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Artificial Intelligence: A Kalam and Sufi Perspective

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26 February 2024

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Abstract
The recent remarkable progress in Artificial Intelligence (AI) has renewed the discussion on the possibility of human-level AI. Despite the difficulty of the problem and the presence of many large gaps and challenges, most people working in the field think that human-level AI is achievable, but they disagree on the date. In this paper, I briefly discuss the possibility of human-level AI from the perspective of two traditions in the Islamic world, namely Kalam and Sufism, and despite their different mode of investigation, they converge to the same conclusion on this topic.
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Subject: Arts and Humanities  -   Philosophy

Introduction

Artificial Intelligence has recently produced many remarkable achievements and it is expected to show more in the future, however it still far from approaching human-level intelligence. Most of this progress come from Deep Learning (DL) which is very promising in pattern recognition, but it is still limited in high-level functions such as reasoning [1,2]. Although recent AI systems show some abstraction and reasoning abilities, however, they seem to use pattern matching, shortcuts, and memorization of some aspect of the reasoning process [50–54]. Despite the remarkable ability of Large Language Models (LLMs) [9,70–74] in learning some patterns of the reasoning process and then apply them in different context, they still lack understanding of the coherent text they produce when they are probed more deeply [68,75–78,86–88]. Another flaw in the argument that deep learning will lead to human-level intelligence is the assumption that intelligence will somehow emerge through training neural networks without providing any convincing justification for this assumption.
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[99]
There is also a growing interest recently in studying artificial general intelligence [3–9,79–81]. Despite few skeptical views [60–62,96], most experts in the field think that human level intelligence is achievable but they disagree on the date [8]. Although a large part of what we do could be automated, however, there is still something beyond these automatable tasks, this key necessary building block is understanding. Searle [10] illustrated the basic idea through the Chinese room experiment, he argued that computation will not be sufficient for thinking, understanding, and consciousness, and the causal power of the brain should be duplicated to achieve this goal. Penrose [11,85] also provided many examples to illustrate why computers will not be able to show human like understanding, he argued that understanding is not governed by rules and it is not a computational procedure. He suggested that understanding can be achieved using non computational processes and quantum mechanics might be a good candidate. Palmer [34] also provided many arguments why quantum mechanics should play a role such as energy efficiency. However, there is a clear categorical difference between understanding and known physical processes and what emerge from them.
To further illustrate the importance of understanding in the scientific discovery context, consider how the idea of quantum computing was developed, to be able to discover the idea, one should have understanding of the binary representation, understanding of how computer programs work, and understanding of how quantum mechanics works. Then to combine these ideas together to come up with the idea of quantum computing, without understanding of each of the above concepts this process cannot be achieved. Another example is the current attempts to unify quantum mechanics and general relativity, although we have an accurate mathematical description that can describe the behavior of these two theories, without a full understanding of both of them, combining them in a consistent way cannot be achieved. This was the case for unifying electricity and magnetism, unifying the weak and the electromagnetic forces, etc. Many other problems at the frontier of science have similar nature and would require understanding to be solved, current AI systems are only showing promising results on narrower problems such as finding the mathematical equation that describes specific phenomenon [59,60,82–84]. Several other examples can be given to show that there is something beyond computational processes and without it, AI systems will keep operating at a shallow level.
Muhtaroglu [13] discussed the possibility of achieving strong AI within both dualist and non-dualist views, where strong AI will be less challenging in a non-dualist view. He also discussed the Kalam view on free will [14]. Ritchie [15] provided a comprehensive discussion on God action in the natural world with focus on human consciousness, she argued that divine action theories that locate divine actions in a nonphysical mind are insufficient, she suggested that theistic naturalism is more compelling, where natural processes do not compete with divine actions, but participate with God in a natural manner.
Despite the potential of the Kalam and Sufi thoughts and the later synthesis between the two in addressing key challenges in the philosophical foundation of modern science and in the philosophical thoughts in general, we can only see few works that started to engage with these challenges. In this work, I only provide a brief summarization of the Kalam and Sufi perspective on the possibility of human-level intelligence in machines.

