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…
Microsoft has launched a comprehensive AI learning journey designed to cater to various roles within an organization, from executives to developers. The four-stage program is aimed at equipping firms with the skills to integrate AI into their business operations, thereby enhancing productivity, innovation, and business transformation.
The first stage, 'Understanding AI', builds a foundational knowledge of…
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…
Radiology departments often deal with massive workloads leading to burnout among radiologists. Therefore, tools to help mitigate these issues are essential. VLMs such as LLaVA-Med have advanced significantly in recent years, providing multimodal capabilities for biomedical image and data analysis. However, the generalization and user-friendliness issues of these models have hindered their clinical adoption.
To…
Large language models (LLMs) have demonstrated impressive performances across various tasks, with their reasoning capabilities playing a significant role in their development. However, the specific elements driving their improvement are not yet fully understood. Current strategies to enhance reasoning focus on enlarging model size and expanding the context length via methods such as chain of…
Large language models (LLMs) have made remarkable strides in many tasks, with their capacity to reason forming a vital aspect of their development. However, the main drivers behind these advancements remain unclear. Current measures to boost reasoning primarily involve increasing the model's size and extending the context length with methods such as the chain of…