In the digital world, the two buzzwords discussed everywhere include Artificial Intelligence and Machine Learning. These technologies have revolutionized the ways businesses function and also the ways we execute our routine tasks. These have gradually seeped into the business world as well as our personal lives. It is through Artificial Intelligence and Machine Learning that every company is on the way to becoming a tech company.
The profound implications of Artificial Intelligence in both business and society have made this technology the next digital frontier. A report by Fortune Business Insights states that the global AI market size is expected to reach USD 202.57 billion by the year 2026.
Another report by Grandview Research states that the global AI and ML market is expected to grow at a Compound Annual Growth Rate or CAGR of a whopping 40.2%.
According to a report by Forbes, Artificial Intelligence and Machine Learning jobs across the world will reach 58 million, eventually paving the way to lucrative careers for IT professionals. Ai and ML find applications in almost every industry, including finance, retail, banking, investment, logistics, media and entertainment, news, manufacturing, Information Technology, Pharmaceuticals and Healthcare, software products, and more.
This is why ed-tech firms are observing a surge in the number of professionals and freshers enrolling in machine learning Bootcamp.
Artificial Intelligence and Machine Learning are the two terms that are generally used interchangeably because they are closely connected. But there is a fine line of difference between the two.
As you can see, Machine Learning is a subcategory of Artificial Intelligence. So, along with the capabilities of Machine Learning, there is a lot more under the umbrella of Artificial Intelligence.
This article will let you go through this fine line of difference.
What is Artificial Intelligence?
Artificial Intelligence basically refers to the capability of a machine to imitate human cognitive abilities that include problem-solving, reasoning, learning, and more. The main objective of Artificial Intelligence is to build self-dependent machines that can perform tasks by learning. A computer system simulates the reasoning made by humans by using math and logic to learn from the information and make decisions through Artificial Intelligence.
The most common application of Artificial Intelligence is virtual assistants such as Apple’s Siri and Amazon’s Alexa, which are capable of following your orders and performing tasks like playing music, voice interaction, playing audiobooks, setting alarms, and also providing you with real-time information such as weather, news, and others.
Types of Artificial Intelligence
Reactive Machines are the form of artificial intelligence that just reacts and don’t use any previous experience to make new decisions or doesn’t store information in the form of memories.
Theory of Mind systems are capable of understanding human emotions and the ways they impact the decision-making process. These systems are trained to alter their behavior according to the situation.
In Limited Memory systems, the information is fed over a period of time and they refer to the past for making decisions. The information referred to is generally short-lived.
Self-awareness systems are specifically designed and crafted to be aware of themselves. These systems are capable of understanding their internal state as well as predicting users’ emotions and responding accordingly.
Artificial Intelligence Applications
- Aibo and Sophia, the AI robots
- Google’s Waymo, an autonomous vehicle
- Google Translate, a machine translation system
- Google Assistant, Amazon Alexa, Apple’s Siri, and other virtual assistants
Now that you have gone through the gist of Artificial Intelligence let us move forward to Machine Learning.
What is Machine Learning?
A subcategory of Artificial Intelligence, Machine Learning refers to the process of utilizing mathematical models to enable a system to learn without the need for direct instructions. Machine Learning is an application of Artificial Intelligence. On the basis of experience, machine learning enables a computer to learn and improve on its own.
Basically, machine learning models are made to learn using neural networks that work on a series of algorithms that are modelled on the basis of the functioning of the human brain. These neural networks enable a computer system to achieve the goals of AI via deep learning.
Types of Machine Learning
Supervised Learning systems are those where the target variable is known, which implies that the data is already labeled. In this method of learning, a system is capable of predicting the outcome on the basis of past experience.
In unsupervised learning systems, unlabeled data is employed to identify patterns from the data on their own. By making the data readable, the similarities are patterns are identified easily.
Reinforcement learning systems usually train an agent to execute a task within an unpredictable environment. Generally, a reinforcement system is based on rewarding the desired behavior and punishing the undesired ones. With the help of reward, the system identifies the success rate and improves accordingly.
Machine Learning Applications
- Fraud detection in banking systems
- Sales forecast
- Recommendations for Stock prices
- Product recommendations
How are AI and ML connected?
A ‘smart’ or ‘intelligent’ system utilizes AI to think and act like humans and carry out tasks on its own. Simply put, machine learning is the way a computer system develops its intelligence.
This is how these technologies work together:
Step 1: an AI system is fabricated utilizing machine learning, neural networks, and other systems.
Step 2: By observing and identifying patterns in data, machine learning models are built
Step 3: these machine learning models are optimized by data scientists on the basis of patterns and information hidden in the data.
Step 4: the steps are repeated and refined on the basis of desired outcomes until the model reaches a high accuracy rate for the specified tasks.
Conclusion
The connection between machine learning and artificial intelligence provides potential benefits to companies across every industry, paving huge career opportunities for you. If you wish to make or advance your career in this domain, you can simply take up an online training course such as that from Simplilearn.
These courses allow you to learn at your own pace, provide you with enterprise-class learning management systems, and training is delivered via industry experts. Ask Me Anything sessions and round-the-clock learning assistance make these courses worth your investment.
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