Monday, June 19, 2017

Weekly Review 19 June 2017

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

  1. An Ask Slashdot discussion on jobs in AI: https://ask.slashdot.org/story/17/06/09/1954230/ask-slashdot-what-types-of-jobs-are-opening-up-in-the-new-field-of-ai
  2. A script-writing AI producing lines for David Hasselhoff: https://arstechnica.com/the-multiverse/2017/04/an-ai-wrote-all-of-david-hasselhoffs-lines-in-this-demented-short-film/
  3. Creating a classifier in SKLearn: http://www.datasciencecentral.com/profiles/blogs/creating-your-first-machine-learning-classifier-model-in-sklearn 
  4. Five things AI (neural networks) can do better than people: https://www.datanami.com/2017/06/08/5-things-ai-better/
  5. Using machine learning to detect identity thieves, from the way they move their mouse: https://qz.com/1003221/identity-theft-can-be-thwarted-by-artificial-intelligence-analysis-of-a-users-mouse-movements/
  6. Combining quantum computing and machine learning: http://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/kickstarting-the-quantum-startup-a-hybrid-of-quantum-computing-and-machine-learning-is-spawning-new-ventures
  7. Predicting suicide risk with machine learning: https://qz.com/1001968/artificial-intelligence-can-now-predict-suicide-with-remarkable-accuracy/
  8. A neural network based negotiating bot: https://qz.com/1004070/facebook-fb-built-an-ai-system-that-learned-to-lie-to-get-what-it-wants/
  9. Automating comment moderation with machine learning: https://www.recode.net/2017/6/13/15789178/new-york-times-expanding-comments-artificial-intelligence-google
  10. A deep learning powered four armed robot that composes marimba music: http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/four-armed-marimba-robot-uses-deep-learning-to-compose-its-own-music
  11. Yes, prospective employers will check your social media presences: https://www.seek.co.nz/career-advice/recruiters-reveal-top-3-social-media-fails
  12. A collection of 150 intelligent agents has beaten the game Ms Pacman: https://techcrunch.com/2017/06/15/microsofts-ai-beats-ms-pac-man/
  13. 8 techniques for data mining: https://www.datanami.com/2017/06/14/8-concrete-data-mining-techniques-will-deliver-best-results/
  14. Microsoft is using deep neural networks to turn photos into art: https://techcrunch.com/2017/06/15/microsoft-pix-can-now-turn-your-iphone-photos-into-art-thanks-to-a-i/
  15. Applying AI to call centres: http://www.techproresearch.com/article/how-artificial-intelligence-is-taking-call-centers-to-the-next-level/
  16. AI is predicted to create 800,000 jobs by 2021: http://www.techrepublic.com/article/can-ai-really-create-800000-jobs-by-2021-this-report-says-yes/

Tuesday, June 13, 2017

IEEE Transactions on Cognitive and Developmental Systems, Volume 9, Number 2, June 2017

1. Yielding Self-Perception in Robots Through Sensorimotor Contingencies
Author(s): P. Lanillos, E. Dean-Leon and G. Cheng
Pages: 100 - 112

2. Online Multimodal Ensemble Learning Using Self-Learned Sensorimotor Representations
Author(s): M. Zambelli and Y. Demirisy
Pages: 113 - 126

3. Perception of Localized Features During Robotic Sensorimotor Development
Author(s): A. Giagkos, D. Lewkowicz, P. Shaw, S. Kumar, M. Lee and Q. Shen
Pages: 113 - 126

4. Building a Sensorimotor Representation of a Naive Agent’s Tactile Space
Author(s): V. Marcel, S. Argentieri and B. Gas
Pages: 141 - 152

5. A Multimodal Model of Object Deformation Under Robotic Pushing
Author(s): V. E. Arriola-Rios and J. L. Wyatt
Pages: 153 - 169

6. Analysis of Cognitive Dissonance and Overload through Ability-Demand Gap Models
Author(s): G. Hossain and M. Yeasin
Pages: 170 - 182

