In-context learning (ICL) in large language models (LLMs) utilizes input-output examples to adapt to new tasks without altering the underlying model architecture. This method has transformed how models handle various tasks by learning from direct examples provided during inference. The problem at hand is the limitation of a few-shot ICL in handling intricate tasks. These tasks often demand a deep comprehension that few-shot learning cannot provide, as it operates under the restriction of minimal input data. This scenario could be better for applications requiring detailed analysis and decisi ..read more

From 512 to 1M+ tokens in 5 years — LLMs have rapidly expanded their context windows. Where’s the limit? Source link The post Towards infinite LLM context windows appeared first on AI Quantum Intelligence ..read more

From 512 to 1M+ tokens in 5 years — LLMs have rapidly expanded their context windows. Where’s the limit? Source link The post Towards infinite LLM context windows appeared first on AI Quantum Intelligence. Source link The post Towards infinite LLM context windows appeared first on AI Quantum Intelligence ..read more

DALL-E: Circadian AI: Harmonizing AGI with Nature’s Rhythms In the rapidly evolving landscape of emerging technologies, the development and integration of Artificial General Intelligence (AGI) into our lives present both unprecedented opportunities and significant challenges. Building on the insights from my previous article, “Spiral Dynamics: The Evolution of Consciousness & Communication,” it is clear that mindful engagement with technology is crucial. As we navigate the accelerating pace of advancements in AGI, quantum computing, and neural interfaces, setting robust ethical and ..read more

In artificial intelligence, a significant focus has been on developing models that simultaneously process and interpret multiple forms of data. These multimodal models are designed to analyze and synthesize information from various sources, such as text, images, and audio, mimicking human sensory and cognitive processes. The main challenge in this field is developing systems that not only excel in single-mode tasks like image recognition or text analysis but can also integrate these capabilities to handle complex interactions between different data types. Traditional models often fall short ..read more

Time series forecasting is increasingly vital across numerous sectors, such as meteorology, finance, and energy management. Its relevance has grown as organizations aim to predict future trends and patterns more accurately. This type of forecasting is instrumental in enhancing decision-making processes and optimizing resource allocation over long periods. However, making accurate long-term forecasts is complex due to the inherently unpredictable nature of the datasets involved and the substantial computational resources required for processing them. Historically, recurrent neural networks (R ..read more

Photo by Vitaly Gariev on Unsplash The cause of their demise could surprise you. Young people may not be familiar with the term, but “Expert Systems” was a major technological initiative during the 1980s and 1990s of the last century. I got my 30y full-time job precisely because of Expert Systems. That was in 1989, and I had just finished my PhD. A friend told me about an opening for… Source link The post What Happened With Expert Systems? | by Rafe Brena, Ph.D. | Apr, 2024 appeared first on AI Quantum Intelligence ..read more

DALL-E: System Design in Circadian AI: Navigating Human Biases and AddictionsNavigating Human Biases and Addictions In the sequel to “Circadian AI: Harmonizing AGI with Nature’s Rhythms,” we delve deeper into the transformative potential of Artificial General Intelligence (AGI) in enhancing human well-being through advanced system design. Building on the foundation of aligning AGI with natural rhythms, this article explores how AGI can assist humans in overcoming biases and addictions, thus fostering healthier lifestyles and more mindful management of time, holding responsible for our ..read more

The interdisciplinary domain of vision-language representation seeks innovative methods to develop systems to understand the nuanced interactions between text and images. This area is pivotal as it enables machines to process and interpret the vast amount of digitally available visual and textual content. Despite significant advances, the challenge persists primarily due to the noisy data sourced from the internet, where image-caption pairs often do not align well, leading to inaccuracies in training models. Researchers from FAIR at Meta, Columbia University, New York University, and the Uni ..read more

Image created by Author   Introduction to Gemini   It’s the era of language models, and Gemini is Google’s latest and most capable model to date.   Gemini is the result of large-scale collaborative efforts by teams across Google, including our colleagues at Google Research. It was built from the ground up to be multimodal, which means it can generalize and seamlessly understand, operate across and combine different types of information including text, code, audio, image and video.   If you are interested in learning about Gemini, language models, and using them to your ..read more