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Bisheng: An Open-Source LLM DevOps Platform Revolutionizing LLM Application Development

Bisheng, launched under the Apache 2.0 License, is an innovative open-source platform designed to facilitate and accelerate the development of Large Language Model (LLM) applications. The platform is named after the inventor of movable type printing, symbolizing its potential to revolutionize the dissemination of knowledge through intelligent applications.

The platform is built to cater to both business users and technical experts. Business users can leverage pre-configured application templates and intuitive form-filling processes to build intelligent applications centered around LLM swiftly. This ease of use ensures that even those without deep technical expertise can develop sophisticated LLM applications.

For developers and experts familiar with LLM technologies, Bisheng offers extensive flexibility. The platform includes hundreds of development components that align with the latest trends in the LLM ecosystem. Users can utilize visual and flexible process orchestration capabilities to create diverse types of LLM applications beyond simple prompting projects.

Bisheng distinguishes itself from other open-source projects by offering enterprise-level features. These include high availability under high concurrency, continuous iteration, and optimization of application operations. The platform is designed to handle real production use cases, making it reliable for enterprise-level deployment. Bisheng also addresses the issue of uneven data quality within enterprises by providing comprehensive unstructured data governance capabilities, which have been honed over years of experience. These capabilities are accessible in the demo environment and are offered without limitations.

The applications that can be built using Bisheng are diverse. They range from analysis report generation, such as contract review and credit investigation reports, to knowledge base Q&A systems, including user manual Q&A and research report knowledge bases. The platform also supports dialogue-based applications like role-playing as an interviewer or foreign language teacher, as well as element extraction tasks from contracts and engineering reports.

Bisheng’s vision extends beyond dialogue-based interactions. The platform aims to support various application forms, including process automation and search functionalities, to meet the evolving needs of enterprise scenarios.

Bisheng offers a robust, flexible, and user-friendly platform for developing LLM applications. By providing tools for both novices and experts and ensuring reliability and scalability for enterprise use, Bisheng is poised to play a significant role in the future of intelligent application development.

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MicroPython Testbed for Federated Learning Algorithms (MPT-FLA) Framework Advancing Federated Learning at the Edge

The Python Testbed for Federated Learning Algorithms (PTB-FLA) is a low-code framework developed for the EU Horizon 2020 project TaRDIS, aimed at simplifying the creation of decentralized and distributed applications for edge systems. Written in pure Python, PTB-FLA is lightweight and easy to install, making it suitable for small IoT devices. It supports both centralized and decentralized federated learning algorithms and peer-to-peer data exchange via time division multiplexing. Designed with a simple API, it allows nonprofessional programmers to develop applications using AI tools like ChatGPT. Its primary limitation is that it currently only runs on a single PC.

Researchers from the University of Novi Sad and RT-RK Institute have developed the MicroPython Testbed for Federated Learning Algorithms (MPT-FLA), overcoming the limitation of its predecessor by enabling individual application instances to run on different network nodes, such as PCs and IoT devices. Retaining the pure Python ideal, MPT-FLA is based on asynchronous I/O and runs on MicroPython, making it suitable for edge systems. The framework was validated on a wireless network with PCs and Raspberry Pi Pico W boards, using adapted application examples from the previous framework, PTB-FLA. The successful validation confirmed that MPT-FLA produces the same numerical results as PTB-FLA.

Current federated learning (FL) frameworks like TensorFlow Federated and BlueFog are effective in cloud-edge environments but unsuitable for edge-only deployment, lack Windows OS support, and are complex to install. A 2021 review by Kholod et al. highlighted the ongoing challenge of developing FL frameworks for edge systems. Unlike complete systems such as CoLearn and FedIoT, the MPT-FLA serves as an “algorithmic” testbed for ML and AI developers in the TaRDIS project. It uses a Single Program Multiple Data (SPMD) pattern, similar to MapReduce, and future work may involve adapting it for high-performance compilation with Codon.

