In a recent study led by the University of Liège researchers delved into the intersection of the fields of entrepreneurship and neuroscience, looking specifically at the cognitive flexibility of habitual entrepreneurs—those who repeatedly launch new businesses—compared to less experienced entrepreneurs and managers.
Just how secure are your passwords? Not very, if an attacker uses a smart-guessing password-cracking algorithm, according to a new Kaspersky analysis.
LIFE after death could in some form be possible if the infinite universe theory is proved to be true, one physicist has claimed.
Our technological prowess is unrivaled. From the simplest inventions to the most complex machines, we have continually pushed the limits of what is possible. But as our capabilities grow exponentially, a looming question arises: are we heading towards a technological singularity that could change the course of humanity?
The concept of technological singularity has moved beyond the fringes of futurism; it is now an imminent possibility. Defined as the hypothetical future point when technologies have become so advanced that humanity undergoes a dramatic and irreversible change, the singularity presents an inescapable, exhilarating, and terrifying problem for philosophers, scientists, and every human being.
Are we prepared for the implications, the ramifications, and the profound changes that a tech-driven future might bring?
These technologies are helping researchers to explore what sets the human brain apart from those of other species, and how its cognitive abilities have evolved. For example, the role of non-invasive imaging in learning about cognitive abilities is discussed in a Perspective article by Feline Lindhout at the Medical Research Council’s Laboratory of Molecular Biology in Cambridge, UK, and her colleagues1. In another article, Evelina Fedorenko at the Massachusetts Institute of Technology in Cambridge and her colleagues also draw on this literature to argue that, in humans, language probably serves mainly as a communication tool rather than as a means for thinking or reasoning2 — and that language is not a prerequisite for complex thought.
One desirable outcome for human neuroscience would be to develop personalized treatments for neurological and psychiatric disorders, because translating the results of studies in animals has not proved successful or sufficient for generating effective therapies at scale. But in grasping these opportunities, researchers must keep in mind that the brain is different from other organs — it’s the seat of people’s memory, experiences and personality. When using the human brain — whether in small cubes removed during neurosurgery, or through 3D organoids made from stem cells and grown in cultures to resemble parts of the developing human brain — for research, scientists must consider the dignity and respect owed to the individuals concerned.
In today’s semiconductor manufacturing industry, the most advanced chips are produced at 7 nm and below where there is little room for error. Despite the difficulty and unrelenting pressures found in this microworld, engineers and scientists remain undeterred in their pursuit of cutting-edge processes, techniques or materials that push the boundaries of Moore’s Law. Through endless experimentation at the nanoscale level, designers and researchers seek to uncover minute improvements that have the potential to translate into millions—if not billions—of dollars in revenue for chipmakers.
The emergence of carbon nanotubes (CNTs) as a compelling alternative material to address inefficiencies in extreme ultraviolet (EUV) lithography has the potential to be one of those innovations. However, contemporary production methods create CNTs that fall short of expectations. To realize the full potential of CNTs requires a new production method that significantly improves their quality. Only then can they help the semiconductor industry deliver on the insatiable demands for advanced chips.
Before exploring the production methods behind creating CNTs, one must first understand why they are so crucial in the semiconductor industry.
Stephen Wolfram explores simple models of biological organisms as computational systems. A study of progressive development, multiway graphs of all possible paths and the need for narrowing the framework space.
