(2) Francesco Bellman (Department of Philosophy and Communication Studies, University of Bologna, Italy)
(3) Heri Nurdiyanto (STMIK Dharma Wacana, Indonesia)
*corresponding author
AbstractThe field of synthetic biology benefits significantly from the application of artificial intelligence. I'd want to make three suggestions, which all have something to do with the past, the present, and the future of artificial intelligence. The works of Turing and von Neumann in biology and artificial systems from the past are exciting to investigate within the new framework of synthetic biology, particularly regarding the concepts of self-modification and self-replication as well as their links to the emergence and the bottom-up approach. The ongoing epistemological investigation into the emergence and the research being conducted on swarm intelligence, superorganisms, and biologically inspired cognitive architecture may result in discoveries on the potential uses of synthetic biology to explain mental processes. Finally, the current discussion on the future of artificial intelligence and the rise of superintelligence may point to some research trends for the future of synthetic biology and help to better define the boundary of concepts such as "life," "cognition," "artificial," and "natural," as well as their interconnections in theoretical synthetic biology. In addition, the rise of superintelligence may point to some research trends for the future of synthetic biology
KeywordsArtificial intelligence Synthetic biology Cognitive systems Emergence Superorganism Superintelligence
|
DOIhttps://doi.org/10.29099/ijair.v7i1.348 |
Article metrics10.29099/ijair.v7i1.348 Abstract views : 960 | PDF views : 1382 |
Cite |
Full TextDownload |
References
A. Xu, F. Qian, C. H. Pai, N. Yu, and P. Zhou, “The Impact of COVID-19 Epidemic on the Development of the Digital Economy of China—Based on the Data of 31 Provinces in China,†Front. Public Heal., vol. 9, Jan. 2022, doi: 10.3389/FPUBH.2021.778671.
R. L. Priya, A. Abirami, and N. Desai, “Machine Learning-Based Emerging Technologies in the Post Pandemic Scenario,†pp. 51–90, 2022, doi: 10.1007/978-3-031-04597-4_3.
R. P. H. Yue, H. F. Lee, and C. Y. H. Wu, “Trade routes and plague transmission in pre-industrial Europe,†Sci. Rep., vol. 7, no. 1, Dec. 2017, doi: 10.1038/S41598-017-13481-2.
J. Castro-Bedriñana, D. Chirinos-Peinado, and G. Castro-Chirinos, “Emergency Remote Education Satisfaction during COVID-19 at a Public University in Central Andes, Peru with Low Resources and Little Online Teaching Experience,†Educ. Sci. Theory Pract., vol. 22, no. 1, pp. 46–51, Apr. 2022, doi: 10.12738/JESTP.2022.1.0005.
Z. Yu et al., “Rapid identification of COVID-19 severity in CT scans through classification of deep features,†Biomed. Eng. Online, vol. 19, no. 1, Aug. 2020, doi: 10.1186/S12938-020-00807-X.
R. M. Wehbe et al., “DeepCOVID-XR: An artificial intelligence algorithm to detect COVID-19 on chest radiographs trained and tested on a large U.S. Clinical data set,†Radiology, vol. 299, no. 1, pp. E167–E176, Apr. 2021, doi: 10.1148/RADIOL.2020203511.
V. Tornincasa et al., “Integrated Digital Health Solutions in the Management of Growth Disorders in Pediatric Patients Receiving Growth Hormone Therapy: A Retrospective Analysis,†Front. Endocrinol. (Lausanne)., vol. 13, Jun. 2022, doi: 10.3389/FENDO.2022.882192.
S. Whitelaw, M. A. Mamas, E. Topol, and H. G. C. Van Spall, “Applications of digital technology in COVID-19 pandemic planning and response,†Lancet Digit. Heal., vol. 2, no. 8, pp. e435–e440, Aug. 2020, doi: 10.1016/S2589-7500(20)30142-4.
J. Zhu, B. Shen, A. Abbasi, M. Hoshmand-Kochi, H. Li, and T. Q. Duong, “Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs,†PLoS One, vol. 15, no. 7 July, Jul. 2020, doi: 10.1371/JOURNAL.PONE.0236621.
X. Xu et al., “A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia,†Engineering, vol. 6, no. 10, pp. 1122–1129, Oct. 2020, doi: 10.1016/J.ENG.2020.04.010.
