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Toward the Starting Line: A Systems Engineering Approach to Strong AI [article]

Tansu Alpcan, Sarah M. Erfani, Christopher Leckie
2017 arXiv   pre-print
After many hype cycles and lessons from AI history, it is clear that a big conceptual leap is needed for crossing the starting line to kick-start mainstream AGI research.  ...  After a broad analysis of the AGI problem from different perspectives, a system-theoretic and engineering-based research approach is introduced, which builds upon the existing mainstream AI and systems  ...  Acknowledgements The authors thank anonymous reviewers and TPC chairs of the AGI 2017 conference for their valuable feedback and suggestions.  ... 
arXiv:1707.09095v2 fatcat:sd2jobjirjbrzhf3ahlyeq43te

The AI for Scientific Discovery Network+

Samantha Kanza, Colin Leonard Bird, Mahesan Niranjan, William McNeill, Jeremy Graham Frey
2021 Patterns  
These challenges are shaping the future directions of the Network+.  ...  The activities, collaborations, and research covered in the first year of the Network+ have highlighted the significant challenges in the chemistry and augmented and artificial intelligence space.  ...  AUTHOR CONTRIBUTIONS All authors contributed equally to the originating proposals that some of this material was drawn from, and to writing the overall paper.  ... 
doi:10.1016/j.patter.2020.100162 pmid:33511363 pmcid:PMC7815949 fatcat:ed4g3tvmxbdk3dqk7na4jqp7d4

Winter Workshop 2019 [article]

University, Nottingham Trent, Connected Everything
2022 figshare.com  
learning and connected devices are helping to shape the future of our smart industry.  ...  The workshop features 13 keynote talks by established researchers and it also provides the opportunity to the researchers to share their research experiences.  ...  Her research interests include system-on-chip, artificial neural networks, embedded systems and multicore processes.  ... 
doi:10.6084/m9.figshare.20291340.v1 fatcat:ff7uztgezvbuboeorgdq4qmzci

AI System Engineering—Key Challenges and Lessons Learned

Lukas Fischer, Lisa Ehrlinger, Verena Geist, Rudolf Ramler, Florian Sobiezky, Werner Zellinger, David Brunner, Mohit Kumar, Bernhard Moser
2020 Machine Learning and Knowledge Extraction  
The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems.  ...  This will be done by taking into account intrinsic conditions of nowadays deep learning models, data and software quality issues and human-centered artificial intelligence (AI) postulates, including confidentiality  ...  environments, and also from changes in activation patterns within layers of deep neural networks.  ... 
doi:10.3390/make3010004 fatcat:35qfecqrn5auxc3epcjodxuez4

Artificial Intelligence and Robotics [article]

Javier Andreu Perez, Fani Deligianni, Daniele Ravi, Guang-Zhong Yang
2018 arXiv   pre-print
and future directions.  ...  To understand the impact of AI, it is important to draw lessons from it's past successes and failures and this white paper provides a comprehensive explanation of the evolution of AI, its current status  ...  There are many lessons that can be learnt from the past successes and failures of AI.  ... 
arXiv:1803.10813v1 fatcat:p2czbmak4jcyxbtncqfqlkxtma

Analyzing Machine Learning Enabled Fake News Detection Techniques for Diversified Datasets

Shubha Mishra, Piyush Shukla, Ratish Agarwal
2022 Wireless Communications and Mobile Computing  
Fake news, or fabric which appeared to be untrue with point of deceiving the open, has developed in ubiquity in current a long time.  ...  In particular, the research describes the fundamental theory of the related work to provide a deep comparative analysis of various literature works that has contributed to this topic.  ...  With the deployment of DL research and applications in the current past, many research works will apply DL techniques including CNNs, deep Boltzmann machines, DNN, and deep autoencoder models in different  ... 
doi:10.1155/2022/1575365 doaj:422ea8aa5c3e4a47b9a1c01d94807fa5 fatcat:6d57kun6wbczdnig523ixm3i7u

Deep learning: from speech recognition to language and multimodal processing

Li Deng
2016 APSIPA Transactions on Signal and Information Processing  
Finally, a number of key issues in deep learning are discussed, and future directions are analyzed for perceptual tasks such as speech, image, and video, as well as for cognitive tasks involving natural  ...  While artificial neural networks have been in existence for over half a century, it was not until year 2010 that they had made a significant impact on speech recognition with a deep form of such networks  ...  The second "quick-fix" above is the only one that has not been resolved as in today's state of the art ASR systems. This direction of future research will be discussed later in this article.  ... 
doi:10.1017/atsip.2015.22 fatcat:rsaafhsbfzeo3l6dxycjewcmi4

Automating Analysis and Feedback to Improve Mathematics Teachers' Classroom Discourse