The experimental WiFi network for evaluating MPT-FLA comprised a Belkin F5D7234-4 router, two Raspberry Pi Pico W boards, and a Dell Latitude 3420 PC. The router supports 802.11g with speeds up to 54Mbps. The Raspberry Pi Pico W, featuring the RP2040 chip, includes a 2.4GHz wireless interface and 264KB of RAM, programmed with the “RPI_PICO_W-20231005-v1.21.0.uf2” firmware. The PC runs Windows 10 Pro, with an Intel Core i7-1165G7 processor, and uses Python 3.12.0 and VS Code for software development. The MPT-FLA tool will be available on GitHub by mid-2024. The MPT-FLA framework evolved from the PTB-FLA framework, which relied on Python’s multiprocessing abstractions (process, queue, clients, and listeners). However, PTB-FLA could not be directly ported due to the lack of support for these abstractions in MicroPython.

The MPT-FLA framework was tested on an experimental WiFi network with a Belkin router, two Raspberry Pi Pico W boards, and a PC. The goal was to ensure the adapted algorithms produced the same numerical results as the originals, which they did, confirming functional correctness. However, performance metrics like execution time and energy consumption were not evaluated as the framework is still in development. Issues encountered included the Pico boards’ repeated attempts to connect to the WiFi, potentially seen as DoS attacks by the router, and severe WiFi interferences, particularly in apartment buildings, causing TCP connection failures and increased latencies.

In conclusion, MPT-FLA is an FL framework that extends the PTB-FLA framework to support applications running on diverse network nodes, such as PCs and IoT devices, primarily in edge systems. Four adapted application examples were used in the experimental validation in a lab setting to demonstrate functional correctness. Key contributions include the MPT-FLA framework, new application examples, and the validation approach and results. MPT-FLA’s advantages over PTB-FLA include support for distributed applications and compatibility with smaller IoT platforms. Future work will involve developing benchmark applications and conducting detailed performance evaluations.

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This AI Paper Discusses How Latent Diffusion Models Improve Music Decoding from Brain Waves

Brain-computer interfaces (BCIs) focus on creating direct communication pathways between the brain and external devices. This technology has applications in medical, entertainment, and communication sectors, enabling tasks such as controlling prosthetic limbs, interacting with virtual environments, and decoding complex cognitive states from brain activity. BCIs are particularly impactful in assisting individuals with disabilities, enhancing human-computer interaction, and advancing our understanding of neural mechanisms.

Decoding intricate auditory information, like music, from non-invasive brain signals presents significant challenges. Traditional methods often require complex data processing and invasive procedures, making real-time applications and broader usage difficult. The problem lies in capturing music’s detailed and multifaceted nature, which involves various instruments, voices, and effects from simple brainwave recordings. This complexity necessitates advanced modeling techniques to reconstruct music from brain signals accurately.

Existing approaches to decoding music from brain signals include using fMRI and ECoG, which, while effective, involve either impractical real-time application or invasive techniques. Non-invasive EEG methods have been explored, but they often require manual data preprocessing and focus on simpler auditory stimuli. For instance, previous studies have successfully decoded music from EEG signals. Still, these methods were limited to simpler, monophonic tunes and required extensive data cleaning and manual channel selection.

Researchers from Ca’ Foscari University of Venice, Sapienza University of Rome, and Sony CSL have introduced a novel method using latent diffusion models to decode naturalistic music from EEG data. This approach aims to improve the quality and complexity of the decoded music without extensive data preprocessing. By leveraging ControlNet, a parameter-efficient fine-tuning method for diffusion models, the researchers conditioned a pre-trained diffusion model on raw EEG signals. This innovative approach seeks to overcome the limitations of previous methods by handling complex, polyphonic music and reducing the need for manual data handling.

The proposed method employs ControlNet to condition a pre-trained diffusion model on raw EEG signals. ControlNet integrates EEG data with the diffusion model to generate high-quality music by mapping brainwave patterns to complex auditory outputs. The architecture uses minimal preprocessing, such as a robust scaler and standard deviation clamping, to ensure data integrity without extensive manual intervention. The EEG signals are mapped to latent representations via a convolutional encoder, which is then used to guide the diffusion process, ultimately producing naturalistic music tracks. This method also incorporates neural embedding-based metrics for evaluation, providing a robust framework for assessing the quality of the generated music.

