Technology Concepts Defined Simply

Why Technology Vocabulary Matters

Someone says: "We'll leverage AI and blockchain to disrupt the market." (Buzzwords without substance)

An article claims: "Your data is stored securely in the cloud." (What does that mean? Where is it actually?)

A tech company promises: "Our algorithm is unbiased and fair." (Algorithms aren't neutral—they reflect their design)

Imprecise technology language creates confusion, enables deception, and prevents informed decision-making.

Technology terms have become ubiquitous in daily life—yet many remain mysterious or misunderstood. API, encryption, open source, machine learning—these aren't just jargon for programmers. They describe the infrastructure of modern life.

Understanding actual technology concepts matters because:

  • Technology shapes work, communication, privacy, power
  • Marketing uses technical terms to confuse or impress
  • Policy debates require technical literacy
  • You can't spot manipulation or nonsense without basic vocabulary

This is the vocabulary that demystifies how digital systems actually work—and why that matters for everyone.

Fundamental Computing Concepts

Algorithm

Definition: Step-by-step set of instructions for solving a problem or completing a task.

Metaphor: Recipe (specific steps to produce result)

Characteristics:

  • Deterministic: Same input → Same output (usually)
  • Finite: Terminates in finite steps
  • Unambiguous: Each step clearly defined

Examples:

  • Google search: Algorithm ranks websites based on relevance
  • GPS navigation: Algorithm calculates shortest route
  • Credit scoring: Algorithm predicts loan default risk
  • Social media feed: Algorithm decides what you see

Simple algorithm (find largest number in list):

1. Set "largest" to first number
2. For each remaining number:
   - If number > largest, set "largest" to this number
3. Return "largest"

Why "algorithm" matters:

  • Algorithms aren't neutral (they reflect designer's choices)
  • Algorithms can encode bias (training data, optimization goals)
  • Algorithms are hidden (you don't see how decisions are made)
  • Algorithms have power (determine what you see, who gets loans, who gets arrested)

Common misconception: "Algorithm" sounds scientific/objective. Reality: Algorithms are human-designed tools with embedded values and trade-offs.

Application: When someone says "the algorithm decided," ask: "Who designed the algorithm? What was it optimized for? What does it ignore?"

Data vs. Information vs. Knowledge

Data:

  • Definition: Raw, unprocessed facts
  • Example: "42, 37, 41, 38, 40"

Information:

  • Definition: Processed data given context and meaning
  • Example: "Daily temperatures (°F) this week"

Knowledge:

  • Definition: Information understood and integrated into mental models; actionable insights
  • Example: "It's been consistently cool this week; bring a jacket"

Progression: Data → Information (add context) → Knowledge (add understanding)

Stage Example Characteristics
Data 1.2 GB, 500 kbps, 12 ms Numbers without context
Information Internet speed: 500 kbps download, 12 ms latency Organized, contextualized data
Knowledge My internet is slow; video calls will lag Understanding, implications, action

Why distinction matters: "Data-driven" sounds impressive, but data without interpretation is meaningless. Need context (information) and understanding (knowledge).

Application: Don't confuse collecting data with gaining insight. Data is raw material; knowledge is the product.

Binary and Bits

Binary:

  • Definition: Number system using only 0 and 1
  • Why computers use it: Digital circuits have two states (on/off, high voltage/low voltage)

Bit (binary digit):

  • Definition: Smallest unit of data; single 0 or 1
  • Storage: One bit can represent 2 values (0 or 1)

Byte:

  • Definition: 8 bits
  • Storage: One byte can represent 256 values (2^8)
  • Uses: One character (letter) typically stored as one byte

Larger units:

  • Kilobyte (KB): ~1,000 bytes (10^3)
  • Megabyte (MB): ~1 million bytes (10^6)
  • Gigabyte (GB): ~1 billion bytes (10^9)
  • Terabyte (TB): ~1 trillion bytes (10^12)

Example scales:

  • Text message: ~1 KB
  • Photo: ~3-5 MB
  • Movie: ~1-5 GB
  • Hard drive: 500 GB - 2 TB

Why it matters: Understanding scale helps evaluate storage needs, bandwidth, data privacy (how much data companies collect).

Application: When company says "we collect minimal data," ask how many bytes. Context matters.

Internet and Networking

Internet vs. Web

Internet:

  • Definition: Global network of interconnected computers using standardized protocols (TCP/IP)
  • Analogy: Highway system (infrastructure)
  • Includes: Email, file transfer, video calls, messaging, and...

