Artificial Intelligence vs. Machine Learning: What Are The Key Differences?

Artificial Intelligence (AI) and Machine Learning (ML) are innovations that have made plenty of buzz in the technical world of late, and for valid reasons. They’re enabling companies to simplify their work processes and identify patterns in data to make smarter business choices. They’re progressing almost every industry by assisting them to work more efficiently, and they’re becoming fundamental tools for organisations to keep a competitive advantage. 

Interest in these technologies— and demand for professionals skilled in them— is exploding. This remarkable development is introducing significant opportunities as well as challenges for organisations. AI and ML, which were once the subjects of sci-fi many years ago, are becoming a norm in organisations today. 

In this article, we closely explore the differences between AI and ML, opportunities and challenges, and how these innovations are set to be a household name in the coming years.

 

Artificial Intelligence vs. Machine Learning: A Basic Understanding

 

Artificial Intelligence (AI):

 

Artificial intelligence is a field of computer science that makes a computer mimic human intelligence. Artificial intelligence systems don’t need to be pre-customised, they utilise specialised algorithms that can work with their own intelligence. It includes machine learning algorithms, for example, reinforcement algorithms and neural networks. Artificial intelligence is being utilised in many spaces in our world, for example, voice assistants (Siri, Google Assistant, Alexa), chatbots, chess-playing, and so forth.

In terms of skills, AI can be classified into three classifications: Weak AI, General AI, and Strong AI. Right now, we are dealing with Weak and General AI. The eventual future of AI is Strong AI, which is expected to be smarter than people. 

 

Machine Learning (ML):

Machine learning empowers a computer to make smarter choices based on predictions or make a few choices using historical information without being specifically programmed. Machine learning uses a huge amount of structured as well as semi-structured data so a machine learning model can produce accurate results or give predictions based on that information. 

Machine learning normally uses algorithms that learn on their own via historical information. However, it works only for specific domains, for example, if we are making a machine learning model to identify pictures of cats, it will just give results for cat pictures, however, if we input other data such as pictures of dogs, it will become unresponsive. Machine learning is likewise being utilised in different real-world domains like online recommendation systems, google search engines, email spam filters, auto-tagging suggestions on Facebook, and so on.

 

Applications of Artificial Intelligence & Machine Learning in Action

 

Applications of AI:

 

  • Advanced Robotics

A modern robot powered by advanced robotics is a genuine illustration of AI in action. Modern robots can monitor their own precision and performance, and detect or identify when support is needed to refrain from heavy downtime. These robots can also act in a new or unknown environment without having to configure everything manually. 

 

  • Virtual Assistants 

One more depiction of AI is virtual assistant tools which are AI-human communication mediums. The most famous virtual assistants are Google Home by Google, Siri by Apple, Alexa by Amazon, and Cortana by Microsoft. These virtual assistants enable users to browse information online, book appointments, add events to their calendars, answer questions, plan meetings, send messages, and so forth. 

 

Applications of ML:

 

  • Online Product Recommendations 

Most internet business websites have machine learning tools that give suggestions of various products dependent on historical data i.e. previous search history or browsing history. For instance, if you have searched for books online previously and you bought one of them, when you visit that page again after a specific timeframe, the landing page of that website will show you a list of books within a similar niche. It likewise makes recommendations dependent on what you have liked, added to your cart, and other related practices. 

 

  • Email and Malware Filtering 

Spam emails and malware have turned into an immense security threat for online users. These days most email service providers are using machine learning tools to automatically detect and filter out spam emails and phishing messages. 

For instance, Gmail and Yahoo mail spam filters accomplish something more than just checking for spam emails utilizing pre-existing rules. They create new rules themselves dependent on what they have learned as they gain experience in their spam filtering operations.

 

What Does The Future Hold for AI and ML?

The advances made by experts in the fields of artificial intelligence and machine learning are speeding up. These developments are equipped for solving increasingly hard issues better than people can. 

This implies that AI and ML are changing quicker than history can be written, so expectations about their future become obsolete as quickly as they are written. The people who believe that this advancement will proceed quickly focus on Strong AI, and regardless of whether it is useful for humankind. Among the people who predict continuous progress, they underscore the advantages of smarter predictive ML methodologies based on historical data and feature extraction, which might save people from the current mishaps; the other part stresses the existential risk of a super-intelligence. 

Given the dramatic development of both these innovations, one thing is inevitable – we are in for a rollercoaster ride full of technological advancements in the fields of ML and AI that will pave the way for extraordinary projects in the future.

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