1045 published stories · page 59 of 70
AI music tools are making song creation faster and cheaper for Indian creators. Platforms like Suno, Udio, ElevenLabs Music, Beatoven.ai, Google Gemini with Lyria 3 and Weights.gg offer features ranging from full song generation and voice cloning to background scores and AI covers, catering to musicians, YouTubers and indie creators.
It is twilight on a desirable street in Chiswick, or it could be Hampstead, Wilmslow or Hove. A spectacular sunset has left a vivid stripe of orange fading into a violet sky. Against this saturated backdrop, a large Victorian house is clearly outlined despite the darkening atmosphere, perhaps thanks to the lights blazing from every single room. The effect is dazzling, in an unhinged, halfway-through-an-exorcism way. It is also quite obviously fake: a digital trick previously achieved with software such as Photos…
An Australian producer has gone from little-known artist to viral sensation in a matter of months, with his hit song catapulting on to global charts and receiving thousands of radio spins. There’s just one problem: music experts and other musicians are questioning whether he produced it. They claim Josh Fawaz’s most popular song, a cover of Madonna’s Like a Prayer which reached the No 1 spot on the National Radio Airplay chart, could have been made using AI. While producers often use software including Ableton L…
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…
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,…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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