PROBLEMS AND OPPORTUNITIES OF ARTIFICIAL INTELLIGENCE

Necmi Gürsakal; Sadullah Çelik; Bülent Batmaz

doi:10.54688/ayd.1104830

EN TR

PROBLEMS AND OPPORTUNITIES OF ARTIFICIAL INTELLIGENCE

Abstract

This article reviews Artificial Intelligence (AI)’s challenges and opportunities and discusses where AI might be headed. In the first part of the article, it was tried to reveal the differences between Symbolic AI and Deep Learning approaches, then long promises but short deliveries of AI were mentioned. When we review the problems of AI in general terms, it is a problem that the media has high expectations about AI and keeps the problems and restrictions it creates low. Today, while AI is stuck with issues such as deepfake applications and carbon footprints that create moral and climatologic problems; on the other hand, it is struggling with problems such as deep learning models requiring huge amounts of data. Another problem with deep learning is that deep learning models are a black-box and not open to improvements because it is not known where mistakes were made. Among the new paths ahead of AI are Hierarchical Temporal Memory (HTM) models and hybrid models that generally try to bridge the gap between Symbolic AI and Connectionist AI. If we consider that the most important leaps in AI have been made with the features of the brain that AI can imitate, then the developed HTM models may also be a new opportunity for AI.

Keywords

Hierarchical Temporal Memory , Deep Learning , GPT-3 , Black-box , Carbon Footprint

PROBLEMS AND OPPORTUNITIES OF ARTIFICIAL INTELLIGENCE

Öz

This article reviews Artificial Intelligence (AI)’s challenges and opportunities and discusses where AI might be headed. In the first part of the article, it was tried to reveal the differences between Symbolic AI and Deep Learning approaches, then long promises but short deliveries of AI were mentioned. When we review the problems of AI in general terms, it is a problem that the media has high expectations about AI and keeps the problems and restrictions it creates low. Today, while AI is stuck with issues such as deepfake applications and carbon footprints that create moral and climatologic problems; on the other hand, it is struggling with problems such as deep learning models requiring huge amounts of data. Another problem with deep learning is that deep learning models are a black-box and not open to improvements because it is not known where mistakes were made. Among the new paths ahead of AI are Hierarchical Temporal Memory (HTM) models and hybrid models that generally try to bridge the gap between Symbolic AI and Connectionist AI. If we consider that the most important leaps in AI have been made with the features of the brain that AI can imitate, then the developed HTM models may also be a new opportunity for AI.

Anahtar Kelimeler

Deep Learning , GPT-3 , Black-box

Kaynakça

Abid, A., Farooqi, M., & Zou, J. (2021). Persistent Anti-Muslim Bias in Large Language Models. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 298–306. https://doi.org/10.1145/3461702.3462624
Ahmad, S., & Scheinkman, L. (2019). How Can We Be So Dense? The Benefits of Using Highly Sparse Representations.
Barlow, H. B. (1961). Possible Principles Underlying the Transformations of Sensory Messages. In W. A. Rosenblith (Ed.), Sensory Communication. https://www.cnbc.cmu.edu/~tai/microns_papers/Barlow-SensoryCommunication-1961.pdf
Bathaee, Y. (2018). The Artificial Intelligence Black Box and the Failure of Intent and Causation. Harvard Journal of Law & Technology (Harvard JOLT), 31. https://heinonline.org/HOL/Page?handle=hein.journals/hjlt31&id=907&div=&collection=
Bautista, I., Sarkar, S., & Bhanja, S. (2020). MatlabHTM: A sequence memory model of neocortical layers for anomaly detection. SoftwareX, 11, 100491. https://doi.org/10.1016/j.softx.2020.100491
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922
Bengio, Y. (2022, January 24). Superintelligence: Futurology vs. Science. https://yoshuabengio.org/2022/01/24/superintelligence-futurology-vs-science/
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., Arx, S. von, Rernstein, M. S., & Liang, P. (2021). On the opportunities and risks of foundation models. https://arxiv.org/pdf/2108.07258.pdf?utm_source=morning_brew
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., Anderson, H., Roff, H., Allen, G. C., Steinhardt, J., Flynn, C., Héigeartaigh, S. Ó., Beard, S., Belfield, H., Farquhar, S., … Amodei, D. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. https://arxiv.org/ftp/arxiv/papers/1802/1802.07228.pdf
Buhrmester, V., Münch, D., & Arens, M. (2021). Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey. Machine Learning and Knowledge Extraction, 3(4), 966–989. https://doi.org/10.3390/make3040048