The performance of the new method was evaluated using various neural embedding-based metrics. The research demonstrated that their model significantly outperformed traditional convolutional networks in generating more accurate musical reconstructions from EEG data. For instance, the ControlNet-2 model achieved a CLAP Score of 0.60, while the baseline convolutional network scored significantly lower. Regarding the Frechet Audio Distance (FAD), the proposed method achieved a score of 0.36, indicating high-quality generation, compared to 1.09 for the baseline. Furthermore, the mean square error (MSE) was reduced to 148.59 in the proposed method, highlighting its superior performance in reconstructing detailed musical characteristics from EEG data. The Pearson Coefficient also reflected improved accuracy, with the ControlNet-2 model achieving a correlation coefficient of 0.018, indicating a closer match between the generated and ground truth tracks.

In conclusion, the research addresses the challenge of decoding complex music from non-invasive brain signals by introducing a novel, minimally invasive method. The proposed diffusion model shows promising results in accurately reconstructing naturalistic music, marking a significant advancement in brain-computer interfaces and auditory decoding. The method’s ability to handle complex, polyphonic music without extensive manual preprocessing sets a new benchmark in EEG-based music reconstruction, paving the way for future developments in non-invasive BCIs and their applications in various domains.

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Quantum Machine Learning for Accelerating EEG Signal Analysis

The origins of quantum computing trace back to Richard Feynman’s ideas of simulating various Hamiltonians using controlled quantum systems, with David Deutsch later formulating the theory of quantum Turing machines. This led to the proposal of numerous quantum algorithms, driving rapid advancements in quantum computing. Quantum machine learning (QML), an interdisciplinary field, aims to accelerate machine learning processes compared to classical methods. Despite achievements, challenges remain, including integrating quantum feature extraction and addressing nonlinear dynamics while applying quantum mechanics to electroencephalogram (EEG) signal processing to expedite computation.

EEG records brain electrical activity via scalp electrodes, which is essential for understanding neural processes and diagnosing disorders. Given the volume of data, automated EEG analysis is vital. Processing involves preprocessing, feature extraction, and classification. Feature extraction is pivotal for brain mapping; key features include sample entropy and power spectra. Current classification methods rely on machine learning, with diverse contributions. Entropy signatures from wavelet packets offer robust classification, inspiring the use of WPEE features via QWPT and an improved QSVM classifier.

A quantum state preparation procedure is devised by the College of Information Engineering, Shanghai Maritime University, Research Center of Intelligent Information Processing and Quantum Intelligent Computing, and  School of Computer Science and Engineering, Anhui University of Science and Technology, for processing classical information on a quantum computer. The EEG signal is initially encoded into an amplitude-encoding quantum state, facilitating multi-channel and multi-sample scenarios. This method extends to other time-series data like speech and stock market trends. A Quantum Wavelet Packet Transformation (QWPT) extracts Wavelet Packet Energy Entropy (WPEE) features from the EEG signal. Extracted features are inputted into a Quantum Machine Learning (QML) classifier, such as an improved Quantum Support Vector Machine (QSVM) with efficient implementation of nonlinear kernel functions. The framework is elucidated via quantum circuitry and mathematical expressions, showcasing significant innovations, including multi-channel EEG signal preparation, QWPT for feature extraction, and enhanced QSVM performance. The proposal demonstrates exponential acceleration over classical methods.

The preparation algorithm encodes EEG signals into quantum states using QRAM and quantum arithmetic. Wavelet packet energy entropy (WPEE) is extracted via Haar quantum wavelet packet transform. A universal nonlinear kernel is implemented efficiently for quantum SVM classification, allowing for nonlinear data separation. Quantum circuits simulate Hamiltonians for kernel approximation. The Universal Quantum Approximation Algorithm enhances the HHL algorithm for nonlinear kernel evaluations. This framework integrates quantum mechanics with EEG signal processing, enabling efficient feature extraction and classification.

