Lifeboat Foundation

Computer simulations of complex systems provide an opportunity to study their time evolution under user control. Simulations of neural circuits are an established tool in computational neuroscience. Through systematic simplification on spatial and temporal scales they provide important insights in the time evolution of networks which in turn leads to an improved understanding of brain functions like learning, memory or behavior. Simulations of large networks are exploiting the concept of weak scaling where the massively parallel biological network structure is naturally mapped on computers with very large numbers of compute nodes. However, this approach is suffering from fundamental limitations. The power consumption is approaching prohibitive levels and, more seriously, the bridging of time-scales from millisecond to years, present in the neurobiology of plasticity, learning and development is inaccessible to classical computers. In the keynote I will argue that these limitations can be overcome by extreme approaches to weak and strong scaling based on brain-inspired computing architectures.

Bio: Karlheinz Meier received his PhD in physics in 1984 from Hamburg University in Germany. He has more than 25years of experience in experimental particle physics with contributions to 4 major experiments at particle colliders at DESY in Hamburg and CERN in Geneva. For the ATLAS experiment at the Large Hadron Collider (LHC) he led a 15 year effort to design, build and operate an electronics data processing system providing on-the-fly data reduction by 3 orders of magnitude enabling among other achievements the discovery of the Higgs Boson. Following scientific staff positions at DESY and CERN he was appointed full professor of physics at Heidelberg university in 1992. In Heidelberg he co-founded the Kirchhoff-Institute for Physics and a laboratory for the development of microelectronic circuits for science experiments. In particle physics he took a leading international role in shaping the future of the field as president of the European Committee for Future Accelerators (ECFA). Around 2005 he gradually shifted his scientific interests towards large-scale electronic implementations of brain-inspired computer architectures. His group pioneered several innovations in the field like the conception of a description language for neural circuits (PyNN), time-compressed mixed-signal neuromorphic computing systems and wafer-scale integration for their implementation. He led 2 major European initiatives, FACETS and BrainScaleS, that both demonstrated the rewarding interdisciplinary collaboration of neuroscience and information science. In 2009 he was one of the initiators of the European Human Brain Project (HBP) that was approved in 2013. In the HBP he leads the subproject on neuromorphic computing with the goal of establishing brain-inspired computing paradigms as tools for neuroscience and generic methods for inference from large data volumes.

A newly invented fuel cell taps into naturally present, and ubiquitous microbes in the soil to generate power.

This soil-powered device, about the size of a regular paperback book, offers a viable alternative to batteries in underground sensors used for precision agriculture.

Northwestern University highlighted the durability of its robust fuel cell, showcasing its ability to withstand various environmental conditions, including both arid soil and flood-prone areas.

An organic artificial neuron that is based on a compact nonlinear electrochemical element can operate in a liquid and responds to the concentration of biological species in its surroundings, allowing its behaviour to be modulated, for example, by interfacing with the membranes of living cells.

A Northwestern University-led team of researchers has developed a new fuel cell that harvests energy from microbes living in dirt.

New tech harvests energy from microbes in soil to power sensors, communications.

Working in the lab, Northwestern alumnus Bill Yen buries the fuel cell in soil.

Interactions between RNA and proteins are the cornerstone of many important biological processes from transcription and translation to gene regulation, yet little is known about the ancient origin of said interactions. We hypothesized that peptide amyloids played a role in the origin of life and that their repetitive structure lends itself to building interfaces with other polymers through avidity. Here, we report that short RNA with a minimum length of three nucleotides binds in a sequence-dependent manner to peptide amyloids. The 3′–5′ linked RNA backbone appears to be well-suited to support these interactions, with the phosphodiester backbone and nucleobases both contributing to the affinity. Sequence-specific RNA–peptide interactions of the kind identified here may provide a path to understanding one of the great mysteries rooted in the origin of life: the origin of the genetic code.

