Of Algorithms and Minds: Navigating the AI-Human Partnership #15 Exploring The Dynamic Synergy Between Artificial Intelligence And Humans

Hey, in this issue: a novel approach to non-invasive blood glucose monitoring using vocal biomarkers and machine learning; a discussion on the significant impact of artificial intelligence (AI) on personalized healthcare; OpenResearcher, an innovative AI-powered platform designed to accelerate scientific research; NudgeRank, an innovative digital algorithmic nudging system designed to promote positive health behaviors at a population scale and more…

RESEARCH ARTICLES

In this issue

1)????? Moving Healthcare AI-Support Systems for Visually Detectable Diseases onto Constrained Devices (arxiv.org)

2)????? Hearing Your Blood Sugar: Non-Invasive Glucose Measurement Through Simple Vocal Signals, Transforming any Speech into a Sensor with Machine Learning (arxiv.org)

3)????? Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models (arxiv.org)

4)????? Innovation and challenges of artificial intelligence technology in personalized healthcare | Scientific Reports (nature.com)

5)????? OpenResearcher: Unleashing AI for Accelerated Scientific Research (arxiv.org) / OpenResearcher: An Open-Source Project that Harnesses AI to Accelerate Scientific Research - MarkTechPost

6)????? Exploring the molecular mechanisms and shared potential drugs between rheumatoid arthritis and arthrofibrosis based on large language model and synovial microenvironment analysis | Scientific Reports (nature.com)

7)????? Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter | Scientific Reports (nature.com)

8)????? A bioactivity foundation model using pairwise meta-learning | Nature Machine Intelligence

9)????? PATopics: An automatic framework to extract useful information from pharmaceutical patents documents (arxiv.org)

10)?? Adaptive Behavioral AI: Reinforcement Learning to Enhance Pharmacy Services (arxiv.org)

11)?? Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings (arxiv.org)

12)?? MLtoGAI: Semantic Web based with Machine Learning for Enhanced Disease Prediction and Personalized Recommendations using Generative AI (arxiv.org)

13)?? NudgeRank: Digital Algorithmic Nudging for Personalized Health (arxiv.org)

14)?? Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method (arxiv.org)

15)?? Oral squamous cell detection using deep learning (arxiv.org)

16)?? MoRA: LoRA Guided Multi-Modal Disease Diagnosis with Missing Modality (arxiv.org)

17)?? Exploring Latent Space for Generating Peptide Analogs Using Protein Language Models (arxiv.org)

18)?? VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs (arxiv.org) / VideoLLaMA 2 Released: A Set of Video Large Language Models Designed to Advance Multimodal Research in the Arena of Video-Language Modeling - MarkTechPost

19)?? Med42-v2: A Suite of Clinical LLMs (arxiv.org) / Med42-v2 Released: A Groundbreaking Suite of Clinical Large Language Models Built on Llama3 Architecture, Achieving Up to 94.5% Accuracy on Medical Benchmarks - MarkTechPost

20)?? DaCapo: a modular deep learning framework for scalable 3D image segmentation (arxiv.org) / DaCapo: An Open-Sourced Deep Learning Framework to Expedite the Training of Existing Machine Learning Approaches on Large and Near-Isotropic Image Data - MarkTechPost

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·??????? Moving Healthcare AI-Support Systems for Visually Detectable Diseases onto Constrained Devices (arxiv.org) - This article discusses the development of a healthcare AI-support system for visually detectable diseases (VDDs) using tinyML on constrained devices. The authors aim to address the issue of limited internet connectivity in remote areas by deploying an image classification model on a Raspberry Pi 3 Model B. They used the HAM10000 dataset, containing 10,000 images of skin lesions, to train a Convolutional Neural Network (CNN) based on the MobileNet-V2 architecture. The system achieved a test accuracy of 78% and a test loss of 1.08. The authors compared their results with existing models in the literature, finding their accuracy higher than some but lower than others. They attribute these differences to factors such as dataset size and algorithm fine-tuning. The article also explores ethical considerations in healthcare AI, including data privacy, the impact of AI-generated diagnoses, and the importance of medical verification and legal approval. The authors emphasize the need for diversity in both AI development teams and training data to mitigate potential biases. The researchers conclude by suggesting future work, including comparing a wider range of algorithms, investigating model compression techniques, and considering more powerful hardware options. They propose that their system could pave the way for low-spec devices to be used for real-time detection of VDDs in remote and low-connectivity areas.