The researchers detail the experimental results of the proposed framework. Initially, the EEG dataset is transformed into a corresponding quantum state. Subsequently, the QWPT module is applied, yielding 64 features per sample. These features serve as input for training and testing the QSVM model. The classification performance varies with different kernels. Specifically, performance is contingent upon hyper-parameter selection; thus, the parameters within the kernel functions are optimized via the grid search algorithm.

In conclusion, the underdeveloping field of QML holds immense promise for advancing artificial intelligence. This study introduces a hierarchically structured quantum mechanics-based framework tailored for EEG signal processing, encompassing preparation, feature extraction, and classification. Each component is meticulously implemented with quantum techniques, showcasing significant potential for extending to diverse time-series data. Notably, a robust method for universally approximating nonlinear kernels in quantum construction is proposed, exemplified by its QSVM application. Experimental validation on real-world data confirms feasibility and efficacy, with the entire framework exhibiting exponential acceleration over classical counterparts in complexity.


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Meet Verba 1.0: Run State-of-the-Art RAG Locally with Ollama Integration and Open Source Models

Retrieval-augmented generation (RAG) is a cutting-edge technique in artificial intelligence that combines the strengths of retrieval-based approaches with generative models. This integration allows for creating high-quality, contextually relevant responses by leveraging vast datasets. RAG has significantly improved the performance of virtual assistants, chatbots, and information retrieval systems by ensuring that generated responses are accurate and contextually appropriate. The synergy of retrieval and generation enhances the user experience by providing detailed and specific information.

One of the primary challenges in AI is delivering precise and contextually relevant information from extensive datasets. Traditional methods often need help maintaining the necessary context, leading to generic or inaccurate responses. This problem is particularly evident in applications requiring detailed information retrieval and a deep understanding of context. The inability to seamlessly integrate retrieval and generation processes has been a significant barrier to advancing AI applications in various fields.

Current methods in the field include keyword-based search engines and advanced neural network models like BERT and GPT. While these tools have significantly improved information retrieval, they cannot often effectively combine retrieval and generation. Keyword-based search engines can retrieve relevant documents but do not generate new insights. On the other hand, generative models can produce coherent text but may need help to retrieve the most pertinent information. 

Researchers from Weaviate have introduced Verba 1.0, a solution that can bridge retrieval and generation to enhance the overall effectiveness of AI systems. Verba 1.0 integrates state-of-the-art RAG techniques with a context-aware database. The tool is designed to improve the accuracy and relevance of AI-generated responses by combining advanced retrieval and generative capabilities. This collaboration has resulted in a versatile tool that can handle diverse data formats and provide contextually accurate information. Check out the release video!

Verba 1.0 employs a variety of models, including Ollama’s Llama3, HuggingFace’s MiniLMEmbedder, Cohere’s Command R+, Google’s Gemini, and OpenAI’s GPT-4. These models support embedding and generation, allowing Verba to process various data types, such as PDFs and CSVs. The tool’s customizable approach enables users to select the most suitable models and techniques for their specific use cases. For instance, Ollama’s Llama3 provides robust local embedding and generation capabilities, while HuggingFace’s MiniLMEmbedder offers efficient local embedding models. Cohere’s Command R+ enhances embedding and generation, and Google’s Gemini and OpenAI’s GPT-4 further expand Verba’s capabilities.

Verba 1.0 has demonstrated significant improvements in information retrieval and response generation. Its hybrid search and semantic caching features enable faster and more accurate data retrieval. For example, Verba’s hybrid search combines semantic search with keyword search, saving and retrieving results based on semantic meaning. This approach has enhanced query precision and the ability to handle diverse data formats, making Verba a versatile solution for numerous applications. The tool’s ability to suggest autocompletion and apply filters before performing RAG has further improved its performance.

Notable results from Verba 1.0 include the successful handling of complex queries and the efficient retrieval of relevant information. The tool’s semantic caching and hybrid search capabilities have significantly enhanced performance. Verba’s support for various data formats, including PDFs, CSVs, and unstructured data, has made it a valuable asset for diverse applications. The tool’s performance metrics indicate substantial improvements in query precision and response accuracy, highlighting its potential to transform AI applications.

