Machine Learning (ML) is NOT the same as Artificial Intelligence. That said, the connection between the two is undoubtedly undeniable. Machine learning is part of AI in which the algorithms allow the system to locate patterns and learn the trends in the data and try to make decisions without human intervention.
ML technology is evolving so rapidly that every generation is entirely different from the last. The first types of ML were just programmed to perform certain tasks in case of a specific event. Today, however, Machine Learning systems are pretty much autonomous and make new decisions based on what they have learned.
Artificial Intelligence, on the other hand, stems from copying human intelligence. This system includes three different processes: learning, reasoning, and self-correction. The learning process involves sifting through loads of data and turning it into actionable information. AI systems are governed by a set of rules which are called algorithms.
In the reasoning step, the system must decide which algorithm to use in each situation, then self-correct the algorithm, and finally modify them to reach the best results.
The similarities between AI and ML
As mentioned before, machine learning is a subset of Artificial intelligence. that is, artificial intelligence and machine learning work together to make a perfect system in which the processes are streamlined, and the tasks are performed flawlessly.
At the beginning of this process, there is only a considerable amount of data, called big data. The data processing step is done by machine learning in order to find the patterns and trends. And then, it is artificial intelligence that tweaks the algorithms until the best results are found.
The differences between AI and ML
Machine learning is designed to perform some tasks autonomously. In fact, using ML, one does not need to set any algorithms to make decisions. It directly uses the data to discern the rules and see what it had to do.
Artificial intelligence is set a series of algorithms to choose from facing different conditions. Therefore, it can be said that the task of AI is to choose between the rules rather than setting the rules by itself.
Machine learning has a more limited scope since it only can use the already existing data to learn stuff. Artificial intelligence, on the other hand, does not have any boundaries. The sky is the limit when designing an AI system: from traffic systems to smart homes, storage facilities, and factories.
The ease of use and versatility drive companies to have their own AI systems in place more than ever. Today’s companies know that the road to success and innovation is through AI-provided efficiency.
Artificial intelligence is usually used to design robotic systems in factories and manufacturing sites. These systems also make our smart home assistants such as Google Assistant and Siri. These AI-based services can perform tasks and answer questions autonomously.
Machine learning’s main application is in product and service recommendation systems. These systems look at the users’ search history and shopping preferences and issue unique recommendations for each person.
Deep learning is a subset of machine learning. In deep learning, the amount of data used to learn is much more massive. This data is collected from a complicated neural network. Therefore, deep learning can solve more profound and complex problems than both AI and ML.
The common applications of ML and AI
Machine learning and artificial intelligence can create a supreme online shopping experience that has everything the seller and the buyer want. This experience involves having an automated storage facility that automatically keeps track of the goods in the facility. However, it also means personalized suggestions for the users on the website and a streamlined ordering process.
Machine learning in healthcare usually falls into genome research. These research projects contain massive amounts of data that can be analyzed and sorted through an ML system. AI is generally used for image recognition and as a diagnostic tool.
AI and ML in banking usually guarantee the safety of customers’ information and money. ML systems make great security tools that protect banks from malware and phishing tools. What’s more, AI is usually used to predict risk factors in investment ventures and stock markets.
The majority of chatbots on retail and sales websites are run by AI and ML services. This makes the process of customer services much more efficient. Additionally, customer preference and customer satisfaction can also be analyzed and predicted through ML services.
AI and ML market size
The AI market is estimated to be 327.5 billion dollars in 2021. This market is predicted to grow by 17% per year until 2024 and reach 554.3 billion dollars. This growth is mainly driven by the high demand for machine learning and artificial intelligence systems in various industries.