AI is everywhere (and it’s not just ChatGPT)
artificial intelligence is all around us—from the voice assistants on our phones to the recommendations we get on streaming platforms.
while many people associate AI primarily with ChatGPT, it’s important to understand that AI encompasses a wide range of technologies and applications.
this guide aims to demystify AI in simple terms, exploring its various categories and real-world uses.
what is artificial intelligence (AI)?
at its core, artificial intelligence refers to machines or software that can perform tasks typically requiring human intelligence.
this includes:
- learning from experience and improving over time
- understanding language and generating human-like responses
- recognizing patterns in data and images
- making decisions based on information and rules
think of AI as teaching machines to “think” and “learn” from data—not by programming every possible response, but by enabling them to find patterns and improve through experience.
AI categories: understanding how machines learn and respond
AI systems can be categorized by how they process information and learn from experience.
reactive machines
what they are: the most basic AI systems that can only react to specific inputs. they don’t store memories or past experiences.
example: IBM’s Deep Blue, a chess-playing computer that could analyse possible moves but had no memory of past games. it looked at the current board state and calculated the best next move without learning from previous matches.
real-world applications:
- basic game-playing algorithms
- simple recommendation systems
- rule-based expert systems
limited memory AI
what they are: systems that can use past experiences to inform future decisions. they have short-term memory that helps them make better choices.
example: self-driving cars observe other vehicles’ speed and direction over time to make better driving decisions. they remember recent traffic patterns to predict what other drivers might do next.
real-world applications:
- autonomous vehicles learning from traffic patterns
- chatbots that remember conversation context
- recommendation systems that learn from your viewing history
- fraud detection systems that learn from transaction patterns
theory of mind AI (still in development)
what it is: AI that aims to understand human emotions, beliefs, and intentions. this would enable machines to interact more naturally with humans by recognizing and responding to emotional states.
current status: still largely theoretical and in early research stages. while we have systems that can recognize facial expressions or vocal tone, truly understanding human mental states remains a significant challenge.
potential future applications:
- truly empathetic customer service systems
- therapeutic AI companions
- advanced educational tutors that adapt to learning styles and motivation
self-aware AI (hypothetical)
what it is: the most advanced form of AI, where machines possess consciousness and self-awareness. this is AI that would understand its own existence and have its own desires and goals.
current status: purely hypothetical and remains a concept explored in science fiction. we’re nowhere close to creating this type of AI, and many experts question whether it’s even possible or desirable.
AI by capability: narrow, general, and superintelligent
beyond how AI processes information, we can also categorize it by the breadth of tasks it can perform.
narrow AI (weak AI)
what it is: AI designed to perform a specific task, such as facial recognition or internet searches. it’s “narrow” because it’s focused on one domain.
key characteristics:
- performs pre-defined, single-domain tasks
- learns and improves through data (machine learning)
- lacks understanding beyond its specific application
- doesn’t possess emotions or consciousness
the reality: most current AI applications, including ChatGPT, fall into this category. even sophisticated systems are specialized tools designed for particular tasks.
general AI (AGI)
what it is: artificial general intelligence refers to machines that possess the ability to understand, learn, and apply knowledge across a wide range of tasks, much like a human.
current status: AGI remains a goal for researchers and is not yet realized. we don’t have systems that can genuinely learn and apply knowledge across domains the way humans do.
the challenge: human intelligence is remarkably flexible—we can learn to cook, then apply abstract reasoning from that experience to business problems. AGI would need this same flexibility.
superintelligent AI
what it is: hypothetical AI that would surpass human intelligence in all aspects—creativity, problem-solving, social intelligence, and more.
current status: purely speculative. while it offers exciting possibilities, it also raises significant ethical and safety concerns that researchers are already discussing.
narrow AI in action: the power behind today’s smart systems
narrow AI is the most prevalent and practical form of artificial intelligence today. let’s look at where you’re already using it daily.
virtual assistants
examples: Siri, Alexa, Google Assistant
what they do:
- understand voice commands using natural language processing
- set reminders, timers, and alarms
- play music and control smart home devices
- answer queries based on integrated data sources
- learn your preferences over time
recommendation systems
examples: Netflix, Amazon, YouTube, Spotify
what they do:
- analyze your behavior, preferences, and patterns
- suggest content, products, or music you’re likely to enjoy
- improve recommendations as they learn more about you
- balance showing you similar content with introducing new options
email filtering
examples: Gmail spam filter, Outlook junk mail
what they do:
- use machine learning to categorize emails
- sort messages into spam, promotions, or primary inbox
- learn from your actions (marking emails as spam or not)
- adapt to new spam tactics over time
chatbots and customer support AI
examples: Zendesk AI, Drift, Intercom, LivePerson
what they do:
- respond to frequently asked questions automatically
- route tickets to appropriate human agents
- assist with purchases or technical support
- handle multiple conversations simultaneously
- learn from successful interactions
facial recognition technology
examples: Apple Face ID, airport security systems
what they do:
- unlock phones by recognizing your face
- verify identities at border control
- tag people in photos automatically
- provide security through facial verification
language translation
examples: Google Translate, DeepL, Microsoft Translator
what they do:
- translate text and speech in real-time
- use deep learning to understand context and nuance
- improve translation quality for commonly used language pairs
- handle idiomatic expressions better than older rule-based systems
navigation and traffic management
examples: Google Maps, Waze
what they do:
- optimize routes based on current traffic
- analyze traffic patterns to predict congestion
- estimate arrival times considering historical data
- suggest alternative routes when problems arise
smart home devices
examples: Nest Thermostat, Roomba vacuum, smart refrigerators
what they do:
- automate climate control based on your schedule
- learn cleaning routines and room layouts
- manage inventory and suggest shopping lists
- adapt to your preferences over time
predictive text and autocorrect
examples: Gboard, Grammarly, Microsoft Word Editor
what they do:
- suggest next words based on context
- correct grammar and spelling errors
- adapt to your writing style
- learn phrases you use frequently
fraud detection systems
examples: PayPal, Stripe, major banking systems
what they do:
- identify unusual transaction patterns
- flag fraudulent activity in real-time
- learn new fraud tactics as they emerge
- minimize false positives while catching real fraud
social media feeds and advertising algorithms
examples: Facebook, Instagram, TikTok, Twitter (X)
what they do:
- prioritize content based on engagement likelihood
- target ads with precision using demographics and interests
- predict what content you’ll interact with
- balance showing you what you like with introducing new content
introducing large language models (LLMs)
large language models are a subset of AI designed to understand and generate human-like text.
