In the field of audio processing, the ability to separate overlapping speech signals amidst noise is a challenging task. Previous approaches, such as Convolutional Neural Networks (CNNs) and Transformer models, while groundbreaking, have faced limitations when processing long-sequence audio. CNNs, for instance, are constrained by their local receptive capabilities while Transformers, though skillful at modeling…
Over two millennia ago, the ancient mathematician Euclid, widely recognized as the father of geometry, shifted our perspective on shapes. Today, Justin Solomon of MIT uses contemporary geometric methods to tackle complex challenges seemingly unrelated to shapes. Solomon utilizes geometric tools to analyze high-dimensional datasets, providing insights about the potential performance of machine learning models.…
Photolithography, the process of using light to etch features onto surfaces for the manufacturing of computer chips and optical devices, often fails to accurately match designer’s intentions due to tiny inconsistencies in the manufacturing process. Researchers at MIT and the Chinese University of Hong Kong have developed a machine-learning digital simulator in an effort to…
A recent study from MIT suggests that computational models built using machine learning could closely mimic the structure and function of the human auditory system. This discovery could potentially help researchers in designing more effective hearing aids, cochlear implants, and brain-machine interfaces.
In the largest-ever examination of deep learning neural networks trained for auditory tasks, the…
Since the 1970s, finding new antibiotics has been challenging. The World Health Organization now considers the antimicrobial resistance crisis as one of the top 10 global public health threats. Bacteria can become resistant to antibiotics, especially when an infection is treated repeatedly. Some bacteria become metabolically inert, avoiding detection by antibiotics that only respond to…
During the AWS re:Invent 2023, Amazon announced the general availability of Knowledge Bases for Amazon Bedrock. This allows secure connection of foundation models in Amazon Bedrock to your company data using a fully managed Retrieval Augmented Generation (RAG) model.
This feature helps in improving the accuracy of the generated responses from foundation models depending upon the…
Data is as valuable as currency in today's world, leading many industries to face the challenge of sharing and enhancing data across various entities while also protecting privacy norms. Synthetic data generation has provided organizations with a means to overcome privacy obstacles and unlock potential for collaborative innovation. This is especially relevant in distributed systems,…
The impressive advancements that have been seen in artificial intelligence, specifically in Large Language Models (LLMs), have seen them become a vital tool in many applications. However, the high cost associated with the computational power needed to train these models has limited their accessibility, stifling wider development. There have been several open-source resources attempting to…
Effector is a new Python library developed to address the limitations of traditional methods used to explain black-box models. Current global feature effect methods, including Partial Dependence Plots (PDP) and SHAP Dependence Plots, often fall short in explaining such models, especially when feature interactions or non-uniform local effects occur, resulting in potentially misleading interpretations.
To overcome…