Abhijit Suresh, Tamara Sumner, Jennifer Jacobs, Bill Foland, Wayne Ward
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Currently, providing teachers with detailed feedback about the talk moves in their lessons requires highly trained observers to hand code transcripts of classroom recordings and analyze talk moves and/  ...  Talk moves can be used by both teachers and learners to construct conversations in which students share their thinking, actively consider the ideas of others, and engage in sustained reasoning.  ...  This variant enables the network to preserve information from the past and the future.  ... 
doi:10.1609/aaai.v33i01.33019721 fatcat:w5hlfxjyq5afpbcvh3i6z777dq

Implementation and Design of Wireless IoT Network using Deep Learning

S.V.Manikanthan Et.al
2021 Turkish Journal of Computer and Mathematics Education  
This provides the client with a rising knowledge of the pattern of power consumption and the usage of electricity using Recurrent Neural Network(RNN).  ...  In designing better cities,embedded devices include this new technology, which the internet is similar to everybody.  ...  A recurrent neural network (RNN) is a class of artificial neural networks where relations between nodes are generated by a directed graph along with a temporal series.  ... 
doi:10.17762/turcomat.v12i3.761 fatcat:6zrmbsn4gbettoldvpyjouhp64

The promise of artificial intelligence in chemical engineering: Is it here, finally?

Venkat Venkatasubramanian
2018 AIChE Journal  
I am also grateful to my former and current students, who worked on various aspects of my AI work cited in this perspective. I am thankful to my former Ph.D. student, Yu Luo, for the cover art.  ...  Acknowledgments In preparing this perspective article, I have benefited greatly from the suggestions of René Bañares-Alcántara, Kyle Bishop, Ted Bowen, Bryan Goldsmith, Ignacio Grossmann, John Hooker,  ...  Second, drawing on these lessons, to identify promising current and future opportunities for AI in chemical engineering.  ... 
doi:10.1002/aic.16489 fatcat:a2rbapguynbwjbaup4fnia4h4a

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program

Grace Bang, Guy Barash, Ryan Bea, Jacques Cali, Mauricio Castillo-Effen, Xin Chen, Niyati Chhaya, Rachel Cummings, Rohan Dhoopar, Sebastijan Dumanci, Huáscar Espinoza, Eitan Farchi (+29 others)
2020 The AI Magazine  
The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence.  ...  This report contains the required reports, which were submitted by most, but not all, of the workshop chairs.  ...  neural network for transactions data, and environmental, social, and governance knowledge and data extraction from sustainability reports.  ... 
doi:10.1609/aimag.v41i4.7398 fatcat:r6bw77vy4zgmrbgyuvsjs5knta

An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications

Taiwo Samuel Ajani, Agbotiname Lucky Imoize, Aderemi A. Atayero
2021 Sensors  
Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded  ...  deep neural networks (DNNs).  ...  Research Directions and Open Issues Embedded machine learning research is still in its early days.  ... 
doi:10.3390/s21134412 pmid:34203119 fatcat:dxmshp4frnf4pcookdy3wjl4fi

Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers [article]

Fred Hohman, Minsuk Kahng, Robert Pienta, Duen Horng Chau
2018 arXiv   pre-print
We conclude by highlighting research directions and open research problems.  ...  However, because of the internal complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are achieving such performance are challenging  ...  RESEARCH DIRECTIONS & OPEN PROBLEMS Now we present research directions and open problems for future research distilled from the surveyed works.  ... 
arXiv:1801.06889v3 fatcat:c5x3ftcf5fbapc5tsyhm5w2dhq

Archives and AI: An Overview of Current Debates and Future Perspectives [article]

Giovanni Colavizza, Tobias Blanke, Charles Jeurgens, Julia Noordegraaf
2021 arXiv   pre-print
We conclude by underlining emerging trends and directions for future work, which include the application of recordkeeping principles to the very data and processes which power modern artificial intelligence  ...  We find four broad themes in the literature on archives and artificial intelligence: theoretical and professional considerations, the automation of recordkeeping processes, organising and accessing archives  ...  Only this way, the core archival mission of building trust in the past record can be sustained. While much progress has been made, more research and direct work in and with archives is needed.  ... 
arXiv:2105.01117v1 fatcat:up2ahotvurabtnwvzlyk32z7yq

A Survey on Visual Navigation for Artificial Agents with Deep Reinforcement Learning

Fanyu Zeng, Chen Wang, Shuzhi Sam Ge
2020 IEEE Access  
Visual navigation for artificial agents with deep reinforcement learning (DRL) is a new research hotspot in artificial intelligence and robotics that incorporates the decision making of DRL into visual  ...  These visual DRL navigation algorithms are reviewed in detail. Finally, we discuss the challenges and some possible opportunities to visual DRL navigation for artificial agents.  ...  A multicity network trains artificial agents in many cities and then freezes the neural network of specific paths and policy networks of many cities, which enables the artificial agent to acquire new knowledge  ... 
doi:10.1109/access.2020.3011438 fatcat:ie6qvu24qbapbjxtiudh7fumgy
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