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What is?

The What is? space is where questions are answered in bitesize content that is easy to understand and digest. The goal is to help you gain a better understanding of different AI related concepts.

01

What is Artificial Intelligence?

Artificial Intelligence, refers to the ability of machines to perform tasks that normally require human intelligence, such as understanding natural language, recognizing images, making decisions, and learning from experience.

This is achieved through a combination of algorithms and computer programs that are designed to process large amounts of data, identify patterns, and make predictions or decisions based on that data. These algorithms and programs can be trained using different techniques, including supervised and unsupervised learning, reinforcement learning, and deep learning.

Examples Include: 

1. Personalized recommendations: Online platforms, such as Netflix and Amazon, use AI algorithms to analyze user data and provide personalized recommendations for movies, TV shows, products, and services.

2. Virtual assistants: Intelligent virtual assistants like Siri, Alexa, and Google Assistant use natural language processing

3. Predictive maintenance: AI is used by industrial companies to predict when equipment is likely to fail and schedule maintenance before a breakdown occurs, creating a reduction in downtime and maintenance costs.

02

What is Machine Learning

Machine Learning (ML), is a subfield of AI that focuses on building computer systems that can learn and improve from experience, without being explicitly programmed. The computer is trained to recognize patterns and make decisions based on data. This training can be supervised, unsupervised, or reinforced.

Machine learning is used in a variety of applications, such as image and speech recognition, natural language processing, fraud detection, and recommendation systems.

Examples Include: 

1. Healthcare: ML is being used to diagnose diseases, predict patient outcomes, and personalize treatment plans. 

2. Banking and finance: ML is used for fraud detection, credit scoring, and predicting market trends.

3. Transportation: ML is used in self-driving cars to detect and avoid obstacles on the road. For example, Tesla's Autopilot system uses ML to analyze camera and radar data to control the car's speed and steering.

03

Supervised learning vs Unsupervised learning vs reinforced learning?

Supervised learning involves training a computer on a set of labeled examples, where the correct output is known, in order to make predictions on new, unseen data.

Example:  Spam detection in emails is an example of a supervised learning model. Using supervised classification algorithms, organizations such as google, yahoo or outlook can train databases to recognize patterns or anomalies in new emails to organize spam content.

Unsupervised learning involves finding patterns in unlabeled data, without any pre-existing knowledge of the correct output.

Example:

Organisations can group their users into multiple groups based on their prior bought items. Lets take -> imagine your supermarket <- they could send a text about a sale on a grocery items specifically to its users of that item such as popcorn rather than sending that text to all its users. 

 

Reinforcement learning involves training a computer to make decisions based on feedback received from its environment.

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