What areas of application of artificial neural networks in information technology are now the most promising?

Artificial neural networks (ANNs) have found numerous applications in information technology, and several areas show great promise for their continued development and utilization. Here are some of the most promising areas of application for artificial neural networks in information technology:

Computer Vision: Artificial neural networks have revolutionized computer vision tasks, such as object detection, image classification, and image segmentation. Convolutional Neural Networks (CNNs) are widely used for these tasks and have achieved remarkable accuracy in various applications like autonomous vehicles, medical imaging, surveillance, and augmented reality.

Natural Language Processing (NLP): Neural networks, especially Recurrent Neural Networks (RNNs) and Transformers, have made significant advancements in NLP tasks. They are used in applications like machine translation, sentiment analysis, text generation, chatbots, and voice recognition. Language models, such as GPT-3, have demonstrated the ability to generate human-like text and have sparked innovations in various language-related applications.

Recommender Systems: Artificial neural networks play a crucial role in building recommendation engines that suggest relevant products, movies, music, or content to users. Collaborative filtering and deep learning techniques are used to analyze user preferences, item features, and historical data to provide personalized recommendations. These systems are widely used in e-commerce, entertainment platforms, and content streaming services.

Financial Forecasting: Neural networks are employed in financial institutions for predicting stock prices, market trends, credit risk assessment, fraud detection, and algorithmic trading. ANNs can process large volumes of financial data, capture intricate patterns, and make accurate predictions, helping financial professionals in decision-making and risk management.

Cybersecurity: Artificial neural networks are utilized in cybersecurity to detect and prevent various types of cyber threats, including malware detection, network intrusion detection, phishing detection, and anomaly detection. Deep learning models, combined with behavioral analysis and advanced algorithms, enhance the security infrastructure by identifying and mitigating potential attacks.

Autonomous Systems: Artificial neural networks are essential components of autonomous systems, such as self-driving cars, drones, and robotics. These networks help in perception tasks, decision-making, and control, enabling machines to navigate and interact with their environments autonomously and safely.

Healthcare and Medical Diagnosis: ANNs have shown promise in healthcare applications, including disease diagnosis, medical imaging analysis, genomics, drug discovery, and personalized medicine. Deep learning models can extract valuable insights from medical data, aiding in early detection, treatment planning, and improving patient outcomes.

Generative Models: Generative models, such as Generative Adversarial Networks (GANs), are used to create realistic and novel content. GANs have been employed in generating images, videos, music, and even deepfake technology. These models have the potential for applications in creative industries, virtual reality, and entertainment.

It's important to note that the field of artificial neural networks is continually evolving, and new applications may emerge as research progresses. The areas mentioned above represent some of the most prominent and promising applications of ANNs in information technology as of my knowledge cutoff in September 2021.

Rasul Barlybaev

> Financial Forecasting: Neural networks are employed in financial institutions for predicting stock prices, market trends, credit risk assessment, fraud detection, and algorithmic trading. ANNs can process large volumes of financial data, capture intricate patterns, and make accurate predictions, helping financial professionals in decision-making and risk management.

Here is the opinion of one PhD, engaged in statistics:

[QUOTE]In econometric studies, the explainability of models is important; neural networks do not provide this. For time series, neural networks can be used, but only with a special architecture, such as LSTM. For tasks of macroeconomic forecasting, as a rule, neural networks are not used. Use ARIMA/SARIMA, VAR, ECM.[/QUOTE]

Another opinion on the subject:

[QUOTE]A typical task in the field of time series analysis is to predict, from a sequence of previous values in a time series, the most likely next/future value. The Large Language Model (LLM - Large Language Model), which underlies the same ChatGPT, predicts which word or phrase will be next in a sentence or phrase, i.e. in a sequence of words in natural language.

The current ChatGPT is implemented using the so-called transformers - neural networks, which after 2017 began to actively replace the older, but also neural network and also sequence-oriented architecture LSTM (long short-term memory networks), and not only in word processing tasks, but also in other areas.[/QUOTE]

For what purposes is it advisable to use small solar linear-focus collectors (PTC, Fresnel reflectors)?

Why "solar" countries not paying due attention to the questions of solar energy using ?

What is the optimal opening (rim) angle of a solar parabolic (dish) concentrator ?

What are the standard books of postgraduate or master degree or doctor for Physiotherapy students , please write title and outher name?

Parallel Computing of MCNP?

How can I model 2D wave propagation ?

How to perform k-fold cross validation for Neural Network in IBM SPSS Statistics?

Is it possible to have gram positive Azotobacter species by gram staining procedure ?

Is it necessary to prepare FMO controls for each sample to be analyzed when there are two or more samples available?

Why does through activated sludge process treatment quality of Effluent found satisfactory but through chemical process not very well coagulation?

What is the most Operating System (OS) which can be INSTALL on Tiny hard devices like Raspberry Pi (RPi) hardware ?

What are some of the research gaps in machine learning and artificial intelligence in africa?

Is there a powerful system for the security of the systems distributed on IoT systems?

How to choose ANN number of hidden layers and nodes?

Is there any Machine Learning/AI algorithm that lets you predict with incomplete features without missing value imputation process?

What are the important topics in the field of Artificial Intelligence in Business world?

Unknown debris sheets observed in cell culture flasks?

How to sample data to increase presence of solar panels in the images?

Suggest me some trending topics for phd research in text mining/AI/machine learning?

Satellite orbit estimation using Recurrent Neural Network - what should I use?