World Wide Web (Web):

  • Definition: System of interlinked documents (web pages) accessed via internet using HTTP/HTTPS
  • Analogy: One specific type of traffic on highway (websites, browsers)
  • Inventor: Tim Berners-Lee (1989)

Relationship: Web runs on internet. Internet existed before web.

Aspect Internet Web
What Network infrastructure Document/information system
Protocol TCP/IP HTTP/HTTPS
Invented 1960s-1980s (ARPANET) 1989 (Berners-Lee)
Includes Email, FTP, VoIP, Web, etc. Websites, browsers

Application: "Internet access" is broader than "web browsing." Many internet services aren't web-based (email apps, messaging, games).

IP Address and DNS

IP Address (Internet Protocol Address):

  • Definition: Unique numerical address assigned to each device on a network
  • Format: IPv4 (192.168.1.1) or IPv6 (2001:0db8:85a3:0000:0000:8a2e:0370:7334)
  • Function: Identifies and locates devices (like postal address for computers)

DNS (Domain Name System):

  • Definition: System that translates human-readable domain names into IP addresses
  • Metaphor: Phone book (name → number)
  • Example: whennotesfly.com → 192.0.2.1

Why needed: Humans remember names (google.com); computers use numbers (142.250.185.46). DNS bridges gap.

Process:

  1. You type: "google.com"
  2. DNS lookup: "What's the IP for google.com?" → "142.250.185.46"
  3. Browser connects to that IP address
  4. Page loads

Application: DNS is critical infrastructure. Controlling DNS can censor or redirect traffic (why DNS security matters).

Bandwidth and Latency

Bandwidth:

  • Definition: Maximum data transfer rate (how much data can flow)
  • Metaphor: Width of pipe (more water flows through wider pipe)
  • Units: Bits per second (bps), kilobits per second (kbps), megabits per second (Mbps)
  • Affects: Download/upload speed, video quality

Latency:

  • Definition: Time delay for data to travel from source to destination
  • Metaphor: Length of pipe (longer pipe = more travel time)
  • Units: Milliseconds (ms)
  • Affects: Responsiveness, real-time interaction (video calls, gaming)

Both matter:

  • High bandwidth, high latency: Downloads fast but delayed response (satellite internet)
  • Low bandwidth, low latency: Slow download but responsive (old dial-up, best case)
  • High bandwidth, low latency: Fast and responsive (ideal—fiber optic)
Activity Bandwidth Need Latency Sensitivity
Email Very low Very low
Web browsing Low-medium Medium
Video streaming High Medium
Video calls Medium High (delay obvious)
Online gaming Medium Very high (lag = death)

Application: "Fast internet" can mean high bandwidth or low latency. Different activities need different things.

Software and Development

API (Application Programming Interface)

Definition: Set of rules and protocols allowing different software programs to communicate and share data.

Metaphor: Restaurant menu

  • You (customer) don't need to know how kitchen works
  • Menu (API) shows what you can order
  • Kitchen (backend system) prepares order
  • Waiter (API) delivers result

Example:

  • Weather app on your phone doesn't measure weather
  • It calls weather service's API: "What's the weather in New York?"
  • API returns data: "72°F, sunny"
  • App displays it

Why APIs matter:

  • Enable integration (different services work together)
  • Hide complexity (use functionality without understanding implementation)
  • Create ecosystems (developers build on platforms via APIs)

Types:

  • Web API: Access over internet (HTTP/HTTPS)
  • Library API: Functions within programming language
  • Operating System API: Access system resources (files, network)

Real-world APIs:

  • Google Maps API (embed maps in your app)
  • Payment APIs (Stripe, PayPal—process payments)
  • Social media APIs (post to Twitter from another app)

Application: When service says "we don't share data," check API documentation. APIs can expose data you didn't realize.

