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.
Artificial intelligence encompasses many fields of research that can make machines capable of carrying out tasks that typically would have required human intelligence and can range from genetic algorithms to natural language processing.
Machine learning is a subset of AI and is defined as the process of teaching a computer to carry out a task rather than programming it how to carry that task out step by step.
Deep learning, in turn, is a subset of machine learning, whose capabilities differ in several key respects from traditional shallow machine learning, allowing computers to solve a host of complex problems that couldn’t otherwise be tackled.
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Machine learning can tackle shallow predictions when fed data, such as determining whether a fruit in a photo is an apple or an orange. Deep learning can solve more complex problems, like recognizing handwritten numbers where a massive amount of data is necessary during training.
In the specific example illustrated below, the computer needs to be able to cope with a huge variety in how the data can be presented. Every digit between 0 and 9 can be written in a myriad of ways: The size and exact shape of each handwritten digit can vary significantly depending on who’s writing and in what circumstance.
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Coping with the variability of these features, and the even bigger mess of interactions between them, is where deep learning and deep neural networks become useful.
Each neuron within a neural network is a mathematical function that takes in data through an input, transforms that data into a more amenable form, and then spits it out via an output. You can think of neurons in a neural network as being arranged in layers, as illustrated in the image below.
