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Artificial Intelligence vs. Machine Learning

Apr 30, 2020

Artificial intelligence. Machine learning. These common IT buzzwords are thrown around in articles and discussions all the time, but do you know what they really mean? You may think about intelligent robots that are coming to life to take over, but that's not really the case. These terms are often misunderstood, used interchangeably, or just tossed into conversation. But it can be extremely beneficial to learn the meaning behind these terms, and understand real-world examples that are all around us. 

If you’re interested in IT or currently working to earn an IT degree, it’s important to understand some of the popular trends and innovations happening currently. AI researchers are working on solving learning problems with new technology that can benefit our daily lives. Today, machine learning and artificial intelligence are two important topics to really understand, as they are shaping the direction technology is going. This guide will help you learn more about artificial intelligence and machine learning, and see how they are influencing the IT landscape around us.

What is artificial intelligence (AI)?

Artificial intelligence, commonly called AI, is the broad concept that machines or robots can carry out tasks in ways that we as humans consider “smart.” AI is the general theory and development of computer systems to help them replicate human intelligence. 

AI is based on the idea that human intelligence can be defined and mimicked by machines to execute tasks. From simple to complex, artificial intelligence is focused on accomplishing all kinds of tasks. AI goals include learning, reasoning, and perception, but the benchmarks for AI are always changing and developing as technology develops. Some technology that was once revolutionary AI is now considered basic computer functions, and that trend of technology growth is likely to continue. Data managers or data scientists help utilize AI and develop ways to keep the data secure and available for us to use. AI research involves helping data-driven machines learn how to take new data as part of their learning problem and solution process.

Artificial intelligence is the larger, broader term for how we utilize machines and help them accomplish tasks. Machine learning is a current application of artificial intelligence that we utilize in our day-to-day lives. Machine-learning systems are a smaller facet of the larger AI systems.

Examples of AI.

There are examples of AI surrounding us in our world today. We may not even recognize all the artificial intelligence applications that we now take for granted or don’t give a second thought. Some of the most common forms of AI are:

  • Chatbots. When you log onto a website and connect with the customer service team, chances are you’re talking to an AI chatbot. These chatbots interact with customers and can pull answers to generic questions based on keywords. They know how to react to certain responses, and are able to direct the customer to a live person if the bot can’t answer a question. Customers are able to get a human-level of interaction quickly and efficiently. 

  • Online shopping. Isn’t it interesting how Amazon makes suggestions about things you might like, and often they are spot on, showing you things you’re genuinely interested in. AI is what allows your favorite online shops to use algorithms and data to display similar items for you. Online retailers are constantly working to learn more about their shoppers and help encourage them to purchase. It could be an email if you didn’t finish a purchase, a display of similar items, ads based on what you’ve shopped for in the past, and more. 

  • Streaming services. Everyone loves a relaxed night with Netflix and ice cream, but when you sit down to stream your favorite show or music, AI is used to display shows and songs you may like. It learns about your preference and uses algorithms to find patterns and give you top suggestions. AI is used in countless ways in your streaming services that you may not even consider.

  • Healthcare technology. New tools to diagnose, develop medicine, monitor patients, and more are all being developed for the healthcare industry right now. AI is used in electronic health records to store and learn from data, it’s used in scheduling services for doctors and patients, and it is used in the many technological devices doctors use daily. AI is helping the healthcare industry provide more efficient and safe care for patients everywhere. 

  • Factory and warehouse systems. Retail and shipping industries are being transformed with AI software. Automated systems are making processes more efficient and safe. Productivity levels are reaching new heights with the help of software programs that utilize artificial intelligence to find patterns, construct schedules, give options, and more. 

  • Educational tools. Teachers and students alike can utilize artificial intelligence for educational purposes every day. Plagiarism checkers, citation finders, research archives, and more are all ways that AI helps in the education industry. There are countless programs that students and educators can use to enhance their work. 

Importance of artificial intelligence.

Artificial intelligence is crucial for the way we live our lives today. It impacts your life in countless ways that you probably don’t even realize. It has and will continue to improve agriculture, healthcare, manufacturing, and more. AI research shows that AI-driven and AI-powered products are impacting every industry, every business, around the world.Artificial intelligence has created important solutions to difficult problems including:

  • Increase of jobs. As AI continues to develop, there will be a need for more professionals to meet the demand. Designing, testing, implementing, and managing these AI systems are all important roles in the tech industry.