Open Source vs. Proprietary Software

Open Source:

  • Definition: Software whose source code is publicly available for anyone to view, modify, and distribute
  • License: Varies (MIT, GPL, Apache—different permissions)
  • Development: Often collaborative (community contributors)
  • Cost: Usually free (but paid support available)

Examples: Linux, Firefox, WordPress, LibreOffice, Python

Advantages:

  • Transparency (can audit code for security/privacy)
  • Customization (modify to fit needs)
  • Community support and innovation
  • No vendor lock-in

Disadvantages:

  • Support may be limited or volunteer-based
  • User interface sometimes less polished
  • Compatibility issues possible

Proprietary (Closed Source):

  • Definition: Software whose source code is secret; controlled by company
  • License: Restrictive (can't modify, often can't share)
  • Development: Internal (company employees)
  • Cost: Often paid (or monetized through ads/data)

Examples: Windows, Microsoft Office, Adobe Photoshop, most mobile apps

Advantages:

  • Professional support
  • Often polished interface
  • Guaranteed compatibility (within ecosystem)

Disadvantages:

  • No transparency (can't audit code)
  • Vendor lock-in (dependent on company)
  • Cost (subscription or licensing fees)
  • Limited customization

Hybrid models: "Open core" (basic version open source, premium features proprietary)

Application: Open source doesn't automatically mean "better" or "more secure"—depends on project. But transparency enables verification.

Cloud Computing

Definition: Storing and accessing data and programs over the internet instead of on local computer's hard drive.

"The cloud": Not magical. Means "someone else's computer" (usually massive data centers).

Types:

1. Software as a Service (SaaS):

  • What: Use software over internet (don't install locally)
  • Examples: Gmail, Google Docs, Salesforce, Dropbox
  • User: General users

2. Platform as a Service (PaaS):

  • What: Development and deployment platform over internet
  • Examples: Heroku, Google App Engine
  • User: Developers (build apps without managing servers)

3. Infrastructure as a Service (IaaS):

  • What: Virtual servers, storage, networking over internet
  • Examples: Amazon Web Services (AWS), Microsoft Azure, Google Cloud
  • User: IT departments (configure own infrastructure)

Advantages:

  • Access from anywhere
  • Scalability (add resources as needed)
  • No hardware maintenance
  • Often cheaper (no upfront infrastructure costs)

Disadvantages:

  • Requires internet connection
  • Privacy/security concerns (data on someone else's servers)
  • Vendor lock-in
  • Ongoing costs (subscription)

Privacy implication: "Cloud storage" means company has access to your data (legally, technically, or both). Encryption matters.

Application: "Cloud" isn't inherently good or bad. Ask: Where is data stored? Who can access it? What happens if service shuts down?

Security and Privacy

Encryption

Definition: Converting information into code (ciphertext) to prevent unauthorized access; only those with key can decode (decrypt) it.

Metaphor: Locking document in safe. Only those with combination can open.

Types:

1. Symmetric Encryption:

  • Method: Same key encrypts and decrypts
  • Example: AES (Advanced Encryption Standard)
  • Use case: Fast, efficient; used for data at rest
  • Challenge: How to securely share key?

2. Asymmetric Encryption (Public Key Cryptography):

  • Method: Two keys—public key (encrypts), private key (decrypts)
  • Example: RSA, ECC
  • Use case: Secure communication over internet (HTTPS)
  • Advantage: Can share public key openly; only private key holder can decrypt

Example - Sending encrypted email:

  1. You encrypt message with recipient's public key
  2. Only recipient (holding private key) can decrypt

End-to-End Encryption (E2EE):

  • Definition: Only sender and recipient can read messages; service provider cannot
  • Examples: Signal, WhatsApp (when enabled), iMessage
  • Contrast: Standard email (Gmail, Yahoo) is not E2EE—provider can read messages

Why encryption matters:

  • Protects privacy (eavesdroppers can't read)
  • Secures data (hackers accessing database get gibberish without keys)
  • Enables secure transactions (online banking, shopping)

Application: Check for HTTPS (encrypted web connection) before entering passwords or payment info. Use E2EE for private conversations.

Cookies and Tracking

Cookie:

  • Definition: Small text file stored on your device by websites
  • Purpose: Remember information (login status, preferences, shopping cart)
  • Types:
    • Session cookies: Temporary (deleted when browser closes)
    • Persistent cookies: Remain for set duration
    • First-party cookies: Set by website you're visiting
    • Third-party cookies: Set by external services (ads, analytics)

Why cookies matter:

  • Enable functionality (stay logged in, remember preferences)
  • Enable tracking (build profile of browsing history)

Tracking technologies:

  • Cookies: Traditional method
  • Browser fingerprinting: Identify you by unique browser configuration
  • Pixel tracking: Invisible images loading from external server (email open tracking)
  • Device IDs: Mobile advertising identifiers

Privacy concerns:

  • Third-party cookies track across websites (build comprehensive profile)
  • Data sold to data brokers
  • Targeted ads based on behavior
  • Potential discrimination (insurance, employment based on profiles)

Controls:

  • Browser settings (block third-party cookies)
  • Private/incognito mode (doesn't save cookies long-term)
  • VPN (hides IP address)
  • Tracker blockers (browser extensions)

Application: "Accept all cookies" means "let us track everything." Read privacy policies or use blocking tools.

