According to Tech Emergence, ‘’ All Machine Learning is Artificial Intelligence but not all Artificial Intelligence is Machine Learning’’. We, at TechQuarters, will be covering what AI and Machine Learning is and how they differ.

What is Machine Learning?

Recently Machine Learning has blown up, more organisations are looking to incorporate Machine Learning in areas such as Marketing, Advertisement. Machine Learning is self-explanatory, the computer learns without being to do something, in other words a proactive system.
We use machine learning at some point in our everyday lives? Machine Learning can apply large sets of data to perform tasks such as facial recognition, speech recognition, object recognition and so on. Machine learning is able to recognize specific patterns and makes its own predicting.

Examples of Machine Learning: Siri, Cortana, Facebook Tagging Technology, Google Maps, PayPal, Netflix, Uber & Spotify.

What is Artificial Intelligence?

Artificial Intelligence is the concept of machines being able to complete tasks that we would consider’ smart’ in comparison to machine learning where data can be access and machines can learn themselves how to complete tasks.

Examples of Artificial Intelligence: Alexa, Tesla, Amazon, Box Ever

What’s the similarities and differences between AI and Machine Learning?

Both technologies have a lot to offer in changing the world to become more digital and improving consumer experience as well as business productivity. Artificial Intelligence offers to complete manual tasks to improve productivity and efficiency, useful for sectors like manufacturing and healthcare.

On the other hand, Machine Learning is an opportunity for Marketers to automate data visuals, content analysis and much more. There is so much data out there that Machine Learning help to identify patterns in the data and make predictions.

What is deep learning?

Neutral Networks in other words Deep Learning is a set of algorithms modelled similar after the human brain that can recognize patterns and sequences. Based on this, predication can be made. Deep Learning is so advanced it requires GPU to run deep learning algorithms. It can help organise data, a great example is email operating systems where outcomes can be set to sort e-mail data, so the labels could be spam, not spam and helps sort emails that are important into the right folder.

Examples of Deep Learning: Speech Recognition, Self-Driving Car, Robotics

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