Kalam Perspective

One key difference between Kalam and other philosophical traditions in the Islamic world is that the Kalam tradition takes the compatibility between the world and divine actions and attributes more seriously when studying the natural world. Due to this key difference, each one of these traditions produced different philosophical foundations of natural science. Altaie [21,22,69] has recently discussed and summarized these key foundational principles with their implications. In this work I will focus on one key principle relevant to this work, namely causality [23–25,90,94].
In this work I will focus mainly on the Ash’arite school of Kalam with major figures like al-Ash’ari, al-Baqilani, al-Isfarayini, Ibn Furak, al-Juiani, al-Ghazali, al-Razi, al-Amidi, al-Iji, al-Taftazani, and to a lesser degree on the mu’tazilite school. The discussion on the rational bases and how these two schools developed their systems is beyond the scope of this paper (see [90,95] for more discussion). After establishing God existence and his attributes, God sovereignty is one of the main theme of the Ash’arite system, nothing in the world can happen outside the will of God, this emphasis allowed their system to be more consistent than the mu’tazilite system in addressing key challenges like the problem of evil. According to the Ash’arites nothing can have genuine causal power over anything and all causal power belong only to God, and any deterministic view that limits God sovereignty and actions in the world is necessarily flawed. The fire does not have a causal power to burn and God is the creator of the act of burning and he might decide not to create that action, but it is God habit to usually do so. The main implication of the Ash’arite view is that the laws of nature can not be deterministic. This view suggests that all laws of physics -not only quantum mechanics- (like gravity) should be probabilistic, similarly the laws of biology should be probabilistic, and finally the laws of intelligence, this might suggest that quantum mechanics could play a role in these fields too. Even if a physical system is completely isolated from external influences, God should be still able to change its behavior, which necessitate the probabilistic nature of the laws. Using similar reasoning, they proposed even a more radical idea namely recreation, which state that the entire world is recreated at each moment by His action, nothing can sustain its existence and without continues recreation everything will be annihilated. According to the Ash’arites, nothing has causal power, nothing can sustain its existence, and nothing can understand.
Although the Ash’arites consider rational investigation one of the main ways of knowing, the Ash’arite system shows that any deterministic view that limits God actions in the physical and the mental worlds is clearly flawed, this implies that human rational investigation cannot escape God sovereignty let alone the machines. Understanding which is a key aspect of knowing is something beyond the form (words, images, mathematics, etc.). Rational investigation does not have the causal power to produce understanding on its own, God may or may not create understanding after rational investigation. According to al-Ash’ari [47,94] God create knowledge at rational investigation usually similar to the creation of burning when there is a fire and He may not create it, which means understanding is also probabilistic, this is more clear in the case of understanding than in the case of other natural laws. According to al-Sabuni [26], there are two types of knowledge, a necessary (daruri) knowledge created by God without human choice like the whole is bigger than its parts, and acquired (iktisabi) knowledge created by God that is acquired by the human through senses and rational investigation. However, some Ash’arites like al-Razi [47,48] argued that knowledge is not probabilistic after rational investigation, but his view is still different from some mu’tazilites who argued for the emergence of knowledge at rational investigations [47], where God according to them placed a causal power in humans to know.
To the Ash’arites, God actions are applicable to possibilities, not to impossibilities, where violating mathematical and logical truths is impossible but understanding them is in the realm of possibilities, and this might be the limit of what machines can achieve from the Ash’arite perspective. Although mathematics and logic are the main factor behind AI achievements, however; assuming that they will lead to understanding is a main flaw in the argument for human-level AI.
Unlike other natural laws which are applicable everywhere and can be harnessed by us, God habit seems to be creating high-level understanding uniquely in humans when a limited aspect of the knowing subject (‘and breathed into him of my spirit’ [43]) is doing rational investigation, even within humans, the same rational investigation by different people not always produce understanding for some. Even under the assumption that everything is conscious, there is still a large gap between the level of understanding of humans and other animals. Although computer systems are based on quantum mechanics, these systems are built in a deterministic way which means that creating something beyond these deterministic processes would be an exception not the norm. Even for the brain itself, there is no evidence suggesting that the brain can be still conscious after death, even if it is provided with the necessary conditions to function. Near death experiences also seem to suggest that the brain alone is not sufficient for consciousness, particularly experiences that give a correct description of something beyond normal sensory abilities.
