Artificial Intelligence (AI) and machine learning (ML) have become the talk of the town lately. These technologies are groundbreaking because they are revolutionizing the working of organizations. The advancements in AI have streamlined the functioning of these firms and have given them access to valuable information. This lets businesses make the right choices and informed decisions, thus moving in the right direction. But it is prevalent even today that people are unable to differentiate between AI and ML. This opinion allows them to clear their concepts with knowledge of AI vs. machine learning.
What is Artificial Intelligence?
Artificial Intelligence (AI) is a broad concept, and this field covers various techniques and tools, machine learning being one of them. The process is based on making super-intelligent machines that perform functions like a human mind. These functions are thinking, decision-making, problem-solving, and other technical tasks. Can you imagine that AI systems adapt to situations, keep learning from new experiences, and use this knowledge to make effective decisions without human intervention?
Common subsets or domains of Artificial Intelligence
Machine Learning (ML) – Machine learning is basically all about training algorithms on existing datasets. This creates models of machine learning, which is a subset of AI. These models then perform various tasks as humans do. It is to be noted that AI vs. machine learning begins with the basic idea that machine learning is just one subset of AI and not an entirely similar thing.
Deep Learning – Deep learning is an important and well-known faction of AI. Its processes involve the use of AANs (Artificial Neural Networks) that copy the ability of the human brain to perform difficult tasks. You would be excited to know that it is all done without any input from a man.
NLP Natural language processing (NLP) – is a sub-field of computer science; you must have heard about it and can translate human language into machine language or machine speech. This is done by developing software that can translate and interpret the language of humans.
Robots – Who does not know about Robotics? Everybody does as it has become a buzzword for a few years. We would say it is the most famous branch of AI that develops robots who look like, speak like, and act like humans. These robots learn and perform even complex tasks in real-world scenarios.
Top Real-World Applications of AI
Machine Translation
Machine translation is, in our opinion, one remarkable application of Artificial Intelligence. You must have experienced the convenience of tools that make your tasks seamless like Google Translate. Google Translate effortlessly bridges language barriers for all of us.
Self-Driving Vehicles
Now comes another exciting AI application, which is self-driven vehicles. Have you not heard about Google’s Waymo? These autonomous vehicles are using AI algorithms to visualize the situation and navigate seamlessly through the traffic. And make quick decisions for a smooth and safe journey.
AI Robots
As mentioned above, AI robots have made this world a new one. Consider Sophia, for example, a renowned robot. Can you guess what Sophia can do? You surely can today. But nobody could imagine a robot having a meaningful conversation in human style and mimicking human emotions just a few years ago. These robots have literally made science fiction a reality.
Speech Recognition
Now let’s talk about everybody’s favorites: Apple’s Siri and OK Google. These are examples where NLP algorithms have entirely changed the way we interact with our gadgets and other electronic devices. Gone are the days when we had to type a message or search for a query. With speech prompts available, we can simply speak and order verbally to get our devices to do whatever we want.
What is Machine Learning?
On the other hand, machine learning focuses on preparing machines by training them to learn and remember from data patterns. These machines then make informed decisions on their own. Machine learning utilizes algorithms that let computers recognize certain patterns, access insights, classify data, and keep on learning from any information that is coming up. This is how they improve on performance and accuracy. ML algorithms are of the following three types:
- supervised (with labeled data for training)
- unsupervised (without labels and designed to find patterns independently)
- semi-supervised (a hybrid approach)
Steps included in ML processes
The following steps provide a general framework for you to understand the machine learning process.
Keep in mind that specific details may vary depending on the nature of the problem and the type of machine-learning technique used.
- Define the Problem
- Collect data
- Data pre-processing
- Select a Model
- Split the Data
- Train the Model
- Test Model
- Evaluate the Model
- Tune Model
- Prediction
Everyday Examples of Machine Learning
Image Recognition
Automatic tagging suggestions for friends on Facebook are the most common use case for facial and image recognition. We are sure you enjoy it the most.
Voice Recognition
Google’s “Voice Search” option is an exciting feature, right? It is also an application of ML. Virtual assistants that talk to you like humans, including Siri, Alexa, Google Assistant, and Cortana, enable you to seek information through voice search without typing anything.
Email Spam Filtering
Go to your inbox to check that all your received emails are usually divided into three sets: normal, important, and spam. Imagine it is done by itself because of machine learning.
Product Recommendations
When you search for a product or service on a website, Google basically “learns” to keep/retain this information and use it later to make recommendations about your interests.
Spelling and Grammar Checker
You can use your smartphone’s autocorrect function to check spelling and grammar; we are sure you do.
Medical Diagnosis
Machine learning is also used in medicine to diagnose various diseases.
How do the two correlate?
Now that we know what AI and ML are, it has become important for us to understand their interplay before we thoroughly read about AI vs. machine learning. AI is the broader umbrella term, incorporating techniques like ML to achieve its objectives. ML, in turn, strengthens AI systems to learn and evolve over time so that they can perform complex tasks, that too with precision and efficiency.
Difference between AI and Machine Learning
AI is a computer software that thinks like humans think to perform certain tasks. These tasks include analysis, reasoning, and of course, learning. On the other hand, machine learning is a specific part or a subset of AI that uses algorithms trained on existing data. The models, hence created, can handle the above-mentioned task effortlessly.
People often use the terms AI and ML interchangeably because of their interplay as concepts. However, one must note that AI is a huge idea, creating software with cognitive abilities like those of humans. Whereas, machine learning is just one approach to achieving this goal.
Summarizing the key differences
The following table is sufficient for everyone to grasp AI vs. machine learning – distinctions between artificial intelligence and machine learning.
Before we wrap up the discussion with a clear understanding of how machine learning and artificial intelligence are different, here’s a crisp answer to your questions like ‘Is machine learning the same as AI?’ or What’s AI vs. machine learning in one or simplest words?
- AI is the decision maker, whereas ML is the learner.
- AI involves making human-like machines, while ML is teaching machines to perform a task.
Want to Learn More? Read the FAQs Below
How are AI and machine learning correlated?
A smart computer uses AI to mimic humans and perform tasks independently. Machine learning is how a computer system develops intelligence using data to solve problems.
Are AI and machine learning the same?
They are closely connected and used interchangeably, but the two differ. Machine learning is actually a subset or a component of AI.
What is artificial intelligence (AI)?
Artificial intelligence is making a machine/computer system to mimic human cognitive functions to solve various problems.
Can AI think and feel like humans?
Currently, AI cannot feel like humans because it lacks emotions, yet it has learned to respond emotionally like humans.
What are the three types of Machine learning algorithms?
- supervised algorithms
- unsupervised algorithms
- semi-supervised algorithms
Name a few real-world applications of AI.
Siri, Google Assistant, Alexa, Waymo, Sophia, etc
Last Word!
In a nutshell, Understanding AI vs. machine learning is important today for everybody as these innovations are gradually becoming part and parcel of the present world. Whether you are from the commerce industry, a data scientist, or, let’s just say, a tech-savvy person, having comprehensive knowledge of AI and machine learning, including their working and applications, is vital. We hope the AI chief’s opinion will help you differentiate between the two concepts.
