AI Glossary
Do you remember your first brush with AI? Perhaps it was with ChatGPT, OpenAI's chatbot. This marvel not only answers questions but crafts poems, designs resumes, and even devises fusion recipes. Think of it as autocomplete, but tenfold more sophisticated.
However, let's not restrict our understanding of AI to just chatbots. The scope of AI is expansive. It's more than ChatGPT helping with your homework or Midjourney crafting exquisite mech images. As per McKinsey Global Institute, AI's potential is poised to enrich the global economy by a staggering $4.4 trillion annually. It's no wonder the buzz around artificial intelligence is only getting louder.
As AI intertwines further with our lives, there's a surge in terminologies. Whether you're aiming to impress at a social gathering or ace that job interview, navigating the world of AI demands some vocabulary knowledge.
To aid in this, we present a continuously updated glossary:
Artificial General Intelligence (AGI):
This refers to an advanced AI, superior to our current understanding, proficient in tasks and continually enhancing itself.
AI Ethics:
Guidelines ensuring AI's benevolent behavior towards humans.
AI Safety:
This explores AI's long-term ramifications, especially if it evolves into a superintelligence.
Algorithm:
Set instructions allowing software to analyze and learn from data.
Alignment:
Adjusting an AI for optimal results.
Anthropomorphism:
Humans attributing human traits to non-humans, like assuming a chatbot has emotions.
Artificial Intelligence (AI):
Simulating human intelligence via technology.
Bias:
Errors in AI models, often a result of the training data.
Chatbot:
Software simulating human conversation.
ChatGPT:
A flagship chatbot from OpenAI.
Cognitive Computing:
Another name for AI.
Data Augmentation:
Diversifying training data for AI.
Deep Learning:
A subtype of machine learning mimicking the human brain.
Diffusion:
A technique in machine learning that adds randomness to data.
Emergent Behavior:
Unanticipated abilities in an AI model.
End-to-end Learning (E2E):
A model comprehensively trained for a task.
Ethical Considerations:
The moral facets of AI, encompassing privacy, fairness, and more.
Foom:
A concept warning about the uncontrollable evolution of AGI.
Generative Adversarial Networks (GANs):
AI models creating novel data.
Generative AI:
AI producing new content, like text or images.
Google Bard:
Google's answer to ChatGPT.
Guardrails:
Policies ensuring AI's ethical functionality.
Hallucination:
Erroneous AI outputs.
Large Language Model (LLM):
AI models trained extensively on text.
Machine Learning (ML):
AI that learns and improves without being explicitly programmed.
Microsoft Bing:
Search engine integrating AI technologies similar to ChatGPT.
Multimodal AI:
AI understanding varied inputs like text or videos.
Natural Language Processing:
AI's capability to grasp human language.
Neural Network:
A computational model reminiscent of the human brain.
Overfitting:
An ML limitation causing narrow data understanding.
Parameters:
Elements defining an LLM's behavior.
Prompt Chaining:
AI's ability to recall past interactions.
Stochastic Parrot:
A term indicating AI's lack of deep understanding.
Style Transfer:
Adapting one image's style to another.
Temperature:
Governs the unpredictability of AI responses.
Text-to-image Generation:
Crafting images from textual cues.
Training Data:
Essential datasets for AI learning.
Transformer Model:
AI understanding context within data.
Turing Test:
A machine's ability to impersonate a human.
Weak AI:
AI limited to specific tasks.
Zero-Shot Learning
An AI model's ability to work without direct training.
AI Consultant Perspective:
As an AI consultant, I believe in empowering individuals and businesses with knowledge. By understanding these terms, you position yourself at the forefront of this technological revolution. The future beckons, and AI is an integral part of it.