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Medium
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Recent Trends of Multi-Agent AI Systems in Robotics Control
- In this blog post, we will explore the potential of MAS in addressing four key challenges that are shaping the landscape of AI and robotics in 2024.
- MAS offers a powerful solution to this challenge by enabling the aggregation of diverse perspectives and solutions from multiple agents.
- MAS provides a collaborative approach where different robots (agents) can share tasks and information, leading to more efficient handling of complex and dynamic environments.
- Multi-agent systems facilitate better coordination and communication among robots and between robots and humans, improving safety and operational efficiency.
- Multi-agent systems can contribute by enabling distributed problem-solving that allows individual robots to operate semi-independently while still coordinating their activities with the collective.
- As we’ve seen through these examples, multi-agent systems offer a powerful approach to addressing various challenges in AI and robotics.
- The power of MAS lies in its ability to leverage diverse perspectives, distribute workloads, and enable coordinated problem-solving.
- As the fields of AI and robotics continue to evolve, embracing the principles of multi-agent systems will be crucial in developing more robust, adaptive, and efficient solutions.
- So, whether you’re a robotics developer, an AI researcher, or simply someone fascinated by these cutting-edge technologies, the time is ripe to explore the potential of multi-agent systems and unlock new frontiers in innovation.
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Medium
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Fixing Mislabeled Demos with Machine Learning
- The author used exploratory data analysis (EDA) to clean and prepare the data for training an ML model.
- The author filtered out unnecessary columns using mean values and .isna() function.
- Feature selection tests --chi2, f_classif, and mutual_info_classif-- were used to rank features.
- The author decided to use FeatureUnion to combine the features from each test.
- Campaign names were added to the categorical features using a ColumnTransformer and pipeline.
- RandomForestClassifier gave the best f1 score of 0.898 for the decision tree model.
- The author could tune hyperparameters of the model, but since it would be used once, deemed it unnecessary.
- Data from unmatched demos was used to predict missing industries with the trained model.
- The random forest model used in this work predicted missing industry ids with an f1 score of ~0.90.
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Medium
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How to Train a KAN Model on the Titanic Dataset for Kaggle
- Training machine learning models on real-world datasets requires careful preprocessing and model configuration.
- In this tutorial, we learn how to train a Knowledge Augmented Network (KAN) model on the Titanic dataset for Kaggle competitions.
- The steps include data preprocessing, model training, and making predictions for submission.
- Key suggestions for improving the model include feature engineering, improved missing value handling, better model configuration, optimizer selection, and regularization techniques.
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Medium
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What Is Synthetic Data?
- Synthetic data is data that is generated artificially through computer algorithms.
- It can be created based on predefined rules or using machine learning models trained on real-world data.
- Synthetic data offers benefits such as data privacy, cost-effectiveness, and control over biases.
- However, it may not always capture the complexity and unpredictability of real-world data.
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Medium
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Synthetic Data for Software Testing
- Synthetic data is a valuable tool for software testing, especially in the context of data privacy and security regulations like GDPR and CCPA.
- It allows testers and developers to create specific scenarios and edge cases that may be difficult to reproduce with real data.
- Generating synthetic data is cost-efficient compared to maintaining and anonymizing real-world data for testing purposes.
- By closely mirroring real-world data, synthetic data helps ensure the application functions correctly in various scenarios.
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Medium
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Here is a blog on coding style, written in simple English, with a comprehensive language and…
- Simplicity is key to good coding style. Avoid complex logic and nested loops.
- Readability is crucial for collaboration and maintenance. Use clear, concise variable names and function names.
- Consistency is essential for coding style. Follow a consistent naming convention, indentation, and formatting throughout your codebase.
- Coding style is not just about writing functional code; it’s about writing code that is easy to read, understand, and maintain.
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Medium
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ST-One: where Industry and University comes together
- Vacuum is a key component in producing vegetable oil for margarine, which is where ST-One, M. Dias Branco, and Federal University of Minas Gerais (UFMG) came together on a project looking to improve the process of creating a vacuum in this process.
- The vacuum distillation process neutralises odours and undesirable tastes and colours in oil and removes volatile components that cause product instabilities. This deodorisation process occurs at low temperatures when a vacuum is applied to reduce the risk of thermal degradation of the oil.
- The three institutions were creating predictive models to forecast problems relating to vacuum breakdowns that could lead to the interruption of the process, improving the production line efficiency.
- The project also created opportunities to see the real-life application of the models created in an industrial environment, and to understand how data interpretation can enhance productivity lines.
- As it stands, the models are still in the application stage, however, the study has already yielded several beneficial results for each of the groups involved.
- The benefits to ST-One were varied, improving knowledge and visualisation of new data, as well as providing an opportunity to engage with more Artificial Intelligence technologies and productivity improvements.
- As Brazilian consumers eat margarine for breakfast, vacuum distillation is an important process that needs to be secured and improved further so that the manufacturers can remain competitive and tasty to the customer.
- The project was carried out over several important stages, from investigation through to mathematical modelling, and testing of results.
- The partnership between industry and academia has resulted in additional resources for the industry and provided valuable practical experience for students and academics alike.
- This study is an excellent example of how academia and industry can work together on innovative projects, creating a positive and productive collaboration, which benefits the parties involved.
