AI

  Speech recognition technology has become a cornerstone for various applications, enabling machines to understand and process human speech. The field continuously seeks advancements in algorithms and models to improve accuracy and efficiency in recognizing speech across multiple languages and contexts. The main challenge in speech recognition is developing models that accurately transcribe speech from…

  Self-supervised learning (SSL) has proven to be an indispensable technique in AI, particularly in pretraining representations on vast, unlabeled datasets. This significantly reduces the dependency on labeled data, often a major bottleneck in machine learning. Despite the merits, a major challenge in SSL, particularly in Joint Embedding (JE) architectures, is evaluating the quality of…

  Processing extensive sequences of linguistic data has been a significant hurdle, with traditional transformer models often buckling under the weight of computational and memory demands. This limitation is primarily due to the quadratic complexity of the attention mechanisms these models rely on, which scales poorly as sequence length increases. The introduction of State Space…

  Language model development has historically operated under the premise that the larger the model, the greater its performance capabilities. However, breaking away from this established belief, Microsoft Research’s Machine Learning Foundations team researchers introduced Phi-2, a groundbreaking language model with 2.7 billion parameters. This model defies the traditional scaling laws that have long dictated…

  The employment of numerous deduction rules and the construction of subproofs allows the complexity of proofs to develop infinitely in many deductive reasoning tasks, such as medical diagnosis or theorem proving. It is not practical to find data to cover guarantees of all sizes due to the huge proof space. Consequently, starting with basic…

  Training large transformer models poses significant challenges, especially when aiming for models with billions or even trillions of parameters. The primary hurdle lies in the struggle to efficiently distribute the workload across multiple GPUs while mitigating memory limitations. The current landscape relies on complex Large Language Model (LLM) scaling frameworks, such as Megatron, DeepSpeed,…

  The capabilities of Artificial Intelligence (AI) are stepping into every industry, be it healthcare, finance, or education. In the field of medicine and veterinary medicine, identifying pain is a crucial first step in administering the right treatments. This identification is especially difficult with individuals who are unable to convey their pain, which calls for…

  Natural language processing (NLP) has entered a transformational period with the introduction of Large Language Models (LLMs), like the GPT series, setting new performance standards for various linguistic tasks. Autoregressive pretraining, which teaches models to forecast the most likely tokens in a sequence, is one of the main factors causing this amazing achievement. Because…

  Reinforcement Learning (RL) is a subfield of Machine Learning in which an agent takes suitable actions to maximize its rewards. In reinforcement learning, the model learns from its experiences and identifies the optimal actions that lead to the best rewards. In recent years, RL has improved significantly, and it today finds its applications in…