Machine Learning vs. Deep Learning vs. Artificial Intelligence

Machine Learning vs. Deep Learning vs. Artificial Intelligence

Modern technologies make our lives easier, yet you’re probably unaware of what technologies are involved. Have you ever considered how refrigerators control their temperatures or how Siri works?

Thanks to modern technologies like AI (artificial intelligence), ML (machine learning), and DL (deep learning), cell phones, home appliances, and automobiles have all become wiser (deep learning). Everyone has certainly heard the technology buzzwords discussed, but only a few people know what they imply and how they differ.

All of these terms appear to be too technical for non-technical users. It’s difficult to tell the difference between deep learning, machine learning, and AI. Although AI, deep learning, and machine learning are all made of the same cloth, they have quite distinct meanings. It’s time to compare them and see how deep learning, machine learning, and artificial intelligence vary.

What is Artificial Intelligence?

When you hear about AI, the first thing that comes to mind is undoubtedly a robot. The reason for this could be due to Hollywood films such as ‘Transformers’ and ‘Chappie,’ which depict AI as human-like robots attempting to take over the planet. However, AI is no longer as intelligent or dangerous as it appears in movies. Instead, AI is employed in a wide range of industries, including retail, education, healthcare, and many more.

Despite this, there is no clear definition of what AI is. The godfather of AI, John McCarthy, defined it as “the science of building intelligent robots.” Here are some more popular Artificial Intelligence definitions:

  • A computer science dealing with the recreation of intelligent human-like behavior in machines. 
  • Machine’s ability to simulate human behavior. 
  • An intelligent computer program able to perform human-like tasks, such as recognizing speech, perceiving pictures, making decisions, and translating between languages.

Artificial intelligence’s major goal is to make machines as intelligent as humans. It aims to make machines think and act like humans. Artificial intelligence (AI)-enabled gadgets are taught to solve problems and learn. The best examples of AI in the present world are robots and self-driving cars.

The truth is that we can’t adequately imbue machines with human intellect. Even so, machines cannot think, work, or function in the same way that people can. Sophia, the first social humanoid robot, was recently introduced to the public, and she can act and even think like a human. She is able to communicate, make jokes, and make decisions. Sophia is the most well-known AI-powered robot.

Three types of Artificial Intelligence 

Artificial intelligence must be viewed through the perspective of business capabilities, not technologies. In other words, instead of looking for AI vs Machine Learning vs Deep Learning instances, we should concentrate on what artificial intelligence can achieve for your company.

Artificial intelligence can support three top business needs:

  • Automate business processes 
  • Collect and analyze data 
  • Engage with customers or employees

Artificial Intelligence for business automation 

Using robotic process automation systems, or RPA, most back-office administrative and financial functions may now be easily automated. RPA can help with the following tasks:

  • Transfer data from call center system or emails into a customer management solution like Salesforce
  • Replace lost ATM or credit cards
  • Extract provisions by reading contracts and other legal papers using natural language processing 

Artificial Intelligence for data collection and analysis

To recognize and comprehend recurring patterns, the great majority of enterprises use AI-based algorithms (to be more precise, machine learning models):

  • Predict follow-up purchases 
  • Identify card fraud 
  • Analyze warranty data 
  • Automate ad targeting

AI-powered systems deliver more precise data insights than traditional analytics in three ways: they can handle large datasets, models obtain better and better insights by studying dozens of datasets, and they can deal with enormous datasets.

Artificial Intelligence for customer engagement 

Many modern organizations rely on natural language processing chatbots, smart agents, and machine learning models. Some organizations deploy AI solutions for their staff (for example, Becton, a US medical technology company, uses Amelia as an internal help-desk representative), while others use AI to give improved customer service (for example, SEBank, a Swedish bank, uses Amelia for customer support). Companies can readily handle a wide range of difficulties by utilizing artificial intelligence capabilities:

  • 24/7 intelligent agents solve a growing array of issues from password request to technical support
  • Internal chatbots answer the most common employee questions on various topics, including employee benefits, HR policy, and IT
  • Service and product recommendation solutions help retailers to provide a more personalized experience, drive engagement rates, and grow sales 
  • Insurance recommendation systems provides more customized care plans

Artificial Intelligence works with:

  • General intelligence 
  • Learning 
  • Nature language processing 
  • Knowledge representation 
  • Planning 
  • Reasoning 
  • Problem solving
  • Social intelligence 

What is Machine Learning?

Both machine learning and deep learning are built on the foundation of artificial intelligence. In other words, without AI, there would be no machine learning or deep learning. Let’s have a look at what machine learning is.

