assignment on artificial intelligence

• Top Courses
• Online Degrees
• Find your New Career
• Join for Free

10 Artificial Intelligence Examples: AI in Practice

Professionals across many industries, such as finance, health care, and film, benefit from the use of AI. Learn more about artificial intelligence by exploring real-world examples.

Artificial intelligence is an exciting field projected to grow exponentially in the next decade. By exploring real-world uses, you can get to know the diverse applications of AI and how you might be able to apply this powerful tool in your industry. 

Read more: How Does AI Work? Basics to Know

What is artificial intelligence?

Artificial intelligence (AI) is a tool designed to mimic human processing and analytic skills to analyze problems, identify solutions, and decide on courses of action. Unlike traditional technology, artificial intelligence algorithms have the ability to adapt to new information and follow “thought processes” similar to those of a human brain. This type of algorithm can help professionals make informed and data-driven decisions.

Professionals across a wide array of fields use artificial intelligence, with notable applications in finance, education, business, film, and health care. As we continue to expand our understanding of AI and its ability to assist humans in different contexts, the applications of AI are likely to grow steadily in coming years.

Read more: What Is Artificial Intelligence? Definition, Uses, and Types

10 artificial intelligence examples across industries

Artificial intelligence has wide-ranging uses, many of which you might have encountered without even realizing it. Here are 10 artificial intelligence examples seen in practice across diverse industries.

1. Risk assessment and risk management

In finance, artificial intelligence is a powerful tool that helps financial firms decide who to lend money to. With AI algorithms, banks and other organizations can use the personal information of applicants to decide whether they have a high risk of defaulting on their loans. One of the benefits of AI in this context is that (theoretically) the algorithms make unbiased decisions. These AI algorithms can provide insights that bankers use to make investment and lending decisions depending on user inputs, such as risk preferences and outcome goals. 

AI also detects overall risk by finding trends and providing insights that can help organizations lower their risk threshold. 

2. Customer service chatbots

Businesses have begun implementing chatbots to improve their customer service. These chatbots use artificial intelligence to understand input from users and provide human-like responses that customers can understand. They can respond to direct questions and help customers obtain needed information. One advantage of chatbots is that they do not require human presence, so they can assist customers at all hours, potentially reducing hiring costs for businesses.

3. Streaming service algorithms

Streaming services like Netflix use AI to improve search results and media recommendations for subscribers. This technology is able to continually iterate on itself to offer more personalized content for users and increase adaptive services. The algorithms take previous searches, ratings, and content watched in order to recommend content that you are more likely to enjoy. Streaming services also use AI to make decisions related to what type of content to produce, who to hire, and which areas to improve. 

4. Online shopping recommendations

Similarly to streaming services, AI algorithms can tailor online shopping content and suggestions based on user activity. This type of algorithm monitors your activity on retail sites and learns your preferences and habits in order to provide suggestions that are more likely to interest you.

5. Smart products

Smart products are growing in popularity due to their ability to make domestic life easier for many people. For example, the Roomba is a vacuum robot that can move around the house on its own and clean floors without human direction. With improvements in smart technologies, these robots are becoming able to take human direction and listen to specific commands, such as spot-cleaning certain areas. Other examples of smart products include smart light bulbs, doorbells, thermostats, and household assistants like Alexa.

6. Precision medicine

In health care, artificial intelligence algorithms can make predictions on how a patient will respond to a given treatment or set of treatments. This type of algorithm can determine which treatment will likely be most effective. To do this, the model is “trained” on a set of data, including previous disease and patient characteristics and what the outcomes were. By identifying trends in outcomes, health care providers can offer more individualized care for their patients. 

7. National security assessment

Government sectors use AI to analyze large amounts of data and find patterns related to suspicious activities. Because AI algorithms can process large amounts of data much faster than humans can, AI is a powerful tool to increase the speed and agility of these types of security methods.

8. Educational assessments and feedback

In education, AI algorithms can grade exams and provide insights into the trends of incorrect answers. For example, if a large proportion of learners missed a certain question, AI algorithms can provide feedback to the instructor on the type of content the learners are missing. This can help inform educational direction and improve learning outcomes. Based on learner needs, AI algorithms can also adapt to different learning styles and provide tailored instruction for each learner.

9. Autonomous vehicle development

Autonomous vehicles have long been a topic of debate because of the potential dangers related to algorithm errors. However, they are a great example of how artificial intelligence algorithms are used in ways that allow machines to “sense” the world around us and make informed decisions without direct human input.

10. Weather forecasting

Artificial intelligence algorithms can help many weather forecasting applications to rapidly predict upcoming weather based on global trends and current information. These models reduce costs and save energy compared to traditional methods and can provide ongoing updates and information changes. They have shown greater accuracy than humans in several tests and are becoming more common in application. One recent example of this is GraphCast, a machine learning and artificial intelligence model funded by Google DeepMind and Alphabet. This model can predict hundreds of weather variables internationally and outperforms current industry standards on 90 percent of tested variables [ 1 ].

