt
A user-provided input or command that initiates a response from an AI system, such as "Create an Instagram caption."
40.Prompt Chaining
A technique that enables AI to maintain context across a series of prompts, allowing it to respond with continuity and logical coherence.
41.Quantization
A process used to reduce the size of an AI model, making it faster and more efficient by lowering the precision of certain data representations without significantly affecting performance.
42.Stochastic Parrot
A metaphor describing AI systems as being capable of mimicking human language without truly understanding it, simply generating plausible responses based on learned patterns.
43.Style Transfer
A technology that applies the artistic style of one image, such as a vintage or Van Gogh painting, to another image, like transforming a regular photo of a cat into a stylized artwork.
44.Synthetic Data
Artificially generated data created by AI, used to train other AI systems. For example, generating images of non-existent people to expand a training dataset.
45.Temperature
A setting that adjusts the creativity or randomness of an AI's output. Lower values yield more cautious and predictable responses, while higher values produce more creative or unexpected results.
46.Text-to-Image Generation
An AI capability that produces images based on textual descriptions. For instance, typing "an elephant flying on Mars" results in a visual depiction of that scenario.
47.Tokens
The basic units of information used in processing text, such as words or syllables. Longer prompts require more tokens.
48.Training Data
The collection of content used to train an AI model, including texts, images, and code, enabling it to learn and perform tasks effectively.
49.Transformer Model
A foundational technology behind Large Language Models (LLMs), which allows AI to understand sentence meaning by processing all input simultaneously, rather than word by word.
50.Turing Test
A method of evaluating AI intelligence, based on whether a person can distinguish between interacting with a human or an AI. If indistinguishable, the AI is considered to have passed the test.
51.Unsupervised Learning
A training method in which AI learns to identify patterns and structures in data without labeled answers, such as grouping customers based on purchasing behavior.
52.Weak AI / Narrow AI
AI systems that are designed to perform specific tasks and lack general intelligence. Examples include language translation tools and spam detection systems.
53. Zero-shot Learning
The ability of AI to solve tasks it was not explicitly trained on. For example, after learning about wolves, it can infer similarities with foxes without prior exposure.
True Digital Park is the largest center for technology and startups in Southeast Asia. It is a vibrant community for the next generation of innovators who are eager to learn, grow, and shape the future together. We believe that learning and understanding these terms is the first step to staying ahead in technology and unlocking genuine opportunities in the modern world.
Cite: https://www.cnet.com/tech/services-and-software/chatgpt-glossary-53-ai-terms-everyone-should-know/
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"One Roof, All Possibilities"
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From ChatGPT, which has become an intelligent personal assistant, to Google’s Search Generative Experience (SGE), which transforms how search results appear by displaying AI-generated summaries at the top, artificial intelligence is now seamlessly and powerfully embedded into our daily lives.
Yet, all of this represents only the tip of the iceberg.
Generative AI: The Transformative Power Behind the Scenes Generative AI, the type of artificial intelligence that can create new content on its own, is currently the most rapidly advancing field within AI. It can generate images, compose music, write code, and even summarize business reports. According to estimates by the McKinsey Global Institute, this technology could contribute up to 4.4 trillion US dollars to the global economy annually.
As AI continues to develop, new technical terms are emerging daily. These are not confined to the tech industry but have begun to influence business, marketing, and creative industries as well. AI terminology is quickly becoming a new language that professionals must understand in order to collaborate effectively with intelligent systems.
If you have ever wondered what LLM stands for, why people are discussing prompt engineering, or what AI hallucination really means, there is no need to worry. True Digital Park has compiled a list of 53 essential AI terms, based on definitions from CNET, to help you stay informed. In a world where AI is a game-changing tool, understanding its vocabulary is the first step to gaining a competitive edge.
1.Artificial General Intelligence (AGI)
This refers to artificial intelligence that possesses general cognitive abilities similar to those of a human brain. AGI can think, analyze, translate languages, plan, and solve new problems independently, without requiring step-by-step instruction. It differs from traditional AI systems, which are usually designed to excel in a single specific domain.
2.Agentive
This describes AI systems that act autonomously to achieve goals without waiting for human commands. For example, autonomous vehicles can analyze road conditions, stop, or make turns without human intervention, relying entirely on built-in decision-making capabilities.
3.AI Ethics
This refers to the set of ethical principles that guide the design and application of AI. The goal is to ensure that AI systems are safe, fair, and socially responsible. Key concerns include privacy protection, non-discrimination, and the avoidance of harmful biases in automated decision-making.
