A team of researchers has developed a new method for improving tensor contraction paths (CPs), which are used to solve problems across numerous areas of research, including machine learning, graph problems, quantum circuits, and model counting. Their technique improves upon the standard greedy algorithm (SGA), incorporating an enhanced cost function that covers a larger range…
Reinforcement learning (RL) is a method of machine learning where agents are trained to make decisions by interacting with their environment. This interaction involves taking action and receiving feedback via rewards or penalties. RL has been crucial in developing complex technologies such as advanced robotics, autonomous vehicles, and strategic game-playing mechanisms and has been instrumental…
Python programming, with its vast number of libraries, is a flexible and powerful tool for programmers. However, a gap in the Python ecosystem has been identified: the lack of no-code studios for developing web front-ends. A handful of low-code tools have been available, such as Streamlit, Taipy, and Gradio, but none have fully automated this…
Generative AI models such as Large Language Models (LLMs) have proliferated over various industries, advancing the future of programming. Historically, the field of programming has been primarily governed by symbolic coding that unites traditional symbolic code and neural networks to solve specific tasks. Symbolic programming's backlash, however is that it often requires developers to manually…
Artificial Intelligence (AI) has dramatically improved numerous areas via sophisticated natural language processing (NLP) frameworks. NLP is upgrading computers' capacities to understand, interpret, and respond intelligently to human language. Significant progress has been achieved in areas like text generation, translation, and sentiment analysis which have made substantial impacts in sectors like healthcare, finance, and customer…
Reinforcement Learning (RL) has been gaining traction within the artificial intelligence (AI) field, and one of its significant advancements is Hierarchical Reinforcement Learning (HRL). HRL simplifies complex tasks by dividing them into manageable sub-tasks. This hierarchical structure improves overall learning efficiency and scalability.
Seemingly unrelated tasks may share useful sub-task policies; HRL promotes the reuse…
In the field of machine learning, multi-task learning (MTL) is a crucial aspect which enables the simultaneous training of interrelated algorithms. Given its ability to enhance model generalizability, it has been successfully utilized in various fields such as biomedicine, computer vision, and natural language processing. However, combining different types of tasks such as regression and…
Artificial intelligence (AI) is significantly contributing to the field of biological research, catalyzing progress in genomics and drug discovery. Several state-of-the-art AI tools have evolved in this domain.
Google's deep neural network-based tool, 'DeepVariant,' processes genetic variants data from DNA sequencing algorithms. 'DNAnexus,' another tool, utilizes cloud technology for genomic data management, accelerating scientific discovery and…
The Technology Innovation Institute (TII) in Abu Dhabi has launched "Falcon," a ground-breaking collection of language models. They're available under the Apache 2.0 license, with Falcon-40B being the first "fully open" model that's equivalent in capabilities to numerous proprietary alternatives. This innovation marks a significant step forward in the field, presenting a wealth of opportunities…
Generative Artificial Intelligence (Gen AI) is leading to significant advancements in sectors such as science, economy, and education. At the same time, it also raises significant concerns that stem from its potential to produce robust content based on input. These advancements are leading to in-depth socio-technical studies to understand the profound implications and assessing risks…