Lifeboat Foundation

To achieve high intrinsic gain (A_i) in OTFTs, it is necessary to enlarge output resistance (r_o) or transconductance (g_m) according to a typical formula of A_i = g_mr_o, which is very difficult for conventional OTFTs because of inherent device structure and operating mode limitations (11, 12). Recently, the “Schottky barrier” (SB) strategy based on metal-semiconductor junction (MS junction) has been adopted in TFTs to pursue high-gain and low-saturation voltage, including subthreshold SB-TFTs (11, 12, 15, 16) and source-gated transistors (17, 18). Unfortunately, the subthreshold transistors are limited in low and narrow subthreshold operating region rather than the normal ON-state region (namely, the normal voltage operating region in a typical TFT), which are difficult to be compatible with typical circuits. As far as we know, the ultrahigh-gain (1000) OTFTs operating in the ON-state region have not been previously reported. On the other hand, the state-of-the-art OTFTs above have mostly suffered from uncontrollable barriers owing to energy-level mismatches and a series of complex interface problems, such as Fermi-level pinning and interface chemical disorder (19). In this case, considerable low-energy carriers are allowed to pass through the junction by thermionic field emission and tunneling models instead of thermionic emission model, which is not conducive to obtaining a high output resistance and high intrinsic gain. Most barrier heights in MS junction do not conform to the prediction value of Schottky-Mott rule. Theoretically, an ideal and high-quality barrier with thermionic emission model allows the rapid depletion of carriers at the source electrode, thus yielding ultrahigh gain, infinite output resistance, and low saturation voltage (11, 12). In addition, infinite output resistance at the saturation regime indicates that the output current is very stable and flat. This performance is helpful because only a single OTFT is used as a simplified current stabilizer in circuits without complex circuit design, which benefits low power and low cost in circuits. Therefore, it is necessary to develop a high-quality barrier strategy to modulate charge injection to meet the requirements of ultrahigh-gain OTFTs.

Here, we demonstrate a metal-barrier interlayer-semiconductor (MBIS) junction to prepare high-performance MBIS-OTFT with an ultrahigh gain of ~10⁴ in the ON-state region, low saturation voltage, almost negligible hysteresis, and good stability. On the basis of low-energy processes and in situ surface oxidation technology, the high-quality van der Waals MBIS junction with wide-bandgap semiconductor (mainly Ga₂O₃) interlayer is achieved, allowing for an adjustable barrier height and thermionic emission properties. A series of in situ experiments and simulations revealed the relationship between the barriers and the device’s performance. Furthermore, as demonstrations, a simplified current stabilizer and an ultrahigh-gain organic inverter are exhibited without complex circuit design.

A team of AI researchers at Google’s DeepMind project have developed a type of AI system that is able to demonstrate social learning capabilities. In their paper published in the journal Nature Communications, the group describes how they developed an AI application that showed it was capable of learning new skills in a virtual world by copying the actions of an implanted “expert.”

Most AI systems, such as ChatGPT, gain their knowledge through exposure to huge amounts of data, such as from repositories on the Internet. But such an approach, those in the industry have noted, is not very efficient. Therefore many in the field continue to look for other ways to teach AI systems to learn.

One of the most popular approaches used by researchers is to attempt to mimic the process by which humans learn. Like traditional AI apps, humans learn by exposure to known elements in an environment and by following the examples of others who know what they are doing. But unlike AI apps, humans pick things up without the need for huge numbers of examples. A child can learn to play the game of Jacks, for example, after watching others play for just a few minutes—an example of cultural transmission. In this new effort, the research team has attempted to replicate this process using AI constrained to a virtual world.

Using single-cell and spatial transcriptomics, human embryonic limb development across space and time and the diversification and cross-species conservation of cells are demonstrated.

The feeling that we belong to something much larger and deeper than ourselves has long been a common human experience. Palaeontologist and Jesuit priest Teilhard de Chardin wrote about “a noosphere” of cognitive realisation evolving towards an “Omega point” of divine planetary spiritualisation. But it is hard to envisage that ever occurring. It is easier to envisage that we belong in an evolving intelligent power that has entered a momentous posthuman dimension though artificial intelligence.

Some futurists believe we are on the way to realising a posthuman world in which we will live on as cyborgs, or in some new embodiment of intelligent power that will absorb and supersede human intelligence. It is no longer fanciful to foresee a future in which we will have everyday interactions with androids that are powered by artificial general intelligence. They will look, move, and seem to think and respond like a human person, be skilled in simulating emotional responses realistically, and greatly out-perform us in mental activities and manual tasks. It may be we will regard them only as tools or mechanical assistants. But from their expression of human-like behaviours we may become attached to them, even to the extent of according them rights. Their design will have to ensure they don’t carry any threat, but will we be able to trust fully that this will remain the case given their technical superiority? And how far can we trust that the military, malicious groups, and rogue states won’t develop androids trained to kill people and destroy property? We know only too well about our human propensity for violent conflict.

It would be ironic if, to gain more power and control over the world, we used our human intelligence to create AI systems and devices which, for all the benefits they bring, end up managing our lives to our detriment, or even controlling us. And irony, as Greek dramatists were well aware, is often a component of fate.

Explore the dynamic interplay between the human brain and AI. Uncover the complexities and contrasts shaping the future of intelligence.

SUMMARY: A soft robot with octopus-inspired sensory and motion capabilities represents significant progress in robotics, offering nimbleness and adaptability in uncertain environments.

Robotic engineers have made a leap forward with the development of a soft robot that closely resembles the dynamic movements and sensory prowess of an octopus. This groundbreaking innovation from an international collaboration involving Beihang University, Tsinghua University, and the National University of Singapore has the potential to redefine how robots interact with the world around them.

The blueprint for this highly adaptable robot draws upon the intelligent, soft-bodied mechanics of an octopus, enabling smooth movements across a variety of surfaces and environments with precision. The sensorized soft arm, lovingly named the electronics-integrated soft octopus arm mimic (E-SOAM), embodies advancements in soft robotics with its incorporation of elastic materials and sophisticated liquid metal circuits that remain resilient under extreme deformation.

Qi et al. used phage-assisted evolution to optimize SlugCas9, a compact Cas9 nuclease, for NNG PAM recognition and developed a SlugCas9-NNG based adenine base editor for single AAV delivery.

This blog post is tailored to capture the interest of a professional management and executive audience, combining theoretical insights with practical advice. It aims to provoke thought and encourage a proactive approach to the integration of AI in the business world.

Google’s new AI model Gemini launched today inside the Bard chatbot. It could go on to advance robotics and other projects, says Demis Hassabis, the AI executive leading the project.