Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Brain Intelligence: Go beyond Artificial Intelligence

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan’s economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing next-generation artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called “Beyond AI”. Specifically, we plan to develop an intelligent learning model called “Brain Intelligence (BI)” that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Siri, https://en.wikipedia.org/wiki/Siri. Accessed 20 April 2017

  2. AlphaGo, https://deepmind.com/research/alphago/. Accessed 20 April 2017

  3. IBM Watson, https://www.ibm.com/watson/. Accessed 20 April 2017

  4. Microsoft Translator Speech API, https://www.microsoft.com/en-us/translator/speech.aspx. Accessed 20 April 2017

  5. Amazon Prime Air, https://www.amazon.com/Amazon-Prime-Air/b?node=8037720011. Accessed 20 April 2017

  6. Taigman Y, Yang M, Ranzato M, Wolf L (2014) “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” IEEE International Conference on Computer Vision and Pattern Recognition (CVPR2014), pp. 1–8

  7. Stanford Artificial Intelligence Laboratory, http://ai.stanford.edu/. Accessed 20 April 2017

  8. MIT BigDog, https://slice.mit.edu/big-dog/. Accessed 20 April 2017

  9. The 4th Science and Technology Basic Plan of Japan, http://www8.cao.go.jp/cstp/english/basic/. Accessed 20 April 2017

  10. AI EXPO, http://www.ai-expo.jp/en/. Accessed 20 April 2017

  11. 2017 Will Be the Year of AI, http://fortune.com/2016/12/30/the-year-of-artificial-intelligence/. Accessed 20 April 2017

  12. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  13. Bengio Y, Ducharme R, Vincent P, Janvin C (2003) A neural probabilistic language model. J Mach Learn Res 3:1137–1155

    MATH  Google Scholar 

  14. Mnih A, Hinton G (2007) “Three new graphical models for statistical language modelling,” In Proc of ICML07, pp. 641–648

  15. T. Mikolov, M. Karafiat, L. Burget, J. Cernocky, S. Khudanpur, (2010) “Recurrent neural network based language model,” In Proc of Interspeech 10, pp. 1045–1048

  16. Sutskever I, Vinyals O, Le Q (2014) “Sequence to sequence learning with neural networks,” In Advances in Neural Information Processing Systems, pp. 3104–3112

  17. Mei H, Bansal M, Walter M (2016) “What to talk about and how? Selective generation using LSTMs with coarse-to-fine alignment,” In NAACL-HLT, pp. 1–11

  18. Luong M, Le Q, Sutskever I, Vinyals O, Kaiser L (2016) “Multitask sequence to sequence learning,” In Proc ICLR, pp. 1–10

  19. Bourlard H, Morgan M (1994) Connnectionist speech recognition: a hybrid approach. Kluwer Academic Publishers, Netherlands

  20. Graves A, Mohamed A, Hinton G (2013) “Speech recognition with deep recurrent neural networks,” In ICASSP 2013, pp. 1–5

  21. Wiederhold B, Riva G, Wiederhold M (2015) Virtual reality in healthcare: medical simulation and experiential interface. ARCTT 13:239

    Google Scholar 

  22. Bartsch G, Mitra A, Mitra S, Almal A, Steven K, Skinner D, Fry D, Lenehan P, Worzel W, Cote R (2016) Use of artificial intelligence and machine learning algorithms with gene expression profiling to predict recurrent nonmuscle invasive urothelial carcinoma of the bladder. J Urol 195:493–498

    Article  Google Scholar 

  23. Labonnote N, Hoyland K (2017) Smart home technologies that support independent living: challenges and opportunities for the building industry – a systematic mapping study. Intell Buildings Int 29(1):40–63

    Article  Google Scholar 

  24. Chetlur S, Woolley C, Vandermersch P, Cohen J, Tran J, Catanzaro B, Shelhamer E (2014) Cudnn: efficient primitives for deep learning, pp. 1–10, arXiv:1410.0759

  25. Coates A, Huval B, Wang T, Wu D, Catanzaro B, Andrew N (2013) Deep learning with cots hpc systems, In Proc of the 30th International Conference on Machine Learning, pp. 1337–1345

