Artificial Intelligence is really changing the way we live and work. It is helping us to do things faster and better. Artificial Intelligence is used in industries and it is making a big impact. However there are also some risks that we need to think about.

In 2026 Artificial Intelligence is being used everywhere so it is very important to know what it can and cannot do. Artificial Intelligence can help us to get things done faster. It can also make mistakes. Artificial Intelligence can be wrong sometimes. It can also be unfair. We need to be careful when we use Artificial Intelligence.

Understanding Why AI Has Risks

Artificial Intelligence systems are like computers that use data and special codes to make decisions. They do not think like humans they just look at the data. Make choices based on what they see. This means that if the data is wrong the choices will be wrong too.

Also Artificial Intelligence is used by a lot of people so if something goes wrong it can affect people. This is why we need to be very careful when we use Artificial Intelligence. We need to make sure that it is working correctly and that it is not making mistakes.

Do’s and Don’ts of Using AI Safely

Dependency on Data Quality We need to make sure that the data is good and that it is handled correctly. This can be work but it is very important if we want Artificial Intelligence to work well. Businesses need to make sure that they are using data so that they can get the most out of Artificial Intelligence.

Over-Reliance on AI If we rely much on Artificial Intelligence we might stop thinking for ourselves. This is a problem because Artificial Intelligence is not perfect and it can make mistakes. We need to use Artificial Intelligence as a tool to help us. We also need to think for ourselves.

Data Privacy and Security Risks Artificial Intelligence systems need a lot of data to work. This data can include personal information. This is a risk because if the data is not handled correctly it can be stolen or used in the wrong way.

Bias in AI Systems Artificial Intelligence systems can also be unfair. They can learn from the data they are given and if the data is unfair the Artificial Intelligence system will be unfair too. This is a problem in areas like hiring and law enforcement.

Accuracy and Reliability Issues One of the risks of Artificial Intelligence is that it can give us wrong information. Artificial Intelligence systems can make mistakes especially when they are dealing with things. This is a problem in important areas like healthcare and finance.

1. Accuracy and Reliability Issues

One of the risks of Artificial Intelligence is that it can give us wrong information. Artificial Intelligence systems can make mistakes especially when they are dealing with things. This is a problem in important areas like healthcare and finance.

The reason this happens is that Artificial Intelligence does not check facts like humans do. It just looks at the data. Makes choices based on what it sees. This means that we need to check the information that Artificial Intelligence gives us before we use it. If we do not we might make decisions.

2. Bias in AI Systems

Artificial Intelligence systems can also be unfair. They can learn from the data they are given and if the data is unfair the Artificial Intelligence system will be unfair too. This is a problem in areas like hiring and law enforcement.

We need to make sure that Artificial Intelligence systems are fair and that they do not discriminate against people. We need to choose the data and make sure that the Artificial Intelligence system is working correctly.

3. Data Privacy and Security Risks

Artificial Intelligence systems need a lot of data to work. This data can include personal information. This is a risk because if the data is not handled correctly it can be stolen or used in the wrong way.

We need to be careful about what information we give to Artificial Intelligence systems. Businesses need to make sure that they are handling the data correctly and that they are following the rules.

4. Over Reliance on AI

If we rely much on Artificial Intelligence we might stop thinking for ourselves. This is a problem because Artificial Intelligence is not perfect and it can make mistakes. We need to use Artificial Intelligence as a tool to help us. We also need to think for ourselves.

Some Artificial Intelligence systems are very complicated. It is hard to understand how they work. This is a problem in areas like finance and law where we need to know how decisions are made.

5. Lack of Transparency (Black Box Problem)

We need to make sure that Artificial Intelligence systems are transparent and that we can understand how they work. This is a challenge but it is very important.

Artificial Intelligence can also change the way we work. It can automate some tasks, which can be good. It can also mean that some people lose their jobs.

6. Job Displacement and Workforce Impact

We need to be prepared for these changes and make sure that we have the skills we need to work with Artificial Intelligence. This is a challenge for both individuals and businesses.

Artificial Intelligence can be used in the way and this can be a big problem. It can be used to create news or to trick people.

7. Ethical and Misuse Concerns

We need to make sure that Artificial Intelligence is used in a way. We need to set rules and make sure that people are following them. If we do not Artificial Intelligence can have consequences.

