When it comes to the world of technology, there are a lot of buzzwords that get thrown around. Two of these buzzwords are AI and machine learning. Already 77% of the devices we use feature one form of AI or another, so if you don’t already have tools powered by either of them, you will surely in the future. ML algorithms are also used in various industries, from finance to healthcare to agriculture. It is not easy to see the difference between AI and Machine Learning.
Then, is Artificial Intelligence the same as Machine Learning? Unfortunately, those two terms are so often used synonymously that it’s hard to tell the difference between them for many people. But even though both are closely related, AI and ML technologies are quite different.
Let’s look at the main differences between Artificial Intelligence and Machine Learning, where both technologies are currently used, and what’s the difference.
What is AI?
Artificial Intelligence is a branch of computer science whose goal is to make a computer or machine capable of mimicking human behaviour and performing human-like tasks. Scientists aim to design a machine that can think, reason, learn from experience, and make its own decisions just like humans.
The concept of Artificial Intelligence first appeared in Alan Turing’s 1950 seminal work, “Computing Machinery and Intelligence”. Often considered the “father of computer science,” Turing asked in the paper the following question: “Can machines think?”. Then he described a method for testing his question, known as the Turing Test.
The test involves a human participant asking questions to the computer and another human participant. If, based on the answers, the person asking the questions can’t recognize which candidate is a human and which is a computer, the computer successfully passes the Turing test.
Turing predicted machines would be able to pass his test by 2000 but come 2022, no AI has yet passed his test. Despite the growing number of tasks that Artificial Intelligence can do well, these machines have not yet developed the ability to interact with people on a truly emotional level that could allow them to “fool” the human participants.
How can AI be used?
AI has existed in some form for more than 50 years. However, in recent years, AI has seen significant breakthroughs thanks to advances in computing power, data availability, and new algorithms.
Artificial Intelligence is now virtually everywhere – it can be used in robotics and big data analysis, but it’s also in our widespread digital assistants like Alexa or voice search in our smartphone app. It has also proven successful in nearly every field where it was used, including healthcare, banking, education, and manufacturing. So that shouldn’t be a surprise for anyone that by 2025, the global
AI market is expected to be almost $60 billion.
Where you can find examples of using AI?
Here are a couple of examples of how Artificial Intelligence already works hard to prove just how helpful it can be:
- Artificial Intelligence is regularly used in the medical field to diagnose cancer, detect abnormalities in medical imaging, spot and mark life-threatening cases, manage chronic diseases, and even predict stroke outcomes.
- AI helps banks and financial institutions gather and analyze big data to get valuable insights about their customers and help tailor their service to them. Moreover, technologies such as digital payments, AI bots, and biometric fraud detection systems further enable them to improve both their customer service and the system’s overall security.
- In law enforcement, Artificial Intelligence (AI) is regularly used to monitor gatherings, and it is also increasingly used for facial identification and detecting anomalies in video footage. In predictive policing, AI is used to identify and analyze large volumes of historical crime data to identify places or people at risk. However, this use of AI is still seen as controversial.
- Many shops and services (such as Amazon and Netflix) use AI to suggest the most relevant products to their customers. AI-based engines draw data from previous customer behaviours on the website (such as searches, clicks, and purchases)and use it to determine what might appeal most to that specific consumer in the future. Using AI-driven product recommendations helps customers find what they are looking for quickly and easily. It also helps brands put their most popular products before new potential customers.
- Chatbots and virtual assistants with natural speech capabilities are only growing in popularity thanks to their convenience for daily use. For 72% of people who own a voice search device, using it has become a part of their daily routine. Instead of typing a question in the search box, you can speak to the assistant just as you would to someone in person and have the bot respond to you or perform simple tasks like ordering groceries.
The potential of AI
Those examples are just the tip of the iceberg, AI has a lot more potential. The number of places where AI-powered devices can be used keeps growing – from automatic traffic lights to business predictions to 24/7 factory equipment monitoring.
And while for some people, that’s a good thing since artificial intelligence machines could help us work smarter and more efficiently, there are as many people worried that machines might eventually take over human jobs and increase unemployment.
Additionally, there are many ethical questions we need to answer before we start relying on artificial Intelligence devices. One of the biggest problems is that AI systems deliver biased results. Since it prioritizes results with the maximum click-through rate, this often leads to the system spreading prejudices and stereotypes from the real world. Although computer scientists are working hard to solve this issue, AI might take a long time to become neutral.
What is Machine Learning?
What makes AI tools so powerful, though? Machine Learning algorithms.
