IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 12, December 2016.

1. Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering
Author(s): Jenni Raitoharju; Serkan Kiranyaz; Moncef Gabbouj
Pages: 2458 - 2471

2. Similarity Constraints-Based Structured Output Regression Machine: An Approach to Image Super-Resolution
Author(s): Cheng Deng; Jie Xu; Kaibing Zhang; Dacheng Tao; Xinbo Gao; Xuelong Li
Pages: 2472 - 2485

3. Deep Learning of Part-Based Representation of Data Using Sparse Autoencoders With Nonnegativity Constraints
Author(s): Ehsan Hosseini-Asl; Jacek M. Zurada; Olfa Nasraoui
Pages: 2486 - 2498

4. A Unified Framework for Representation-Based Subspace Clustering of Out-of-Sample and Large-Scale Data
Author(s): Xi Peng; Huajin Tang; Lei Zhang; Zhang Yi; Shijie Xiao
Pages: 2499 - 2512

5. A Theoretical Foundation of Goal Representation Heuristic Dynamic Programming
Author(s): Xiangnan Zhong; Zhen Ni; Haibo He
Pages: 2513 - 2525

6. Sequential Compact Code Learning for Unsupervised Image Hashing
Author(s): Li Liu; Ling Shao
Pages: 2526 - 2536

7. Organizing Books and Authors by Multilayer SOM
Author(s): Haijun Zhang; Tommy W. S. Chow; Q. M. Jonathan Wu
Pages: 2537 - 2550

8. Generalized Higher Order Orthogonal Iteration for Tensor Learning and Decomposition
Author(s): Yuanyuan Liu; Fanhua Shang; Wei Fan; James Cheng; Hong Cheng
Pages: 2551 - 2563

9. Dynamic Learning From Neural Control for Strict-Feedback Systems With Guaranteed Predefined Performance
Author(s): Min Wang; Cong Wang; Peng Shi; Xiaoping Liu
Pages: 2564 - 2576

10. Online Solution of Two-Player Zero-Sum Games for Continuous-Time Nonlinear Systems With Completely Unknown Dynamics
Author(s): Yue Fu; Tianyou Chai
Pages: 2577 - 2587

11. Shortcomings/Limitations of Blockwise Granger Causality and Advances of Blockwise New Causality
Author(s): Sanqing Hu; Xinxin Jia; Jianhai Zhang; Wanzeng Kong; Yu Cao
Pages: 2588 - 2601

12. Semisupervised Multiclass Classification Problems With Scarcity of Labeled Data: A Theoretical Study
Author(s): Jonathan Ortigosa-Hernández;  I?aki Inza;  Jose A. Lozano
Pages: 2602 - 2614

13. Integration-Enhanced Zhang Neural Network for Real-Time-Varying Matrix Inversion in the Presence of Various Kinds of Noises
Author(s): Long Jin; Yunong Zhang; Shuai Li
Pages: 2615 - 2627

14. Scalable Linear Visual Feature Learning via Online Parallel Nonnegative Matrix Factorization
Author(s): Xueyi Zhao; Xi Li; Zhongfei Zhang; Chunhua Shen; Yueting Zhuang; Lixin Gao; Xuelong Li
Pages: 2628 - 2642

15. Information Theoretic Subspace Clustering
Author(s): Ran He; Liang Wang; Zhenan Sun; Yingya Zhang; Bo Li
Pages: 2643 - 2655

16. Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering
Author(s): Lei Meng; Ah-Hwee Tan; Donald C. Wunsch
Pages: 2656 - 2669

17. K-MEAP: Multiple Exemplars Affinity Propagation With Specified K Clusters
Author(s): Yangtao Wang; Lihui Chen
Pages: 2670 - 2682

18. Landslide Displacement Prediction With Uncertainty Based on Neural Networks With Random Hidden Weights
Author(s): Cheng Lian; Zhigang Zeng; Wei Yao; Huiming Tang; Chun Lung Philip Chen
Pages: 2683 - 2695

19. Impulsive Synchronization of Reaction–Diffusion Neural Networks With Mixed Delays and Its Application to Image Encryption
Author(s): Wu-Hua Chen; Shixian Luo; Wei Xing Zheng
Pages: 2696 - 2710

20. MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multicategory Classification
Author(s): Lingfeng Wang; Xu-Yao Zhang; Chunhong Pan
Pages: 2711 - 2717

21. Data-Driven Modeling for UGI Gasification Processes via an Enhanced Genetic BP Neural Network With Link Switches
Author(s): Shida Liu; Zhongsheng Hou; Chenkun Yin
Pages: 2718 - 2729

22. Is a Complex-Valued Stepsize Advantageous in Complex-Valued Gradient Learning Algorithms?
Author(s): Huisheng Zhang; Danilo P. Mandic
Pages: 2730 - 2735

23. Enhanced Logical Stochastic Resonance in Synthetic Genetic Networks
Author(s): Nan Wang; Aiguo Song
Pages: 2736 - 2739

24. A Boosting Approach to Exploit Instance Correlations for Multi-Instance Classification
Author(s): Yali Li; Shengjin Wang; Qi Tian; Xiaoqing Ding
Pages: 2740 - 2747

25. Using Digital Masks to Enhance the Bandwidth Tolerance and Improve the Performance of On-Chip Reservoir Computing Systems
Author(s): Bendix Schneider; Joni Dambre; Peter Bienstman
Pages: 2748 - 2753

26. Synchronization Analysis and Design of Coupled Boolean Networks Based on Periodic Switching Sequences
Author(s): Huaguang Zhang; Hui Tian; Zhanshan Wang; Yanfang Hou
Pages: 2754 - 2759

27. Power Quality Analysis Using a Hybrid Model of the Fuzzy Min–Max Neural Network and Clustering Tree
Author(s): Manjeevan Seera; Chee Peng Lim; Chu Kiong Loo; Harapajan Singh
Pages: 2760 - 2767

28. Max-Margin-Based Discriminative Feature Learning
Author(s): Changsheng Li; Qingshan Liu; Weishan Dong; Fan Wei; Xin Zhang; Lin Yang
Pages: 2768 - 2775

Weekly Review 5 December 2016

Below are some of the interesting links I Tweeted about in the last week.