7. Constructing a Language From Scratch: Combining Bottom–Up and Top–Down Learning Processes in a Computational Model of Language Acquisition
Author(s): J. Gaspers, P. Cimiano, K. Rohlfing and B. Wrede
Pages: 183 - 196

8. Behavior-Based SSVEP Hierarchical Architecture for Telepresence Control of Humanoid Robot to Achieve Full-Body Movement
Author(s): J. Zhao, W. Li, X. Mao, H. Hu, L. Niu and G. Chen
Pages: 197 - 209


Monday, June 12, 2017

IEEE Transactions on Fuzzy System, Volume 25, Issue 3, June 2017

1. A Profit Maximizing Solid Transportation Model Under a Rough Interval Approach
Author(s): A. Das, U. Kumar Bera and M. Maiti
Pages: 485-498

2. Design of State Feedback Adaptive Fuzzy Controllers for Second-Order Systems Using a Frequency Stability Criterion
Author(s): K. Wiktorowicz
Pages: 499-510

3. Dynamic Output-Feedback Dissipative Control for T–S Fuzzy Systems With Time-Varying Input Delay and Output Constraints
Author(s): H. D. Choi, C. K. Ahn, P. Shi, L. Wu and M. T. Lim
Pages: 511-526

4. Adaptive Predefined Performance Control for MIMO Systems With Unknown Direction via Generalized Fuzzy Hyperbolic Model
Author(s): L. Liu, Z. Wang, Z. Huang and H. Zhang
Pages: 527-542

5. Revisiting Fuzzy Set and Fuzzy Arithmetic Operators and Constructing New Operators in the Land of Probabilistic Linguistic Computing
Author(s): S. C. Ngan
Pages: 543-555

6. Asymptotic Fuzzy Tracking Control for a Class of Stochastic Strict-Feedback Systems
Author(s): C. Chen, Z. Liu, Y. Zhang, C. L. P. Chen and S. Xie
Pages: 556-568

7. Approaches to T–S Fuzzy-Affine-Model-Based Reliable Output Feedback Control for Nonlinear Itô Stochastic Systems
Author(s): Y. Wei, J. Qiu, H. K. Lam and L. Wu
Pages: 569-583

8. Pixel Modeling Using Histograms Based on Fuzzy Partitions for Dynamic Background Subtraction
Author(s): Z. Zeng, J. Jia, D. Yu, Y. Chen and Z. Zhu
Pages: 584-593

9. Varying Spread Fuzzy Regression for Affective Quality Estimation
Author(s): K. Y. Chan and U. Engelke
Pages: 594-613

10. Ranking of Multidimensional Uncertain Information Based on Metrics on the Fuzzy Ellipsoid Number Space
Author(s): G. Wang and Y. Li
Pages: 614-626

11. The Spatial Disaggregation Problem: Simulating Reasoning Using a Fuzzy Inference System
Author(s): J. Verstraete
Pages: 627-641

12. Adaptive Fuzzy Backstepping Tracking Control for Strict-Feedback Systems With Input Delay
Author(s): H. Li, L. Wang, H. Du and A. Boulkroune
Pages: 642-652

13. Dynamic Output Feedback-Predictive Control of a Takagi–Sugeno Model With Bounded Disturbance
Author(s): B. Ding and H. Pan
Pages: 653-667

14. Command-Filtered-Based Fuzzy Adaptive Control Design for MIMO-Switched Nonstrict-Feedback Nonlinear Systems
Author(s): Y. Li and S. Tong
Pages: 668-681

15. Type-2 Fuzzy Alpha-Cuts
Author(s): H. Hamrawi, S. Coupland and R. John
Pages: 682-692

16. A New Look at Type-2 Fuzzy Sets and Type-2 Fuzzy Logic Systems
Author(s): L. X. Wang
Pages: 693-706

17. LMI-Based Stability Analysis for Piecewise Multi-affine Systems
Author(s): A. T. Nguyen, M. Sugeno, V. Campos and M. Dambrine
Pages: 707-714

