Scientific realism is, at the most general level, the view that the world described by science is the real world, as it is, independent of what we might take it to be. Within philosophy of science, it is often framed as an answer to the question \.
Like the Buddhist concept of Emptiness sunyata.
The purpose of this lecture is to defend a reading of Nietzsche that attributes to him an ontology that reduces everything to relations of force against the charge that such an ontology is wildly implausible. Rather than rehearsing the textual evidence supporting such a view, the tactic employed in this chapter is to show that Nietzsche’s relational ontology of force can be understood as a proto-form of a view currently being given serious consideration in contemporary philosophy of science: Ontic Structural Realism. Ontic structural realism is a form of structural realism that eliminates things-in-themselves and inflates the ontological significance of relations vis-à-vis relata. Because Nietzsche appeals to the natural sciences to justify his rejection of things-in-themselves in favour of a relational ontology of force, it is argued that Nietzsche should be understood as offering a proto-version of ontic structural realism.
Informational Structural Realism (ISR) has been presented by Luciano Floridi and others as an alternative form of structural realism that can overcome many of structural realism’s shortcomings. The essential idea behind ISR is that nature is ultimately informational in nature and scientific theories present data structures that represent this information. In this talk I assess whether ISR is able to reconcile the two most important arguments in the realism-antirealism debate: the \.
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Graham Cooks and his team at Purdue University have discovered a chemical process that has exciting implications for people who believe that life could have emerged spontaneously and through natural means. The idea that the building blocks of life started in a primordial ocean now has a competitor: airborne tiny water droplets.
Estimating spectral features of quantum many-body systems has attracted great attention in condensed matter physics and quantum chemistry. To achieve this task, various experimental and theoretical techniques have been developed, such as spectroscopy techniques1,2,3,4,5,6,7 and quantum simulation either by engineering controlled quantum devices8,9,10,11,12,13,14,15,16 or executing quantum algorithms17,18,19,20 such as quantum phase estimation and variational algorithms. However, probing the behaviour of complex quantum many-body systems remains a challenge, which demands substantial resources for both approaches. For instance, a real probe by neutron spectroscopy requires access to large-scale facilities with high-intensity neutron beams, while quantum computation of eigenenergies typically requires controlled operations with a long coherence time17,18. Efficient estimation of spectral properties has become a topic of increasing interest in this noisy intermediate-scale quantum era21.
A potential solution to efficient spectral property estimation is to extract the spectral information from the dynamics of observables, rather than relying on real probes such as scattering spectroscopy, or direct computation of eigenenergies. This approach capitalises on the basics in quantum mechanics that spectral information is naturally carried by the observable’s dynamics10,20,22,23,24,25,26. In a solid system with translation invariance, for instance, the dynamic structure factor, which can be probed in spectroscopy experiments7,26, reaches its local maximum when both the energy and momentum selection rules are satisfied. Therefore, the energy dispersion can be inferred by tracking the peak of intensities in the energy excitation spectrum.
That statement, now signed by twice as many concerned citizens, warned about the risk of human extinction from AI, which was perhaps a bit of an overreach, because … well, extinction? Come on! That’s just a movie with Arnold Schwarzenegger.
What they should have warned about was jobs — the redundancy and destitution of most of humanity, unless there’s some kind of universal income funded by taxes on robots.
What no-one talks about, as the AI revolution unfolds in stock market hype and scientific gung-ho, is what they’re all really trying to do.
Ibogaine—a psychoactive plant derivative—has attracted attention for its anti-addictive and anti-depressant properties. But ibogaine is a finite resource, extracted from plants native to Africa like the iboga shrub (Tabernanthe iboga) and the small-fruited voacanga tree (Voacanga africana). Further, its use can lead to irregular heartbeats, introducing safety risks and an overall need to better understand how its molecular structure leads to its biological effects.
In a study appearing in Nature Chemistry, researchers at the University of California, Davis Institute for Psychedelics and Neurotherapeutics (IPN) report the successful total synthesis of ibogaine, ibogaine analogs and related compounds from pyridine—a relatively inexpensive and widely available chemical.
The team’s strategy enabled the synthesis of four naturally occurring ibogaine-related alkaloids as well as several non-natural analogs. Overall yields ranged from 6% to 29% after only six or seven steps, a marked increase in efficiency from previous synthetic efforts to produce similar compounds.
Engineering researchers at Lawrence Livermore National Laboratory (LLNL) have achieved breakthroughs in multi-material 3D printing through the power of capillary action. The LLNL team printed lattice structures with a series of custom-designed unit cells to selectively absorb fluid materials and precisely direct them into patterns – making it possible to fabricate complex structures with unprintable materials or materials with vastly different properties.
According to the researchers, the technique, featured in Advanced Materials Technologies, would help engineers design and optimize structures for properties like extreme strength-to-weight ratios, large surface areas, or precision deformation.
“By decoupling some of the printing and patterning techniques, you could achieve some complex multi-material structures, and you wouldn’t always have to be able to print the material,” said Hawi Gemeda, Materials Engineering Division (MED) staff engineer at LLNL and the paper’s lead author.