1. Why the results of Google's neural network based image enhancement system RAISR should be mistrusted: https://thestack.com/world/2016/11/15/raisr-is-googles-ai-driven-image-resizing-algorithm-dishonest/
2. Another way companies can abuse big data and modelling: http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=11757150
3. List of machine learning data sets: https://www.analyticsvidhya.com/blog/2016/11/25-websites-to-find-datasets-for-data-science-projects/
4. Why the results of Google's neural network based image enhancement system RAISR should be mistrusted: https://thestack.com/world/2016/11/15/raisr-is-googles-ai-driven-image-resizing-algorithm-dishonest/
5. Another way companies can abuse big data and modelling: http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=11757150
6. List of machine learning data sets: https://www.analyticsvidhya.com/blog/2016/11/25-websites-to-find-datasets-for-data-science-projects/
7. A basic introduction to some of the more popular machine learning algorithms: http://www.kdnuggets.com/2016/11/intro-machine-learning-developers.html 
8. Are the well-publicised failures of machine learning really such bad failures? http://www.datasciencecentral.com/profiles/blogs/why-so-many-machine-learning-implementations-fail 
9. Stealing (really reverse engineering) machine learning models via public API: http://www.kdnuggets.com/2016/11/arxiv-spotlight-stealing-machine-learning-models-prediction-apis.html 
10. Dreaming in deep neural networks makes learning 10x faster: https://www.extremetech.com/extreme/240163-googles-deepmind-ai-gives-robots-ability-dream 
11. Paper on dreaming in deep neural networks: https://arxiv.org/pdf/1611.05397.pdf 
12. Using machine learning to predict dangerous seismic events in coal mines: https://deepsense.io/machine-learning-models-predicting-dangerous-seismic-events/ 
13. Bad article title-it's not AI that's gone too far, rather the people building and applying the AI: http://www.datasciencecentral.com/profiles/blogs/has-ai-gone-too-far-automated-inference-of-criminality-using-face 
14. The next 3 industries that will be disrupted by AI: http://dataconomy.com/artificial-intelligence-retail-healthcare-finance/ 
15. Teaching neural networks fear: http://www.theregister.co.uk/2016/11/30/artificial_intelligence_intrinsic_fear/ 
16. So much for a classless society: https://www.technologyreview.com/s/602987/china-turns-big-data-into-big-brother/
17. Deep neural networks generate song lyrics from pictures of a scene: https://www.theguardian.com/technology/2016/nov/29/its-no-christmas-no-1-but-ai-generated-song-brings-festive-cheer-to-researchers 
18. MusicNet is an annotated set of classicial music performances for training machine learning models: https://techcrunch.com/2016/11/30/musicnet-aims-to-give-machine-learning-algorithms-a-taste-for-beethoven/ 
19. There seems to be something of a shortage of trained AI practitioners: http://www.techproresearch.com/downloads/research-companies-lack-skills-to-implement-and-support-ai-and-machine-learning/?ftag=tip185eb84 
20. Amazon launches its AI web platform: https://techcrunch.com/2016/11/30/amazon-launches-amazon-ai-to-bring-its-machine-learning-smarts-to-developers/ 
21. The US government is continuing to take AI seriously. Is the NZ government going to do the same? http://www.techrepublic.com/article/us-senate-subcommittee-meets-on-the-dawn-of-ai-today-livestream-available/ 
22. The optimisation problems with deep neural networks: http://www.kdnuggets.com/2016/12/hard-thing-about-deep-learning.html 
23. Does Google have the edge in cloud-based AI? http://www.techrepublic.com/article/the-cloud-war-moves-to-machine-learning-does-google-have-an-edge/ 
24. Learn maths if you want to get into AI, according to Facebook's head of AI research: https://techcrunch.com/2016/12/01/facebooks-advice-to-students-interested-in-artificial-intelligence/ 
25. Bringing AI to logo design-sounds like an interactive evolutionary algorithm: https://techcrunch.com/2016/12/01/logojoy-makes-designers-unemployed/ 
26. Detecting diabetic retinopathy (damage to the retina caused by diabetes) with machine learning: http://betanews.com/2016/11/29/google-machine-learning-diabetes-retinopathy-eyes-vision/
27. Facebook is developing AI to flag "offensive" videos: http://www.reuters.com/article/us-facebook-ai-video-idUSKBN13Q52M
28. The 10 biggest failures in applications of artificial intelligence for 2016: http://www.techrepublic.com/article/top-10-ai-failures-of-2016/ 
29. Microsoft in embedding image recognition AI into some of its Office applications: http://www.theverge.com/2016/12/2/13825590/microsoft-office-apps-ai-word-powerpoint-accessibility 
30. Random forests in Python: http://www.kdnuggets.com/2016/12/random-forests-python.html 
31. How can governments regulate ecommerce (and the AI that drives it): https://www.theguardian.com/commentisfree/2016/dec/04/how-do-you-throw-book-at-an-algorithm-internet-big-data

Weekly Review 28 November 2016

Below are some of the interesting links I Tweeted about in the last week.

1. Not to be too glib, but this is what being a post-doc is like: http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=11752215
2. Pictorial description of backpropagation ANN training: http://www.datasciencecentral.com/profiles/blogs/neural-networks-the-backpropagation-algorithm-in-a-picture 
3. Implementing Human ActivityRecognition in TensorFlow: http://www.kdnuggets.com/2016/11/implementing-cnn-human-activity-recognition-tensorflow.html
4. Combining text analytics and machine learning: https://www.datanami.com/2016/11/21/text-analytics-machine-learning-virtuous-combination/ 
5. Top 20 open source Python machine learning projects: http://www.kdnuggets.com/2016/11/top-20-python-machine-learning-open-source-updated.html 
6. Questions to ask when moving machine learning systems into production: http://www.kdnuggets.com/2016/11/moving-machine-learning-practice-production.html 
7. Choosing a programming language for machine learning applications: http://www.datasciencecentral.com/profiles/blogs/python-machine-learning-and-language-wars-a-highly-subjective-poi 
8. Baidu is releasing Chinese-language speech recognition APIs: https://www.datanami.com/2016/11/23/baidu-ups-ai-ante-deep-learning-release/ 
9. Google's deep-learning based translation system seems to have learned its own internal language model: https://techcrunch.com/2016/11/22/googles-ai-translation-tool-seems-to-have-invented-its-own-secret-internal-language/ 
10. Tech leaders need to be aware of biased models: http://www.techrepublic.com/article/algorithms-can-be-racist-why-cxos-should-understand-the-assumptions-behind-predictive-analytics/ 
11. A deep-learning based system learned to lipread by watching television, performs better than a human: http://www.theverge.com/2016/11/24/13740798/google-deepmind-ai-lip-reading-tv 
12. AI is making inroads into the music industry: http://www.theregister.co.uk/2016/11/24/big_music_goes_mad_for_chat_bots_and_ai/ 
13. An introduction (with Python code) to linear regression: http://www.kdnuggets.com/2016/11/linear-regression-least-squares-matrix-multiplication-concise-technical-overview.html 
14. How Google uses cloud-based machine learning to help companies fill job vacancies: http://www.techrepublic.com/article/how-googles-new-cloud-jobs-api-uses-machine-learning-to-help-companies-fill-jobs/ 
15. Why neural networks won't take over from human translators: http://motherboard.vice.com/en_au/read/shitloads-and-zingers-the-perils-of-machine-translation

IEEE Transactions on Fuzzy Systems, vol. 24, issue 5, 2016

1. On Pythagorean and Complex Fuzzy Set Operations
Author(s): Scott Dick; Ronald R. Yager; Omolbanin Yazdanbakhsh
Pages: 1009- 1021

2. Classification of Type-2 Fuzzy Sets Represented as Sequences of Vertical Slices
Author(s): Lorenzo Livi; Hooman Tahayori; Antonello Rizzi; Alireza Sadeghian; Witold Pedrycz
Pages: 1022- 1034

3. Power Average of Trapezoidal Intuitionistic Fuzzy Numbers Using Strict t-Norms and t-Conorms
Author(s): Shu-Ping Wan; Zhi-Hong Yi
Pages: 1035- 1047

4. Aperiodic Sampled-Data Sliding-Mode Control of Fuzzy Systems With Communication Delays Via the Event-Triggered Method
Author(s): Shiping Wen; Tingwen Huang; Xinghuo Yu; Michael Z. Q. Chen; Zhigang Zeng
Pages: 1048- 1057

5. Fault Detection and Isolation for Affine Fuzzy Systems With Sensor Faults
Author(s): Huimin Wang; Guang-Hong Yang; Dan Ye
Pages: 1058- 1071

6. Knowledge Measure for Atanassov's Intuitionistic Fuzzy Sets
Author(s): Kaihong Guo
Pages: 1072- 1078

7. Takagi–Sugeno–Kang Transfer Learning Fuzzy Logic System for the Adaptive Recognition of Epileptic Electroencephalogram Signals
Author(s): Changjian Yang; Zhaohong Deng; Kup-Sze Choi; Shitong Wang
Pages: 1079- 1094

8. A Survey of Adaptive Fuzzy Controllers: Nonlinearities and Classifications
Author(s): Meng Joo Er; Sayantan Mandal
Pages: 1095- 1107

9. Optimal Design of Constraint-Following Control for Fuzzy Mechanical Systems
Author(s): Ruiying Zhao; Ye-Hwa Chen; Shengjie Jiao
Pages: 1108- 1120

10. Multiscale Opening of Conjoined Fuzzy Objects: Theory and Applications
Author(s): Punam K. Saha; Subhadip Basu; Eric A. Hoffman
Pages: 1121- 1133

