Understanding Artificial Intelligence, Machine Learning and Deep Learning

| Last Updated: 4 August 2022

Artificial intelligence (AI) is a widely used term in all different industries. It is changing every industry and business function, resulting in an increased interest in AI and its subdomains, such as machine learning and deep learning. AI can be used to personalize marketing, to predict sales, to analyze customers, to get real-time analytics, to provide an early diagnosis in healthcare, to deploy AI-powered autonomous security systems, and to provide a 24/7 digital assist. But what is AI actually? And what are the subdomains of AI?  

According to the MIT Technology Review AI refers to “Machines that can learn, reason, and act for themselves. They can make their own decisions when faced with new situations, in the same way, that humans and animals can.”   

Most of the AI applications and advancements refer to a category of algorithms, called machine learning, a more specific subset of AI that trains machines how to learn. These machine learning algorithms use statistics to find patterns in huge amounts of data after which predictions can be made. The used data is collected from all kinds of platforms people use. The predictions made can be about everything: what film you might like on Netflix, when you should sell your house, or what diagnosis you got based on your MRI. Machine learning is a small, incredibly powerful subdomain of AI.  

The overall idea of AI is to develop something looking like the same as human intelligence. AI has the potential to set up a human-to-machine interaction. When the machines become ‘intelligent’, they have the possibility to understand requests, connect data points and provide insides. This can be achieved with the use of machine learning, deep learning, and enough data. Machine learning algorithms namely use statistics to find patterns in a huge amount of data. Data can be, for example, numbers, words, images, videos, etc., everything that is digitally stored can be data for a machine learning algorithm.  

In addition, deep learning, a subset of machine learning uses “a technique that gives machines an enhanced ability to find—and amplify—even the smallest patterns. This technique is called a deep neural network—deep because it has many, many layers of simple computational nodes that work together to munch through data and deliver a final result in the form of the prediction.” This kind of AI is seen as the most promising outlook of all AI applications. It takes advantage of the extensive advances in computing power and improved training techniques.  

Machine learning and so deep learning exist of three types: supervised, unsupervised, and reinforcement learning.  

  • Supervised: the most prevalent form of learning, where the data is labeled to the machine exactly what patterns it should look for.  
  • Unsupervised: where the data isn’t labeled, and the machine has to look for any kind of pattern.  
  • Reinforcement: a reinforcement algorithm learns by trial and error to achieve an objective.  

How AI makes the difference  

Artificial intelligence has long been seen as something temporarily. But because of the proliferation of data and the maturity of other innovations, the adoption of AI is growing faster than ever. Businesses are starting to see how AI can provide value for them as a source of business innovation. The automation caused by AI reduces the costs, accelerate the business processes and drive growth.  

According to Accenture “Companies that scale successfully see 3X the return on their AI investments compared to those who are stuck in the pilot stage.”.  

In addition, as a result of machine learning and deep learning, AI applications can learn from data and results in (near) real time, analyzing new information from various sources and adapting accordingly, with a level of accuracy that is invaluable to business.  

Do you want to learn more about how Clappform can help you with this AI adoption to automate your business processes? Request a free demo or contact us.  

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