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…
As artificial intelligence (AI) evolves, the risk of misuse in critical fields such as autonomy, cybersecurity, biosecurity, and machine learning increases. Google DeepMind has introduced the Frontier Safety Framework to counter these threats posed by advanced AI models, which may develop potentially harmful capabilities.
Current AI safety protocols primarily deal with existing AI system risks…
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…
Artificial Intelligence (AI) has improved email writing by automating tasks, prioritizing messages, and providing insightful answers. AI email assistants can write and send messages, so users have more time to concentrate on the most critical emails. These email assistants have diverse applications, from office employees, business owners, to individual entrepreneurs and students.
Many AI email assistants…
Large Vision-Language Models (VLMs) have proven to have impressive capacities as adaptable agents who are able to solve many tasks. They can be optimized through fine-tuning with specific visual instruction-following data, thus enhancing their performance. However, this strategy can be limited as it mostly relies on supervised learning from pre-collected data. Consequently, it may not…