11. Decentralized State Feedback Control of Uncertain Affine Fuzzy Large-Scale Systems With Unknown Interconnections
Author(s): Huimin Wang; Guang-Hong Yang
Pages: 1134- 1146

12. Fuzzy Adaptive Control With State Observer for a Class of Nonlinear Discrete-Time Systems With Input Constraint
Author(s): Yan-Jun Liu; Shaocheng Tong; Dong-Juan Li; Ying Gao
Pages: 1147- 1158

13. Fuzzy-Based Goal Representation Adaptive Dynamic Programming
Author(s): Yufei Tang; Haibo He; Zhen Ni; Xiangnan Zhong; Dongbin Zhao; Xin Xu
Pages: 1159- 1175

14. Nonparametric Statistical Active Contour Based on Inclusion Degree of Fuzzy Sets
Author(s): Maoguo Gong; Hao Li; Xiang Zhang; Qiunan Zhao; Bin Wang
Pages: 1176- 1192

15. The Role of Crisp Elements in Fuzzy Ontologies: The Case of Fuzzy OWL 2 EL
Author(s): Fernando Bobillo
Pages: 1193- 1209

16. Transfer Prototype-Based Fuzzy Clustering
Author(s): Zhaohong Deng; Yizhang Jiang; Fu-Lai Chung; Hisao Ishibuchi; Kup-Sze Choi; Shitong Wang
Pages: 1210- 1232

17. Observer-Based Fuzzy Control for Nonlinear Networked Systems Under Unmeasurable Premise Variables
Author(s): Hongyi Li; Chengwei Wu; Shen Yin; Hak-Keung Lam
Pages: 1233- 1245

18. Adaptive Fuzzy Hysteresis Internal Model Tracking Control of Piezoelectric Actuators With Nanoscale Application
Author(s): Pengzhi Li; Peiyue Li; Yongxin Sui
Pages: 1246- 1254

Weeky Review 21 November 2016

Below are some of the interesting links I Tweeted about in the last week.

1. The AI market is predicted to keep growing: http://www.nextbigfuture.com/2016/10/ai-market-is-projected-to-grow-from-8.html 
2. Making music with neural networks: http://www.theregister.co.uk/2016/11/11/ai_pop_music_maker/ 
3. Point and click chatbot builder: https://techcrunch.com/2016/11/10/kitt-ais-chartflow-helps-you-build-better-chatbots/ 
4. Implementing machine learning algorithms in parallel using GPU: http://www.kdnuggets.com/2016/11/parallelism-machine-learning-gpu-cuda-threading.html 
5. Spotting insider trading with data mining: https://www.datanami.com/2016/11/08/sec-mines-data-spot-insider-trading/ 
6. A descriptive overview of convolutional neural networks: http://www.kdnuggets.com/2016/11/intuitive-explanation-convolutional-neural-networks.html 
7. The current state of machine intelligence: http://www.datasciencecentral.com/profiles/blogs/the-current-state-of-machine-intelligence-3-0 
8. Overview of computer vision: https://techcrunch.com/2016/11/13/wtf-is-computer-vision/ 
9. Examining the relationships we have with present primitive AI: https://techcrunch.com/2016/11/13/defining-our-relationship-with-early-ai/ 
10. An argument that it makes more economic sense for AI to replace highly-paid workers first: http://www.theregister.co.uk/2016/11/14/the_sharks_of_ai_will_attack_expensive_and_scarce_workers_faster_than_they_eat_drivers/ 
11. Adobe is developing Sensei, its own intelligent assisstant: https://techcrunch.com/2016/11/14/adobe-makes-big-bets-on-ai-and-the-public-cloud/ 
12. Has Microsoft made a break-through in machine language comprehension? http://www.techrepublic.com/article/microsoft-has-found-a-way-to-bring-human-language-intelligence-to-our-dumb-computers/ 
13. It seems that Facebook uses machine learning to identify fake news content: https://techcrunch.com/2016/11/14/facebook-fake-news/ 
14. The shortcomings of deep learning: http://www.kdnuggets.com/2016/11/shortcomings-deep-learning.html 
15. Getting to grips with neural networks with Google's AI Experiments showcase: https://techcrunch.com/2016/11/15/googles-ai-experiments-help-you-understand-neural-networks-by-playing-with-them 
16. Some predictions on the future of artificial intelligence: http://www.kdnuggets.com/2016/11/13-forecasts-on-artificial-intelligence.html 
17. An AI-based task manager: https://techcrunch.com/2016/11/15/gluru/ But is it better than my textfile named ToDo.txt?
18. Semantic Scholar, an AI-based search engine for research papers: https://techcrunch.com/2016/11/11/scientists-gain-a-versatile-modern-search-engine-with-the-ai-powered-semantic-scholar/ 
19. Can AI replace HR? https://www.linkedin.com/pulse/can-robots-replace-hr-michael-gretczko?trk=hp-feed-article-title-comment 
20. Machine learning based upsampling of images: http://www.theverge.com/2016/11/16/13649016/google-machine-learning-low-res-image-raisr 
21. The crucial elements missing from chatbot AI: http://www.techrepublic.com/article/mobile-ai-chatbot-intelligence-masquerading-as-the-real-deal/ 
22. Google's ANN-based doodle classifier: http://www.theverge.com/2016/11/15/13641876/google-ai-experiments-quick-draw-image-recognition-game 
23. OpenAI has chosen Microsoft Azure as its cloud platform of choice: http://www.techrepublic.com/article/microsoft-partners-with-openai-to-advance-ai-research-with-azure/ 
24. Google is expanding its cloud-based AI services: http://www.theverge.com/2016/11/15/13640420/google-cloud-service-machine-learning-ai-translation-computer-vision 
25. Nexar is using machine learning in car dash cams to predict collisions: https://techcrunch.com/2016/11/15/nexars-vehicle-to-vehicle-network-will-use-dash-cam-ai-to-prevent-accidents/ 
26. The future of AI is inseparable from humans: http://www.theverge.com/a/verge-2021/humanity-and-ai-will-be-inseparable 
27. List of and commentaries on useful tools for building chatbots: https://chatbotsmagazine.com/the-tools-every-bot-creator-must-know-c0e9dd685094#.2fclmqz8w 
28. Colour me skeptical about the claim that the system recognises handwriting better than humans: https://techcrunch.com/2016/11/17/searchink-unlocking-the-handwritten-past-and-present-with-machine-learning/ 
29. Someone who spent more time talking to bots than their spouse wouldn't have a spouse for long: https://www.datanami.com/2016/11/16/ai-powered-bots-gearing-up-serve-you/ 
30. Overview of opinion mining: http://dataconomy.com/opinion-mining-extracting-opinions/ 
31. Classifying porn with ANN: http://www.theregister.co.uk/2016/1/18/ai_gives_smut_peddlers_helping_hand/ 
32. I suspect this is a case of either seriously biased data or outright fraud: http://www.theregister.co.uk/2016/11/18/ai_can_tell_if_youre_a_criminal/ 
33. Where to apply machine learning first in your business: http://techemergence.com/where-to-apply-machine-learning-first/ 
34. Bias in machine learning models and how to prevent it: http://www.techrepublic.com/article/bias-in-machine-learning-and-how-to-stop-it/ 
35. Automated medical diagnostic tools are still not as good as human doctors: http://spectrum.ieee.org/the-human-os/biomedical/diagnostics/doctors-still-struggle-to-make-the-most-of-computer-aided-diagnosis/?utm_source=humanosalert&utm_medium=email&utm_campaign=111616 
36. British government report on the future implications of AI: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/566075/gs-16-19-artificial-intelligence-ai-report.pdf 
37. Howto: Deep learning-based object recognition in Microsoft Cognitive Toolkit: https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/ 
38. Why "Reply All" is not a good idea: http://www.businessinsider.com.au/reply-all-email-chain-1-2-million-nhs-employees-2016-11?r=US&IR=T

Evolving Systems, Volume 7, Issue 4, December 2016

1. A speed FPGA hardware accelerator based FSBMA-VBSME used in H.264/AVC
Author(s): Amira Yahi, Salah Toumi, El-bay Bourennane & Kamel Messaoudi
Pages: 233-241

2. On cluster validity indices with its application to interleaved radar pulse separation through fuzzy-based representation
Author(s): A. H. Mazinan
Pages: 243-254

3. On fuzzy feature selection in designing fuzzy classifiers for high-dimensional data
Author(s): Eghbal G. Mansoori & Khadijeh S. Shafiee
Pages: 255-265

4. Adaptive PID controller design for wing rock suppression using self-recurrent wavelet neural network identifier
Author(s): Milad Malekzadeh, Jalil Sadati & Mojtaba Alizadeh
Pages: 267-275

5. Adaptive cow movement detection using evolving spiking neural network models
Author(s): Tao Gao & Nikola Kasabov
Pages: 277-285

6. An inventory model for controllable probabilistic deterioration rate under shortages
Author(s): Umakanta Mishra
Pages: 287-307

Review August - November 2016

 It's been a while since my last review post. Below are some of the interesting links I Tweeted about in the last few weeks.

