IEEE Transactions on Emerging Topics in Computational Intelligence, Volume, 1, Issue 4, August 2017

1) Dendritic Cell Algorithm Applied to Ping Scan Investigation Revisited: Detection Quality and Performance Analysis
Author(s): Guilherme Costa Silva, Walmir Matos Caminhas and Luciano de Errico
Pages: 236-247

2) An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots
Author(s): Grazziela P. Figueredo, Isaac Triguero, Mohammad Mesgarpour, Alexandre M. Guerra, Jonathan M. Garibaldi and Robert I. John
Pages: 248-258

3) On the Reconstruction Method for Negative Surveys with Application to Education Surveys 
Author(s): Hao Jiang, Wenjian Luo, Li Ni and Bei Hua
Pages: 259-269

4) Automatic EEG Artifact Removal Techniques by Detecting Influential Independent Components 
Author(s): Sim Kuan Goh, Hussein A. Abbass, Kay Chen Tan, Abdullah Al-Mamun, Chuanchu Wang and Cuntai Guan
Pages: 270-279

5) A Computationally Fast Convergence Measure and Implementation for Single, Multiple and Many-Objective Optimization
Author(s): Kalyanmoy Deb, Mohamed Abouhawwash and Haitham Seada
Pages: 280-293

6) Behavior Recognition Using Multiple Depth Cameras Based on a Time-Variant Skeleton Vector Projection
Author(s): Chien-Hao Kuo, Pao-Chi Chang and Shih-Wei Sun
Pages: 294-304

7) A Collective Neurodynamic System for Distributed Optimization with Applications in Model Predictive Control
Author(s): Xinyi Le, Zheng Yan and Juntong Xi
Pages: 305-314

CFP: ICACI 2018

10th International Conference on Advanced Computational Intelligence
(www.icaci2018.org)
March 29-31, 2018
Xiamen, China

Technical cosponsor: IEEE Systems, Man and Cybernetics Society

ICACI 2018 aims to provide a high-level international forum for scientists, engineers, and educators to present the state-of-the-art research and applications in computational intelligence. The conference will feature plenary speeches given by world renowned scholars, regular sessions with broad coverage, and special sessions focusing on popular topics. In addition, best paper awards will be given during the conference. The proceedings of ICACI 2018 will be submitted to the IEEE Xplore and EI Compendex. Moreover, selected papers will be published in special issues of related journals. The conference will favor papers representing advanced theories and innovative applications in computational intelligence.

Call for Papers and Special Sessions
Prospective authors are invited to contribute high-quality papers to ICACI 2018. In addition, proposals for special sessions within the technical scopes of the conference are solicited. Special sessions, to be organized by internationally recognized experts, aim to bring together researchers in special focused topics. A special session proposal should include the session title, a brief description of the scope and motivation, names, contact information and brief biographical information on the organizers. Researchers interested in organizing special sessions are invited to submit formal proposals to the special sessions chairs (auyqli@scut.edu.cn, qqs@aber.ac.uk or shiyh@sustc.edu.cn).

Topic Areas
Topics areas include, but not limited to, computational neuroscience, connectionist theory and cognitive science, mathematical modeling of neural systems, neurodynamic analysis, neurodynamic optimization and adaptive dynamic programming, embedded neural systems, probabilistic and information-theoretic methods, principal and independent component analysis, hybrid intelligent systems, supervised, unsupervised and reinforcement learning, deep learning, brain imaging and neural information processing, neuroinformatics and bioinformatics, support vector machines and kernel methods, autonomous mental development, data mining, pattern recognition, time series analysis, image and signal processing, robotic and control applications, telecommunications, transportation systems, intrusion detection and fault diagnosis, hardware implementation, real-world applications, big data processing, fuzzy systems, fuzzy logic, fuzzy set theory, fuzzy decision making, fuzzy information processing, fuzzy logic control, evolutionary computation, ant colony optimization, genetic algorithms, parallel and distributed algorithms, particle swarm optimization, evolving neural networks, evolutionary fuzzy systems, evolving neuro-fuzzy systems, evolutionary games and multi-agent systems, intelligent systems applications.

Important Dates
Special session proposals deadline: Sep. 15, 2017
Paper submission deadline: Nov.15, 2017
Notification of acceptance: Dec. 15, 2017
Camera-ready copy and author registration: Jan. 15, 2018

For latest and additional information, please visit www.icaci2018.org

Call for Papers - WCCI 2018

2018 IEEE World Congress on Computational Intelligence

 8-13 July 2018, Windsor Convention Centre, Rio de Janeiro, BRAZIL
www.ieee-wcci.org

On behalf of the IEEE WCCI 2018 Organizing Committee, it is our great pleasure to invite you to the bi-annual IEEE World Congress on Computational Intelligence (IEEE WCCI), which is the largest technical event in the field of computational intelligence. The IEEE WCCI 2018 will host three conferences:

• 2018 International Joint Conference on Neural Networks (IJCNN 2018 - co-sponsored by International Neural Network Society - INNS)
• 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018)
• 2018 IEEE Congress on Evolutionary Computation (IEEE CEC 2018)

It encourages cross-fertilization of ideas among the three big areas and provides a forum for intellectuals from all over the world to discuss and present their research findings on computational intelligence.

IEEE WCCI 2018 will be held at the Windsor Convention Centre, Rio de Janeiro, Brazil. Known as one of the most beautiful cities in the World, Rio de Janeiro is the first city to receive the certificate of World Heritage for its Cultural Landscape.

Special Sessions

 Special session proposals should include the title, aim and scope (including a list of main topics), a short biography of all organizers, and a list of potential contributors. All special sessions proposals should be submitted to the Special Sessions Co-Chairs, according to the most appropriate topic:

• Cesare Alippi (IJCNN), Politecnico di Milano, Italy
• Patricia Melin (FUZZ-IEEE), Tijuana Institute of Technology, Mexico
• Chuan-Kang Ting (CEC), National Chung Cheng University, Taiwan
• Mengjie Zhang (Cross-Disciplinary and CI Applications), School of Engineering & Computer Science, New Zealand

Tutorials

Tutorials offer a unique opportunity to disseminate in-depth information on specific topics in computational intelligence. If you are interested in proposing a tutorial, would like to recommend someone who might be interested, or have questions about tutorials, please contact the Tutorials Co-Chairs:
Keeley Crocket, Manchester Metropolitan University, UK
Andre Carvalho, University of Sao Paulo, Brazil
Carmelo Filho, University of Pernambuco, Brazil

Competitions

Prospective competition organizers are invited to submit their proposals to the Competitions Co-Chairs below.

