Deep neural networks: The hidden brain that powers AI

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  Quantumrun Foresight
• April 6, 2023

Algorithms and big data have become the go-to buzzwords in the artificial intelligence (AI) space, but artificial neural networks (ANN) are what allow them to become powerful tools. These ANNs are used to recognize patterns, classify data, and make decisions based on input data. 

Deep neural networks context

Artificial neural networks attempt to mimic the complexity of human intelligence by building a network of software, codes, and algorithms to process input (data/patterns) and match them with the most viable output (effect/results). The ANN is the hidden layer that processes and connects relationships between data and decision-making. The more ANN is built between input and output, the more the machine learns because of the availability of more complex data. The multiple ANN layers are known as deep neural networks because they can burrow into high volumes of training data and develop the best solution or patterns. 

A machine is “educated” further through backpropagation, the process of adjusting existing parameters to train the algorithms to come up with the best result/analysis. Artificial neural networks can be trained to perform various tasks, such as image and speech recognition, language translation, and even playing games. They do this by adjusting the strengths of the connections between neurons, known as weights, based on the input data they receive during the training process. This method allows the network to learn and adapt over time, improving its performance on the task. There are many types of ANNs, including feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Each type is designed to be particularly well-suited to a specific task or data class.

Disruptive impact

There’s hardly any industry today that doesn’t use deep neural networks and AI to automate business processes and gather market intelligence. Perhaps the most obvious use case of deep neural networks is the marketing industry, where AI processes millions of customer information to accurately identify particular groups more likely to buy a product or service. Because of the increasingly high accuracy of these data analyses, marketing campaigns have become much more successful through hypertargeting (identifying specific customer subsets and sending them extremely customized messages). 

Another emerging use case is facial recognition software, an area of debate relating to cybersecurity and data privacy. Facial recognition is currently being used from app authentication to law enforcement and is enabled by deep neural networks processing police records and user-submitted selfies. Financial services is yet another industry that highly benefits from deep neural networks, using AI to forecast market movements, analyze loan applications, and identify potential fraud.

Deep neural networks can also analyze medical images, such as x-rays and magnetic resonance imaging (MRI), to help diagnose diseases and predict patient outcomes. They can also be used to analyze electronic health records to identify trends and risk factors for certain conditions. Neural networks also have the potential to be used in drug discovery, personalized medicine, and population health management. However, it is important to note that ANNs should aid in medical decision-making rather than replacing the expertise and judgment of trained medical professionals.

Applications of deep neural networks

Wider applications of deep neural networks may include:

• Algorithms becoming increasingly sophisticated through more complex datasets and better technologies, resulting in high-level tasks such as providing consultancy services and investment advice. In 2022, powerful consumer-friendly algorithms, such as Open AI’s ChatGPT demonstrated the power, versatility, and applicability of an AI system trained on sufficiently large datasets. (White collar workers worldwide experienced a collective shudder.)
• Artificial intelligence being increasingly used in the military to provide real-time information and intelligence to support war strategies.
• Deep neural networks enabling the Metaverse to create a complex digital ecosystem composed of real-time information such as demographics, customer behaviors, and economic forecasts.
• ANNs being trained to recognize patterns in data that are indicative of fraudulent activity, and being used to flag suspicious transactions in fields such as finance and e-commerce.
• Deep neural networks being employed to recognize objects, people, and scenes in images and videos. This method is used in applications such as self-driving cars, security systems, and social media tagging.

Questions to consider

• How else do you think deep neural networks will change society over the next three years?
• What might be the potential challenges and risks?