The above discussion shows that from the Ash’arite perspective, achieving human-level understanding in machines seems to be very unlikely unless He decides to create it in machines.

Sufi Perspective

The Sufis stress the limit of rational investigation in acquiring true knowledge, according to them there is a type of knowledge that cannot be acquired by rational investigation, similar to knowing the sweetness of honey. According to al-Ghazali [27] the true nature of things and the divine knowledge can be reflected in the mirror of the heart only if the mirror is purified and polished, when the heart is illuminated by the light of God [65].
The senses alone will give a partial and limited knowledge as demonstrated by al-Ghazali through many examples [32], and although rational investigation can provide a higher form of knowledge than the senses, this knowledge will be shaped and limited by the limits of the investigation method and by the limits of the investigator. The self can escape from these limitations only by a mean of ‘external objective reference’ [33]. Probably the best way to summarize this is by what attributed to Ibn Arabi describing Ibn Sina ‘It is a wonder how far this blind man reached with his crutch’, blindness here refer to blindness to unveiling (kashf), and the crutch refer to rational investigation, or Iqbal ‘When Ibn Sina was looking for the traces of the caravan, Rumi was lifting the veil from the face of the beloved’. However, there are several strict criteria [30,46,49] that should be met for this source of knowledge, this was the subject of many disagreements between the Kalam scholars and the Sufis [31,56], and between the Sufis themselves [30,46,49].
According to Ibn Arabi [33] the world itself is a veil, and except for the people of unveiling, all other knowledge such as rational investigation is knowledge of the veil through the veils of the self. The purification of the heart is the main way to receive this kind of knowledge, a transformational process that removes the veils to allow the self to see itself and others. The perspective and the way the self sees the world is changed by each stage of the transformation, until the veils are completely lifted and one reaches to his true self that can have true knowledge. According to the Sufis, the shoe of the I and the shoe of all things other than God should be left at the shore of the Ocean of Nonexistence. A light and a breeze from pre-eternity starts to awaken the spirit [39], the journey now is through the veils of lights to the inner most subtleties [93]. The sun of pre-eternity starts to appear at the middle of the sky, the shadows and the traces are no more. Moses had to take off his shoes when he approached the fire, and after the annihilation in the fire of divine love, he heard ‘listen to what is revealed’ [92]. This transformational process leads to the perfect human, at this stage the sight is sharpened from the timeless sight, the Sufi starts to know by God, it is by Him he sees and knows [89], ‘When I Love him, I am his hearing with which he hears, and his sight with which he sees’ [41]. The Sufi path is a journey in the world of meaning to the Infinite, therefore there is no limits to the knowledge that humans can have, and any end other than Him in the two worlds is just another veil.
The above discussion shows that it is hard to accept the possibility of having this second important source of knowledge namely unveiling in machines unless He decides to create it in machines.

Conclusions

In this paper, the Kalam and Sufi perspective on AI was briefly discussed. The paper showed that there is a limit on what machines can achieve. Therefore achieving human-level intelligence in machines seems to be very unlikely from the Ash’arite and the Sufi perspectives.

