Synthetic Biology and Artificial Intelligence: Toward Cross-Fertilization

2018 ◽  
Vol 27 (3) ◽  
pp. i-vii
Author(s):  
Luisa Damiano ◽  
◽  
Yutetsu Kuruma ◽  
Pasquale Stano ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Synthetic Biology ◽  
Cross Fertilization

scholarly journals Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo

2020 ◽  
Vol 23 (3) ◽  
pp. S1-S24
Author(s):  
Mitsuru Igami
Keyword(s):  
Artificial Intelligence ◽  
Value Function ◽  
Structural Estimation ◽  
Structural Models ◽  
Policy Network ◽  
Value Network ◽  
Deep Blue ◽  
Dynamic Structural Models ◽  
Similarities And Differences ◽  
Cross Fertilization

Summary This article clarifies the connections between certain algorithms to develop artificial intelligence (AI) and the econometrics of dynamic structural models, with concrete examples of three 'game AIs'. Chess-playing Deep Blue is a calibrated value function, whereas shogi-playing Bonanza is an estimated value function via Rust’s nested fixed-point (NFXP) method. AlphaGo’s 'supervised-learning policy network' is a deep-neural-network implementation of the conditional-choice-probability (CCP) estimation reminiscent of Hotz and Miller's first step; the construction of its 'reinforcement-learning value network' is analogous to their conditional choice simulation (CCS). I then explain the similarities and differences between AI-related methods and structural estimation more generally, and suggest areas of potential cross-fertilization.

Digging deeper on “deep” learning: A computational ecology approach

2017 ◽  
Vol 40 ◽  
Author(s):  
Massimo Buscema ◽  
Pier Luigi Sacco
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Learning Processes ◽  
Human Intelligence ◽  
Fundamental Feature ◽  
Alternative Approach ◽  
Multiple Domains ◽  
Computational Ecology ◽  
Cross Fertilization

AbstractWe propose an alternative approach to “deep” learning that is based on computational ecologies of structurally diverse artificial neural networks, and on dynamic associative memory responses to stimuli. Rather than focusing on massive computation of many different examples of a single situation, we opt for model-based learning and adaptive flexibility. Cross-fertilization of learning processes across multiple domains is the fundamental feature of human intelligence that must inform “new” artificial intelligence.

New Military Technologies: Dangers for International Security and Peace

2020 ◽  
Vol 38 (1) ◽  
pp. 36-42
Author(s):  
Jürgen Altmann
Keyword(s):  
Artificial Intelligence ◽  
Additive Manufacturing ◽  
Synthetic Biology ◽  
International Security ◽  
Gene Editing ◽  
Arms Control ◽  
Arms Races ◽  
Weapon Systems ◽  
The Usa ◽  
Cyber War

New military technologies are being developed at a high pace, with the USA in the lead. Intended application areas are space weapons and ballistic missile defence, hypersonic missiles, autonomous weapon systems, and cyber war. Generic technologies include artificial intelligence, additive manufacturing, synthetic biology and gene editing, and soldier enhancement. Problems for international security and peace - arms races and destabilisation - will likely result from properties shared by several technologies: wider availability, easier access, smaller systems; shorter times for attack, warning and decisions; and conventional-nuclear entanglement. Preventive arms control is urgently needed.

Synthetic biology and (embodied) artificial intelligence: opportunities and challenges

2018 ◽  
Vol 26 (1) ◽  
pp. 41-44 ◽  
Author(s):  
Pasquale Stano ◽  
Yutetsu Kuruma ◽  
Luisa Damiano
Keyword(s):  
Artificial Intelligence ◽  
Synthetic Biology ◽  
Artificial Life ◽  
Research Question ◽  
Contemporary Debate ◽  
European Conference ◽  
Scientific Exploration

On the 4th of September 2017, the 14th European Conference on Artificial Life (ECAL 2017, Lyon, France) hosted a satellite workshop dedicated to a frontier research question: ‘What can Synthetic Biology offer to (Embodied) Artificial Intelligence (and vice versa)?’ This workshop, as the previous three of the ‘Synthetic Biology (SB)–Artificial Intelligence (AI)’ workshop series, brought together specialists from different disciplines to address the contemporary debate on the evolution of embodied artificial intelligence from a new angle. In a few words: defining the possible roles that SB – an emerging research line combining biology and engineering – can play in the process of establishment of the so-called ‘Embodied paradigm’ in the scientific exploration of cognition and, in particular, in artificial intelligence.

scholarly journals A New Definition of “Artificial” for Two Artificial Sciences

2021 ◽  
Author(s):  
Francesco Bianchini
Keyword(s):  
Artificial Intelligence ◽  
Synthetic Biology ◽  
New Technologies ◽  
Point Of View ◽  
Theoretical Frameworks ◽  
Conceptual Issue ◽  
Clear Cut ◽  
Engineering Sciences ◽  
Close Relationship ◽  
Definition Of

AbstractIn this article, I deal with a conceptual issue concerning the framework of two special sciences: artificial intelligence and synthetic biology, i.e. the distinction between the natural and the artificial (a long-lasting topic of history of scientific though since the ancient philosophy). My claim is that the standard definition of the “artificial” is no longer useful to describe some present-day artificial sciences, as the boundary between the natural and the artificial is not so sharp and clear-cut as it was in the past. Artificial intelligence and synthetic biology, two disciplines with new technologies, new experimental methods, and new theoretical frameworks, all need a new, more specific, and refined definition of (the) “artificial”, which is also related to the use of the synthetic method to build real world entities and in open-ended (real or virtual) environments. The necessity of a new definition of the artificial is due to the close relationship of AI and synthetic biology with biology itself. They both are engineering sciences that are moving closer and closer, at least apparently, towards (natural) biology, although from different and opposite directions. I show how the new concept of the artificial is, therefore, the result of a new view on biology from an engineering and synthetic point of view, where the boundary between the natural and the artificial is far more blurred. From this, I try to formulate a brand-new, more useful definition for future understanding, practical, and epistemological purposes of these two artificial sciences.

The Practice of UN Treaty-Making concerning Science

2019 ◽  
pp. 320-338
Author(s):  
Sam Johnston
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Synthetic Biology ◽  
New Technologies ◽  
Social Economic ◽  
Critical Role ◽  
Applied Science ◽  
Global Commons ◽  
Institutional Expectations

This chapter describes the growing influence of science in UN treaties, which centers around four main roles; scientific influence in the treaty-making process, promoting access to existing science, supporting research, and managing the threats posed by science. It also highlights the challenges UN treaties face in using science such as; resolving the tensions that exist between pure and applied science; maintaining science’s role as a peaceful activity in the global commons; ensuring that scientific input is not lost among the increasing complex and crowded nature of treaty-making; ensuring that science is more inclusive, holistic, and balanced; and improving its relevance while retaining its credibility. The UN will also need to use science to respond to new and emerging areas such as managing new technologies including nanotechnologies, synthetic biology, or artificial intelligence, or new threats such as cyberwarfare and security. Failures of science in predicting and managing threats from climate change, epidemics, and nuclear disasters have revealed the uncertainties underlying many of its areas of practice and has demonstrated the critical role that social, economic, and institutional expectations play. Recognizing that science is not neutral or objective is an important step in addressing the key shortcomings facing the role of science in UN treaties. Determining what measures need to be taken to balance social and economic influences is another important side of this challenge. Reconciling these enduring challenges will be increasingly important in all areas where UN treaty-making processes and science intersect.

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