Showing posts with label AI Accuracy. Show all posts

Monday, October 19, 2026

AI Fluency Module 5: Capabilities and Limitations | AI Strengths, Hallucinations, Accuracy & Risks

AI FLUENCY · PHASE 1: AWARENESS

Module 5 — Capabilities and Limitations

Discover what AI can do well, why it sometimes gets things wrong, and how to use it responsibly.

Artificial Intelligence offers powerful capabilities for learning, creativity, problem-solving, and productivity. But AI also has limitations. Understanding both its strengths and its risks is essential for AI Fluency.

4 Lessons 4 Videos 4 Text Lessons Beginner Friendly

AIYING — Accomplishing with AI

Welcome to Module 5

In the first four modules, we explored what Artificial Intelligence is, how it learns, how language models work, and the wider AI landscape.

Now we examine an important question:

What can AI do well, and what should we be careful about?

Modern AI can recognize patterns, generate content, explain information, support problem-solving, and assist with everyday tasks.

However, AI may also generate invented information, express unwarranted confidence, reflect unfair patterns, or create privacy and safety risks.

Through four lessons, you will learn to recognize these opportunities and limitations and develop habits for using AI responsibly.

How to Use This Learning Hub

Each lesson includes a short visual YouTube video and a detailed written explanation.

WATCH: Begin with the embedded video to understand the central concept.

READ: Open the linked article for explanations and everyday examples.

UNDERSTAND: Connect the lesson to your learning, work, or daily activities.

REFLECT: Answer the questions and think about how you would apply the concept responsibly.

Four Lessons at a Glance

017. AI Capabilities

Discover how AI recognizes patterns, generates content, explains information, supports problem-solving, and assists with tasks.

018. AI Hallucinations

Understand why AI sometimes generates invented facts, false references, and misleading explanations.

019. Confidence & Accuracy

Learn why AI can sound convincing even when its information is incomplete or incorrect.

020. Responsible AI Risks

Explore bias, privacy, safety, safeguards, and the importance of human oversight.

LESSON 017 · AI CAPABILITIES

What Can Today's AI Do Well?

AI can support a wide range of human activities.

Its capabilities include recognizing patterns, generating content, explaining information, exploring possible solutions, and assisting with tasks.

Everyday Example

A student preparing a presentation about climate change can ask AI to explain the topic, develop an outline, suggest examples, and help draft slide content.

The student then checks important facts and prepares the final presentation.

Key Takeaway

AI can recognize, generate, explain, explore, and assist. Its capabilities are most valuable when combined with human goals and judgment.

Think About It: Which activity in your daily life could become easier or more creative with AI assistance?

Read Text Lesson 017 → Watch on YouTube →

LESSON 018 · AI HALLUCINATIONS

What Are AI Hallucinations?

Artificial Intelligence can produce answers that sound natural and convincing but contain invented or incorrect information.

These errors are commonly called AI hallucinations.

Everyday Example

A student asks an AI assistant to suggest research papers about plastic packaging waste.

The system generates a convincing reference that does not actually exist.

Without checking the original publication, the student could unknowingly cite a fabricated source.

Key Takeaway

AI can generate convincing answers without generating correct answers. Important information must be verified.

Think About It: How would you verify an AI-generated research reference before using it in an assignment?

Read Text Lesson 018 → Watch on YouTube →

LESSON 019 · AI CONFIDENCE AND ACCURACY

Why Can AI Be Wrong Even When It Sounds Confident?

An AI assistant can produce clear, well-structured, confident-sounding answers even when its information is incomplete or inaccurate.

Language fluency and factual reliability are not the same.

Everyday Example

You ask AI for Bengaluru's exact population today.

The AI gives a precise figure without identifying whether it is an official census count, an estimate, or a number based on different city boundaries.

The answer sounds definite, although the evidence may not support that certainty.

Key Takeaway

Confidence is not correctness. Judge AI-generated information using evidence, sources, and appropriate human judgment.

Think About It: If two AI assistants give different answers and both sound confident, how would you determine which answer is supported by evidence?

Read Text Lesson 019 → Watch on YouTube →

LESSON 020 · RESPONSIBLE AI RISKS

What Are AI Bias, Privacy, and Safety Risks?

AI systems can produce unfair outcomes, create privacy concerns, or contribute to harmful decisions.

