Current Trends in Deep Learning Research · Foundation Models and Scaling · Multimodal AI · Efficiency and Sustainability · Security and Ethics.
The deep learning revolution is only beginning to influence robotics, in large part because it is far more difficult to acquire the large labeled data sets that have driven other learning-based areas of AI. Work in this area has facilitated advances in other subfields of AI, including computer vision and NLP, by enabling large amounts of labeled training data and/or human interaction data to be collected in a short amount of time. Topics receiving attention include computational mechanism design (an economic theory of incentive design, seeking incentive-compatible systems where inputs are truthfully reported), computational social choice (a theory for how to aggregate rank orders on alternatives), incentive aligned information elicitation (prediction markets, scoring rules, peer prediction) and algorithmic game theory (the equilibria of markets, network games, and parlor games such as Poker—a game where significant advances have been made in recent years through abstraction techniques and no-regret learning).
Neural networks, the European Journal of Computer Science and Information Technology,13(17),88-98, 2025 Print ISSN: 2054-0957 (Print) Online ISSN: 2054-0965 (Online) Website: https://www.eajournals.org/ Publication of the European Centre for Research Training and Development -UK 89 cornerstone of deep learning, have shown exceptional performance in tasks such as image and speech recognition, natural language processing, and autonomous decision-making. European Journal of Computer Science and Information Technology,13(17),88-98, 2025 Print ISSN: 2054-0957 (Print) Online ISSN: 2054-0965 (Online) Website: https://www.eajournals.org/ Publication of the European Centre for Research Training and Development -UK 94 Reinforcement Learning The integration of deep learning with reinforcement learning has led to significant breakthroughs in AI capabilities: Deep Reinforcement Learning: Researchers have achieved remarkable results in complex decision-making tasks by combining deep neural networks with reinforcement learning. Fig. 2: Quantitative Impacts of Deep Learning Advancements in AI Research [3, 6] European Journal of Computer Science and Information Technology,13(17),88-98, 2025 Print ISSN: 2054-0957 (Print) Online ISSN: 2054-0965 (Online) Website: https://www.eajournals.org/ Publication of the European Centre for Research Training and Development -UK 96 Future Prospects As computational resources continue to expand and datasets grow larger, the potential for deep learning and neural networks in AI is boundless.
The machine learning (ML) and deep learning (DL) field is quickly progressing due to improvements in computational power, data access,
[Skip to main content](https://online.nyit.edu/blog/deep-learning-and-neural-networks#main-content). * [Curriculum](https://online.nyit.edu/ms-data-science/curriculum). * [Careers](https://online.nyit.edu/ms-data-science/careers). * [Study at New York Tech](https://online.nyit.edu/ms-data-science/new-york). * [Apply Now](https://online.nyit.edu/blog/deep-learning-and-neural-networks#apply-now). [](https://online.nyit.edu/). * [Curriculum](https://online.nyit.edu/ms-data-science/curriculum). * [Careers](https://online.nyit.edu/ms-data-science/careers). * [Study at New York Tech](https://online.nyit.edu/ms-data-science/new-york). * [Apply Now](https://online.nyit.edu/blog/deep-learning-and-neural-networks#apply-now). [Home](https://online.nyit.edu/)[Online Degrees Blog at New York Tech](https://online.nyit.edu/blog)Deep Learning and Neural Networks: The Future of Machine Learning. . In contrast, deep learning programs use thousands of layers to train a model.2. An [Online Master’s in Data Science](https://online.nyit.edu/ms-data-science) from the New York Institute of Technology can equip you with the knowledge and skills you need to thrive in high-demand, data-driven careers. 2. Retrieved on May 9, 2025, from [ibm.com/think/topics/deep-learning](https://www.ibm.com/think/topics/deep-learning). 8. Retrieved on May 9, 2025, from [neurond.com/blog/10-applications-of-deep-learning-in-artificial-intelligence](https://www.neurond.com/blog/10-applications-of-deep-learning-in-artificial-intelligence). New York Institute of Technology has engaged [Everspring](https://online.nyit.edu/privacy-policy), a leading provider of education and technology services, to support select aspects of program delivery. [](https://online.nyit.edu/).