·??????? Hearing Your Blood Sugar: Non-Invasive Glucose Measurement Through Simple Vocal Signals, Transforming any Speech into a Sensor with Machine Learning (arxiv.org) - This article presents a novel approach to non-invasive blood glucose monitoring using vocal biomarkers and machine learning. The researchers developed a logistic regression model that analyzes voice recordings to classify blood glucose levels as high or low, based on a 100 mg/dL threshold. The study collected voice samples and corresponding blood glucose measurements from 49 participants, including 6 with type-1 diabetes. The researchers extracted 596 vocal features using the Disvoice library, which were then reduced to 124 features based on correlation with the target variable. Principal Component Analysis (PCA) was applied to further reduce dimensionality, resulting in 8 principal components used as inputs for the model. The logistic regression model achieved high accuracy in predicting blood glucose levels, with 87.1% accuracy on the training set and 85.7% on the test set. Leave-one-out cross-validation was used to assess the model's generalization ability, yielding 86.5% accuracy on the training set and 84.4% on the test set. The study's findings suggest that changes in voice characteristics, particularly jitter (variations in vocal fold vibrations), correlate significantly with blood glucose levels. This approach offers a potentially more convenient and cost-effective alternative to traditional glucose monitoring methods. The researchers acknowledge limitations such as the small sample size and potential confounding factors like stress or vocal strain. They recommend future studies with larger, more diverse populations and longitudinal designs to validate and refine the model. Overall, this research demonstrates the promising potential of using vocal biomarkers and AI for non-invasive glucose monitoring in diabetes management.

·??????? Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models (arxiv.org) - This study examines the relationship between pre-training and fine-tuning in large language models by analyzing multiple intermediate pre-trained checkpoints of the OLMo-1B model. The researchers found that some tasks are learned during pre-training, while others only show improvement after fine-tuning. Continued pre-training can enhance model performance in ways that become apparent only after fine-tuning. Tasks already mastered during pre-training benefit less from fine-tuning compared to those not previously learned. The study also reveals that fine-tuning can cause models to forget previously acquired knowledge or capabilities for tasks not included in the fine-tuning process. Additionally, fine-tuned models exhibit sensitivity to evaluation prompts, which can be mitigated through extended pre-training. The findings suggest that early stopping in pre-training may not negatively impact downstream fine-tuning performance, and that fine-tuning could potentially offer greater benefits than continued pre-training. The researchers also explored what models learn and forget during fine-tuning, considering factors such as task format, task transfer, and domain knowledge. The paper concludes by discussing the implications of these insights for model training strategies and advocates for the release of pre-training checkpoints to facilitate future research in this area.

·??????? Innovation and challenges of artificial intelligence technology in personalized healthcare | Scientific Reports (nature.com) - This article discusses the significant impact of artificial intelligence (AI) on personalized healthcare. It explores various AI applications, including virtual assistant chatbots, wearable devices for patient monitoring, predictive models for disease progression, personalized treatment recommendations, and automated appointment scheduling. The authors highlight the potential benefits of AI in improving patient care, enhancing diagnostic accuracy, and streamlining healthcare processes. However, the article also addresses several challenges and concerns associated with AI implementation in healthcare. These include data security and privacy issues, potential biases in AI algorithms, regulatory hurdles, and the need for patient acceptance. The authors emphasize the importance of developing robust regulatory frameworks, ensuring data protection, and addressing ethical considerations in AI deployment. The paper recommends future research directions, emphasizing the need for collaboration between researchers and clinicians to develop AI technologies that are both innovative and clinically relevant. It also stresses the importance of establishing comprehensive policies to govern the responsible and ethical use of AI in healthcare. Overall, the article presents a balanced view of AI's transformative potential in healthcare while acknowledging the complexities and challenges that must be addressed to ensure its successful and ethical integration into medical practice.

