ARCHAEOLOGY & ARTIFICIAL INTELLIGENCE (QUADERNI DI VICINO ORIENTE XVIII - 2026) - Lorenzo Nigro
di
- Anno Edizione:
- 2026
- Argomento:
- Archeologia e tutela del patrimonio archeologico - Saggi e Ricerche
- ISBN:
- 978-88-98154-30-2
Prezzo: € 30.00
Descrizione:
f.to 17x24, 80 pp. con ill.ni
€ 12,00 per studenti e privati
QUADERNI DI VICINO ORIENTE
XVIII
SAPIENZA UNIVERSITÀ DI ROMA
TABLE OF CONTENTS
CHAPTER 1. AI ENTERS ARCHAEOLOGY 1
1.1 Historical Archaeology and the technological challenge 1
1.2 The new expansion of the digital in the sciences of the Past 2
1.3 From Computational Archaeology to Artificial Intelligence 3
1.4 The growth of studies, projects, and applications 4
1.5 Auxiliary tool or further cognitive and interpretive possibility? 5
1.6 The aim of this short essay 6
CHAPTER 2. WHAT IS MEANT BY AI IN ARCHAEOLOGY 9
2.1 Machine learning, deep learning, generative models 9
2.2 Automation, classification, prediction, simulation 9
2.3 Difference between pattern recognition and historical understanding 10
2.4 The lexicon of “revolution”: opportunities and ambiguities 10
CHAPTER 3. THE MAIN FIELDS OF APPLICATION 13
3.1 Remote sensing, satellite imagery, and LiDAR 13
3.2 Predictive identification of archaeological sites and structures 14
3.3 Automatic classification of artefacts 14
3.4 Pottery, typologies, and visual recognition 14
3.5 3D reconstruction, virtual anastylosis, and fragment reassembly 15
3.6 Epigraphy, palaeography, OCR, and textual analysis 16
3.7 Robotics, survey, monitoring, and maintenance of contexts 18
3.8 Generative AI, visualization, and hypothetical reconstructions 20
3.9 With AI towards the mind of the ancients 20
3.10 Limits emerging from applied case studies 21
CHAPTER 4. THE REAL ADVANTAGES OF AI FOR ARCHAEOLOGICAL WORK 23
4.1 Acceleration in the processing of Large Masses of Data 23
4.2 Support for documentation, ordering, and comparison 24
4.3 Recognition of regularities/anomalies not immediately visible 24
4.4 Greater integration among heterogeneous data 25
4.5 New forms of visualization, simulation, and access 25
4.6 AI as the archaeologist’s operational extension 26
CHAPTER 5. THE PROBLEM OF DATA 27
5.1 Archaeological data are never “raw” nor neutral 27
5.2 Excavation, classification, and description as already interpretive acts 27
5.3 Incompleteness, lacunae, and heterogeneity of datasets 28
5.4 The problem of limited, closed, or non-standardized corpora 28
5.5 Bias in training data 29
5.6 “Garbage in, garbage out” in archaeology 29
5.7 Standardization and loss of historical complexity 30
CHAPTER 6. THE EPISTEMOLOGICAL KNOT 31
6.1 Knowledge, models, and the construction of data 31
6.2 The AI “black box” and the problem of the intelligibility of data 31
6.3 Statistical correlation and historical explanation 32
6.4 The false objectivity of algorithmic output 33
6.5 The risk of classificatory determinism 33
6.6 Explainable AI: possibilities and limits 34
6.7 When the algorithm fixes categories that research ought to revise 35
CHAPTER 7. ARCHAEOLOGY AND HISTORICAL INTERPRETATION 37
7.1 Archaeology as a historical, contextual, and critical discipline 37
7.2 Stratigraphy, association, chronology, and meaning 38
7.3 The provisional nature of archaeological classifications 39
7.4 The importance of anomalies, exceptions, and liminal cases 40
7.5 Interpretive disagreement as a scientific resource 40
CHAPTER 8. ETHICAL, POLITICAL, AND DISCIPLINARY ISSUES 43
8.1 Cultural biases and the reproduction of dominant paradigms 43
8.2 Cultural and political perspectives in training data 43
8.3 Ownership, control, and governance of archaeological data and their potential dual use 44
8.4 Private infrastructures, technological dependence, and the risk of marginalization 44
8.5 Public archaeology, citizen science, and open and shared access 45
8.6 The risk of replacing critical thought with automation 46
CHAPTER 9. FOR CRITICAL USE OF AI IN ARCHAEOLOGY 47
9.1 Theory In, Theory Out: theory before the algorithm 47
9.2 Formulating archaeological questions before training models 47
9.3 Human validation, intersubjective comparison, and verifiability 48
9.4 Collaboration among archaeologists, computer scientists, and philosophers of science 48
9.5 Transparency of processes and documentation of choices 49
9.6 AI as support, not as a substitute for interpretation 49
9.7 Towards a critical or post-digital archaeology 49
CHAPTER 10. CONCLUSIONS 51
10.1 AI between νοῦς and μίμησις 51
10.2 Archaeology vs. AI: provisional results 52
