Q1: What is Artificial Intelligence (AI)?

AI is the field of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence. This includes reasoning, learning from past experiences, finding meaning in complex data, and making autonomous decisions.

Q2: How are AI, Machine Learning (ML), and Deep Learning (DL) related?

These terms represent specific layers of technology:

• AI: The broad goal of creating "smart" machines.

• Machine Learning (ML): A subset of AI focused on algorithms that learn patterns from data to make predictions, rather than following rigid, hand-coded rules.

• Deep Learning (DL): A specialized subset of ML that uses multi-layered artificial neural networks (ANNs) to process data in a way inspired by the biological brain. It is the engine behind modern image recognition and language translation.

Q3: What is the difference between Narrow AI and Artificial General Intelligence (AGI)?

• Narrow AI (Weak AI): Designed for a specific task (e.g., identifying a specific ship class in satellite imagery). It cannot perform outside its programmed domain. Virtually all AI in existence today is Narrow AI.

• Artificial General Intelligence (AGI): A theoretical system that possesses the ability to understand, learn, and apply intelligence across any task a human can. Despite significant progress in 2025-2026, AGI remains a subject of research and is not currently operational.

Q4: What are Neural Networks and how do they "learn"?

Neural networks are computational models consisting of interconnected nodes (neurons) organized in layers. They learn through a process called backpropagation, where the model minimizes a loss function that represents the difference between the actual output and the predicted output. During training, the system adjusts the mathematical weights of internal connections to improve accuracy over time.

Q5: What is Computer Vision (CV)?

Computer Vision enables systems to identify and process objects in visual media—images and video—the same way humans do. In a military context, this is foundational for Automated Target Recognition (ATR) and autonomous navigation for Unmanned Systems (UxS).

Q1: What is Generative AI?

Generative AI creates new content, including text, images, synthetic data, and software code. It works by predicting the most likely next element in a sequence based on massive training datasets.

Q2: What is a Large Language Model (LLM)?

An LLM is a type of generative AI trained on vast amounts of text. It uses a Transformer architecture to understand context and relationships between words (tokens), allowing it to generate human-like prose, summarize documents, and translate languages.

Q3: What is an AI "Hallucination"?

A hallucination occurs when an LLM generates a response that is grammatically correct and confident but factually incorrect. This happens because the model is a probabilistic "next-token predictor," not a database of verified facts.

Q4: What is Retrieval-Augmented Generation (RAG)?

RAG is a technique used to reduce hallucinations and provide "ground truth" for military users. It allows an LLM to look up specific, authoritative documents (like Navy Warfare Publications or technical manuals) before generating an answer. This ensures the output is grounded in official data.

Q5: What is Prompt Engineering?

Prompt Engineering is the practice of refining the input (the prompt) provided to an AI to get the most accurate output. It involves providing clear context, constraints, and "few-shot" examples to guide the model's reasoning.

Q1: How does AI support the Joint Targeting Cycle (F2T2EA)?

AI accelerates the Find, Fix, Track, Target, Engage, and Assess (F2T2EA) cycle by automating the analysis of massive sensor feeds. This reduces "sensor-to-shooter" timelines, ensuring commanders can act faster than the adversary.

Q2: What is "Decision Advantage" in 2026?

Decision advantage is the ability to perceive, understand, and act upon information more rapidly and accurately than an opponent. AI provides this by filtering out noise, identifying patterns in Electronic Warfare (EW) data, and simulating thousands of potential Courses of Action (COAs) in seconds.

Q3: What is the difference between Autonomous and Remotely Piloted Systems?

• Remotely Piloted: A human is "in-the-loop," controlling the platform via a data link.

• Autonomous: The platform can execute tasks (navigation, obstacle avoidance, target identification) independently based on onboard AI, though humans remain "on-the-loop" for mission-level oversight.

Q4: What is Predictive Maintenance (PdM)?

PdM uses AI to analyze sensor data from aircraft engines, hull vibrations, or vehicle telematics. The AI identifies the "signature" of an impending failure before it happens, allowing for repairs that prevent operational downtime and reduce the logistical burden of "just-in-case" part replacement.

Q5: What are AI-powered swarms?

AI swarms involve multiple autonomous systems (drones, surface vessels, or sub-surface gliders) that coordinate their actions through distributed intelligence. They can saturate enemy defenses, perform wide-area ISR, or provide resilient communication networks in contested environments.