Editing Neural network (machine learning) (section)

== Recent advancements and future directions ==
Artificial neural networks (ANNs) have undergone significant advancements, particularly in their ability to model complex systems, handle large data sets, and adapt to various types of applications. Their evolution over the past few decades has been marked by a broad range of applications in fields such as image processing, speech recognition, natural language processing, finance, and medicine.{{citation needed|date=October 2024}}

=== Image processing ===
In the realm of image processing, ANNs are employed in tasks such as image classification, object recognition, and image segmentation. For instance, deep convolutional neural networks (CNNs) have been important in handwritten digit recognition, achieving state-of-the-art performance.<ref name=":07">{{Cite journal |last=Huang |first=Yanbo |date=2009 |title=Advances in Artificial Neural Networks – Methodological Development and Application |journal=Algorithms |language=en |volume=2 |issue=3 |pages=973–1007 |doi=10.3390/algor2030973 |issn=1999-4893 |doi-access=free }}</ref> This demonstrates the ability of ANNs to effectively process and interpret complex visual information, leading to advancements in fields ranging from automated surveillance to medical imaging.<ref name=":07"/>

=== Speech recognition ===
By modeling speech signals, ANNs are used for tasks like speaker identification and speech-to-text conversion. Deep neural network architectures have introduced significant improvements in large vocabulary continuous speech recognition, outperforming traditional techniques.<ref name=":07"/><ref name=":15">{{Cite journal |last1=Kariri |first1=Elham |last2=Louati |first2=Hassen |last3=Louati |first3=Ali |last4=Masmoudi |first4=Fatma |date=2023 |title=Exploring the Advancements and Future Research Directions of Artificial Neural Networks: A Text Mining Approach |journal=Applied Sciences |language=en |volume=13 |issue=5 |page=3186 |doi=10.3390/app13053186 |issn=2076-3417 |doi-access=free }}</ref> These advancements have enabled the development of more accurate and efficient voice-activated systems, enhancing user interfaces in technology products.{{citation needed|date=October 2024}}

=== Natural language processing ===
In natural language processing, ANNs are used for tasks such as text classification, sentiment analysis, and machine translation. They have enabled the development of models that can accurately translate between languages, understand the context and sentiment in textual data, and categorize text based on content.<ref name=":07"/><ref name=":15"/> This has implications for automated customer service, content moderation, and language understanding technologies.{{citation needed|date=October 2024}}

=== Control systems ===
In the domain of control systems, ANNs are used to model dynamic systems for tasks such as system identification, control design, and optimization. For instance, deep feedforward neural networks are important in system identification and control applications.{{citation needed|date=October 2024}}

=== Finance ===
{{more|Applications of artificial intelligence#Trading and investment}}
ANNs are used for [[quantitative investing|stock market prediction]] and [[credit scoring]]: 
*In investing, ANNs can process vast amounts of financial data, recognize complex patterns, and forecast stock market trends, aiding investors and risk managers in making informed decisions.<ref name=":07"/> 
*In credit scoring, ANNs offer data-driven, personalized assessments of creditworthiness, improving the accuracy of default predictions and automating the lending process.<ref name=":15"/> 
ANNs require high-quality data and careful tuning, and their "black-box" nature can pose challenges in interpretation. Nevertheless, ongoing advancements suggest that ANNs continue to play a role in finance, offering valuable insights and enhancing [[financial risk management|risk management strategies]].{{citation needed|date=October 2024}}

=== Medicine ===
ANNs are able to process and analyze vast medical datasets. They enhance diagnostic accuracy, especially by interpreting complex [[medical imaging]] for early disease detection, and by predicting patient outcomes for personalized treatment planning.<ref name=":15"/> In drug discovery, ANNs speed up the identification of potential drug candidates and predict their efficacy and safety, significantly reducing development time and costs.<ref name=":07"/> Additionally, their application in personalized medicine and healthcare data analysis allows tailored therapies and efficient patient care management.<ref name=":15" /> Ongoing research is aimed at addressing remaining challenges such as data privacy and model interpretability, as well as expanding the scope of ANN applications in medicine.{{citation needed|date=October 2024}}

=== Content creation ===
ANNs such as generative adversarial networks (GAN) and [[Transformer (machine learning model)|transformers]] are used for content creation across numerous industries.<ref name=":09">{{Cite journal |last1=Fui-Hoon Nah |first1=Fiona |last2=Zheng |first2=Ruilin |last3=Cai |first3=Jingyuan |last4=Siau |first4=Keng |last5=Chen |first5=Langtao |date=3 July 2023 |title=Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration |journal=Journal of Information Technology Case and Application Research |language=en |volume=25 |issue=3 |pages=277–304 |doi=10.1080/15228053.2023.2233814 |issn=1522-8053|doi-access=free }}</ref> This is because deep learning models are able to learn the style of an artist or musician from huge datasets and generate completely new artworks and music compositions. For instance, [[DALL-E]] is a deep neural network trained on 650 million pairs of images and texts across the internet that can create artworks based on text entered by the user.<ref>{{Cite web |title=DALL-E 2's Failures Are the Most Interesting Thing About It – IEEE Spectrum |url=https://spectrum.ieee.org/openai-dall-e-2 |access-date=9 December 2023 |website=[[IEEE]] |language=en |archive-date=15 July 2022 |archive-url=https://web.archive.org/web/20220715204154/https://spectrum.ieee.org/openai-dall-e-2 |url-status=live }}</ref> In the field of music, transformers are used to create original music for commercials and documentaries through companies such as [[AIVA]] and [[Jukedeck]].<ref>{{Cite journal |last=Briot |first=Jean-Pierre |date=January 2021 |title=From artificial neural networks to deep learning for music generation: history, concepts and trends |journal=Neural Computing and Applications |language=en |volume=33 |issue=1 |pages=39–65 |doi=10.1007/s00521-020-05399-0 |issn=0941-0643|doi-access=free }}</ref> In the marketing industry generative models are used to create personalized advertisements for consumers.<ref name=":09" /> Additionally, major film companies are partnering with technology companies to analyze the financial success of a film, such as the partnership between Warner Bros and technology company Cinelytic established in 2020.<ref>{{Cite journal |last=Chow |first=Pei-Sze |date=6 July 2020 |title=Ghost in the (Hollywood) machine: Emergent applications of artificial intelligence in the film industry |journal=NECSUS_European Journal of Media Studies |doi=10.25969/MEDIAREP/14307 |issn=2213-0217}}</ref> Furthermore, neural networks have found uses in video game creation, where Non Player Characters (NPCs) can make decisions based on all the characters currently in the game.<ref>{{Cite book |last1=Yu |first1=Xinrui |last2=He |first2=Suoju |last3=Gao |first3=Yuan |last4=Yang |first4=Jiajian |last5=Sha |first5=Lingdao |last6=Zhang |first6=Yidan |last7=Ai |first7=Zhaobo |chapter=Dynamic difficulty adjustment of game AI for video game Dead-End |date=June 2010 |pages=583–587 |title=The 3rd International Conference on Information Sciences and Interaction Sciences |publisher=IEEE |doi=10.1109/icicis.2010.5534761|isbn=978-1-4244-7384-7 |s2cid=17555595 }}</ref>