·??????? OpenResearcher: Unleashing AI for Accelerated Scientific Research (arxiv.org) / OpenResearcher: An Open-Source Project that Harnesses AI to Accelerate Scientific Research - MarkTechPost - This article introduces OpenResearcher, an innovative AI-powered platform designed to accelerate scientific research. OpenResearcher uses Retrieval-Augmented Generation (RAG) to combine Large Language Models (LLMs) with up-to-date, domain-specific knowledge from scientific literature and the internet. The system features various tools for query understanding, information retrieval, content filtering, answer generation, and refinement. It can flexibly use these tools to create customized workflows for different types of queries. Unlike passive AI assistants, OpenResearcher actively engages with users to clarify their questions, making it particularly helpful for junior researchers. The article describes OpenResearcher's architecture, including its data routing strategy for efficient ?retrieval, and its ability to provide citations for generated content. The system was evaluated against other industry applications using both human preferences and GPT-4 judgments, demonstrating superior performance in terms of information correctness, relevance, and richness. The authors conducted experiments using a dataset of 109 research questions across various scientific domains. Results showed that OpenResearcher outperformed other applications, including Perplexity AI, iAsk, You.com, and Phind, as well as a naive RAG baseline. Overall, OpenResearcher aims to streamline the research process by providing accurate, comprehensive answers to a wide range of scientific queries, potentially saving researchers significant time and effort in literature review and knowledge acquisition.

·??????? Exploring the molecular mechanisms and shared potential drugs between rheumatoid arthritis and arthrofibrosis based on large language model and synovial microenvironment analysis | Scientific Reports (nature.com) - This article explores the molecular mechanisms and potential shared drug treatments between rheumatoid arthritis (RA) and arthrofibrosis (AF) using large language models and synovial microenvironment analysis. The study found high gene and functional similarity between RA and AF among 12 joint diseases examined. Key pathogenic cell types, including specific fibroblast and macrophage subtypes, were identified as being increased in both conditions. The researchers developed a novel drug repositioning method based on differentially expressed genes in these pathogenic cells. This approach identified several potential drugs for repurposing in RA and AF treatment, with the results correlating well with supporting literature. The study provides new insights into the shared pathogenesis of RA and AF, highlights the importance of fibroblast-macrophage interactions in these conditions, and suggests new avenues for drug development. However, the authors acknowledge limitations such as the need for experimental validation of their computational findings.

·??????? Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter | Scientific Reports (nature.com) - This study investigates the impact of COVID-19 on mental health, specifically post-traumatic stress disorder (PTSD), using Twitter data. The researchers collected over 3.96 million tweets from users who reported being COVID-19 positive between March 2020 and November 2021. They developed a machine learning model to classify tweets as PTSD-positive or PTSD-negative based on ICD-11 guidelines. The researchers used various machine learning classifiers, including Support Vector Machine (SVM), Na?ve Bayes, K-Nearest Neighbor, and Random Forest. They experimented with different feature selection strategies, finding that unigrams and their combinations with other n-grams yielded the best results. The SVM classifier achieved the highest accuracy of 83.29% using unigrams as features. The study revealed that posts related to "Other Affective & Biological Symptoms related to PTSD" were more prevalent than other categories. The researchers also observed shifts in user behavior between "Avoidance" and "Non-PTSD Related" categories over time. This research contributes to understanding the mental health implications of the COVID-19 pandemic and demonstrates the potential of social media data and machine learning techniques in identifying PTSD symptoms. The authors acknowledge limitations, such as the focus on English-language tweets, and suggest future work to expand the dataset and analyze sentiments in replies to PTSD-positive posts.

