AI Systems - overview
The tremendous progress in AI in recent years has been fuelled by advances in Systems for AI. This progress would have been hard to achieve without data parallel frameworks such as MapReduce and Spark, deep-learning frameworks such as TensorFlow and PyTorch, ubiquitous vectorization on GPUs, special-purpose hardware for both training and prediction, and innovations in human-computer interfaces, vizualization, and programming languages. However, AI also poses unique challenges for computer systems. AI has an ever-increasing need for storage and compute, is hard to debug let alone explain, must delicately balance human-in-the-loop input, and needs to be carefully managed in order to be trusted with important data and decisions.
The mission of the AI Systems PIC is to facilitate discussion (internally and externally), knowledge transfer, and collaborative research in designing, building, deploying, and studying systems for AI, including but not limited to systems for machine learning, systems for deep learning, and AI for systems.
The key academic conferences for the AI Systems PIC are:
- SysML / SML (Conference on Systems and Machine Learning) https://systemsandml.org/
- IUI (Conference on Intelligent User Interfaces): http://iui.acm.org/2020/
- MSR (Conference on Mining Software Repositories): https://2020.msrconf.org/
- IAAI (Conference on Innovative Applications of Artificial Intelligence): https://aaai.org/Conferences/AAAI-19/
- PROBPROG (Conference on Probabilistic Programming): http://probprog.cc/
In addition, top-tier systems conferences often have a few good AI papers or colocated workshops, and top-tier AI conferences often have a few good Systems papers or colocated workshops.
Exemplary AI Systems papers by IBM researchers:
- "Towards Extracting Web API Specifications from Documentation", Jinqiu Yang , Erik Wittern, Annie Ying, Julian Dolby, and Lin Tan. Conference on Mining Software Repositories (MSR), 2018. Distinguished Paper Award. https://2018.msrconf.org/track/msr-2018-papers
- "DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers", Akshay Sethi, Anush Sankaran, Naveen Panwar, Shreya Khare, and Senthil Mani. Conference on Artificial Intelligence (AAAI), 2018. https://arxiv.org/abs/1711.03543
- "Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD", Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, and Priya Nagpurkar. Conference on Artificial Intelligence and Statistics (AIStats), 2018. http://www.aistats.org/accepted.html
- "LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks", Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister, and Alexander M. Rush. Transactions on Visualization and Computer Graphics (TVCG), 2017. https://doi.org/10.1109/TVCG.2017.2744158
- "IBM Deep Learning Service", Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi, Parijat Dube, Ben Herta, Vatche Ishakian, K. R. Jayaram, Rania Khalaf, Avesh Krishna, Yu Bo Li, Vinod Muthusamy, Ruchir Puri, Yufei Ren, Florian Rosenberg, Seetharami R. Seelam, Yandong Wang, Jian Ming Zhang, and Li Zhang. IBM Journal of Research and Development, 2017. https://doi.org/10.1147/JRD.2017.2716578
- "Generating Chat Bots from Web API Specifications", Mandana Vaziri, Louis Mandel, Avraham Shinnar, Jérôme Siméon, and Martin Hirzel. Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!), 2017. https://doi.org/10.1145/3133850.3133864
- "Deep learning with limited numerical precision", Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan. International Conference on Machine Learning (ICML), 2015. http://proceedings.mlr.press/v37/gupta15.pdf
- "Building an efficient RDF store over a relational database", Mihaela A. Bornea, Julian Dolby, Anastasios Kementsietsidis, Kavitha Srinivas, Patrick Dantressangle, Octavian Udrea, and Bishwaranjan Bhattacharjee. International Conference on Management of Data (SIGMOD) 2013. https://doi.org/10.1145/2463676.2463718
- "M3R: Increased Performance for In-Memory Hadoop Jobs. Avraham Shinnar, David Cunningham, Ben Herta, and Vijay Saraswat. Conference on Very Large Data Bases (VLDB) 2012. https://doi.org/10.14778/2367502.2367513
- "Using probabilistic generative models for ranking risks of Android apps", Hao Peng, Chris Gates, Bhaskar Sarma, Ninghui Li, Yuan Qi, Rahul Potharaju, Cristina Nita-Rotaru, and Ian Molloy. Conference on Computer and Communications Security (CCS) 2012. https://doi.org/10.1145/2382196.2382224
- "SystemML: Declarative machine learning on MapReduce", Amol Ghoting, Rajasekar Krishnamurthy, Edwin Pednault, Berthold Reinwald, Vikas Sindhwani, Shirish Tatikonda, Yuanyuan Tian, and Shivakumar Vaithyanathan. International Conference on Data Engineering (ICDE) 2011. https://doi.org/10.1109/ICDE.2011.5767930
- "SystemT: An Algebraic Approach to Declarative Information Extraction", Laura Chiticariu, Rajasekar Krishnamurthy, Yunyao Li, Sriram Raghavan, Frederick Reiss, and Shivakumar Vaithyanathan. Annual Meeting of the Association for Computational Linguistics (ACL) 2010. http://www.aclweb.org/anthology/P10-1014