At this point large language models and machine learning are becoming more frequently integrated into our tools, our products and our lives. In order for engineers to stay competitive, it’s crucial that they (we!) develop a strong understanding of the fundamentals of these technologies. To help you navigate the world of artificial intelligence, I’ve compiled a glossary of essential AI terms that every software engineer should know along with some explanations of their relevance.
Yes, it’s a listicle, but I promise this will be helpful.
AI Terms Every Developer Should Know (Comprehensive Guide)
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Machine Learning (ML)
Definition: A subset of AI where algorithms learn from data and make decisions or predictions without being explicitly programmed.
Relevance: ML powers various applications like recommendation engines, fraud detection, and personalization algorithms.
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Neural Networks
Definition: Computational models inspired by the human brain’s structure, consisting of layers of interconnected nodes (neurons).
Relevance: Fundamental to deep learning and AI tasks, such as image and speech recognition.
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Deep Learning
Definition: A branch of machine learning that uses multiple layers of neural networks to model complex patterns in data.
Relevance: Deep learning powers major AI breakthroughs in fields like computer vision, speech recognition, and NLP.
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Natural Language Processing (NLP)
Definition: A branch of AI focused on enabling machines to understand and process human language.
Relevance: NLP is central to building chatbots, virtual assistants, and sentiment analysis systems.
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Reinforcement Learning (RL)
Definition: A machine learning method where agents learn to make decisions through trial and error, receiving rewards or penalties.
Relevance: Widely used in robotics, gaming, and automated systems where optimal decision-making is essential.
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Transfer Learning
Definition: A technique where a model trained on one task is repurposed for another related task, often requiring fewer resources.
Relevance: Transfer learning allows developers to adapt pre-trained models for specific use cases, reducing training time and data requirements.
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Fine-Tuning
Definition: Adjusting a pre-trained model for a specific task by training it further on a smaller, task-specific dataset.
Relevance: Fine-tuning allows models like GPT-3 to be adapted for domain-specific applications like legal document analysis.
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Overfitting
Definition: A phenomenon where a model performs well on training data but fails to generalize to new, unseen data.
Relevance: Preventing overfitting is crucial to ensure that models perform well in real-world situations.
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Supervised Learning
Definition: A type of learning where a model is trained on labeled data, learning to map inputs to known outputs.
Relevance: This is the most common learning method, used in tasks like classification and regression.
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Unsupervised Learning
Definition: A method where models learn from data without labeled responses, identifying hidden patterns or groupings.
Relevance: Common in clustering, anomaly detection, and data compression tasks.
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Embeddings
Definition: Representing high-dimensional data (e.g., words, phrases) as dense vectors in a lower-dimensional space to capture semantic meaning.
Relevance: Embeddings enable NLP models to process text meaningfully by converting words into vectors that reflect their meanings.
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Retrieval-Augmented Generation (RAG)
Definition: A hybrid model that combines retrieval (fetching relevant information) with generation (creating new content) for more accurate responses.
Relevance: RAG models allow for dynamic, context-rich AI applications by integrating external knowledge with the model’s learned information.
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Foundation Model
Definition: Large, pre-trained models (e.g., GPT-4 or BERT) that serve as a base for fine-tuning across various downstream tasks.
Relevance: Foundation models are adaptable, allowing developers to build task-specific models with minimal retraining. Often times these models are used as a starting point for a new model and given the speed of development, they often outpace fine-tuned models in a matter of months.
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Small Language Model
Definition: A lighter, more efficient version of a large language model, optimized for specific use cases or devices with limited computational resources.
Relevance: Small language models are essential for running AI on mobile or embedded systems. Oftentimes, a large language model will initially be introduced for an AI implementation and then fine-tuned or replaced by a small language model for optimization.
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Tokens
Definition: The smallest units of text (e.g., words, subwords, or characters) that language models process during training or inference.
Relevance: Understanding tokens is crucial for optimizing model performance and efficiency.
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Transformer
Definition: A type of machine learning model architecture that is at the heart modern large language models. Transformer architecture was introduced in the paper “Attention is All You Need” by Vaswani et al. and has since become the foundation for models like BERT, GPT, and others.
Relevance: Transformers have revolutionized NLP, forming the foundation of models like BERT, GPT, and others.
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Hallucination
Definition: When an AI model generates information that is incorrect or nonsensical.
Relevance: Hallucinations can lead to misleading or false outputs, particularly in sensitive applications like healthcare or finance.
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Explainable AI (XAI)
Definition: AI models that provide transparency into their decision-making processes, allowing humans to understand why certain decisions were made.
Relevance: Crucial for AI adoption in regulated industries like healthcare and finance, where decisions must be explainable for compliance purposes.
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Feature Engineering
Definition: The process of selecting, modifying, or creating new input features to improve the performance of machine learning models.
Relevance: Proper feature engineering is crucial for building effective models, as it helps capture the most relevant patterns in data.
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Agents
Definition: Autonomous entities in AI that interact with their environment, make decisions, and execute actions to achieve a specific goal.
Relevance: Agents are integral in fields like robotics, gaming, and complex simulation environments where decision-making is required.