The important thing for Artificial Intelligence systems is the data they are given. If the data is bad the Artificial Intelligence system will not work well.

8. Dependency on Data Quality

We need to make sure that the data is good and that it is handled correctly. This can be work but it is very important if we want Artificial Intelligence to work well. Businesses need to make sure that they are using data so that they can get the most out of Artificial Intelligence.

Artificial Intelligence is a tool but it is not perfect. We need to be careful when we use it and make sure that we are using it in a way. Artificial Intelligence can help us to do things but we need to make sure that we are, in control.

Do’s and Don’ts

Using Artificial Intelligence safely has less to do with understanding the technology and more to do with building a few habits around it. The three rules below cover the mistakes that cause the most damage in practice: trusting an output that nobody checked, handing over a decision that genuinely needed a person, and pasting sensitive information into a system you do not control. None of them slow you down much, and every one of them is far cheaper than cleaning up afterwards.

FAQs

These are the questions people ask most often before bringing Artificial Intelligence into work that actually matters. The answers below stay short and practical.

1. What are the main risks of AI?

The main risks of Artificial Intelligence include things like getting things wrong being biased and having problems with data privacy and relying much on automation.

2. Can AI make mistakes?

Artificial Intelligence can generate information and this is why it is very important to always check the information that Artificial Intelligence generates.

3. Is AI biased?

Artificial Intelligence can also have biases because it learns from the data it is trained on and this can lead to results.

4. How can AI affect jobs?

Artificial Intelligence can do some tasks automatically. It also creates new opportunities for people.

5. Is AI safe to use?

Artificial Intelligence is generally safe to use long as people use it in a responsible way and have the right safeguards in place.

6. What is the black box problem?

One of the problems, with Artificial Intelligence is that it is not always clear how Artificial Intelligence systems make their decisions.

7. How can AI risks be reduced?

To make Artificial Intelligence work well people need to check the information it generates deal with any biases and make sure that data privacy is protected.

8. Should businesses be concerned about AI risks?

Yes it is very important to understand and manage the risks of Artificial Intelligence in order to use it effectively.

Artificial Intelligence is used in industries but people often get Machine Learning and Deep Learning mixed up. They are both part of Artificial Intelligence. Have the same goals but they work in very different ways. Machine Learning and Deep Learning are different in how they look at data find patterns and give results. It is really important for people who want to use Artificial Intelligence to understand the difference between Machine Learning and Deep Learning.

What is Machine Learning

In 2026 Machine Learning and Deep Learning are used for things like recommendation systems stopping fraud, self driving cars and tools that make things.. It is not always easy to choose between Machine Learning and Deep Learning. Each one has its good and bad points and they are better for different things. This guide will help you understand when to use Machine Learning and when to use Deep Learning.

What is Deep Learning

Machine Learning is a part of Artificial Intelligence that lets systems learn from data and get better without being told what to do. Of following rules Machine Learning models look for patterns in data and use those patterns to make guesses or decisions. Machine Learning is used to make things like recommendation systems stop fraud and predict what will happen.

Key Differences Between Machine Learning and Deep Learning

The two approaches part ways long before you write any code, and the differences are practical rather than academic. Six areas separate them most clearly: how much data each one needs, how the features get chosen, how much computing power is involved, how accurate the results are, how easily the model can be explained, and how long training takes.

1. Data Requirements

Machine Learning usually works with data that’s organized and easy to understand. It also needs people to help choose the features and adjust the model. This means that people who know a lot about the subject are really important for making Machine Learning models. Some common algorithms used in Machine Learning are decision trees, linear regression and support vector machines. These models are used a lot in things like recommendation systems stopping fraud and predicting what will happen.

2. Feature Engineering

Deep Learning is a kind of Machine Learning that uses neural networks with many layers to look at data. These networks are made to work like the brain so they can learn complicated patterns and understand things. Deep Learning is used for things like recognizing pictures understanding language and making new things.

Deep Learning is different from Machine Learning because it can automatically find features in raw data. This makes it really good for data that is not organized like pictures, sound and text.. Deep Learning models need a lot of data and powerful computers to work well.

3. Complexity and Computation

Machine Learning models can work well with amounts of data especially if the data is organized and easy to understand. They use chosen features to make guesses, which means they do not need as much data. Machine Learning is often used for things like recommendation systems. Predicting what will happen.