Machine learning is a set of algorithms that search for data, learn from it, and then use what they’ve learned to make better decisions. For example, ML-based algorithms are used by online music streaming services like YouTube Music and Apple Music to determine which new song or singer to recommend.

The basic concept behind machine learning is that you build a data collection, feed it to ML algorithms to learn from it, and then the ML algorithms produce predictions or recommendations based on the data processed. In other words, such algorithms are hand-coded and are incapable of learning. When given different inputs, such algorithms always reply in the same way.

When it comes to spotting anomalies, ML algorithms are unrivalled. They look for incidents that are considerably different from others. In the banking industry, machine learning is commonly used. Stripe, for example, employs machine learning-based anomaly detection to detect any fraudulent activity. Stripe’s Radar also performs admirably.

Stripe includes a radar feature that works right out of the box. It gathers fraud-related data from all of Stripe’s operations. Because fraud tendencies fluctuate, firms must mark payments to assist Radar in detecting irregularities.

To establish a behavioral pattern, the algorithm examines every payment made by billions of Stripe users. The country of origin of the card, the IP address from which the payment was made, and the email domain all provide useful information for anticipating fraudulent behavior.

Not only Stripe takes advantage of machine learning. Many of the products we use on a daily basis also use machine learning models. 

  • Apple, Google, Amazon, and Microsoft power up their voice assistants (Siri, Alexa, Cortana, and Google Assistant) with machine learning models. They’re used for lots of cases, from text prediction to app recommendation. 
  • Amazon takes advantage of machine learning to recommend items to buyers. Amazon’s recommendation engine is based on what people have bought after purchasing a particular item. Amazon can recommend up to four items to facilitate a follow-up purchase. 
  • Facebook and Google use machine learning models to adjust what ads to show you based on your last search query or newsfeed. 

What businesses can benefit from utilizing Machine Learning?

  • Financial
  • Healthcare
  • Government
  • Oil and gas
  • Transportation
  • Retail

What is Deep Learning?

When we look at the distinctions between machine learning and deep learning with examples, we can see that they only have one thing in common: they’re both founded on AI concepts.

Deep learning, which is based on artificial neural networks, is the newest branch of artificial intelligence. Deep learning, on the other hand, might be considered a subset of machine learning because it uses data to understand how to solve problems or act in specific situations. When comparing machine learning and deep learning, it’s important to note that the major difference between the two is that machine learning is self-learning.

Despite the fact that many people confuse machine learning and deep learning, the two systems have vastly distinct capabilities. Machine learning uses algorithms, but deep learning uses both processing power and neural networks.

When we examine neural networks more closely, we can see that they are founded on the biology of our brains. As a result, the network closely resembles the connections between brain neurons. However, unlike the human brain, where all neurons are connected within a certain physical area, neural networks include disconnected layers, interconnections, and several data reproduction directions. In other words, unlike traditional machine learning models, neural networks may learn and mimic human decision-making.

Deep learning is being used by academics for a wide range of tasks, from simple pattern identification to medical diagnosis and automatic language translation. We recommend Google’s AlphaGo as one of the best instances of AI vs Machine Learning vs Deep Learning.

AlphaGo is the most recent application of deep learning. It’s the first time a computer program has mastered and won the board game ‘Go.’ ‘Go’ is not a simple board game to play with your children. It necessitates a keen mind as well as intuition. And, believe it or not, AlphaGo won the first match against Mr. Fan Hui, the three-time European Champion, with a score of 5-0!

Google launched AlphaGo Zero as a result of its success. Unlike AlphaGo, AlphaGo Zero does not require thousands of games with both professional and amateur players. Because each game is radically distinct from the last, it can learn by playing against itself. AlphaGo Zero makes new plays and develops unique strategies that have beaten the World ‘Go’ Champions in a matter of moves.

Where can we use Deep Learning?

  • Oil and gas
  • Construction
  • Finance
  • Cybersecurity
  • Healthcare
  • Social Media

Machine Learning vs. Deep Learning vs. Artificial Intelligence

All three concepts are linked in some way and deal with large amounts of data. Machine learning and deep learning are two types of artificial intelligence. Big data and current technologies have made it easier for firms to collect, analyse, and use information. All of these forces combined to form a new subject called Data Science, which arose from the intersection of AI, ML, and DL.

Data science is the study of how to use data to solve analytical problems. Its main purpose is to uncover hidden patterns and assist businesses in increasing revenues and productivity.

Before data scientists can use it to train machine learning or deep learning models, it must first go through six steps:

  • Data discovery 
  • Preparation 
  • Planning 
  • Building 
  • Communicating results 
  • Operationalizing 

Data science isn’t used only for machine learning and deep learning model training. It deals with everything from data collection to data manipulation. Data scientists help businesses make the right decision based on the collected information. 

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