·??????? A bioactivity foundation model using pairwise meta-learning | Nature Machine Intelligence - This article introduces ActFound, a novel bioactivity foundation model for drug discovery and development. ActFound uses pairwise meta-learning to predict compound bioactivity across diverse assay types, addressing challenges like limited labeled data and incompatible measurements between assays. The model was trained on over 1.6 million bioactivity datapoints from ChEMBL and BindingDB. ActFound demonstrates superior performance in both in-domain and cross-domain bioactivity prediction compared to existing methods. It shows strong generalization across assay types and molecular scaffolds. The authors also applied ActFound to free energy perturbation (FEP) benchmarks, where it achieved comparable or better performance than the resource-intensive FEP+(OPLS4) tool using only a fraction of the data for fine-tuning. Additionally, ActFound was evaluated on cancer drug response prediction, exhibiting promising results in a zero-shot setting. The model's effectiveness stems from its ability to learn relative bioactivity differences between compound pairs within assays, circumventing issues of incompatibility across assays. Overall, ActFound represents a significant advancement in bioactivity prediction, offering a versatile foundation model that can be applied to various aspects of drug discovery and development. Its ability to generalize across domains and perform well with limited data makes it a potentially valuable tool for accelerating drug development processes.

·??????? PATopics: An automatic framework to extract useful information from pharmaceutical patents documents (arxiv.org) - This article introduces PATopics, a framework designed to automatically extract and summarize information from pharmaceutical patent documents. The framework uses topic modeling and natural language processing techniques to group patents into thematic topics, correlate them with inventors, companies, and molecules, and present the information through an intuitive web interface. The authors instantiated PATopics using 4,832 pharmaceutical patents covering 809 molecules from 478 companies. They demonstrate the framework's utility for different user profiles, including researchers, chemists, and companies interested in patent information. Four case studies illustrate how PATopics can be used to analyze topics, companies, molecules, and compare patents. Key features of PATopics include its ability to centralize patent data from multiple sources, group related patents automatically, and provide quick insights into patent trends, key players, and emerging technologies in the pharmaceutical industry. The framework aims to streamline the often time-consuming process of patent analysis and make patent information more accessible. The article discusses the technical details of the framework, including its use of advanced text representation methods and topic modeling algorithms. It also outlines potential future work to further enhance the system's capabilities. Overall, PATopics is presented as a valuable tool for managing and analyzing the complex landscape of pharmaceutical.

·??????? Adaptive Behavioral AI: Reinforcement Learning to Enhance Pharmacy Services (arxiv.org) - This article introduces an adaptive behavioral AI system that uses reinforcement learning to enhance pharmacy services, particularly in low- and middle-income countries. The system is integrated into SwipeRx, a mobile app for pharmacists in Southeast Asia, to deliver personalized interventions and product recommendations. The authors describe their methodology, which includes contextual bandits for sequential decision-making and a rule-based algorithm for item pair recommendations. They conducted two experiments to test the effectiveness of these recommendations in increasing basket size and discovering new products. The results indicate a small but consistent positive impact on pharmacy expenditure across both experiments. The system showed potential in helping pharmacists discover new products, with successful recommendations leading to purchases in over 20% of cases. The authors also observed regional effects and differences based on users' typical purchasing frequencies. Overall, this adaptive AI approach demonstrates promise in supporting and improving pharmacy services by providing personalized nudges and recommendations through digital tools. The framework has broader potential applications in optimizing healthcare delivery beyond pharmacy operations.

·??????? Optimizing HIV Patient Engagement with Reinforcement Learning in Resource-Limited Settings (arxiv.org) - This article describes the development of CHARM (Community Health Access & Resource Management), an AI-powered mobile app designed to support community health workers (CHWs) in resource-limited settings, with a focus on HIV care. Created through a partnership between Causal Foundry and mothers2mothers, CHARM aims to enhance healthcare delivery by streamlining case management, improving communication, and providing personalized interventions. The app integrates artificial intelligence, particularly reinforcement learning techniques like contextual and restless bandits, to optimize patient engagement and CHW efficiency. Key features include biometric login, patient and visit management, risk assessments, and a referral system. The platform also incorporates predictive modeling to identify high-risk patients and adaptive interventions to tailor healthcare strategies. While the article focuses on HIV care, the CHARM system could be readily adapted to address other diseases and public health challenges. The core technologies and methodologies described - such as personalized risk assessments, adaptive interventions, and resource optimization - are broadly applicable across various health domains. For instance, the app's risk assessment capabilities could be tailored to identify early signs of chronic diseases like diabetes or cardiovascular conditions. The referral system could be adapted to manage complex care pathways for cancer patients or those with multiple comorbidities. The adaptive intervention strategies could be applied to improve medication adherence for tuberculosis patients or to support behavioral changes in obesity management. The resource allocation algorithms could help optimize community health worker deployment for maternal and child health programs or vaccination campaigns. Additionally, the predictive modeling techniques could be used to forecast disease outbreaks or identify populations at risk for mental health issues. By leveraging AI and mobile technology, the CHARM approach offers a scalable and adaptable framework for improving healthcare delivery and patient outcomes across a wide range of diseases and public health challenges in resource-limited settings.

