​[[{“value”:” Voice interaction technology has significantly evolved with the advancements in artificial intelligence (AI). The field focuses on enhancing natural communication between humans and machines, aiming to make interactions more intuitive and human-like. Recent developments have made it possible to achieve high-precision speech recognition, emotion… Read More »FunAudioLLM: A Multi-Model Framework for Natural, Multilingual, and Emotionally Expressive Voice Interactions Nikhil Artificial Intelligence Category – MarkTechPost

​In recent years, the evolution of end-to-end (E2E) automatic speech recognition (ASR) models has been remarkable, largely due to advances in deep learning architectures like transformer. On top of E2E systems, researchers have achieved substantial accuracy improvement by rescoring E2E model’s N-best hypotheses with a… Read More »Enhancing CTC-based Speech Recognition with Diverse Modeling Units Apple Machine Learning Research

​Consider the supervised learning setting where the goal is to learn to predict labels y given points x from a distribution. An omnipredictor for a class L of loss functions and a class C of hypotheses is a predictor whose predictions incur less expected loss… Read More »Omnipredictors for Regression and the Approximate Rank of Convex Functions Apple Machine Learning Research

​Consider a multi-class labelling problem, where the labels can take values in [k], and a predictor predicts a distribution over the labels. In this work, we study the following foundational question: Are there notions of multi-class calibration that give strong guarantees of meaningful predictions and… Read More »On Computationally Efficient Multi-Class Calibration Apple Machine Learning Research

​Self-attention and masked self-attention are at the heart of Transformers’ outstanding success. Still, our mathematical understanding of attention, in particular of its Lipschitz properties — which are key when it comes to analyzing robustness and expressive power — is incomplete. We provide a detailed study… Read More »How Smooth Is Attention? Apple Machine Learning Research

​Despite the successes of large language models (LLMs), they exhibit significant drawbacks, particularly when processing long contexts. Their inference cost scales quadratically with respect to sequence length, making it expensive for deployment in some real-world text processing applications, such as retrieval-augmented generation (RAG). Additionally, LLMs… Read More »Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation Apple Machine Learning Research

​Beyond minimizing a single training loss, many deep learning estimation pipelines rely on an auxiliary objective to quantify and encourage desirable properties of the model (e.g. performance on another dataset, robustness, agreement with a prior). Although the simplest approach to incorporating an auxiliary loss is… Read More »Careful With That Scalpel: Improving Gradient Surgery With an EMA Apple Machine Learning Research

​ It’s no exaggeration to say that chatbots are a mainstream technology. A whopping 88% of web users chatted with bots in the previous year. What’s more, 7 out of 10 find the experience positive. However, despite these promising statistics, there’s still a significant portion… Read More »Is Your Chatbot Sabotaging Your Business? The Tell-Tale Signs and a Rescue Plan Master Of Code Global Chatbots Life – Medium

​ Natural Language Processing (NLP) is a fascinating field that bridges the gap between human communication and machine understanding. One of the fundamental steps in NLP is text preprocessing, which transforms raw text data into a format that can be effectively analyzed and utilized by… Read More »Exploring NLP Preprocessing Techniques: Stopwords, Bag of Words, and Word Cloud Ravjot Singh Becoming Human: Artificial Intelligence Magazine – Medium

​[[{“value”:” Personalized review generation within recommender systems is an area of increasing interest, particularly in creating custom reviews based on users’ historical interactions and preferences. This involves utilizing data about users’ previous purchases and feedback to produce reviews that accurately reflect their unique preferences and… Read More »Review-LLM: A Comprehensive AI Framework for Personalized Review Generation Using Large Language Models and User Historical Data in Recommender Systems Asif Razzaq Artificial Intelligence Category – MarkTechPost