Under the Hood
Modeled after the human brain, are a mathematical system that actively learns skills by identifying and analyzing statistical patterns in data. This system features multiple layers of artificial neurons, which are computational models inspired by the neurons in our brain
These artificial neurons process information and transmit signals to other connected neurons. While the first layer processes the input data, the final layer delivers the results (Hardesty, 2017). Intriguingly, even the experts who meticulously design these neural networks often find themselves puzzled by the intricate processes occurring between the layers.
What is a Neural Network?
This term in AI models refers to output errors caused by skewed training data. It can cause models to produce inaccurate, offensive, or misleading predictions. Biased AI models arise when algorithms prioritize irrelevant or misleading data traits over meaningful patterns (Smith, 2019).
What is Bias?
This is the practice of designing effective prompts to guide an AI model’s output. This involves setting roles, specifying format, adding constraints, or giving examples to improve the quality, tone, or relevance of the response.
What is Prompt Engineering?
Neural networks known as THIS work by forecasting word sequences. Their capabilities are rapidly advancing and continue to evolve with increased use. They can now hold dialogues, write prose, and scrutinize enormous text quantities from the internet.
What are Large Language Models?
The architecture that processes sentences simultaneously
Transformer Model
We call these occurrences where large language models generate factually inaccurate or illogical answers due to data and architecture constraints.
What are Hallucinations?
Forcing the AI to reason step-by-step
Chain-of-thought Prompting
The computational linguistics subfield
NLP (Natural Language Processing)
The numerical values dictating construction and behavior
Parameters
We use this term to describe the habit of assigning human-like qualities to AI. While AI systems can imitate human emotions or speech, they don’t possess feelings or consciousness. We might interact with various AI models as if they were colleagues or thought partners, but in reality, they serve as tools for learning and resource development.
What is anthropomorphism?
The hidden, behind-the-scenes instructions
Meta Prompt / System Prompt
The broad tech approach for creating text, image, and video
Generative AI
The model's short-term "memory" capacity
Context Window
The unexpected, unprogrammed skills models develop
Emergent Behavior
The dial controlling creativity vs. determinism
Temperature
Models that handle various inputs like text, audio, and vision
Multimodal Model
The smallest unit of text/data
Token
Feeding the model your own external documents
RAG (Retrieval-Augmented Generation)
Autonomous entities calling tools and making decisions
Agents