Nobel Turing Challenge: creating the engine for scientific discovery

Kitano, Hiroaki

doi:10.1038/s41540-021-00189-3

Cited by 59 publications

(43 citation statements)

References 77 publications

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“…Each of these motifs, particularly in combinations described by the SI stack, allows this process to happen faster and more effectively -from probabilistic programming decoupling modeling and inference, to surrogate modeling allowing for rapid and powerful simulation of systems when detailed mechanistic modeling is infeasible, to simulation-based causal discovery deriving the true mechanisms of disease processes, to physics-infused learning deriving the governing equations of unknown physics, and so on. The SI driven scientific methods can help practitioners produce optimal and novel solutions across domains and use-cases in science and intelligence, and provide an essential step towards the Nobel Turing Challenge, creating the machine intelligence engine for scientific discovery [228].…”

Section: Discussionmentioning

confidence: 99%

“…Further, one can make the case that human-machine inference methods (Fig. 12) embodied with causal reasoning are necessary to make progress towards the Nobel Turing Challenge [228] to develop AI-scientists capable of autonomously carrying out research to make major scientific discoveries -it'd difficult to imagine such an AI-scientist without, for example, the ability to intelligently explore-exploit a system to derive its causal structure as in Fig. 16.…”

Section: Future Directionsmentioning

confidence: 99%

“…One promising direction is uncertainty-driven open-ended search. We've described the future benefits in optimization settings, and also suggest these same methods may be key in the hypothesis-generation part of automating the scientific method -Kitano [228] describes these pursuits -and helping shape new scientific methods (for instance, built on principles of uncertainty and complexity, rather than falsifiability).…”

Section: Uncertainty Reasoningmentioning

confidence: 99%

“…Right: Open-ended search space for science (black), and subsets representing what has been achieved thus far (purple), what is achievable by humans (yellow), and what is achievable in general (red). (Diagrams are not to scale, inspired by[228]. )…”

mentioning

confidence: 99%

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Simulation Intelligence: Towards a New Generation of Scientific Methods

Lavin¹,

Zenil²,

Krakauer³

et al. 2021

Preprint

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The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. 1 We present the Nine Motifs of Simulation Intelligence, a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence "operating system" stack (SI-stack) and the motifs therein:1. Multi-physics and multi-scale modeling 2. Surrogate modeling and emulation 3. Simulation-based inference 4. Causal modeling and inference 5. Agent-based modeling 6. Probabilistic programming 7. Differentiable programming 8. Open-ended optimization Machine programmingWe believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.

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Section: Discussionmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Section: Uncertainty Reasoningmentioning

confidence: 99%

mentioning

confidence: 99%

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Simulation Intelligence: Towards a New Generation of Scientific Methods

Lavin¹,

Zenil²,

Krakauer³

et al. 2021

Preprint

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“…If AI is used to try to find novel ways to solve problems, it is counterproductive to constrain it to be risk-averse, trying only minute variations of the old approaches. The best approach available to "'AI Scientists' may not resemble the scientific process conducted by human scientist" (Kitano, 2021).…”

Section: Decisions Where Risk-seeking Is Appropriatementioning

confidence: 99%

Rawls’s Original Position and Algorithmic Fairness

Franke

2021

Philos. Technol.

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Modern society makes extensive use of automated algorithmic decisions, fueled by advances in artificial intelligence. However, since these systems are not perfect, questions about fairness are increasingly investigated in the literature. In particular, many authors take a Rawlsian approach to algorithmic fairness. This article aims to identify some complications with this approach: Under which circumstances can Rawls’s original position reasonably be applied to algorithmic fairness decisions? First, it is argued that there are important differences between Rawls’s original position and a parallel algorithmic fairness original position with respect to risk attitudes. Second, it is argued that the application of Rawls’s original position to algorithmic fairness faces a boundary problem in defining relevant stakeholders. Third, it is observed that the definition of the least advantaged, necessary for applying the difference principle, requires some attention in the context of algorithmic fairness. Finally, it is argued that appropriate deliberation in algorithmic fairness contexts often require more knowledge about probabilities than the Rawlsian original position allows. Provided that these complications are duly considered, the thought-experiment of the Rawlsian original position can be useful in algorithmic fairness decisions.

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