Byrne, F. (2015). Encoding reality: prediction-assisted cortical learning algorithm in hierarchical temporal memory. https://arxiv.org/pdf/1509.08255.pdf
Chaudhuri, S., Ellis, K., Polozov, O., Singh, R., Solar-Lezama, A., & Yue, Y. (2021). Neurosymbolic Programming. Foundations and Trends® in Programming Languages, 7(3), 158–243. https://doi.org/10.1561/2500000049
Dickson, B. (2019, June 5). What happens when you combine neural networks and rule-based AI? . https://bdtechtalks.com/2019/06/05/mit-ibm-hybrid-ai/
Dickson, B. (2022, March 14). Neuro-symbolic AI brings us closer to machines with common sense. https://bdtechtalks.com/2022/03/14/neuro-symbolic-ai-common-sense/
Egri-Nagy, A., & Törmänen, A. (2022). Advancing Human Understanding with Deep Learning Go AI Engines. IS4SI 2021, 22. https://doi.org/10.3390/proceedings2022081022
Feldman, P., Dant, A., & Massey, A. (2019). Integrating Artificial Intelligence into Weapon Systems.
Fraternali, P., Milani, F., Nahime Torres, R., Zangrando, N., & di Milano Piazza Leonardo, P. (2022). Black-box error diagnosis in deep neural networks: a survey of tools.
Garcez, A. S. d’Avila, Broda, K., & Gabbay, D. M. (2002). Neural-Symbolic Learning Systems: Foundations and Applications . Springer Science & Business Media. https://books.google.com.tr/books?hl=tr&lr=&id=6NZYVSOyD-UC&oi=fnd&pg=PA1&dq=Garcez+A.+S.+d.,+K.+B.+Broda,+D.+M.+Gabbay,+Neuralsymbolic+learning+systems:+foundations+and+applications,+2002,++Springer+Science+%26+Business+Media.&ots=sUdRBFNTa8&sig=EA0zcWKtyfTS-GNs_uZXIiG3Y_I&redir_esc=y#v=onepage&q&f=false
Hao, K. (2019, June 6). Training a single AI model can emit as much carbon as five cars in their lifetimes. https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/
Hawkins, J. (2021). A Thousand Brains: A New Theory of Intelligence Hardcover.
Hawkins, J., & Ahmad, S. (2016). Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex. Frontiers in Neural Circuits, 10. https://doi.org/10.3389/fncir.2016.00023
Hawkins, J., & Blakeslee, S. (2004). On Intelligence. Owl Books/Times Books.
Hawkins, J., Lewis, M., Klukas, M., Purdy, S., & Ahmad, S. (2019). A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex. Frontiers in Neural Circuits, 12. https://doi.org/10.3389/fncir.2018.00121
Hoover, A. K., Spryszynski, A., & Halper, M. (2019). Deep Learning in the IT Curriculum. Proceedings of the 20th Annual SIG Conference on Information Technology Education, 49–54. https://doi.org/10.1145/3349266.3351406
Kahneman, D. (2011). Thinking, fast and slow. http://103.38.12.142:8081/jspui/bitstream/123456789/541/1/Thinking%2C%20Fast%20and%20Slow.pdf
Kahneman, D. (2012). Of 2 Minds: How Fast and Slow Thinking Shape Perception and Choice [Excerpt] . Scientific American.
Krestinskaya, O., Ibrayev, T., & James, A. P. (2018). Hierarchical Temporal Memory Features with Memristor Logic Circuits for Pattern Recognition. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(6), 1143–1156. https://doi.org/10.1109/TCAD.2017.2748024
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25. http://code.google.com/p/cuda-convnet/
Kurfess, F. J. (2003). Artificial Intelligence. In Encyclopedia of Physical Science and Technology (pp. 609–629). Elsevier. https://doi.org/10.1016/B0-12-227410-5/00027-2
Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019). Quantifying the Carbon Emissions of Machine Learning. https://mlco2.github.
Lannelongue, L., Grealey, J., Inouye, M., Lannelongue, L., & Inouye, M. (2021). Green Algorithms: Quantifying the Carbon Footprint of Computation. https://doi.org/10.1002/advs.202100707
Lehky, S. R., Tanaka, K., & Sereno, A. B. (2021). Pseudosparse neural coding in the visual system of primates. Communications Biology, 4(1), 50. https://doi.org/10.1038/s42003-020-01572-2
Lomonaco, V. (2019, October 24). A Machine Learning Guide to HTM (Hierarchical Temporal Memory). https://numenta.com/blog/2019/10/24/machine-learning-guide-to-htm
Mao, J., Gan, C., Kohli, P., Tenenbaum, J. B., & Wu, J. (2019, April 26). The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. ICLR 2019. https://arxiv.org/pdf/1904.12584.pdf
Marcus, G. (2022, March 10). Deep Learning Is Hitting a Wall. https://nautil.us/deep-learning-is-hitting-a-wall-14467/
Marcus, G., & Davis, E. (2020, August 22). GPT-3, Bloviator: OpenAI’s language generator has no idea what it’s talking about. https://www.technologyreview.com/2020/08/22/1007539/gpt3-openai-language-generator-artificial-intelligence-ai-opinion/
Masson-Delmotte, V., Zhai, P., Chen, Y., Goldfarb, L., Gomis, M. I., Matthews, J. B. R., Berger, S., Huang, M., Yelekçi, O., Yu, R., Zhou, B., Lonnoy, E., Maycock, T. K., Waterfield, T., Leitzell,
K., & Caud, N. (2021). Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change Edited by. www.ipcc.ch
Mortensen, J. (2022, March 16). Is Black Box AI Dangerous? . Tech Evaluate? https://www.techevaluate.com/is-black-box-ai-dangerous/
Naser, M. Z., & Ross, B. (2022). An opinion piece on the dos and don’ts of artificial intelligence in civil engineering and charting a path from data-driven analysis to causal knowledge discovery. Civil Engineering and Environmental Systems, 1–11. https://doi.org/10.1080/10286608.2022.2049257
Niu, D., Yang, L., Cai, T., Li, L., Wu, X., & Wang, Z. (2022). A New Hierarchical Temporal Memory Algorithm Based on Activation Intensity. Computational Intelligence and Neuroscience, 2022, 1–17. https://doi.org/10.1155/2022/6072316
Numenta. (2017). Biological And Machine Intelligence (BAMI). https://numenta.com/assets/pdf/biological-and-machine-intelligence/BAMI-Complete.pdf
Olshausen, B. A., & Field, D. J. (2004). Sparse coding of sensory inputs. Current Opinion in Neurobiology, 14(4), 481–487. https://doi.org/10.1016/J.CONB.2004.07.007
Pati, S. (2022, March 7). The Future of Artificial Intelligence and Metaverse in 2030. https://zipe-education.com/the-future-of-artificial-intelligence-and-metaverse/
Purdue University News. (2021, March 5). Think the brain is always efficient? Think again. https://www.purdue.edu/newsroom/releases/2021/Q1/think-the-brain-is-always-efficient-think-again..HTML
Riganelli, O., Saltarel, P., Tundo, A., Mobilio, M., & Mariani, L. (2021). Cloud Failure Prediction with Hierarchical Temporal Memory: An Empirical Assessment. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 785–790. https://doi.org/10.1109/ICMLA52953.2021.00130
Schuller, B. W., Akman, A., Chang, Y., Coppock, H., Gebhard, A., Kathan, A., Rituerto-González, E., Triantafyllopoulos, A., & Pokorny, F. B. (2022). Climate Change & Computer Audition: A Call To Action And Overview On Audio Intelligence To Help Save The Planet A Preprint.
Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63. https://doi.org/10.1145/3381831
Shah, D., Ghate, P., Paranjape, M., & Kumar, A. (2021). Application of Hierarchical Temporal Memory Theory for Document Categorization.
Soon, O. Y., & Hui, L. K. (2022, April 16). Making artificial intelligence work for sustainability. https://technologymagazine.com/ai-and-machine-learning/making-artificial-intelligence-work-for-sustainability
Soviany, P., Ionescu, R. T., Rota, P., & Sebe, N. (2022). Curriculum Learning: A Survey.
Strubell, E., Ganesh, A., & Mccallum, A. (2019). Energy and Policy Considerations for Deep Learning in NLP. https://bit.ly/2JTbGnI
Struye, J., & Latré, S. (2020). Hierarchical temporal memory and recurrent neural networks for time series prediction: An empirical validation and reduction to multilayer perceptrons. Neurocomputing, 396, 291–301. https://doi.org/10.1016/j.neucom.2018.09.098
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013). Intriguing properties of neural networks. Intriguing Properties of Neural Networks. https://arxiv.org/pdf/1312.6199.pdf?source=post_page
Taylor, P. (2022, January 24). IBM sells off large parts of Watson Health business -. https://pharmaphorum.com/news/ibm-sells-off-large-parts-of-watson-health-business/
Thoppilan, R., Freitas, D. de, Hall, J., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., Li, Y., Lee, H., Zheng, S. H., Ghafouri, A., & Le, Q. (2022). LaMDA: Language Models for Dialog Applications. https://arxiv.org/pdf/2201.08239.pdf
Trafton, A. (2022, February 17). Dendrites may help neurons perform complicated calculations. MIT News | Massachusetts Institute of Technology. https://news.mit.edu/2022/dendrites-help-neurons-perform-0217
Urbina, F., Lentzos, F., Invernizzi, C., & Ekins, S. (2022). Dual use of artificial-intelligence-powered drug discovery. Nature Machine Intelligence, 4(3), 189–191. https://doi.org/10.1038/s42256-022-00465-9
Vale, D., El-Sharif, A., & Ali, M. (2022). Explainable artificial intelligence (XAI) post-hoc explainability methods: risks and limitations in non-discrimination law. AI and Ethics, 1, 3. https://doi.org/10.1007/s43681-022-00142-y
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., Kenton, Z., Brown, S., Hawkins, W., Stepleton, T., Biles, C., Birhane, A., Haas, J., Rimell, L., Hendricks, L. A., … Com>,
Yang, G., Hu, E. J., Babuschkin, I., Sidor, S., Liu, X., Farhi, D., Ryder, N., Pachocki, J., Chen, W., & Gao, J. (2022). Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer.
Zednik, C. (2021). Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence. Philosophy & Technology, 34(2), 265–288. https://doi.org/10.1007/s13347-019-00382-7

Ayrıntılar

Birincil Dil

İngilizce

Konular

Ekonomi

Bölüm

Derleme

Yazarlar

Necmi Gürsakal
0000-0002-7909-3734
Türkiye

Sadullah Çelik ^*
0000-0001-5468-475X
Türkiye

Bülent Batmaz
0000-0003-4706-5808
Türkiye

Yayımlanma Tarihi

30 Haziran 2022

Gönderilme Tarihi

17 Nisan 2022

Kabul Tarihi

15 Haziran 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 13 Sayı: 1

DOI

https://doi.org/10.54688/ayd.1104830

IZ

https://izlik.org/JA43MR27LM

APA

Gürsakal, N., Çelik, S., & Batmaz, B. (2022). PROBLEMS AND OPPORTUNITIES OF ARTIFICIAL INTELLIGENCE. Journal of Academic Approaches, 13(1), 203-225. https://doi.org/10.54688/ayd.1104830