Deep Learning models need a lot of data to work well. This is because they learn features automatically and need to see a lot of examples to find patterns. When there is not data Machine Learning is often a better choice. Deep Learning is used for things like recognizing pictures and understanding language.

4. Performance and Accuracy

In Machine Learning choosing the features is a really important step. Experts need to find and choose the important features from the data to make the model work better. This can take a lot of time. It gives you more control over the model. Machine Learning models are generally less complicated. Need less powerful computers. They can run on hardware and are easier to set up and maintain.

Deep Learning models are more complicated. Need powerful computers like GPUs. Training these models can take a lot of time and resources. This makes Deep Learning better for organizations that have access to technology.

5. Interpretability

Deep Learning models are often better than Machine Learning models at tasks that involve unorganized data. For example they are really good at recognizing pictures, understanding speech and understanding language.

For organized data and simpler tasks Machine Learning models can do just as well or even better with less complexity. Choosing the approach depends on the problem and the data you have. Machine Learning models are generally easier to understand which means it is easier to see how they make decisions. This is important in applications where you need to be transparent like finance or healthcare.

6. Training Time

Deep Learning models are often hard to understand, which can be a limitation in some cases. Machine Learning models usually take time to train and can be used quickly. This makes them good for applications where you need to make changes

Deep Learning models take longer to train because they are complicated and need a lot of data.. Once they are trained they can give very accurate results for complicated tasks. Machine Learning and Deep Learning are both parts of Artificial Intelligence and understanding the difference between them is crucial, for using Artificial Intelligence effectively.

Machine Learning vs Deep Learning: Comparison Table

Set the two side by side and the pattern is easy to read. Machine Learning works best on organized data, needs people to choose the features, runs on ordinary hardware and trains quickly. Deep Learning works best on unorganized data such as pictures, sound and text, finds its own features without being told what to look for, needs a lot of data and powerful computers like GPUs, and takes considerably longer to train.

Accuracy is not a straight contest between the two. Deep Learning pulls ahead on tasks like recognizing pictures, understanding speech and understanding language, where the patterns are too complicated for hand picked features to capture. For organized data and simpler problems, Machine Learning often matches or beats it with far less complexity, which is why decision trees, linear regression and support vector machines still do so much of the work in production systems.

The last row is the one people forget to read. Machine Learning models are easier to understand, so you can show how a decision was reached. That matters enormously in finance and healthcare, where being able to explain an outcome is not optional. Deep Learning models are frequently hard to interpret, which stays a real limitation whatever their accuracy. Read any comparison table with your own constraints in mind: the data you actually have, the hardware you can afford, and whether somebody will need to justify the model's decisions later.

Do’s and Don’ts

Most of the trouble teams run into with these two approaches comes from choosing one on reputation rather than fit. The points below are the habits worth carrying into any project, whichever technique you land on. None of them need a big budget, only a little discipline before you start training anything.

FAQs

These are the questions that come up most often when people are first sorting out how the two approaches differ in practice.

1. What is the main difference between machine learning and deep learning?

Machine learning needs data that is organized. It needs people to select the important features but deep learning uses neural networks to find patterns in big datasets on its own.

2. Is deep learning better than machine learning?

That is not always true. Deep learning is good for tasks but machine learning is better for simpler tasks because it is faster.

3. Which requires more data?

Deep learning needs a lot of data more than machine learning does.

4. Can machine learning work without deep learning?

Yes machine learning can work by itself. It is used in a lot of things.

5. Why is deep learning called “deep”?

This is because of the layers in neural networks that help process the data.

6. Which is easier to implement?

Machine learning is usually easier to set up. It does not need as many resources as deep learning does.

7. Where is deep learning used?

Deep learning is used for things, like recognizing pictures, processing speech and making things with generative artificial intelligence.

8. Can they be used together?

Yes using both machine learning and deep learning together often gives us results.

Generative AI is a technology that has changed the way we create and use content. It is no longer just used by researchers and tech companies. Now businesses, artists, developers and regular people use AI. It can do things like write articles make images create music and make videos.

For people who’re new to generative AI it can seem hard to understand. Terms like models and neural networks can be confusing. This guide will help explain what generative AI is and how it works. The goal is to make it easy for people to use AI without feeling overwhelmed.

