Health tests and reports suggest that Bryan Johnson, 45, has the heart of a 37-year-old and gets erections like a teenager, Bloomberg reported.
Ever since the invention of computers in the 1940s, machines matching general human intelligence have been greatly anticipated. In other words, a machine that possesses common sense and an effective ability to learn, reason, and plan to meet complex information-processing challenges across a wide range of natural as well as abstract domains, would qualify as having a human-level machine intelligence. Currently, our machines are far inferior to humans in general intelligence. However, according to philosopher Nick Bostrom at the University of Oxford, there are several pathways that could lead to human-level intelligence in machines such as whole brain emulation, biological cognition, artificial intelligence, human-machine interfaces, as well as networks and organizations. Once this happens, it would only be a matter of time until superhuman-level machine intelligence, or simply, superintelligence is unlocked. But what exactly do we mean by ‘superintelligence’? And are there different forms of superintelligence that our A.I.s can attain in the future? Let’s take a look at what Nick Bostrom has to say in this matter!
In his book, ‘Superintelligence’ Nick Bostrom defines the term ‘superintelligence’ “to refer to intellects that greatly outperform the best current human minds across many very general cognitive domains.” So, a super-intelligent intellect, would in principle, have the capacity to completely surpass the best human minds in practically every field, including science, philosophy, arts, general wisdom, and even social skills.
The ability to see invisible structures in our bodies, like the inner workings of cells, or the aggregation of proteins, depends on the quality of one’s microscope. Ever since the first optical microscopes were invented in the 17th century, scientists have pushed for new ways to see more things more clearly, at smaller scales and deeper depths.
Randy Bartels, professor in the Department of Electrical Engineering at Colorado State University, is one of those scientists. He and a team of researchers at CSU and Colorado School of Mines are on a quest to invent some of the world’s most powerful light microscopes—ones that can resolve large swaths of biological material in unimaginable detail.
The name of the game is super–resolution microscopy, which is any optical imaging technique that can resolve things smaller than half the wavelength of light. The discipline was the subject of the 2014 Nobel Prize in Chemistry, and Bartels and others are in a race to keep circumventing that diffraction limit to illuminate biologically important structures inside the body.
With so much death all around us, from the pandemic to the war in Ukraine to all the mass shootings, you might wonder what it all means. Queen Elizabeth gone. Betty White gone. And perhaps even a loved one of yours gone. They no longer exist, right? They are just memories, at least from a rational scientific perspective. But what if you’re wrong?
Dr. Caroline Soames-Watkins also believed that the world around her existed as a hard, cold reality ticking away like a clock. Death was a foregone conclusion—until she learned different. Caro, the protagonist of my new novel co-written with award-winning sci-fi author Nancy Kress, also thought she had the world figured out. Not her personal world, which has been upended by controversy, but how the physical world works and how her consciousness operates within it. Broke and without a job, she accepts a job offer from her great-uncle, a Nobel Prize-winning scientist who runs a research facility studying the space between biology and consciousness—between the self and what we assume is reality. They are on the verge of a humanity-altering discovery, which throws Caro into danger—love, loss, and death—that she could never have imagined possible.
Observer takes Caro on a mind-expanding journey to the very edge of science, challenging her to think about life and the power of the imagination in startling new ways. The ideas behind Observer are based on real science, starting with the famous two-slit experiments, in which the presence of an observer affects the path taken by a sub-atomic particle, and moves step-by-step into cutting-edge science about quantum entanglement, on-going experiments applying quantum-level physics to the macro-world, the multiverse, and the nature of time and consciousness itself.
The brain is often regarded as a soft-matter chemical computer, but the way it processes information is very different to that of conventional silicon circuits. Three groups now describe chemical systems capable of storing information in a manner that resembles the way that neurons communicate with one another at synaptic junctions. Such ‘neuromorphic’ devices could provide very-low-power computation and act as interfaces between conventional electronics and ‘wet’ chemical systems, potentially including neurons and other living cells themselves.
At a synapse, the electrical pulse or action potential that travels along a neuron triggers the release of neurotransmitter molecules that bridge the junction to the next neuron, altering the state of the second neuron by making it more or less likely to fire its own action potential. If one neuron repeatedly influences another, the connection between them may become strengthened. This is how information is thought to become imprinted as a memory, a process called Hebbian learning. The ability of synapses to adjust their connectivity in response to input signals is called plasticity, and in neural networks it typically happens on two timescales. Short-term plasticity (STP) creates connectivity patterns that fade quite fast and are used to filter and process sensory signals, while long-term plasticity (LTP, also called long-term potentiation) imprints more long-lived memories. Both biological processes are still imperfectly understood.
Neuromorphic circuits that display such learning behaviour have been developed previously using solid-state electronic devices called memristors, two-terminal devices in which the relationship between the current that passes through and the voltage applied depends on the charge that passed through previously. Memristors may retain this memory even when no power is applied – they are ‘non-volatile’ – meaning that neuromorphic circuits can potentially process information with very low power consumption, a feature crucial to the way our brains can function without overheating. Typically, memristor behaviour manifests as a current–voltage relationship on a loop, and the response varies depending on whether the voltage is increasing or decreasing: a property called hysteresis, which itself represents a kind of memory as the device behaviour is contingent on its history.