M. van der Schaar et al., “How artificial intelligence and machine learning can help healthcare systems respond to COVID-19,†Mach. Learn., vol. 110, no. 1, pp. 1–14, Jan. 2021, doi: 10.1007/S10994-020-05928-X.
K. Wang, S. Kang, R. Tian, X. Zhang, and Y. Wang, “Imaging manifestations and diagnostic value of chest CT of coronavirus disease 2019 (COVID-19) in the Xiaogan area,†Clin. Radiol., vol. 75, no. 5, pp. 341–347, May 2020, doi: 10.1016/J.CRAD.2020.03.004.
R. M. Summers, “Artificial intelligence of COVID-19 imaging: A hammer in search of a nail,†Radiology, vol. 298, no. 3, pp. E169–E171, Mar. 2021, doi: 10.1148/RADIOL.2020204226.
M. H. Tayarani N., “Applications of artificial intelligence in battling against covid-19: A literature review,†Chaos, Solitons and Fractals, vol. 142, Jan. 2021, doi: 10.1016/J.CHAOS.2020.110338.
L. R. Sultan and C. M. Sehgal, “A Review of Early Experience in Lung Ultrasound in the Diagnosis and Management of COVID-19,†Ultrasound Med. Biol., vol. 46, no. 9, pp. 2530–2545, Sep. 2020, doi: 10.1016/J.ULTRASMEDBIO.2020.05.012.
A. Ramsetty and C. Adams, “Impact of the digital divide in the age of COVID-19,†J. Am. Med. Informatics Assoc., vol. 27, no. 7, pp. 1147–1148, Jul. 2020, doi: 10.1093/JAMIA/OCAA078.
F. Shi et al., “Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19,†IEEE Rev. Biomed. Eng., vol. 14, pp. 4–15, 2021, doi: 10.1109/RBME.2020.2987975.
T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images,†Comput. Biol. Med., vol. 121, Jun. 2020, doi: 10.1016/J.COMPBIOMED.2020.103792.
X. Pu, K. Chen, J. Liu, J. Wen, S. Zhneng, and H. Li, “Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19,†Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, vol. 37, no. 3, pp. 365–372, Jun. 2020, doi: 10.7507/1001-5515.202003045.
A. Shamekh, A. Mahmoodpoor, and S. Sanaie, “COVID-19: Is it the black death of the 21st century?,†Heal. Promot. Perspect., vol. 10, no. 3, pp. 166–167, Jul. 2020, doi: 10.34172/HPP.2020.27.
tahir öztürk, eyup zengin, and fırat erpala, “Volar Locking Plate Fixations for Displaced Distal Radius Fractures: An Evaluation of Functional and Radiographic Outcomes,†Hand Microsurg., no. 0, p. 1, 2020, doi: 10.5455/HANDMICROSURG.87738.
W. Naudé, “Artificial intelligence vs COVID-19: limitations, constraints and pitfalls,†AI Soc., vol. 35, no. 3, pp. 761–765, Sep. 2020, doi: 10.1007/S00146-020-00978-0.
E. Mbunge, “Integrating emerging technologies into COVID-19 contact tracing: Opportunities, challenges and pitfalls,†Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 6, pp. 1631–1636, Nov. 2020, doi: 10.1016/J.DSX.2020.08.029.
F. Shaikh et al., “Current Landscape of Imaging and the Potential Role for Artificial Intelligence in the Management of COVID-19,†Curr. Probl. Diagn. Radiol., vol. 50, no. 3, pp. 430–435, May 2021, doi: 10.1067/J.CPRADIOL.2020.06.009.
Z. Mastaneh and A. Mouseli, “Technology and its Solutions in the Era of COVID-19 Crisis: A Review of Literature,†Evid. Based Heal. Policy, Manag. Econ., Jun. 2020, doi: 10.18502/JEBHPME.V4I2.3438.
B. K. Scott et al., “Advanced digital health technologies for COVID-19 and future emergencies,†Telemed. e-Health, vol. 26, no. 10, pp. 1226–1233, Oct. 2020, doi: 10.1089/TMJ.2020.0140.
M. Reyes et al., “On the interpretability of artificial intelligence in radiology: Challenges and opportunities,†Radiol. Artif. Intell., vol. 2, no. 3, May 2020, doi: 10.1148/RYAI.2020190043.
