AI Definition Dictionary

In this rapidly evolving landscape, it’s crucial to separate the wheat from the chaff when it comes to AI information. As leaders, we need to focus on verifiable facts, practical applications, and ethical considerations rather than getting caught up in sensationalism or unfounded fears. All while not sounding like idiots….

I have extensive experience in tech and as an executive, I am often asked what AI is (this spans all walks of life. To help, I have created this pseudo dictionary …

Please feel free to comment and add as you see fit. This landscape is changing a pace I’ve never seen (three new additions while I was drafting this post), I promise I will release an update when appropriate or when the definition is no longer accurate.

AI is a big bucket, the best way I can describe it is like generically referring to “tool” for items in your toolbox, or silverware for all the items in your cutlery drawer. The term AI encompasses a broad spectrum of technologies and solutions that evolve exponentially. The term works, but to distinguish what you mean (think spoon vs silverware from your cutlery drawer), it helps to be a bit more specific.

Computer Vision: AI systems that interpret visual data from the world. Used for facial recognition, autonomous driving, medical imaging diagnostics, and surveillance. (e.g. Google’s DeepMind for Go and eye disease diagnosis.)

Decision-Support Systems: AI tools designed to assist people in making complex decisions by analyzing large datasets. Used in financial modeling, supply chain optimization, and medical diagnostics. (e.g. IBM Watson Health for oncology.)

Edge AI: AI algorithms processed locally on hardware devices instead of in the cloud. Currently used in smart home devices, autonomous vehicles, and industrial IoT applications for real-time decision making. (e.g. Facial recognition systems in smartphones that process data on the device for enhanced privacy and speed)

Explainable AI (XAI): AI systems designed to provide clear explanations for their decisions and actions. Currently used in healthcare for treatment recommendations, finance for credit decisions, and autonomous vehicles for understanding driving choices. (e.g. A hospital’s AI system explaining why it recommended a specific cancer treatment plan to doctors)

Generative AI: Creates new content by learning from existing data. It does this by learning from existing data and applying your query to it. Currently used to create marketing content, graphic design, video production, and creative industries. (e.g. DALL-E by OpenAI, Deepfake technology.)

Large Language Models (LLMs): AI models trained on vast amounts of text data to understand and generate human-like text. Currently used for writing assistance, chatbots, research, translation services, and code generation. (e.g. ChatGPT, CoPilot, Gemini.)

Machine Learning (ML) Algorithms: Algorithms that learn patterns from data to make predictions or decisions without explicit programming. Often used to provide predictive analytics, personalized recommendations, financial forecasting, and anomaly detection. (e.g. Amazon’s shopping recommendations, credit card fraud detection.)

Natural Language Processing (NLP): Focuses on the interaction between computers and humans in natural language. Currently these tools provide sentiment analysis, language translation, and text summarization. (e.g. Twitter’s sentiment analysis for public opinion.)

Neural Networks: A subset of ML, designed to mimic the human brain’s structure and function. Used for image recognition, pattern recognition, and complex data analysis. (e.g. Instagram’s object identification in photos, AlphaGo by DeepMind.)

Recommendation Systems: Algorithms designed to predict user preferences based on past behavior or interactions. Currently used for e-commerce personalization, media streaming, and online learning platforms. (e.g. Netflix’s recommendation engine.)

Reinforcement Learning: A type of machine learning where an AI agent learns to make decisions by taking actions in an environment to maximize a reward. Currently used in robotics, game playing, and industrial automation. (e.g. DeepMind’s AlphaGo learning to play the game of Go by repeatedly playing against itself)

Robotics and Automation: Physical systems powered by AI to perform tasks autonomously or semi-autonomously. Often used for warehouse operations, healthcare robotics, and inspection in hazardous environments. (e.g. Amazon’s robotic fulfillment centers, Boston Dynamics’ Spot.)

Speech Recognition and Synthesis: AI solutions that convert speech to text or text to speech. These tools are used for virtual assistants, transcription services, accessibility tools, and meeting minutes. (e.g. Siri, Alexa, Google Translate’s voice feature.)

These categories are expanding quickly as people learn how they can apply AI theories to solve, augment, collaborate, and inspire for their own purposes.

I’ll be updating this post periodically to reflect the latest definitions in AI. Follow me for future updates and insights!