1. 7 ways AI could be a good thing: https://www.theguardian.com/technology/2016/aug/07/seven-benefits-of-artificial-intelligence
2. A fairly detailed overview of IBM's Watson AI: http://www.techrepublic.com/article/ibm-watson-the-smart-persons-guide/
3. Using deep learning to automate spearphishing attacks: https://www.technologyreview.com/s/602109/this-ai-will-craft-tweets-that-youll-never-know-are-spam/
4. And publishers are still being dicks about Sci-Hub: https://www.insidehighered.com/news/2016/08/08/letter-publishers-group-adds-debate-over-sci-hub-and-librarians-who-study-it
5. Getting started understanding machine vision: http://www.kdnuggets.com/2016/08/seven-steps-understanding-computer-vision.html
6. AI applied to marketing and advertising: http://techemergence.com/artificial-intelligence-in-marketing-and-advertising-5-examples-of-real-traction/
7. The Nervana chip optimised for deep learning: http://www.nextbigfuture.com/2016/08/startup-nervana-making-deep-learning.html
8. AI and machine learning in finance: http://www.datasciencecentral.com/profiles/blogs/interview-with-flowcast-cto-ai-machine-learning-in-fintech
9. A guide to Google deepmind: http://www.techrepublic.com/article/google-deepmind-the-smart-persons-guide/
10. Tutorial on neural networks in R: http://www.kdnuggets.com/2016/08/begineers-guide-neural-networks-r.html
11. Brief step-by-step guide to building an expert system: http://www.techrepublic.com/article/40-year-old-ai-innovation-may-solve-your-big-data-problems/
12. Recent advances in quantum computers are promising for AI: https://www.datanami.com/2016/08/11/quantum-researchers-eye-ai-advances/
13. 3 thoughts from Yann LeCunn on why deep learning works so well: http://www.kdnuggets.com/2016/08/yann-lecun-3-thoughts-deep-learning.html
14. AI in healthcare-where it is, and where it's going: https://techcrunch.com/2016/08/12/the-healing-power-of-ai/
15. Why movies like The Terminator are not to blame for the bad journalism around AI: http://www.kdnuggets.com/2016/08/stop-blaming-terminator-for-bad-ai-journalism.html
16. Recognising hand gestures using IBM's neuromorphic chips: http://www.theverge.com/circuitbreaker/2016/8/12/12458330/samsung-ibm-truenorth-brain-chip-gesture-app
17. I've said it time and again-biased data produces biased models: http://www.nytimes.com/2016/06/26/opinion/sunday/artificial-intelligences-white-guy-problem.html?_r=2
18. 10 signs of a bad place to work: http://www3.forbes.com/leadership/ten-unmistakable-signs-of-a-bad-place-to-work/
19. The dangers of using biased data in training a model - with Donald Trump as the cautionary example: https://mathbabe.org/2016/08/11/donald-trump-is-like-a-biased-machine-learning-algorithm/
20. Why we're experiencing an AI boom: http://venturebeat.com/2016/08/12/why-is-now-the-time-for-artificial-intelligence/
21. Using ensembles of models to boost performance: http://www.datasciencecentral.com/profiles/blogs/improving-predictions-with-ensemble-model
22. AI in medical apps: https://www.datanami.com/2016/08/08/ai-initiative-targets-medical-apps/
23. Creating an invisible user interface with AI: https://techcrunch.com/2016/08/15/using-artificial-intelligence-to-create-invisible-ui/
24. Machine learning in finance-where it is, and where it's going: http://techemergence.com/machine-learning-in-finance-applications/
25. No matter how good the model, if it isn't interpreted or applied properly, it is useless: http://www.theverge.com/2016/8/19/12552384/chicago-heat-list-tool-failed-rand-test
26. Samsung demos its camera based on IBM's neuromorphic chip: http://www.extremetech.com/extreme/233747-samsung-demonstrates-camera-sensors-hooked-to-ibms-brain-imitating-silicon
27. An AI-based assistant for firefighters: http://www.jpl.nasa.gov/news/news.php?feature=6590
28. Deep learning is now being used in the Dragon speech recognition system: https://techcrunch.com/2016/08/16/dragon-15/
29. Companies continue to invest big in AI: http://www.theregister.co.uk/2016/08/17/chip_giants_invest_heavily_to_boost_changes_in_embedded_ai_platforms/?mt=1472434359193
30. OpenAI is set to receive the first "deep learning in a box" system: http://www.techrepublic.com/article/elon-musk-backed-openai-project-will-get-first-deep-learning-supercomputer-in-a-box/
31. Facebook open sources its fast text processing system: https://techcrunch.com/2016/08/18/facebooks-artificial-intelligence-research-lab-releases-open-source-fasttext-on-github/
32. How to train an AI doctor: http://www.datasciencecentral.com/profiles/blogs/training-an-ai-doctor-by-tyler-schnoebelen
33. Boosting your competitive advantage using machine learning: http://www.datasciencecentral.com/profiles/blogs/machine-learning-becomes-mainstream-how-to-increase-your
34. Mapping poverty using machine learning: http://motherboard.vice.com/en_au/read/artificial-intelligence-is-predicting-human-poverty-from-space
35. Machine learning systems now out-perform humans in diagnosing cancer biopsies: http://www.extremetech.com/extreme/233746-ai-beats-doctors-at-visual-diagnosis-observes-many-times-more-lung-cancer-signals
36. An Android malware detection system using machine learning: http://www.techrepublic.com/article/droidol-android-malware-detection-based-on-online-machine-learning/
37. A chatbot to help homeless people get government housing: http://uk.businessinsider.com/chatbot-helps-homeless-josh-browder-2016-8?utm_content=bufferb3a52&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer?r=US&IR=T
38. Tracking Beijing pickpockets with machine learning: http://www.economist.com/news/science-and-technology/21705296-artful-dodger-your-time-may-be-up-cutpurse-capers
39. Demystifying deep reinforcement learning: https://www.nervanasys.com/demystifying-deep-reinforcement-learning/
40. Getting up to speed on deep learning: https://medium.com/the-mission/up-to-speed-on-deep-learning-august-update-part-1-25afc11aea6b#.ikqxgmnp0
41. So, which professions fit these criteria the most? 6 signs your job is going to be automated: http://www.fastcompany.com/3062739/the-future-of-work/six-very-clear-signs-that-your-job-is-due-to-be-automated
42. "Academic clickbait" - the right choice of title for a paper can massively increase its reach: https://www.insidehighered.com/views/2016/08/24/review-article-using-clickbait-techniques-scholarly-titles
43. Baidu has open sourced their deep learning toolkit: http://www.theverge.com/2016/9/1/12725804/baidu-machine-learning-open-source-paddle
44. Predicting air quality in South Africa with machine learning: http://spectrum.ieee.org/energywise/energy/environment/tackling-air-quality-prediction-in-south-africa-with-machine-learning?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+IeeeSpectrum+%28IEEE+Spectrum%29&utm_content=FaceBook
45. Samsung is embedding neural networks in the chips for their new phones: http://www.theregister.co.uk/2016/08/22/samsung_m1_core/
46. Identifying regions of poverty from satelite images using machine learning: http://www.theverge.com/2016/8/18/12522764/poverty-measurement-satellite-algorithms-night-vs-day-imaging
47. Yandex is using machine learning to target users with less-annoying ads: https://techcrunch.com/2016/08/23/yandex-applies-ai-to-filter-annoying-ads-on-android-powered-by-user-reports/
48. On how important sleep is for resetting the brain: https://www.theguardian.com/science/2016/aug/23/sleep-resets-brain-connections-crucial-for-memory-and-learning-study-reveals