Simon Lucas, University of Essex, UK
Chang-Shing Lee, National University of Tainan, Taiwan

Workshops

The overall purpose of a workshop is to provide participants with the opportunity to present and discuss novel research ideas on active and emerging topics of Computational Intelligence. Prospective workshop organizers are invited to submit their proposals to the Workshops Co-Chairs below:

Richard Duro, University of Coruna, Spain
Robi Polikar, Rowan University, USA

Important Dates

• Special Session & Workshop Proposals Deadline: 15 December 2017
• Competition & Tutorial Proposals Deadline: 15 December 2017
• Paper Submission Deadline: 15 January 2018
• Paper Acceptance Notification Date: 15 March 2018
• Final Paper Submission & Early Registration Deadline: 1st May 2018
• IEEE WCCI 2018 conference: 08-13 July 2018

For more information on the congress, please visit: www.ieee-wcci.org

2018 IEEE WCCI General Co-Chairs and Conference Chairs

Marley Vellasco, General Co-Chair
Pontifical Catholic University of
Rio de Janiero, Brazil
INNS Director    

Pablo Estevez, General Co-Chair
University of Chile, Santiago
IEEE-CIS President 2016-2017

Teresa Ludermir IJCNN Conference Chair, Federal University of Pernambuco, Brazil

Gary Yen, IEEE-CEC Conference Chair, Oklahoma State University, USA

Fernando Gomide, IEEE-FUZZ Conference Chair, University of Campinas, Brazil

   

CFP: IEEE TFS Special Issue on Uncertain Multi-Criteria Decision Making Using Evolutionary Algorithms

1. AIMS AND SCOPE

Uncertain multi-criteria decision making (UMCDM) is to select or rank objects based on the evaluation done by the decision-maker on several criteria under uncertainty. UMCDM has been proved as a useful means in diverse fields like management, finance, economics, education, environmental protection, medicine, engineering and so on. Due to numerous successful applications, it becomes more and more prevailing.

It becomes quite a challenging task, as far as the solution methodologies of UMCDM is concerned. The complexity becomes more and more significant in terms of problem size (e.g., number of criteria, size of the search space). Moreover, the solution time has to be reasonable for most of the problems encountered in practice. Hence, the development of advanced multi-criteria evolutionary algorithms has been widely investigated.

This Special Issue aims to collect the most recent outstanding contributions in both theory and practice, which apply evolutionary algorithms to solve multi-criteria decision making problems under uncertain environments. The original studies that propose novel multi-criteria decision making models under uncertainty and creative solution methodologies by classical and/or evolutionary algorithms are especially welcome.

2. TOPICS COVERED

The topics include but are not limited to:

• Theoretical foundations of UMCDM
• Evolutionary computation in UMCDM
• Innovative applications on UMCDM
• Multi-criteria decision support systems and knowledge-based systems
• Risk analysis/modelling, sensitivity/robustness analysis

3. SUBMISSION GUIDELINES

All authors should read ‘Information for Authors’ before submitting a manuscript http://cis.ieee.org/ieee-transactions-on-fuzzy-systems.html

Submissions should be through the IEEE TFS journal website http://mc.manuscriptcentral.com/tfs-ieee.

It is essential that your manuscript is identified as a Special Issue contribution:

• Ensure you choose ‘Special Issue’ when submitting.
• A cover letter must be included which includes the title ‘Special Issue on Uncertain Multi-Criteria Decision Making Using Evolutionary Algorithms (DMEA)’

4. IMPORTANT DATES

• 31 December 2017: Submission deadline
• 31 March 2018: Notification of the first round review
• 31 May 2018: Revised submission due
• 31 July 2018: Final notice of acceptance/reject
• October 2018: Special Issue publication

5. GUEST EDITORS

Prof. Xiang Li
Beijing University of Chemical Technology, Beijing, China
Email: lixiang@mail.buct.edu.cn

Prof. Samarjit Kar
National Institute of Technology Durgapur, Durgapur, India
Email: samarjit.kar@maths.nitdgp.ac.in

IEEE Transactions on Fuzzy Systems, Volume 25, Issue 4, August 2017

1. Admissibility Analysis and Control Synthesis for T–S Fuzzy Descriptor Systems
Author(s): L. Qiao, Q. Zhang and G. Zhang
Pages: 729-740

2. A Fitting Model for Feature Selection With Fuzzy Rough Sets
Author(s): C. Wang, Y. Qi, M. Shao, Q. Hu, D. Chen, Y. Qian and Y. Lin
Pages: 741-753

3. Subspace-Based Takagi–Sugeno Modeling for Improved LMI Performance
Author(s): R. Robles, A. Sala, M. Bernal and T. González
Pages: 754-767

4. Maxitive Belief Structures and Imprecise Possibility Distributions
Author(s): R. R. Yager and N. Alajlan
Pages: 768-774

5. An SOS-Based Control Lyapunov Function Design for Polynomial Fuzzy Control of Nonlinear Systems
Author(s): R. Furqon, Y. J. Chen, M. Tanaka, K. Tanaka and H. O. Wang
Pages: 775-787

6. Measuring Similarity and Ordering Based on Interval Type-2 Fuzzy Numbers
Author(s): G. Hesamian
Pages: 788-798

7. Statistical Inference in Rough Set Theory Based on Kolmogorov–Smirnov Goodness-of-Fit Test
Author(s): D. Hu, X. Yu and J. Wang
Pages: 799-812

8. Event-Triggered Control for Nonlinear Systems Under Unreliable Communication Links
Author(s): H. Li, Z. Chen, L. Wu and H. K. Lam
Pages: 813-824

9. Active Sample Selection Based Incremental Algorithm for Attribute Reduction With Rough Sets
Author(s): Y. Yang, D. Chen and H. Wang
Pages: 825-838

10. Generalized Adaptive Fuzzy Rule Interpolation
Author(s): L. Yang, F. Chao and Q. Shen
Pages: 839-853

11. State-Based Decentralized Diagnosis of Bi-Fuzzy Discrete Event Systems
Author(s): W. Deng and D. Qiu
Pages: 854-867

12. Detection of Resource Overload in Conditions of Project Ambiguity
Author(s): M. Pelikán, H. Štiková and I. Vrana
Pages: 868-877

13. Reachable Set Estimation of T–S Fuzzy Systems With Time-Varying Delay
Author(s): Z. Feng, W. X. Zheng and L. Wu
Pages: 878-891, Aug. 2017.