The purpose of the attention schema theory is to explain how an information-processing device, the brain, arrives at the claim that it possesses a non-physical, subjective awareness and assigns a high degree of certainty to that extraordinary claim. The theory does not address how the brain might actually possess a non-physical essence. It is not a theory that deals in the non-physical. It is about the computations that cause a machine to make a claim and to assign a high degree of certainty to the claim. The theory is offered as a possible starting point for building artificial consciousness. Given current technology, it should be possible to build a machine that contains a rich internal model of what consciousness is, attributes that property of consciousness to itself and to the people it interacts with, and uses that attribution to make predictions about human behavior. Such a machine would “believe” it is conscious and act like it is conscious, in the same sense that the human machine believes and acts.

This article is part of a special issue on consciousness in humanoid robots. The purpose of this article is to summarize the attention schema theory (AST) of consciousness for those in the engineering or artificial intelligence community who may not have encountered previous papers on the topic, which tended to be in psychology and neuroscience journals. The central claim of this article is that AST is mechanistic, demystifies consciousness and can potentially provide a foundation on which artificial consciousness could be engineered. The theory has been summarized in detail in other articles (e.g., Graziano and Kastner, 2011; Webb and Graziano, 2015) and has been described in depth in a book (Graziano, 2013). The goal here is to briefly introduce the theory to a potentially new audience and to emphasize its possible use for engineering artificial consciousness.

The AST was developed beginning in 2010, drawing on basic research in neuroscience, psychology, and especially on how the brain constructs models of the self (Graziano, 2010, 2013; Graziano and Kastner, 2011; Webb and Graziano, 2015). The main goal of this theory is to explain how the brain, a biological information processor, arrives at the claim that it possesses a non-physical, subjective awareness and assigns a high degree of certainty to that extraordinary claim. The theory does not address how the brain might actually possess a non-physical essence. It is not a theory that deals in the non-physical. It is about the computations that cause a machine to make a claim and to assign a high degree of certainty to the claim. The theory is in the realm of science and engineering.

Complexity of biological forms has fascinated humankind over the years. Different species of plants have different leaf shapes. Have you ever wondered why it is so? Why does this shape diversity exist? Plants can change their leaf shapes over time and space. But how?

Does the distinct shape of leaf forms play a significant role in energy optimization? In fact, the shape of leaves has a lot to do with adapting to their surrounding environment. How is the unfolding of shape related to the evolutionary process of nature? These intriguing questions have led us to focus on quantitative approaches to the complexity of plant leaves.

Quantifying leaf shapes using Euclidean shapes, such as circles, triangles, etc., are appropriate to only a few plant species. Therefore, various quantitative measures of leaf shapes have been developed with varying accuracy. But Is the shape of an object really its actual shape? Visual perception of definite shape or geometry of physical objects is only an abstraction.

CryocrastinationDescription: Biostasis techniques such as cryopreservation, which allow recently-living organisms to be stored indefinitely without deteriora…

Dr. Avshalom Cyrus Elitzur (Hebrew: אבשלום כורש אליצור; born 30 May 1957) is an Israeli physicist, philosopher and professor at Chapman University. He is also the founder of the Israeli Institute for Advanced Physics. He obtained his PhD under Yakir Aharanov. Elitzur became a household name among physicists for his collaboration with Lev Vaidman in formulating the “bomb-testing problem” in quantum mechanics, which has been validaded by two Nobel-prize-winning physicists. Elitzur’s work has sparked extensive discussions about the foundations of quantum mechanics and its interpretations, including the Copenhagen interpretation, many-worlds interpretation, and objective collapse models. His contributions have had a profound impact on both physics and philosophy, influencing debates about measurement, the role of observers, and the ontology of quantum states. Elitzur has also engaged in discussions about consciousness, the arrow of time, and other foundational topics, including a recent breakthrough in bio-thermodynamics and the “ski-lift” pathway.

Elitzur’s Google Scholar page: https://tinyurl.com/5n7a8hd6
Elitzur’s Wikipedia page: https://en.wikipedia.org/wiki/Avshalo…
IAI Article: https://iai.tv/articles/a-radical-new…

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