What is deep learning? Everything you need to know

Nowadays, deep learning can be everywhere, from groundbreaking AI companies to the voice assistant in your smartphone.

Here are just a few of the most popular deep-learning applications:

ChatGPT

OpenAI’s chatbot uses deep learning and is one of the largest deep-learning models available. ChatGPT uses OpenAI’s 3.5 version of a generative pre-trained transformer (GPT 3.5), which touts 175 billion parameters. The neural network that makes ChatGPT so efficient is trained to learn patterns and relationships in language. 

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The fourth version of this generative pre-trained transformer (GPT-4) expertly performs natural language processing (NLP) tasks with the largest architecture of large language models (LLM), consisting of a trillion parameters.

Virtual assistants

Voice assistants, such as Google Assistant, Amazon Alexa, and Apple’s Siri, use deep learning for speech recognition and NLP. They apply these deep-learning techniques to process what you tell them and respond accordingly and accurately. 

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These deep-learning algorithms can also learn from patterns in user interactions to continuously improve the user experience. 

Fraud detection

Various entities can use deep learning to detect and prevent fraud. Financial institutions, for example, use different algorithms to detect fraud. One example you might be familiar with is long short-term memory (LSTM), a deep-learning model that flags suspicious activity that strays from the data it has been trained on. 

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LSTM is a recurrent neural network (RNN) that handles sequential data and stores information about what it processes to recognize a standout event, like a potentially fraudulent transaction, to flag for human intervention.

Healthcare

Artificial intelligence has already made a significant impact in healthcare. Deep-learning technology has been found useful in diagnosing eye diseases, including diabetic retinopathy and glaucoma, and even certain cancers. 

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The advancements of AI in medicine are only just beginning.