  • Faster, error-free work. Without human error, AI is able to get things done more efficiently and productively. Computers are able to run constantly, be efficient in their work, and avoid errors as part of their programming. This means that work is much more productive when done with AI.

  • Secure. AI can drastically increase security for computer systems. Data and personal information can be kept safe with the help of AI programs and systems. In a world that has increasing numbers of cyber attacks and threats, the increased security AI offers is extremely valuable.

What is machine learning (ML)?

Machine learning is an application of AI that is based around the idea that we can give machines data, and allow them to learn for themselves. Machine learning utilizes neural networks to take data, and use algorithms to solve pieces of the problem, and produce an output. Machine learning encompasses one small part of the larger AI system—machine learning focuses on a specific way that computers can learn and adapt based on what they know.

Deep learning is a facet of machine learning, simply meaning that the neural networks used are larger to parse bigger data sets or more complex problems. Deep learning utilizes the same neural networks and machine learning models, but on a much larger scale. This deep learning is important for larger data sets—deep learning is the way that we can get more information, parsing more data than has ever been possible before.

Machine learning and deep learning focus on ensuring a program can continue to learn and develop based on what outputs it has come up with before. There are three different kinds of intelligence systems involved in machine learning models and machine learning algorithms.

Supervised learning focuses on giving an input and an output, and helping the machine get there. Supervised learning helps an intelligent machine understand how their algorithms should get to the final output. Supervised learning is more hands-on that other types of intelligent machine learning.

Unsupervised learning focuses on giving a robot or intelligent machine the input, and then letting the algorithms do the rest. You give the robot the chance to take what you've given them and figure out the output. Unsupervised learning has a higher risk of error than supervised learning, because you aren't telling it what the answer is. Unsupervised learning focuses on helping enhance intelligence within a machine and its algorithms, allowing it to learn and improve as it figures out the output.

Reinforcement learning allows a machine to meet goals while it is utilizing its intelligence and algorithms to understand what it is doing well. Reinforcement learning focuses on helping a machine understand what it is doing correctly as it gets toward the output. Reinforcement learning may or may not have an output, so it can be similar to both supervised learning and unsupervised learning. Reinforcement learning is sometimes classified as semi-supervised.

Examples of machine learning.

  • Smart home assistants. When you talk to Google, Siri, or Alexa you’re utilizing machine learning models! Your machine uses speech recognition, learns from the routines that you set up, connects to your other devices or services to remind you about them, and more. The algorithms used in your smart home devices are extremely advanced forms of deep learning, and are getting smarter all the time. Software updates help these machines get even better at learning. It's true that these are a form of robots that are learning more about how to serve you best.

  • Social media. You may not believe it, but it’s true! Your social media platforms utilize machine learning algorithms and intelligence to serve you ads, to display content that goes with your preferences, and more. Your social media profiles learn about you and then are able to produce more content based on what you would like. 

  • Translation services. Language translation services rely heavily on machine learning algorithms to translate quickly and accurately. AI programs are able to look into neural networks, solve tiny pieces of the translation puzzle, and come out with an output. Prediction is a crucial element of translation services, which is made possible thanks to neural networks. Algorithms are used in translation services to help with grammar, vocabulary, and sentence structure. 

  • Autonomous vehicles. Machine learning algorithms are critical to help autonomous technology work. Cars need to learn from situations they are in, recognize how traffic signs impact their route, observe pedestrians, other vehicles, and more. Neural networks are critical in helping a car quickly determine what output they need to make, learn from what happens around them, and more. 

Importance of machine learning.

Machine learning is crucial as data and information gets larger and larger. Processing is expensive, and machine learning helps data processing get done much faster and more efficiently. It becomes faster and easier to analyze big, complex data sets and get the most accurate results. Machine learning can additionally help avoid errors that can be made by humans.

Machine learning allows technology to do the analyzing and learning, making our life more convenient and simple. As technology continues to evolve, machine learning is becoming a regular occurrence that helps systems move quickly and effectively. 

If you’re interested in IT, machine learning and AI are important topics that are likely to be part of your future. The more you understand machine learning and AI, the more likely you are to be able to implement it as part of your future career.

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