For the Children - Walk the Talk (NOW in Spanish & French!)
Download PDFImagine a world where poverty, hunger, and lack of education are relics of the past. A world where communities not only survive, but thrive, with access to healthcare, sanitation, and sustainable practices. This vision isn't just a dream; it's the foundation for achieving all 17 UN Sustainable Goals, and OneKind's Diana Project is actively building it.
Breaking the Cycle, Building a Better World:
The UN's Sustainable Development Goals (SDGs) provide a roadmap for a more just and equitable world. OneKind's Diana Project directly addresses many of these goals, fostering a world transformed:
Empowering Orphans, Building a Stronger World:
Orphans are often vulnerable to exploitation and violence. The Diana Project provides them with a safe haven, education, and opportunities for self-sufficiency, contributing to a more peaceful and just world (SDG 16).
The Ripple Effect of Peace:
OneKind's Diana Project isn't just about solving immediate problems; it's about building a more peaceful and sustainable future in alignment with all 17 UN Sustainable Goals. By addressing the root causes of conflict, fostering collaboration, and promoting responsible practices, the project sets in motion a ripple effect of positive change that spreads outwards.
Join the Movement, Be the Change:
OneKind's Diana Project offers a compelling vision for a better world. By joining our mission, you can be a part of the solution. Together, we can plant the seeds of hope and cultivate a world where peace and sustainability blossom for all.
Imagina un mundo donde la pobreza, el hambre y la falta de educación sean reliquias del pasado. Un mundo donde las comunidades no solo sobreviven, sino que prosperan, con acceso a la atención médica, el saneamiento y las prácticas sostenibles. Esta visión no es solo un sueño; es la base para lograr los 17 Objetivos de Desarrollo Sostenible de la ONU, y el Proyecto Diana de OneKind lo está construyendo activamente.
Rompiendo el ciclo, construyendo un mundo mejor:
Los Objetivos de Desarrollo Sostenible (ODS) de la ONU proporcionan una hoja de ruta para un mundo más justo y equitativo. El Proyecto Diana de OneKind aborda directamente muchos de estos objetivos, fomentando un mundo transformado:
Empoderando a los huérfanos, construyendo un mundo más fuerte:
Los huérfanos a menudo son vulnerable a la explotación y la violencia. El Proyecto Diana les brinda un refugio seguro, educación y oportunidades de autosuficiencia, contribuyendo a un mundo más pacífico y justo (ODS 16).
El efecto dominó de la paz:
El Proyecto Diana de OneKind no se trata solo de resolver problemas inmediatos; se trata de construir un futuro más pacífico y sostenible alineado con los 17 Objetivos de Desarrollo Sostenible de la ONU. Al abordar las causas fundamentales del conflicto, fomentar la colaboración y promover prácticas responsables, el proyecto pone en marcha un efecto dominó de cambio positivo que se extiende hacia afuera.
Únete al movimiento, sé el cambio:
El Proyecto Diana de OneKind ofrece una visión convincente de un mundo mejor. Al unirse a nuestra misión, puede ser parte de la solución. Juntos, podemos plantar las semillas de la esperanza y cultivar un mundo donde la paz y la sostenibilidad florezcan para todos.
#onekindscience #thedianaproject #elcorazondediana
Imaginez un monde où la pauvreté, la faim et le manque d'éducation ne soient plus que des vestiges du passé. Un monde où les communautés non seulement survivent, mais prospèrent, avec un accès aux soins de santé, à l'assainissement et aux pratiques durables. Cette vision n'est pas qu'un rêve, c'est la base pour atteindre les 17 objectifs de développement durable de l'ONU, et le projet Diana de OneKind y contribue activement.
Briser le cycle, construire un monde meilleur:
Les objectifs de développement durable (ODD) de l'ONU fournissent une feuille de route pour un monde plus juste et équitable. Le projet Diana de OneKind s'attaque directement à plusieurs de ces objectifs, favorisant un monde transformé :
Donner du pouvoir aux orphelins, bâtir un monde plus fort:
Les orphelins sont souvent vulnérables à l'exploitation et à la violence. Le projet Diana leur offre un refuge sûr, une éducation et des possibilités d'autosuffisance, contribuant ainsi à un monde plus pacifique et plus juste (ODD 16).