·??????? MLtoGAI: Semantic Web based with Machine Learning for Enhanced Disease Prediction and Personalized Recommendations using Generative AI (arxiv.org) - This article presents MLtoGAI, an innovative approach to disease prediction and personalized healthcare recommendations that integrates Semantic Web technology, Machine Learning (ML), and Generative AI. The system comprises three key components: a reusable disease ontology, a diagnostic classification model, and an integration of Semantic Web Rule Language (SWRL) with ChatGPT for generating clear, personalized health advice. The researchers developed a comprehensive dataset of diseases and symptoms, which was used to create an ontology and train ML models. They implemented SWRL rules to enhance the reasoning capabilities of the ontology and improve disease classification accuracy. The system processes user-input symptoms, matches them using the Jaccard coefficient, and predicts potential diseases using ML algorithms, with Logistic Regression showing the best performance. The authors validated their approach using synthetic patient data and compared it with existing methods, demonstrating improved accuracy and interpretability. They also evaluated the ontology using various metrics such as Attribute Richness and Inheritance Richness. The integration of ChatGPT allows for personalized and user-friendly explanations of the predictions and recommendations. The researchers suggest that this combined approach of ontology-based knowledge representation, rule-based reasoning, machine learning, and natural language processing can significantly enhance disease prediction and decision-making in healthcare. The paper concludes by outlining future work, including expanding the dataset, developing a more user-friendly interface, incorporating real-time data from electronic health records, and validating the model in clinical settings. Overall, MLtoGAI shows promise in improving the accuracy, transparency, and accessibility of disease prediction and healthcare decision support systems.

·??????? NudgeRank: Digital Algorithmic Nudging for Personalized Health (arxiv.org) - This article introduces NudgeRank, an innovative digital algorithmic nudging system designed to promote positive health behaviors at a population scale. Developed by researchers at CueZen, Inc., NudgeRank uses a combination of Graph Neural Networks and an extensible Knowledge Graph to deliver personalized, context-aware health nudges to over 1.1 million users daily. The system was deployed in collaboration with Singapore's Health Promotion Board and evaluated over a 12-week period. Results showed statistically significant improvements in health outcomes, including a 6.17% increase in daily steps and 7.61% more exercise minutes for users receiving nudges compared to a control group. The system also demonstrated high user engagement, with a 13.1% nudge open rate compared to baseline systems' 4%. NudgeRank addresses key challenges in behavior change and technical implementation. It provides personalized nudges based on user preferences and context, handles cold start problems for new users and nudges, and incorporates business rules and constraints to maintain a positive user experience. The system is designed for scalability, performance, and reliability, capable of generating nudges for over 1 million users daily while maintaining efficiency on commodity compute resources. The researchers discuss potential future improvements, including the use of Reinforcement Learning for goal-directed recommendations and the integration of Large Language Models to enrich the Knowledge Graph. They emphasize that this work represents a first step in improving population-level health behaviors through personalization, with the potential for broader applications in various health domains.

