269 research outputs found
Holmes Rolston III: interview by Theo Horesh
No full textRolston on how we might more wisely approach the "perfect moral storm" of climate change. Ethicists will watch the worldview, the interpretation, the value choices underlying economic analyses. We are at a hinge point in human and Earth history. We have enormous amounts of power but have not learned to control our appetites. How do we value the extinctions of species we are causing? How do we value diversity on our wonderland planet? What are the dangers of entering an Anthropocene Epoch? We can think of Earth as a promised land, a gift. Rolston has seen radical changes in human attitudes and behaviors in his lifetime. He challenges the millennial generation to press for more caring for Earth.Theo Horesh interviewed Holmes Rolston III in 2013
Shell model of two-dimensional turbulence in polymer solutions
No full textWe address the effect of polymer additives on two-dimensional turbulence, an issue that was studied recently in experiments and direct numerical simulations. We show that the same simple shell model that reproduced drag reduction in three-dimensional turbulence reproduces all the reported effects in the two-dimensional case. The simplicity of the model offers a straightforward simulation of all the major effects under consideration
Theory of concentration dependence in drag reduction by polymers and of the maximum drag reduction asymptote
No full textA simple model of the effect of polymer concentration on the amount of drag reduction in turbulence is presented, simulated, and analyzed. The qualitative phase diagram of drag coefficient versus Reynolds number (Re) is recaptured in this model, including the theoretically elusive onset of drag reduction and the maximum drag reduction (MDR) asymptote. The Re-dependent drag and the MDR are analytically explained, and the dependence of the amount of drag on material parameters is rationalized
Fast Reed-Solomon Interactive Oracle Proofs of Proximity
No full textThe family of Reed-Solomon (RS) codes plays a prominent role in the construction of quasilinear probabilistically checkable proofs (PCPs) and interactive oracle proofs (IOPs) with perfect zero knowledge and polylogarithmic verifiers. The large concrete computational complexity required to prove membership in RS codes is one of the biggest obstacles to deploying such PCP/IOP systems in practice.
To advance on this problem we present a new interactive oracle proof of proximity (IOPP) for RS codes; we call it the Fast RS IOPP (FRI) because (i) it resembles the ubiquitous Fast Fourier Transform (FFT) and (ii) the arithmetic complexity of its prover is strictly linear and that of the verifier is strictly logarithmic (in comparison, FFT arithmetic complexity is quasi-linear but not strictly linear). Prior RS IOPPs and PCPs of proximity (PCPPs) required super-linear proving time even for polynomially large query complexity.
For codes of block-length N, the arithmetic complexity of the (interactive) FRI prover is less than 6 * N, while the (interactive) FRI verifier has arithmetic complexity <= 21 * log N, query complexity 2 * log N and constant soundness - words that are delta-far from the code are rejected with probability min{delta * (1-o(1)),delta_0} where delta_0 is a positive constant that depends mainly on the code rate. The particular combination of query complexity and soundness obtained by FRI is better than that of the quasilinear PCPP of [Ben-Sasson and Sudan, SICOMP 2008], even with the tighter soundness analysis of [Ben-Sasson et al., STOC 2013; ECCC 2016]; consequently, FRI is likely to facilitate better concretely efficient zero knowledge proof and argument systems.
Previous concretely efficient PCPPs and IOPPs suffered a constant multiplicative factor loss in soundness with each round of "proof composition" and thus used at most O(log log N) rounds. We show that when delta is smaller than the unique decoding radius of the code, FRI suffers only a negligible additive loss in soundness. This observation allows us to increase the number of "proof composition" rounds to Theta(log N) and thereby reduce prover and verifier running time for fixed soundness
Proceedings of”Thinking Fast and Slow and Other Cognitive Theories in AI”, a AAAI 2022 fall symposium
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Autophobia? : Israel's geo-politics in the early 'Chinese century'
No full textRecent popular unrest across the Arab world has sparked fresh debate over the scope of multinational intervention in 21st-century emergent humanitarian crises and the perimeters of promoting democratization in the developing world; it has also prompted reassessment of the United States' ability to maintain its system of longstanding regional alliances in the face of economic recession at home, and amid President Obama's apparent disowning of his predecessor's ebullient pre-emptive posture
Combining Fast and Slow Thinking for Human-like and Efficient Decisions in Constrained Environments
No full textCurrent AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. We believe that existing cognitive theories of human decision making, such as the thinking fast and slow theory, can provide insights on how to advance AI systems towards some of these capabilities. In this paper, we propose a general architecture that is based on fast/slow solvers and a metacognitive component. We then present experimental results on the behavior of an instance of this architecture, for AI systems that make decisions about navigating in a constrained environment. We show how combining the fast and slow decision modalities, which can be implemented by learning and reasoning components respectively, allows the system to evolve over time and gradually pass from slow to fast thinking with enough experience, and that this greatly helps in decision quality, resource consumption, and efficiency
Value-based Fast and Slow AI Nudging
No full textNudging is a behavioral strategy aimed at influencing people's thoughts and actions. Nudging techniques can be found in many situations in our daily lives, and these nudging techniques can targeted at human fast and unconscious thinking, e.g., by using images to generate fear or the more careful and effortful slow thinking, e.g., by releasing information that makes us reflect on our choices. In this paper, we propose and discuss a value-based AI-human collaborative framework where AI systems nudge humans by proposing decision recommendations. Three different nudging modalities, based on when recommendations are presented to the human, are intended to stimulate human fast thinking, slow thinking, or meta-cognition. Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities. Examples of values are decision quality, speed, human upskilling and learning, human agency, and privacy. Several values can be present at the same time, and their priorities can vary over time. The framework treats values as parameters to be instantiated in a specific decision environment
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