[[DOI](https://doi.org/10.1109/ACCESS.2018.2869577)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=IEEE%20Access&title=Deep%20learning%20approach%20combining%20sparse%20autoencoder%20with%20svm%20for%20network%20intrusion%20detection&author=M%20Al-Qatf&author=Y%20Lasheng&author=M%20Al-Habib&author=K%20Al-Sabahi&volume=6&publication_year=2018&pages=52843-52856&doi=10.1109/ACCESS.2018.2869577&)]. [[DOI](https://doi.org/10.1109/MSP.2017.2743240)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=IEEE%20Signal%20Process%20Mag&title=Deep%20reinforcement%20learning:%20a%20brief%20survey&author=K%20Arulkumaran&author=MP%20Deisenroth&author=M%20Brundage&author=AA%20Bharath&volume=34&issue=6&publication_year=2017&pages=26-38&doi=10.1109/MSP.2017.2743240&)]. [[DOI](https://doi.org/10.1016/j.asoc.2020.106912)] [[PMC free article](/articles/PMC7673219/)] [[PubMed](https://pubmed.ncbi.nlm.nih.gov/33230395/)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Appl%20Soft%20Comput&title=Cnn-based%20transfer%20learning-bilstm%20network:%20a%20novel%20approach%20for%20covid-19%20infection%20detection&author=MF%20Aslan&author=MF%20Unlersen&author=K%20Sabanci&author=A%20Durdu&volume=98&publication_year=2021&pages=106912&pmid=33230395&doi=10.1016/j.asoc.2020.106912&)]. [[DOI](https://doi.org/10.1016/j.future.2019.04.041)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Futur%20Gener%20Comput%20Syst&title=A%20smart%20agriculture%20iot%20system%20based%20on%20deep%20reinforcement%20learning&author=F%20Bu&author=X%20Wang&volume=99&publication_year=2019&pages=500-507&doi=10.1016/j.future.2019.04.041&)]. [[DOI](https://doi.org/10.1109/ACCESS.2019.2908843)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=IEEE%20Access&title=A%20deep%20learning-based%20intelligent%20medicine%20recognition%20system%20for%20chronic%20patients&author=W-J%20Chang&author=L-B%20Chen&author=C-H%20Hsu&author=C-P%20Lin&author=T-C%20Yang&volume=7&publication_year=2019&pages=44441-44458&doi=10.1109/ACCESS.2019.2908843&)]. [[DOI](https://doi.org/10.1016/j.apenergy.2017.01.003)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Appl%20Energy&title=A%20gpu%20deep%20learning%20metaheuristic%20based%20model%20for%20time%20series%20forecasting&author=IM%20Coelho&author=VN%20Coelho&author=J%20da%20Eduardo&author=S%20Luz&author=LS%20Ochi&volume=201&publication_year=2017&pages=412-418&doi=10.1016/j.apenergy.2017.01.003&)]. [[DOI](https://doi.org/10.1007/s10462-019-09744-1)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Artif%20Intel%20Rev&title=Recommendation%20system%20based%20on%20deep%20learning%20methods:%20a%20systematic%20review%20and%20new%20directions&author=A%20Da'u&author=N%20Salim&volume=53&issue=4&publication_year=2020&pages=2709-48&doi=10.1007/s10462-019-09744-1&)]. [[DOI](https://doi.org/10.1561/2000000039)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Found%20Trends%20Signal%20Process&title=Deep%20learning:%20methods%20and%20applications&author=L%20Deng&author=Yu%20Dong&volume=7&issue=3%E2%80%934&publication_year=2014&pages=197-387&doi=10.1561/2000000039&)]. [[DOI](https://doi.org/10.1016/j.matpr.2020.05.450)] [[PMC free