14. Graph Matching Using Hierarchical Fuzzy Graph Neural Networks
Author(s): D. Krleža and K. Fertalj
Pages: 892-904

15. Robust Tracking Control of MIMO Underactuated Nonlinear Systems With Dead-Zone Band and Delayed Uncertainty Using an Adaptive Fuzzy Control
Author(s): T. S. Wu, M. Karkoub, H. Wang, H. S. Chen and T. H. Chen
Pages: 905-918

16. Using Fuzzy Logic to Leverage HTML Markup for Web Page Representation
Author(s): A. P. García-Plaza, V. Fresno, R. M. Unanue and A. Zubiaga
Pages: 919-933

17. Intelligent Decision Support System for Detection and Root Cause Analysis of Faults in Coal Mills
Author(s): V. Agrawal, B. K. Panigrahi and P. M. V. Subbarao
Pages: 934-944

18. Models of Mathematical Programming for Intuitionistic Multiplicative Preference Relations
Author(s): Z. Zhang and W. PedryczZ. Zhang and W. Pedrycz
Pages: 945-957

19. Stability and Stabilization Analysis of Positive Polynomial Fuzzy Systems With Time Delay Considering Piecewise Membership Functions
Author(s): X. Li, H. K. Lam, F. Liu and X. Zhao
Pages: 958-971

20. Reachability in Fuzzy Game Graphs
Author(s): H. Pan, Y. Li, Y. Cao and D. Li
Pages: 972-984

21. A Linear Programming Approach for Minimizing a Linear Function Subject to Fuzzy Relational Inequalities With Addition–Min Composition
Author(s): S. M. Guu and Y. K. Wu
Pages: 985-992

22. Type-2 Fuzzy Entropy Sets
Author(s): L. Miguel, H. Santos, M. Sesma-Sara, B. Bedregal, A. Jurio, H. Bustince, and H. Hagras
Pages: 993-1005

23. A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification
Author(s): Y. Deng, Z. Ren, Y. Kong, F. Bao and Q. Dai
Pages: 1006-1012

24. Extending Information-Theoretic Validity Indices for Fuzzy Clustering
Author(s): Y. Lei, J. C. Bezdek, J. Chan, N. X. Vinh, S. Romano and J. Bailey
Pages: 1013-1018

Neural Networks, Volume 93, Pages 1-266, September 2017

1. A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal   
Author(s): Naoya Oosugi, Keiichi Kitajo, Naomi Hasegawa, Yasuo Nagasaka, Kazuo Okanoya, Naotaka Fujii
Pages: 1-6

2. An online supervised learning method based on gradient descent for spiking neurons   
Author(s): Yan Xu, Jing Yang, Shuiming Zhong
Pages: 7-20

3. Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system   
Author(s): Yawei Wei, Ganesh Kumar Venayagamoorthy
Pages: 21-35

4. Sparse subspace clustering for data with missing entries and high-rank matrix completion   
Author(s): Jicong Fan, Tommy W.S. Chow
Pages: 36-44

5. Hybrid neuro-heuristic methodology for simulation and control of dynamic systems over time interval   
Author(s): Marcin Woźniak, Dawid Połap
Pages: 45-56

6. Emergence of ultrafast sparsely synchronized rhythms and their responses to external stimuli in an inhomogeneous small-world complex neuronal network   
Author(s): Sang-Yoon Kim, Woochang Lim
Pages: 57-75

7. Ordinal regression based on learning vector quantization   
Author(s): Fengzhen Tang, Peter Tiňo
Pages: 76-88

8. Synchronization of stochastic reaction–diffusion neural networks with Dirichlet boundary conditions and unbounded delays   
Author(s): Yin Sheng, Zhigang Zeng
Pages: 89-98

9. Nonredundant sparse feature extraction using autoencoders with receptive fields clustering   
Author(s): Babajide O. Ayinde, Jacek M. Zurada
Pages: 99-109

10. Fractional-order leaky integrate-and-fire model with long-term memory and power law dynamics   
Author(s): Wondimu W. Teka, Ranjit Kumar Upadhyay, Argha Mondal
Pages: 110-125

11. Collective neurodynamic optimization for economic emission dispatch problem considering valve point effect in microgrid   
Author(s): Tiancai Wang, Xing He, Tingwen Huang, Chuandong Li, Wei Zhang
Pages: 126-136

12. Bayesian geodesic path for human motor control   
Author(s): Ken Takiyama
Pages: 137-142

13. Pinning synchronization of memristor-based neural networks with time-varying delays   
Author(s): Zhanyu Yang, Biao Luo, Derong Liu, Yueheng Li
Pages: 143-151

14. Memristor standard cellular neural networks computing in the flux–charge domain   
Author(s): Mauro Di Marco, Mauro Forti, Luca Pancioni
Pages: 152-164

15. Master–slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control   
Author(s): Xiaofan Li, Jian-an Fang, Huiyuan Li
Pages: 165-175

16. Hybrid impulsive and switching Hopfield neural networks with state-dependent impulses   
Author(s): Xianxiu Zhang, Chuandong Li, Tingwen Huang
Pages: 176-184

17. Deep neural mapping support vector machines   
Author(s): Yujian Li, Ting Zhang
Pages: 185-194

18. Adaptive near-optimal neuro controller for continuous-time nonaffine nonlinear systems with constrained input   
Author(s): Kasra Esfandiari, Farzaneh Abdollahi, Heidar Ali Talebi
Pages: 195-204

19. Robust recursive absolute value inequalities discriminant analysis with sparseness   
Author(s): Chun-Na Li, Zeng-Rong Zheng, Ming-Zeng Liu, Yuan-Hai Shao, Wei-Jie Chen
Pages: 205-218

20. Accelerating deep neural network training with inconsistent stochastic gradient descent   
Author(s): Linnan Wang, Yi Yang, Renqiang Min, Srimat Chakradhar
Pages: 219-229

21. Multi-scale modeling of altered synaptic plasticity related to Amyloid image effects   
Author(s): Takumi Matsuzawa, László Zalányi, Tamás Kiss, Péter Érdi
Pages: 230-239

22. Novel density-based and hierarchical density-based clustering algorithms for uncertain data   
Author(s): Xianchao Zhang, Han Liu, Xiaotong Zhang
Pages: 240-255