Responsible AI use requires appropriate safeguards, careful evaluation, and human accountability.

Everyday Example

A company uses AI to help shortlist job applicants.

The system could produce unfair recommendations, mishandle personal information, or influence important decisions through unreliable outputs.

Appropriate testing, privacy protections, and human review help reduce these risks.

Key Takeaway

AI capability creates possibilities. Responsible AI use protects people. Fairness, privacy, safety, and human oversight matter.

Think About It: If an AI system helped you make a decision affecting another person, what safeguards would you put in place?

Read Text Lesson 020 → Watch on YouTube →

Connecting the Four Lessons

Module 5 introduces four connected ideas.

1. CAPABILITY

Understand what AI can do well and which tasks benefit from its assistance.

↓

2. HALLUCINATIONS

Recognize that AI may generate invented or unsupported information.

↓

3. UNCERTAINTY

Understand why fluent and confident answers are not necessarily accurate.

↓

4. RESPONSIBILITY

Consider fairness, privacy, safety, and human accountability when using AI.

Together, these concepts help us recognize both AI's potential and the precautions needed for responsible use.

Before You Trust an AI Answer

Use this simple checklist:

1. Appropriate Capability
Is this a task the AI system is well suited to perform?

2. Factual Accuracy
Have I verified important claims using reliable sources?

3. Uncertainty
Does the answer distinguish facts from estimates and assumptions?

4. Fairness and Privacy
Could the output be unfair, or could the task expose sensitive information?

5. Human Judgment
Who should review the result and remain responsible for the final decision?

AI Fluency means knowing when to use AI, when to question it, and when to rely on independent evidence.

Check Your Understanding

After completing all four lessons, try answering these questions without referring to the material.

What are five important capabilities of modern AI?
What is an AI hallucination? Give an everyday example.
Why can a confident AI answer still be incorrect?
How are AI bias, privacy, and safety risks different?
What verification and human oversight practices would you use before relying on an important AI recommendation?

Try explaining your answers with examples from learning, work, or everyday life.

Module 5 Reflection Challenge

Choose a real task where you might use AI.

For example:

Preparing a presentation
Researching a topic
Drafting a professional document
Planning an activity
Analyzing information

Now answer four questions:

What can AI help you accomplish?
What might AI get wrong?
What risks could arise?
How would you verify and review the result?

The objective is not merely to use AI, but to use it with awareness and judgment.

Module 5 Complete!

You have now explored AI's capabilities and limitations across four learning lessons.

Across Modules 1–5, you have completed access to twenty foundational video and reading lessons.

20 of 24 Awareness Lessons

Only one planned module remains in the Awareness phase.

COMING NEXT

Module 6 — AI and the Human World

Having explored what AI can do and where it can go wrong, we now consider how AI is changing people, institutions, and society.

Lesson 021: How Is AI Changing Learning and Education?

Lesson 022: How Is AI Changing Jobs and Work?

Lesson 023: Why Do AI Ethics, Governance, and Human Oversight Matter?

Lesson 024: What Is AI Fluency, and Why Does Everyone Need It?

Module 6 completes Phase 1 — Awareness and prepares learners for the next phase: Appreciation.

Your AI Fluency Journey

AI Fluency progresses through four connected phases.

AWARENESS → APPRECIATION → APPLICATION → ACCOMPLISHMENT

Explore the complete 36-module roadmap and continue building your AI Fluency.

Explore the Complete AI Fluency Roadmap →

19. Why Can AI Be Wrong Even When It Sounds Confident? | AI Fluency Lesson 019

AI FLUENCY · PHASE 1: AWARENESS

Module 5 — Capabilities and Limitations | Lesson 019

Why Can AI Be Wrong Even When It Sounds Confident?

Discover why convincing AI answers are not always correct, and learn to judge information using evidence.

Watch the 54-second visual lesson, then explore the explanations, examples, and reflection questions below.

Watch on YouTube →

Introduction

Have you ever asked an AI assistant a question and received an answer that sounded completely certain?

The response may have been clear, well organized, and convincing.

But what happens when you discover that the answer was incorrect?

This reveals an important distinction: the ability to generate fluent language is not the same as the ability to establish factual truth.

In Lesson 018, we explored AI hallucinations.

Now we examine why AI systems can express information confidently even when their answers are inaccurate.