49. Research begins on using deep learning to segment cancerous tissue in scans: https://www.uclh.nhs.uk/News/Pages/Researchbeginstodeveloppioneeringtechnologytoplanradiotherapytreatment.aspx
50. Comparison of deep learning and AI: http://www.datasciencecentral.com/profiles/blogs/6448529:BlogPost:459267
51. Part 1 of a gentle introduction to TensorFlow: http://www.kdnuggets.com/2016/08/gentlest-introduction-tensorflow-part-1.html
52. Part 2 of a gentle introduction to TensorFlow: http://www.kdnuggets.com/2016/08/gentlest-introduction-tensorflow-part-2.html
53. Using GPU to turn a PC into a supercomputer: http://spectrum.ieee.org/tech-talk/computing/hardware/use-a-gpu-to-turn-a-pc-into-a-supercomputer?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+IeeeSpectrum+%28IEEE+Spectrum%29&utm_content=FaceBook
54. 10 programming career tips: http://www.techrepublic.com/article/10-programming-career-tips-for-students-and-new-professionals/
55. 5 requirements for a successful mobile app: http://www.techrepublic.com/article/five-things-your-branded-mobile-app-needs-to-succeed/
56. 10 ways to make your mobile app fail: http://www.informationweek.com/software/productivity-collaboration-apps/10-ways-to-doom-your-next-mobile-app/d/d-id/1326590?image_number=11
57. 9 ways bad managers drive good employees away: https://www.entrepreneur.com/article/249903
58. Microsoft is putting deep neural networks in a fridge: https://techcrunch.com/2016/09/02/microsoft-is-putting-cortana-machine-learning-in-a-fridge/
59. Some basic stuff - Why neural networks need activation functions: http://www.kdnuggets.com/2016/08/role-activation-function-neural-network.html
60. 10 Java machine learning libraries: http://www.datasciencecentral.com/profiles/blogs/25-java-machine-learning-tools-libraries
61. How is growing up around AI going to affect the next generation? https://techcrunch.com/2016/09/03/growing-up-in-generation-ai/
62. How convolutional neural networks work: http://www.kdnuggets.com/2016/08/brohrer-convolutional-neural-networks-explanation.html
63. How search at eBay got a boost from machine learning: https://www.datanami.com/2016/08/22/search-engines-get-machine-language-boost/
64. What one university did to graduate more women from computer science: http://www.techrepublic.com/article/what-universities-can-do-to-graduate-more-women-in-comp-sci-7-tips-from-harvey-mudd/
65. Sturgeon's law applies to AI as well - 90 % of them are crap: http://motherboard.vice.com/en_au/read/why-an-ai-judged-beauty-contest-picked-nearly-all-white-winners
66. Skin colour is not the only attribute distinguishing different populations - think the programmers forgot that: http://motherboard.vice.com/en_au/read/why-an-ai-judged-beauty-contest-picked-nearly-all-white-winners
67. PayPal is using machine learning to detect fraud. All based on open-source tools, too: http://www.americanbanker.com/news/bank-technology/how-paypal-is-taking-a-chance-on-ai-to-fight-fraud-1091068-1.html
68. Microsoft open sources its deep learning toolkit: http://blogs.microsoft.com/next/2016/01/25/microsoft-releases-cntk-its-open-source-deep-learning-toolkit-on-github/#sm.00002eomx1dc9fa2te714z6of4st4
69. Classifying cucumbers using deep neural networks: https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow
70. A neural network based app to find local food: http://www.dealstreetasia.com/stories/russia-digest-neural-network-based-app-launched-52383/
71. Machine checking of statistics in published papers: http://motherboard.vice.com/en_au/read/scientists-are-worried-about-peer-review-by-algorithm-statcheck
72. 82 free data science e-books from O'Reilly: http://www.oreilly.com/data/free/archive.html
73. Classifying urban sounds using deep neural networks: http://aqibsaeed.github.io/2016-09-03-urban-sound-classification-part-1/
74. Why you should stop trying to multi-task: https://www.insidehighered.com/blogs/call-action-marketing-and-communications-higher-education/stop-multitasking
75. Text-to-speech using deep ANN to directly generate waveforms rather than sequences of syllables: https://deepmind.com/blog/wavenet-generative-model-raw-audio/
76. Some thoughts on how AI could manage your money for you: https://techcrunch.com/2016/09/08/ai-can-make-your-money-work-for-you/
77. Some military uses of machine learning: https://www.datanami.com/2016/09/12/pentagon-eyes-ai-battlefield/
78. Computer vision as a service: https://techcrunch.com/2016/09/12/restb-ai-offers-custom-computer-vision-as-a-service/
79. Overview of reinforcement learning: http://www.datasciencecentral.com/profiles/blogs/reinforcement-learning-and-ai
80. How Apple's wireless earbuds could lead to always-on AI: https://techcrunch.com/2016/09/13/apples-ai-if-by-air/
81. The dangers of relying on a supervised learning model when making decisions: http://www.kdnuggets.com/2016/09/deception-of-supervised-learning.html
82. The future of AI and machine learning: http://www.datasciencecentral.com/profiles/blogs/a-sneak-peek-at-the-future-of-artificial-intelligence-the-newest
83. Nvidia's low-power AI optimised computer: https://techcrunch.com/2016/09/13/nvidias-tiny-new-self-driving-ai-computer-sips-power/
84. Glad this kind of thing doesn't happen much in NZ. Since I'm in a mixed-race marriage myself, it's very troubling: https://www.insidehighered.com/news/2016/09/19/racist-chalk-messages-college-directed-against-presidents-children-and-diversity
85. Music mastering with machine learning: http://betakit.com/landr-wants-to-make-the-music-industry-more-accessible-with-machine-learning-platform/
86. AI and automation will eliminate 6% of jobs in the US by 2021: https://www.theguardian.com/technology/2016/sep/13/artificial-intelligence-robots-threat-jobs-forrester-report
87. An overview of decision trees: http://www.kdnuggets.com/2016/09/decision-trees-disastrous-overview.html
88. Description of the bagging approach used in random forests: http://www.kdnuggets.com/2016/09/reandom-forest-criminal-tutorial.html
89. Career opportunities in AI: http://www.datasciencecentral.com/profiles/blogs/finding-career-opportunities-in-ai
90. Learning machine learning in a year: http://www.kdnuggets.com/2016/09/machine-learning-year-total-noob-effective-practitioner.html
91. Microsoft wants to use AI to "solve" cancer: https://techcrunch.com/2016/09/20/microsoft-wants-to-crack-the-cancer-code-using-artificial-intelligence/
92. Number of jobs replaced by AI will be smaller than expected: http://www.techrepublic.com/article/why-the-number-of-jobs-that-will-be-replaced-by-robots-is-lower-than-you-think/
93. An AI that learned how to play deathmatch Doom from pixel data: https://techcrunch.com/2016/09/21/scientists-teach-machines-to-hunt-and-kill-humans-in-doom-deathmatch-mode/
94. Paper on the Doom deathmatch AI: https://arxiv.org/abs/1609.05521
95. Overview of 9 key papers in deep learning: http://www.kdnuggets.com/2016/09/9-key-deep-learning-papers-explained.html
96. Machine-generated peer reviews pass for the real thing: https://www.timeshighereducation.com/news/robot-written-reviews-fool-academics
97. How publish or perish selects for bad research: https://www.theguardian.com/science/2016/sep/21/cut-throat-academia-leads-to-natural-selection-of-bad-science-claims-study?CMP=share_btn_tw
98. FuzzyML is the first IEEE standard to come out of the Computational Intelligence Society: http://standardsinsight.com/ieee_company_detail/the-value-and-process-of-creating-standards-cis-develops-its-first-standard
