Spatially resolved single-cell transcriptomics offers insights into gene expression within tissues, but current technologies are limited by their ability to measure only a small number of genes. To address this, algorithms have been developed to predict or impute the expression of additional genes. These methods often use paired single-cell RNA sequencing data, embedding spatial and…
In a significant development for the forecasting community, Nixtla has announced the release of NeuralForecast, an advanced library designed to offer a robust and user-friendly collection of neural forecasting models. This library aims to bridge the gap between complex neural networks and their practical application, addressing the persistent challenges faced by forecasters in terms of…
In a seminal announcement, Black Forest Labs has emerged as a new player in the generative AI landscape. With deep roots in the research community, this innovative company aims to revolutionize the field of generative deep learning models, particularly focusing on media such as images and videos. Their mission is clear: to push the boundaries…
Reinforcement learning (RL) focuses on how agents can learn to make decisions by interacting with their environment. These agents aim to maximize cumulative rewards over time by using trial and error. This field is particularly challenging due to the need for large amounts of data and the difficulty in handling sparse or absent rewards in…
Ensuring data privacy and security during computational processes presents a significant challenge, particularly when using cloud services. Traditional encryption methods require data to be decrypted before processing, exposing it to potential risks. Homomorphic encryption offers a promising solution, allowing computations on encrypted data without revealing the underlying information. Apple introduces a new open-source Swift package,…
Large Language Models (LLMs) have gained significant traction in various domains, revolutionizing applications from conversational agents to content generation. These models demonstrate exceptional capabilities in comprehending and producing human-like text, enabling sophisticated applications across diverse fields. However, the deployment of LLMs necessitates robust mechanisms to ensure safe and responsible user interactions. Current practices often employ…
LLMs have shown impressive abilities in handling complex question-answering tasks, supported by advancements in model architectures and training methods. Techniques like chain-of-thought (CoT) prompting have gained popularity for improving the explanation and accuracy of responses by guiding the model through intermediate reasoning steps. However, CoT prompting can result in longer outputs, increasing the time needed…
Large Language Model (LLM) agents are experiencing rapid diversification in their applications, ranging from customer service chatbots to code generation and robotics. This expanding scope has created a pressing need to adapt these agents to align with diverse user specifications, enabling highly personalized experiences across various applications and user bases. The primary challenge lies in…
Hackers finding a way to mislead their AI into disclosing critical corporate or consumer data is the possible nightmare that looms over Fortune 500 company leaders as they create chatbots and other generative AI applications. Meet Lakera AI, a GenAI security company and cool start-up that uses AI to shield businesses from LLM flaws in…
Recent advancements in video generation have been driven by large models trained on extensive datasets, employing techniques like adding layers to existing models and joint training. Some approaches use multi-stage processes, combining base models with frame interpolation and super-resolution. Video Super-Resolution (VSR) enhances low-resolution videos, with newer techniques using varied degradation models to better mimic…