Octopuses have fascinated scientists and the public with their remarkable intelligence, from using tools to engaging in creative play, problem-solving, and even escaping from aquariums. Now, their cognitive abilities may provide significant insight into understanding the evolution of complex life and cognition, including the human brain.
An international team of researchers from Dartmouth College and the Max Delbrück Center (MDC) in Germany has published a study in the journal Science Advances.
<em>Science Advances</em> is a peer-reviewed, open-access scientific journal that is published by the American Association for the Advancement of Science (AAAS). It was launched in 2015 and covers a wide range of topics in the natural sciences, including biology, chemistry, earth and environmental sciences, materials science, and physics.
Year 2017 face_with_colon_three
Biological molecules, like organisms themselves, are subject to genetic drift and may even become “extinct”. Molecules that are no longer extant in living systems are of high interest for several reasons including insight into how existing life forms evolved and the possibility that they may have new and useful properties no longer available in currently functioning molecules. Predicting the sequence/structure of such molecules and synthesizing them so that their properties can be tested is the basis of “molecular resurrection” and may lead not only to a deeper understanding of evolution, but also to the production of artificial proteins with novel properties and even to insight into how life itself began.
This video covers the world in a billion years and its future technologies. Watch this next video about the world in a million years: https://bit.ly/3xe50by.
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Quantum-enhanced single-parameter estimation is an established capability, with non-classical probe states achieving precisions beyond what can be reached by the equivalent classical resources in photonic1,2,3, trapped-ion4,5, superconducting6 and atomic7,8 systems. This has paved the way for quantum enhancements in practical sensing applications, from gravitational wave detection9 to biological imaging10. For single-parameter estimation, entangled probe states are sufficient to reach the ultimate allowed precisions. However, for multi-parameter estimation, owing to the possible incompatibility of different observables, entangling resources are also required at the measurement stage. The ultimate attainable limits in quantum multi-parameter estimation are set by the Holevo Cramér–Rao bound (Holevo bound)11,12. In most practical scenarios, it is not feasible to reach the Holevo bound as this requires a collective measurement on infinitely many copies of the quantum state13,14,15,16 (see Methods for a rigorous definition of collective measurements). Nevertheless, it is important to develop techniques that will enable the Holevo bound to be approached, given that multi-parameter estimation is fundamentally connected to the uncertainty principle17 and has many physically motivated applications, including simultaneously estimating phase and phase diffusion18,19, quantum super-resolution20,21, estimating the components of a three-dimensional field22,23 and tracking chemical processes24. Furthermore, as we demonstrate, collective measurements offer an avenue to quantum-enhanced sensing even in the presence of large amounts of decoherence, unlike the use of entangled probe states25,26.
To date, collective measurements for quantum multi-parameter metrology have been demonstrated exclusively on optical systems27,28,29,30,31,32. Contemporary approaches to collective measurements on optical systems are limited in their scalability: that is, it is difficult to generalize present approaches to measuring many copies of a quantum state simultaneously. The limited gate set available can also make it harder to implement an arbitrary optimal measurement. Indeed, the collective measurements demonstrated so far have all been restricted to measuring two copies of the quantum state and, while quantum enhancement has been observed, have all failed to reach the ultimate theoretical limits on separable measurements33,34. Thus, there is a pressing need for a more versatile and scalable approach to implementing collective measurements.
In this work, we design and implement theoretically optimal collective measurement circuits on superconducting and trapped-ion platforms. The ease with which these devices can be reprogrammed, the universal gate set available and the number of modes across which entanglement can be generated, ensure that they avoid many of the issues that current optical systems suffer from. Using recently developed error mitigation techniques35 we estimate qubit rotations about the axes of the Bloch sphere with a greater precision than what is allowed by separable measurements on individual qubits. This approach allows us to investigate several interesting physical phenomena: we demonstrate both optimal single-and two-copy collective measurements reaching the theoretical limits33,34. We also implement a three-copy collective measurement as a first step towards surpassing two-copy measurements. However, due to the circuit complexity, this measurement performs worse than single-copy measurements. We investigate the connection between collective measurements and the uncertainty principle. Using two-copy collective measurements, we experimentally violate a metrological bound based on known, but restrictive uncertainty relations36. Finally, we compare the metrological performance of quantum processors from different platforms, providing an indication of how future quantum metrology networks may look.
The wetware in a casket of bone that we each carry on our shoulders is 1 million times more efficient than the AI models run by services like ChatGPT, Stable Diffusion, or DALL-E.
In this TechFirst with John Koetsier we chat for a second time with Gordon Wilson, CEO of Rain AI, which is building a neuromorphic artificial brain simulating the structure of our biological brains, and aiming at 10,000 to 100,000 greater energy efficiency than current AI architectures.
