Researchers from Harvard and Stanford universities have developed a new meta-algorithm known as SPRITE (Spatial Propagation and Reinforcement of Imputed Transcript Expression) to improve predictions of spatial gene expression. This technology serves to overcome current limitations in single-cell transcriptomics, which can currently only measure a limited number of genes.
SPRITE works by refining predictions from existing…
Deep learning has transformed the field of pathological voice classification, particularly in the evaluation of the GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain) scale. Unlike traditional methods that involve manual feature extraction and subjective analysis, deep learning leverages 1D convolutional neural networks (1D-CNNs) to autonomously extract relevant features from raw audio data. However, background noise can…
Video captioning is crucial for content understanding, retrieval, and training foundational models for video-related tasks. However, it's a challenging field due to issues like a lack of high-quality data, the complexity of captioning videos compared to images, and the absence of established benchmarks.
Despite these challenges, recent advancements in visual language models have improved video…
In developing AI-based applications, developers often grapple with memory management challenges. High costs, restricted access due to closed-source tools, and poor support for external integration have posed barriers to creating robust applications such as AI-driven dating or health diagnostics platforms.
Typically, memory management for AI applications can be expensive, closed-sourced, or lack comprehensive support for external…
Speech recognition technology, a rapidly evolving area of machine learning, allows computers to understand and transcribe human languages. This technology is pivotal for services including virtual assistants, automated transcription, and language translation tools. Despite recent advancements, developing universal speech recognition systems that cater to all languages, particularly those that are less common and understudied, remains…
The use of AI (Artificial Intelligence) models is increasingly becoming important in the development of modern applications that contain both backend and frontend code. However, developers often face challenges in accessing these models, which affects their ability to integrate AI into their applications. To bridge this gap, GitHub is launching GitHub Models, aimed at providing…
As corporations' use of Artificial Intelligence (AI) increases, so too does their risk of security breaches. Hackers could potentially manipulate AI into revealing crucial corporate or consumer data, a genuine concern for leaders of Fortune 500 companies developing chatbots and other AI applications. Lakera AI, a start-up in the field of GenAI security, addresses this…
Large Language Model (LLM) agents are seeing a vast number of applications across various sectors including customer service, coding, and robotics. However, as their usage expands, the need for their adaptability to align with diverse consumer specifications has risen. The main challenge is to develop LLM agents that can successfully adopt specific personalities, enabling them…
With advancements in model architectures and training methods, Large Language Models (LLMs) such as OpenAI's GPT-3 have showcased impressive capabilities in handling complex question-answering tasks. However, these complex responses can also lead to hallucinations, where the model generates plausible but incorrect information. This is also compounded by the fact that these LLMs generate responses word-by-word,…
Large Language Models (LLMs) have gained significant traction in various applications but they need robust safety measures for responsible user interactions. Current moderation solutions often lack detailed harm type predictions or customizable harm filtering. Now, researchers from Google have introduced ShieldGemma, a suite of content moderation models ranging from 2 billion to 27 billion parameters,…