18. An Extended Type-Reduction Method for General Type-2 Fuzzy Sets
Author(s): B. K. Xie and S. J. Lee
Pages: 715-724

19. Critique of “A New Look at Type-2 Fuzzy Sets and Type-2 Fuzzy Logic Systems"
Author(s): J. M. Mendel and D. Wu
Pages: 725-727

Saturday, June 10, 2017

Weekly Review 9 June 2017

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

  1.  Microsoft has released version 2.0 of its deep learning toolkit: https://techcrunch.com/2017/06/01/microsoft-releases-version-2-0-of-its-deep-learning-toolkit/
  2. An overview of tensors, as used in TensorFlow: http://www.kdnuggets.com/2017/06/deep-learning-demystifying-tensors.html 
  3. How rat brains are inspiring the next generation of artificial neural networks: http://spectrum.ieee.org/biomedical/imaging/ai-designers-find-inspiration-in-rat-brains
  4. Despite the article title Watson isn't diagnosing cancer, it's helping plan how to treat it: https://news.fastcompany.com/ibm-says-watson-healths-ai-is-getting-really-good-at-diagnosing-cancer-4039394
  5. An overview of neurocomputing hardware - hardware inspired by the brain: http://spectrum.ieee.org/computing/hardware/the-brain-as-computer-bad-at-math-good-at-everything-else
  6. Four tips for job interviews - these apply to interviews for academic positions as well: https://www.seek.co.nz/career-advice/4-ways-to-prepare-for-an-interview
  7. Three things intelligent machines need to copy from the human brain: http://spectrum.ieee.org/computing/software/what-intelligent-machines-need-to-learn-from-the-neocortex
  8. Hawkins' book "On Intelligence" is very good, must re-read sometime soon: http://spectrum.ieee.org/computing/software/what-intelligent-machines-need-to-learn-from-the-neocortex
  9. So valedictorians (dux in NZ) go on to lead good lives, but seldom achieve particularly highly: http://time.com/money/4779223/valedictorian-success-research-barking-up-wrong/
  10. Why we need to build an artificial brain or, why neuromorphic computing is a good idea: http://spectrum.ieee.org/computing/hardware/can-we-copy-the-brain
  11. We could build an artificial brain now, but probably wouldn't want to pay for it: http://spectrum.ieee.org/computing/hardware/we-could-build-an-artificial-brain-right-now
  12. If you're looking for a job, be careful what you say on social media (including Twitter!) http://jobs.ieee.org/jobs/content/Looking-for-a-Job-Double-Check-Your-Social-Media-Accounts-First-2017-04-18
  13. Deep learning can't tell what Homer Simpson is doing. Or, why DeepMind built an enormous database of videos: http://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/deepmind-shows-ai-has-trouble-seeing-homer-simpson-actions
  14. If I am advertising for a new staff member, I will read the cover letters of applicants: https://www.seek.co.nz/career-advice/does-anyone-actually-read-cover-letters
  15. Using AI in children's educational apps: https://techcrunch.com/2017/06/07/sesame-workshop-and-ibm-team-up-to-test-a-new-a-i-powered-teaching-method/
  16. Combining relational networks with deep learning: https://www.theregister.co.uk/2017/06/09/deepmind_teaches_ai_to_reason/
  17. Paper on adding relational reasoning to deep neural networks: https://arxiv.org/abs/1706.01427
  18. How AI can make things worse, instead of better: http://www.kdnuggets.com/2017/06/unintended-consequences-machine-learning.html

Friday, June 9, 2017

Neural Networks, Volume 92, Pages 1-98, August 2017

Special Issue "Advances in Cognitive Engineering Using Neural Networks" Edited by Minho Lee, Steven Bressler and Robert Kozma

1. Advances in Cognitive Engineering Using Neural Networks  
Author(s): Minho Lee, Steven Bressler, Robert Kozma
Pages: 1-2

2. How can a recurrent neurodynamic predictive coding model cope with fluctuation in temporal patterns? Robotic experiments on imitative interaction   
Author(s): Ahmadreza Ahmadi, Jun Tani
Pages: 3-16

