Visual Language Models (VLMs) have proven instrumental in tasks such as image captioning and visual question answering. However, the efficiency of these models is often hampered by challenges such as data scarcity, high curation costs, lack of diversity, and noisy internet-sourced data. To combat these setbacks, researchers from Google DeepMind have introduced Synth2, a method…
Machine learning (ML) workflows have become increasingly complex and extensive, prompting a need for innovative optimization approaches. These workflows, vital for many organizations, require vast resources and time, driving up operational costs as they adjust to various data infrastructures. Handling these workflows involved dealing with a multitude of different workflow engines, each with their own…
In the realm of artificial intelligence, notable advancements are being made in the development of language agents capable of understanding and navigating human social dynamics. These sophisticated agents are being designed to comprehend and react to cultural nuances, emotional expressions, and unspoken social norms. The ultimate objective is to establish interactive AI entities that are…
Google Research has recently launched FAX, a high-tech software library, in an effort to improve federated learning computations. The software, built on JavaScript, has been designed with multiple functionalities. These include large-scale, distributed federated calculations along with diverse applications including data center and cross-device provisions. Thanks to the JAX sharding feature, FAX facilitates smooth integration…
Data science is an in-demand field, yet many aspirants often encounter challenges in landing a sought-after job due to high competition or the preference of employers for senior data scientists. However, the skills learned in data science can be used in multiple alternative careers, such as Machine Learning Engineer, Data Engineer, Business Intelligence Analyst, Data…
Visual Language Models (VLMs), which are powerful tools for processing visual and textual data, can face difficulties due to limited data availability. Recent research developments have shown that pre-training these models on larger image-text datasets can enhance their performance in downstream tasks. However, creating these datasets can be challenging because of paired data scarcity, high…
Machine learning (ML) workflows are crucial for enabling data-driven innovations. Yet as they continue to grow in complexity and scale, they become increasingly resource-intensive and time-consuming, raising operational costs. These workflows also require management across a range of unique workflow engines, each with its own Application Programming Interface (API), complicating optimization efforts across different platforms.…
Scientists from the McGovern Institute for Brain Research at MIT, the Broad Institute of MIT and Harvard, and the National Center for Biotechnology Information at the National Institutes of Health have developed a new algorithm that can sift through massive amounts of genomic data to identify unique CRISPR systems. Known as Fast Locality-Sensitive Hashing-based clustering…
Frame interpolation is a technique employed in video processing to generate extra frames between the existing ones in a video sequence. This method is used to enhance the frame rate of a video, which can subsequently lead to smoother motion and superior visual quality. These benefits are particularly noticeable when creating slow-motion effects or when…
In the field of digital replication of human motion, researchers have long faced two main challenges: the computational complexities of these models, and capturing the intricate, fluid nature of human movement. Utilising state space models, particularly the Mamba variant, has yielded promising advancements in handling long sequences more effectively while reducing computational demands. However, these…
The Retrieval Augmented Generation (RAG) approach is a sophisticated technique employed within language models that enhances the model's comprehension by retrieving pertinent data from external sources. This method presents a distinct challenge when evaluating its overall performance, creating the need for a systematic way to gauge the effectiveness of applying external data in these models.
Several…