A. Liberati et al., “The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration,†J. Clin. Epidemiol., vol. 62, no. 10, pp. e1–e34, Oct. 2009, doi: 10.1016/J.JCLINEPI.2009.06.006.
L. Li et al., “Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy,†Radiology, vol. 296, no. 2, pp. E65–E71, Aug. 2020, doi: 10.1148/RADIOL.2020200905.
S. Lalmuanawma, J. Hussain, and L. Chhakchhuak, “Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review,†Chaos, Solitons and Fractals, vol. 139, Oct. 2020, doi: 10.1016/J.CHAOS.2020.110059.
S. Kundu, A. D. Burman, S. K. Giri, S. Mukherjee, and S. Banerjee, “Selective harmonics elimination for three-phase seven-level CHB inverter using backtracking search algorithm,†Int. J. Power Electron., vol. 11, no. 1, pp. 1–19, 2020, doi: 10.1504/IJPELEC.2020.103947.
H. Kim, Gyoonhee Han, and Jae-Hoon Song, “A Review for Artificial Intelligence Proving to Fight Against COVID-19 Pandemic and Prefatory Health Policy,†J. Med. Biomed. Appl. Sci., vol. 8, no. 8, pp. 494–506, Aug. 2020, doi: 10.15520/JMBAS.V8I8.247.
S. A. Harmon et al., “Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets,†Nat. Commun., vol. 11, no. 1, Dec. 2020, doi: 10.1038/S41467-020-17971-2.
M. E. H. Chowdhury et al., “Can AI Help in Screening Viral and COVID-19 Pneumonia?,†IEEE Access, vol. 8, pp. 132665–132676, 2020, doi: 10.1109/ACCESS.2020.3010287.
A. S. Albahri et al., “Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods,†Int. J. Inf. Technol. Decis. Mak., vol. 19, no. 5, pp. 1247–1269, Aug. 2020, doi: 10.1142/S0219622020500285.
O. S. Albahri et al., “Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects,†J. Infect. Public Health, vol. 13, no. 10, pp. 1381–1396, Oct. 2020, doi: 10.1016/J.JIPH.2020.06.028.
O. S. Albahri et al., “Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods,†Comput. Methods Programs Biomed., vol. 196, Nov. 2020, doi: 10.1016/J.CMPB.2020.105617.
I. E. Agbehadji, B. O. Awuzie, A. B. Ngowi, and R. C. Millham, “Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing,†Int. J. Environ. Res. Public Health, vol. 17, no. 15, pp. 1–16, Aug. 2020, doi: 10.3390/IJERPH17155330.
S. Kundu, H. Elhalawani, J. W. Gichoya, and C. E. Kahn, “How Might AI and Chest Imaging Help Unravel COVID-19’s Mysteries?,†Radiol. Artif. Intell., vol. 2, no. 3, p. e200053, May 2020, doi: 10.1148/RYAI.2020200053.
Q. Ni et al., “A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images,†Eur. Radiol., vol. 30, no. 12, pp. 6517–6527, Dec. 2020, doi: 10.1007/S00330-020-07044-9.
N. Tsiknakis et al., “Interpretable artificial intelligence framework for COVID‑19 screening on chest X‑rays,†Exp. Ther. Med., vol. 20, no. 2, pp. 727–735, May 2020, doi: 10.3892/ETM.2020.8797.
J. Song et al., “End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT,†Eur. J. Nucl. Med. Mol. Imaging, vol. 47, no. 11, pp. 2516–2524, Oct. 2020, doi: 10.1007/S00259-020-04929-1.
H. tao Zhang et al., “Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software,†Eur. J. Nucl. Med. Mol. Imaging, vol. 47, no. 11, pp. 2525–2532, Oct. 2020, doi: 10.1007/S00259-020-04953-1.
S. Debnath et al., “Machine learning to assist clinical decision-making during the COVID-19 pandemic,†Bioelectron. Med., vol. 6, no. 1, Dec. 2020, doi: 10.1186/S42234-020-00050-8.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
________________________________________________________
The International Journal of Artificial Intelligence Research
Organized by: Departemen Teknik Informatika
Published by: STMIK Dharma Wacana
Jl. Kenanga No.03 Mulyojati 16C Metro Barat Kota Metro Lampung
Email: jurnal.ijair@gmail.com
This work is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.