AI, Machine Learning, and Automation — the differences.

Artificial Intelligence, or AI, is a term you’ve probably heard a lot about. It pops up in discussions about the latest tech, sci-fi movies, and even in conversations about the future of work and everyday life. But what exactly is AI? How does it relate to machine learning and what role does something like ChatGPT play? And what about automation — how does that fit into the picture? In this article, we’ll dive into these topics, breaking them down in a way that’s easy to understand. By the end, you’ll have a clearer idea of what these terms mean and how they’re shaping our world.

To start, let’s understand what AI really is. Artificial Intelligence is the idea of creating machines or software that can perform tasks which normally require human intelligence. These tasks include things like understanding language, recognizing pictures, making decisions, and solving problems.

Think of AI as a super-smart computer program that can learn and adapt. When you play a video game against a computer opponent that gets better the more you play, or when you ask your phone’s assistant to set a reminder, you’re interacting with AI.

Levels of AI

AI comes in different levels, based on how smart and capable these systems are. The simplest form is called Narrow AI. Narrow AI is designed to do one specific thing. It’s very good at that one thing but can’t do anything else. For example, the spam filter in your email that catches junk mail is a type of Narrow AI. It’s great at identifying spam messages, but it can’t help you with your math homework or play chess with you.

Next up is General AI. This is the kind of AI that can understand, learn, and apply knowledge across a wide range of tasks, much like a human can. General AI doesn’t just excel at one task but can perform many different ones, switching between them as needed. Imagine a robot that can cook, clean, help you study, and even have a meaningful conversation with you about your day. As of now, General AI is still something we’re working towards and hasn’t been fully realized yet.

Finally, there’s Super Intelligent AI. This is a level of AI that would surpass human intelligence in every aspect. It would not only perform tasks better and faster than humans but also come up with ideas and solutions beyond human capabilities. This kind of AI remains in the realm of science fiction for now, as we’re far from creating anything like it.

Machine Learning

Now, let’s talk about machine learning. Machine learning is a big part of AI, but it’s more specific. It’s a way to teach computers to learn from data. Instead of programming a computer with exact instructions for every possible situation, we give it lots of data and let it figure out patterns and rules by itself.

Imagine you have a computer program that you want to teach to recognize dogs in pictures. Instead of telling it exactly what a car looks like, you show it thousands of pictures of cars and thousands of pictures of other things. The computer analyzes these pictures and learns the patterns that make a car a car.

This process of learning from examples is what machine learning is all about.

There are different ways to do machine learning. One common way is called supervised learning. In supervised learning, you train the computer with a set of data where the answers are already known. For instance, if you’re teaching a program to recognize cars, each picture you show it is labeled as “car” or “not car.” The computer uses these labels to learn what features are associated with cars.

Another way is unsupervised learning. Here, you don’t give the computer any labels. Instead, it tries to find patterns on its own. For example, it might group pictures with similar colors or shapes together without knowing what any of the objects are.

There’s also reinforcement learning, where a computer learns by trial and error, much like how you might learn to ride a bike. It gets feedback, usually from you, based on its actions and tries to maximize the correct answer.

ChatGPT

ChatGPT is a specific type of AI. It’s designed to understand and generate human-like text based on the input it receives. If you’ve ever chatted with an online assistant that can answer questions or help you with tasks, it might be powered by something similar ChatGPT. What makes ChatGPT unique is that it uses a technique called deep learning, which is a type of machine learning. Deep learning involves using very large networks of computers to learn from vast amounts of data, kind of like building a very complex brain for the computers.

ChatGPT works by first being trained on a massive amount of text data from the internet. This process, called pre-training, helps it learn grammar, facts, and even some (very limited) reasoning skills. After this, it goes through fine-tuning, where it gets better at specific tasks by receiving feedback from the developers and YOU. When you ask ChatGPT a question, it uses all this learning to generate a response that makes sense based on the context.

It’s important to note that while ChatGPT is a form of machine learning, it’s specifically designed for working with language. Making it a Narrow AI.

Not all machine learning models are like this. Some might be designed to recognize images, while others might predict weather patterns. ChatGPT’s main job is to understand and generate text, making it a powerful tool for things like chatbots.

Automation

Automation is another concept that often gets mentioned alongside AI and machine learning, but it’s different. Automation is all about making machines or software do tasks on their own without human help. These tasks are usually repetitive and follow a clear set of steps. For example, think about an automatic washing machine. Once you load your clothes and start it, the machine goes through a series of steps to wash your clothes without needing any further input from you. That’s automation.

Automation doesn’t necessarily require AI. For example, a simple conveyor belt system in a factory that moves products from one place to another is automated, but it doesn’t have any intelligence. It’s just following a pre-programmed set of instructions.

However, when you combine automation with AI, you get smart automation. This is where the machine can adapt and make decisions based on the data it receives. For example, a smart thermostat that learns your schedule and adjusts the temperature automatically is a combination of automation and AI.

So, how do AI, machine learning, and automation differ from each other?

• AI is the broad concept of creating intelligent machines.
• Machine learning is a specific approach within AI where machines learn from data.
• ChatGPT is a specific approach within Machine Learning called Deep Learning.
• Automation is about making machines or software perform tasks on their own, often without any need for intelligence. Automation is not AI.

When you put them together, you get powerful systems that can do amazing things, like self-driving cars that navigate traffic on their own, or smart assistants that manage your daily tasks.

