Lifeboat Foundation

NPL, in collaboration with London Biofoundry and BiologIC Technologies Ltd, have released an analysis on existing and emerging DNA Synthesis technologies in Nature Reviews Chemistry, featuring the work on the front cover.

The study, which was initiated by DSTL, set out to understand the development trajectory of DNA Synthesis as a major industry drive for the UK economy over the next 10 years. The demand for synthetic DNA is growing exponentially. However, our ability to make, or write, DNA lags behind our ability to sequence, or read, it. The study reviewed existing and emerging DNA synthesis technologies developed to close this gene writing gap.

DNA or genes provide a universal tool to engineer and manipulate living systems. Recent progress in DNA synthesis has brought up limitless possibilities in a variety of industry sectors. Engineering biology, therapy and diagnostics, data storage, defense and nanotechnology are all set for unprecedented breakthroughs if DNA can be provided at scale and low cost.

Summary: Researchers have developed a more powerful and energy-efficient memristor, based on the structure of the human brain, that combines data storage and processing. The new technology, made from nanocrystals of halogenated perovskite, is not yet ready for use as it is difficult to integrate with existing computer chips, but it has the potential for parallel processing of large amounts of data.

Source: Politecnico di Milano.

Inspired by the brain’s energy efficiency, copying its structure to create more powerful computers, a team of researchers from Politecnico di Milano, Empa and ETH Zurich has developed a memristor that is more powerful and easier to produce than its predecessors: the results have been published in Science Advances.

In 2022, the physics Nobel prize was awarded for experimental work showing that the quantum world must break some of our fundamental intuitions about how the Universe works.

Many look at those experiments and conclude that they challenge “locality” – the intuition that distant objects need a physical mediator to interact. And indeed, a mysterious connection between distant particles would be one way to explain these experimental results.

Others instead think the experiments challenge “realism” – the intuition that there’s an objective state of affairs underlying our experience. After all, the experiments are only difficult to explain if our measurements are thought to correspond to something real.

The Eqs. (3a) and (3b) suggest two important features of the location of neutrons and the spin by switching the choice of the post-selection: (i) The first lines indicate that the neutrons are found to be localized in different paths by switching the choice of the post-selection; they are found in the path I and II by applying the post-selection \({|{\Psi ^{+}_f}\rangle }\) and \({|{\Psi ^{-}_f}\rangle }\), respectively. (ii) The lines of the second part of the equations indicate that the spin in the different paths is found to be affected by switching the choice of the post-selection; the spin in path II and I is affected by applying the post-selection \({|{\Psi ^{+}_f}\rangle }\) and \({|{\Psi ^{-}_f}\rangle }\), respectively. Note that, in both choices of the post-selection, neutron and spin are localized in different paths, i.e., the location of the cat itself and its grin are interchanged by switching the choices of the post-selection. Since measurement of the locations of the neutron and the spin in the interferometer can be carried out independently of the delayed-choice process, the picking of a direction for post-selection, the influence of the delayed-choice on the preceding measurements can be investigated. We would like to point out that the experimental proposal in a recent publication³⁵, contains a delayed choice scenario, too. The difference to the experiment presented in this report is that the authors of³⁵ suggest a setup where two properties of the same system, represented by two non-commuting observables, are separated. In contrast to that, we deal in our experiment with the separation of one property from the system itself, hereby constituting the phenomenon of disembodiment. Further we would like to point out that in their Gedanken-experiment the effect of a change in the pre-selection is discussed that in our view has no retro-causal implications.

The experiment was carried out at the S18 silicon-perfect-crystal interferometer beam line at the high flux reactor at the Institute Laue Langevin. A schematic view of the experimental set-up is shown in Fig. 2.

An international team of planetary scientists has characterized some of the features of an exoplanet named HD-207496-b, located approximately 138 light years from Earth. In their paper accepted for publication in the journal Astronomy & Astrophysics, and currently posted on the arXiv preprint server, the group describes their study of the exoplanet and the two theories regarding its likely makeup.

The exoplanet HD-207496-b was discovered as part of a larger effort to characterize naked core planets. As such, the team was analyzing HARPS spectroscopic observations of HD-207496—a bright k dwarf. By adding TESS photometry data, the group was able to measure the stars’ brightness and wavelength, and by studying the exoplanet’s transit characteristics, the team was able to calculate its period, mass, radius and density. That led them to a bit of a conundrum—was the exoplanet gaseous or watery?

The researchers calculated that the exoplanet had a radius 2.25 times that of Earth, with an orbit of 6.44 days. And it had a mass that was approximately 6.1 times Earth’s. Simple math showed that the exoplanet had a density of 3.27 grams per cubic centimeter, which is less than that of Earth.

He was ranked the number 1 most influential neuroscientist in the world by Semantic Scholar in 2016, and has received numerous awards and accolades for his work. His appointment as chief scientist of Verses not only validates their platform’s framework for advancing AI implementations but also highlights the company’s commitment to expanding the frontier of AI research and development.

Friston is short listed for a Nobel Prize, is one of the most cited scientists in human history with over 260,000 academic citations, and invented all of the mathematics behind the fMRI scan. As one pundit put it, “what Einstein was to physics, Friston is to Intelligence.”

Indeed Friston’s expertise will be invaluable in helping the company execute its vision of deploying a plethora of technologies working toward a smarter world through AI.

This physics-inspired circus performance enthralled attendees of the American Physical Society’s March Meeting, but a casual observer may have missed some of the scientific concepts that performers enacted.

By Karmela Padavic-Callaghan

With the development of computing and data, autonomous agents are gaining power. The need for humans to have some say over the policies learned by agents and to check that they align with their goals becomes all the more apparent in light of this.

Currently, users either 1) create reward functions for desired actions or 2) provide extensive labeled data. Both strategies present difficulties and are unlikely to be implemented in practice. Agents are vulnerable to reward hacking, making it challenging to design reward functions that strike a balance between competing goals. Yet, a reward function can be learned from annotated examples. However, enormous amounts of labeled data are needed to capture the subtleties of individual users’ tastes and objectives, which has proven expensive. Furthermore, reward functions must be redesigned, or the dataset should be re-collected for a new user population with different goals.

New research by Stanford University and DeepMind aims to design a system that makes it simpler for users to share their preferences, with an interface that is more natural than writing a reward function and a cost-effective approach to define those preferences using only a few instances. Their work uses large language models (LLMs) that have been trained on massive amounts of text data from the internet and have proven adept at learning in context with no or very few training examples. According to the researchers, LLMs are excellent contextual learners because they have been trained on a large enough dataset to incorporate important commonsense priors about human behavior.

To divine that wonderful arts lie hid behind trivial and childish things is a conception for superhuman talents.