Recurrent neural networks (RNNs): Predictive algorithms that can anticipate human behavior

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  Quantumrun Foresight
• September 16, 2022

Insight summary

Recurrent neural networks (RNNs) handle sequential data, excelling in tasks like language processing and speech recognition, thanks to their unique feedback loop structure. They come in various types, each suited for specific applications like image recognition or sentiment analysis, and are powered by algorithms that enable learning from past data. RNNs significantly impact areas like market research, cybersecurity, and user interface development, offering enhanced predictive accuracy and pattern recognition in diverse applications.

Recurrent neural networks (RNNs) context

Recurrent neural networks are a type of artificial neural network specifically designed to handle sequential data. This analysis may include natural language processing (NLP), speech recognition, and machine translation. RNNs are composed of interconnected neurons designed to process sequential data and recognize patterns in it.

RNNs have a feedback loop, which allows them to remember information from previous inputs. This advantage enables them to make more accurate predictions, as they can incorporate past data into their calculations. These networks are composed of three layers: an input layer, a hidden layer, and an output layer. The hidden layer contains a temporal loop that allows the network to remember the state of the last neuron and pass that information on to itself in the "future." This process enables the network to learn from past data to understand future data better.

There are three primary types of RNNs: 

1. one input to multiple outputs, 
2. several inputs to one output, and 
3. many inputs to many outputs. 

Each type of RNN is well-suited for different applications. For example, one input to multiple outputs RNNs is often used in image recognition. Whereas with several inputs to one output, RNNs are commonly used in sentiment analysis. 

The two critical algorithms behind RNNs are backpropagation through time and long short-term memory units. Backpropagation through time allows the network to learn from previous information. Long short-term memory units enable the network to recognize patterns that follow a specific order.

Disruptive impact

Because of its advanced predictive potential, RNN has several business applications. In market research, recurrent neural networks can analyze and understand customer behavior and preferences, which helps plan effective marketing and product strategies. In product analytics, sentiment analysis manages and analyzes customer feedback to improve the product or service.

Meanwhile, sentiment analysis helps anticipate customer needs and expectations in customer support. In particular, more intuitive and user-friendly chatbots are made possible because of NLP. Natural language processing allows these tools to perform conversational user interface (UI) tasks that combine knowledge base and potential behavioral scenarios. 

Cybersecurity is another area where RNNs deliver benefits. In research conducted by computer engineers, it was discovered that RNN is more effective in Android malware classification and incident and fraud detection than traditional machine learning methodologies. Ad fraud, spam detection, and bot detection are additional applications of RNNs. In these cases, the network can identify suspicious or abnormal behavior. NLP tools can recognize general patterns in automated algorithms and block spam messages. 

Implications of recurrent neural networks (RNNs)

Wider implications of recurrent neural networks (RNNs) may include: 

• Cybersecurity firms increasing their use of RNNs to train their systems to detect common malware and spam content and aid in reducing automated cyberattacks.
• Companies increasing the use of text-to-speech machines/systems that can read content in a more human-like fashion.
• Audio recordings that can be quickly translated into various languages and devices that can perform translations more accurately.
• Intuitive chatbots and virtual assistants improving their ability to understand motives and predict preferences, e.g., smart home ecosystems.
• Improved facial recognition and optical character recognition tools. 
• RNNs being used for stock price forecasting, which anticipates future prices based on historical performance.

Questions to consider

• What might be other uses of RNNs?
• Which RNN-enabled features/technologies have you interacted with? What was the experience like?