Two-Factor Authentication (2FA)

Definition: Security method requiring two different forms of verification to log in.

Factors:

  1. Something you know: Password, PIN
  2. Something you have: Phone, security key, authentication app
  3. Something you are: Fingerprint, face recognition

Common 2FA methods:

Method Factor Security Level Convenience
SMS code Phone (have) Medium (SIM swapping risk) High
Authenticator app (Google Authenticator, Authy) Phone (have) High High
Security key (YubiKey) Physical device (have) Very high Medium
Biometric Fingerprint/face (are) Varies Very high

Why 2FA matters:

  • Even if password stolen, attacker needs second factor
  • Dramatically reduces account compromise risk

Best practice: Enable 2FA on critical accounts (email, banking, social media).

Application: Password alone is insufficient. Use 2FA wherever possible, preferably authenticator app or hardware key (more secure than SMS).

Emerging Technology Concepts

Artificial Intelligence (AI) and Machine Learning (ML)

Artificial Intelligence (AI):

  • Definition: Broad field—systems performing tasks typically requiring human intelligence
  • Includes: Learning, reasoning, problem-solving, perception, language understanding
  • Example: Chess-playing computer, voice assistant, self-driving car

Machine Learning (ML):

  • Definition: Subset of AI—computers learn patterns from data without explicit programming for each case
  • Process: Feed data → Algorithm finds patterns → Makes predictions on new data
  • Example: Email spam filter (learns what spam looks like from examples)

Relationship: ML is technique within broader AI field.

Types of ML:

1. Supervised Learning:

  • Method: Learn from labeled examples (input + correct output)
  • Example: Show 1,000 cat photos labeled "cat" → Algorithm learns to recognize cats
  • Use: Classification, prediction

2. Unsupervised Learning:

  • Method: Find patterns in unlabeled data
  • Example: Group customers by purchasing behavior (no predefined categories)
  • Use: Clustering, pattern discovery

3. Reinforcement Learning:

  • Method: Learn through trial and error (rewards for good actions, penalties for bad)
  • Example: Game-playing AI (AlphaGo)
  • Use: Strategy, optimization

Why AI/ML matters:

  • Increasingly makes decisions affecting you (hiring, loans, bail, medical diagnosis)
  • Can encode bias (biased training data → biased decisions)
  • Often opaque ("black box"—even creators don't fully understand how decisions are made)

Myth vs. Reality:

  • Myth: AI is intelligent like humans
  • Reality: AI is pattern recognition—very good at narrow tasks, no general understanding

Application: When AI makes decision about you, ask: What data was it trained on? What is it optimizing? Can decision be appealed?

Blockchain

Definition: Distributed ledger (record-keeping system) shared across network; data stored in "blocks" linked in "chain."

Key characteristics:

  • Decentralized: No single authority controls it
  • Immutable: Once added, data can't be changed (very hard)
  • Transparent: All participants can see ledger (usually)
  • Cryptographically secured: Blocks linked using cryptographic hashes

How it works (simplified):

  1. Transaction requested
  2. Broadcast to network
  3. Network validates transaction
  4. Transaction added to new block
  5. Block added to chain
  6. Transaction complete

Use cases:

  • Cryptocurrency: Bitcoin, Ethereum (digital money without central bank)
  • Supply chain: Track product origin, authenticity
  • Smart contracts: Self-executing agreements (conditions met → Action automatic)
  • Voting: Tamper-resistant records

Advantages:

  • No central point of failure
  • Transparent and auditable
  • Resistant to tampering

Disadvantages:

  • Slow (compared to centralized databases)
  • Energy-intensive (especially Proof of Work systems like Bitcoin)
  • Hard to fix errors (immutability is feature and bug)
  • Scalability challenges

Hype vs. Reality: Blockchain is useful for specific problems (distrust in central authority, need for transparency). Not useful for everything—often slower and more expensive than traditional databases.

Application: Don't assume "blockchain" makes something legitimate or revolutionary. Ask: Why is decentralization needed here? What problem does this solve?

Internet of Things (IoT)

Definition: Network of physical devices ("things") embedded with sensors, software, connectivity—can collect and exchange data.

