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With a daily presence in headlines and increasingly rapid advances, AI is undeniably the talk of the town. Staying up to date with everything AI offers in professional and daily life involves a clear understanding of critical concepts surrounding this technological phenomenon. This article provides a 5-minute reading to dive into the essential building blocks of AI, from foundational notions like algorithms and training data to cutting-edge trends and topics like generative AI and ethical considerations. Whether you are new to AI or looking to refresh your knowledge, this quick guide provides you with a solid grasp of 10 key concepts driving today’s technological revolution.
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1. Artificial Intelligence
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Of course, there’s no better way to start a tour of essential AI concepts than by defining AI itself. AI is a discipline of computer science that studies the development of systems capable of undertaking and solving a variety of complex tasks using capabilities similar to those involved in certain human cognitive processes: learning, reasoning, making inferences and predictions, optimization, task automation, and so on. Most AI systems are designed to acquire one or a small subset of these “intelligence” skills for performing a specific task. This goes in contrast with the notion of Artificial General Intelligence (AGI) which aims to replicate human-level intelligence in a broader sense so that a single system is capable of solving a wide range of activities. Many experts claim true AIG has not been reached yet, although most advanced LLMs and autonomous vehicles are examples of systems that could be positioned halfway between “narrow” AI and AGI.
AI has grown so substantially that it is no longer pictured as a subarea of computer science, but rather as a discipline of its own. As such, it has several closely interrelated subareas, a few of which we will explore in some of the next concepts.
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Main areas of AI
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2. AI Algorithms and Models
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Are AI algorithms and AI models the same? Not quite. An algorithm is a set of instructions for solving a problem, and in an AI algorithm, those instructions are designed to give computers the ability to learn by themselves how to solve the problem. Meanwhile, an AI model is like a prebuilt box that contains the result of a learning (or training) process by being exposed to data: think of an AI model as a readily available solution to make predictions or perform tasks based on new data.
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3. Machine Learning
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The largest subarea within AI is undoubtedly machine learning (ML), which focuses on systems that learn from data to perform tasks like classifying images, estimating sales, and detecting suspicious bank transactions. ML is often used interchangeably with AI, but as shown in the above diagram, ML is still just one part of AI, alongside other subareas. Nonetheless, the most advanced and evolved forms of ML systems today are designed to strongly overlap with other AI areas.
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4. Training Data
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A central concept in ML and the construction of AI—and more specifically, ML—models is training data. These are the data utilized to teach the model how to recognize patterns and make predictions. For instance, by processing large amounts of training data consisting of images of diverse bird species, the gradually model learns to identify patterns in the data that help distinguish one species from another, getting eventually on the specific task it is designed for, such as image classification.
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Training data are like the fuel used by ML models to learn and ‘build themselves’
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5. Deep Learning and Computer Vision
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Deep Learning is an advanced branch of ML that specializes in handling more challenging problems and complex data, using artificial neural network architectures that mimic how human and animal brains operate. One of the main applications of deep learning models is in computer vision tasks, which involve enabling machines to understand visual information, like recognizing objects in images or videos.
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6. Natural Language Processing
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NLP is another application-oriented AI area that, like computer vision, is tightly coupled with deep learning architectures today: it focuses on tasks related to processing, understanding, and producing human language (text and speech), thereby helping enable human-machine communication. Example NLP tasks include analyzing and classifying text, summarization, translation, and question-answering.
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7. Generative AI
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Generative AI refers to AI systems that have been trained to create new content, such as text, images, or music, as a result of learning patterns from existing data. This area of AI is mostly founded on advanced deep learning architectures, and it’s behind much of the explosion of available apps and tools we are witnessing today to bring closer AI capabilities to the general public, particularly those related to creativity.
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8. Large Language Models
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Large Language Models (LLMs) are AI systems of a huge magnitude: they’re trained on vast text datasets (up to billions of documents) to understand and generate human-like language at an unprecedented level. Tools like ChatGPT and Claude are well-known examples of LLMs deployed in the real world.
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9. Responsible AI
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Responsible AI is a subject of study focused on developing frameworks for the ethical development and deployment of AI systems to ensure fairness, transparency, and accountability. The higher the capabilities of the latest AI systems like LLMs, advanced computer vision systems, etc., the more relevance that should be given to responsible AI practices for ensuring these powerful tools are used in the right way.
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Example of biased AI system for classifying job applications: training data are biased by gender
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10. AI Bias
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A central concept within responsible AI, concretely under the lens of fairness and ethics. AI Bias occurs when AI systems produce unfair outcomes or decisions, often due to biases present in its training data. For instance, if a system trained to accept or decline bank loan applications has been trained on data where most low-income customers were denied loans, the system might inadvertently become biased in classifying future customers. The good news, there are techniques to help mitigate these biases.
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Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.