A look at the enduring relevance.of Invasion of the Body Snatchers.
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As artificial intelligence models become increasingly advanced, electronics engineers have been trying to develop new hardware that is better suited for running these models, while also limiting power-consumption and boosting the speed at which they process data. Some of the most promising solutions designed to meet the needs of machine learning algorithms are platforms based on memristors.
Memristors, or memory resistors, are electrical components that can retain their resistance even in the absence of electrical power, adjusting their resistance based on the electrical charge passing through them. This means that they can simultaneously support both the storage and processing of information, which could be advantageous for running machine learning algorithms.
Memristor-based devices could be used to develop more compact and energy-efficient hardware for running AI models, including emerging distributed computing solutions referred to as edge computing systems. Despite their advantages, many existing memristor-based platforms have been found to have notable limitations, adversely impacting their reliability and endurance.
Sound localization is one of the many learning tasks accomplished by the brain based on the binaural signals of the ears. Here, Wu et al demonstrate in-situ learning of sound localization function using a memristor array, with dramatic improvements in energy efficiency.
Xanadu developed Aurora, a universal photonic quantum computer with a modular architecture of interconnected server racks for scalability.
Reward and emotional evaluations interact to guide decisions regarding the punishment of a social norm-violating proposer, with these processes being mediated by distinct neurofunctional computation.
The human brain spends 30–50% of its waking hours engaged in mind-wandering (MW), a common phenomenon in which individuals either spontaneously or deliberately shift their attention away from external tasks to task-unrelated internal thoughts. Despite the significant amount of time dedicated to MW, its underlying reasons remain unexplained. Our pre-registered study investigates the potential adaptive aspects of MW, particularly its role in predictive processes measured by statistical learning. We simultaneously assessed visuomotor task performance as well as the capability to extract probabilistic information from the environment while assessing task focus (on-task vs. MW). We found that MW was associated with enhanced extraction of hidden, but predictable patterns.
A team of researchers from the University of Ottawa has developed innovative methods to enhance frequency conversion of terahertz (THz) waves in graphene-based structures, unlocking new potential for faster, more efficient technologies in wireless communication and signal processing.
THz waves, located in the far-infrared region of the electromagnetic spectrum, can be used to perform non-invasive imaging through opaque materials for security and quality control applications. Additionally, these waves hold great promise for wireless communication.
Advances in THz nonlinear optics, which can be used to change the frequency of electromagnetic waves, are essential for the development of high-speed wireless communication and signal processing systems for 6G technologies and beyond.