References

  1. Marcus, G. Deep learning: A critical appraisal. arXiv 2018, arXiv:1801.00631. [Google Scholar]
  2. Mitchell, M. Why AI is harder than we think. arXiv 2021, arXiv:2104.12871. [Google Scholar]
  3. Adams, S.; Arel, I.; Bach, J.; Coop, R.; Furlan,, R.; Goertzel, B.; Shapiro, S.C. Mapping the landscape of human-level artificial general intelligence. AI magazine 2012, 33, 25–42. [Google Scholar] [CrossRef]
  4. Lake, B. M.; Ullman,, T. D.; Tenenbaum,, J. B.; Gershman, S. J. Building machines that learn and think like people. Behavioral and brain sciences, 2017; 40. [Google Scholar]
  5. Clune, J. AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence. arXiv 2019, arXiv:1905.10985. [Google Scholar]
  6. Hutter, M. Universal artificial intelligence: Sequential decisions based on algorithmic probability; Springer Science & Business Media, 2004. [Google Scholar]
  7. Goertzel, B. The General Theory of General Intelligence: A Pragmatic Patternist Perspective. arXiv 2021, arXiv:2103.15100. [Google Scholar]
  8. Müller, V.C.; Bostrom, N. Future progress in artificial intelligence: A survey of expert opinion. In Fundamental issues of artificial intelligence; Springer: Cham, 2016; pp. 555–572. [Google Scholar]
  9. Webb, T.; Holyoak,, K.J.; Lu, H. Emergent Analogical Reasoning in Large Language Models. arXiv 2022, arXiv:2212.09196. [Google Scholar] [CrossRef] [PubMed]
  10. Searle, J. R. Minds, brains, and programs. Behavioral and brain sciences 1980, 3, 417–424. [Google Scholar] [CrossRef]
  11. Penrose, R.; Mermin, N.D. The emperor’s new mind: Concerning computers, minds, and the laws of physics; 1990. [Google Scholar]
  12. Hadamard, J. The mathematician’s mind: The psychology of invention in the mathematical field; 1996. [Google Scholar]
  13. Muhtaroglu, N. (2018) “An Analysis of the Strong AI in terms of Two Ontologies”, Templeton Science and Religion Conference: Consciousness and Artificial Intelligence, Trinity Hall, University of Cambridge and Cambridge Muslim College, Cambridge, England, 2018. https://www.youtube.com/watch?v=21y_Nnf3uSE.
  14. Muhtaroglu, N. An Occasionalist Defence of Free Will. In Classic Issues in Islamic Philosophy and Theology Today; Springer: Dordrecht, 2010; pp. 45–62. [Google Scholar]
  15. Ritchie, S. L. Divine action and the human mind; Cambridge University Press, 2019; Volume 14. [Google Scholar]
  16. Chalmers, D. The hard problem of consciousness. The blackwell companion to consciousness 2017, 2, 32–42. [Google Scholar]
  17. Wolfson, H. A. The philosophy of the Kalam; Harvard University Press, 1976; Volume 4. [Google Scholar]
  18. Walzer, R. Early Islamic Philosophy. In The Cambridge History of Later Greek and Early Medieval Philosophy, Cambridge; 1967; pp. 643–669. [Google Scholar]
  19. Harvey, R. Transcendent God, Rational World: A Maturidi Theology; Edinburgh University Press, 2021. [Google Scholar]
  20. Erlwein, H. C. Arguments for God’s Existence in Classical Islamic Thought; De Gruyter, 2019. [Google Scholar]
  21. Altaie, M. B. Daqiq al-Kalam: A Basis for an Islamic Philosophy of Science. Cambridge Muslim College Papers 2017, 4. [Google Scholar]
  22. Altaie, M. B. God, nature and the cause: Essays on Islam and science; Kalam Research et Media, 2016. [Google Scholar]
  23. Al-Ghazali, M. Tahafut al-Falasifa, p. 166-171.
  24. Marmura, M. E. The Incoherence of the Philosophers (Tahafut al-Falasifa). 1998. [Google Scholar]
  25. Altaie, M. B. Daqiq al-Kalam, Chapter 4. 2010.
  26. Al-Taftazani. Sharh al-’aqa’id al-nasafiyah. Maktabet al-kuliat al-azhariah, Cairo, p. 15-22.
  27. Lumbard, J. Abū Ḥāmid al-Ghazālī and the Art of Knowing. In Light upon Light: Essays in Islamic Thought and History in Honor of Gerhard Bowering; Brill, 2019; pp. 401–419. [Google Scholar]
  28. Ibn Arabi, Al-Futuhat al-Makkea (Part 3 – Maratib al-Ulowm).
  29. Arif, S. Sufi Epistemology: Ibn’Arabi on Knowledge. Afkar-Jurnal Akidah & Pemikiran Islam 2002, 3, 81–94. [Google Scholar]