23. Recommender system based on scarce information mining   
Author(s): Wei Lu, Fu-lai Chung, Kunfeng Lai, Liang Zhang
Pages: 256-266

IEEE Transactions on Neural Networks and Learning Systems, Volume 28, Issue 8, August 2017

1. A Survey of Memristive Threshold Logic Circuits
Author(s): Akshay Kumar Maan; Deepthi Anirudhan Jayadevi; Alex Pappachen James
Page(s): 1734 - 1746

2. A Collective Neurodynamic Approach to Distributed Constrained Optimization
Author(s): Qingshan Liu; Shaofu Yang; Jun Wang
Page(s): 1747 - 1758

3. Spectrum-Diverse Neuroevolution With Unified Neural Models
Author(s): Danilo Vasconcellos Vargas; Junichi Murata
Page(s): 1759 - 1773

4. Designing and Implementation of Stable Sinusoidal Rough-Neural Identifier
Author(s): Ghasem Ahmadi; Mohammad Teshnehlab
Page(s): 1774 - 1786

5. MR-NTD: Manifold Regularization Nonnegative Tucker Decomposition for Tensor Data Dimension Reduction and Representation
Author(s): Xutao Li; Michael K. Ng; Gao Cong; Yunming Ye; Qingyao Wu
Page(s): 1787 - 1800

6. Understanding Social Causalities Behind Human Action Sequences
Author(s): Ruichu Cai; Zhenjie Zhang; Zhifeng Hao; Marianne Winslett
Page(s): 1801 - 1813

7. Bridging the Gap Between Imitation Learning and Inverse Reinforcement Learning
Author(s): Bilal Piot; Matthieu Geist; Olivier Pietquin
Page(s): 1814 - 1826

8. Passivity of Directed and Undirected Complex Dynamical Networks With Adaptive Coupling Weights
Author(s): Jin-Liang Wang; Huai-Ning Wu; Tingwen Huang; Shun-Yan Ren; Jigang Wu
Page(s): 1827 - 1839

9. Stability of Markovian Jump Generalized Neural Networks With Interval Time-Varying Delays
Author(s): Ramasamy Saravanakumar; Muhammed Syed Ali; Choon Ki Ahn; Hamid Reza Karimi; Peng Shi
Page(s): 1840 - 1850

10. Shrinkage Degree in L2-Rescale Boosting for Regression
Author(s): Lin Xu; Shaobo Lin; Yao Wang; Zongben Xu
Page(s): 1851 - 1864

11. Extending the Peak Bandwidth of Parameters for Softmax Selection in Reinforcement Learning
Author(s): Kazunori Iwata
Page(s): 1865 - 1877

12. Exponential Synchronization of Memristive Neural Networks With Delays: Interval Matrix Method
Author(s): Xinsong Yang; Jinde Cao; Jinling Liang
Page(s): 1878 - 1888

13. A Memristive Multilayer Cellular Neural Network With Applications to Image Processing
Author(s): Xiaofang Hu; Gang Feng; Shukai Duan; Lu Liu
Page(s): 1889 - 1901

14. An Adaptive NN-Based Approach for Fault-Tolerant Control of Nonlinear Time-Varying Delay Systems With Unmodeled Dynamics
Author(s): Shen Yin; Hongyan Yang; Huijun Gao; Jianbin Qiu; Okyay Kaynak
Page(s): 1902 - 1913

15. Lazy-Learning-Based Data-Driven Model-Free Adaptive Predictive Control for a Class of Discrete-Time Nonlinear Systems
Author(s): Zhongsheng Hou; Shida Liu; Taotao Tian
Page(s): 1914 - 1928

16. Hamiltonian-Driven Adaptive Dynamic Programming for Continuous Nonlinear Dynamical Systems
Author(s): Yongliang Yang; Donald Wunsch; Yixin Yin
Page(s): 1929 - 1940

17. Event-Triggered Adaptive Dynamic Programming for Continuous-Time Systems With Control Constraints
Author(s): Lu Dong; Xiangnan Zhong; Changyin Sun; Haibo He
Page(s): 1941 - 1952

18. Dynamical Analysis of the Hindmarsh–Rose Neuron With Time Delays
Author(s): S. Lakshmanan; C. P. Lim; S. Nahavandi; M. Prakash; P. Balasubramaniam
Page(s): 1953 - 1958

19. Adaptation to New Microphones Using Artificial Neural Networks With Trainable Activation Functions
Author(s): Sabato Marco Siniscalchi; Valerio Mario Salerno
Page(s): 1959 - 1965

20. Design of Probabilistic Boolean Networks Based on Network Structure and Steady-State Probabilities
Author(s): Koichi Kobayashi; Kunihiko Hiraishi
Page(s): 1966 - 1971

Weekly Review 30 July 2017

Below are some of the interesting links I Tweeted about recently.

1. Adding 'imagination' to deep neural networks: http://www.theregister.co.uk/2017/07/21/deepmind_ai_imagination/
2. Half of low-skilled jobs will be replaced by AI / automation: http://www.techrepublic.com/article/50-of-low-skilled-jobs-will-be-replaced-by-ai-and-automation-report-claims/ 
3. What's old is new again in AI, we've just got better computers: http://www.theregister.co.uk/2017/07/21/artificial_intelligent/ 
4. How Google is using AI to help run its data centres: http://www.techrepublic.com/article/google-says-ai-will-help-run-datacenters-in-the-near-future/ 
5. Why quality, labelled data is so important for deep learning: https://techcrunch.com/2017/07/21/why-the-future-of-deep-learning-depends-on-finding-good-data/ 
6. The next version of HoloLens will have an onboard neural network chip: https://www.theverge.com/2017/7/24/16018558/microsoft-ai-coprocessor-hololens-hpu 
7. Overview of some of the hardware being applied to deep neural networks: http://www.theregister.co.uk/2017/07/24/ai_hardware_development_plans/ 
8. Qualcomm has open sourced its Neural Processing Engine: http://www.theregister.co.uk/2017/07/26/qualcomm_neural_processing_engine_freed/ 
9. A neural network based facial expression recogniser that gauges movie audience enjoyment: https://techcrunch.com/2017/07/25/this-facial-recognition-system-tracks-how-youre-enjoying-a-movie/ 
10. Diagnosing schizophrenia from brain scans using a support vector machine: http://www.theregister.co.uk/2017/07/26/ibm_and_uni_alberta_tackle_schizophrenia/ 
11. Elon Musk is a clever guy, but I think he is way off-beam about AI: https://www.theguardian.com/technology/2017/jul/25/elon-musk-mark-zuckerberg-artificial-intelligence-facebook-tesla 
12. AI is already causing problems, we should be worrying about them, not hypothetical killer robots: https://www.theverge.com/platform/amp/2017/7/25/16024444/ai-safety-threat-elon-musk-mark-zuckerberg 
13. Building decision trees in Scikit-Learn: http://www.dxbydt.com/decision-trees-classification-interpretation-using-scikit-learn/
14. Deep learning, AI, machine learning, made really simple: http://www.kdnuggets.com/2017/07/ai-deep-learning-explained-simply.html
15. Situations where you should not use deep learning: http://www.kdnuggets.com/2017/07/when-not-use-deep-learning.html 
16. List of Python machine learning tutorials: http://www.kdnuggets.com/2017/07/machine-learning-exercises-python-introductory-tutorial-series.html 
17. How to build a team that does AI: http://www.theregister.co.uk/2017/07/27/assembling_an_ai_team/ 
18. Nanoneurons for neuromorphic chips: http://spectrum.ieee.org/nanoclast/semiconductors/devices/nanoneurons-enable-neuromorphic-chips-for-voice-recognition
19. Current AI is so resource-intensive only a few big companies can effectively use it: https://www.datanami.com/2017/07/25/exposing-ais-1-problem/
20. Machines still struggle with conversational Chinese: https://www.technologyreview.com/s/608249/for-computers-too-its-hard-to-learn-to-speak-chinese/
21. Never mind terminators, this kind of AI can kill you just as dead: https://www.theverge.com/2017/7/28/16054834/mushroom-identifying-app-machine-vision-ai-dangerous

