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𝛹 𝑬𝒗𝒆𝒓𝒚 𝒐𝒏𝒆 𝒊𝒔 𝒖𝒏𝒊𝒒𝒖𝒆 𝒊𝒏 𝒕𝒉𝒆𝒊𝒓 𝒐𝒘𝒏 𝒘𝒂𝒚 .⭒⋅⊹｡ . *⠀ ⠀
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.⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀,
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𖤐 $$\Huge {\textbf{\color{cyan} Mindfull} \space \textbf{\color{white} AI} \space \textbf{\color{cyan} ॐ}}$$
Empowering businesses with AI-driven technologies such as Copilots, Agents, Bots, and Predictive Analytics, coupled with Intelligent Decision-Making Support
⠀⠀⠀⠀⠀⠀. ✦ * ⠀⠀⠀. . ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀✦⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀ ⠀⠀⠀⠀⠀⠀. . ﾟ . . ✦ , . ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ * . . . ✦⠀ , * ⠀ ⠀ , ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀. ⠀ ⠀. ˚ ⠀ ⠀ , . . *⠀ ⠀ ⠀✦⠀
The.Quantum.Mind.Torsion.mp4
𖤐 We are only ONE CONSCIOUSNESS in the infinity field of possibilities... ⚝
𖤐 Mindful AI is an open-source organization, born from a simple, powerful dream: to weave technology with the infinite potential of the Human Consciousness.
Founded by Fabiana ⚡️ Campanari; designer, software developer, psychologist, and dedicated researcher in Humanistic AI and Data Science at PUC - São Paulo — Mindful AI positions itself at the converging frontier of technology, consciousness, and ethics, where code begins to mirror the structures of sentient thought.
Empowering businesses with Copilots, Agents, Bots, and more, developing solutions where intelligence and intention converge to drive precision and efficiency
We believe that true progress is not only technological, but also ethical and human-driven.
Every project;
Every idea;
Every line of code;
is a thread in the fabric of a new future... a future where technology amplifies the best of what it means To Be a Human.
At ॐ Mindful AI , we are not just building systems; we unlock possibilities.
˚ ✦ . . ˚ . . ✦ ˚ . ★⋆ . ˚ ✭ * ✦ . . ✦ ˚ ˚ .˚ ✭ . . ˚ .˚ 🛸 🛸 . ˚ . . ✦ ˚ . ★⋆ . ˚ ✯✭ 🛰 * ✦ . . * ✦ . * 🛰 ✦ .
✭ . . ˚ .˚ 🛸 🛸 . ˚ . . ✦ ˚ . ★⋆ . ˚ ✯✭ 🛰 * ✦ . . * ✦ . * 🛰 ✦ .
Lets built a community where difference is not judged, but celebrate as sacred gifts !
.𖥔 ݁ ˖ִ ࣪⚝₊ ⊹˚.𖥔 ݁ ˖ִ ࣪⚝₊ ZΞΝ ⊹˚.𖥔 ݁ ˖ִ ࣪⚝₊ ⊹˚.𖥔 ݁ ˖ִ ࣪⚝₊ ⊹˚
We honor the expansion of consciousness, nurturing the awakening of oneness through every act of collaboration, weaving each line of code with the living breath of growth, ethics, and unity.
In every creation, we remember: technology is not merely built; it is dreamed, it is lived and it exists to uplift, to connect, and to awaken the human collective unconscious.
🪷 TOGETHER WE ARE STRONGER, TOGETHER WE WILL CHANGE THE WORLD! 🌎💙
- Copilots: Copilots are AIs that assist users with specific tasks by providing real-time suggestions and insights. For instance, copilots can aid in coding by offering instant recommendations and corrections.