What is Generative AI

Generative AI is a type of intelligence that can make new things. It does not just look at data. It can make text, images, audio and video based on what it has learned. Generative AI is different from types of artificial intelligence. It can create things instead of just looking at old data.

At its core generative AI works by looking for patterns in data. Then it uses those patterns to make things. For example a generative AI model that is trained on text data can write articles and answer questions. Models that are trained on images can make pictures that look like things. This ability to make things is what makes generative AI special.

How Generative AI Works

Generative AI uses machine learning techniques. These techniques are called networks. They are trained on datasets that have examples of the things they are supposed to make. When they are trained they learn patterns and relationships in the data.

Once they are trained they can make things. They do this by guessing what comes next based on what they have learned. For example when you give an AI model a prompt it guesses the most relevant words or elements to make a good response. This happens quickly. Is designed to be fast and relevant.

Types of Generative AI Models

It is important to remember that generative AI does not really understand what it is making. It just makes things based on statistics. This is why sometimes the things it makes are not accurate. Knowing how it works helps people use it better.

Text Generation Models

There are types of generative AI models. Some can make text, like emails and articles. These models are trained on datasets of text and can make things that make sense. They are useful for making content helping customers and answering questions. However they often need people to edit what they make to make sure it is accurate.

Image Generation Models

There are also models that can make images. These models are used in design, marketing and art. They can make pictures based on what you tell them. This helps people make pictures without needing to be good at design. It also saves time and money.

Image generation models work from a text prompt. You describe the picture you want in plain language, and the model draws on the patterns it learned from millions of training images to produce something new that matches your description. You can usually set a style too, such as a photograph, a watercolor painting, a cartoon, or a product mockup. The more specific your prompt is about the subject, the setting, the lighting and the mood, the closer the result will be to what you had in mind. Most tools generate several options at once so you can pick the best one and refine it with a follow up prompt.

There are a few things beginners should keep in mind. Generated images can look impressive at first glance but contain odd details when you look closely, so always inspect the result before you use it. Check the usage rights of the tool you choose, because rules about commercial use differ from service to service. And be transparent when it matters, since passing off a generated image as a real photograph can mislead people. Used carefully, these models are one of the fastest ways to turn an idea into a visual.

Audio and Music Generation

Generative AI can also make audio, like music and voiceovers. These models can make music or copy styles they have learned. This is useful in entertainment, advertising and making content.

Audio models split into two big groups. Music generators can produce a complete track from a short description, such as a calm piano piece for a study video or an upbeat tune for an advert. Voice generators turn written text into natural sounding speech, which is how many audiobooks, video voiceovers and accessibility tools are made today. Some voice tools can even clone a specific voice from a sample, which is powerful but also easy to misuse, so reputable services ask for clear permission from the voice owner first.

For beginners, the practical uses are simple. You can add background music to a video without licensing a stock track, narrate a presentation without recording yourself, or prototype a podcast intro in minutes. Listen closely before publishing, because generated audio can drift in strange ways, a melody may repeat awkwardly or a voice may mispronounce names. As with images, check the terms of the tool you use, especially for commercial projects, and never generate a real person's voice without their consent. Treated as a starting point rather than a finished product, audio generation saves hours of work.

Video Generation Models

Generative AI can even make videos. These models can make clips, animations and realistic scenes. This is still an area but it has the potential to change many industries.

Video is the hardest thing for generative AI to make, because a video is really thousands of images that have to stay consistent from frame to frame. That is why most tools today focus on short clips, usually a few seconds long, rather than full scenes with dialogue. You describe the shot you want, sometimes add a reference image, and the model produces a moving clip that matches. Results have improved quickly, and short generated clips are already being used for social media posts, product teasers, animated backgrounds and early drafts of bigger ideas.

If you want to experiment, start small. Simple subjects with clear motion, like waves rolling onto a beach or steam rising from a coffee cup, work far better than complex action with people. Expect to generate several versions before one looks right, and plan to edit clips together with normal video software. Watch for telltale glitches such as flickering details or objects that change shape mid clip. Because realistic generated video can also be used to deceive, always label AI made footage honestly. For beginners, treat video generation as a creative sketchpad, not a replacement for a camera.

Real World Applications of Generative AI

Generative AI is used in industries. It helps make content efficiently. In software development it helps write code and find mistakes. In healthcare it is being used to find medicines and do research. In education it helps make learning materials.

