AI Cheat Sheets Archives

The Faces You See Aren’t Real: How AI Is Quietly Replacing Real Models
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The Faces You See Aren’t Real: How AI Is Quietly Replacing Real Models

Franchesca Asignacion2025-09-12T11:23:40+00:00

Image Generated by ChatGPT AI “She looked perfect… maybe too perfect.” That's what many of us are starting to think when we scroll past new fashion campaigns or pass by dazzling billboards on the street. The faces are flawless, the poses are fierce — yet there’s something slightly unreal about them. And here’s the twist: they might not be real at all. Artificial intelligence is creeping into the heart of the modeling industry — and it’s not creeping quietly. Thanks to powerful tools like Google Veo, brands can now create entire campaigns using models who aren’t human [...]

The Faces You See Aren’t Real: How AI Is Quietly Replacing Real ModelsFranchesca Asignacion2025-09-12T11:23:40+00:00

What is Multimodal AI?
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What is Multimodal AI?

Ace Kenneth Batacandulo2025-09-08T17:57:35+00:00

Multimodal AI refers to systems or models that can process and integrate data from multiple sources or modalities, such as text, images, video, audio, and other sensory data, to produce more accurate and comprehensive outputs. Unlike traditional AI systems that focus on one modality (e.g., text or images), multimodal AI combines different data types to improve understanding and decision-making. How It Works: Multimodal AI systems combine information from various modalities (e.g., visual data + textual data) to process inputs. This can involve: Text: Natural language processing (NLP) to understand meaning. Images/Video: Computer vision techniques to analyze visual data. Audio: Speech [...]

What is Multimodal AI?Ace Kenneth Batacandulo2025-09-08T17:57:35+00:00

What is Responsible AI?
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What is Responsible AI?

Ace Kenneth Batacandulo2025-09-08T15:20:00+00:00

Responsible AI is developing and deploying artificial intelligence (AI) systems that prioritize ethical, transparent, and fair practices while minimizing harm and ensuring accountability. Key Principles: Fairness: Avoid biases and discrimination in AI algorithms. Ensure equitable outcomes for all users. Transparency: Make AI decision-making processes transparent and understandable. Provide access to how AI models work and their limitations. Accountability: Assign responsibility for the outcomes of AI systems. Implement systems for auditing and monitoring AI performance. Privacy and Security: Protect user data privacy and ensure secure AI systems. Adhere to data protection regulations (e.g., GDPR) Inclusivity: Design AI systems that serve diverse [...]

What is Responsible AI?Ace Kenneth Batacandulo2025-09-08T15:20:00+00:00

Generative AI Security Scoping Matrix
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Generative AI Security Scoping Matrix

Nikee Tomas2025-09-03T02:56:19+00:00

Generative AI Security Scoping Matrix Cheat Sheet The Generative AI Security Scoping Matrix is a framework to classify generative AI (GenAI) use cases by the level of ownership and control over the models and data. It helps organizations assess and prioritize security requirements based on their generative AI deployment approach. The matrix defines 5 scopes from least to most ownership and control: Governance & Compliance Legal & Privacy Risk Management Controls Resilience Scopes of Generative AI Use Cases Buying Generative AI (Low Ownership) Scope 1: Consumer app Uses free or paid public third-party services like ChatGPT and Midjourney. No ownership or [...]

Generative AI Security Scoping MatrixNikee Tomas2025-09-03T02:56:19+00:00

What is Chain of Thought Prompting?
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What is Chain of Thought Prompting?

Ace Kenneth Batacandulo2025-08-31T16:52:13+00:00

A prompting technique in Large Language Models (LLMs) where the model is guided to show intermediate reasoning steps before arriving at the final answer. Inspired by how humans solve problems step by step. Helps LLMs handle complex reasoning tasks such as math, logic, and multi-step decision-making. Key Concepts Step-by-Step Reasoning: Instead of jumping to an answer, the model explains its thought process. Intermediate Steps: Similar to “showing work” in math problems. Better Accuracy: Effective in arithmetic, logical reasoning, and multi-hop questions. Prompt Example: “Let’s think step by step.” Benefits Improves reasoning accuracy. Makes the model’s output more interpretable. Reduces errors [...]