In conclusion, Verba 1.0 addresses the challenges of precise information retrieval and context-aware response generation by integrating advanced RAG techniques and supporting multiple data formats. The tool’s ability to combine retrieval and generative capabilities has enhanced query precision and efficiently handled diverse data formats. Verba 1.0’s innovative approach and robust performance make it a valuable addition to the AI toolkit, promising to improve the quality and relevance of generated responses across various applications.


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Sony Bans Use Of Copyright Rights For Artificial Intelligence Development – VOI English

This post was originally published on this site JAKARTA – On May 16, a letter from Sony spread to more than 700 companies warning that Sony was banning the use of its music for training, development, or commercialization of artificial intelligence systems (AI). Sony also rejected any form of text and data mining from its

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TRANSMI: A Machine Learning Framework to Create Baseline Models Adapted for Transliterated Data from Existing Multilingual Pretrained Language Models mPLMs without Any Training

The increasing availability of digital text in diverse languages and scripts presents a significant challenge for natural language processing (NLP). Multilingual pre-trained language models (mPLMs) often struggle to handle transliterated data effectively, leading to performance degradation. Addressing this issue is crucial for improving cross-lingual transfer learning and ensuring accurate NLP applications across various languages and scripts, which is essential for global communication and information processing.

Current methods, including models like XLM-R and Glot500, perform well with text in their original scripts but struggle significantly with transliterated text due to ambiguities and tokenization issues. These limitations degrade their performance in cross-lingual tasks, making them less effective when handling text converted into a common script such as Latin. The inability of these models to accurately interpret transliterations poses a significant barrier to their utility in multilingual settings. 

Researchers from the Center for Information and Language Processing, LMU Munich, and Munich Center for Machine Learning (MCML) introduced TRANSMI, a framework designed to enhance mPLMs for transliterated data without requiring additional training. TRANSMI modifies existing mPLMs using three merge modes—Min-Merge, Average-Merge, and Max-Merge—to incorporate transliterated subwords into their vocabularies, thereby addressing transliteration ambiguities and improving cross-lingual task performance.

TRANSMI integrates new subwords tailored for transliterated data into the mPLMs’ vocabularies, particularly excelling in the Max-Merge mode for high-resource languages. The framework is tested using datasets that include transliterated versions of texts in scripts such as Cyrillic, Arabic, and Devanagari, showing that TRANSMI-modified models outperform their original versions in various tasks like sentence retrieval, text classification, and sequence labeling. This modification ensures that models retain their original capabilities while adapting to the nuances of transliterated text, thus enhancing their overall performance in multilingual NLP applications.

The datasets used to validate TRANSMI span a variety of scripts, providing a comprehensive assessment of its effectiveness. For example, the FURINA model using Max-Merge mode shows significant improvements in sequence labeling tasks, demonstrating TRANSMI’s capability to handle phonetic scripts and mitigate issues arising from transliteration ambiguities. This approach ensures that mPLMs can process a wide range of languages more accurately, enhancing their utility in multilingual contexts.

The results indicate that TRANSMI-modified models achieve higher accuracy compared to their unmodified counterparts. For instance, the FURINA model with Max-Merge mode demonstrates notable performance improvements in sequence labeling tasks across different languages and scripts, showcasing clear gains in key performance metrics. These improvements highlight TRANSMI’s potential as an effective tool for enhancing multilingual NLP models, ensuring better handling of transliterated data and leading to more accurate cross-lingual processing.

In conclusion, TRANSMI addresses the critical challenge of improving mPLMs’ performance on transliterated data by modifying existing models without additional training. This framework enhances mPLMs’ ability to process transliterations, leading to significant improvements in cross-lingual tasks. TRANSMI offers a practical and innovative solution to a complex problem, providing a strong foundation for further advancements in multilingual NLP and improving global communication and information processing.