Machine Learning is a branch of Artificial Intelligence and computer science that uses data and algorithms to mimic human learning, steadily improving its accuracy over time.
Here, scientists aim to develop computer programs that can access and use data to learn for themselves. The learning process begins with observation or data, like examples, direct experience, or instruction, to find patterns in data. The learning algorithms then use these patterns to make better decisions in the future. Basically, the main aim here is to allow the computers to understand the situation without human input and then adjust its actions accordingly.
There are four main types of ML methods: supervised, semi-supervised, unsupervised, and reinforcement.
Supervised machine learning
The algorithm is given a dataset with desired results and must figure out how to achieve them. Then, using the data, the algorithm identifies data patterns and makes predictions confirmed or corrected by the scientists. The process continues until the algorithm reaches a high level of accuracy/performance in a given task.
Unsupervised machine learning
Machine learning algorithms also study data to identify patterns in this type, but it doesn’t get specific instructions or expected results. Rather, the machine is expected to analyze the data, figure out the relationships and correlations, and then organize the data accordingly.
Semi-supervised machine learning
It is similar to supervised learning, but here scientists use labelled (clearly described) and unlabeled (not defined) data to improve the algorithm’s accuracy.
The algorithm is given a reinforcement learning set of actions, parameters, and end values. After analyzing and understanding the rules, the system then evaluates various options and possibilities to find the optimal solution for a given task. Using this method, the machine can learn from its experience and adapt its approach to a situation to achieve the best possible results.
The “deep learning” teaching method is also popular among computer scientists and is often used in speech recognition, natural language processing, machine translation, or medical image analysis.
Since deep learning methods are typically based on neural network architectures, they are sometimes called deep neural networks. The term “deep” here refers to the number of layers in the neural network since traditional neural networks contain only 2-3 hidden layers, but deep networks can have up to 150.
This type of machine learning involves training the computer to gain knowledge similar to humans, which means learning about basic concepts and then understanding abstract and more complex ideas.
One of the biggest advantages of deep learning is its ability to work with unstructured data such as text, images, and voice and then organize it
accordingly. More importantly, the multiple layers in deep neural networks enable models to become more effective at learning complex features. That also allows it to eventually learn from its own mistakes, verify the accuracy of its predictions/outputs and make necessary adjustments.
How can machine learning be used?
There are many applications for Machine Learning across various fields, and the number continues to grow. For example, we already use ML algorithms in internet search engines, email filters that filter spam, banking software that detects unusual transactions, and many apps on our phones that recognize voices. Here’s just are a couple of examples of where those algorithms are already being used successfully:
- A group of scientists at the Commonwealth Scientific and Industrial Research Organisation in Australia developed a machine-learning technique to identify people who fit specific trials using patient medical records.
- As ML systems can scan through vast data sets to detect unusual activity or anomalies and flag them instantly, they are ideally suited for combating fraud in financial transactions.
- Computer vision and ML algorithms can be used in agriculture to detect and distinguish weeds cheaply, without causing environmental harm and with fewer side effects. These technologies may even be used to power robots that destroy weeds, reducing the need for herbicides.
In general, machine learning algorithms are useful wherever large volumes of data are needed to uncover patterns and trends. However, the main issue with those algorithms is that they are very prone to errors. Adding incorrect or incomplete data can cause havoc in the algorithm interface, as all subsequent predictions and actions made by the algorithm might be skewed.
For example, if you have a faulty factory equipment monitor sensor, the inaccurate data it provides may cause the machine learning program to behave unexpectedly, all because it used the wrong data as a basis for an algorithm update. For this reason, the data added to the program must be regularly checked, and the ML actions must also be periodically monitored.
AI vs Machine Learning. What’s the ultimate difference?
Even though Machine Learning is a component of Artificial Intelligence, those are two different things. Artificial Intelligence aims to create a computer that can “think” like a human and solve complex problems. Meanwhile, ML helps the computer do that by enabling it to make predictions or take decisions using historical data without any human instructions.
AI is also capable of much more than ML algorithms. Scientists are working on creating intelligent systems that can perform complex tasks, whereas ML machines can only perform those specific tasks for which they are trained but do so with extraordinary accuracy.
There is a close connection between AI and machine learning – the rapid evolution of AI technology is partly due to groundbreaking developments in ML.
Eventually, thanks to both, we can create artificially intelligent human-like machines. The recent technological advances have certainly brought us closer to that goal than ever before. This article provided a basic overview of AI vs Machine Learning and their differences. Now it is time to use them in your further projects.