1. Why Does AI Sound Confident?

Large Language Models generate responses by predicting likely sequences of tokens using learned statistical patterns and the available context.

Their training helps them produce natural, coherent, and well-structured language.

However, the generation of a confident-sounding sentence does not require the model to have independently verified every factual claim.

In Simple Words

AI can produce language that sounds certain even when the information behind it is uncertain.

A confident writing style should therefore not be interpreted as proof of accuracy.

2. Fluency, Accuracy, and Reliability

These three concepts are related, but they are not the same.

Fluency

💬

How naturally and clearly an answer is expressed.

Accuracy

🎯

Whether the information or result is correct.

Reliability

🛡️

How dependably a system performs under relevant conditions and tasks.

Important Distinction

Fluent language does not guarantee accurate information, and one accurate answer does not establish reliability.

3. Everyday Example: Bengaluru's Population

Imagine asking an AI assistant:

Your Question

"What is the exact population of Bengaluru today?"

The AI responds with a precise number and presents it confidently.

But several important questions remain:

Is the number from an official census or an estimate?
What year does the figure represent?
Does the figure refer to the city, its municipal boundary, or a larger metropolitan area?
What is the original source?

A population figure can appear precise while concealing uncertainty about its meaning and origin.

The problem is not necessarily that one number is completely fabricated.

The answer may be outdated, based on an estimate, or use a different geographical definition.

A More Responsible Answer

"There may not be a verified exact population count for today. Official census figures and more recent estimates may differ. We should identify the year, source, and geographical boundary before selecting a figure."

This kind of answer communicates uncertainty rather than hiding it behind a precise number.

4. Why Doesn't AI Always Express Uncertainty?

Several factors can contribute to overconfident AI responses.

Learned Language Patterns

Training data includes many confidently written statements, which can influence response style.

Question Wording

A question demanding an exact answer may encourage an overly definite response.

Missing Context

The model may lack the information needed to identify uncertainties or important distinctions.

Imperfect Calibration

Expressions or estimates of confidence may not match actual correctness.

These factors help explain why conversational confidence and factual reliability can become disconnected.

Some AI systems can acknowledge uncertainty effectively, especially when prompted or supported by relevant evidence.

However, uncertainty statements are not always accurate either.

5. How Should We Evaluate Confident AI Answers?

A useful AI Fluency habit is to evaluate information based on evidence rather than conversational style.

From Confident Answer to Informed Judgment

1. QUESTION THE CLAIM

What exactly is the AI asserting?

↓

2. LOOK FOR EVIDENCE

Find reliable sources supporting the important statements.

↓

3. IDENTIFY UNCERTAINTY

Check assumptions, dates, definitions, and missing information.

↓

4. APPLY HUMAN JUDGMENT

Decide what can be trusted, qualified, corrected, or rejected.

Question → Evidence → Uncertainty → Judgment

For everyday low-risk activities, a quick review may be enough.

For academic, financial, legal, medical, or other consequential decisions, stronger verification and appropriate expert oversight are necessary.

6. Ask Better Questions

You can encourage more careful AI responses by asking for evidence and limitations.

Instead of Asking

"Tell me the exact answer."

Try Asking

"Explain what is known, identify any uncertainty, distinguish facts from estimates, and provide sources that I can independently verify."

This can improve the usefulness of the response, but it does not guarantee accuracy.

An AI model can also invent sources or provide incorrect explanations of its own uncertainty.

Always check critical claims against appropriate evidence.

Key Takeaway

Confidence is not correctness.

Judge AI answers by evidence, not by how convincing they sound.

AI can support our understanding, but factual reliability requires appropriate verification and informed human judgment.

Think About It

Imagine asking the same factual question to two AI assistants.

Both provide different answers, and both sound confident.

Which answer should you trust?

How would you determine whether either answer is correct?

Your challenge: Choose one AI-generated factual answer, identify its important assumptions, and verify its central claim using an authoritative source.

What Have You Learned?

After completing Lesson 019, you should be able to:

Explain why AI can produce confident-sounding incorrect answers.
Distinguish fluency, accuracy, and reliability.
Recognize uncertainty behind apparently precise claims.
Ask questions that encourage evidence and appropriate qualification.
Apply human judgment when evaluating AI responses.