  26. Lacey G, Taylor G, Areibi S (2016) Deep learning on FPGAs: past, present, and future, pp. 1–8, ar Xiv: 1602.04283

  27. Lin W, Lin S, Yang T (2017) Integrated business prestige and artificial intelligence for corporate decision making in dynamic environments. Cybern Syst:1–22. https://doi.org/10.1080/01969722.2017.1284533

  28. Ratnaparkhi A, Pilli E, Joshi R (2016) Survey of scaling platforms for deep neural networks, In Proc of International Conference on Emerging Trends in Communication Technologies, pp. 1–6

  29. Raina R, Madhavan A, Ng A (2009) Large-scale deep unsupervised learning using graphics processors, In Proc of 26th Annual International Conference on Machine Learning, pp. 873–880

  30. Catanzaro B (2013) Deep learning with COTS HPC systems, In Proc of the 30th International Conference on Machine Learning, pp. 1337–1345

  31. Dean J, Corrado G, Monga R, Chen K, Devin M, Le Q, Mao M, Ranzato M, Senior A, Tucker P, Yang K, Ng A (2012) Large scale distributed deep networks, In Proc of Advances in Neural Information Processing Systems, pp. 1223–1231

  32. Chilimbi T, Suzue Y, Apacible J, Kalyanaraman K (2014) Project adam: Building an efficient and scalable deep learning training system, In Proc of 11th USENIX Symposium on Operating Systems Design and Implementation, pp. 571–582

  33. Qualcomm Zeroth, https://www.qualcomm.com/invention/cognitive-technologies/zeroth. Accessed 27 April 2017

  34. Merolla P, Arthur J, Alvarez-lcaza R, Cassidy A, Sawada J, Akopyan F, Jackson B, Imam N, Guo C, Nakamura Y, Brezzo B, Vo I, Esser S, Appuswamy R, Taba B, Amir A, Flickner M, Risk W, Manohar R, Modha D (2014) A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197):668–673

    Article  Google Scholar 

  35. Khan M, Lester D, Plana L, Rast A, Jin X, Painkras E, Furber S (2008) SpiiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor, In Proc of IEEE International Joint Conference on Neural Networks, pp. 2849–2856

  36. Lacity M, Willcocks L (2016) A new approach to automating services. MIT Sloan Manag Rev 2016:1–16

    Google Scholar 

  37. Mikolov T, Karafiat M, Burget L, Cernocky J, Khudanpur S (2010) Recurrent neural network based language model, In Proc of Interspeech 2010, pp. 1045–1048

  38. Schuster M, Paliwal K (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673–2681

    Article  Google Scholar 

  39. Graves A, Jaitly N, Mohamed A (2013) Hybrid speech recognition with deep bidirectional LSTM, In Proc of IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 1–4

  40. Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architecutres. Neural Netw 18(5–6):602–610

    Article  Google Scholar 

  41. Mishra A, Desai V (2006) Drought forecasting using feed-forward recursive neural network. Ecol Model 198(1–2):127–138

    Article  Google Scholar 

  42. Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Li F (2014) Large-scale video classification with convolutional neural networks, In Proc of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732

  43. LeCun Y, Boser B, Denker J, Henderson D, Howard R, Hubbard W, Jackel L (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551

    Article  Google Scholar 

  44. Bell R, Koren Y (2007) Lessons from the Netflix prize challenge. ACM SIGKDD Explorations Newsletter 9(2):75–79

    Article  Google Scholar 

  45. Simonyan K, Zisserman A (2015) “Very deep convolutional networks for large-scale image recognition,” In Proc of IEEE ICLR2015, pp. 1–14

  46. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions, In Proc of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–12

  47. Arora S, Bhaskara A, Ge R, and Ma T (2013) “Provable bounds for learning some deep representations,” arXiv:abs/1310.6343

  48. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, In Proc. Of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–12

  49. Chen M, Ma Y, Li Y, Wu D, Zhang Y (2017) Wearable 2.0: enabling human-cloud integration in next generation healthcare systems. IEEE Commun Mag 54(12):3–9

    Google Scholar 

  50. Song J, Zhang Y (2016) TOLA: Topic-oriented learning assistance based on cyber-physical system and big data. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2016.05.040

  51. Zhang Y (2016) Grorec: a group-centric intelligent recommender system integrating social, mobile and big data technologies. IEEE Trans Serv Comput 9(5):786–795

