We have come across the terms Machine Learning and Artificial Intelligence many times. But, do we really know the right meaning and application of these two terms? The scope of both of these emerging technologies is immense, which is why today we will map out the key differences between Artificial Intelligence & Machine Learning.
This article aims at knowing the difference between the two technologies and their respective scope. We will also talk about the courses you can take to equip yourself with the concepts of Machine Learning and Artificial Intelligence. Let us delve into the article.
Artificial Intelligence and Machine Learning are different fields of computer science. Both the terms are used interchangeably, but that is an incorrect approach to the two fields of computer science. Let us first learn what Artificial Intelligence and Machine Learning really mean.
Artificial Intelligence
Artificial Intelligence is about making machines that are adept enough to behave like human intelligence and accordingly take decisions & perform specific actions. To clarify further:
- AI-enabled machines have abilities like visual perception, speech recognition, language processing, etc.
- AI, based on input data, can learn and reason. Based on its learning, it can make decisions
Machine Learning
Machine Learning is a sub-field of Artificial Intelligence. It is all about tutoring a machine to learn on its own by reading the existing data, without really programming it explicitly. To clarify further:
- Machine Learning is quite adept at finding a pattern in a given set of data; it becomes helpful in catching and predicting upcoming trends
- ML’s sets of learning are used as a foundation by AL to plan future courses of action
Let us look at the difference between AI and ML
| Artificial Intelligence | Machine Learning |
| AI is all about simulating human behavioral patterns | ML is all about making machines learn based on past experience without programming it to do so |
| AI centers on thinking like a human so that it can solve complex problems/ deal with complex situations | ML centers around collecting data and results, on a specific area, and learning from the collected data to perform better |
| AI is adept in dealing with unstructured, semi-structured and totally structured data | ML is adept in dealing only with semi-structured, and totally structured data |
| Work Flow: Learn → Reason → Self-Correct | Work Flow: Learn → Correct after an input of fresh data |
| AI aims at enhancing the success rate | ML targets accuracy, irrespective of the outcome |
| AI has a huge scope with its application possible in all facets of life | ML is still about training the machine to do a specific task accurately |
| There are 3 broad categories of AI: Artificial Narrow Intelligence (ANI)Artificial General Intelligence (AGI)Artificial Super Intelligence (ASI) | There are 3 broad categories of ML: Supervised Learning Unsupervised Learning Reinforcement Learning |
| Some of the classic examples of AI applications are: customer service catboats, online gaming, intelligent humanoid robots, Siri, etc. | Some of the classic examples of ML applications are: Google search algorithms, Facebook auto-tagging, online recommendation systems |
| AI is designed to work without any human involvement or little involvement, depending upon the requirements of the task in hand. AI is capable in making decisions and performing actions, based on the data and input it has. | ML is dependent on human involvement because it needs to be trained and optimized. ML needs timely interventions from data scientists, engineers and other IT professionals. |
Various Aspects of Artificial Intelligence
Many online marketplaces like Amazon use AI to suggest products to their customers on the basis of their search history and past purchase.
Various learning platforms like Udemy, Simplilearn, edX, etc. use AI to suggest the right courses to students, as per their career goal and their capabilities. The platforms also provide customized feedback that helps in the development of the student
- Natural Language Processing
NLP is one of the aspects of AI that focuses on communication between humans and machines. Voice-activated assistants of various online platforms use this technique to interact with their customers in their choice of language.
Self-driven cars use this AI-enabled technology to recognize all the obstacles, people, etc. in the front so that they are able to navigate effortlessly on roads.
Security systems across globe, uses face recognition technology to confirm identity of a person. This helps in authenticating the usage of any system or grant permission to use a specific technology. Apple uses this technology in Face ID technology to authorize payments and unlock Apple products.
AI provides a realistic gaming experience to all the gaming fiends out there. With the help of AI, one can create intelligent behavior patterns amongst non-playing characters and make the game even more fun and interesting.
Various Aspects of Machine Learning
- Clinical Decision Support System
There are many medical decisions that a doctor needs to make on the basis of the medical history of their patients. This can be done effortlessly and with accuracy by using ML.
- Insurance Fraud Detection
With the help of patterns, ML can identify all the claims that fall under fraudulent activities
- Predict the need for lifesaving Drugs in the Market
With the help of ML, any company can predict the shortage of a specific lifesaving medicine in the market and prevent this from happening.
We have come across Chatbots that provide a basic customer service experience by providing information regarding a particular order, payment status, etc.
Using Machine Learning with Artificial Intelligence
AI analyzes traffic situations on a particular road and suggests alternate less traffic routes to drivers. If we talk about ML, there are autonomous vehicles that use ML to avoid obstacles on the road and navigate better on roads.
With the help of ML, it becomes easier to achieve efficiency in medical tests and examinations. With the help of AI, personalized treatment plans can be offered to different patients and help them recover faster.
ML and AI together are being used to find cures for rare diseases, by evaluating the benefits of existing drugs in the market.
With the help of ML and AI, different retailers can predict their consumer’s purchasing patterns and predict an upcoming trend. This data can be used to restock a store accordingly.
Career Scope in Machine Learning and Artificial Intelligence Industries
- Data Scientist
Data scientists collect, analyze, and process data to identify patterns. They even use AI to interpret data and predict the future of a particular trend. To become a data scientist, you will need to be adept in basic machine languages like Python, Scala, Perl, etc.
- Machine Learning Engineer
ML engineers are required to write codes and design algorithms that can help machines learn better. You will need a very strong knowledge of programming to become a machine learning engineer.
Designing and building robotic systems needs immense caliber and a strong knowledge of mechanical engineering and computer science. Robotics engineering is quite a lucrative career if we think of the coming future.
- Business Intelligence Analyst
Business analysts use the trends predicted by ML and AI and help various businesses make profits by grabbing current trends.
Prepare for Career Dependent upon ML and AI
We have already discussed that you will need to have a strong foundational knowledge of machine languages, programming, coding, etc. to make your career as a data scientist or an engineer.
The good part is that you can check out various technical courses offered by online learning platforms like Udemy, edX, etc. and enroll in one.
Features of Various Courses Offered by Online Learning Platforms
- You will find short-term courses as well as long-term diploma courses on the above-mentioned platforms.
- You can complete a course as per your learning pace and also attend classes when it is comfortable for you (this option is available for many courses if not all the courses)
- You also get a completion certificate/ diploma for course completion; the certificate/diploma is valid for applying for jobs in many companies
- You can also attend a trial session, before actually enrolling in the desired course
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