·??????? Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method (arxiv.org) - This article presents a novel approach to predicting startup success in venture capital using GraphRAG technology and multivariate time series analysis. It addresses limitations in existing predictive models by incorporating inter-company relationships like competition and collaboration. The study uses large-scale Chinese financial and news datasets to build a comprehensive knowledge graph of startup ecosystems, which is then integrated into a sequence-to-sequence LSTM model. This method outperforms traditional deep learning approaches in predicting startup success. For someone starting an AI and healthcare startup, this article offers valuable insights. It emphasizes the importance of considering the broader ecosystem and relationships between companies when evaluating startup potential, which is crucial in the complex AI and healthcare space. The use of diverse data sources, including unstructured text data, demonstrates the value of leveraging multiple information streams to gain a comprehensive understanding of a startup's position and potential. The focus on predicting long-term performance rather than just binary outcomes suggests that startups should prioritize sustained value creation. The methodology's ability to handle sparse data conditions could be particularly relevant in healthcare AI, where data might be limited due to privacy concerns. While the specific technical implementation might not be directly applicable, the general approach of combining structured financial data with unstructured information about industry relationships could inform strategy development and investor pitches. The emphasis on capturing complex, non-linear relationships in startup ecosystems aligns well with the intricate nature of the healthcare and AI industries. Overall, this research underscores the importance of a holistic, data-driven approach to evaluating and positioning startups. For entrepreneurs in AI and healthcare, it highlights the need to consider industry dynamics, potential collaborations, and long-term value creation when developing their business strategies and communicating with investors. The article's insights could help startups in these sectors navigate their complex and rapidly evolving industries more effectively.

·??????? Oral squamous cell detection using deep learning (arxiv.org) - The article discusses the use of deep learning techniques for detecting oral squamous cell carcinoma (OSCC), a common type of oral cancer. The authors propose using convolutional neural networks (CNNs), particularly the EfficientNetB3 model, for image classification of OSCC. They compared several CNN architectures including ResNet101, VGG-16, DenseNet121, DenseNet201, and EfficientNetB3. The EfficientNetB3 model performed best, achieving 98.33% accuracy, 97.82% precision, and 97.82% recall. The study utilized a dataset of histopathological images from the Oral Cancer Database. The authors emphasize the potential of deep learning to improve early detection and diagnosis of OSCC, which could lead to better patient outcomes. This approach could be applied to other types of cancer detection and diagnosis. The deep learning techniques described could be adapted for analyzing medical images of other cancer types, such as lung, breast, or skin cancer. CNNs could be trained on histopathological images or radiological scans from various cancer types to assist in early detection and classification. The methods for data preprocessing, augmentation, and model evaluation could be applied to develop AI-assisted diagnostic tools for different cancers. Transfer learning techniques could allow models trained on one type of cancer to be fine-tuned for detecting others, potentially improving efficiency in developing new cancer detection systems. The approach of comparing multiple CNN architectures could be replicated to find optimal models for other cancer types.

·??????? MoRA: LoRA Guided Multi-Modal Disease Diagnosis with Missing Modality (arxiv.org) - This article introduces MoRA (Modality-aware Low-Rank Adaptation), a method for improving multi-modal disease diagnosis when facing missing modalities. The authors address challenges in applying pre-trained multi-modal models to medical diagnosis, including performance degradation with missing modalities and computational costs of fine-tuning. MoRA builds on Low-Rank Adaptation (LoRA) by projecting inputs to a low-dimensional space and using modality-specific up-projections for adaptation. This approach aims to enhance robustness to missing modalities while requiring minimal computational resources. MoRA is implemented in the first block of a pre-trained model, using only 1.6% of the total parameters for fine-tuning. The method is evaluated on two medical datasets: Chest X-rays (CXR) and Ocular Disease Intelligent Recognition (ODIR). Experiments compare MoRA to existing techniques, demonstrating superior performance and robustness across various modality-missing scenarios. The authors also conduct ablation studies to analyze MoRA's sensitivity to different factors like missing rates, plugged blocks, and rank. Results show that MoRA outperforms baseline methods, particularly in extreme modal missing situations. It achieves better performance while requiring less GPU memory and training time. The study concludes that MoRA improves both robustness and performance in multi-modal disease diagnosis while saving computational resources. Future work aims to extend the method to larger pre-trained models and further explore its application in disease diagnosis.