99. Apparently it's possible to use TensorFlow on D-Wave's quantum computers: http://www.computerworld.com/article/3122512/computer-hardware/d-wave-plans-to-ship-a-2000-qubit-quantum-computer-in-17.html
100. Sounds a lot like an ecosystem of Darwinian bots: https://techcrunch.com/2016/09/16/bazillion-beings-are-ai-driven-bots-that-have-to-earn-their-keep-or-die/
101. Microsoft claims their ANN-based speech recognition system is the most accurate: http://www.theregister.co.uk/2016/09/15/microsoft_lowest_error_rate_ai_speech_recognition/
102. Why AI is booming now: http://www.nytimes.com/2016/09/19/technology/artificial-intelligence-software-is-booming-but-why-now.html?partner=IFTTT&_r=1
103. Looks like it isn't easy to make a living with open source AI: http://venturebeat.com/2016/09/24/machine-learning-startup-h2o-lays-off-10-of-employees/
104. Deep learning leads to more accurate processing of mammograms: http://futurism.com/artificial-intelligence-reads-mammograms-with-99-accuracy/
105. A neural network zoo: http://www.asimovinstitute.org/neural-network-zoo/
106. Tried to build a system to identify sepsis, ended up with a system that predicted deaths: https://www.buzzfeed.com/stephaniemlee/how-a-failed-hospital-algorithm-could-save-lives?utm_term=.sigva50LA#.njbNW6Ryp
107. Google open sources its image auto-captioning system, based on TensorFlow: http://www.zdnet.com/article/whats-in-that-photo-google-open-sources-caption-tool-in-tensorflow-that-can-tell-you/
108. I regularly tell my daughter to not do a PhD-instead, choose a career with better security than academia: https://www.insidehighered.com/advice/2016/09/23/faculty-member-no-longer-advises-her-students-go-academe
109. The limits of machine learning: http://nautil.us/blog/the-fundamental-limits-of-machine-learning
110. Building a robot with object recognition using TensorFlow: https://www.oreilly.com/learning/how-to-build-a-robot-that-sees-with-100-and-tensorflow
111. Of course our AI are going to be racist/sexist. Biased data leads to biased models. http://www.dailymail.co.uk/sciencetech/article-3808834/Are-making-AIs-racist-sexist-Researchers-warn-machines-learning-human-biases.html
112. Why is this still surprising to people? I was teaching my undergrad students this 16 years ago: http://boingboing.net/2016/09/06/weapons-of-math-destruction-i.html
113. k-Means vs Expectation-maximization clustering: http://www.kdnuggets.com/2016/09/comparing-clustering-techniques-concise-technical-overview.html
114. A pretty weak argument from the IEEE on why people shouldn't use Sci-Hub: http://theinstitute.ieee.org/blogs/blog/scihubs-free-articles-are-anything-but-free
115. Deep learning-based language translation: https://techcrunch.com/2016/09/27/google-unleashes-deep-learning-tech-on-language-with-neural-machine-translation/
116. Paper on deep learning-based translation: https://arxiv.org/pdf/1609.08144v1.pdf
117. Description of what machine learning actually is: http://techemergence.com/what-is-machine-learning/
118. Microsoft's move towards AI: https://techcrunch.com/2016/09/26/microsoft-ceo-satya-nadella-on-how-ai-will-transform-his-company/
119. What companies get wrong about machine learning: http://fortune.com/2016/09/27/machine-learning/
120. How Microsoft is integrating AI into Office 365: https://techcrunch.com/2016/09/26/microsoft-brings-new-ai-powered-features-to-office-365-and-dynamics-365/
121. The points made in this article are even more important now: http://www.bioone.org/doi/full/10.1641/0006-3568%282005%29055%5B0390%3APOP%5D2.0.CO%3B2
122. You'd be hard-pressed to find a research paper author opposed to Sci-Hub, because more downloads==more citations: http://theinstitute.ieee.org/blogs/blog/scihubs-free-articles-are-anything-but-free
123. Don't think authors-content creators-are opposed to Sci-Hub. Publishers are,it threatens their extortionate profits: http://theinstitute.ieee.org/blogs/blog/scihubs-free-articles-are-anything-but-free
124. We're doomed: https://techcrunch.com/2016/09/28/facebook-amazon-google-ibm-and-microsoft-come-together-to-create-historic-partnership-on-ai/
125. List of 15 tutorials on deep learning: http://www.datasciencecentral.com/profiles/blogs/15-deep-learning-tutorials
126. Is a Java deep learning library worth $3M? https://techcrunch.com/2016/09/28/skymind-raises-3m-to-bring-its-java-deep-learning-library-to-the-masses/ Would have thought an open source project could do it.
127. Google's cloud-based deep learning API is in beta: http://www.infoworld.com/article/3125095/artificial-intelligence/google-cloud-machine-learning-hits-public-beta-with-additions.html
128. More opportunities than risks when investing in AI: https://techcrunch.com/2016/09/24/investing-in-ai-offers-more-rewards-than-risks/
129. Investment advisers are betting big on machine learning: http://www.ifa.com.au/news/16855-advisers-look-to-ai-for-future-of-investment-survey-shows
130. Five safety problems with AI: https://openai.com/blog/concrete-ai-safety-problems/ Most of which seem concerned with the safety of the AI...
131. I don't think any human-created AI can be unbiased, all humans have some biases: http://motherboard.vice.com/en_au/read/to-make-ai-less-biased-give-it-a-worldview-racism-fairness-algorithm
132. Machine learning is seen as a savior for security: http://www.techrepublic.com/article/how-machine-learning-and-ai-will-save-the-entire-security-industry/
133. Reverse engineering cloud-based machine learning models: http://www.theregister.co.uk/2016/10/01/steal_this_brain/
134. Paper on reverse-engineering machine learning models: https://regmedia.co.uk/2016/09/30/sec16_paper_tramer.pdf
135. Predicting future human behaviour with deep learning: http://www.kdnuggets.com/2016/09/predicting-future-human-behavior-deep-learning.html
136. Yahoo has open sourced its porn-detecting convolutional neural network: https://techcrunch.com/2016/09/30/yahoo-open-sources-its-porn-detecting-neural-network/
137. Reading people's facial expressions using Google's Cloud Vision API: http://www.theregister.co.uk/2016/09/30/we_feel_your_pain_sometimes/
138. Why human curation still has a place among algorithmic organisation of information: https://www.theguardian.com/technology/2016/sep/30/age-of-algorithm-human-gatekeeper
139. Researchers claim that there is no inborn aptitude for programming, it can all be taught: http://www.theregister.co.uk/2016/09/28/geek_gene_denied/ Only studied 1 university
140. Looks like a primitive version of the personality constructs from Neuromancer: http://www.theverge.com/a/luka-artificial-intelligence-memorial-roman-mazurenko-bot
141. The next generation of neural networks will use spiking neurons: http://www.datasciencecentral.com/profiles/blogs/beyond-deep-learning-3rd-generation-neural-nets
142. We need a Data Mining Code of Ethics, to prevent this kind of sloppy work affecting the public: https://mic.com/articles/156286/crime-prediction-tool-pred-pol-only-amplifies-racially-biased-policing-study-shows#.c5TAsSbvF
143. Review of six cloud-based machine learning services: http://www.infoworld.com/article/3068519/artificial-intelligence/review-6-machine-learning-clouds.html#tk.ifw-infsb
144. What happens if you are not careful with building your model? You get a "weapon of math destruction" http://spectrum.ieee.org/tech-talk/computing/software/are-you-making-a-weapon-of-math-destruction
