Explore The Differences Between Artificial Intelligence & Machine Learning
With the many wonders of technology and science at their disposal, industries are now evolving at an impressive pace. Two of the most popular fields of study today are Artificial Intelligence and Machine Learning, and they are here to stay. Steadily and speedily, these curiosity-inducing concepts are helping businesses leverage the true power of Big Data, especially those that belong to the software industry.
How exactly do organizations build AI and ML-powered applications? With the help of programming languages that are flexible to easily accommodate the complexities that AI and ML concepts bring. Based on their skill-set and industry experience, developers are the ones who choose the language of preference for AI and ML development. Today, most developers turn to Python for the same purpose.
Why Python above all the other popular choices? The answer lies in its impressive list of features. An easy learning curve, great support community, versatile programming style, and a treasure trove of libraries available make Python one of the most sought-after programming languages. It enables developers to build and launch AI-powered Python applications at a faster rate in the market, all while maintaining the highest excellent quality standards.
Given that both AI and ML are products of innovation and advanced technology, the lines can get a little blurry. As such, it is only natural to ponder upon million-dollar questions like – ‘Is Artificial Intelligence just Machine Learning’? This blog will answer just that, along with exploring the main differences between Artificial Intelligence and Machine Learning.
What Is Artificial Intelligence?
John McCarthy, a professor emeritus of computer science at Stanford and the man who coined the term ‘Artificial intelligence’ defines it as “the science and engineering of making intelligent machines.” Today, AI is a vast field of study in the data science sector. The primary objective of this concept is to create smart machines that are designed to perform human tasks with the same level of rigor and intelligence.
The concept finds its origin in Alan Turing’s question – ‘Can machines think’ – in his 1950 paper ‘Computing Machinery and Intelligence’. In its simplest sense, AI is the branch of computer science that is striving to find a positive answer to this question. All successful innovations in the field are a steady step toward enabling machines to think and function with human intelligence such as observing, thinking, practical learning, rationalizing, and making informed decisions.
What Is Machine Learning?
Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, defined ML as “a field of study that gives computers the ability to learn without being explicitly programmed.” Machine learning is essentially a category that falls under the Artificial Intelligence umbrella. It involves developing software and algorithms that help in making predictions from data. Technically speaking, it is a human-made intelligence (AI) application that permits systems to learn and improve from experience.
Machine Learning algorithms come with the ability to automatically study data and learn from it, without any assistance or expertise. ML machines and applications are designed to process large quantities of data very quickly using algorithms that change and get better with time. The primary purpose of machine learning is to enable machines to teach themselves by reading data and making precise predictions.
Key Differences Between AI & ML
| Artificial Intelligence | Machine Learning |
| John McCarthy, a professor emeritus of computer science at Stanford, coined the term ‘artificial intelligence’. He provided the field with a firm foundation for more than five decades. | Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term ‘machine learning’ in 1959. His contribution to the field has been of key importance. |
| Artificial intelligence is the overall appearance of being smart. | Machine learning is, first and foremost, associated with patterns and precise results. |
| Artificial Intelligence involves the creation of intelligent machines or applications that can brilliantly replicate even the most complex of human behaviors like thinking, analyzing, predicting, making sound decisions, and such. | ML is the branch of Artificial Intelligence where machines/applications learn from data provided to them, without the need of being programmed. They are designed with the ability to analyze data and learn from it, just as humans do. |
| AL-powered systems and applications are capable of solving complex problems and performing advanced tasks with a human approach. | ML-powered machines learn from data and give an accurate output. The systems can only perform particular tasks that they have been trained for, but expertly so. |
| The scope of AI is truly vast, the possibilities can go beyond one’s imagination. | So far, the scope of Machine learning is limited given that is it, after all, a subset of AI. |
| Popular AI applications: Personal assistants like Siri and Alexa, online customer support using chatbots, online gaming, smart humanoid robots, and such. | Popular ML applications: email spam filters, online recommendation systems, Google search algorithms, Facebook auto friend tagging suggestions, and such. |
Artificial Intelligence & Machine Learning Examples
1. AI-powered Personal Assistant Tools:
Almost all modern-day homes and offices are equipped with personal assistant systems that serve as great interactive gadgets, keeping us updated and on time. The most popular examples are Google Home by Google, Siri by Apple, Alexa by Amazon, and Cortana by Microsoft.
2. Manufacturing & Industrial robots:
Industrial robots are physical evidence of how well the human imagination and technology perform together. These robots can do it all – from performing complex actions to monitoring their performance to identifying if and when maintenance is required.
3. Ad Recommendations:
Personalized ads are a buzzing trend in the marketing and advertising industry today. Using ML, brands can present different ads to different users based on Google search. For example, if one searches for sports shoes, they will be recommended with the right ads.
4. 24×7 Chatbot Support:
The presence of chatbots can be traced across diverse sectors like Finance, E-commerce, Education, Healthcare, and more. They enable businesses to provide consumers with 24×7 online support, addressing their multiple queries at the same time.
5. Self-driving Cars:
This innovation has been nothing short of a revolution in the global automobile sector. Although still in their learning stage, these driverless cars are adept at carefully studying other cars and educating themselves in the matters of ideal speeds and a good sense of direction.
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