CONTINUE LEARNING

Next: Lesson 020

What Are AI Bias, Privacy, and Safety Risks?

In the final lesson of Module 5, we will explore three important areas of responsible AI use:

Bias: Why AI systems may produce unfair or unrepresentative outcomes.

Privacy: Why personal and confidential information requires protection.

Safety: Why AI systems need appropriate safeguards and human oversight.

Lesson 020 completes Module 5 — Capabilities and Limitations.

Further Learning Resources

OpenAI Academy

Claude Academy

NIST AI Risk Management Framework

Module 5 — Learning Progress

017. What Can Today's AI Do Well?

018. What Are AI Hallucinations?

019. Why Can AI Be Wrong Even When It Sounds Confident?

020. What Are AI Bias, Privacy, and Safety Risks?

Your AI Fluency Journey

AI Fluency progresses through four connected phases.

AWARENESS → APPRECIATION → APPLICATION → ACCOMPLISHMENT

From understanding Artificial Intelligence to using it responsibly to accomplish meaningful goals.

Explore the Complete AI Fluency Roadmap →

18. What Are AI Hallucinations? Why AI Makes Up Facts | AI Fluency Lesson 018

AI FLUENCY · PHASE 1: AWARENESS

Module 5 — Capabilities and Limitations | Lesson 018

What Are AI Hallucinations?

Why AI sometimes invents facts, citations, and explanations — and how to recognize misleading answers.

Watch the visual lesson. Then read the examples, explanations, and verification tips below.

Watch on YouTube →

Introduction

Imagine asking an AI assistant for information about a scientific discovery.

It responds with a detailed explanation, names of researchers, publication dates, and convincing references.

The answer sounds authoritative. But when you investigate, some of the information turns out to be incorrect or entirely invented.

This is an example of what is commonly called an AI hallucination.

Understanding hallucinations is important because AI can generate language that sounds natural and convincing without guaranteeing factual accuracy.

1. What Is an AI Hallucination?

An AI hallucination occurs when an AI system generates information that is false, fabricated, or unsupported, while presenting it as though it were reliable.

The term is especially common when discussing Generative AI and Large Language Models.

In Simple Words

An AI hallucination is a convincing AI-generated answer that contains invented or incorrect information.

Hallucinations do not necessarily mean the AI system is deliberately trying to deceive someone.

The term describes a problem with the generated output, not a human-like experience or conscious intention.

2. What Do AI Hallucinations Look Like?

Hallucinations can take several forms.

❌ Invented Facts

AI describes an event that never happened or provides an incorrect date, number, or historical detail.

📄 False References

AI generates a publication title, author name, or citation that does not correspond to a real source.

💬 Fabricated Quotations

AI attributes words to someone who never made the statement.

💡 Incorrect Explanations

AI produces a plausible explanation that includes false reasoning or misleading conclusions.

These errors can be difficult to recognize because the response may be grammatically correct, well organized, and confident.

3. Why Do AI Hallucinations Happen?

Many language models generate responses by predicting likely sequences of tokens based on learned patterns and the information available in the current context.

This process can produce useful and accurate explanations.

However, producing plausible language is not the same as checking every statement against reliable evidence.

Common Contributing Factors

Missing information: The model may not have access to the necessary facts.
Ambiguous questions: An unclear request may encourage unsupported assumptions.
Incorrect inferences: The model may combine patterns into a conclusion that is wrong.
Insufficient grounding: The response may not be supported by relevant, trustworthy sources.
Task complexity: Long or complicated tasks can create more opportunities for unsupported details.

Some AI systems can search the web, retrieve documents, or consult databases.

These features can help ground answers in external information, but they do not guarantee correctness.

4. Everyday Example: The Invented Research Paper

Imagine a student preparing an assignment about plastic packaging waste.

The student asks:

Student's Question

"Suggest five scientific research papers about the environmental impact of plastic packaging, including authors and publication years."

The AI generates five references. The titles appear professional, and each entry contains author names and publication details.

The student searches for one of the papers but cannot find the publication.

After checking relevant scholarly databases and publisher records, the student discovers that the reference was fabricated.

Illustrative Example

AI-generated reference: A convincing article title, author list, publication year, and journal name.

Verification result: No matching publication can be found in the expected journal or reliable scholarly records.

Conclusion: The reference should not be cited as an authentic publication without sufficient verification.