article](/articles/PMC7283081/)] [[PubMed](https://pubmed.ncbi.nlm.nih.gov/32837917/)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Mater%20Today%20Proc&title=An%20intelligent%20chatbot%20using%20deep%20learning%20with%20bidirectional%20rnn%20and%20attention%20model&author=M%20Dhyani&author=R%20Kumar&volume=34&publication_year=2021&pages=817-824&pmid=32837917&doi=10.1016/j.matpr.2020.05.450&)]. [[DOI](https://doi.org/10.1109/TIE.2020.2982085)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=IEEE%20Trans%20Ind%20Electron&title=Domain%20knowledge-based%20deep-broad%20learning%20framework%20for%20fault%20diagnosis&author=J%20Feng&author=Y%20Yao&author=S%20Lu&author=Y%20Liu&volume=68&issue=4&publication_year=2020&pages=3454-3464&doi=10.1109/TIE.2020.2982085&)]. [[DOI](https://doi.org/10.1109/TMM.2019.2893549)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=IEEE%20Trans%20Multimed&title=Hybrid%20deep-learning-based%20anomaly%20detection%20scheme%20for%20suspicious%20flow%20detection%20in%20sdn:%20a%20social%20multimedia%20perspective&author=S%20Garg&author=K%20Kaur&author=N%20Kumar&author=JJPC%20Rodrigues&volume=21&issue=3&publication_year=2019&pages=566-578&doi=10.1109/TMM.2019.2893549&)]. [[DOI](https://doi.org/10.1109/TPAMI.2015.2389824)] [[PubMed](https://pubmed.ncbi.nlm.nih.gov/26353135/)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=IEEE%20Trans%20Pattern%20Anal%20Mach%20Intell&title=Spatial%20pyramid%20pooling%20in%20deep%20convolutional%20networks%20for%20visual%20recognition&author=K%20He&author=X%20Zhang&author=S%20Ren&author=J%20Sun&volume=37&issue=9&publication_year=2015&pages=1904-1916&pmid=26353135&doi=10.1109/TPAMI.2015.2389824&)]. [[DOI](https://doi.org/10.1162/neco.2006.18.7.1527)] [[PubMed](https://pubmed.ncbi.nlm.nih.gov/16764513/)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Neural%20Comput&title=A%20fast%20learning%20algorithm%20for%20deep%20belief%20nets&author=GE%20Hinton&author=S%20Osindero&author=Y-W%20Teh&volume=18&issue=7&publication_year=2006&pages=1527-1554&pmid=16764513&doi=10.1162/neco.2006.18.7.1527&)]. [[DOI](https://doi.org/10.3390/s18072220)] [[PMC free article](/articles/PMC6069282/)] [[PubMed](https://pubmed.ncbi.nlm.nih.gov/29996546/)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Sensors&title=A%20deep%20cnn-lstm%20model%20for%20particulate%20matter%20(pm2.%205)%20forecasting%20in%20smart%20cities&author=C-J%20Huang&author=P-H%20Kuo&volume=18&issue=7&publication_year=2018&pages=2220&pmid=29996546&doi=10.3390/s18072220&)]. [[DOI](https://doi.org/10.1089/big.2018.0023)] [[PubMed](https://pubmed.ncbi.nlm.nih.gov/29924649/)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Big%20Data&title=Deep%20learning%20method%20for%20denial%20of%20service%20attack%20detection%20based%20on%20restricted%20Boltzmann%20machine&author=Y%20Imamverdiyev&author=F%20Abdullayeva&volume=6&issue=2&publication_year=2018&pages=159-169&pmid=29924649&doi=10.1089/big.2018.0023&)]. [[DOI](https://doi.org/10.1016/j.imu.2020.100412)] [[PMC free article](/articles/PMC7428728/)] [[PubMed](https://pubmed.ncbi.nlm.nih.gov/32835084/)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Inf%20Med%20Unlock&title=A%20combined%20deep%20cnn-lstm%20network%20for%20the%20detection%20of%20novel%20coronavirus%20(covid-19)%20using%20x-ray%20images&author=MZ%20Islam&author=MM%20Islam&author=A%20Asraf&volume=20&publication_year=2020&pages=100412&pmid=32835084&doi=10.1016/j.imu.2020.100412&)]. [[DOI](https://doi.org/10.1016/j.ins.2018.04.092)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Inf%20Sci&title=Zero-day%20malware%20detection%20using%20transferred%20generative%20adversarial%20networks%20based%20on%20deep%20autoencoders&author=J-Y%20Kim&author=B%20Seok-Jun&author=S-B%20Cho&volume=460&publication_year=2018&pages=83-102&doi=10.1016/j.ins.2018.04.092&)]. [[DOI](https://doi.org/10.1038/s41598-020-67544-y)] [[PMC