Large Language Models (LLMs) are advanced Artificial Intelligence tools designed to understand, interpret, and respond to human language in a similar way to human speech. They are currently used in various areas such as customer service, mental health, and healthcare, due to their ability to interact directly with humans. However, recently, researchers from the National…
Artificial Intelligence (AI) projects require a high level of processing power to function efficiently and effectively. Traditional hardware often struggles to meet these demands, resulting in higher costs and longer processing times. This presents a significant challenge for developers and businesses that are seeking to harness the impact and potential of AI application. Previous options…
AI integration into low-powered Internet of Things (IoT) devices such as microcontrollers has been enabled by advances in hardware and software. Holding back deployment of complex Artificial Neural Networks (ANNs) to these devices are constraints such as the need for techniques such as quantization and pruning. Shifts in data distribution between training and operational environments…
Data curation, particularly high-quality and efficient data curation, is crucial for large-scale pretraining models in vision, language, and multimodal learning performances. Current approaches often depend on manual curation, making it challenging to scale and expensive. An improvement to such scalability issues lies in model-based data curation that selects high-quality data based on training model features.…
In the field of software development, large coding projects often come with their fair share of difficulties. Common problems include battling with unfamiliar technology, managing extensive backlogs, and spending significant time on repetitive tasks. Current tools and methods often fall short when it comes to efficiently handling these challenges, causing delays and frustration for developers.
Existing…
Traditional protein design, which relies on physics-based methods like Rosetta can encounter difficulties in creating functional proteins due to parametric and symmetric constraints. Deep learning tools such as AlphaFold2 have revolutionized protein design by providing more accurate prediction abilities and the capacity to analyze large sequence spaces. With these advancements, more complex protein structures can…
Generative Domain Adaptation (GDA) is a machine learning technique used to adapt a model trained in one domain (source) using a few examples from another domain (target). This is beneficial in situations where it is expensive or impractical to obtain substantial labeled data from the target domain. While existing GDA solutions focus on enhancing a…
Booth AI is an artificial intelligence (AI) startup focused on revolutionizing the online product photography industry. This ground-breaking tool provides a solution for streamlining, cost reduction and creativity unleashing for brands and creators looking to enhance their product images for e-commerce and online marketing.
The company provides a generative AI app builder service which operates exclusively…
Adversarial attacks, efforts to deceitfully force machine learning (ML) models to make incorrect predictions, have presented a significant challenge to the safety and dependability of crucial machine learning applications. Neural networks, a form of machine learning algorithm, are especially susceptible to adversarial attacks. These attacks are especially concerning in applications such as facial recognition systems,…
Controllable Learning (CL) is being recognized as a vital element of reliable machine learning, one that ensures learning models meet set targets and can adapt to changing requirements without the need for retraining. This article examines the methods and applications of CL, focusing on its implementation within Information Retrieval (IR) systems, as demonstrated by researchers…
Retrieval-augmented generation (RAG) is a technique that enhances large language models’ capacity to handle specific expertise, offer recent data, and tune to specific domains without changing the model’s weight. RAG, however, has its difficulties. It struggles with handling different chunked contexts efficiently, often doing better with a lesser number of highly relevant contexts. Similarly, ensuring…
Advancements in Vision-and-Language Models (VLMs) like LLaVA-Med propose exciting opportunities in biomedical imaging and data analysis. Still, they also face challenges such as hallucinations and imprecision risks, potentially leading to misdiagnosis. With the escalating workload in radiology departments and professionals at risk of burnout, the need for tools to mitigate these problems is pressing.
In response…