When was the term “artificial intelligence” first introduced?
The term “artificial intelligence” was first used in 1956 by John McCarthy.
When was the first AI program created?
The first AI program was created in 1951.
What happened to AI research in the 1970s and 1980s?
AI experienced a decline in funding and interest, often referred to as an “AI winter.”
What caused the resurgence of AI in the 21st century?
Advances in computing power, availability of large datasets, and the development of deep learning techniques.
What is the current era of AI often called?
The current era is often called Generative AI (GenAI).
What defines Generative AI?
Generative AI creates original content such as text, images, or music by learning patterns from existing data.
How were early neural networks inspired by biology?
They were inspired by biological neurons, modeling inputs as dendrites and outputs as axons.
What is a perceptron?
A perceptron is an early neural network model that learns simple linear patterns.
Why did increasing network size improve learning performance?
Larger and denser networks can represent more complex patterns, similar to how a denser brain supports intelligence.
What does “we got inspired again” refer to in AI history?
It refers to revisiting and improving biologically inspired models with better algorithms and computing power.
What triggered the rise of deep learning?
Increased data availability, improved hardware (GPUs), and better training algorithms enabled deep neural networks.
What key insight did biology provide for modern AI architectures?
The brain is not uniform; different regions are specialized for different functions.
How did this biological insight influence AI model design?
It led to architectures with specialized components rather than flat, uniform networks.
What are Transformer models?
Transformer models are neural network architectures designed to efficiently learn sequences, language, and images.
Why are Transformers important for GenAI and LLMs?
They scale well, capture long-range dependencies, and enable powerful language and multimodal models.
What are Large Language Models (LLMs)?
LLMs are large neural networks trained on massive datasets to understand and generate language.
What does “generation” mean in Generative AI?
Generation refers to producing new data such as text, images, or code.
How are Generative AI models trained?
They are trained on vast datasets to learn patterns, structures, and relationships in data.
What are common uses of Generative AI?
Virtual assistants, creative tools, content generation, and problem-solving applications.
Why is GenAI not considered a “parrot”?
It does not simply repeat training data but generates new, creative outputs based on learned patterns.
What capabilities does GenAI offer beyond content creation?
Reasoning, decision-making, multimodal understanding, agent-based actions, and new approaches to robotics.
What are reasoning capabilities in LLMs?
The ability to make informed, context-aware decisions across a wide range of tasks.
What are multimodal LLMs?
Models that process and integrate multiple data types such as text, images, and speech.
Why is multimodality important for AI systems?
It enables richer context understanding, similar to how humans process multiple sensory inputs.
What is Agentic AI?
Agentic AI refers to systems that can plan, decide, and take actions to achieve goals.
Why is action important in addition to decision-making?
Many real-world tasks require execution, not just reasoning, such as booking services or controlling systems.
How does Agentic AI support collaboration?
Multiple AI agents can coordinate and divide tasks to solve complex problems.
How does GenAI change traditional robotics?
It may provide machines with a more intuitive “sense” of physics rather than relying only on precise measurements.
What is the overall trend shown by the history of AI?
AI evolves through cycles of inspiration, limitation, and resurgence, driven by data, computation, and new architectures.
Last changed11 days ago