Benefits of Generative AI

Generative AI has benefits. It can save time. Help people focus on more important things. It can also make a lot of content quickly which is useful for businesses. Additionally it can help people be more creative by giving them ideas and options. These benefits help people be more productive and innovative.

Challenges and Limitations

However generative AI also has limitations. One of the challenges is that it can make mistakes. It can also lack depth and context. There are also concerns, about privacy and using it in a way. To address these challenges people need to use AI carefully and make sure it is working correctly.

Generative AI is a tool that can be used in many ways. It can make text, images, audio and video. It is used in industries and has many benefits. However it also has limitations that need to be considered. By understanding how generative AI works and using it people can get the most out of this technology.

Do’s and Don’ts

Generative AI rewards good habits and punishes careless ones. Before you make these tools part of your routine, keep this quick reference nearby. It sums up the most important guidance from this guide in one place.

FAQs

What is generative AI in simple terms?

Generative AI is a kind of intelligence. It makes things like text, images or audio. It learns from data patterns.

How does generative AI work?

It uses computer models trained on lots of data. These models. Make things based on what you tell them.

What are examples of generative AI?

Examples are tools that write text create images and make music.

Is generative AI the same as AI?

Generative AI is not all of AI. It is a part that focuses on making content. It is not, for analyzing or predicting.

Can generative AI replace humans?

Generative AI helps people. It does not replace creativity, good judgment and feelings.

Is generative AI safe to use?

You can use Generative AI if you are careful. Do not share information.

What are the benefits of generative AI?

Using Generative AI makes some tasks easier. It helps with projects and makes new ideas.

What are its limitations?

Generative AI can make mistakes. It does not really understand things like people do.

Artificial Intelligence has become really good at doing things that people used to think only humans could do. It can make content look at data and even help with decisions. Artificial Intelligence is being used more and more in work. This has started a discussion about how Artificial Intelligence compares to human intelligence and if it can one day replace it. It is really important to understand the differences between Artificial Intelligence and human intelligence. This is especially true for people who use Artificial Intelligence tools at work. Artificial Intelligence is great at doing things handling a lot of work and finding patterns. On the hand human intelligence is great at being creative understanding emotions and making sense of things.

Artificial Intelligence is a type of machine that can do things that usually require intelligence. These things include learning from data finding patterns making decisions and creating things like text or pictures. Artificial Intelligence machines are trained on a lot of data. Use special rules to process information quickly.

What is Artificial Intelligence

Unlike people Artificial Intelligence machines do not have feelings or know who they are. They work based on what they have learned and what’s likely to happen. This lets them process a lot of information fast. However it also means that Artificial Intelligence does not really understand things and only knows what it has been taught. Its ability to think is only good for tasks. Human intelligence is a thing that includes being able to reason, learn, be creative understand emotions and adapt. It is not about processing information but also about understanding what is going on making good choices and knowing what things mean.

One of the things about human intelligence is that it can be used in many different areas. People can learn from a bit of information adapt to new situations and make good choices even when they do not have all the facts. Also being able to understand emotions is a part of how people interact with each other. This lets us be empathetic communicate and understand each other. These things make human intelligence very different from Artificial Intelligence.

What is Human Intelligence

Artificial Intelligence machines learn by being trained on a lot of data. They need to see many examples to be good at what they do. They use models and rules to understand data and get better over time. However they are not very good at adapting to things. People on the hand can learn from just a little bit of information and use what they know in many different situations. A person can understand an idea quickly and use it in a creative way in a different situation. This ability to adapt makes human intelligence very good in situations where things are changing fast.

One of the things about Artificial Intelligence is how fast it can work. Artificial Intelligence machines can process a lot of information in a few seconds. This makes them very good at things like looking at data finding patterns and automating tasks. This helps businesses do work and make decisions faster.

Key Differences Between AI and Human Intelligence

The contrast in the AI vs human intelligence debate becomes clearest when you put the two side by side. The differences below cover how each one learns, how fast it works, where creativity and emotion come in, and what each needs to make a good decision. Together they explain why the two are better treated as partners than rivals.

Learning and Adaptability

People are slower at processing information. They can understand things deeply. They can look at the things consider many different views and make good choices that are not just based on data. This balance between speed and depth shows that Artificial Intelligence and human intelligence work together.