145. Yes, machine learning models can be sexist, classist, racist, otherwise biased if data used to train them is biased: https://techcrunch.com/2016/10/11/is-machine-learning-sexist/
146. What will people do when AI replaces all of the jobs? https://philipdodson.wordpress.com/2016/10/15/when-work-doesnt-exist-what-will-you-do/
147. AI-written poetry. It's really bad. But I expect it will get better: http://www.aipoem.com/easypoem/
148. Google investigates a framework to deal with bias in machine learning: https://thestack.com/world/2016/10/11/google-brain-machine-learning-discrimination/
149. Paper on equal opportunity in machine learning: https://drive.google.com/file/d/0B-wQVEjH9yuhanpyQjUwQS1JOTQ/view 
150. Paper on external memory for deep neural networks: http://www.nature.com/articles/nature20101.epdf?referrer_access_token=frOErGoDiMdDbAcGAiOinNRgN0jAjWel9jnR3ZoTv0MggmpDmwljGswxVdeocYSuWA28NTxakh-dRc-_0c4BVXvapExdTwoFcAqeeInLf9sHqUdOmQFGF_e6ZjH8WoY_s2ttYIgDzb9ecBAHMb7VcnxXLJau2ZJIZLecBqbtchd4IvmmzjDPLjuvFnm4y0x8eZ5IdVTyVGNaTcM3Oytc15GurUgTWnjmHuHAwVKQzv19W3Md8UgYuguYGAFdHPi54xevgLJXA24IrGZkk34C1--AjZNdv-Yw9QvDs3FG3_jhrH9nwBYCWpL82141wbWyFQ544nsEcPz6s1leHCs0zfn1R2kfAl-TsNWZAvLq7PjT_hGVe3U98Z-BzCkp3lKXOxbEGSbsZZ-RcD4MAgpme_2tR-RDEi6aBmRbt4QlR-U%3D&tracking_referrer=spectrum.ieee.org
151. How to compete in the age of AI: http://dataconomy.com/competing-age-of-ai/
152. Simple advice for getting an academic job: https://www.insidehighered.com/advice/2016/09/29/simple-advice-getting-job-academe-essay
153. Collective learning in a deep neural network: http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/google-wants-robots-to-acquire-new-skills-by-learning-from-each-other?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+IeeeSpectrum+%28IEEE+Spectrum%29&utm_content=FaceBook
154. Are schools preparing students for the age of AI? https://www.theguardian.com/technology/2016/oct/12/schools-not-preparing-children-to-succeed-in-an-ai-future-mps-warn
155. Smart traffic lights make commuting more efficient: http://spectrum.ieee.org/cars-that-think/robotics/artificial-intelligence/pittsburgh-smart-traffic-signals-will-make-driving-less-boring?utm_source=SocialFlow&utm_medium=Facebook&utm_campaign=Social&utm_content=FaceBook
156. Myself, I'd prefer Sir Patrick Stewart: https://www.theguardian.com/technology/2016/oct/14/robert-downey-jr-mark-zuckerberg-digital-assistant-jarvis-iron-man
157. If a monkey can't own the copyright on a selfie it took, a computer shouldn't be able to own a patent: http://www.theregister.co.uk/2016/10/17/ai_computers_to_register_their_own_patents/
158. AI makes us better at what we are already good at: http://www.cio.com/article/3128870/analytics/artificial-intelligence-making-us-better-at-the-things-we-do-best.html
159. Overview of ANN and deep learning: http://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html
160. Reducing patient no-shows with machine learning: https://www.datanami.com/2016/10/12/predictor-looks-reduce-patient-no-shows/
161. Promoting collaboration in software development using machine learning: https://www.datanami.com/2016/10/11/ai-platform-targets-coder-shortage/
162. Paper on using deep reinforcement learning to play first-person shooter games: https://arxiv.org/abs/1609.05521
163. Microsoft claims human-level speech recognition performance: http://www.theverge.com/2016/10/18/13326434/microsoft-speech-recognition-human-parity
164. The Royal Navy is investigating the use of AI in threat assessment: http://www.theregister.co.uk/2016/10/18/royal_navy_ai_software/
165. More on spiking ANN: http://www.datasciencecentral.com/profiles/blogs/more-on-3rd-generation-spiking-neural-nets
166. An AI corporate executive: http://www.theregister.co.uk/2016/1/19/ai_bot_will_guide_finnish_it_firm/
167. Using machine learning to detect expenses fraud: https://techcrunch.com/2016/10/18/internal-expense-fraud-is-next-on-machine-learnings-list/
168. Is Facebook slipping behind in AI? https://www.datanami.com/2016/10/18/facebook-ai-efforts-seen-lagging/
169. A role for machine learning in predictive analytics for marketing: http://techemergence.com/predictive-analytics-for-marketing/
170. 3 things to consider when deciding if a business needs AI: http://www.techrepublic.com/article/does-your-business-need-ai-consider-these-3-things/
171. Paper: The Mythos of Model Interpretability: https://arxiv.org/abs/1606.03490
172. How Airbnb makes use of machine learning: http://techemergence.com/airbnb-machine-learning-data-social-science-make-work/
173. How will AI deal with moral dilemmas? https://techcrunch.com/2016/10/19/ai-autonomous-cars-and-moral-dilemmas/
174. Intro to using neural networks in Python: http://www.kdnuggets.com/2016/10/beginners-guide-neural-networks-python-scikit-learn.html
175. How Uber uses machine learning in most of what it does: http://www.techrepublic.com/article/how-data-and-machine-learning-are-part-of-ubers-dna/
176. I remember seeing nonsensical conference papers well before we had autocorrect: https://www.theguardian.com/science/2016/oct/22/nonsense-paper-written-by-ios-autocomplete-accepted-for-conference
177. I suspect that this is a symptom of us approaching peak-hype for machine learning: http://www.theregister.co.uk/2016/10/21/machine_learning_craze_reaches_freelance_market/
178. Brief summary of Stephen Hawking's thoughts on AI: http://betanews.com/2016/10/21/artificial-intelligence-stephen-hawking/
179. Assessing Clinton & Trumps emotional intelligence with AI: https://www.fastcompany.com/3064863/election-2016/watch-this-ai-platform-assess-trumps-and-clintons-emotional-intelligence
180. Why analog computing is a good match with AI: http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/analog-and-neuromorphic-chips-will-rule-robotic-age?utm_source=SocialFlow&utm_medium=Facebook&utm_campaign=Social&utm_content=FaceBook
181. Deep Fried Data: http://idlewords.com/talks/deep_fried_data.htm … How machine learning can make anything "taste" good
182. I always preferred the character of Chandler myself: https://www.theguardian.com/technology/2016/oct/20/joey-friends-virtual-digital-avatar-chatbot
183. The blind spot in AI research: http://www.nature.com/news/there-is-a-blind-spot-in-ai-research-1.20805
184. List of five free ebooks on machine learning: http://www.kdnuggets.com/2016/10/5-free-ebooks-machine-learning-career.html
185. Universities helping their staff with online profiles, but could it lead to unwanted uniformity? https://www.timeshighereducation.com/news/secret-shoppers-pimp-academics-online-profiles
186. A rather inspiring post by my old school-mate LenaRobinson: http://www.kiwigray.com/blog-kiwigray/2016/10/24/play-to-win-just-like-the-all-blacks
187. Why we need more diversity in AI: http://spectrum.ieee.org/tech-talk/at-work/tech-careers/computer-vision-leader-feifei-li-on-why-ai-needs-diversity?utm_source=SocialFlow&utm_medium=Facebook&utm_campaign=Social&utm_content=FaceBook
188. AI is not out to get us: https://www.scientificamerican.com/article/ai-is-not-out-to-get-us/
189. An AI "judge" that predicts the outcome of cases: https://www.theguardian.com/technology/2016/oct/24/artificial-intelligence-judge-university-college-london-computer-scientists - I wonder if they called it Dredd?
190. Awesome falsehoods - should be required reading for every programmer: https://github.com/kdeldycke/awesome-falsehood
191. Why advances in AI should be kept in the public eye: https://techcrunch.com/2016/10/23/advancements-in-artificial-intelligence-should-be-kept-in-the-public-eye/
