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877 published stories · page 48 of 59

Evidence-backed problem

‘It’s a scam’: Americans express unease over SpaceX’s influence on retirement savings

Elon Musk became the world’s first trillionaire last week after SpaceX debuted on the stock market with a valuation of $1.77tn. Millions of Americans could soon become indirect investors in SpaceX and other emerging AI-focused companies as US markets increasingly shift toward AI-driven investments. Many Americans’ retirement savings are heavily tied to the US stock market through private 401(k) retirement savings plans. Those plans are heavily invested in index funds that track the major stock market indices. So…

The Guardian · Business

‘It’s a scam’: Americans express unease over SpaceX’s influence on retirement savings

Evidence-backed gain

Google DeepMind launches AI tool to help identify genetic drivers of disease

Researchers at Google DeepMind have unveiled their latest artificial intelligence tool and claimed it will help scientists identify the genetic drivers of disease and ultimately pave the way for new treatments. AlphaGenome predicts how mutations interfere with the way genes are controlled, changing when they are switched on, in which cells of the body, and whether their biological volume controls are set to high or low. Most common diseases that run in families, including heart disease and autoimmune disorders,…

Health

Both readings

Federated Machine Learning

Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated tran…

Science

Evidence-backed gain

Accurate prediction of protein structures and interactions using a three-track neural network

Deep learning takes on protein folding In 1972, Anfinsen won a Nobel prize for demonstrating a connection between a protein’s amino acid sequence and its three-dimensional structure. Since 1994, scientists have competed in the biannual Critical Assessment of Structure Prediction (CASP) protein-folding challenge. Deep learning methods took center stage at CASP14, with DeepMind’s Alphafold2 achieving remarkable accuracy. Baek et al . explored network architectures based on the DeepMind framework. They used a three…

Science

Both readings

Overcoming catastrophic forgetting in neural networks

Significance Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks. This approach, inspired by synaptic consolidation in neuroscience, enable…

Science

Evidence-backed gain

Deep learning

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters th…

Science

Evidence-backed gain

Random Forest

For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become increasingly common. Particularly in this era of "big data" and machine learning, survival analysis has become methodologically broader. This paper aims to explore one t…

Health

Artificial intelligence: A powerful paradigm for scientific research

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming…

Science

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a hig…

Science

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

Deep forest

Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized differentiable non-linear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules such as decision trees. After a discussion about the mystery behind deep neural networks, particularly by contrasting them with shallow neural networks and traditional machine-learning techniques such as decision trees and boosting mac…

Science

Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, machine learning alone ign…

Science

Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

Highly accurate protein structure prediction with AlphaFold

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1-4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6,7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to addr…

Science

Highly accurate protein structure prediction with AlphaFold

AI in Everyday Life: How Algorithmic Systems Shape Social Relations, Opportunity, and Public Trust

Artificial intelligence is often framed as a neutral technical tool that enhances efficiency and consistency in institutional decision-making. This article challenges that framing by showing that automated systems now operate as social and institutional actors that reshape recognition, opportunity, and public trust in everyday life. Focusing on employment screening, welfare administration, and digital platforms, the study examines how algorithmic systems mediate social relations and reorganise how individuals ar…

Policy

AI in Everyday Life: How Algorithmic Systems Shape Social Relations, Opportunity, and Public Trust

XAI-driven Data Mining for Self-defending IoT Systems: Enhancing Cybersecurity Transparency in the Age of Smart Cities

The rapid expansion of Internet of Things (IoT) technologies in smart cities, healthcare, and industrial automation has intensified the need for cybersecurity frameworks capable of operating at scale and in real time under increasingly sophisticated threat conditions. Traditional security mechanisms and opaque AI-based models are no longer adequate for protecting interconnected urban infrastructures, especially as regulatory and societal expectations move toward transparency and accountability. Although prior su…

Crime

The impact of robot-assisted language learning (RALL) on EFL students’ classroom engagement and willingness to attend classes (WTAC): A technology acceptance model (TAM) perspective

Recently, a surge of scholarly attention has been paid to the impacts of Artificial Intelligence (AI) tools on second/foreign language (L2) education. Nonetheless, there is a lack of evidence on the role of AI-assisted robots or robot-assisted language learning (RALL) in English as a foreign language (EFL) students’ psycho-emotional factors and academic behaviors. To address this gap, drawing on the technology acceptance model (TAM), the present study probed into the impact of robot-assisted L2 education on Chin…

Education

The impact of robot-assisted language learning (RALL) on EFL students’ classroom engagement and willingness to attend classes (WTAC): A technology acceptance model (TAM) perspective