-
Researchers at Stanford Present A Novel Artificial Intelligence Method that can Effectively and Efficiently Decompose Shading into a Tree-Structured Representation
Stanford researchers introduce a novel approach to inferring detailed object shading from a single image. By utilizing shade tree representations, they break down object surface shading into an interpretable and user-friendly format, allowing for efficient and intuitive editing. Their method combines auto-regressive inference with optimization algorithms, outperforming existing techniques. Experimental results demonstrate its effectiveness across…
-
Meet Concept2Box: Bridging the Gap Between High-Level Concepts and Fine-Grained Entities in Knowledge Graphs – A Dual Geometric Approach
The Concept2Box approach bridges the gap between high-level concepts and specific entities in knowledge graphs. It employs dual geometric representations, with concepts represented as box embeddings and entities represented as vectors. This approach allows for the learning of hierarchical structures and complex relationships within knowledge graphs. Experimental evaluations have shown the effectiveness of Concept2Box in…
-
Researchers at the Shibaura Institute of Technology Revolutionize Face Direction Detection with Deep Learning: Navigating Challenges of Hidden Facial Features and Expanding Horizon Angles
Researchers from the Shibaura Institute of Technology have developed a novel AI solution for face orientation estimation. By combining deep learning techniques with gyroscopic sensors, they have overcome the limitations of traditional methods and achieved accurate results with a smaller training dataset. This innovation has potential applications in driver monitoring systems, human-computer interaction, and healthcare…
-
New tools are available to help reduce the energy that AI models devour
A team at the MIT Lincoln Laboratory Supercomputing Center (LLSC) is developing techniques to reduce energy consumption in data centers, specifically in relation to artificial intelligence (AI) models. Their methods include power capping hardware and stopping AI training early, with minimal impact on model performance. The team hopes their work will inspire other data centers…
-
Improve prediction quality in custom classification models with Amazon Comprehend
This article discusses how organizations can use Amazon Comprehend, an AI/ML service, to build and optimize custom classification models. It provides guidelines on data preparation, model creation, and model tuning. The article also explores techniques for handling underrepresented data classes and mentions the cost of using Amazon Comprehend.
-
Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium
Large language models (LLMs) like Llama 2 have gained popularity among developers, scientists, and executives. Llama 2, recently released by Meta, can be fine-tuned on AWS Trainium to reduce training time and cost. The model uses the Transformer’s decoder-only architecture, has three sizes, and pre-trained models are trained on 2 trillion tokens. Distributed training is…
-
Top 5 Data Analytics Certifications
The post discusses the importance of data analytics in today’s data-driven world and recommends obtaining a Data Analytics Certification as a valuable and indispensable tool for success and innovation in various industries.
-
How to create a digital marketing strategy with AI
AI has revolutionized the marketing landscape, offering insights, predictive analytics, and personalized customer experiences. AI marketing tools help save time, increase efficiency, and optimize efforts. AI can analyze customer data, personalize content, generate content ideas, and make real-time decisions. Seven AI tools for marketing strategy include Adzooma, Jasper AI, HubSpot, Murf AI, Adobe Sensei, ClickUp,…
-
Researchers from ETH Zurich and Microsoft Introduce SCREWS: An Artificial Intelligence Framework for Enhancing the Reasoning in Large Language Models
Researchers from ETH Zurich and Microsoft introduce SCREWS, a modular framework for improving reasoning in Large Language Models (LLMs). The framework includes three core components: Sampling, Conditional Resampling, and Selection. By combining different techniques, SCREWS improves the accuracy of LLMs in tasks such as question answering, arithmetic reasoning, and code debugging. The framework also emphasizes…
-
How to Generate Audio Using Text-to-Speech AI Model Bark
Bark is an open-source AI model created by Suno.ai that can generate realistic, multilingual speech with background noise, music, and sound effects. Unlike typical TTS engines, Bark produces highly natural-sounding audio using a GPT-style architecture.