  30. Al-Muhasibi, Al-Ri’ayah Li’Huquq-il-lah, p. 92-94.
  31. Rustom, M. Ibn ʿArabī’s Letter to Fakhr al-Dīn al-Rāzī: A Study and Translation. Journal of Islamic Studies 2014, 25, 113–137. [Google Scholar] [CrossRef]
  32. Al-Ghazali, M. Almunqidh min al-Dalal.
  33. Chittick, W. C. The self-disclosure of God: principles of Ibn al-’Arabi’s cosmology; SUNY Press, 2015. [Google Scholar]
  34. Palmer, T. Human creativity and consciousness: Unintended consequences of the brain’s extraordinary energy efficiency? Entropy 2020, 22, 281. [Google Scholar] [CrossRef] [PubMed]
  35. Ogunnaike, O. Sufism and Ifa: Ways of knowing in two West African intellectual traditions. 2015. [Google Scholar]
  36. Ogunnaike, O. Deep knowledge: ways of knowing in Sufism and Ifa, two West African intellectual traditions; Penn State Press, 2020; Volume 5. [Google Scholar]
  37. Ogunnaike, O. Shining of the Lights and the Veil of the Sights in the Secrets Bright: An Akbarī Approach to the Problem of Pure Consciousness. Journal of the Muhyiddin Ibn ‘Arabi Society 2017, 61. [Google Scholar]
  38. Casewit, Y. Shushtarī’s Treatise On the Limits of Theology and Sufism: Discursive Knowledge (ʿilm), Direct Recognition (maʿrifa), and Mystical Realization (tahqiq) in al-Risala al-Qusariyya. Religions 2020, 11, 226. [Google Scholar] [CrossRef]
  39. Ansari. A, Manazil al-Sa’irin (Chapter 66).
  40. Zargar, C. A. Transcending Character as a Quest for Union: The Place of al-Jamʿ in Sufi Ethical Commentaries on Khwaja Abdallah Ansari’s Waystations. In Mysticism and Ethics in Islam; 2022. [Google Scholar]
  41. Bukhari, 6502.
  42. Ibn Arabi, M. Tarjuman Al-Ashwaq.
  43. Quran 15:29.
  44. Villani, C. Birth of a Theorem: a mathematical adventure; Farrar, Straus and Giroux, 2015. [Google Scholar]
  45. Chollet, F. On the measure of intelligence. arXiv 2019, arXiv:1911.01547. [Google Scholar]
  46. Sohravardi, O, Awaref al-ma’aref (Chapter 57).
  47. al-Iji, A. A. D. al-Mawaqif fi Ilm al-Kalam. ‘Alam al-kutub, Beirut (p. 27-28).
  48. al-Razi, F. A. D. Muhasal afkar al-mutaqadmen wa al-muta’kheren min al-olama’ wa al-hukama’ wa al-mutakalimen. Maktabet al-kuliat al-azhariah, Cairo (p. 47-48).
  49. Sirhindi, A. Maktubat (Chapter 286).
  50. Saxton, D.; Grefenstette, E.; Hill, F.; Kohli, P. Analysing mathematical reasoning abilities of neural models. arXiv 2019, arXiv:1904.01557. [Google Scholar]
  51. Geirhos, R.; Jacobsen, J.H.; Michaelis, C.; Zemel, R.; Brendel, W.; Bethge, M.; Wichmann, F.A. Shortcut learning in deep neural networks. Nature Machine Intelligence 2020, 2, 665–673.
