AI Research
On this page we intend to highlight some Machine Intelligence research related resources that are relevant for those interested to stay abreast of the latest development in this field. See the following resources as a starting point:
- Future of Machine Intelligence, Perspectives from leading Practitioners
- Deep Learning Research Groups – see Deep Learning Research Groups
- Machine Intelligence Conferences focusing on machine leanring and pattern recognition
- Machine Intelligence Journals
- Data Science Journals, Publications and Magazines
- Leading organizations from an academic publications’ perspective (from a machine intelligence perspective)
If you know of any other research material that we should share or highlight for the rest of the community on the MIIA website or via our real-time messaging platform (MIIA on Slack), please let us know either on Slack or via info@machineintelligenceafrica.org.
The Future of Machine Intelligence
Perspectives from Leading Practitioners
By David BeyerPublisher: O’ReillyReleased: March 2016
Advances in both theory and practice are throwing the promise of machine learning into sharp relief. The field has the potential to transform a range of industries, from self-driving cars to intelligent business applications. Yet machine learning is so complex and wide-ranging that even its definition can change from one person to the next.
The series of interviews in this exclusive report unpack concepts and innovations that represent the frontiers of ever-smarter machines. You’ll get a rare glimpse into this exciting field through the eyes of some of its leading minds.
In these interviews, these ten practitioners and theoreticians cover the following topics:
- Anima Anandkumar: high-dimensional problems and non-convex optimization
- Yoshua Bengio: Natural Language Processing and deep learning
- Brendan Frey: deep learning meets genomic medicine
- Risto Miikkulainen: the startling creativity of evolutionary algorithms
- Ben Recht: a synthesis of machine learning and control theory
- Daniela Rus: the autonomous car as a driving partner
- Gurjeet Singh: using topology to uncover the shape of your data
- Ilya Sutskever: the promise of unsupervised learning and attention models
- Oriol Vinyals: sequence-to-sequence machine learning
- Reza Zadeh: the evolution of machine learning and the role of Spark
Deep Learning Research Groups
Some labs and research groups that are actively working on deep learning:
University of Toronto – Machine Learning Group (Geoffrey Hinton, Rich Zemel, Ruslan Salakhutdinov, Brendan Frey, Radford Neal)
Université de Montréal – MILA Lab (Yoshua Bengio, Pascal Vincent, Aaron Courville, Roland Memisevic)
New York University – Yann Lecun, Rob Fergus, David Sontag and Kyunghyun Cho
Stanford University – Andrew Ng, Christopher Manning‘s, Fei-fei Li‘s group
University of Oxford – Deep learning group, Nando de Freitas and Phil Blunsom, Andrew Zisserman
Google Research – Jeff Dean, Geoffrey Hinton, Samy Bengio, Ilya Sutskever, Ian Goodfellow, Oriol Vinyals, Dumitru Erhan, Quoc Le et al
Google DeepMind – Alex Graves, Karol Gregor, Koray Kavukcuoglu, Andriy Mnih, Guillaume Desjardins, Xavier Glorot, Razvan Pascanu, Volodymyr Mnih et al
Facebook AI Research(FAIR) – Yann Lecun, Rob Fergus, Jason Weston, Antoine Bordes, Soumit Chintala, Leon Bouttou, Ronan Collobert, Yann Dauphin et al.
Twitter’s Deep Learning Group – Hugo Larochelle, Ryan Adams, Clement Farabet et al
Microsoft Research – Li Deng et al
SUPSI – IDSIA (Jurgen Schmidhuber‘s group)
UC Berkeley – Bruno Olshausen‘s group, Trevor Darrell‘s group, Pieter Abbeel
UCLA – Alan Yuille
University of Washington – Pedro Domingos‘ group
IDIAP Research Institute – Ronan Collobert‘s group
University of California Merced – Miguel A. Carreira-Perpinan‘s group
University of Helsinki – Aapo Hyvärinen‘s Neuroinformatics group
Université de Sherbrooke – Hugo Larochelle‘s group
University of Guelph – Graham Taylor‘s group
University of Michigan – Honglak Lee‘s group
Technical University of Berlin – Klaus-Robert Muller‘s group
Baidu – Kai Yu‘s and Andrew Ng’s group
Aalto University – Juha Karhunen and Tapani Raiko group
U. Amsterdam – Max Welling‘s group
CMU – Chris Dyer
U. California Irvine – Pierre Baldi‘s group
Ghent University – Benjamin Shrauwen‘s group
University of Tennessee – Itamar Arel‘s group
IBM Research – Brian Kingsbury et al
University of Bonn – Sven Behnke’s group
Gatsby Unit @ University College London – Maneesh Sahani, Peter Dayan
Computational Cognitive Neuroscience Lab @ University of Colorado Boulder
Machine Intelligence conferences
Machine Intelligence Journals
Data Science Journals, Publications and Magazines*
- ICML – International Conference on Machine Learning
- epjdatascience
- Journal of Data Science – an international journal devoted to applications of statistical methods at large
- Big Data Research
- Journal of Big Data
- Big Data & Society
- Data Science Journal
* https://github.com/okulbilisim/awesome-datascience
Leading Organizations from an academic publications’ perspective