3. Dual-memory neural networks for modeling cognitive activities of humans via wearable sensors   
Author(s): Sang-Woo Lee, Chung-Yeon Lee, Dong-Hyun Kwak, Jung-Woo Ha, Jeonghee Kim, Byoung-Tak Zhang
Pages: 17-28

4. Understanding human intention by connecting perception and action learning in artificial agents   
Author(s): Sangwook Kim, Zhibin Yu, Minho Lee
Pages: 29-38

5. Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation   
Author(s): Peerajak Witoonchart, Prabhas Chongstitvatana
Pages: 39-46

6. Reprint of “Two-stage sparse coding of region covariance via Log-Euclidean kernels to detect saliency”   
Author(s): Ying-Ying Zhang, Cai Yang, Ping Zhang
Pages: 47-59

7. Evaluating deep learning architectures for Speech Emotion Recognition   
Author(s): Haytham M. Fayek, Margaret Lech, Lawrence Cavedon
Pages: 60-68

8. Weighted spatial based geometric scheme as an efficient algorithm for analyzing single-trial EEGS to improve cue-based BCI classification   
Author(s): Fatemeh Alimardani, Reza Boostani, Benjamin Blankertz
Pages: 69-76

9. Prediction of advertisement preference by fusing EEG response and sentiment analysis   
Author(s): Himaanshu Gauba, Pradeep Kumar, Partha Pratim Roy, Priyanka Singh, Debi Prosad Dogra, Balasubramanian Raman
Pages: 77-88

10. A Hypergraph and Arithmetic Residue-based Probabilistic Neural Network for classification in Intrusion Detection Systems   
Author(s): M.R. Gauthama Raman, Nivethitha Somu, Kannan Kirthivasan, V.S. Shankar Sriram
Pages: 89-97

Thursday, June 8, 2017

IEEE Transactions on Emerging Topics in Computational Intelligence, Volume 1, Issue 3, June 2017

Special Issue on Computational Intelligence for Software Engineering and Services Computing

1. WebAPIRec: Recommending Web APIs to Software Projects via Personalized Ranking
Author(s): F. Thung, R. J. Oentaryo, D. Lo and Y. Tian
Pages: 145-156

2. Context-Aware, Adaptive, and Scalable Android Malware Detection Through Online Learning
Author(s): A. Narayanan, M. Chandramohan, L. Chen and Y. Liu
Pages: 157-175

3. A Model-Driven Approach to Enable Adaptive QoS in DDS-Based Middleware
Author(s): J. F. Inglés-Romero, A. Romero-Garcés, C. Vicente-Chicote and J. Martínez
Pages: 176-187

4. Search-Based Energy Optimization of Some Ubiquitous Algorithms
Author(s): A. E. I. Brownlee, N. Burles and J. Swan
Pages: 188-201

5. Dynamic Selection of Classifiers in Bug Prediction: An Adaptive Method
Author(s): D. Di Nucci, F. Palomba, R. Oliveto and A. De Lucia
Pages: 202-212

6. Epistasis Based ACO for Regression Test Case Prioritization
Author(s): Y. Bian, Z. Li, R. Zhao and D. Gong
Pages: 213-223

7. A Model-Driven Methodology for the Design of Autonomic and Cognitive IoT-Based Systems: Application to Healthcare
Author(s): E. Mezghani, E. Exposito and K. Drira
Pages: 224-234

Saturday, June 3, 2017

Complex & Intelligent Systems, Vol. 3, No. 2, June 2017

1. Constructing enhanced default theories incrementally
Author(s): Ghassan Beydoun, Achim Hoffmann, Asif Gill
Pages: 83-92

2. Age estimation from a face image in a selected gender-race group based on ranked local binary patterns
Author(s): Andrey Rybintsev
Pages: 93-104

3. An insight into imbalanced Big Data classification: outcomes and challenges
Author(s): Alberto Fernández, Sara del Río, Nitesh V. Chawla, Francisco Herrera
Pages: 105-120

4. An evolutionary hybrid method to predict pistachio price
Author(s): Azim Heydari, Farshid Keynia, Nasser Shahsavari-Pour, Reza Sedaghat
Pages: 121-132

