Difference B/W Artificial Intelligence and Machine Learning

Nowadays, Artificial Intelligence (AI) and Machine learning (ML) technologies are the two most trending technologies. Many companies are investing in AI and ML applications to transform the existing business processes.
Most of the people are confused about the difference between Artificial Intelligence and Machine Learning. So, we are here to clear your confusion!
Today, In this article, we will be giving a detail about what is AI? What is ML? And what is the major difference between AI and ML technologies.

What is Artificial Intelligence (AI)?

Artificial Intelligence is defined as a smart concept that enables machines to perform various tasks done by humans. AI becomes more popular nowadays with its automation and intelligent features.
AI has been in talks since long back. Gradually, the technology is moving to the next level. The researchers continue to invent something new in AI. Artificial Intelligence machines can solve complex calculations.
AI along with ML techniques, it has been scientifically proven to reflect human decision processes and improve machine intelligence.

How Does AI Works and Why Is AI So Important?

• AI can automate the every task which is done by human previously
• AI frequently performs high-volume machine tasks efficiently
• Industries are improving their tasks using AI capabilities
• AI-based apps, conversational tools, and chatbots help companies in improving digital marketing
• AI can build fraud detection systems to identify and track illegal access to data systems or network
• AI uses ML to predict the future outcomes
• AI apps in healthcare used to detect diseases with high accuracy
• AI in automobile used for developing autonomous cars

The field of Applied AI is still observing advancements. We can state that advancements in AI is welcoming more innovations in ML. As a subset of AI, machine learning program is giving more valuable insights and predictions into data. Thus, ML is supporting new research works in AI.

What is Machine Learning?

The machine learning is best defined as an important application of AI, which allows a computer or machine to learn from input data and improve the experience without the need for explicit programming. The primary aim of the advanced machine learning algorithms is to allow systems to learn automatically without one’s interaction.

Rapid Growth of Machine Learning

Driven by the advancements in AI, the demand for ML techniques is expanding rapidly. ML allows the software to predict future outcomes accurately.
In addition, a vast amount of digital data over the internet is increasing the demand for ML solutions. In particular, digital businesses are highly adopting ML, and deep learning apps to manage their customers proficiently.
The researchers thought that instead of training machines how to perform, it’s better to code them once to do repeated tasks automatically. This trend increased the demand for the development of machine learning, deep learning, data analysis, and predictive analytics.

How Does Machine Learning Works?

• Step 1: Learns from a trained data set
• Step 2: Identifies dissimilar data from a group of similar data and hence measures error rate
• Step 3: Identifies noise attributes to improve the processing capacity
• Step 4: Data validation and testing processes to deliver accurate error measure
• Step 5: Insights into data

Artificial Intelligence Vs Machine Learning

Neural Networks

The primary reason for the development of Neural Networks is to train the systems to replicate exactly like humans.
A Neural Network system can categorize the data in a manner as human brain do. These systems can recognize images and categorize them based on the elements they comprise. In the image below, the nervous system takes an input image, processes it, and finally identifies objects using previously gained experiences.

Based on the trained data, it can make decisions, predictions, and statements with confidence. Along with the feedback loop, it can decide the predicted decisions are wrong or right. Thus, neural network systems can modify the approach it takes in the future.
Accordingly ML apps can read and understand the input text and categorize whether that text is a complaint or greetings. In addition, ML applications can also listen to music and determines whether it makes a person happy or sad.
All these are a few applications of ML and neural network systems. The major idea behind all research works is connecting digital data and electronic devices intelligently. To reach this, AI also uses natural language processing (NLP) to efficiently understand human language.
NLP is highly dependent on ML techniques. The NLP-based apps can interpret written/spoken language and respond to the user in the same way.

Machine Learning Vs Neural Networks

Advanced Artificial Intelligence and Machine Learning Market Overview:

Increased investment in AI technologies, the growing need to process large amounts of data and the lack of experienced technicians to manage business tasks are key growth factors of the artificial intelligence market size. In between 2016-2025, the market size is expected to project $169.41 billion by 2025 from $4.06 billion in 2016.

To Conclude, Next-Level of AI and ML Offers Huge Opportunities to Businesses

Despite the difference b/w Artificial Intelligence and Machine Learning technologies, we can understand that the combination of AI and machine learning models provides intelligent business processes. Different industries ranging from healthcare and banking to manufacturing and e-commerce are widening business opportunities. Thus, AI, ML, deep learning, and neural networks expand your brand awareness.
For instance, the sales and marketing teams are using ML systems to detect the behavior of its customers search. Thus, AI & ML apps for marketing and sales industry are providing growth benefits to them. Multiple developments in AI leads to the development of ML technology even more.
Connect with USM to know more the benefits of AI and ML Technologies.
Hope, this article makes you understand the basic difference between AI and ML. We would like to add more valuable information related to Artificial intelligence, reinforcement learning, computer science, data science, big data, and deep learning technologies.