Designing computation workflows for AI applications faces complexities, requiring the management of various parameters such as prompts and machine learning hyperparameters. Improvements made post-deployment are often manual, making the technology harder to update. Traditional optimization methods like Bayesian Optimization and Reinforcement Learning often call for greater efficiency due to the intricate nature of these systems.…
Deep learning's exceptional performance across a wide range of scientific fields and its utilization in various applications have been proven. However, these models often come with many parameters that require a substantial amount of computational power for training and testing. The improvement of these models has been a primary focus of advancement in the field,…
Optimal transport is a mathematical field focused on the most effective methods for moving mass between probability distributions. It has a broad range of applications in disciplines such as economics, physics, and machine learning. However, the optimization of probability measures in optimal transport frequently faces challenges due to complex cost functions influenced by various factors…
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…
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…
Traditional protein design, which relies on physics-based methods like Rosetta can encounter difficulties in creating functional proteins due to parametric and symmetric constraints. Deep learning tools such as AlphaFold2 have revolutionized protein design by providing more accurate prediction abilities and the capacity to analyze large sequence spaces. With these advancements, more complex protein structures can…
Artificial Neural Networks (ANNs) have long been used in artificial intelligence but are often criticized for their static structure which struggles to adapt to changing circumstances. This has restricted their use in areas such as real-time adaptive systems or robotics. In response to this, researchers from the IT University of Copenhagen have designed an innovative…
Artificial Neural Networks (ANNs), while transformative, have traditional shortcomings in terms of adaptability and plasticity. This lack of flexibility poses a significant challenge for their applicability in dynamic and unpredictable environments. It also inhibits their effectiveness in real-time applications like robotics and adaptive systems, making real-time learning and adaptation a crucial achievement for artificial intelligence…
Model selection is a critical part of addressing real-world data science problems. Traditionally, tree ensemble models such as XGBoost have been favored for tabular data analysis. However, deep learning models have been gaining traction, purporting to offer superior performance on certain tabular datasets. Recognising the potential inconsistency in benchmarking and evaluation methods, a team of…
Research conducted by DeepMind has shed new light on the complexities of machine learning and neural representation, providing insights into the dissociations between representation and computation in deep networks. High capacity deep networks frequently demonstrate an implicit bias towards simplicity amidst their learning dynamics and structure. The employed simple functions allow for easier learning of…
Artificial Intelligence (AI) and complex neural networks are growing rapidly, necessitating efficient hardware to handle power and resource constraints. One potential solution is In-memory computing (IMC) which focuses on developing efficient devices and architectures that can optimize algorithms, circuits, and devices. The explosion of data from the Internet of Things (IoT) has propelled this need…