This fictional scenario illustrates how a hallucination can enter academic work if references are accepted without checking.

5. Why Are Hallucinations Important?

A hallucinated answer may be a minor inconvenience in a casual conversation, but it can have greater consequences in important tasks.

🎓 Education

Invented references can weaken assignments and research projects.

💼 Professional Work

Incorrect claims may lead to poor decisions or unreliable reports.

📢 Communication

False statements can spread when AI-generated content is shared without verification.

🔬 Research

Fabricated evidence can affect the validity of conclusions.

The more important the decision, the more carefully AI-generated information should be evaluated.

6. How Can You Reduce the Risk?

Although hallucinations cannot always be eliminated, you can reduce the risk of relying on incorrect answers.

A Simple AI Verification Workflow

1. IDENTIFY THE CLAIM

What specific fact, number, quotation, or reference needs checking?

↓

2. FIND THE SOURCE

Locate the original publication, official record, or other authoritative evidence.

↓

3. COMPARE THE DETAILS

Check dates, authors, quotations, figures, and surrounding context.

↓

4. USE HUMAN JUDGMENT

Accept, correct, qualify, or reject the information based on the evidence.

Identify → Find → Compare → Judge

Practical Verification Habits

Verify important factual claims using reliable sources.
Check research references in scholarly databases or publisher websites.
Ask AI to distinguish confirmed information from uncertain statements.
Open and read the original source, rather than trusting a generated citation alone.
For high-stakes matters, seek appropriate expert review.

Asking AI to double-check itself may be helpful for identifying possible problems, but it is not independent verification.

A second confident AI answer can still be incorrect.

7. Does Every Incorrect AI Answer Count as a Hallucination?

Not necessarily.

AI can produce incorrect results for several reasons, including calculation mistakes, misunderstood instructions, outdated information, or failures in reasoning.

The term hallucination is most useful for describing generated content that is fabricated, false, or unsupported but presented as factual.

In practice, the boundaries between different AI errors can overlap.

What matters most for the learner is recognizing that an apparently credible answer may still require verification.

Key Takeaway

AI can generate convincing answers without generating correct answers.

Fluency is not factual accuracy.

Verify important facts, quotations, and references before using or sharing them.

Think About It

Imagine an AI assistant gives you a surprising scientific fact or a quotation from a famous researcher.

How would you check whether the statement is genuine?

Which sources would you trust?

What could happen if you shared the claim without checking it?

Your challenge: Choose one factual AI-generated answer and practice verifying its key claims using reliable evidence.

What Have You Learned?

After completing Lesson 018, you should be able to:

Explain what an AI hallucination is.
Identify fabricated facts, references, and quotations.
Describe why language models can generate unsupported information.
Recognize the risks of accepting AI answers without verification.
Apply a simple process for checking AI-generated claims.

CONTINUE LEARNING

Next: Lesson 019

Why Can AI Be Wrong Even When It Sounds Confident?

Now that you understand AI hallucinations, the next lesson explores another important question:

Why can an AI system produce a confident-sounding response even when its answer is incorrect?

We will examine why language fluency and factual reliability are different, and how to interpret AI-generated confidence.

Further Learning Resources

OpenAI Academy

Claude Academy

Google Scholar — Research Reference Verification

Crossref — Search Scholarly Publications

Module 5 — Learning Progress

017. What Can Today's AI Do Well?

018. What Are AI Hallucinations?

019. Why Can AI Be Wrong Even When It Sounds Confident?

020. What Are AI Bias, Privacy, and Safety Risks?

Your AI Fluency Journey

AI Fluency progresses through four connected phases.

AWARENESS → APPRECIATION → APPLICATION → ACCOMPLISHMENT

From understanding AI to using it responsibly to accomplish meaningful goals.

Explore the Complete AI Fluency Roadmap →

Monday, October 5, 2026

The Minimalist AI Checker — AI Checkers Explained in 100 Words

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The Minimalist AI Checker

AI Checkers Explained in 100 Words

10 ideas × 10 words = 100 words.

Everything AI. For Everyone.

Why This Book?

Generative AI can produce convincing answers in seconds. Convincing, however, does not necessarily mean correct.

AI can misunderstand context, reproduce bias, make unsupported claims, confuse sources, or generate information that sounds plausible but is false.

This makes checking AI output an essential human skill.

An AI Checker is not merely a tool. It is a disciplined way of examining AI-generated information before trusting, sharing, publishing, or acting upon it.

This minimalist book explains that checking process through ten essential ideas.

How to Read This Book

Start with the bold ten-word sentence under each idea.

Each sentence captures one essential principle. The explanation underneath helps you understand how that principle works when checking AI output.

Follow the ideas in sequence:

Claim → Source → Evidence → Accuracy → Bias → Hallucination → Context → Risk → Human Judgment → Decision

Together, they create a simple human-centered method for deciding whether an AI output is trustworthy and fit for use.

1. Claim

Identify exactly what claim the AI output asks you to trust.

Checking begins by identifying the actual claim. Separate facts, predictions, interpretations, recommendations, and opinions. A vague response becomes easier to evaluate when converted into specific claims that can be examined individually.

2. Source

Trace important claims back to reliable, relevant, original information sources.

Ask where the information came from. Prefer authoritative and primary sources whenever possible. A confident AI response without a traceable source should not automatically be treated as established fact.

3. Evidence

Look for evidence that independently supports the AI generated claim.

A source alone is not enough. Determine whether the available evidence actually supports the claim being made. Strong conclusions require relevant, sufficient, and independently verifiable evidence.

4. Accuracy

Verify facts, numbers, names, dates, quotations, references before using them.

AI outputs can contain small factual errors hidden inside otherwise useful responses. Check important details against dependable sources, especially when the information affects decisions, publications, research, or professional work.

5. Bias

Examine whether hidden assumptions unfairly shape the AI generated answer.

AI can reproduce patterns and assumptions present in data, prompts, and surrounding context. Look for missing perspectives, stereotypes, imbalanced framing, and conclusions that privilege one viewpoint without adequate justification.

6. Hallucination

Detect plausible sounding information that AI generated without factual support.

Generative AI can produce fluent statements that are unsupported or invented. References, quotations, events, statistics, names, and technical details deserve particular attention whenever they cannot be independently verified.

7. Context

Check whether context changes the meaning, relevance, or usefulness significantly.

Information may be technically correct yet inappropriate for a particular situation. Consider audience, location, time, purpose, assumptions, limitations, and surrounding circumstances before deciding that an answer is useful.

8. Risk

Increase checking rigor when consequences of errors become more serious.

Not every AI output requires the same level of scrutiny. A brainstorming suggestion carries different consequences from medical, financial, legal, safety, governance, or high-value organizational advice. Higher stakes demand stronger verification.

9. Human Judgment

Apply human knowledge, experience, responsibility, and judgment before accepting outputs.

Checking cannot be completely delegated back to AI. Humans remain responsible for interpreting evidence, recognizing consequences, questioning assumptions, and deciding whether an output deserves trust.

10. Decision

Decide whether to accept, revise, reject, or investigate AI output.

Checking ends with action. An AI output may be accepted, corrected, refined, independently verified, escalated for expert review, or rejected. The goal is not simply to detect errors—it is to make better decisions.

The Entire Book in 100 Words

Identify exactly what claim the AI output asks you to trust.

Trace important claims back to reliable, relevant, original information sources.

Look for evidence that independently supports the AI generated claim.

Verify facts, numbers, names, dates, quotations, references before using them.

Examine whether hidden assumptions unfairly shape the AI generated answer.

Detect plausible sounding information that AI generated without factual support.

Check whether context changes the meaning, relevance, or usefulness significantly.

Increase checking rigor when consequences of errors become more serious.

Apply human knowledge, experience, responsibility, and judgment before accepting outputs.

Decide whether to accept, revise, reject, or investigate AI output.

From AI Output to Human Decision

AI makes generating information easier. That makes checking information more important.

The objective is not to distrust everything AI produces. Nor is it to accept everything because it sounds intelligent.

The better habit is:

Generate with AI.
Check with evidence.
Decide with human judgment.

The more consequential the decision, the stronger the checking should be.

The Minimalist Series

The Minimalist Series explains complex ideas through a deliberately simple format:

10 essential ideas × 10 words each = 100 words.

Everything AI. For Everyone.

Explore The Minimalist Series and other AI resources at:

https://dastikop.blogspot.com

Ravindra Dastikop
AI Educator • Advisor • Mentor

Everything AI. For Everyone.

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