  52. Razeghi, Y.; Logan IV, R.L.; Gardner, M.; Singh, S. Impact of pretraining term frequencies on few-shot reasoning. arXiv 2022, arXiv:2202.07206. [Google Scholar]
  53. Zhang, H.; Li, L.H.; Meng, T.; Chang, K.W.; Broeck, G.V.D. On the Paradox of Learning to Reason from Data. arXiv 2022, arXiv:2205.11502. [Google Scholar]
  54. Srivastava, A.; Rastogi, A.; Rao, A.; Shoeb, A.A.M.; Abid, A.; Fisch, A.; Kim, H. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. arXiv 2022, arXiv:2206.04615. [Google Scholar]
  55. Chittick, W.C. The Sufi path of knowledge: Ibn al-Arabi’s metaphysics of imagination. Suny Press, 2010; p. 162. [Google Scholar]
  56. Al-Taftazani. Sharh al-’aqa’id al-nasafiyah. Maktabet al-kuliat al-azhariah, Cairo, p. 22.
  57. Foudeh, S. (2017) Maturidi Kalam and Modern Challenges to Theology, Understanding Maturidi Kalam: Legacy, Present & Future Challenges Conference. https://www.youtube.com/watch?v=8WAgKnV25VM.
  58. Udrescu, S. M.; Tegmark, M. AI Feynman: A physics-inspired method for symbolic regression. Science Advances 2020, 6, eaay2631. [Google Scholar]
  59. Udrescu, S.M.; Tegmark, M. Symbolic pregression: Discovering physical laws from distorted video. Physical Review E 2021, 103, 043307. [Google Scholar] [CrossRef] [PubMed]
  60. Fjelland, R. Why general artificial intelligence will not be realized. Humanities and Social Sciences Communications 2020, 7, 1–9. [Google Scholar] [CrossRef]
  61. Larson, E.J. The Myth of Artificial Intelligence. In The Myth of Artificial Intelligence; Harvard University Press, 2021. [Google Scholar]
  62. Landgrebe, J.; Smith, B. Why Machines Will Never Rule the World: Artificial Intelligence without Fear; 2022. [Google Scholar]
  63. Brown, J. H. The problem of reductionism in philosophy of mind and its implications for theism and the principle of soul: Framing the issue for further Islamic inquiry; Tabah Foundation, 2013. [Google Scholar]
  64. Lahham, K. The Anatomy of Knowledge & the Ontological Necessity of First Principles. Tabah Research. 2021. [Google Scholar]
  65. Spiker, H. Things as They are: Nafs al-Amr and the Metaphysical Foundations of Objective Truth. Tabah Research. 2021. [Google Scholar]
  66. Qaysari, Matla’ khusus al-kalim, 64.
  67. Malik, S.A.; Muhtaroglu, N. How Much Should or Can Science Impact Theological Formulations? An Ash’ari Perspective on Theology of Nature. European Journal of Analytic 2022, 18, S8–35. [Google Scholar]
  68. Mahowald, K.; Ivanova, A.A.; Blank, I.A.; Kanwisher, N.; Tenenbaum, J.B.; Fedorenko, E. Dissociating language and thought in large language models: a cognitive perspective. arXiv 2023, arXiv:2301.06627. [Google Scholar]
  69. Altaie, M. B. Islam and natural philosophy, principles of daqiq al-kalam. 2023.
  70. Lewkowycz, A.; Andreassen, A.; Dohan, D.; Dyer, E.; Michalewski, H.; Ramasesh, V.; Slone, A.; Anil, C.; Schlag, I.; Gutman-Solo, T.; Wu, Y.; Neyshabur, B.; Gur-Ari, G.; Misra, V. Solving quantitative reasoning problems with language models. arXiv 2022, arXiv:2206.14858. [Google Scholar]
  71. Bubeck, S.; Chandrasekaran, V.; Eldan, R.; Gehrke, J.; Horvitz, E.; Kamar, E.; Lee, P.; Lee, Y.T.; Li, Y.; Lundberg, S.; Nori, H.; Palangi, H.; Ribeiro, M.; Zhang, Y. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv 2023, arXiv:2303.12712. [Google Scholar]
  72. Kadavath, S.; Conerly, T.; Askell, A.; Henighan, T.; Drain, D.; Perez, E.; Kaplan, J. Language models (mostly) know what they know. arXiv 2022, arXiv:2207.05221. [Google Scholar]