IEEE Transactions on Evolutionary Computation, Volume 21, Issue 4, August 2017

1. Efficient Resource Allocation in Cooperative Co-Evolution for Large-Scale Global Optimization
Author(s): Ming Yang; Mohammad Nabi Omidvar; Changhe Li; Xiaodong Li; Zhihua Cai; Borhan Kazimipour; Xin Yao
Pages: 493-505

2. Changing the Intensity of Interaction Based on Individual Behavior in the Iterated Prisoner’s Dilemma Game
Author(s): Jiaqi Li; Chunyan Zhang; Qinglin Sun; Zengqiang Chen; Jianlei Zhang
Pages: 506-517

3. Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications
Author(s): Xiaodong Li; Michael G. Epitropakis; Kalyanmoy Deb; Andries Engelbrecht
Pages: 518-538

4. Improving Diversity in Evolutionary Algorithms: New Best Solutions for Frequency Assignment
Author(s): Carlos Segura; Arturo Hernández-Aguirre; Francisco Luna; Enrique Alba
Pages: 539-553

5. Matching-Based Selection With Incomplete Lists for Decomposition Multiobjective Optimization
Author(s): Mengyuan Wu; Ke Li; Sam Kwong; Yu Zhou; Qingfu Zhang
Pages: 554-568

6. Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification
Author(s): Muhammad Iqbal; Bing Xue; Harith Al-Sahaf; Mengjie Zhang
Pages: 569-587

7. Personalized Search Inspired Fast Interactive Estimation of Distribution Algorithm and Its Application
Author(s): Yang Chen; Xiaoyan Sun; Dunwei Gong; Yong Zhang; Jong Choi; Scott Klasky
Pages: 588-600

8. An Evolutionary Transfer Reinforcement Learning Framework for Multiagent Systems
Author(s): Yaqing Hou; Yew-Soon Ong; Liang Feng; Jacek M. Zurada
Pages: 601-615

9. Stochastic Runtime Analysis of the Cross-Entropy Algorithm
Author(s): Zijun Wu; Michael Kolonko; Rolf H. Möhring
Pages: 616-628

10. Toward a Steady-State Analysis of an Evolution Strategy on a Robust Optimization Problem With Noise-Induced Multimodality
Author(s): Hans-Georg Beyer; Bernhard Sendhoff
Pages: 629-643

11. Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems
Author(s): Chaoli Sun; Yaochu Jin; Ran Cheng; Jinliang Ding; Jianchao Zeng
Pages: 644-660

Weekly Review 23 July 2013

Below are some of the interesting links I Tweeted about recently.