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Medium
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Last Week in AI (May 17, 2024)
- OpenAI unveiled GPT-4o with enhanced capabilities in text, vision, audio, and more.
- Google released AlphaFold 3 to predict molecular interactions and made updates to Gemini model.
- Apple introduced upgraded iPads with the new M4 chip and showcased AI-powered accessibility features in iOS 18.
- OpenAI experienced leadership changes with the departure of co-founder Ilya Sutskever and Jan Leike.
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Arstechnica
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Slack users horrified to discover messages used for AI training
- Slack users are concerned about the use of their messages for AI training.
- Slack's policy states that customer data, including messages and files, is used to train its global AI models.
- A Slack engineer acknowledges the need for clearer privacy principles regarding the use of customer data for AI training.
- Engineers and writers are calling for a more transparent policy language from Slack regarding data sharing.
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Medium
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Intro to Deep Learning
- Deep learning is a subfield of machine learning that involves the use of deep neural networks to model and solve complex problems.
- In a neural network, multiple single neurons are stacked together to form a larger neural network.
- Deep learning has achieved significant success in various fields and is expected to grow further with the availability of more data and powerful computing resources.
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Medium
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How Stylometric Analysis works part4(Machine Learning 2024)
- Function word adjacency networks (WANs) are used to study the authorship of plays from the Early Modern English period.
- WANs consist of nodes representing function words and directed edges representing the relative frequency of co-appearance.
- Author profile networks are generated by aggregating the WANs of analyzed plays.
- WANs prove to be accurate for authorship attribution, outperforming other frequency-based methods for Early English plays.
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Medium
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How Stylometric Analysis works part3(Machine Learning 2024)
- In this study, Japanese stylometric features of texts generated by GPT-3.5 and GPT-4 were compared to those written by humans.
- Multi-dimensional scaling (MDS) was performed to analyze the distributions of texts based on stylometric features.
- The distributions of GPT-3.5, GPT-4, and human-generated texts were found to be distinct.
- The classification performance using random forest (RF) for Japanese stylometric features showed high accuracy in distinguishing GPT-generated from human-written texts.
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Medium
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How Stylometric Analysis works part2(Machine Learning 2024)
- Large language models (LLMs) like GPT-4, PaLM, and Llama have increased the generation of AI-crafted text.
- Neural authorship attribution is a forensic effort to trace AI-generated text back to its originating LLM.
- The LLM landscape can be divided into proprietary and open-source categories.
- An empirical analysis of LLM writing signatures highlights the contrasts between proprietary and open-source models and scrutinizes variations within each group.
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Medium
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ContextDecode: Reverse Engineering for Automated Interpretation of Contextualized Embedding…
- CWEs obtained from pre-trained large language models, such as BERT, GPT, or RoBERTa, are powerful and provide a solid foundation for many NLP tasks.
- Contextualized representations encode additional information from the sentence context, allowing for detecting subtle nuances in meaning that static embeddings cannot capture.
- Despite the benefits, a significant challenge remains: understanding which specific features of the context determine the similarity of these representations.
- This tool aims to address this challenge by interpreting the clusters of CWEs, using the K-Means algorithm to cluster sentences based on the contextual representations of a word and training a classifier to predict the cluster of each sentence using context features.
- Recursive feature elimination (RFE) is then applied to identify the top features responsible for cluster formation, and these features are analyzed to reveal which contextual features drive the clustering process.
- The method can be used to assess whether the encoded information is relevant and useful for specific NLP tasks, such as Word Sense Disambiguation.
- Examples in the article, such as analyzing sentences containing the words “foot” and “buy”, reveal the features that affect cluster formation and signal different senses of the words.
- This tool can potentially provide deeper insights into the encoded information in contextualized embeddings and improve their interpretability.
- More research is needed in this area to expand the tool's potential and application in various NLP tasks.
- The method described in this article is detailed in the paper “Assessing the Significance of Encoded Information in Contextualized Representations to Word Sense Disambiguation” by Deniz Ekin Yavas.
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Medium
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Metric Learning for Classification
- Many state-of-the-art papers leave crucial questions unanswered, particularly regarding performance on unseen classes.
- New methods, utilizing the latest architectures and optimal hyperparameters, are often pitted against older methods that rely on outdated architectures and suboptimal hyperparameters.
- The goal of the project is to determine whether Metric Learning can replace classification methods by using various metrics.
- The CUB-200–2011 Dataset was chosen for the experiments.
- Before starting off a ML project, it is important to think carefully about evaluation metrics and how to make fair comparisons among experiments.
- Siamese Networks and Triplet loss are the foundational stones of Metric Learning.
- Resnet-18 Architecture was chosen with the last classification layer replaced with Linear layer which outputs an embedding dimension of 128, with imagenet-1k pretrained weights and Euclidean distance metric.
- Choosing SGD seemed logical to improve generalization, giving a bump of 25 % in test accuracy. Augmentations during training improved it further by 3%.
- Resnet-50 showed a notable increase of 11% test accuracy w.r.t baseline.
- ArcFace and ProxyNCA were chosen as the loss functions, with ArcFace showing promise for test accuracy but the highest drop of precision for unseen classes, making it the worst generalizer for unseen classes.
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