  73. Burns, C.; Ye, H.; Klein, D.; Steinhardt, J. Discovering latent knowledge in language models without supervision. arXiv 2022, arXiv:2212.03827. [Google Scholar]
  74. Sun, W.; Yan, L.; Ma, X.; Ren, P.; Yin, D.; Ren, Z. Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent. arXiv 2023, arXiv:2304.09542. [Google Scholar]
  75. Valmeekam, K.; Sreedharan, S.; Marquez, M.; Olmo, A.; Kambhampati, S. On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark). arXiv 2023, arXiv:2302.06706. [Google Scholar]
  76. Liu, J.; Xia, C.S.; Wang, Y.; Zhang, L. Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation. arXiv 2023, arXiv:2305.01210. [Google Scholar]
  77. Schaeffer, R.; Miranda, B.; Koyejo, S. Are Emergent Abilities of Large Language Models a Mirage? arXiv 2023, arXiv:2304.15004. [Google Scholar]
  78. Liu, N. F.; Zhang, T.; Liang, P. Evaluating Verifiability in Generative Search Engines. arXiv 2023, arXiv:2304.09848. [Google Scholar]
  79. Adams, S.; Arel, I.; Bach, J.; Coop, R.; Furlan, R.; Goertzel, B.; Shapiro, S. C. Mapping the landscape of human-level artificial general intelligence. AI magazine 2012, 33, 25–42. [Google Scholar] [CrossRef]
  80. Goertzel, B. Artificial general intelligence: concept, state of the art, and future prospects. Journal of Artificial General Intelligence 2014, 5, 1–48. [Google Scholar] [CrossRef]
  81. Lake, B. M.; Ullman, T. D.; Tenenbaum, J. B.; Gershman, S. J. Building machines that learn and think like people. Behavioral and brain sciences 2017, 40. [Google Scholar] [CrossRef] [PubMed]
  82. Zheng, D.; Luo, V.; Wu, J.; Tenenbaum, J. B. Unsupervised learning of latent physical properties using perception-prediction networks. arXiv 2018, arXiv:1807.09244. [Google Scholar]
  83. Iten, R.; Metger, T.; Wilming, H.; Del Rio, L.; Renner, R. Discovering physical concepts with neural networks. arXiv 2018, arXiv:1807.10300. [Google Scholar] [CrossRef] [PubMed]
  84. Schawinski, K.; Turp, M.D.; Zhang, C. Exploring galaxy evolution with generative models. arXiv 2018, arXiv:1812.01114. [Google Scholar] [CrossRef]
  85. Penrose, R. The road to reality: A complete guide to the physical universe. Jonathan Cape. 2004. [Google Scholar]
  86. Dziri, N.; Lu, X.; Sclar, M.; Li, X. L.; Jian, L.; Lin, B. Y.; Choi, Y. Faith and Fate: Limits of Transformers on Compositionality. arXiv 2023, arXiv:2305.18654. [Google Scholar]
  87. Wang, X.; Hu, Z.; Lu, P.; Zhu, Y.; Zhang, J.; Subramaniam, S.; Wang, W. SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models. arXiv 2023, arXiv:2307.10635. [Google Scholar]
  88. Arkoudas, K. (2023). GPT-4 Can’t Reason. Preprints, 2023080148. [CrossRef]
  89. al-Aidarous, A. Al-kibrit al-ahmar (p. 49, p.53).
  90. al-Ghazali, M. Al-Iqtisad fi al-’i’tiqad.
  91. Ibn Arabi, Al-Futuhat al-Makkea (Part 2 – 498).
  92. Quran 20:13.
  93. Sirhindi, A. Maktubat (Chapter 260, 257, 253, 58).
  94. al-Jurjani, A. B. M. Sharh al-Mawaqif.
  95. al-Razi al-matalib al-aliyah.
  96. van Rooij, I.; Guest, O.; Adolfi, F. G.; de Haan, R.; Kolokolova, A.; Rich, P. Reclaiming AI as a theoretical tool for cognitive science. 2023. [Google Scholar]
  97. al-Ghazali, M. Mishkat al-anwār, “The niche of lights”.
  98. Chittick, W. C. The Sufi path of knowledge: Ibn al-Arabi’s metaphysics of imagination. Suny Press, 2010; p. 96. [Google Scholar]
  99. https://twitter.com/Beautif69668200.
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