1. AI will help you do your job better, not replace you, according to Microsoft: https://www.technologyreview.com/s/608272/microsoft-thinks-ai-will-fill-your-blind-spots-not-take-over-your-job/
2. Using machine learning to map groups of neurons to behaviours in D. melanagaster: https://www.theregister.co.uk/2017/07/14/ai_deciphers_fruit_fly_brain/
3. How AI is going to increase social inequality: https://www.theverge.com/2017/7/13/15963710/robots-ai-inequality-social-mobility-study
4. 150+ tutorials on Python and machine learning: https://unsupervisedmethods.com/over-150-of-the-best-machine-learning-nlp-and-python-tutorials-ive-found-ffce2939bd78
5. The many ways UK industry is using AI: https://www.theregister.co.uk/2017/07/13/british_ai_smbs/
6. The maths you need to know to master machine learning: http://dataconomy.com/2017/02/mathematics-machine-learning/
7. A crash-course to machine learning with the Julia language: http://www.kdnuggets.com/2017/07/guerrilla-guide-machine-learning-julia.html
8. Introduction to using Python for big data: http://dataconomy.com/2016/10/big-data-python/
9. 3 educational pathways to get into machine learning: https://www.datanami.com/2017/07/10/machine-learning-education-3-paths-get-started/
10. A brief description of Creative Adversarial Networks: http://www.kdnuggets.com/2017/07/creative-adversarial-network.html
11. Paper on Creative Adversarial Networks: https://arxiv.org/abs/1706.07068
12. Online AI soccer players: https://www.cnet.com/news/after-chess-people-are-now-building-ai-to-beat-us-at-soccer/
13. It's not that the AI are biased, or even that the developers are biased, it's biased data sets that cause problems: https://www.axios.com/algorithms-discriminate-and-big-tech-doesnt-care-2458083425.html
14. Cargo Cult Computer Science, or, what I think is wrong with AI research: https://computational-intelligence.blogspot.com/2017/07/cargo-cult-computer-science.html
15. It looks like for the meantime AI will be assisting radiologists rather than replacing them: http://money.cnn.com/2017/07/14/technology/business/radiology-doctors-artificial-intelligence/index.html
16. When Generative Adversarial Networks start making up their own language: https://www.fastcodesign.com/90132632/ai-is-inventing-its-own-perfect-languages-should-we-let-it
17. Personally, I find people misusing AI far more frightening than AI itself: https://www.recode.net/2017/7/15/15976744/elon-musk-artificial-intelligence-regulations-ai
18. Profile of neural network based speech recognition pioneer Tony Robinson: http://www.theregister.co.uk/2017/07/17/tony_robinson_speechmatics/ 
19. A hierarchy of malicious algorithms: https://www.theguardian.com/technology/2017/jul/16/how-can-we-stop-algorithms-telling-lies 
20. Why it is cheaper to buy your own hardware for machine learning than use the cloud: http://www.theregister.co.uk/2017/07/17/hardware_for_machine_learning/ 
21. Determining if a business is ready to utilise AI - 3 questions to ask: https://www.datanami.com/2017/07/14/business-ready-ai-ask-3-questions/
22. The limits of deep learning: https://venturebeat.com/2017/04/02/understanding-the-limits-of-deep-learning/ 
23. Speeding-up convolutional neural networks on a Raspberry Pi: http://cv-tricks.com/artificial-intelligence/deep-learning/accelerating-convolutional-neural-networks-on-raspberry-pi/ 
24. The top 10 programming languages for 2017: http://spectrum.ieee.org/computing/software/the-2017-top-programming-languages 
25. AI industry hype-it takes more than a bunch of 'if' statements to qualify as an AI: http://www.theregister.co.uk/2017/07/18/it_vendors_mass_over_hype_artificial_intelligence_abilities/ 
26. A tutorial on Keras TensorFlow: http://cv-tricks.com/tensorflow-tutorial/keras/
27. Yandex open sources its machine learning library CatBoost: http://www.theregister.co.uk/2017/07/18/yandex_open_sources_machine_learning_library_catboost/ 
28. Why AI needs to look to neuroscience more: https://www.theverge.com/2017/7/19/1599
29. I think implants will be interfaces to external tech for a while yet-organ replacements will be bio-engineered: https://www.theverge.com/2017/7/21/15999544/biohacking-finger-magnet-human-augmentation-loss 
30. Top ten programming languages for employment: http://spectrum.ieee.org/static/interactive-the-top-programming-languages-2017
31. A lot of businesses call their products AI, but that doesn't make them so: https://www.axios.com/report-many-firms-are-ai-washing-claims-of-intelligent-products-2461919261.html
32. Using neural networks to control physical rehabilitation equipment: http://spectrum.ieee.org/the-human-os/biomedical/devices/ai-can-help-patients-recover-ability-to-stand-and-walk
33. Intel's $79 USB 3 deep learning stick: https://www.siliconrepublic.com/machines/intel-movidius-neural-compute-stick
34. Yet another reason why you shouldn't review papers for Elsevier journals: https://forestsandco.wordpress.com/2017/07/18/thanks-for-your-invitation-to-review-but/

Cargo Cult Computer Science

I recently attended a presentation by a post-graduate student that I thought was a little bit funny. The presentation was about the experiments they had done on classifying classical music. At the end of the presentation, they proudly declared that algorithm X could identify the composer of a piece (one of Vivaldi, Bach or Mozart), from half a second of music.

The first query I raised was, how many notes are you going to get in half a second? Classical music tends to have a relatively relaxed pace (at least, compared to the music I enjoy) so I doubt there would be more than one or two notes in each sample. The response was, algorithm X is really good at classification so half a second is enough.

The second query I raised was as follows: there was only one piece from each composer in each sample, and the Vivaldi was entirely strings, the Mozart was entirely piano, and the Bach was a mixture of instruments. How do they know that the algorithm didn't just learn to classify instruments?

This is similar to the famous example from the early days of neural networks, when perceptrons were being trained to distinguish photographs that contained images of tanks and those that did not. After some very good results at the start of the project, a second batch of images utterly failed. The reason for that failure was traced to the fact that the photographs with tanks had been developed using a slightly different process to that used to develop the photographs without tanks. That resulted in a slight difference in the overall brightness of the photographs. The neural network had simply learned to distinguish between lighter and darker photographs.

Now, the people who were looking for tanks did one thing right: they tested their algorithm with more data. The post-graduate student at the start of this story didn't do that. They just looked at the results they got, which fit their expectations, and stopped there. That meant that the conclusions they were drawing were not supported by the evidence.

The American physicist Richard Feynman famously spoke of "Cargo Cult Science". This is research that has the superficial form of science, but does not follow the rigor expected of the scientific method.

The scientific method is a process that has developed over many centuries, and requires a certain rigor and self-criticism that is intended to prevent erroneous conclusions being made. It requires scientists to be completely honest with themselves, to consider every objection to their research method and possible factors that could be influencing their results. The scientific method is supposed to prevent researchers from just seeing what they want to see and instead see the reality. The post-graduate student did not do this, and so their conclusions are not necessarily valid.

I've seen this in a lot of papers in computer science, and in more than a few post-graduate theses. Experiments are performed, results are gathered, and conclusions are confidently espoused about the value of their approach. Yet they never consider what else could explain those results. They never consider whether their data is biased in some way, or if their method is flawed so that certain results are favoured over others.

I think there are several reasons this occurs. Students in computer science are not necessarily trained in the scientific method, so they can hardly be blamed for not following it. It is human nature that researchers want their approach, their new algorithm, to work, so they develop a kind of wilful blindness to the flaws in their experimental approach. Finally, and more insidiously, researchers are under immense pressure to publish: "publish or perish" applies in computer science just as much as any other field of academia. It is only through publishing papers that researchers gain employment, get promotion, and secure research funding. There is, then, a system set up to favour rapid and uncritical publication of supportive results and to suppress unfavourable results. We have created a system that favours Cargo Cult Computer Science.

Computer science, if it is to remain worthy of the appellation "science" must fully embrace the scientific method. This means being rigorous, and being self-critical. The consequences of not doing so, could be severe for everyone in the field.

Weekly Review 13 July 2017

Below are some of the interesting links I Tweeted about recently.