Being creative is something that human intelligence’s still better at than Artificial Intelligence. People can think in ways come up with new ideas and innovate in ways that are not just based on what has been done before. This is because people have imagination, experience and emotional context.

Speed and Efficiency

Artificial Intelligence can make content and ideas. It does this by putting together things it has already seen. While this can be useful it is not truly original. Artificial Intelligence is not as good at being creative as people are because people have purpose and understanding.

Human intelligence includes being aware of emotions being empathetic and understanding people. These things are necessary for communicating, leading and working with others. People can understand how others feel, respond in a way and build relationships.

Creativity and Innovation

Artificial Intelligence on the hand does not have feelings. While it can pretend to respond to emotions it does not really understand them. This means it is not good at jobs that require a lot of interaction and emotional understanding.

Artificial Intelligence makes decisions based on data and rules. It can look at things and make suggestions quickly which is helpful for making decisions based on data. However its decisions are limited by how good its data’s

Emotional Intelligence

People make decisions based on intuition, experience and what is right. They can make choices even when things are not clear and consider things that cannot be measured. This makes human intelligence more reliable in situations.

Artificial Intelligence machines need a lot of data to work well. If they do not have data they do not work as well. They also struggle with things they have not seen before.

Decision Making Ability

People however can work well with limited information. They can use reasoning and what they already know to make choices. This means they do not need much data and can adapt more easily.

Artificial Intelligence and human intelligence are different. Artificial Intelligence is good at some things and human intelligence is good at things. They can work together to do things that neither could do alone.

Dependency on Data

Understanding Artificial Intelligence and human intelligence is important. It can help us use Artificial Intelligence in a way that’s good for everyone. We can use Artificial Intelligence to do things that’re hard for people and we can use human intelligence to do things that are hard, for Artificial Intelligence.

In the end Artificial Intelligence and human intelligence are not competing with each other. They are working together to make things better. We just need to understand how they are different and how they can work together. This will help us make the most of both Artificial Intelligence and human intelligence.

AI vs Human Intelligence: Comparison Table

Put side by side, the pattern is easy to see. Artificial Intelligence processes huge amounts of information in seconds, finds patterns, automates repetitive work, and keeps improving as long as it is fed enough good data. Human intelligence works from the other direction: it reasons, imagines, reads emotions, and makes sound choices from just a little information, even when the situation is unclear. AI has no feelings or awareness of what it is doing, so its answers are only as good as its training data, while people bring context, ethics, and experience that no dataset captures. The comparison table below sums up these differences at a glance.

That is why the comparison ends in partnership rather than replacement. Let AI carry the data heavy, repetitive, high speed work it is built for, and keep humans on creativity, empathy, strategy, and the judgment calls where being wrong costs something. Understood this way, AI vs human intelligence is less a contest than a division of labor, and the people and businesses that learn to split the work sensibly will get the most out of both.

Do’s and Don’ts

Working well alongside AI is mostly a matter of habits. The short table below sums up the practical rules this comparison points to, where to lean on AI and where human judgment has to stay in charge. Treat it as a quick reference for everyday use.

FAQs

What is the main difference between AI and human intelligence?

The big difference between intelligence and human intelligence is how they process information. Artificial intelligence uses data and algorithms. Human intelligence uses reasoning and emotions and experience.

Can AI replace human intelligence completely?

No artificial intelligence cannot completely replace intelligence because it does not have creativity or emotional understanding or the ability to think about things in context.

Is AI smarter than humans?

Artificial intelligence is really fast and efficient at doing tasks but it is not more intelligent than humans when it comes to general things.

What are the advantages of AI over humans?

Artificial intelligence is great at doing things accurately and handling large amounts of data, which makes it perfect for tasks that are repetitive and involve a lot of data.

What are the strengths of human intelligence?

Human intelligence includes being creative and able to adapt to things and having emotional intelligence and being able to make complicated decisions.

How do AI and humans work together?

Artificial intelligence is good at handling data and automating tasks. Humans are better at coming up with strategies and being creative and making decisions.

Will AI surpass human intelligence in the future?

Artificial intelligence may get a lot better in the future. It is still going to be hard for it to be as intelligent as humans in general.

Why is human judgment still important?

Human judgment is necessary, for understanding what is going on in a situation and making decisions and dealing with uncertain things.