5. Big data for secure healthcare system: a conceptual design
Author(s): Bikash Kanti Sarkar
Pages: 133-151

Friday, June 2, 2017

Weeky Review 2 June 2017

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

  1. An interview on how deep learning is used to infer human emotions: https://www.oreilly.com/ideas/how-ai-is-used-to-infer-human-emotion
  2. Why quantum computing is necessary for the future of AI: http://theinstitute.ieee.org/ieee-roundup/blogs/blog/artificial-intelligences-potential-will-be-realized-by-quantum-computing 
  3. Who is liable when an AI misdiagnoses a patient? https://qz.com/989137/when-a-robot-ai-doctor-misdiagnoses-you-whos-to-blame/ A society being over-litigious makes this a problem.
  4. Shifting jobs in IT - what is going to become obsolete, and what is going to become more important: http://www.infoworld.com/article/3196022/it-careers/the-working-dead-it-jobs-bound-for-extinction.html
  5. AlphaGo has defeated the world's best human Go player: https://www.theguardian.com/technology/2017/may/23/alphago-google-ai-beats-ke-jie-china-go
  6. "Music" composed by a neuromorphic chip: http://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/a-neuromorphic-chip-that-makes-music 
  7. AI in healthcare works better alongside humans, not replacing them: http://www.informationweek.com/healthcare/analytics/ai-works-better-with-human-intelligence-too/d/d-id/1328903?
  8. The relationship between AI and statistics (and abuse thereof): http://www.theregister.co.uk/2017/05/24/fear_of_statistics/ 
  9. AlphaGo has defeated the world's best human Go player in 3 out of 3 matches: https://techcrunch.com/2017/05/24/alphago-beats-planets-best-human-go-player-ke-jie/ 
  10. Time for academics to again control academic journals, rather than greedy, predatory corporations: https://www.theguardian.com/higher-education-network/2017/may/25/its-time-for-academics-to-take-back-control-of-research-journals 
  11. Using facial recognition technology to track if students are paying attention to a lecture: https://www.theverge.com/2017/5/26/15679806/ai-education-facial-recognition-nestor-france 
  12. A list of free tools for visualising data: http://computerworld.com/article/2507728/enterprise-applications/enterprise-applications-22-free-tools-for-data-visualization-and-analysis.html
  13. How could we tell if a general AI were truly conscious? http://spectrum.ieee.org/computing/hardware/can-we-quantify-machine-consciousness 
  14. Applying deep learning to sports analytics: https://www.datanami.com/2017/05/26/deep-learning-revolutionize-sports-analytics-heres/ 
  15. Is Apple developing a neural network chip? https://www.theverge.com/2017/5/26/15702248/apple-neural-engine-ai-chip-iphone-ipad 
  16. Not so much racist algorithms, as the benchmark data sets are lacking diversity: https://www.theguardian.com/technology/2017/may/28/joy-buolamwini-when-algorithms-are-racist-facial-recognition-bias 
  17. An AI that detects other AI: https://motherboard.vice.com/en_us/article/this-dystopian-wearable-detects-ais-pretending-to-be-humans 
  18. Brief explanation of what an ontology is: http://www.kdnuggets.com/2017/05/ontology-simplest-definition.html 
  19. Quite a bit of disagreement over when AI will overtake humans: http://www.theregister.co.uk/2017/05/26/job_automation_by_ai_timeline/ 
  20. Why it's a silly idea to try to teach everyone to code: http://www.techrepublic.com/article/why-the-notion-that-everyone-should-learn-to-code-is-complete-rubbish/ 
  21. The challenges of identifying trust with machine learning: http://www.kdnuggets.com/2017/05/challenges-machine-learning-trust.html 
  22. The second wave of AI, and how it will impact humanity: http://www.techrepublic.com/article/why-ai-will-force-businesses-to-rethink-balance-between-the-work-of-humans-and-machines/ 
  23. Nuts-ml, a Python library intended to preprocess data for machine learning algorithms: http://www.kdnuggets.com/2017/05/data-pre-processing-deep-learning-nuts-ml.html 
  24. How Google is using machine learning to improve security in GMail: http://www.techrepublic.com/article/google-looks-to-machine-learning-to-boost-security-in-gmail/
  25. Is China going to outstrip the USA in AI? https://www.nytimes.com/2017/05/27/technology/china-us-ai-artificial-intelligence.html?_r=1
  26. A system that uses a neural network to generate code from a screenshot of a GUI: https://siliconangle.com/blog/2017/05/28/startup-uses-ai-create-gui-source-code-simple-screenshots/
  27. The New Zealand AI Forum is launching: http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=11867287