key characteristics:
- trained on vast amounts of text data from the internet
- can perform tasks like translation, summarization, and question-answering
- generate coherent and contextually relevant responses
- don’t actually “understand” in the human sense—they recognize patterns
popular LLMs beyond ChatGPT
| model | key features |
|---|---|
| Claude • developed by Anthropic • known for ethical conversational AI • human-like, nuanced responses • strong safety features | Google Gemini • suite of advanced AI models • handles both text and images • integrated memory system • real-time data access via APIs |
| Microsoft Copilot • integrates with Microsoft 365 • AI-powered assistance across Word, Excel, etc. • enterprise-focused features • deep integration with business tools | Perplexity AI • real-time information retrieval • citation-based searches • factual information focus • transparent source attribution |
| Meta’s LLaMA • family of open-source models • optimized for dialogue • conversational AI applications • research and commercial use | Mistral AI • open-source models • Mistral 7B outperforms larger models • efficient and cost-effective • strong performance-to-size ratio |
| DeepSeek • Chinese AI chatbot • competitive performance • lower development costs • caused market disruption |
real-world use cases by industry
healthcare
current applications:
- assisting in diagnosing diseases from medical images
- personalizing treatment plans based on patient data
- managing patient records and administrative tasks
- summarizing medical research for healthcare providers
LLM applications:
- summarizing medical records for quick review
- providing health information to patients in plain language
- helping with medical coding and billing
- translating medical jargon for patient understanding
finance
current applications:
- detecting fraudulent activities in transactions
- automating trading based on market patterns
- providing personalized financial advice
- assessing credit risk for loan applications
the advantage: AI can process vast amounts of financial data faster than humans, spotting patterns that might indicate fraud or investment opportunities.
retail
current applications:
- personalizing product recommendations
- managing inventory and predicting demand
- providing customer service through chatbots
- optimizing pricing based on demand and competition
LLM applications:
- generating product descriptions
- answering customer questions about products
- providing styling advice and recommendations
- handling returns and exchanges through chat
manufacturing
current applications:
- AI-driven robots handling assembly lines
- predictive maintenance preventing equipment failures
- quality control through visual inspection
- optimizing production schedules
the impact: manufacturers using AI for predictive maintenance report 25-30% reduction in maintenance costs and 70% decrease in equipment downtime.
transportation
current applications:
- autonomous vehicles navigating roads
- optimizing logistics and delivery routes
- improving traffic management systems
- predicting maintenance needs for vehicles
quick reference: spotting AI in your daily life
- email filters: spam detection keeping unwanted emails at bay
- streaming services: Netflix recommending shows based on viewing history
- navigation apps: Google Maps suggesting optimal routes and predicting traffic
- shopping: Amazon showing "customers who bought this also bought"
- social media: your feed being curated based on engagement patterns
- banking: fraud alerts when unusual transactions are detected
what AI can and can’t do (reality check)
what AI is genuinely good at
pattern recognition: spotting patterns in large datasets faster and more accurately than humans
repetitive tasks: handling routine work consistently without fatigue or boredom
processing speed: analyzing vast amounts of information quickly
specific expertise: performing narrow, well-defined tasks with high accuracy
what AI struggles with
common sense reasoning: understanding context the way humans naturally do
genuine creativity: creating truly novel ideas rather than remixing existing patterns
emotional intelligence: truly understanding and responding to human emotions
general flexibility: adapting to completely new situations without retraining
the bottom line
artificial intelligence isn’t one monolithic technology—it’s a diverse set of tools and approaches, each suited to different tasks.
most AI you encounter daily is narrow AI: specialized systems designed to do specific things really well. from recommending your next favorite show to catching fraudulent transactions, these systems are already integrated into your life.
understanding AI categories and capabilities helps you:
- make informed decisions about which AI tools to use
- set realistic expectations for what AI can and can’t do
- recognize AI applications in your daily life
- evaluate vendor claims and marketing hype
the key is matching the right type of AI to the right problem—not expecting one AI system to solve everything.
as AI continues to evolve, the businesses and individuals who understand its categories, capabilities, and limitations will be best positioned to use it effectively.
you don’t need to become an AI expert. you just need to understand enough to ask the right questions and make informed decisions about where AI can genuinely help your business.
ready to understand which AI tools are right for your business challenges? let’s chat about matching AI capabilities to your specific needs.