10.3 Materiality and serendipity of discovery 53
10.4 Will AI help us write history? 53
GLOSSARY OF TECHNICAL TERMS 57
BIBLIOGRAPHY 63
€ 12,00 per studenti e privati
QUADERNI DI VICINO ORIENTE
XVIII
SAPIENZA UNIVERSITÀ DI ROMA
TABLE OF CONTENTS
CHAPTER 1. AI ENTERS ARCHAEOLOGY 1
1.1 Historical Archaeology and the technological challenge 1
1.2 The new expansion of the digital in the sciences of the Past 2
1.3 From Computational Archaeology to Artificial Intelligence 3
1.4 The growth of studies, projects, and applications 4
1.5 Auxiliary tool or further cognitive and interpretive possibility? 5
1.6 The aim of this short essay 6
CHAPTER 2. WHAT IS MEANT BY AI IN ARCHAEOLOGY 9
2.1 Machine learning, deep learning, generative models 9
2.2 Automation, classification, prediction, simulation 9
2.3 Difference between pattern recognition and historical understanding 10
2.4 The lexicon of “revolution”: opportunities and ambiguities 10
CHAPTER 3. THE MAIN FIELDS OF APPLICATION 13
3.1 Remote sensing, satellite imagery, and LiDAR 13
3.2 Predictive identification of archaeological sites and structures 14
3.3 Automatic classification of artefacts 14
3.4 Pottery, typologies, and visual recognition 14
3.5 3D reconstruction, virtual anastylosis, and fragment reassembly 15
3.6 Epigraphy, palaeography, OCR, and textual analysis 16
3.7 Robotics, survey, monitoring, and maintenance of contexts 18
3.8 Generative AI, visualization, and hypothetical reconstructions 20
3.9 With AI towards the mind of the ancients 20
3.10 Limits emerging from applied case studies 21
CHAPTER 4. THE REAL ADVANTAGES OF AI FOR ARCHAEOLOGICAL WORK 23
4.1 Acceleration in the processing of Large Masses of Data 23
4.2 Support for documentation, ordering, and comparison 24
4.3 Recognition of regularities/anomalies not immediately visible 24
4.4 Greater integration among heterogeneous data 25
4.5 New forms of visualization, simulation, and access 25
4.6 AI as the archaeologist’s operational extension 26
CHAPTER 5. THE PROBLEM OF DATA 27
5.1 Archaeological data are never “raw” nor neutral 27
5.2 Excavation, classification, and description as already interpretive acts 27
5.3 Incompleteness, lacunae, and heterogeneity of datasets 28
5.4 The problem of limited, closed, or non-standardized corpora 28
5.5 Bias in training data 29
5.6 “Garbage in, garbage out” in archaeology 29
5.7 Standardization and loss of historical complexity 30
CHAPTER 6. THE EPISTEMOLOGICAL KNOT 31
6.1 Knowledge, models, and the construction of data 31
6.2 The AI “black box” and the problem of the intelligibility of data 31
6.3 Statistical correlation and historical explanation 32
6.4 The false objectivity of algorithmic output 33
6.5 The risk of classificatory determinism 33
6.6 Explainable AI: possibilities and limits 34
6.7 When the algorithm fixes categories that research ought to revise 35
CHAPTER 7. ARCHAEOLOGY AND HISTORICAL INTERPRETATION 37
7.1 Archaeology as a historical, contextual, and critical discipline 37
7.2 Stratigraphy, association, chronology, and meaning 38
7.3 The provisional nature of archaeological classifications 39
7.4 The importance of anomalies, exceptions, and liminal cases 40
7.5 Interpretive disagreement as a scientific resource 40
CHAPTER 8. ETHICAL, POLITICAL, AND DISCIPLINARY ISSUES 43
8.1 Cultural biases and the reproduction of dominant paradigms 43
8.2 Cultural and political perspectives in training data 43
8.3 Ownership, control, and governance of archaeological data and their potential dual use 44
8.4 Private infrastructures, technological dependence, and the risk of marginalization 44
8.5 Public archaeology, citizen science, and open and shared access 45
8.6 The risk of replacing critical thought with automation 46
CHAPTER 9. FOR CRITICAL USE OF AI IN ARCHAEOLOGY 47
9.1 Theory In, Theory Out: theory before the algorithm 47
9.2 Formulating archaeological questions before training models 47
9.3 Human validation, intersubjective comparison, and verifiability 48
9.4 Collaboration among archaeologists, computer scientists, and philosophers of science 48
9.5 Transparency of processes and documentation of choices 49
9.6 AI as support, not as a substitute for interpretation 49
9.7 Towards a critical or post-digital archaeology 49
CHAPTER 10. CONCLUSIONS 51
10.1 AI between νοῦς and μίμησις 51
10.2 Archaeology vs. AI: provisional results 52
10.3 Materiality and serendipity of discovery 53
10.4 Will AI help us write history? 53
GLOSSARY OF TECHNICAL TERMS 57
BIBLIOGRAPHY 63