4.AI safety
A concept focused on the long-term safety of artificial intelligence, especially in cases where systems may become too intelligent to be controlled. This involves forecasting potential risks AI may pose in the future and developing strategies to mitigate them in advance.
5.Algorithm
A set of rules or instructions that enables AI to process information and make decisions. For example, if you often watch romantic movies, the system learns from your behavior and automatically recommends similar films.
6.Alignment
The process of ensuring that AI systems understand and align with human intentions. For instance, if a user requests unbiased information, the AI should provide straightforward, impartial answers without introducing bias or misinformation.
7.Anthropomorphism
The human tendency to attribute human characteristics to AI, such as assuming a chatbot "understands" you. In reality, the AI responds based on programmed data and learned patterns without conscious awareness or emotions.
8.Artificial Intelligence (AI)
Technology that enables machines or computers to perform tasks that typically require human intelligence, such as data analysis, customer service interactions, or automated image editing.
9.Autonomous agents
AI systems capable of performing entire tasks independently from start to finish without human intervention. For example, delivery robots that plan routes, avoid obstacles, and complete deliveries on their own.
10.Bias
A distortion or lack of fairness in AI decision-making, often caused by biased training data. For example, if an AI model is trained mostly on images of male doctors, it may incorrectly associate the profession exclusively with men.
11.Chatbot
A computer program designed to communicate with humans through text. Common applications include answering questions on websites, assisting app users, or processing commands.
12.ChatGPT
An advanced chatbot developed by OpenAI, capable of responding to a wide range of questions and understanding natural language. It exemplifies the capabilities of large-scale language models.
13.Cognitive computing
An alternative term for AI that emphasizes simulating human thought processes, such as reasoning, memory, and learning, to help computers understand and make decisions more like humans.
14.Data augmentation
A method used to enhance training data diversity to improve AI learning. This can involve flipping images, changing colors, adding noise, or rotating visuals to help the model learn from varied representations.
15.Dataset
A collection of information used to train AI systems, including large volumes of images, text, or audio recordings. These datasets serve as the foundation for machine learning.
16.Deep learning
A branch of AI involving neural networks with multiple layers, designed to analyze complex data. This technique allows AI to perform tasks such as facial recognition or speech interpretation.
17.Diffusion
A technique that trains AI to generate images by starting with a noisy or distorted version and progressively refining it until it becomes a clear and realistic visual.
18.Emergent behavior
Unexpected capabilities that AI systems develop, even if not explicitly programmed. For example, the ability to tell jokes or translate rare languages without direct training.
19.End-to-end learning
A training approach where all steps of a task are handled within a single model, from data input to output generation. This enables comprehensive learning without dividing the process into separate stages.
20.Ethical consideration
Moral and social questions that must be addressed when designing or using AI systems, such as data ownership, safety risks, and potential societal impacts.
21.Foom
A term derived from "Fast Takeoff," referring to a hypothetical scenario where AI rapidly evolves to surpass human intelligence in a short period, raising concerns about loss of control.
22.Generative Adversarial Networks (GANs)
An AI training technique where two systems work in opposition: one generates synthetic content, while the other evaluates its authenticity. This process helps produce highly realistic outputs, such as images or audio.
23.Generative AI
Artificial intelligence that can independently create new content, including articles, images, music, or code, based on prior learning from existing data.
24.Google Gemini
An AI system developed by Google, comparable to ChatGPT, with the added advantage of integrating seamlessly with Google services such as Maps, Gmail, and Docs.
25.Guardrails
Predefined constraints built into AI systems to prevent misuse, such as restricting dangerous responses or avoiding the generation of harmful content.
26.Hallucination
A phenomenon where AI provides confident but incorrect information, often fabricating details without credible sources—a common issue in large language models.
27.Inference
The application of previously acquired knowledge by AI to new, unseen situations. For instance, after learning the rules of chess, the AI can play new matches without retraining.
28.Large Language Model (LLM)
A type of AI trained on massive amounts of text data, enabling it to understand language, engage in conversation, and answer questions effectively. Examples include ChatGPT, Claude, and Gemini.
29.Latency
This refers to the time delay required for an AI system to process a request and provide a response. For example, after a user inputs a question, they may need to wait two seconds for the answer to appear.
30.Machine Learning (ML)
A method that enables computers to learn from data without being explicitly programmed for every step. For instance, feeding the system numerous images of cats allows it to eventually recognize what a cat is.
