A team from Harvard University and the Kempner Institute at Harvard University have conducted an extensive comparative study on optimization algorithms used in training large-scale language models. The investigation targeted popular algorithms like Adam - an optimizer lauded for its adaptive learning capacity, Stochastic Gradient Descent (SGD) that trades adaptive capabilities for simplicity, Adafactor with…
Researchers from the Massachusetts Institute of Technology, University of Toronto, and Vector Institute for Artificial Intelligence have developed a new method called IF-COMP for improving the estimation of uncertainty in machine learning, particularly in deep learning neural networks. These fields place importance on not only accurately predicting outcomes but quantifying the uncertainty involved in these…
Methods for evaluating the dependability of a multi-functional AI model prior to its implementation.
Foundation models, or large-scale deep-learning models, are becoming increasingly prevalent, particularly in powering prominent AI services such as DALL-E, or ChatGPT. These models are trained on huge quantities of general-purpose, unlabeled data, which is then repurposed for various uses, such as image generation or customer service tasks. However, the complex nature of these AI tools…
The field of robotics has seen significant changes with the integration of generative methods such as Large Language Models (LLMs). Such advancements are promoting the development of systems that can autonomously navigate and adapt to diverse environments. Specifically, the application of LLMs in the design and control processes of robots signifies a massive leap forward…
Robotic technology is quickly evolving, with large language models (LLMs) driving significant advances in the sector. These generative methods allow for the creation of intricate systems capable of independent navigation and adaptation to various settings, improving efficiency and the ability to complete complex tasks.
Designing optimal robot structures is a significant challenge due to the extensive…
Conversational Recommender Systems (CRS) are systems that leverage advanced machine learning techniques to offer users highly personalized suggestions through interactive dialogues. Unlike traditional recommendation systems that present pre-determined options, CRS allows users to dynamically state and modify their preferences, leading to an intuitive and engaging user experience. These systems are particularly relevant for small and…
Artificial intelligence (AI) has significantly impacted traditional research, taking it to new heights. However, its application is yet to be fully realized in areas such as causal reasoning. Training AI models in causal reasoning is a crucial aspect of AI, with traditional methods heavily dependent on huge datasets containing explicitly labeled causal relationships. These datasets…
Google researchers have been investigating how large Transformer models can be efficiently used for large natural language processing projects. Although these models have revolutionised the field, they require careful planning and memory optimisations. The team have focused on creating techniques for multi-dimensional positioning that can work for TPU v4 slices. In turn, these have been…
Improving Major Language Models (LLMs) on CPUs: Strategies for Increased Precisions and Performance.
Large Language Models (LLMs), particularly those built on the Transformer architecture, have recently achieved significant technological advances. These models have displayed remarkable proficiency in understanding and generating human-like text, bringing a significant impact to various Artificial Intelligence (AI) applications. However, implementing these models in environments with limited resources can be challenging, especially in instances where…
Evaluating the performance of large language model (LLM) inference systems comes with significant difficulties, especially when using conventional metrics. Existing measurements such as Time To First Token (TTFT), Time Between Tokens (TBT), normalized latency and Time Per Output Token (TPOT) fail to provide a complete picture of the user experience during actual, real-time interactions. Such…
Large language model (LLM) inference systems have become vital tools in the field of AI, with applications ranging from chatbots to translators. Their performance is crucial in ensuring optimal user interaction and overall experience. However, traditional metrics used for evaluation, such as Time To First Token (TTFT) and Time Between Tokens (TBT), have been found…
Large Language Models (LLMs) have transformed our interactions with AI, notably in areas such as conversational chatbots. Their efficacy is heavily reliant on high-quality instruction data used post-training. However, the traditional ways of post-training, which involve human annotations and evaluations, face issues such as high cost and limited availability of human resources. This calls for…