IEEE Transactions on Neural Networks and Learning Systems, Volume 28, Issue 6, June 2017

1. Holographic Graph Neuron: A Bioinspired Architecture for Pattern Processing
Author(s): Denis Kleyko; Evgeny Osipov; Alexander Senior; Asad I. Khan; Yaşar Ahmet Şekerciogğlu
Page(s): 1250 - 1262

2. Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection
Author(s): Xiaofeng Zhu; Xuelong Li; Shichao Zhang; Chunhua Ju; Xindong Wu
Page(s): 1263 - 1275

3. Rate of Convergence of the FOCUSS Algorithm
Author(s): Kan Xie; Zhaoshui He; Andrzej Cichocki; Xiaozhao Fang
Page(s): 1276 - 1289

4. Action and Event Recognition in Videos by Learning From Heterogeneous Web Sources
Author(s): Li Niu; Xinxing Xu; Lin Chen; Lixin Duan; Dong Xu
Page(s): 1290 - 1304

5. Mapping Temporal Variables Into the NeuCube for Improved Pattern Recognition, Predictive Modeling, and Understanding of Stream Data
Author(s): Enmei Tu; Nikola Kasabov; Jie Yang
Page(s): 1305 - 1317

6. Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints
Author(s): Ziting Chen; Zhijun Li; C. L. Philip Chen
Page(s): 1318 - 1330

7. Co-Operative Coevolutionary Neural Networks for Mining Functional Association Rules
Author(s): Bing Wang; Kathryn E. Merrick; Hussein A. Abbass
Page(s): 1331 - 1344

8. Quantum Ensemble Classification: A Sampling-Based Learning Control Approach
Author(s): Chunlin Chen; Daoyi Dong; Bo Qi; Ian R. Petersen; Herschel Rabitz
Page(s): 1345 - 1359

9. A Regularizer Approach for RBF Networks Under the Concurrent Weight Failure Situation
Author(s): Chi-Sing Leung; Wai Yan Wan; Ruibin Feng
Page(s): 1360 - 1372

10. Clustering Through Hybrid Network Architecture With Support Vectors
Author(s): Emrah Ergul; Nafiz Arica; Narendra Ahuja; Sarp Erturk
Page(s): 1373 - 1385

11. Design and Application of a Variable Selection Method for Multilayer Perceptron Neural Network With LASSO
Author(s): Kai Sun; Shao-Hsuan Huang; David Shan-Hill Wong; Shi-Shang Jang
Page(s): 1386 - 1396

12. Robustly Fitting and Forecasting Dynamical Data With Electromagnetically Coupled Artificial Neural Network: A Data Compression Method
Author(s): Ziyin Wang; Mandan Liu; Yicheng Cheng; Rubin Wang
Page(s): 1397 - 1410

13. Efficient Training of Supervised Spiking Neural Network via Accurate Synaptic-Efficiency Adjustment Method
Author(s): Xiurui Xie; Hong Qu; Zhang Yi; Jürgen Kurths
Page(s): 1411 - 1424

14. Sparseness Analysis in the Pretraining of Deep Neural Networks
Author(s): Jun Li; Tong Zhang; Wei Luo; Jian Yang; Xiao-Tong Yuan; Jian Zhang
Page(s): 1425 - 1438

15. Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy
Author(s): Zi-Jun Jia; Yong-Duan Song
Page(s): 1439 - 1451