    Article  Google Scholar 

  52. Liu Q, Ma Y, Alhussein M, Zhang Y, Peng L (2016) Green data center with IoT sensing and cloud-assisted smart temperature controlling system. Comput Netw 101:104–112

    Article  Google Scholar 

  53. Chen D, Manning C (2014) A fast and accurate dependency parser using neural networks, In Proc of Empirical Methods in Natural Language Processing, pp. 740–750

  54. Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences, In Proc of Annual Meeting of the Association for Computational Linguistics, pp. 655–665

  55. Ciresan D, Meier U, Masci J, Schmidhuber J (2012) Multi-column deep neural network for traffic sign classification. Neural Netw 32:333–338

    Article  Google Scholar 

  56. Santos C, Xiang B, Zhou B (2015) Classifying relations by ranking with convolutional neural networks, In Proc of Annual Meeting of the Association for Computational Linguistics, pp. 626–634

  57. Hu B, Tu Z, Lu Z, Chen Q (2015) Context-dependent translation selection using convolutional neural network, In Proc of Annual Meeting of the Association for Computational Linguistics, pp. 536–541

  58. Li Y, Lu H, Li J, Li X, Li Y, Serikawa S (2016) Underwater image de-scattering and classification by deep neural network. Comput Electr Eng 54:68–77

    Article  Google Scholar 

  59. Lu H, Li B, Zhu J, Li Y, Li Y, Xu X, He L, Li X, Li J, Serikawa S (2017) Wound intensity correction and segmentation with convolutional neural networks. Concurr Comput: Pract Experience 29(6):1–8

    Article  Google Scholar 

  60. Lu H, Li Y, Uemura T, Ge Z, Xu X, He L, Serikawa S, Kim H (2017) FDCNet: filtering deep convolutional network for marine organism classification, Multimedia Tools and Applications, pp. 1–14

  61. Lu H, Li Y, Zhang L, Serikawa S (2015) Contrast enhancement for images in turbid water. J Opt Soc Am 32(5):886–893

    Article  Google Scholar 

  62. Serikawa S, Shimomura T (1998) Proposal of a system of function-discovery using a bug type of artificial life. Trans IEE Jpn 118-C(2):170–179

    Google Scholar 

  63. Stanley K, Miikkulainen R (2002) Evolving neural networks through augmenting topologies. Evol Comput 10(2):99–127

    Article  Google Scholar 

  64. Schrum J, Miikkulainen R (2014) Evolving multimodal behavior with modular neural networks in Ms. Pac-Man, In Proc of the Genetic and Evolutionary Computation Conference, pp. 325–332

  65. Stanley K, Ambrosio D, Gauci J (2009) A hypercube-based encoding for evolving large-scale neural networks. Artif Life 15(2):185–212

    Article  Google Scholar 

  66. Emmert-Streib F, Dehmer M, Haibe-Kains B (2014) Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks. Front Cell Dev Biol 2(38):1–7

    Google Scholar 

  67. Dinh H, Aubert M, Noman N, Fujii T, Rondelez Y, Iba H (2015) An effective method for evolving reaction networks in synthetic biochemical systems. IEEE Trans Evol Comput 19(3):374–386

    Article  Google Scholar 

  68. Hwang K, Chen M (2017) Big-data analytics for cloud, IoT and cognitive computing. Press, Wiley, 432 pages

    Google Scholar 

Download references

Acknowledgements

This work was supported by Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Grants-in-Aid for Scientific Research of JSPS (17 K14694), Research Fund of Chinese Academy of Sciences (No.MGE2015KG02), Research Fund of State Key Laboratory of Marine Geology in Tongji University (MGK1608), Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University (1510), Research Fund of The Telecommunications Advancement Foundation, and Fundamental Research Developing Association for Shipbuilding and Offshore.

Author information

Authors and Affiliations

  1. Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu, Hyoungseop Kim & Seiichi Serikawa

  2. Yangzhou University, Yangzhou, China

    Yujie Li

  3. Huazhong University of Science and Technology, Wuhan, China

    Min Chen

Authors
  1. Huimin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yujie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hyoungseop Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Seiichi Serikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huimin Lu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, H., Li, Y., Chen, M. et al. Brain Intelligence: Go beyond Artificial Intelligence. Mobile Netw Appl 23, 368–375 (2018). https://doi.org/10.1007/s11036-017-0932-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-017-0932-8

Keywords

Search

Navigation