free article](/articles/PMC7329868/)] [[PubMed](https://pubmed.ncbi.nlm.nih.gov/32612129/)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Sci%20Rep&title=Domain-specific%20cues%20improve%20robustness%20of%20deep%20learning-based%20segmentation%20of%20ct%20volumes&author=M%20Kloenne&author=S%20Niehaus&author=L%20Lampe&author=A%20Merola&author=J%20Reinelt&volume=10&issue=1&publication_year=2020&pages=1-9&pmid=32612129&doi=10.1038/s41598-020-67544-y&)]. [[DOI](https://doi.org/10.1038/nature14539)] [[PubMed](https://pubmed.ncbi.nlm.nih.gov/26017442/)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Nature&title=Deep%20learning&author=Y%20LeCun&author=Y%20Bengio&author=G%20Hinton&volume=521&issue=7553&publication_year=2015&pages=436-44&pmid=26017442&doi=10.1038/nature14539&)]. [[DOI](https://doi.org/10.1109/5.726791)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Proc%20IEEE&title=Gradient-based%20learning%20applied%20to%20document%20recognition&author=Y%20LeCun&author=L%20Bottou&author=Y%20Bengio&author=P%20Haffner&volume=86&issue=11&publication_year=1998&pages=2278-2324&doi=10.1109/5.726791&)]. [[DOI](https://doi.org/10.1109/TSC.2017.2662008)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=IEEE%20Trans%20Serv%20Comput&title=A%20new%20deep%20learning-based%20food%20recognition%20system%20for%20dietary%20assessment%20on%20an%20edge%20computing%20service%20infrastructure&author=C%20Liu&author=Y%20Cao&author=Y%20Luo&author=G%20Chen&author=V%20Vokkarane&volume=11&issue=2&publication_year=2017&pages=249-261&doi=10.1109/TSC.2017.2662008&)]. [[DOI](https://doi.org/10.1016/j.neucom.2016.12.038)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Neurocomputing&title=A%20survey%20of%20deep%20neural%20network%20architectures%20and%20their%20applications&author=W%20Liu&author=Z%20Wang&author=X%20Liu&author=N%20Zeng&author=Y%20Liu&volume=234&publication_year=2017&pages=11-26&doi=10.1016/j.neucom.2016.12.038&)]. [[DOI](https://doi.org/10.1016/j.eswa.2019.112963)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Expert%20Syst%20Appl&title=Application%20of%20deep%20reinforcement%20learning%20to%20intrusion%20detection%20for%20supervised%20problems&author=M%20Lopez-Martin&author=B%20Carro&author=A%20Sanchez-Esguevillas&volume=141&publication_year=2020&pages=112963&doi=10.1016/j.eswa.2019.112963&)]. [[DOI](https://doi.org/10.1109/ACCESS.2019.2958962)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=IEEE%20Access&title=A%20survey%20on%20deep%20learning%20empowered%20iot%20applications&author=X%20Ma&author=T%20Yao&author=H%20Menglan&author=Y%20Dong&author=W%20Liu&volume=7&publication_year=2019&pages=181721-181732&doi=10.1109/ACCESS.2019.2958962&)]. [[DOI](https://doi.org/10.1016/j.comcom.2020.01.050)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Comput%20Commun&title=Deep%20learning-based%20intelligent%20face%20recognition%20in%20iot-cloud%20environment&author=M%20Masud&author=G%20Muhammad&author=H%20Alhumyani&author=SS%20Alshamrani&author=O%20Cheikhrouhou&volume=152&publication_year=2020&pages=215-222&doi=10.1016/j.comcom.2020.01.050&)]. [[DOI](https://doi.org/10.1016/j.eswa.2020.114285)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Expert%20Syst%20Appl&title=A%20reinforcement%20learning%20and%20deep%20learning%20based%20intelligent%20system%20for%20the%20support%20of%20impaired%20patients%20in%20home%20treatment&author=M%20Naeem&author=G%20Paragliola&author=A%20Coronato&volume=168&publication_year=2021&pages=114285&doi=10.1016/j.eswa.2020.114285&)]. [[DOI](https://doi.org/10.1145/3412842)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=ACM%20Trans%20Internet%20Technol%20(TOIT)&title=Predictive%20analytics%20for%20smart%20parking:%20A%20deep%20learning%20approach%20in%20forecasting%20of%20iot%20data&author=F%20Piccialli&author=F%20Giampaolo&author=E%20Prezioso&author=D%20Crisci&author=S%20Cuomo&volume=21&issue=3&publication_year=2021&pages=1-21&doi=10.1145/3412842&)]. [[DOI](https://doi.org/10.1016/j.iot.2020.100344)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Internet%20of%20Things&title=The%20architectural%20design%20of%20smart%20blind%20assistant%20using%20iot%20with%20deep%20learning%20paradigm&author=MW%20Rahman&author=SS%20Tashfia&author=R%20Islam&author=MM%20Hasan&author=SI%20Sultan&volume=13&publication_year=2021&pages=100344&doi=10.1016/j.iot.2020.100344&)]. [[DOI](https://doi.org/10.1109/TII.2018.2867174)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=IEEE%20Trans%20Ind%20Inf&title=A%20knowledge-based%20recommendation%20system%20that%20includes%20sentiment%20analysis%20and%20deep%20learning&author=RL%20Rosa&author=GM%20Schwartz&author=WV%20Ruggiero&author=DZ%20Rodr%C3%ADguez&volume=15&issue=4&publication_year=2018&pages=2124-2135&doi=10.1109/TII.2018.2867174&)]. [[DOI](https://doi.org/10.1186/s40537-019-0258-4)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=J%20Big%20Data&title=Context-aware%20rule%20learning%20from%20smartphone%20data:%20survey,%20challenges%20and%20future%20directions&author=IH%20Sarker&volume=6&issue=1&publication_year=2019&pages=1-25&doi=10.1186/s40537-019-0258-4&)]. [[DOI](https://doi.org/10.1016/j.iot.2019.01.007)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Internet%20of%20Things&title=A%20machine%20learning%20based%20robust%20prediction%20model%20for%20real-life%20mobile%20phone%20data&author=IH%20Sarker&volume=5&publication_year=2019&pages=180-193&doi=10.1016/j.iot.2019.01.007&)]. [[DOI](https://doi.org/10.1016/j.iot.2021.100393)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Internet%20of%20Things&title=Cyberlearning:%20effectiveness%20analysis%20of%20machine%20learning%20security%20modeling%20to%20detect%20cyber-anomalies%20and%20multi-attacks&author=IH%20Sarker&volume=14&publication_year=2021&pages=100393&doi=10.1016/j.iot.2021.100393&)]. [[DOI](https://doi.org/10.1007/s42979-021-00592-x)] [[PMC free article](/articles/PMC7983091/)] [[PubMed](https://pubmed.ncbi.nlm.nih.gov/33778771/)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Science&title=Machine%20learning:%20Algorithms,%20real-world%20applications%20and%20research%20directions.