Lifeboat Foundation

Generative AI and other easy-to-use software tools can help employees with no coding background become adept programmers, or what the authors call citizen developers. By simply describing what they want in a prompt, citizen developers can collaborate with these tools to build entire applications—a process that until recently would have required advanced programming fluency.

Information technology has historically involved builders (IT professionals) and users (all other employees), with users being relatively powerless operators of the technology. That way of working often means IT professionals struggle to meet demand in a timely fashion, and communication problems arise among technical experts, business leaders, and application users.

Citizen development raises a critical question about the ultimate fate of IT organizations. How will they facilitate and safeguard the process without placing too many obstacles in its path? To reject its benefits is impractical, but to manage it carelessly may be worse. In this article the authors share a road map for successfully introducing citizen development to your employees.

Will we ever decipher the language of molecular biology? Here, I argue that we are just a few years away from having accurate in silico models of the primary biomolecular information highway — from DNA to gene expression to proteins — that rival experimental accuracy and can be used in medicine and pharmaceutical discovery.

Since I started my PhD in 1996, the computational biology community had embraced the mantra, “biology is becoming a computational science.” Our ultimate ambition has been to predict the activity of biomolecules within cells, and cells within our bodies, with precision and reproducibility akin to engineering disciplines. We have aimed to create computational models of biological systems, enabling accurate biomolecular experimentation in silico. The recent strides made in deep learning and particularly large language models (LLMs), in conjunction with affordable and large-scale data generation, are propelling this aspiration closer to reality.

LLMs, already proven masters at modeling human language, have demonstrated extraordinary feats like passing the bar exam, writing code, crafting poetry in diverse styles, and arguably rendering the Turing test obsolete. However, their potential for modeling biomolecular systems may even surpass their proficiency in modeling human language. Human language mirrors human thought providing us with an inherent advantage, while molecular biology is intricate, messy, and counterintuitive. Biomolecular systems, despite their messy constitution, are robust and reproducible, comprising millions of components interacting in ways that have evolved over billions of years. The resulting systems are marvelously complex, beyond human comprehension. Biologists often resort to simplistic rules that work only 60% or 80% of the time, resulting in digestible but incomplete narratives. Our capacity to generate colossal biomolecular data currently outstrips our ability to understand the underlying systems.

The hyperactivity disorder, usually referred to as ADHD, is an independent risk factor for several common and serious mental health issues, finds research published in the open access journal BMJ Mental Health.

It is associated with major depression, post traumatic stress disorder, the eating disorder anorexia nervosa, and suicide attempts, the findings show, prompting the researchers to recommend vigilance by health professionals in a bid to ward off these disorders later on.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition in children and teens that extends into adulthood in up to around two thirds of cases. Worldwide, its prevalence is estimated to be around 5% in children/teens and 2.5% in adults.

Belinda Howell, Non-Executive Director at Digital Catapult, walks Reuters Plus through the potential that AI has to reduce carbon emissions and mitigate climate change.

By improving hospital care pathways, researchers from The University of Texas MD Anderson Cancer Center successfully reduced inpatient opioid use by 50% after pancreatic cancer surgery and cut the median opioid prescription volumes at discharge to zero. This approach, described in a study published in JAMA Surgery, could help reduce the risk of long-term opioid dependence in patients.

In this cohort study, which involved 832 patients undergoing pancreatic resection surgery, the researchers investigated how making incremental modifications to post-surgery procedures affected the amounts of opioids used by inpatients and at the point of discharge.

In less than four years, the total inpatient oral morphine equivalents (OME) decreased from a median of 290 mg to 129 mg, while OME at discharge decreased from a median of 150 mg to 0 mg. Over 75% of patients were discharged with ≤ 50 mg OME, which is fewer than 10 pills.

In new research published in BMC Medicine, the authors recruited a large cohort of participants in order to assess how plant-based foods affect aging trajectories [1].

Previous research has shown that consumption of plant-based foods is associated with healthy aging [2,3]. It can also help to decrease the risk of mortality [4], prevent the development of chronic diseases [5,6], and improve neurological health, such as by lowering the risk of dementia [7] and cognitive impairment [8].

This new study aimed to determine the influence of a plant-based diet on the aging trajectory of the middle-aged Asian population. Researchers recruited over 10,000 people 50 years and older in Taiwan. Participants provided health data four times during the eight years after enrollment, underwent physical examinations, and filled out relevant questionnaires.

Physicists at CERN’s Large Hadron Collider (LHC) have made the first ever direct observation of neutrinos in a particle accelerator.

Neutrinos are tiny, near massless and chargeless particles. They are among the elementary particles that make up the Standard Model of particle physics. Of all the particles in the Standard Model, neutrinos are among the least understood.

Even seeing a neutrino is extremely difficult, despite the fact they are among the most numerous particles in the universe. An estimated 100 trillion (100 million million) neutrinos pass through your body every second!

We may have become the most complex living creature in part by accident and replication of error.

Quantum computers, technologies that perform computations leveraging quantum mechanical phenomena, could eventually outperform classical computers on many complex computational and optimization problems. While some quantum computers have attained remarkable results on some tasks, their advantage over classical computers is yet to be conclusively and consistently demonstrated.

Ramis Movassagh, a researcher at Google Quantum AI, who was formerly at IBM Quantum, recently carried out a theoretical study aimed at mathematically demonstrating the notable advantages of quantum computers. His paper, published in Nature Physics, mathematically shows that simulating random quantum circuits and estimating their outputs is so-called #P-hard for classical computers (i.e., meaning that is highly difficult).

“A key question in the field of quantum computation is: Are quantum computers exponentially more powerful than classical ones?” Ramis Movassagh, who carried out the study, told Phys.org. “Quantum supremacy conjecture (which we renamed to Quantum Primacy conjecture) says yes. However, mathematically it’s been a major open problem to establish rigorously.”