L'effet d'entraînement de la paix:
Le projet Diana de OneKind ne se contente pas de résoudre les problèmes immédiats ; il vise à construire un avenir plus pacifique et durable, en accord avec les 17 objectifs de développement durable de l'ONU. En s'attaquant aux causes profondes des conflits, en favorisant la collaboration et en promouvant des pratiques responsables, le projet déclenche un effet d'entraînement de changements positifs qui se propagent vers l'extérieur.
Rejoignez le mouvement, incarnez le changement:
Le projet Diana de OneKind offre une vision convaincante d'un monde meilleur. En rejoignant notre mission, vous pouvez faire partie de la solution. Ensemble, nous pouvons planter les graines de l'espoir et cultiver un monde où la paix et la durabilité s'épanouissent pour tous.
#onekindscience #thedianaproject #lecoeurdediana
Building a Global Safety Net for Orphans: A Gryphon Orphanage & OneKind Science Foundation Initiative
This proposal outlines a 10-year plan to establish a holistic global support system for orphaned and fostered children. This system leverages the strengths of both the Gryphon Orphanages and the OneKind Science Foundation's Diana Project, creating a network that prioritizes child well-being, sustainability, and global collaboration.
Core Principles:
Creando una Red de Seguridad Global para Huérfanos: Una Iniciativa del Orfanato Gryphon y la Fundación OneKind Science
Esta propuesta describe un plan de 10 años para establecer un sistema holístico de apoyo global para niños huérfanos y acogidos. Este sistema aprovecha las fortalezas tanto de los Orfanatos Gryphon como del Proyecto Diana de la Fundación OneKind Science, creando una red que prioriza el bienestar infantil, la sostenibilidad y la colaboración global.
Principios Fundamentales:
Cette proposition décrit un plan décennal visant à établir un système global holistique de soutien aux enfants orphelins et placés en famille d'accueil. Ce système s'appuie sur les forces combinées des Orphelinats Gryphon et du Projet Diana de la Fondation OneKind Science, créant un réseau qui accorde la priorité au bien-être de l'enfant, à la durabilité et à la collaboration internationale.
Principes fondamentaux :
Imagine this: You step off the plane into the warm embrace of Africa, excitement thrumming through your veins...
Be a part of the future of OneKind Science and Subscribe! Thank you to all our AI Industry Champions whom we learned from their approaches on how to make our Quantum AI Articulated Paradigm SynergySyncSEO Notebook Engine from OneKind Science’s Digital Reflex Media (DRM). It is by working with this technology that all our architecture is aligned for the systems of tomorrow. Searching for a solution to a pioneer operating system for AI that became Maple 1.0 we developed our ORCAS/PAAM foundation for success. We would like to thank the pioneers and champions of AI we look forward to strengthening our synergies. Looking at how you work with AI let us build our ORCAS/PAAM engine from SynergySyncSEO research showing the power of AI omnichannel/omniprescence technology construct. Ai Artificial Intelligence SynergySyncSEO Thank you to all the explorers and inventors and technology Google: TensorFlow: An open-source machine learning framework for building and deploying various AI models. PyTorch: A popular open-source machine learning library favored for its dynamic computation graphs and natural language processing capabilities. Keras: A user-friendly API for building and experimenting with neural networks, often used as a frontend for TensorFlow. Scikit-learn: A widely used Python library for classical machine learning algorithms, offering simple and efficient tools for data mining and analysis. Caffe: A deep learning framework known for its speed and effectiveness in image recognition tasks. Microsoft Cognitive Toolkit (CNTK): An open-source deep learning framework focusing on performance, scalability, and flexibility. Apache MXNet: An open-source deep learning framework known for its scalability and distributed computing capabilities. Theano: A Python library for defining, optimizing, and evaluating mathematical expressions, especially useful for deep learning research. OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms. RapidMiner: An integrated data science platform facilitating building machine learning models without extensive coding knowledge. H2O.ai: An open-source machine learning platform designed for enterprises, offering scalable machine learning and deep learning solutions. IBM Watson Studio: IBM's cloud-based data science platform integrating various tools for data analysis, AI model development, and deployment. Apache Spark MLlib: A scalable machine learning library built on top of Apache Spark, offering distributed algorithms for data processing and machine learning tasks. NLTK (Natural Language Toolkit): A Python library