31.Microsoft
Bing A search engine developed by Microsoft that incorporates AI to provide intelligent, summarized responses directly within the search interface, similar to ChatGPT.
32.Multimodal AI
AI systems that can process and integrate multiple types of information simultaneously, such as audio, images, and text, to gain a more comprehensive understanding of user input.
33.Natural Language Processing (NLP)
A field of AI focused on enabling computers to understand and use human language. Applications include reading news, summarizing articles, and engaging in conversations.
34.Neural Network
A structure that mimics the human brain, composed of multiple layers of artificial neurons, enabling the system to learn and analyze complex data more effectively.
35.Overfitting
A common issue where AI memorizes the training data too precisely, leading to poor performance on new, unseen data, as it lacks the ability to generalize.
36.Paperclips
A thought experiment illustrating the potential risks of poorly defined AI objectives. If an AI is told to maximize paperclip production, it might go to extreme lengths, even attempting world domination, to achieve its goal. This underscores the importance of setting goals responsibly.
37.Parameters
The internal values within an AI model that determine how it behaves, such as how polite it should respond or how it connects information. A greater number of parameters generally enhances the AI's capabilities.
38.Perplexity
An AI system that combines the functionality of a chatbot with a search engine, capable of retrieving information and citing reliable sources.
39.Prompt
A user-provided input or command that initiates a response from an AI system, such as "Create an Instagram caption."
40.Prompt Chaining
A technique that enables AI to maintain context across a series of prompts, allowing it to respond with continuity and logical coherence.
41.Quantization
A process used to reduce the size of an AI model, making it faster and more efficient by lowering the precision of certain data representations without significantly affecting performance.
42.Stochastic Parrot
A metaphor describing AI systems as being capable of mimicking human language without truly understanding it, simply generating plausible responses based on learned patterns.
43.Style Transfer
A technology that applies the artistic style of one image, such as a vintage or Van Gogh painting, to another image, like transforming a regular photo of a cat into a stylized artwork.
44.Synthetic Data
Artificially generated data created by AI, used to train other AI systems. For example, generating images of non-existent people to expand a training dataset.
45.Temperature
A setting that adjusts the creativity or randomness of an AI's output. Lower values yield more cautious and predictable responses, while higher values produce more creative or unexpected results.
46.Text-to-Image Generation
An AI capability that produces images based on textual descriptions. For instance, typing "an elephant flying on Mars" results in a visual depiction of that scenario.
47.Tokens
The basic units of information used in processing text, such as words or syllables. Longer prompts require more tokens.
48.Training Data
The collection of content used to train an AI model, including texts, images, and code, enabling it to learn and perform tasks effectively.
49.Transformer Model
A foundational technology behind Large Language Models (LLMs), which allows AI to understand sentence meaning by processing all input simultaneously, rather than word by word.
50.Turing Test
A method of evaluating AI intelligence, based on whether a person can distinguish between interacting with a human or an AI. If indistinguishable, the AI is considered to have passed the test.
51.Unsupervised Learning
A training method in which AI learns to identify patterns and structures in data without labeled answers, such as grouping customers based on purchasing behavior.
52.Weak AI / Narrow AI
AI systems that are designed to perform specific tasks and lack general intelligence. Examples include language translation tools and spam detection systems.
53. Zero-shot Learning
The ability of AI to solve tasks it was not explicitly trained on. For example, after learning about wolves, it can infer similarities with foxes without prior exposure.
True Digital Park is the largest center for technology and startups in Southeast Asia. It is a vibrant community for the next generation of innovators who are eager to learn, grow, and shape the future together. We believe that learning and understanding these terms is the first step to staying ahead in technology and unlocking genuine opportunities in the modern world.
Cite: https://www.cnet.com/tech/services-and-software/chatgpt-glossary-53-ai-terms-everyone-should-know/
Opportunity Is Here - Your Opportunity Is Yours !
At True Digital Park, where opportunity meets endless possibilities
"One Roof, All Possibilities"
---------------------------------------
Fanpage: TrueDigitalPark : https://www.facebook.com/TrueDigitalPark
Instagram: https://www.instagram.com/truedigitalpark/?hl=en
Line Official Account: https://lin.ee/cA6Ryda
LinkedIn: https://www.linkedin.com/company/true-digital-park/
TiKTok: https://www.tiktok.com/@truedigitalparkbkk
Youtube: https://www.youtube.com/c/TrueDigitalPark
Twitter: https://x.com/TDigitalpark
#AIforEveryone #Upskill #TrueDigitalPark #OpportunityIsHere #OneRoofAllPossibilities
