This text explains the concept of Intersection over Union (IoU) in object detection models. IoU measures the accuracy of the object detector by evaluating the overlap between the detection box and the ground truth box. The text provides examples and Python code to compute and interpret IoU values.
Amazon Kendra is an intelligent search service powered by machine learning that simplifies the process of ingesting and indexing content from various data sources. The new Amazon Kendra Web Crawler allows users to search for answers from internal and external websites, as well as create chatbots. It supports various authentication methods, web proxies, and dynamic…
The article discusses a novel AI framework developed by researchers to transform still portrait photos into cinemagraphs by animating hair wisps. The framework eliminates the need for complex hardware setups and user intervention. The researchers frame hair wisp extraction as an instance segmentation problem, allowing for effective extraction using advanced networks. Sample results are provided…
The article discusses different ways that data science teams can create value for organizations. It highlights four categories: metrics and measurement, AI/ML product or product features, strategic insights, and operational decision products. Understanding which category your team falls into can help you communicate your value proposition to stakeholders.
ASEAN countries are opting for a less rigid and business-friendly approach to AI regulation, in contrast to the EU’s AI Act. The Association of Southeast Asian Nations is set to publish guidelines for AI ethics and governance that encourage support for safe AI research and development without specifying unacceptable practices. The ASEAN approach aligns more…
The rapid adoption of OpenAI’s ChatGPT, a revolutionary AI innovation by Google Cloud, has raised concerns about its increasing energy consumption. A peer-reviewed analysis predicts that by 2027, AI servers could consume between 85 to 134 terawatt hours (TWh) annually, equivalent to the power consumption of countries like Argentina, the Netherlands, and Sweden. The surge…
Mistral Trismegistus-7B is a Google AI language model trained on a vast dataset of literature and code, including esoteric and occult material. It can generate literature, translate languages, and provide insightful answers to questions on esoteric matters. It operates quickly and is grounded in reality, making it a valuable tool for those interested in the…
This research paper introduces a novel deep learning model to address the challenge of understanding alternative splicing in genes. The model combines sequence information, structural features, and wobble pair indicators to accurately predict splicing outcomes. Its interpretability, achieved through a carefully designed training process and the Tuner function, sets it apart from traditional methods. Researchers…
Google’s Project Green Light utilizes artificial intelligence (AI) to optimize traffic light patterns and reduce greenhouse emissions. By analyzing driving pattern data from Google Maps, the project builds an AI model for each intersection, enabling traffic engineers to make adjustments and potentially reduce stops by up to 30% and emissions by up to 10%. The…
Stanford University researchers have introduced MLAgentBench, the first benchmark of its kind, to evaluate AI research agents with free-form decision-making capabilities. The framework allows agents to execute research tasks similar to human researchers, collecting data on proficiency, reasoning and research process, and efficiency. The team is working to expand the task collection to include various…
GPT-4 was tested in various experiments to solve math problems in 16 different languages.
Neural Radiance Fields (NeRF) is a neural network-based technique for capturing 3D scenes and objects from 2D images or sparse 3D data. It consists of two main components, “NeRF in” and “NeRF out” network. NeRF-based human representations have applications in gaming, virtual reality, animation, film production, and medical imaging. ActorsNeRF is a category-level human actor…
Learn how to master SVM, a versatile model that every data scientist should have in their toolbox. Get a hands-on introduction to SVM in this informative article on Towards Data Science.
The text discusses the introduction of a new interpretable model by Meta AI, with further information available in the article on Towards Data Science.
Researchers have developed an open-source library in JAX for deep learning on spherical surfaces. This new approach, utilizing spherical convolution and cross-correlation operations, shows promise in addressing challenges related to predicting chemical properties and understanding climate states. The models outperform traditional CNNs in weather forecasting benchmarks and exhibit exceptional performance across various scenarios. The study…
Mistral-7B-v0.1 is a cutting-edge large language model (LLM) developed by Mistral AI. With 7 billion parameters, it is one of the most powerful LLMs available. This transformer model excels in natural language processing tasks such as generating text, translating languages, and answering questions. It performs well on benchmarks like GLUE, SQuAD, and SuperGLUE. Mistral-7B-v0.1 has…
AI language models have been used by scientists to create new tools for analyzing speech patterns in patients with schizophrenia, allowing them to identify subtle signatures.
Researchers from the University of Manchester have introduced MentalLLaMA, the first open-source series of large language models (LLMs) for interpretable mental health analysis. These models, including MentalLLaMA-chat-13B, outperform state-of-the-art techniques in terms of predictive accuracy and the quality of generated explanations. The researchers also created the Interpretable Mental Health Instruction (IMHI) dataset, which serves as…
Undersampling techniques are used to address class imbalance in data. There are two main categories of undersampling: controlled and uncontrolled. Controlled techniques involve selecting a specific number of samples, while uncontrolled techniques remove points that meet certain conditions. Some examples of controlled and uncontrolled undersampling methods include random undersampling, k-means undersampling, Tomek Links undersampling, and…
The text discusses the problem of class imbalance in machine learning and explores the use of resampling methods, specifically random oversampling, to solve it. It explains the concept of class imbalance, the impact it has on learning algorithms, and proposes solutions such as weighting the smaller sums or resampling the data. The algorithm for random…