for working with human language data, providing tools for tokenization, stemming, tagging, parsing, and more. GPT (Generative Pre-trained Transformer): A family of language generation models known for their capabilities in natural language understanding and generation. BERT (Bidirectional Encoder Representations from Transformers): A transformer-based language representation model excelling in understanding context in natural language processing tasks. XGBoost: An efficient and scalable gradient boosting library used for supervised learning tasks, known for its performance in structured/tabular data problems. fast.ai: A high-level deep learning library built on top of PyTorch, providing simplified APIs for training models and conducting cutting-edge research. AutoML (Automated Machine Learning): Various platforms and libraries automate the process of building machine learning models. AllenNLP: A natural language processing library built on PyTorch, specifically designed for research in deep learning-based NLP. Stanford CoreNLP: A suite of NLP tools providing various language analysis capabilities. Dlib: A C++ library used for machine learning, computer vision, and image processing tasks, known for its effectiveness in face recognition and object detection. Julia: A programming language offering high performance for technical computing tasks, including machine learning and scientific computing. PaddlePaddle: A deep learning platform developed by Baidu, offering tools and libraries for building and deploying machine learning models. Microsoft: Azure Machine Learning: Microsoft's cloud-based machine learning platform for building, training, and deploying machine learning models at scale. Azure Cognitive Services: A suite of AI services providing pre-built APIs for vision, speech, language, and decision-making capabilities. Azure Databricks: A unified analytics platform that integrates with Azure to accelerate big data analytics and AI tasks. Microsoft Cognitive Toolkit (CNTK): An open-source deep learning framework developed by Microsoft, known for its scalability and performance. Microsoft Bot Framework: A platform for building, deploying, and managing intelligent bots across various channels. Azure Custom Vision: Allows users to build and deploy custom image recognition models using machine learning. Azure Speech Services: Provides speech-to-text and text-to-speech capabilities, enabling developers to integrate speech into applications. Azure Translator Text API: Offers text translation capabilities between languages using neural machine translation technology. Azure Form Recognizer: A service that extracts information from forms and documents using AI-powered machine learning models. Microsoft Azure Face API: Enables face detection, recognition, and identification in images and videos. Azure Language Understanding (LUIS): Helps developers build natural language understanding into applications for intent recognition and entity extraction. Microsoft AI School: Offers online courses, tutorials, and resources for learning about Microsoft's AI technologies and tools. Microsoft Research AI: Microsoft's research division focused on advancing the field Other Companies: IBM Watson: IBM's AI platform offering various services for natural language understanding, speech recognition, and machine learning. Amazon Web Services (AWS) AI: Provides AI and machine learning services on the AWS cloud, including SageMaker for building ML models. NVIDIA Deep Learning Institute (DLI): Offers training and certification in AI, deep learning, and accelerated computing. PyTorch: An open-source machine learning library developed by Facebook's AI Research lab, known for its flexibility and ease of use. Apple Core ML: Apple's framework for integrating machine learning models into iOS, macOS, watchOS, and tvOS apps. OpenAI: A research organization focused on developing artificial general intelligence, known for projects like GPT (Generative Pre-trained Transformer) models. Fast.ai: Offers practical deep learning for coders, providing free courses and libraries built on PyTorch. Salesforce Einstein: Salesforce's AI platform embedded in its CRM software, offering AI-driven insights and predictions. Alibaba Cloud AI: Alibaba's cloud services with AI capabilities, including natural language processing, computer vision, and machine learning. Baidu AI Cloud: Baidu's AI services and solutions, covering speech recognition, image analysis, and natural language processing. Huawei HiAI: Huawei's AI platform focused on integrating AI capabilities into their devices and cloud services. Caffe: A deep learning framework developed by Berkeley Vision and Learning Center (BVLC), known for its expressive architecture. Kaggle: A platform for data science competitions and collaboration, providing datasets, notebooks, and AI challenges. TensorRT: NVIDIA's high-performance