·??????? Exploring Latent Space for Generating Peptide Analogs Using Protein Language Models (arxiv.org) - This article presents a novel method for generating peptide analogs using protein language models. The authors propose exploring the latent space of protein embeddings to create new peptides with desired properties, requiring only a single input sequence. This approach addresses limitations of traditional methods that often need large datasets or structural information. The method utilizes autoencoder-shaped models, specifically ProtT5 and ESM-2, to project peptide sequences into a continuous latent space. By introducing controlled noise to the embeddings and then decoding them back to sequences, the approach generates peptide analogs with similar properties to the original. The researchers evaluated their method against baseline models using three similarity metrics: Morgan Fingerprints, RDKit Descriptors, and QSAR Descriptors. Results showed that the proposed method significantly outperformed baselines across all metrics, with ProtT5 excelling in RDKit descriptor similarity and ESM-2 in Morgan fingerprint and QSAR similarities. To further validate their approach, the authors conducted molecular dynamics simulations on TIGIT inhibitor peptides identified through wet lab experiments. The simulations demonstrated that the generated peptide analogs exhibited behavior similar to the original peptides, with some generated sequences showing potential improvements in binding affinity. The study concludes that this method can accelerate peptide screening processes by efficiently generating analogs with desired properties using minimal input data. The authors suggest future work should focus on testing the method in actual wet lab experiments to further validate its effectiveness in real-world applications.

·??????? VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs (arxiv.org) / VideoLLaMA 2 Released: A Set of Video Large Language Models Designed to Advance Multimodal Research in the Arena of Video-Language Modeling - MarkTechPost - VideoLLaMA 2 is a new set of advanced video large language models (Video-LLMs) developed by researchers at DAMO Academy, Alibaba Group. These models are designed to improve spatial-temporal modeling and audio understanding in video-related tasks. VideoLLaMA 2 builds upon previous models by incorporating a custom Spatial-Temporal Convolution (STC) connector to better handle video dynamics and an integrated Audio Branch for enhanced multimodal understanding. The model excels at tasks like video question answering and captioning, outperforming many open-source models and rivaling some proprietary ones. The model's architecture includes separate Vision-Language and Audio-Language branches that connect pre-trained visual and audio encoders to a large language model. This modular design ensures effective visual and auditory data integration, enhancing VideoLLaMA 2's multimodal capabilities. Evaluations show that VideoLLaMA 2 consistently performs well across various benchmarks for video and audio understanding tasks. It demonstrates strong performance in multiple-choice video question answering, open-ended video question answering, video captioning, and audio-visual question answering. While the article does not explicitly discuss medical implications, advanced multimodal AI models like VideoLLaMA 2 could potentially have applications in healthcare. These may include analyzing medical imaging videos, assisting in diagnostics, or improving patient care through better understanding of audiovisual clinical data. However, further research and development would be needed to adapt such models for specific medical use cases.

·??????? Med42-v2: A Suite of Clinical LLMs (arxiv.org) / Med42-v2 Released: A Groundbreaking Suite of Clinical Large Language Models Built on Llama3 Architecture, Achieving Up to 94.5% Accuracy on Medical Benchmarks - MarkTechPost - The article introduces Med42-v2, a suite of clinical large language models (LLMs) built on the Llama3 architecture. These models are specifically designed for healthcare applications, addressing limitations of generic LLMs in medical settings. Med42-v2 underwent a two-stage training process: clinical fine-tuning using specialized medical datasets, followed by preference alignment to ensure outputs meet user expectations. The models demonstrate superior performance compared to original Llama3 models and even outperform GPT-4 on various medical benchmarks. For instance, the 70B parameter version of Med42-v2 achieved up to 94.5% accuracy on some tasks. The article highlights the importance of domain-specific models in healthcare, where precision and reliability are crucial. While Med42-v2 shows promise in handling clinical queries and supporting medical decision-making, the authors acknowledge potential limitations such as hallucinations, biases, and ethical concerns. They emphasize the need for further evaluation in real-world clinical settings and plan to develop a new framework to assess the models' clinical utility, safety, and reasoning capabilities. The research team aims to address the challenges of applying AI in healthcare by creating models that can understand complex medical terminology, reason through clinical scenarios, and provide accurate, context-appropriate responses. The Med42-v2 suite represents a significant step towards more effective and reliable AI assistance in medical applications.

