This research introduces StemGen, an end-to-end music generation model, leveraging non-autoregressive, transformer-based techniques to respond to musical context. It incorporates innovative training approaches, achieves state-of-the-art audio quality, and is validated through objective metrics and subjective Mean Opinion Score tests. The model demonstrates robust musical alignment with context and presents significant strides in deep learning-based music…
The article explores Stable Diffusion and its inpainting variant for interior design. For more detailed information, please refer to the original article on Towards Data Science.
AWS recognizes the transformative potential of AI and emphasizes responsible use through collaboration with customers and adherence to ISO 42001. The international standard provides guidelines for managing AI systems within organizations, promoting responsible AI practices. AWS actively contributes to the standard’s development, aiming to foster global cooperation in implementing responsible AI solutions and demonstrate commitment…
PixelLLM, a new vision-language model introduced by Google Research and UC San Diego, achieves fine-grained localization and alignment by aligning each word of the language model output to a pixel location. It supports diverse vision-language tasks, demonstrating superior results in location-conditioned captioning and referencing localization. Learn more about the project at the provided link.
The emergence of generative AI is profoundly changing today’s enterprises, with 76% of global organizations already using or planning to adopt this technology. Despite its benefits, leaders must carefully strategize, overcome challenges, and ensure data sufficiency. External providers can offer valuable expertise, and investments in talent, data, and privacy solutions are crucial for success.
The text describes the use of a user-friendly tool for creating intricate visualizations. For further details, refer to the original article on Towards Data Science.
OpenAI’s board can override the CEO’s decisions on releasing new AI models, as outlined in their safety guidelines. After CEO dismissal and reinstatement, concerns over model safety and valuation arose. OpenAI’s preparedness team and safety framework aim to address catastrophic risks, assessing AI systems and categorizing risks for model release. The internal safety advisory group…
Federated Learning (FL) trains models using distributed data. Differential Privacy (DP) provides privacy guarantees. The goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP. However, DP-noise increases as model size grows, hindering convergence. Partial Embedding Updates (PEU) is proposed to decrease noise by…
Large language models (LLMs) excel at text-based natural language processing tasks through creative prompt engineering and in-context learning. However, their performance on spoken language understanding (SLU) tasks relies heavily on speech-to-text conversion by an off-the-shelf automation speech recognition (ASR) system, constraining their accuracy in this setup.
Introduced in May 2023 and available on iOS 17 in September 2023, Personal Voice is a voice replicator tool designed for individuals at risk of losing their ability to speak, such as those with ALS. It creates a synthesized voice for use in FaceTime, phone calls, assistive communication apps, and in-person conversations, supporting speaking ability.
Former Prime Minister of Pakistan, Imran Khan, utilized AI to deliver a four-minute speech at a virtual rally while in prison. The AI-generated voice closely resembled his own, delivering a message of resilience and defiance against political constraints faced by his party. The rally gained over five million views despite reported internet outages. AI’s political…
This blog post serves as the conclusion to a series on training BERT from scratch. It discusses the significance of BERT in Natural Language Processing, reviews the previous parts of the series, and outlines the process of building and training a BERT model. The post emphasizes understanding the model’s inner workings and shares insights on…
The year 2023 saw significant developments in the Generative AI landscape, marked by the release of multiple LLMs and the emergence of LLMOps. While there were challenges in production, it was a year of experimentation and getting to know Generative AI. Looking ahead to 2024, the focus will likely be on successfully deploying Generative AI…
The text is a comprehensive explanation of computer simulations and their applications in understanding and predicting astronomical events. It covers various scenarios of transit phenomena, including exoplanet transits, asteroid belts’ influence, and hypothetical scenarios like simulating an exoplanet with an exomoon and detecting alien megastructures. It also highlights the advantages of simulations in scientific research.…
The paper explores training End-to-End Automatic Speech Recognition (ASR) models using Federated Learning (FL) and its impact on minimizing the performance gap with centralized models. It examines adaptive optimizers, loss characteristics, model initialization, and carrying over modeling setup from centralized training to FL.
The paper “Bootstrap Your Own Variance: Understanding Model Uncertainty with SSL and Bayesian Methods” was accepted at the Self-Supervised Learning workshop at NeurIPS 2023. It proposes BYOV, combining BYOL SSL algorithm with BBB Bayesian method to estimate model posteriors, showing that BYOV’s predictive standard deviation aligns well with a Gaussian distribution.
Multimodal datasets play a crucial role in recent AI advancements like Stable Diffusion and GPT-4. However, their design is not as researched as model architectures or training algorithms. To tackle this, DataComp introduces a testbed for dataset experiments using 12.8 billion image-text pairs from Common Crawl, allowing participants to create and evaluate new datasets.
A Multi-Strategy AI with Deep Reinforcement Learning has achieved victory over GPT3.5 in a Chess Match. For more details, please visit Towards Data Science.
The text outlines the challenges faced by industries without real-time forecasts and introduces the integration of MongoDB’s time series data management capabilities with Amazon SageMaker Canvas for overcoming these challenges. It details the solution architecture, prerequisites, and step-by-step processes for setting up the solution using MongoDB Atlas and Amazon SageMaker Canvas. The post concludes with…
The text describes the concept and process of building stacked ensembles in machine learning using H2O.ai and Optuna. The author outlines the steps involved in training a stacked ensemble, including the training of base models such as Deep Neural Networks, XGBoost, and LightGBM, and subsequently training the meta-model using H2OStackedEnsembleEstimator. The summary provides an in-depth…