Machine Learning vs Artificial Intelligence

Introduction :

Machine learning and artificial intelligence are two closely related fields that are revolutionizing the way we interact with technology. Machine learning refers to the process of teaching computers to learn from data, without being explicitly programmed to do so. This involves using algorithms and statistical models to find patterns in data, and then using these patterns to make predictions or decisions.

Artificial intelligence, on the other hand, is a broader field that encompasses machine learning as well as other approaches to building intelligent systems. Artificial intelligence is concerned with creating machines that can perform tasks that would normally require human intelligence, such as recognizing speech, understanding natural language, and making decisions based on complex data.

The goal of both machine learning and artificial intelligence is to create machines that can learn and adapt to new situations, without the need for explicit programming. By enabling computers to learn from data and make decisions based on that data, we can create systems that are more accurate, more efficient, and more effective at performing a wide range of tasks.

Machine learning and artificial intelligence are being used in a wide variety of applications, from self-driving cars and virtual assistants to medical diagnosis and fraud detection. As the technology continues to advance, we can expect to see even more innovative applications of machine learning and artificial intelligence in the future.

Machine Learning and Artificial Intelligence are creating a huge buzz worldwide. The plethora of applications in Artificial Intelligence has changed the face of technology. The terms Machine Learning and Artificial Intelligence are often used interchangeably. However, there is a stark difference between the two that is still unknown to industry professionals. 

Let’s start by taking an example of Virtual Personal Assistants which have been familiar to most of us for quite some time now. 

Machine learning and artificial intelligence (AI) are related but distinct fields.

Machine learning is a subset of AI that involves the development of algorithms and statistical models that enable computers to learn and make predictions or decisions without being explicitly programmed. Machine learning algorithms can be trained on data to identify patterns and make predictions about future events.

Artificial intelligence, on the other hand, is a broader field that encompasses machine learning as well as other techniques for creating intelligent systems. AI involves the development of computer systems that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing images, and making decisions.

There are several types of machine learning, including:

Supervised learning: The algorithm is trained on a labeled dataset, where the desired output is already known.
Unsupervised learning: The algorithm is not given any labeled data, it must find the underlying structure in the data on its own.
Reinforcement learning: The algorithm learns from the feedback it receives from its actions in an environment.
There are also several types of AI, including:

Strong AI: Capable of performing any intellectual task that a human can.
Weak AI: Specialized for a specific task.

Working of Virtual Personal Assistants:

Siri(part of Apple Inc.’s iOS, watchOS, macOS, and tvOS operating systems), Google Now (a feature of Google Search offering predictive cards with information and daily updates in the Google app for Android and iOS.), Cortana (Cortana is a virtual assistant created by Microsoft for Windows 10) are intelligent digital personal assistants on the platforms like iOS, Android and Windows respectively. To put it plainly, they help to find relevant information when requested using voice. For instance, for answering queries like ‘What’s the temperature today?’ or ‘What is the way to the nearest supermarket’ etc. and the assistant will react by searching for information, transferring that information from the phone, or sending commands to various other applications. 

AI is critical in these applications, as they gather data on the user’s request and utilize that data to perceive speech in a better manner and serve the user with answers that are customized to his inclination. Microsoft says that Cortana “consistently finds out about its user” and that it will in the end build up the capacity to anticipate users’ needs and cater to them. Virtual assistants process a tremendous measure of information from an assortment of sources to find out about users and be more compelling in helping them arrange and track their data. Machine learning is a vital part of these personal assistants as they gather and refine the data based on users’ past participation with them. Thereon, this arrangement of information is used to render results that are custom-made to users’ inclinations. 

Roughly speaking, Artificial Intelligence (AI) is when a computer algorithm does intelligent work. On the other hand, Machine Learning is a part of AI that learns from the data that also involves the information gathered from previous experiences and allows the computer program to change its behavior accordingly. Artificial Intelligence is the superset of Machine Learning i.e. all Machine Learning is Artificial Intelligence but not all AI is Machine Learning. 

Artificial Intelligence	Machine Learning
AI manages more comprehensive issues of automating a system. This computerization should be possible by utilizing any field such as image processing, cognitive science, neural systems, machine learning, etc.	Machine Learning (ML) manages to influence users’ machines to gain from the external environment. This external environment can be sensors, electronic segments, external storage gadgets, and numerous other devices.
AI manages the making of machines, frameworks, and different gadgets savvy by enabling them to think and do errands as all people generally do.	What ML does, depends on the user input or a query requested by the client, the framework checks whether it is available in the knowledge base or not. If it is available, it will restore the outcome to the user related to that query, however, if it isn’t stored initially, the machine will take in the user input and will enhance its knowledge base, to give a better value to the end-user
Objective is to maximize the chance of success.	Objective is to maximize accuracy.
Artificial intelligence uses logic and decision tree.	Machine learning uses statistical models.
AI is concerned with knowledge dissemination and conscious Machine actions.	ML is concerned with knowledge accumulation.
Focuses on giving machines cognitive and intellectual capabilities similar to thone of humans.	Focuses on providing the means for algorithms and system to learn from the experience with data and use that experience to improve overtime.
AI encompasses  a collection of intelligence concepts, including elements of perception, planning and prediction.	ML can be done through supervised , unsupervised or reinforcement learning approaches.

Future Scope:

• Artificial Intelligence is here to stay and is going nowhere. It digs out the facts from algorithms for a meaningful execution of various decisions and goals predetermined by a firm.
• Artificial Intelligence and Machine Learning are likely to replace the current model of technology that we see these days, for example, traditional programming packages like ERP and CRM are certainly losing their charm.
• Firms like Facebook, and Google are investing a hefty amount in AI to get the desired outcome at a relatively lower computational time.
• Artificial Intelligence is something that is going to redefine the world of software and IT in the near future.

Advantages :

The advantages of machine learning and artificial intelligence are many, and include:

1. Efficiency: Machine learning and artificial intelligence can automate complex processes and make them more efficient. This can save time and resources, and allow businesses to focus on more strategic tasks.
2. Accuracy: Machine learning algorithms can analyze data and make predictions with a high degree of accuracy. This can lead to better decision-making and more accurate results.
3. Personalization: Machine learning and artificial intelligence can be used to personalize products and services to individual users, based on their preferences and behavior.
4. Scalability: Machine learning and artificial intelligence algorithms can be applied to large amounts of data, allowing organizations to scale their operations and handle larger volumes of information.
5. Innovation: Machine learning and artificial intelligence can be used to identify new opportunities and create innovative solutions to complex problems.
6. Cost savings: By automating processes and increasing efficiency, machine learning and artificial intelligence can help organizations save money and reduce costs.

Dis-advantages :

1. Complexity: Machine learning and artificial intelligence systems can be complex and difficult to implement, requiring specialized expertise and resources.
2. Bias: Machine learning algorithms can sometimes produce biased results, depending on the data that is used to train them. This can lead to unfair or discriminatory outcomes.
3. Lack of transparency: Some machine learning and artificial intelligence systems are considered “black boxes,” meaning that it can be difficult to understand how they arrived at a particular decision or prediction.
4. Security concerns: Machine learning and artificial intelligence systems can be vulnerable to attacks and hacking attempts, which could compromise sensitive data and systems.
5. Job displacement: As automation becomes more prevalent, there may be concerns about job displacement and the impact on the workforce.
6. Data quality: Machine learning and artificial intelligence systems rely on high-quality data to function effectively. Poor quality data can lead to inaccurate predictions and decisions.