Neuromorphic Computing, Quantum Computing for AI, Explainable AI (XAI), AI-augmented Design and Creativity, Autonomous Vehicles and Robotics, AI in Cybersecurity, and AI for Environmental Sustainability are the seven key areas where AI advancements are considerably changing several sectors.
Neuromorphic Computing is a technology that is designed to mirror the structure and functioning of the human brain.…
Understanding and processing Hebrew language has always been a challenge due to its morphologically rich structure and the use of prefixes, suffixes, and infixes that change the meaning and tense of words. This has posed particular challenges for AI language models, which often struggle to interpret the subtleties of lesser-known, low-resource languages accurately. Addressing this…
Language models play a crucial role in advancing artificial intelligence (AI) technologies, revolutionizing how machines interpret and generate text. As these models grow more intricate, they employ vast data quantities and advanced structures to improve performance and effectiveness. However, the use of such models in large scale applications is challenged by the need to balance…
"Finetuned adapters" play a crucial role in generative image models, permitting custom image generation and reducing storage needs. Open-source platforms that provide these adapters have grown considerably, leading to a boom in AI art. Currently, over 100,000 adapters are available, with the Low-Rank Adaptation (LoRA) method standing out as the most common finetuning process. These…
Researchers from MIT have discovered that doctors underperform when diagnosing skin diseases in patients with darker skin based on image assessment. Their study included over a 1,000 dermatologists and general practitioners, revealing that dermatologists accurately identified diseases on images around 38% of the time, but their success rate dropped to 34% when it came to…
Amazon SageMaker JumpStart provides built-in algorithms, pre-trained models, and pre-built solution templates to assist data scientists and machine learning practitioners in quickly training and deploying ML models. This post looks at how to use the text classification and fill-mask models on Hugging Face with SageMaker JumpStart for text classification on a custom dataset. The tutorial…
AI21 Labs has launched a new model, the Jamba-Instruct, which is designed to revolutionize natural language processing tasks for businesses. It does this by improving upon the limitations of traditional models, particularly their limited context capabilities. These limitations affect model effectiveness in tasks such as summarization and conversation continuation.
The Jamba-Instruct model significantly enhances this capability…
AI21 Labs has unveiled its Jamba-Instruct model, a solution designed to tackle the challenge of using large context windows in natural language processing for business applications. Traditional models usually have constraints in their context capabilities, impacting their effectiveness in tasks such as summarising lengthy documents or continuing conversations. In contrast, Jamba-Instruct overcomes these barriers by…
In the rapidly evolving domain of Artificial Intelligence, Natural Language Processing (NLP), and Information Retrieval, the advent of advanced models like Retrieval Augmented Generation (RAG) has stirred considerable interest. Despite this, many data science experts advise against jumping into complex RAG models until the evaluation pipeline is fully reliable and robust.
Performing comprehensive assessments of RAG…
Retrieval Augmented Generation (RAG) models have become increasingly important in the fields of Artificial Intelligence, Natural Language Processing (NLP), and Information Retrieval. Despite this, there's a cautionary note from data science experts advising against a rush into using sophisticated RAG models until the evaluation pipeline is reliable and robust.
Emphasising the importance of examining RAG…
Doctors face greater challenges in identifying diseases while examining images of darker skin tones.
A study conducted by researchers at the Massachusetts Institute of Technology (MIT) found that doctors and dermatologists accomplish lower diagnostic accuracy rates when examining images of darker skin tones compared to lighter ones. Technologically-assisted diagnosis offered greater improvements when assessing lighter skin.
More than 1,000 practitioners, including dermatologists and general practitioners, categorized an array of 364…