Real World Applications

Now, let’s explore some real-life examples of how these technologies are used. One of the most exciting areas where AI is making a big impact is healthcare. Doctors and researchers are using AI to help diagnose diseases, analyze medical images, and even discover new drugs. For instance, an AI system can look at thousands of medical images and learn to identify signs of cancer with high accuracy. This helps doctors make faster and more accurate diagnoses, potentially saving lives. In another example, AI is being used to analyze genetic data to understand how different genes are related to diseases, leading to more personalized treatments for patients.

In the world of finance, AI is used to detect fraudulent transactions. Banks and credit card companies use AI systems to analyze millions of transactions and identify patterns that might indicate fraud. This helps them catch suspicious activity early and protect their customers. AI is also used for automated trading, where machines make trading decisions based on real-time data. These systems can analyze market trends and execute trades much faster than humans, potentially leading to higher profits.

Education is another field where AI is making a difference. Intelligent tutoring systems can provide personalized learning experiences for students. These systems analyze how a student is performing and adjust the content and pace of lessons to match their needs. For example, if a student is struggling with a particular math concept, the AI tutor can provide additional practice problems and explanations to help them understand. This kind of personalized attention can help students learn more effectively and at their own pace.

AI is also transforming the way we shop and interact with businesses. Online retailers use AI to recommend products based on your browsing and purchase history. When you see a section on a website that says “Customers who bought this also bought,” that’s AI at work. These recommendations are based on analyzing patterns in the data from millions of users. AI-powered chatbots are another example. They can handle customer service inquiries, answer questions, and even help you place orders. This makes it easier for businesses to provide 24/7 support to their customers.

Transportation is an area where AI and automation are coming together in a big way. Self-driving cars are one of the most exciting developments. These vehicles use a combination of AI, machine learning, and sensors to navigate roads and traffic. They can detect obstacles, read traffic signs, and even predict the actions of other drivers. Companies like Tesla and Google are leading the way in developing self-driving technology, and it’s expected to become more common in the future. AI is also used in ride-sharing services like Uber and Lyft. These companies use AI algorithms to match riders with drivers, optimize routes, and predict demand, making the service more efficient.

In the entertainment industry, AI is used to create more immersive and personalized experiences. Streaming services like Netflix and Spotify use AI to recommend movies, TV shows, and music based on your preferences. These recommendations are based on analyzing your viewing or listening history and comparing it with other users. AI is also used in video games to create more realistic and challenging opponents. The computer-controlled characters you play against can learn from your actions and adapt their strategies, making the game more engaging.

AI is even making its way into our homes. Smart home devices like Amazon Echo and Google Home use AI to control various aspects of your home, from adjusting the thermostat to turning on the lights. You can talk to these devices and ask them to play music, set reminders, or answer questions. They learn from your interactions and get better at understanding your preferences over time. Smart appliances like refrigerators and washing machines are also using AI to optimize their performance and make your life easier.

AI Concerns

As exciting as all these advancements are, it’s important to think about the impact of AI, machine learning, and automation on society. One concern is job displacement. As machines become capable of performing more tasks, some jobs may become obsolete. For example, self-driving trucks could reduce the need for truck drivers, and automated customer service systems could replace human agents. However, new jobs will also be created in areas like AI development, data analysis, and maintenance of these systems. It’s important for education and training programs to prepare people for these new roles.

Another concern is privacy. AI systems often rely on large amounts of data to function effectively. This data can include personal information, like your browsing history, purchase habits, and even your voice recordings. It’s important for companies to handle this data responsibly and ensure that it’s protected from misuse. Regulations and policies are needed to ensure that AI is used ethically and that people’s privacy is respected.

There are also ethical considerations around AI decision-making. For example, how do we ensure that AI systems are fair and unbiased? If an AI system is used to make decisions about things like job applications, loans, or medical treatments, it’s crucial that these decisions are made fairly. Bias can creep into AI systems if the data they’re trained on contains biases. For instance, if a hiring algorithm is trained on data where certain groups are underrepresented, it might unfairly disadvantage those groups. Researchers and developers are working on ways to identify and mitigate bias in AI systems to ensure they’re fair and equitable.

In addition to these concerns, there’s the question of control. As AI systems become more advanced and autonomous, how do we ensure that we remain in control? This is especially important when it comes to AI systems that can make decisions on their own, like self-driving cars or automated weapons. Establishing clear guidelines and oversight mechanisms is crucial to ensure that AI is used responsibly and safely.

Conclusion

AI, machine learning, and automation are fascinating and transformative fields that are reshaping our world. AI is the broad concept of creating smart machines, machine learning is a way for these machines to learn from data, and automation is about getting things done without human help. As these technologies continue to evolve, they will bring new opportunities and challenges. Understanding them is the first step to being a part of this inevitable future.

Tyler Wall is the founder of Cyber NOW Education by night and works full time in the cybersecurity industry as his day job. He creates cybersecurity training material in his free time, often after feeling the need to shout what he’s just learned and also because a little bit of passive income never hurt anyone.

He holds bills for a Master of Science from Purdue University, and also CISSP, CCSK, CFSR, CEH, Sec+, Net+, A+ certifications

You can connect with him on LinkedIn.

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For a limited of time get a free copy of Jump-start Your SOC Analyst Career eBook that was published June 1, 2024, in exchange for a review on Amazon. Email tyler@cybernoweducation.com