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CinePile: A Novel Dataset and Benchmark Specifically Designed for Authentic Long-Form Video Understanding

Video understanding is one of the evolving areas of research in artificial intelligence (AI), focusing on enabling machines to comprehend and analyze visual content. Tasks like recognizing objects, understanding human actions, and interpreting events within a video come under this domain. Advancements in this domain find crucial applications in autonomous driving, surveillance, and entertainment industries. Enhancing the ability of AI to process and understand videos, researchers aim to improve the performance and reliability of various technologies that rely on visual data.

The main challenge in video understanding lies in the complexity of interpreting dynamic and multi-faceted visual information. Traditional models need help accurately analyzing temporal aspects, object interactions, and plot progression within scenes. These limitations hinder the development of robust systems capable of comprehensive video comprehension. Addressing this challenge requires innovative approaches that can manage the intricate details and vast amounts of data present in video content, pushing the boundaries of current AI capabilities.

Current methods for video understanding often rely on large multi-modal models that integrate visual and textual information. These models typically use annotated datasets where human-written questions and answers are generated based on specific scenes. However, these approaches are labor-intensive and prone to errors, making them less scalable and unreliable. Existing benchmarks, like MovieQA and TVQA, offer some insights but must cover the full spectrum of video understanding, particularly in handling complex interactions and events within scenes.

Researchers from the University of Maryland and Weizmann Institute of Science have introduced a novel approach called CinePile, which was developed by a team that included members from Gemini and other companies. This method leverages automated question template generation to create a large-scale, long-video understanding benchmark. The system integrates visual and textual data to generate detailed and diverse questions about movie scenes. CinePile aims to bridge the gap between human performance and current AI models by providing a comprehensive dataset that challenges the models’ understanding and reasoning capabilities.

CinePile uses a multi-stage process to curate its dataset. Initially, raw video clips are collected and annotated with scene descriptions. A binary classification model distinguishes between dialogue and visual descriptions. These annotations are then used to generate question templates through a language model, which are applied to the video scenes to create comprehensive question-answer pairs. The process involves shot detection algorithms to pick and annotate important frames using the Gemini Vision API. The concatenated text descriptions produce a visual summary of each scene. This summary then generates long-form questions and answers, focusing on various aspects like character dynamics, plot analysis, thematic exploration, and technical details.

The CinePile benchmark features approximately 300,000 questions in the training set and about 5,000 in the test split. The evaluation of current video-centric models, both open-source and proprietary, showed that even state-of-the-art systems need to catch up to human performance. For example, the models often must adhere more strictly to instructions, producing verbose responses instead of concise answers. The researchers noted that open-source models like Llava 1.5-13B, OtterHD, mPlug-Owl, and MinGPT-4 showed high fidelity in image captioning but struggled with hallucinations and unnecessary text snippets. This highlights the complexity and challenges inherent in video understanding tasks and underscores the need for more sophisticated models and evaluation methods.

In conclusion, the research team addressed a critical gap in video understanding by developing CinePile. This innovative approach enhances the ability to generate diverse and contextually rich questions about videos, paving the way for more advanced and scalable video comprehension models. The work underscores the importance of integrating multi-modal data and automated processes in advancing AI capabilities in video analysis. CinePile sets a new standard for evaluating video-centric AI models by providing a robust benchmark, driving future research and development in this vital field.

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Study including WVU and Marshall analyzes cyber threats to Artificial Intelligence systems – West Virginia MetroNews

This post was originally published on this siteMORGANTOWN, W.Va. — Researchers from West Virginia University, Marshall University, and Florida International University are exploring the cybersecurity needs of artificial intelligence technologies with a $1.75 million grant from the Defense Advanced Research Projects Agency (DARPA). Anurag Srivastava Professor and Chairman of the Statler College of Engineering and

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Predictive Quantum Artificial Intelligence Lab 1950.Ai Launches to Advance AI through Research and Collaboration – Yahoo Finance

This post was originally published on this site The 17th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2022), hosted by and led by Dr. Shahid Masood, convened in Salamanca, Spain. Over 200 global experts gathered to discuss AI advancements, ethics, and applications in healthcare, finance, and autonomous systems, solidifying Salamanca’s status as a

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