deep learning inference optimizer and runtime for deploying trained models. H2O.ai: Provides AI and machine learning platforms for data science and analytics, including AutoML functionalities. Intel AI: Intel's AI technologies and frameworks, including tools optimized for AI workloads on Intel hardware. SAS AI & Analytics: Offers AI-powered analytics solutions for businesses, covering areas like fraud detection and customer intelligence. Databricks: A unified analytics platform built on Apache Spark, facilitating big data analytics and AI tasks. DeepMind: A subsidiary of Alphabet (Google's parent company) focused on artificial general intelligence research and reinforcement learning. Theano: A Python library used for defining, optimizing, and evaluating mathematical expressions, especially useful for deep learning research. Apache MXNet: An open-source deep learning framework used for training and deploying neural networks. Orange: An open-source data visualization and analysis tool with machine learning and AI components. RapidMiner: An integrated data science platform offering machine learning, data preparation, and model deployment functionalities. BigML: Provides a machine learning platform for predictive analytics and machine learning automation. DataRobot: An automated machine learning platform designed to assist in building and deploying machine learning models. Additional Resources: OpenAI GPT-3: A language model based on transformers, utilizing 175 billion parameters for natural language processing tasks with extensive use in language generation and understanding. DeepMind AlphaFold: An AI system utilizing deep learning and attention mechanisms to predict protein structure from amino acid sequences, advancing protein folding predictions in bioinformatics. Facebook AI Research (FAIR): Facebook's research division focused on AI, employing convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for computer vision, natural language processing, and reinforcement learning. Google Brain: Google's AI research division employing deep neural networks (DNNs), recurrent networks, and attention mechanisms for various AI applications across Google services. AI Dungeon: An AI-generated text adventure game using language models like GPT-3 to generate interactive narratives based on user inputs. Generative Adversarial Networks (GANs): A class of neural networks comprising a generator and a discriminator, used for unsupervised learning and generating realistic synthetic data. NeuroSymbolic AI: A field combining neural networks with symbolic reasoning techniques, aiming to integrate neural networks' pattern recognition with logic-based reasoning systems. Evolutionary Algorithms: Optimization algorithms inspired by biological evolution, using techniques like genetic algorithms and genetic programming for machine learning tasks. Quantum Machine Learning: Exploring quantum computing principles like quantum gates and superposition for solving machine learning problems, potentially achieving faster computations for certain tasks. Reinforcement Learning: A machine learning paradigm focused on learning to make sequences of decisions by interacting with an environment, utilizing methods like Q-learning and policy gradients. Explainable AI (XAI): Research focused on interpretable models employing XAI to market Tools and Resources: IBM AI Explainability 360: A comprehensive open-source toolkit providing various explainability algorithms for machine learning models. SHAP (SHapley Additive exPlanations): A model-agnostic approach for explaining individual predictions of machine learning models. LIME (Local Interpretable Model-agnostic Explanations): Provides explanations for individual model predictions locally around the prediction to be explained. DeepLIFT: A method for understanding the contributions of different input features to a specific output prediction. Anchors: Identifies minimal subsets of features that are sufficient to explain model predictions. Counterfactual Explanations: Explains model predictions by generating alternative scenarios where the prediction would have been different. Model Cards: Document model capabilities, limitations, and biases, providing transparency and understanding of model behavior. Fairness Tooling: Tools for assessing and mitigating potential biases in machine learning models, including fairness metrics and bias detection algorithms. InterpretML: A Python library for interpreting black-box models using various explainability techniques. Captum: A PyTorch library for gradient-based explainability methods, offering insights into model predictions. Sign up to hear from us about events, news, and how you too can be a volunteer, intern, educator, scientist, programmer, or other leader of the future of science. Ready Now? Use the Contact Us on the Home Page
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