16. Label Propagation via Teaching-to-Learn and Learning-to-Teach
Author(s): Chen Gong; Dacheng Tao; Wei Liu; Liu Liu; Jie Yang
Page(s): 1452 - 1465

17. Optimized Kernel Entropy Components
Author(s): Emma Izquierdo-Verdiguier; Valero Laparra; Robert Jenssen; Luis Gómez-Chova; Gustau Camps-Valls
Page(s): 1466 - 1472

18. Quantized Iterative Learning Consensus Tracking of Digital Networks With Limited Information Communication
Author(s): Wenjun Xiong; Xinghuo Yu; Yao Chen; Jie Gao
Page(s): 1473 - 1480

19. Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control
Author(s): Yongping Pan; Haoyong Yu
Page(s): 1481 - 1487

Tuesday, May 23, 2017

Weekly Review 23 May 2017

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

  1. The importance of visual (image) data to future developments in AI: https://techcrunch.com/2017/05/17/the-war-over-artificial-intelligence-will-be-won-with-visual-data/
  2. One-shot visual learning of tasks from humans in VR with neural networks: http://www.theregister.co.uk/2017/05/16/openai_teaches_robot_to_learn_from_human_demonstrations_in_vr/ 
  3. Detecting masks and guns in CCTV feeds, using deep learning: https://techcrunch.com/2017/05/16/deep-science-ai-monitors-security-feeds-for-masks-and-guns-to-quicken-response-times/ 
  4. Machine learning can eliminate bias in hiring, but only if the training data is unbiased: http://www.techrepublic.com/article/how-machine-learning-can-help-companies-eliminate-bias-in-hiring/ 
  5. Diagnosing concussions with deep learning: https://techcrunch.com/2017/05/16/brightlamp-wants-to-use-ai-to-spot-concussions/ 
  6. Conference submission deadline: ICONIP 2017 https://computational-intelligence.blogspot.com/2017/05/conference-submission-deadline-iconip.html 
  7. How to build a text classifier utilising machine learning: https://blog.monkeylearn.com/how-to-create-text-classifiers-machine-learning/
  8. This guy does not like the R language, and not without some good reasons: http://www.datasciencecentral.com/profiles/blogs/why-r-is-bad-for-you 
  9. Academic resumes tend to be long, but it helps to put the main points on the first or second page: https://www.seek.co.nz/career-advice/5-things-employers-wish-they-could-say-about-your-resume
  10. 5 different types of recommenders explained: http://www.datasciencecentral.com/profiles/blogs/5-types-of-recommenders 
  11. Google has brought TensorFlow to Android: https://techcrunch.com/2017/05/17/googles-tensorflow-lite-brings-machine-learning-to-android-devices/
  12. A neural network that invents new paint colours and names: http://lewisandquark.tumblr.com/post/160776374467/new-paint-colors-invented-by-neural-network "Clardic fug" is probably my favourite paint name.
  13. How to build, and convert into code, decisions trees in Python: http://www.kdnuggets.com/2017/05/simplifying-decision-tree-interpretation-decision-rules-python.html 
  14. Google's AI is starting to manage relationships between people: https://www.theverge.com/2017/5/19/15660610/google-photos-ai-relationship-emotional-labor 
  15. AI in agriculture-a bit vague about what the AI is, though: http://www.datasciencecentral.com/profiles/blogs/artificial-intelligence-in-agriculture-what-s-next 
  16. Google is making its Tensor Processing Units available via the cloud: https://www.datanami.com/2017/05/18/cloud-tpu-bolsters-googles-ai-first-strategy/ 
  17. An argument that AI is not driving cloud adoption: https://www.theregister.co.uk/2017/05/18/baby_steps_not_ai_drives_cloud_growth/ 
  18. So Canada's developing a nation-wide AI strategy? http://www.datasciencecentral.com/profiles/blogs/pan-canadian-artificial-intelligence-strategy 
  19. An AI that is "outrageous in a great way": http://www.theregister.co.uk/2017/05/22/deeptingle_ai_transforms_writing/