%20SN%20Computer&author=IH%20Sarker&volume=2&issue=3&publication_year=2021&pages=1-21&pmid=33778771&doi=10.1007/s42979-021-00592-x&)]. [[DOI](https://doi.org/10.3390/sym12050754)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Symmetry&title=Intrudtree:%20a%20machine%20learning%20based%20cyber%20security%20intrusion%20detection%20model&author=IH%20Sarker&author=YB%20Abushark&author=F%20Alsolami&author=AI%20Khan&volume=12&issue=5&publication_year=2020&pages=754&doi=10.3390/sym12050754&)]. [[DOI](https://doi.org/10.1007/s11036-019-01443-z)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Mob%20Netw%20Appl&title=Behavdt:%20a%20behavioral%20decision%20tree%20learning%20to%20build%20user-centric%20context-aware%20predictive%20model&author=IH%20Sarker&author=A%20Colman&author=J%20Han&author=AI%20Khan&author=YB%20Abushark&volume=25&issue=3&publication_year=2020&pages=1151-1161&doi=10.1007/s11036-019-01443-z&)]. [[DOI](https://doi.org/10.1016/j.jnca.2020.102762)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=J%20Netw%20Comput%20Appl&title=Abc-ruleminer:%20User%20behavioral%20rule-based%20machine%20learning%20method%20for%20context-aware%20intelligent%20services&author=IH%20Sarker&author=ASM%20Kayes&volume=168&publication_year=2020&pages=102762&doi=10.1016/j.jnca.2020.102762&)]. [[DOI](https://doi.org/10.1186/s40537-020-00318-5)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=J%20Big%20data&title=Cybersecurity%20data%20science:%20an%20overview%20from%20machine%20learning%20perspective&author=IH%20Sarker&author=ASM%20Kayes&author=S%20Badsha&author=H%20Alqahtani&author=P%20Watters&volume=7&issue=1&publication_year=2020&pages=1-29&doi=10.1186/s40537-020-00318-5&)]. [[DOI](https://doi.org/10.1186/s40537-019-0219-y)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=J%20Big%20Data&title=Effectiveness%20analysis%20of%20machine%20learning%20classification%20models%20for%20predicting%20personalized%20context-aware%20smartphone%20usage&author=IH%20Sarker&author=ASM%20Kayes&author=P%20Watters&volume=6&issue=1&publication_year=2019&pages=1-28&doi=10.1186/s40537-019-0219-y&)]. [[DOI](https://doi.org/10.1109/TCBB.2018.2822803)] [[PubMed](https://pubmed.ncbi.nlm.nih.gov/29993662/)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=IEEE/ACM%20Trans%20Comput%20Biol%20Bioinf&title=Transfer%20learning%20for%20molecular%20cancer%20classification%20using%20deep%20neural%20networks&author=RK%20Sevakula&author=V%20Singh&author=NK%20Verma&author=C%20Kumar&author=Y%20Cui&volume=16&issue=6&publication_year=2018&pages=2089-2100&pmid=29993662&doi=10.1109/TCBB.2018.2822803&)]. [[DOI](https://doi.org/10.1007/s12652-018-0803-6)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=J%20Ambient%20Intell%20Humaniz%20Comput&title=Effective%20android%20malware%20detection%20with%20a%20hybrid%20model%20based%20on%20deep%20autoencoder%20and%20convolutional%20neural%20network&author=W%20Wang&author=M%20Zhao&author=J%20Wang&volume=10&issue=8&publication_year=2019&pages=3035-3043&doi=10.1007/s12652-018-0803-6&)]. [[DOI](https://doi.org/10.1109/TVT.2020.3003933)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=IEEE%20Trans%20Veh%20Technol&title=A%20real-time%20collision%20prediction%20mechanism%20with%20deep%20learning%20for%20intelligent%20transportation%20system&author=X%20Wang&author=J%20Liu&author=T%20Qiu&author=M%20Chaoxu&author=C%20Chen&volume=69&issue=9&publication_year=2020&pages=9497-9508&doi=10.1109/TVT.2020.3003933&)]. [[DOI](https://doi.org/10.1109/ACCESS.2019.2925828)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=IEEE%20Access&title=An%20optimization%20method%20for%20intrusion%20detection%20classification%20model%20based%20on%20deep%20belief%20network&author=P%20Wei&author=Y%20Li&author=Z%20Zhang&author=H%20Tao&author=Z%20Li&volume=7&publication_year=2019&pages=87593-87605&doi=10.1109/ACCESS.2019.2925828&)]. [[DOI](https://doi.org/10.1109/ACCESS.2018.2836950)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Ieee%20access&title=Machine%20learning%20and%20deep%20learning%20methods%20for%20cybersecurity&author=Y%20Xin&author=L%20Kong&author=Z%20Liu&author=Y%20Chen&author=Y%20Li&volume=6&publication_year=2018&pages=35365-35381&doi=10.1109/ACCESS.2018.2836950&)]. [[DOI](https://doi.org/10.1016/j.eswa.2016.10.017)] [[Google Scholar](https://scholar.google.com/scholar_lookup?journal=Expert%20Syst%20Appl&title=Text%20summarization%20using%20unsupervised%20deep%20learning&author=M%20Yousefi-Azar&author=L%20Hamey&volume=68&publication_year=2017&pages=93-105&doi=10.1016/j.eswa.2016.10.017&)].
Model Performance 90% CI 10× to 900× 1 to 4 months 1 to 3 OOM](https://epoch.ai/trends#model-performance)[Compute stock growth 3.4 ×/year 6.8 months 0.53 OOM/year The total computing power of the stock of AI chips is growing at a rate of 3.4×/year. AI Companies 90% CI 3.2× to 3.7× 6.4 to 7.2 months 0.51 to 0.57 OOM](https://epoch.ai/trends#ai-companies)[Training compute 5 ×/year 5.2 months 0.7 OOM/year Training compute for frontier language models has been growing at 5× per year since 2020. Training Runs 90% CI 4× to 6× 4.6 to 6.0 months 0.6 to 0.8 OOM](https://epoch.ai/trends#training-runs)[Software progress ÷ 3.0 ×/year 7.6 months 0.5 OOM/year Pre-training compute efficiency is improving at roughly 3.0× per year. Training Runs 90% CI 2.8× to 4.4× 5.6 to 8.1 months 0.4 to 0.6 OOM](https://epoch.ai/trends#training-runs)[Largest AI data center 700,000 H100e The largest known AI data center has computing power equivalent to 700,000 NVIDIA H100 chips. Data Centers 90% CI 500k to 1M H100e](https://epoch.ai/trends#data-centers)[FLOP/s per dollar 1.37 ×/year 2.2 years 0.14 OOM/year AI chip performance per dollar has improved by 37% per year.
Data and AI leaders in Randy’s 2025 AI & Data Leadership Executive Benchmark Survey said they are confident that GenAI value is being generated: Fifty-eight percent said that their organization has achieved exponential productivity or efficiency gains from AI, presumably mostly from generative AI. In our trend article last year, we noted that Randy’s survey found that the percentage of company respondents who said that their organization had “created a data and AI-driven organization” and “established a data and AI-driven organizational culture” both doubled over the prior year (from 24% to 48% for creating data- and AI-driven organizations, and from 21% to 43% for establishing data-driven cultures). Fewer than half of data leaders (mostly chief data officers) who responded to Randy’s AI & Data Leadership Executive Benchmark Survey said their function is very successful and well established, and only 51% said they feel that the job is well understood within their organizations.