What is Chain of Thought Prompting?Ace Kenneth Batacandulo2025-08-31T16:52:13+00:00

What is Model Context Protocol (MCP)?
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What is Model Context Protocol (MCP)?

Ace Kenneth Batacandulo2025-11-16T12:35:08+00:00

An open, model‑agnostic protocol introduced by Anthropic in November 2024, designed to standardize how AI systems (huge language models, LLMs) connect with external data sources and tools via a JSON‑RPC interface. Often likened to a “USB‑C port for AI,” offering a universal interface rather than bespoke integrations per system. Key Benefits of MCP Provides a standardized interface so LLMs can easily connect to multiple tools and data sources without custom adapters. Solves the “N×M” problem, removing the need to build a unique connector for every AI–tool combination. Ensures structured and validated exchanges, supporting better debugging, version control, and reliability in [...]

What is Model Context Protocol (MCP)?Ace Kenneth Batacandulo2025-11-16T12:35:08+00:00

What is Federated Learning?
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What is Federated Learning?

Ace Kenneth Batacandulo2025-08-26T16:51:20+00:00

A machine learning technique where multiple devices or servers collaboratively train a shared model without sharing raw data. Instead of sending data to a central server, only the model updates (gradients/parameters) are sent, keeping sensitive information local. Key Concepts Decentralized Training: Data stays on local devices (e.g., smartphones, IoT, edge devices). Model Aggregation: A central server collects and averages model updates to improve the global model. Privacy-Preserving: Minimizes risk of exposing personal or sensitive data. Communication Efficiency: Reduces the need for large-scale raw data transfer. Edge AI Integration: Often paired with edge computing for real-time AI. How Federated Learning Works [...]

What is Federated Learning?Ace Kenneth Batacandulo2025-08-26T16:51:20+00:00

What are Clustering Algorithms in Machine Learning?
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What are Clustering Algorithms in Machine Learning?

Ace Kenneth Batacandulo2025-08-25T06:43:36+00:00

Clustering is an unsupervised learning technique that groups similar data points without predefined labels. It helps discover hidden patterns, segment data, and reduce dimensionality in datasets. Key Concepts Clustering: Grouping data points based on similarity or distance metrics. Unsupervised Learning: No labeled data; the model identifies structure independently. Distance Metrics: Commonly used metrics include Euclidean, Manhattan, and Cosine similarity. Popular Clustering Algorithms 1. K-Means Clustering Divides data into K clusters by minimizing the variance within each cluster. Fast, easy to implement, and works well with large datasets. It requires predefining K and is sensitive to outliers. Customer segmentation, image compression. [...]

What are Clustering Algorithms in Machine Learning?Ace Kenneth Batacandulo2025-08-25T06:43:36+00:00

Microsoft Foundry
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Microsoft Foundry

Irene Bonso2026-03-30T06:19:24+00:00

Microsoft Foundry Cheat Sheet Microsoft Foundry is a unified platform that enables enterprises to design, customize, and manage AI applications and agents at scale. It integrates tools, models, and workflows to streamline the development and deployment of AI solutions. Key Components AI Foundry: A development environment for building, testing, and deploying AI models and applications. Model Catalog: A repository of prebuilt models from Microsoft, OpenAI, and other partners, facilitating model selection and deployment. Prompt Flow: A tool for designing and orchestrating language model workflows, enabling systematic experimentation and refinement. Agent Service: A platform for securely designing, deploying, and scaling AI [...]

Microsoft FoundryIrene Bonso2026-03-30T06:19:24+00:00

What Is the Difference Between AI, ML, DL, and Generative AI?
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What Is the Difference Between AI, ML, DL, and Generative AI?

Ace Kenneth Batacandulo2025-07-22T17:40:30+00:00

Imagine a world where machines compose music, diagnose diseases, write code, drive cars, and even generate original artwork. That world isn't the future, it's now. Artificial Intelligence (AI) is no longer a buzzword; it's a driving force behind the most significant innovations of our time. But here's the catch: while AI is everywhere, many still confuse its core components: Understanding the differences between these technologies isn't just helpful, it's essential. Whether you're a student, a tech professional, a business leader, or AI-curious, this guide will give you a crystal-clear breakdown of these foundational terms in 2025 and beyond. What is [...]

What Is the Difference Between AI, ML, DL, and Generative AI?Ace Kenneth Batacandulo2025-07-22T17:40:30+00:00