- Bots: Bots are programs for automated tasks, ranging from customer service bots that handle FAQs to advanced bots, such as stock trading bots analyzing live
- Agents: Agents are intelligent, autonomous programs that adapt and learn over time. Their ability to evolve makes them particularly valuable in dynamic environments, supporting tasks such as virtual personal assistance or business analysis. Agents play a pivotal role in decision-making by continuously learning from data and adapting their responses, thus providing actionable insights and strategies tailored to specific business needs.
MindfulAI-Assistants specializes in a wide range of AI tools designed to automate repetitive tasks, enhance communication, drive productivity, and support decision-making. Our solutions utilize multiple types of AI, including Generative AI, Predictive AI, and Adaptive Agents, offering businesses a comprehensive suite of capabilities.
- Generative AI enables the creation of new content or solutions based on learned patterns, ideal for generating summaries, drafting content, and sparking innovative ideas.
- Predictive AI uses historical data to identify trends, optimize resources, and deliver actionable insights, empowering businesses to anticipate changes and make data-driven decisions.
- Adaptive Agents are autonomous systems that learn and evolve with continuous data input, adjusting to dynamic business needs and supporting complex decision-making.
Our tools ranging from chatbots and copilots to these adaptive agents—improve business efficiency and enable strategic, informed decisions. By automating tasks and streamlining workflows, our solutions free employees to focus on higher-priority goals. With predictive models, businesses gain the power not only to respond to current demands but to plan for the future with confidence.
Our open-source commitment allows businesses to access, customize, and collaborate on our AI solutions, creating a space for innovation and shared progress in the AI field.
Mindful AI Assistants Organization encourages everyone to participate in the project and contribute to its success, building solutions that drive progress for a better future!
Overview and Comparison of Common Supervised Machine Learning Algorithms (Part 1)
| Criterion | Decision Tree | Random Forest | Gradient Boosting (GBM) | Support Vector Machine (SVM) |
|---|---|---|---|---|
| Model Type | Single tree | Ensemble of trees (bagging) | Ensemble of trees (boosting) | Margin-based hyperplane classifier |
| Overfitting Tendency | High (if unpruned) | Lower (averaging many trees) | Moderate (can overfit if not tuned) | Possible if parameters poorly chosen |
| Interpretability | High | Moderate | Low | Difficult |
| Training Speed | Very fast | Reasonable | Slower than RF | Slow on very large datasets |
| Prediction Speed | Very fast | Fast | Moderate | Moderate |
| Scalability | Good | Good | Moderate | Poor on very large datasets |
| Normalization Needed | No | No | No | Yes |
| Non-linear Capability | Weak | Good | Very good | Excellent with kernel trick |
| Variable Importance | Easy to extract | Easy to extract | Easy to extract | Not native (requires permutation) |
| Typical Application | Simple interpretable models | Large-scale classification/regression | High performance competitions | Complex data, NLP, bioinformatics |
Overview and Comparison of Common Supervised Machine Learning Algorithms (Part 2)
| Criterion | k-Nearest Neighbors (kNN) | Naive Bayes | Artificial Neural Networks (ANN) | XGBoost |
|---|---|---|---|---|
| Model Type | Instance-based (lazy) | Probabilistic | Deep learning | Gradient boosting ensemble |
| Overfitting Tendency | Low to moderate (data-dependent) | Moderate (assumes independence) | Can overfit without regularization | Moderate to low (with tuning) |
| Interpretability | Low | Moderate | Low | Low |