1. When AI research collides head-on with privacy laws: https://www.theregister.co.uk/2017/07/03/google_deepmind_trial_failed_to_comply_with_data_protection_law/
2. A list of logical fallacies used in arguments - make sure you don't fall for these in your research: https://www.lifehacker.com.au/2017/06/spot-the-flaw-in-a-politicians-argument-with-this-guide-to-logical-fallacies/
3. Chinese tech sector's concerns about AI: https://www.technologyreview.com/s/608183/chinas-tech-moguls-warn-of-ais-troubling-trajectory/
4. Should the heads of universities be academics? https://www.insidehighered.com/news/2017/07/03/madison-professors-fight-keep-requirement-administrators-be-academics
5. Using AI to assist with drug design: https://www.theregister.co.uk/2017/07/03/gsk_signs_ai_deal_british_firm/
6. Ten simple rules to make your data science /AI research reproducible: http://dataconomy.com/2017/07/10-rules-results-data-science/ 
7. Relax, machine learning is not going to revolutionise business just yet: http://www.theregister.co.uk/2017/07/05/rethink_machine_learning/ 
8. I am starting to think that ethics in AI is a more complicated problem than AI itself: http://www.theregister.co.uk/2017/07/04/ai_ethics_and_what_next/ 
9. Using deep neural networks to observe football games and bet on them: https://www.theverge.com/2017/7/6/15923784/ai-predict-sport-betting-gambling-stratagem 
10. In two years every successful software product will be expected to include some form of AI: http://www.techrepublic.com/article/why-there-will-be-no-more-ai-startups-in-2-years/ 
11. Automating machine learning so non-technical people can use it: https://techcrunch.com/2017/07/06/h2o-ais-driverless-ai-automates-machine-learning-for-businesses/ 
12. Using machine learning to detect hot jupiter planets orbiting other stars: http://www.theregister.co.uk/2017/07/07/machine_learning_algos_and_hot_jupiters/ 
13. Google is funding the development of machine-written news articles: https://www.theverge.com/2017/7/7/15933224/google-press-association-ai-news-writers 
14. Google's unified deep learning model: https://www.datanami.com/2017/07/06/google-mimics-human-brain-unified-deep-learning-model/
15. A session with an AI therapist: http://www.theregister.co.uk/2017/07/07/ai_robodoc_asks_me_personal_questions/ 
16. Why machine learning doesn't work for some problems: http://www.datasciencecentral.com/profiles/blogs/the-e-dimension-why-machine-learning-doesn-t-work-well-for-some 
17. Preparing for the future of ubiquitous machine learning: https://www.datanami.com/2017/07/03/machine-learning-everywhere-preparing-future/ 
18. List of articles on TensorFlow: http://www.datasciencecentral.com/profiles/blogs/9-great-articles-about-tensorflow 
19. Another attempt at using a deep neural network to name colours: https://arstechnica.com/information-technology/2017/07/new-experiments-reveal-that-ai-are-still-terrible-at-naming-paint-colors/ 
20. Getting into deep learning: how they built the Not Hotdog app: http://www.theregister.co.uk/2017/07/10/skills_for_ai/ 
21. How machine learning / AI is being used in US hospitals: https://www.techemergence.com/top-5-hospitals-using-machine-learning/
22.  How AI can help to manage data: http://www.techrepublic.com/article/how-ai-and-machine-learning-can-help-solve-its-data-management-problem/
23. Why AI will change the business world: http://informationweek.com/big-data/software-platforms/artificial-intelligence-will-redefine-the-world/a/d-id/1329254
24. List of resources on self-driving cars: http://www.kdnuggets.com/2017/07/5-free-resources-getting-started-self-driving-vehicles.html
25. The ten IT jobs most likely to be taken over by AI: http://informationweek.com/strategic-cio/10-it-jobs-that-will-be-done-by-ai/d/d-id/1329310?
26. Detecting heart arrhythmia using deep learning: https://www.technologyreview.com/s/608234/the-machines-are-getting-ready-to-play-doctor/
27. AI is now, finally, starting to deliver in the enterprise: http://computerworld.com/article/3203029/artificial-intelligence/a-i-starts-to-deliver-in-the-enterprise-at-last.html
28. DeepMind is letting virtual figures teach themselves how to walk, run etc through a virtual environment: https://www.theverge.com/tldr/2017/7/10/15946542/deepmind-parkour-agent-reinforcement-learning
29. How AI is being used in mobile apps: http://computerworld.com/article/3204906/mobile-wireless/here-s-what-really-matters-when-it-comes-to-ai-and-mobile-apps.html
30. Detecting heart arrhythmia using deep learning: https://www.technologyreview.com/s/608234/the-machines-are-getting-ready-to-play-doctor/ 
31. Google is looking seriously at bias in AI: https://www.theverge.com/2017/7/10/15947358/google-ai-pair-bias-fairness-equality
32. The role of AI in the 'digital brain' of a business: http://www.kdnuggets.com/2017/07/why-every-company-needs-digital-brain.html 
33. AI is moving further into the enterprise: https://www.datanami.com/2017/07/11/ai-makes-inroads-enterprise-software/
     

IEEE Transactions on Neural Networks and Learning Systems, Volume 28, Issue 7, July 2017.

1. Feature Selection Based on Structured Sparsity: A Comprehensive Study
Author(s): Jie Gui; Zhenan Sun; Shuiwang Ji; Dacheng Tao; Tieniu Tan
Pages: 1490 - 1507

2. Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer’s Disease
Author(s): Liqiang Nie; Luming Zhang; Lei Meng; Xuemeng Song; Xiaojun Chang; Xuelong Li
Pages: 1508 - 1519

3. Observer-Based Adaptive NN Control for a Class of Uncertain Nonlinear Systems With Nonsymmetric Input Saturation
Author(s): Yong-Feng Gao; Xi-Ming Sun; Changyun Wen; Wei Wang
Pages: 1520 - 1530

4. Neural Approximation-Based Adaptive Control for a Class of Nonlinear Nonstrict Feedback Discrete-Time Systems
Author(s): Yan-Jun Liu; Shu Li; Shaocheng Tong; C. L. Philip Chen
Pages: 1531 - 1541

5. Characterization of Linearly Separable Boolean Functions: A Graph-Theoretic Perspective
Author(s): Yanyi Rao; Xianda Zhang
Pages: 1542 - 1549

6. Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks
Author(s): Chunjie Zhang; Chao Liang; Liang Li; Jing Liu; Qingming Huang; Qi Tian
Pages: 1550 - 1559

7. Impulsive Multisynchronization of Coupled Multistable Neural Networks With Time-Varying Delay
Author(s): Yan-Wu Wang; Wu Yang; Jiang-Wen Xiao; Zhi-Gang Zeng
Pages: 1560 - 1571

8. Embedded Streaming Deep Neural Networks Accelerator With Applications
Author(s): Aysegul Dundar; Jonghoon Jin; Berin Martini; Eugenio Culurciello
Pages: 1572 - 1583