Learn more on Coursera.

You can expand your knowledge of artificial intelligence, machine learning, deep learning, and related fields with courses offered by top universities and organizations on Coursera. As a beginner, consider building foundational skills with the Introduction to Artificial Intelligence (AE) course offered by IBM.

Article sources

Science. “ Learning skillful medium-range global weather forecasting , https://www.science.org/stoken/author-tokens/ST-1550/full.” Accessed March 29, 2024.

Keep reading

Coursera staff.

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

• Lectures: Mon/Wed 1:30pm-2:50pm in NVIDIA Auditorium .
• Problem sessions: Fri 1:30-2:20pm in Thornton 102 .
• Office hours, homework parties: see the Calendar and the HW OH Queue .
• Try our new LLM powered bot on slack . Note: do not direct message any members of course staff on Slack.
• To contact all teaching staff, use Ed .
• For personal/sensitive matters, email [email protected] .
• Modules : All the course content has been broken up into short modules , which include slides, recorded videos, and notes.
• Lectures: Instructors go over the main modules more slowly and interactively. All lectures will be recorded and available on Canvas.
• Problem sessions: CAs work through practice homework and exam problems.
• Homework parties : CAs help students work through homework problems in small groups.
• Office Hours: Meet 1:1 with instructors and CAs. There are two types of CA office hours: homework OH (for help with homework questions) and general OH (to ask questions about course content from lecture).
• Looking at the writeup or code of another student.
• Showing your writeup or code to another student.
• Discussing homework problems in such detail that your solution (writeup or code) is almost identical to another student's solution.
• Uploading your writeup or code to a public repository (e.g., GitHub) so that it can be accessed by other students.
• Looking at solutions from previous years, either official or written up by another student, or found online.

Generative AI Policy: Each student is expected to submit their own solutions to the CS221 homeworks. You may use generative AI tools such as Co-Pilot and ChatGPT as you would use a human collaborator. This means that you may not directly ask generative AI tools for answers or copy solutions, and acknowledge generative AI tools as collaborators. The use of generative AI tools to substantially complete an assignment or exam (e.g. by directly copying) is prohibited and will result in honor code violations. We will be checking students' homework to enforce this policy.

Anyone violating the honor code policy will be referred to the Office of Judicial Affairs. If you think you violated the policy (it can happen, especially under time pressure!), please reach out to us; the consequences will be much less severe than if we approach you.

• Note that messages on public channels in slack are visible to other students and course staff.
• It is a strict violation of course policies to direct message course staff on slack, please keep interaction to public threads or reach out via Ed or the lead staff mailing list if you have questions.
• The student honor code still applies to messages and interactions on slack.
• (Required) Programming CS 106A , CS 106B
• (Required) Discrete math, mathematical rigor: CS 103
• (Required) Probability: CS 109
• (Required) Linear algebra: Math 51
• (recommended, but not required) Algorithms: CS 161
• (recommended, but not required) Systems: CS 107
• Russell and Norvig. Artificial Intelligence: A Modern Approach. A comprehensive reference for all the AI topics that we will cover.
• Koller and Friedman. Probabilistic Graphical Models. Covers factor graphs and Bayesian networks (this is the textbook for CS228 ).
• Sutton and Barto. Reinforcement Learning: An Introduction. Covers Markov decision processes and reinforcement learning (free online).
• Hastie, Tibshirani, and Friedman. The Elements of Statistical Learning. Covers machine learning from a rigorous statistical perspective (free online).
• Tsang. Foundations of Constraint Satisfaction. Covers constraint satisfaction problems (free online).
• Exam 1 (30%): Nov 2nd, 6-8 PM on Campus .
• Exam 2 (30%): Dec 13th, 3:30 - 5:30 PM on Campus .
• If you have a major time conflict for either exam, you should fill out this form by Friday, October 13 (week 3) .

Both exams will be in-person.

Exam Conflicts: Please reach out via the lead-staff mailing list if you have exam conflicts.

SCPD Students: SCPD students will need to nominate exam monitors for both exams and coordinate the exam process with the SCPD exams team . Please refer to this link for more information on the process. For any additional questions, please reach out to the SCPD exams team .

• Projects should be done in groups of 1-4 students.
• There are 5 milestones for the project throughout the quarter: interest form, proposal, progress report, video/poster, final report.
• Each project group will be assigned a CA mentor who will give feedback and answer questions.
• For inspiration, check out previous CS221 projects .
• See the project page for more details.
• --> Project interest form [p-interest] (due Tue Oct--> )--> Project proposal [p-proposal] (due Tue Oct--> )--> Project progress report [p-progress] (due Tue--> )--> Project final report and video [p-final] (due Tue--> )-->