Lifeboat Foundation

Functional trade-offs can affect phenotypic variation. Here, the authors examine trade-offs between bite force and speed in 132 carnivore species, finding that optimising for velocity can be obtained in more ways than optimising for force, and this may impact morphological variability.

I found this on NewsBreak: The big idea of Grand Unified Theories of physics.

Karl Friston, Joscha Bach, and Curt Jaimungal delve into death, neuroscientific models of Ai, God, and consciousness. SPONSOR: HelloFresh: Go to https://HelloFresh.com/theoriesofever… and use code theoriesofeverythingfree for FREE breakfast for life!

TIMESTAMPS:
- 00:00:00 Introduction.
- 00:01:47 Karl and Joscha’s new paper.
- 00:09:13 Sentience vs. consciousness vs. The Self.
- 00:21:00 Self-organization, thingness, and self-evidencing.
- 00:29:02 Overlapping realities and physics as art.
- 00:41:05 Mortal computation and substrate-agnostic AI
- 00:56:38 Beyond Von Neumann architectures.
- 01:00:23 AI surpassing human researchers.
- 01:20:34 Exploring vs. Exploiting (the risk of curiosity in academia)
- 01:27:02 Incompleteness and interdependence.
- 01:32:25 Defining consciousness.
- 01:53:36 Multiple overlapping consciousnesses.
- 02:03:03 Unified experience and schizophrenia \.

How the neocortex works is a mystery. In this paper we propose a novel framework for understanding its function. Grid cells are neurons in the entorhinal cortex that represent the location of an animal in its environment. Recent evidence suggests that grid cell-like neurons may also be present in the neocortex. We propose that grid cells exist throughout the neocortex, in every region and in every cortical column. They define a location-based framework for how the neocortex functions. Whereas grid cells in the entorhinal cortex represent the location of one thing, the body relative to its environment, we propose that cortical grid cells simultaneously represent the location of many things. Cortical columns in somatosensory cortex track the location of tactile features relative to the object being touched and cortical columns in visual cortex track the location of visual features relative to the object being viewed. We propose that mechanisms in the entorhinal cortex and hippocampus that evolved for learning the structure of environments are now used by the neocortex to learn the structure of objects. Having a representation of location in each cortical column suggests mechanisms for how the neocortex represents object compositionality and object behaviors. It leads to the hypothesis that every part of the neocortex learns complete models of objects and that there are many models of each object distributed throughout the neocortex. The similarity of circuitry observed in all cortical regions is strong evidence that even high-level cognitive tasks are learned and represented in a location-based framework.

The human neocortex learns an incredibly complex and detailed model of the world. Each of us can recognize 1000s of objects. We know how these objects appear through vision, touch, and audition, we know how these objects behave and change when we interact with them, and we know their location in the world. The human neocortex also learns models of abstract objects, structures that don’t physically exist or that we cannot directly sense. The circuitry of the neocortex is also complex. Understanding how the complex circuitry of the neocortex learns complex models of the world is one of the primary goals of neuroscience.

Vernon Mountcastle was the first to propose that all regions of the neocortex are fundamentally the same. What distinguishes one region from another, he argued, is mostly determined by the inputs to a region and not by differences in intrinsic circuitry and function. He further proposed that a small volume of cortex, a cortical column, is the unit of replication (Mountcastle, 1978). These are compelling ideas, but it has been difficult to identify what a column could do that is sufficient to explain all cognitive abilities. Today, the most common view is that the neocortex processes sensory input in a series of hierarchical steps, extracting more and more complex features until objects are recognized (Fukushima, 1980; Riesenhuber and Poggio, 1999).

A single-walled carbon nanotube spring stores three times more mechanical energy than a lithium-ion battery, while offering wide temperature stability and posing no explosion risk.

A multi-institutional team of geneticists and genomic and genotyping specialists in Japan has sequenced the genomes of thousands of Japanese people from across the country, looking to settle the debate surrounding the ancestry of the Japanese people.

Dark energy’s grip on the cosmos could be more fickle than scientists once believed.

Quantum chaos focuses on the quantum manifestations of classical chaos. A characteristic of classical chaos is the exponential sensitivity of the dynamics with respect to infinitesimal changes in the initial conditions. Thus, to classify classical dynamics it is sufficient to follow phase space trajectories starting infinitesimally close to each other and to determine the evolution of their distances with respect to each other with time. Because of the uncertainty relation, this is no longer possible in the corresponding quantum system. One important aspect of quantum chaos is the understanding of features of the classical dynamics in terms of the fluctuation properties in the energy spectra of closed quantum systems or of the fluctuations exhibited by the scattering matrix elements describing open ones. The fluctuation properties are predicted to be universal, that is, to be the same for systems belonging to the same universality class and exhibiting the same chaotic behavior in the corresponding classical dynamics and to be describable by random matrix theory. Furthermore, random-matrix models that had been developed for the scattering matrix associated with compound-nuclear reactions have been shown to be applicable to quantum-chaotic scattering processes. A second important aspect within the field of quantum chaos concerns the semiclassical approach. In this context, one of the most important achievements was the periodic orbit theory pioneered by Gutzwiller, which led to understanding the impact of the classical dynamics on the properties of the quantum system in terms of purely classical quantities. The focus of research within the field of quantum chaos has been extended to relativistic quantum systems and to many-body quantum systems with focus on random matrix theory and the semiclassical approach. In distinction to single-particle systems, many-body systems like atomic nuclei do not have a classical analogue. In recent years different measures of chaos and models have been developed. Here, a prominent model is the Sachdev-Ye-Kitaev model which serves as a paradigm for the study of quantum chaos in strongly interacting many-body systems. The school is aimed at PhD students, post-docs and outstanding master students and the first part will provide a survey of single-and many-body quantum chaos and applications based on random-matrix theory and the semiclassical approach. The second part of the school will focus on current aspects of research in the context of many-body quantum chaos. There is no registration fee and limited funds are available for travel and local expenses. Organizers: Hilda Cerdeira (IFT-UNESP, Brazil) Barbara Dietz-Pilatus (Institute for Basic Science (IBS), Republic of Korea)

Check out the math & physics courses that I mentioned (many of which are free!) and support this channel by going to https://brilliant.org/Sabine/ where you can create your Brilliant account. The first 200 will get 20% off the annual premium subscription. You have probably heard people saying that the problem with quantum mechanics is that it’s non-local or that it’s impossible to understand or that it defies common sense. But the problem is much simpler, it’s that quantum mechanics is a linear theory and therefore doesn’t correctly reproduce chaos. Physicists have known this for a long time but it’s rarely discussed. In this video I explain what the problem is, what physicists have done to try and solve it, and why that solution doesn’t work. Subscribe to my weekly science newsletter: https://sabinehossenfelder.com/ You find the estimate for Saturn’s moon Hyperion in Zurek’s review https://arxiv.org/abs/quant-ph/0105127 A much easier to digest and more readable review by Michael Berry is here: https://michaelberryphysics.files.wor… you can find a brief summary on Sean Carroll’s blog https://www.preposterousuniverse.com/… 0:00 Intro 0:27 The trouble with Hyperion 4:04 The alleged solution 6:02 The trouble with the solution 7:46 What a real solution requires 10:31 Sponsor message.