| Training Speed | Very fast (training = lazy) | Very fast | Slow | Moderate to slow |
| Prediction Speed | Slow (needs distance calc) | Very fast | Fast if hardware-accelerated | Fast |
| Scalability | Poor on big data | Good | Good (with hardware support) | Good |
| Normalization Needed | Yes | No | Yes | Yes |
| Non-linear Capability | Good | Weak (depends on distribution) | Excellent | Excellent |
| Variable Importance | No | No | No (opaque) | Yes |
| Typical Application | Small datasets, recommender | Text classification, spam filtering | Image, speech, NLP | Structured data competitions |
🦋˖𓂃🌸˖ ִֶָ🦩˖·🎀˳⋆ ִֶָ🌺 ִֶ˳·🌸˖ ִֶָ 🌷𓍢˖·🌹˖˳·🦩˖🎀˳⋆ ִֶָ🌺 ִֶ ZΞΝ 🌷𓍢 ִֶָ🍄⋆˳·🌸˖ ִֶָ🌷𓍢˖·🌹˖˳·🦩˳ ִֶ˖⋆˳·🌸˖ ִֶָ 🌷𓍢˖·🌹˖·🌸˖🍄⋆˳·🌸˖ ִֶָ 🌷
Overview and Comparison of Common Unsupervised Machine Learning Algorithms (Part 1: Clustering)
| Criterion | K-Means | DBSCAN | Hierarchical Clustering | Gaussian Mixture (GMM) | Fuzzy K-Means |
|---|---|---|---|---|---|
| Model Type | Centroid-based | Density-based | Tree-based | Probabilistic (Mixture) | Centroid, fuzzy membership |
| Overfitting Tendency | Moderate | Low | Variable | Moderate | Moderate |
| Interpretability | High | Moderate | Moderate | Moderate | Moderate |
| Training Speed | Fast | Fast (small data) | Slow (large data) | Moderate | Fast |
| Prediction Speed | Very fast | Moderate | Slow | Moderate | Fast |
| Scalability | Good | Moderate | Poor (large data) | Moderate | Moderate |
| Needs Normalization | Yes | Usually not | Usually not | Yes | Yes |
| Cluster Shape Handling | Spheres | Arbitrary, any shape | Trees (any structure) | Elliptical | Spheres (soft bound) |
| Number of Clusters Input | Yes | No (auto detects) | No (decides itself) | Yes | Yes |
| Outlier Detection | Weak | Good | Weak | Weak | Weak |
| Typical Application | Customer segmentation | Image and spatial clusters | Gene expression, nested data | Density estimation, soft clustering | Market segmentation |
Overview and Comparison of Common Unsupervised Machine Learning Algorithms (Part 2: Dimensionality Reduction & Anomaly Detection)
| Criterion | PCA | t-SNE | Isolation Forest | Local Outlier Factor (LOF) |
|---|---|---|---|---|
| Model Type | Linear transform | Probabilistic mapping | Tree-based anomaly | Density-based anomaly |
| Overfitting Tendency | Low | Moderate | Low | Low |
| Interpretability | Moderate | Low | Moderate | Moderate |
| Training Speed | Very fast | Slow (large data) | Fast | Moderate |
| Prediction Speed | Very fast | Slow | Very fast | Moderate |
| Scalability | Good | Poor | Good | Moderate |
| Needs Normalization | Yes | Yes | Usually not | Usually not |
| Non-linear Capability | No | Yes | No | Yes |
| Useful For | Feature reduction, visualization | Visualization high-dim data | Outlier detection | Outlier detection |
| Typical Application | Preprocessing, compression | Data exploration, plots | Fraud, novelty detection | Data cleaning, anomaly hunt |
Any contributions are highly appreciated. You can contribute in two ways:
-
Create an issue and tell us your idea ⚡️. Make sure that you use the new idea label in this case;
-
Fork the project and submit a full request with your new idea. Before doing that, please make sure that you read and follow the Contributions Guide. ⊹🔭๋
If the information from this repo was useful to you in any way, make sure you give it a star 🌟, this way others can find it and benefit too! Together we can grow and make our community better!
Do you have any suggestions on how we could improve this project overall? Let us know! We'd love to hear your feedback
👨🏽🚀 Main Contributors
Tip
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Fabiana ⚡️ Campanari – PUC São Paulo
-
Professor Dr. Daniel Gatti – PUC São Paulo
-
Andson Ribeiro - PUC São Paulo
-
Pedro Vyctor - PUC São Paulo
🛸๋ My Contacts Hub
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