Examples:

  • Smart home devices (thermostats, lights, locks)
  • Wearables (fitness trackers, smartwatches)
  • Connected appliances (refrigerators, washing machines)
  • Industrial sensors (manufacturing, agriculture)
  • Smart city infrastructure (traffic lights, parking meters)

How it works:

  • Devices collect data (sensors)
  • Send data to cloud or local network
  • Data analyzed (sometimes AI)
  • Action taken (automation, alerts, insights)

Benefits:

  • Convenience (automate tasks)
  • Efficiency (optimize energy, reduce waste)
  • Insights (data-driven decisions)

Risks:

  • Security: Many IoT devices poorly secured (vulnerable to hacking)
  • Privacy: Constant data collection (who has access?)
  • Reliability: Depend on internet connection
  • Obsolescence: Manufacturer stops supporting → Device becomes useless or insecure

Famous incident: Mirai botnet (2016)—hackers infected IoT devices (cameras, DVRs), used them to launch massive cyberattack.

Application: Before buying IoT device, ask: Is it necessary? How is data used? Can it be secured? What happens if company shuts down?

Practical Digital Literacy

Why this vocabulary matters:

1. Make informed decisions:

  • Understand what you're agreeing to (privacy policies, terms of service)
  • Evaluate product claims (AI, blockchain, encryption)

2. Protect yourself:

  • Recognize security threats
  • Understand privacy implications
  • Use technology safely

3. Participate in important debates:

  • Policy (encryption backdoors, net neutrality, algorithmic bias)
  • Ethics (AI decision-making, surveillance, data ownership)
  • Future (how technology should be regulated)

4. Spot manipulation and nonsense:

  • Buzzword bingo ("AI-powered blockchain synergy")
  • Misleading claims ("military-grade encryption" for consumer product)
  • False equivalences ("open source = insecure")

Technology isn't neutral. Every design choice reflects values, priorities, trade-offs. Understanding the vocabulary helps you see past marketing, ask better questions, and make intentional choices.

Don't let jargon intimidate you. Most tech concepts are simple once explained clearly.

Learn the language. Question the claims. Make informed choices.

Essential Readings

General Technology Literacy:

  • Petzold, C. (2000). Code: The Hidden Language of Computer Hardware and Software. Redmond, WA: Microsoft Press. [How computers work from first principles]
  • Gleick, J. (2011). The Information: A History, A Theory, A Flood. New York: Pantheon. [History and theory of information]
  • Rushkoff, D. (2010). Program or Be Programmed. New York: OR Books. [Digital literacy principles]

Internet and Networking:

  • Blum, A. (2012). Tubes: A Journey to the Center of the Internet. New York: Ecco. [Physical infrastructure of internet]
  • Wu, T. (2010). The Master Switch. New York: Knopf. [History of information networks]

Security and Privacy:

  • Schneier, B. (2015). Data and Goliath. New York: W. W. Norton. [Surveillance and privacy]
  • Singh, S. (1999). The Code Book. New York: Doubleday. [History of cryptography]
  • Zuboff, S. (2019). The Age of Surveillance Capitalism. New York: PublicAffairs. [Data collection and power]

Algorithms and AI:

  • O'Neil, C. (2016). Weapons of Math Destruction. New York: Crown. [Algorithmic bias and harm]
  • Christian, B., & Griffiths, T. (2016). Algorithms to Live By. New York: Henry Holt. [Algorithms in everyday decisions]
  • Marcus, G., & Davis, E. (2019). Rebooting AI. New York: Pantheon. [Realistic view of AI limitations]

Software and Development:

  • Raymond, E. S. (1999). The Cathedral and the Bazaar. Sebastopol, CA: O'Reilly. [Open source development]
  • Brooks, F. P. (1995). The Mythical Man-Month (Anniversary ed.). Boston: Addison-Wesley. [Software engineering]

Blockchain and Emerging Tech:

  • Narayanan, A., Bonneau, J., Felten, E., Miller, A., & Goldfeder, S. (2016). Bitcoin and Cryptocurrency Technologies. Princeton: Princeton University Press.
  • Tapscott, D., & Tapscott, A. (2016). Blockchain Revolution. New York: Portfolio/Penguin.

Digital Ethics and Society:

  • Eubanks, V. (2018). Automating Inequality. New York: St. Martin's Press. [Technology and social policy]
  • Noble, S. U. (2018). Algorithms of Oppression. New York: NYU Press. [Search engines and bias]
  • Lessig, L. (2006). Code: Version 2.0. New York: Basic Books. [How code regulates behavior]

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