192. TechCrunch explains WTF machine learning is: https://techcrunch.com/2016/10/23/wtf-is-machine-learning/ But article describes ANN rather than machine learning
193. No, I don't think that AI is going to end humanity: http://www.theregister.co.uk/2016/10/25/will_ai_spell_the_end_of_humanity_the_tech_industry_wants_you_to_think_so/
194. Overview of recurrent neural networks: http://www.datasciencecentral.com/profiles/blogs/recurrent-neural-nets-the-third-and-least-appreciated-leg-of-the-
195. Some sociological issues of intelligent machines: http://www.kdnuggets.com/2016/10/when-bow-down-machine-overlords.html
196. Overview of deep learning on GPU: http://www.datasciencecentral.com/profiles/blogs/accelerated-computing-and-deep-learning
197. IBM has made Watson AI available as a cloud service: http://www.techrepublic.com/article/ibm-says-new-watson-data-platform-will-bring-machine-learning-to-the-masses/
198. An AI for digesting research papers: https://techcrunch.com/2016/10/25/iris-ai-for-science/ Poor AI
199. Tutorial on implementing classification measures in Python: http://machinelearningmastery.com/implement-machine-learning-algorithm-performance-metrics-scratch-python/
200. Microsoft has released the next version of its Cognitive Toolkit to beta: https://techcrunch.com/2016/10/25/microsoft-launches-the-next-version-of-its-deep-learning-toolkit-into-beta/
201. Embedding AI in airport security scanners: https://www.theguardian.com/technology/2016/oct/25/airport-body-scanner-artificial-intelligence Badder than a bad thing that's very, very bad.
202. Even a flower-order business is betting on AI to increase it's business: http://computerworld.com/article/3135380/artificial-intelligence/1-800-flowers-wants-to-transform-its-business-with-ai.html
203. The impact of machine learning / AI on privacy: https://techcrunch.com/2016/10/26/the-darker-side-of-machine-learning/
204. Is AI going to create more jobs than it destroys? http://computerworld.com/article/3135081/artificial-intelligence/ai-and-robots-arent-gunning-for-your-job-white-house-economist-says.html
205. Pick your pastiche with deep learning: https://techcrunch.com/2016/10/26/deep-learning-tool-lets-you-pick-your-pastiche-mostly-monet-a-dab-of-dore-and-a-pinch-of-picasso/
206. AI-generated encryption: https://techcrunch.com/2016/10/28/googles-ai-creates-its-own-inhuman-encryption/
207. Paper on AI-generated encryption: https://arxiv.org/pdf/1610.06918v1.pdf
208. Identifying malicious URLs using machine learning: http://www.kdnuggets.com/2016/10/machine-learning-detect-malicious-urls.html
209. IBM says that in 5 years Watson will be behind every business & personal decision: http://computerworld.com/article/3135852/artificial-intelligence/ibm-in-5-years-watson-ai-will-be-behind-your-every-decision.html
210. Humans and machines together give better language understanding: http://dataconomy.com/ai-language-understanding/
211. Microsoft's Cognitive Toolkit is intended to bring machine learning to the masses: http://www.networkworld.com/article/3134877/application-development/microsoft-wants-to-bring-maching-learning-into-the-mainstream.html
212. What I look for when examining a postgraduate thesis: …http://computational-intelligence.blogspot.com/2016/10/examining-postgraduate-theses.html
213. An argument for why academics should not spend time on social media: https://www.timeshighereducation.com/blog/why-academics-should-not-make-time-social-media
214. A fairly comprehensive introduction to using neural networks in TensorFlow: https://www.analyticsvidhya.com/blog/2016/10/an-introduction-to-implementing-neural-networks-using-tensorflow
215. An argument that current AI is not good enough to justify a universal basic income: https://www.technologyreview.com/s/602747/todays-artificial-intelligence-does-not-justify-basic-income/?utm_campaign=internal&utm_medium=homepage&utm_source=top-stories_2
216. Generative adversarial neural networks: http://www.kdnuggets.com/2016/10/deep-learning-research-review-generative-adversarial-networks.html
217. Human intelligence + artificial intelligence is the future, not AI alone: https://techcrunch.com/2016/11/01/how-combined-human-and-computer-intelligence-will-redefine-jobs/
218. CCTV operators will be the next group made redundant by AI: http://www.theregister.co.uk/2016/11/02/nec_cctv_ai/
219. How to get good at R: http://www.kdnuggets.com/2016/11/data-science-101-good-at-r.html
220. A Bot-builder for non-programmers: https://techcrunch.com/2016/11/02/general-catalyst-backed-octane-will-make-you-a-bot/
221. 8 pitfalls for developers moving from R to Python: http://www.kdnuggets.com/2016/11/r-user-frustrating-learning-python.html
222. Automating customer complaints with machine learning: https://techcrunch.com/2016/11/02/resolver/
223. How Bayesian inference works: http://www.datasciencecentral.com/profiles/blogs/how-bayesian-inference-works
224. Microsoft Concept Graph: giving machines and AI common sense: https://techcrunch.com/2016/11/01/microsoft-strives-to-give-computers-common-sense-with-concept-graph/
225. Why AI and machine learning is hard: http://www.techrepublic.com/article/why-ai-and-machine-learning-are-so-hard-facebook-and-google-weigh-in/
226. A piece on my home town: http://www.nzherald.co.nz/travel/news/article.cfm?c_id=7&objectid=11739176
227. How machine learning is used in higher education: https://www.datanami.com/2016/11/01/data-analytics-higher-education/
228. How AI will transform business: http://blog.leadcrunch.com/ai-business-interviews-olin-hyde-ai-will-transform-businesses-as-profoundly-as-the-advent-of-the-internet?utm_campaign=Olin%20External%20Guest%20Blogs%20and%20Content&utm_content=41697510&utm_medium=social&utm_source=facebook
229. Even with AI entering the workforce, people are still needed: http://www.theverge.com/a/verge-2021/stacy-brown-philpot-taskrabbit-ceo-interview-ai-gig-economy
230. DeepMind is planning on taking on Starcraft 2 next: https://www.theguardian.com/technology/2016/nov/04/starcraft-ii-deepmind-game-ai
231. Paper on the good and the bad of using machine learning in higher education: https://www.newamerica.org/education-policy/policy-papers/promise-and-peril-predictive-analytics-higher-education/
232. There's more and more competition for fewer and fewer full-time permanent academic positions, so people are leaving: http://www.abc.net.au/news/2016-11-07/lack-of-funding-sees-scientists-leaving-labs-in-droves/7996604
233. An advance in automated lip reading accuracy, using deep learning over a fairly restricted data set: http://www.theverge.com/2016/11/7/13551210/ai-deep-learning-lip-reading-accuracy-oxford
234. Creating an unbiased model is hard as long as data sets are unbalanced: https://techcrunch.com/2016/11/07/why-its-so-hard-to-create-unbiased-artificial-intelligence/
235. When AI have "erotic" dreams (NSFW): https://open_nsfw.gitlab.io
236. Cleaning podcasts with deep learning: http://www.kdnuggets.com/2016/11/deep-learning-cleans-podcast-ahem-sounds.html
237. A brief and basic introduction to neural networks: http://www.kdnuggets.com/2016/11/quick-introduction-neural-networks.html
238. Using machine learning to count dugongs in drone images: https://techcrunch.com/2016/11/09/counting-endangered-sea-cows-is-hard-so-were-going-to-make-ai-do-it/
239. Classifying books by genre from their cover art, using deep learning: https://www.technologyreview.com/s/602807/deep-neural-network-learns-to-judge-books-by-their-covers/