9. Solution Path for Pin-SVM Classifiers With Positive and Negative τ Values
Author(s): Xiaolin Huang; Lei Shi; Johan A. K. Suykens
Pages: 1584 - 1593

10. Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems
Author(s): Lu Dong; Xiangnan Zhong; Changyin Sun; Haibo He
Pages: 1594 - 1605

11. An Alternating Identification Algorithm for a Class of Nonlinear Dynamical Systems
Author(s): Yajun Zhang; Tianyou Chai; Dianhui Wang
Pages: 1606 - 1617

12. Exponential Synchronization for Markovian Stochastic Coupled Neural Networks of Neutral-Type via Adaptive Feedback Control
Author(s): Huabin Chen; Peng Shi; Cheng-Chew Lim
Pages: 1618 - 1632

13. Observer-Based Adaptive Neural Network Trajectory Tracking Control for Remotely Operated Vehicle
Author(s): Zhenzhong Chu; Daqi Zhu; Simon X. Yang
Pages: 1633 - 1645

14. Structural Minimax Probability Machine
Author(s): Bin Gu; Xingming Sun; Victor S. Sheng
Pages: 1646 - 1656

15. Global Synchronization of Multiple Recurrent Neural Networks With Time Delays via Impulsive Interactions
Author(s): Shaofu Yang; Zhenyuan Guo; Jun Wang
Pages: 1657 - 1667

16. Batch Mode Active Learning for Regression With Expected Model Change
Author(s): Wenbin Cai; Muhan Zhang; Ya Zhang
Pages: 1668 - 1681

17. Prediction Reweighting for Domain Adaptation
Author(s): Shuang Li; Shiji Song; Gao Huang
Pages: 1682 - 1695

18. Switched-Observer-Based Adaptive Neural Control of MIMO Switched Nonlinear Systems With Unknown Control Gains
Author(s): Lijun Long; Jun Zhao
Pages: 1696 - 1709

19. μ-Stability of Nonlinear Positive Systems With Unbounded Time-Varying Delays
Author(s): Tianping Chen; Xiwei Liu
Pages: 1710 - 1715

20. Learning With Auxiliary Less-Noisy Labels
Author(s): Yunyan Duan; Ou Wu
Pages: 1716 - 1721

21. Import Vector Domain Description: A Kernel Logistic One-Class Learning Algorithm
Author(s): Sergio Decherchi; Walter Rocchia
Pages: 1722 - 1729

Weekly Review 1 July 2017

Below are some of the interesting links I Tweeted about recently.

1. What Garry Kasparov thinks of AI: http://www.bbc.com/future/story/20170616-garry-kasparov-why-the-world-should-embrace-ai
2. Top 15 Python libraries for data science: http://www.kdnuggets.com/2017/06/top-15-python-libraries-data-science.html
3. A lot of money is getting invested in AI: https://siliconangle.com/blog/2017/06/17/ai-explodes-investors-pour-big-bucks-startups/
4. A collection of cheat sheets for machine learning and deep learning libraries: https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5
5. The General AI Challenge has a $5M prize: http://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/in-the-general-ai-challenge-teams-compete-for-5-million
6. A method of measuring progress in AI research: https://www.eff.org/ai/metrics
7. What it means for humans to be smart, in the age of AI: https://hbr.org/2017/06/in-the-ai-age-being-smart-will-mean-something-completely-different
8. My father was in his mid-40s when I was born: https://www.theguardian.com/science/2017/jun/20/older-men-fathers-geekier-sons-study-geek-index
9. I've seen a few cover letters with pretty bad errors in them: http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=11879982
10. It seems like the only people opposed to sci-hub are the journal publishers: https://www.nature.com/news/us-court-grants-elsevier-millions-in-damages-from-sci-hub-1.22196
11. TEU is being disingenuous - NZ universities have been profit-driven for decades: http://www.nzherald.co.nz/nz/news/article.cfm?c_id=1&objectid=11884020
12. A brief overview of machine learning: http://www.kdnuggets.com/2017/06/making-sense-machine-learning.html
13. Using machine learning to clean data before applying machine learning to it: http://dataconomy.com/2017/06/machine-learning-for-enterprise/
14. List of five deep learning code demos: http://www.kdnuggets.com/2017/06/deep-learning-demos-code-beginners.html
15. Neural networks explained using a corporate analogy: https://myabakhova.blogspot.com/2017/06/
16. Why you should never answer questions about salary in a job interview: http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=11883755
17. The killer business apps of deep learning: http://informationweek.com/big-data/what-execs-should-know-about-deep-learning/a/d-id/1329196?
18. A series of tutorials on the TensorFlow API: http://www.kdnuggets.com/2017/06/using-tensorflow-api-tutorial-series.html
19. Using AI to get business insights: http://dataconomy.com/2017/06/business-insights-artificial-intelligence/
20. Potential applications of AI in banking: http://informationweek.com/big-data/how-artificial-intelligence-will-revolutionize-banking/a/d-id/1329218?
21. How ratty data messes up machine learning algorithms: http://dataconomy.com/2017/06/faulty-data-machine-learning/
22. Finding how deep neural networks arrive at decisions: https://techcrunch.com/2017/06/30/mit-csail-research-offers-a-fully-automated-way-to-peer-inside-neural-nets/
23. Enabling sales with machine learning: https://www.techemergence.com/machine-learning-in-sales-enablement-applications/
24. Brief example of web scraping with R: http://www.kdnuggets.com/2017/06/web-scraping-r-online-food-blogs.html
25. Instagram is using machine learning to filter hurtful comments: https://techcrunch.com/2017/06/29/instagram-implements-an-ai-system-to-fight-mean-and-harassing-comments/
26. Trust your head, not your gut, your gut is the one part of your body that's guaranteed to be full of cr*p: http://www.kdnuggets.com/2017/06/who-cares-evidence.html
27. Might to time to start holding these kinds of competitions in countries other than the USA: https://www.theverge.com/2017/6/30/15903206/afghanistan-robot-team-denied-us-visa
28. The WCCI 2018 call for papers is out: http://www.ecomp.poli.br/~wcci2018/
29. Generative adversarial networks producing new styles of art: https://www.newscientist.com/article/2139184-artificially-intelligent-painters-invent-new-styles-of-art/
30. Richard Feynman's essay on cargo cult science: http://calteches.library.caltech.edu/51/2/CargoCult.htm