02 December, 2024 | 10 mins read
Table of Contents
| Term | Definition | Business Application |
Key Benefits
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| Attention Mechanism | A method that allows models to focus on the most important parts of the input for more accurate predictions. | Improves accuracy in natural language processing tasks, enhancing chatbots and automated customer support systems. |
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| AGI (Artificial General Intelligence) | A type of AI that can perform any intellectual task that a human can do. | While not currently available, AGI could revolutionize businesses by automating complex decision-making across all departments. |
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| AI (Artificial Intelligence) | The simulation of human intelligence by machines, especially computer systems. | Automates complex tasks, enhances decision-making, and improves customer experiences across various business functions. |
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| Backpropagation | An algorithm used to minimize errors in training by adjusting weights in neural networks. | Improves the accuracy and effectiveness of AI models used in various business applications. |
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| Batch Size | The number of training samples used in one iteration of model training. | Helps optimize AI model training for specific business needs and computational resources. |
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| Bias Mitigation | Techniques used to reduce unwanted biases in AI models. | Ensures fairness in AI-driven decisions, crucial for HR applications and customer-facing services. |
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| CFG (Classifier-Free Guidance) | A method to enhance model outputs by guiding text generation towards or away from certain attributes. | Helps in generating more targeted and brand-aligned marketing materials or product descriptions. |
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| Converging | In Stable Diffusion, this refers to the model gradually approaching a stable state during training, where generated images become more realistic as the model’s parameters stabilize. | Helps in developing high-quality AI-generated visual content for marketing and product design. |
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| DL (Deep Learning) | A subfield of ML that uses neural networks with many layers to analyze complex data. | Powers advanced image and speech recognition, natural language processing, and complex data analysis. |
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| Embedding | A vector representation of data, often used to encode words or other data into machine-readable format. | Enhances text analysis for sentiment analysis in customer feedback or content recommendation systems. |
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| Epoch | One full cycle through the entire training dataset in model training. | Helps in fine-tuning AI models for specific business needs, balancing training time and model performance. |
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| Explainability | The practice of making AI outputs understandable for users. | Builds trust in AI systems, crucial for regulatory compliance and user acceptance in fields like finance or healthcare. |
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| Fine-Tuning | The process of adjusting a pre-trained model on new data to specialize in specific tasks. | Allows businesses to customize AI models for industry-specific needs without extensive resources. |
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| Gradient Descent | An optimization algorithm that adjusts model parameters to minimize error. | Improves the accuracy of predictive models used in sales forecasting, risk assessment, and other business analytics. |
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| Guidance Scale | A parameter that controls the influence of the textual prompt on the generated image. | Allows businesses to fine-tune AI-generated visual content to match specific brand guidelines or design requirements. |
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| Inference | The process of running a trained model on new data to generate predictions. | Enables real-time decision-making in various business processes, from fraud detection to personalized marketing. |
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| Inference Steps | The number of steps the model takes to generate an image from a text prompt. | Allows businesses to balance between image quality and generation speed in AI-driven design tools. |
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| Latent Space | A lower-dimensional representation of data that captures essential features, enabling the model to generate new data points by sampling from this space. | Useful in product design, allowing businesses to generate new design concepts or variations. |
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| Learning Rate | A hyperparameter that determines the step size at each iteration while moving toward a minimum of the loss function during training. | Helps optimize AI model training, ensuring efficient and effective learning for business-specific applications. |
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| LLM (Large Language Model) | A type of neural network trained on vast amounts of text data to generate human-like text. | Powers advanced chatbots, content generation, and language translation services. |
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| LoRA (Low-Rank Adaptation) | A technique to fine-tune large models efficiently by training low-rank adaptations. | Allows businesses to customize large AI models for specific industry needs without extensive computational resources. |
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| ML (Machine Learning) | A subset of AI that enables systems to learn from data and improve from experience. | Drives predictive analytics, process automation, and personalized customer experiences. |
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| Negative Prompt | A technique where undesired elements are specified to guide the model away from including them in the generated output. | Helps businesses refine AI-generated content, ensuring brand consistency and quality in marketing materials. |
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| Neural Network | A series of algorithms that recognize patterns by simulating the way human brains operate. | Enables advanced pattern recognition for applications like quality control in manufacturing or fraud detection in finance. |
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| Noise | Random variations added to data during training to help the model generalize better and prevent overfitting. | Improves model robustness, making AI systems more reliable for critical business applications. |
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| Overfitting | A scenario where a model learns the training data too well, including its noise and outliers, leading to poor performance on new, unseen data. | Understanding overfitting helps businesses develop more reliable AI models that generalize well to new data. |
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| Pretrained Model Name | The identifier of a model that has been previously trained on a large dataset and can be fine-tuned for specific tasks. | Allows businesses to leverage existing AI models, saving time and resources in developing AI solutions. |
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| Privacy-Preserving AI | Approaches that protect user privacy while processing data, like federated learning. | Ensures compliance with data protection regulations while still leveraging AI capabilities. |
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| Prompt | A text input given to an AI model to guide its output. | Enables businesses to generate customized content or get specific insights from AI models. |
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| RAG (Retrieval-Augmented Generation) | A method where the model retrieves relevant documents or information to enhance response generation. | Improves accuracy of AI-powered customer support and information retrieval systems. |
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| Regularization | Techniques used during training to prevent overfitting by adding a penalty to the loss function for large coefficients. | Helps create more generalizable AI models, improving their performance on new, unseen business data. |
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| SafeTensor | A data format that enables safe and efficient sharing of model weights for AI development. | Facilitates secure and efficient AI model deployment and updates across business systems. |
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| Seed | An initial value used to generate random numbers in models, ensuring reproducibility of results. | Ensures consistent and reproducible AI model outputs for testing and auditing purposes. |
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| Temperature | A parameter in text generation that controls randomness; higher values lead to more creative outputs, lower values to more predictable ones. | Allows businesses to control the creativity vs. accuracy trade-off in AI-generated content. |
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| Tensor | A mathematical object, like a multidimensional array, that stores data for neural network processing. | Enables efficient processing of complex business data in AI applications. |
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| Transformer | A model architecture that uses self-attention mechanisms for natural language tasks. | Powers advanced natural language processing applications like chatbots, content analysis, and language translation tools. |
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| Underfitting | A situation where a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and new data. | Understanding underfitting helps businesses develop more sophisticated AI models that can capture complex patterns in their data. |
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| Zero-Shot Learning | A model’s ability to perform tasks without prior examples or explicit training on those tasks. | Enables AI systems to adapt to new business scenarios or tasks without extensive retraining. |
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Pek Pongpaet
Helping enterprises and startups achieve their goals through product strategy, world-class user experience design, software engineering and app development.
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