mlsanityData to 7 Oct 2026

Recent Trends in Artificial Intelligence Technology: A Scoping Review

arXiv 2305.04532 ↗First posted 8 May 2023cs.LG cs.AI cs.CVcited by 1

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Abstract (arXiv)

Artificial intelligence is more ubiquitous in multiple domains. Smartphones, social media platforms, search engines, and autonomous vehicles are just a few examples of applications that utilize artificial intelligence technologies to enhance their performance. This study carries out a scoping review of the current state-of-the-art artificial intelligence technologies following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The goal was to find the most advanced technologies used in different domains of artificial intelligence technology research. Three recognized journals were used from artificial intelligence and machine learning domain: Journal of Artificial Intelligence Research, Journal of Machine Learning Research, and Machine Learning, and articles published in 2022 were observed. Certain qualifications were laid for the technological solutions: the technology must be tested against comparable solutions, commonly approved or otherwise well justified datasets must be used while applying, and results must show improvements against comparable solutions. One of the most important parts of the technology development appeared to be how to process and exploit the data gathered from multiple sources. The data can be highly unstructured, and the technological solution should be able to utilize the data with minimum manual work from humans. The results of this review indicate that creating labeled datasets is very laborious, and solutions exploiting unsupervised or semi-supervised learning technologies are more and more researched. The learning algorithms should be able to be updated efficiently, and predictions should be interpretable. Using artificial intelligence technologies in real-world applications, safety and explainable predictions are mandatory to consider before mass adoption can occur.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 47 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

14 of 47 references.

References · 14

  1. 2020Reward Machines: Exploiting Reward Function Structure in Reinforcement Learningcited by 183
  2. 2020Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Surveycited by 83
  3. 2020SAMBA: Safe Model-Based & Active Reinforcement Learningcited by 28
  4. 2020SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networkscited by 26
  5. 2021Wavelet-Packets for Deepfake Image Analysis and Detectioncited by 25
  6. 2018XAI Beyond Classification: Interpretable Neural Clusteringcited by 24
  7. 2020Simultaneous Machine Translation with Visual Contextcited by 17
  8. 2022On Efficient Reinforcement Learning for Full-length Game of StarCraft IIcited by 14
  9. 2021CoLLIE: Continual Learning of Language Grounding from Language-Image Embeddingscited by 11
  10. 2021Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysiscited by 8
  11. 2022Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learningcited by 8
  12. 2020Towards Multimodal Simultaneous Neural Machine Translationcited by 7
2 more
  1. 2022Predicting Survival Outcomes in the Presence of Unlabeled Datacited by 3
  2. 2022Supervised Visual Attention for Simultaneous Multimodal Machine Translationcited by 2

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Cited by year

Citing papers per year: 2023: 1.

1 paper.