Do you have clarity about the terms AI, ML, and DL, or confused by their similarities? Then, stick to this blog, as we will clarify the terms and provide distinctive facts about them. The influence of AI or artificial intelligence is all over the internet, and we have heard many real-life use cases of it. One such example is ChatGPT, which helps people write and generate ideas for writing, photos, presentations, and so on. According to a study conducted by Exploding Topics, 77% of companies are using or exploring AI to improve business structure and profitability. 

 

Slowly, the terms ML and DL are grabbing the attention of the internet. ML refers to machine learning, and DL denotes deep learning, which are cogs of AI only. Machine learning is a part of AI and has a few advanced intelligence properties. On the other hand, deep learning is a branch of ML. All three have their distinct features and usage besides the similarity of mimicking human intelligence. Let us describe their features before going through their differences. 

 

What is AI? 
 

The term AI became popular in the past few years, but artificial intelligence was invented and conceptualized long back in the 1950s. Since then, numerous research and experiments have been done worldwide to enable machines to mimic human intelligence. The aim was to make and train a machine to artificially process information like humans and act like one. Well, we have had success in this case. Many companies have developed AI-powered applications and devices to solve many real-life problems across industries. 

 

Do you know that 60% of YouTube videos are played because of AI recommendations? 

A few successful uses of AI are self-driven cars, Amazon Alexa, Apple’s Siri, self-learning tools, etc. However, AI is still evolving like a growing child and needs human intervention for training. Artificial narrow intelligence, artificial general intelligence, and artificial super intelligence are the types of AI.  

 

What is ML? 
 

ML, or machine learning, is a part of artificial intelligence with a few advanced properties. Machine Learning aims to copy the learning pattern of the human brain. It uses the labeled and structured data set to learn the patterns. That is why it needs some pre-processing of data to structure and label it. ML is certainly not fully automated yet and needs human intervention. 

 

ML can process a large data set to learn from it about past incidents and then predict the future as a result of processing. Many businesses use ML features to navigate through real-life challenges. Such as predicting future sales, fraud prediction for baking, stock price predictions, product recommendations, etc. Supervised learning, unsupervised learning, and reinforcement learning are the types of machine learning. 

 

What is DL? 
 

Deep learning is a branch or part of machine learning with more advanced intelligence. Its algorithm can process an enormous set of data and predict more accurately. Deep learning uses neural networks to process data as the human brain does. DL takes data with an input layer and then creates numerous layers to process the input data to learn from it before giving output.  

 

Deep learning is more automated and needs minimal human intervention compared to machine learning. It can process both structured and unstructured data and extract features automatically. DL is mostly used in the detection of medical problems like tumors and cancer. It is also used in detecting objects, generating music and images, and generating captions for images.  

  

Differences Between AI, ML, and DL 
 

Although all of them are parts of AI, they are different from each other in various aspects. Here, we have mentioned them. 

 

• Purpose 
   

AI is a process to make machines mimic humans for decision-making abilities. ML, as part of AI, is made to develop algorithms to learn patterns by processing statistical data. DL is made to process larger data sets using neural networks to automate the learning process and feature extraction. 

 

• Algorithm 
   

AI is an algorithm that teaches computers to make decisions like humans. Machine learning is part of the AI algorithm that can process larger data sets. Deep learning is a more advanced part of the machine learning algorithm that can process numerous data sets.  

 

• Accuracy 
   

AI focuses more on the success of the output than the accuracy of the output. ML provides more accurate output, but it does not focus on success rates. DL provides the best output of maximum accuracy among AI, ML, and DL. 

 

• Efficiency 
   

The efficiency of AI depends on the efficiency of ML and DL. On the other hand, DL is more efficient than ML as DL is capable of processing larger data sets than ML and can automate the process. 

 

• Mathematics 
   

AI uses the most complex maths. A clear idea of the logic involved in ML can help you visualize its complex functionalities like K-mean, Support Vector Machines, etc. If you know the math involved in DL but need clarity, then you can break down complex functionalities into lower dimensions by adding more layers to it. 

 

Categories 
 

• AI Categories - It has three broad divisions. 
• Artificial narrow intelligence,  
• Artificial general intelligence,  
• Artificial super intelligence 
• ML Categories - ML also has three major categories. 
• Supervised learning,  
• Unsupervised learning,  
• Reinforcement learning 
• DL Categories - DL can be divided by the neural networks it uses. 
• Unsupervised Pre-trained Networks,  
• Convolutional Neural Networks,  
• Recurrent Neural Networks,  
• Recursive Neural Networks. 
   

Use Cases 
 

• AI Applications 
• Self-driven cars 
• AI robots 
• Speech recognition assistance - Siri 
• Translations assistance  
• ML Application 
• Fraud analysis - for banking sectors 
• Stock price predictions  
• Future sales and demand predictions 
• Product recommendations 
• DL Applications 
• Detection of medical conditions - comparing data 
• Generating music 
• Generating images 
• Caption for images 

 

Wrapping Up 
 

Artificial intelligence has already intruded into most aspects of life and helped humans solve many real-life problems. AI made laborious and difficult jobs easier and faster for humans. Machine learning and deep learning are more intricate forms of AI that provide more accuracy and help in solving more critical problems. ML and DL are capable of processing larger data sets to provide more accurate results than AI. On the other hand, DL is capable of automating the learning process and extraction of features and needs minimal human intervention compared to AI and ML.