·??????? DaCapo: a modular deep learning framework for scalable 3D image segmentation (arxiv.org) / DaCapo: An Open-Sourced Deep Learning Framework to Expedite the Training of Existing Machine Learning Approaches on Large and Near-Isotropic Image Data - MarkTechPost - The article introduces DaCapo, an open-source deep learning framework designed for large-scale 3D image segmentation, particularly for near-isotropic datasets such as those from FIB-SEM imaging. DaCapo addresses challenges in scaling existing machine learning approaches to handle terabyte-sized datasets. It offers a modular structure with customizable components for various segmentation tasks, network architectures, and compute infrastructures. Key features of DaCapo include efficient experiment management, blockwise distributed deployment, and scalability across local, cluster, or cloud environments. The framework supports both semantic and instance segmentation, various neural network architectures, and data augmentation techniques. It manages the entire process from training to model deployment, including validation and parameter optimization. DaCapo's flexibility allows users to easily switch between different segmentation approaches and prediction targets. It includes pre-built model architectures and the ability to use pretrained models. The framework employs blockwise inference and post-processing to handle large datasets efficiently, and offers customizable compute contexts for different infrastructures. The article concludes by highlighting DaCapo's value for researchers working with large volume image data, emphasizing its customizability and scalability. The authors invite the community to contribute to the ongoing development of the framework and mention plans for future enhancements, including improved user interface and expanded model repositories.

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AI TOOLS

·??????? Announcing Spanner Graph | Google Cloud Blog - The article announces the launch of Spanner Graph, a new offering from Google Cloud that combines graph database capabilities with Google's Spanner database. Spanner Graph aims to address common challenges in adopting standalone graph databases, such as data fragmentation, scalability issues, and ecosystem friction. It offers a unified database that integrates graph, relational, search, and AI capabilities with virtually unlimited scalability. Key features of Spanner Graph include native graph experience using ISO Graph Query Language (GQL), unified relational and graph models, built-in search capabilities, industry-leading scalability and consistency, and AI-powered insights through integration with Vertex AI. The article highlights how Spanner Graph bridges the gap between relational and graph worlds, allowing users to leverage both SQL and GQL within a single database. The article provides code examples demonstrating how Spanner Graph can be used for tasks like product recommendations and price history analysis. It also explains how tables can be mapped to graphs without data migration, offering flexibility in data modeling and querying. Spanner Graph's integration with vector search and full-text search is emphasized, allowing users to find graph contents based on semantic meaning or specific keywords. The article also mentions Spanner Graph's scalability, consistency, and availability inherited from Spanner. Finally, the article outlines various use cases for Spanner Graph, including product recommendations, financial fraud detection, social networks, gaming, network security, and GraphRAG (Retrieval Augmented Generation with knowledge graphs). It concludes by providing resources for users to learn more about and get started with Spanner Graph.

·??????? Imagen 3 (arxiv.org) / Google AI Released the Imagen 3 Technical Paper: Showcasing In-Depth Details - MarkTechPost – The article describes the development and evaluation of Imagen 3, a state-of-the-art text-to-image generation model by Google. The model is a latent diffusion model designed to produce high-quality images based on text prompts, performing particularly well in photorealism and complex prompt adherence. It outperforms previous versions, like Imagen 2, and other competing models, such as DALL·E 3 and Midjourney v6, in several aspects, including prompt-image alignment and numerical reasoning. The article details the data curation, evaluation processes, and responsible deployment strategies, emphasizing safety and ethical considerations in its deployment. Extensive human and automated evaluations confirm Imagen 3's leading position in text-to-image generation, despite ongoing challenges in areas like numerical reasoning and complex scene generation.

IN THE MEDIA

·??????? The Turing Test and our shifting conceptions of intelligence | Science - This article discusses the Turing Test, proposed by Alan Turing in 1950 as a thought experiment to determine if machines can think. The test involves a human judge conversing with both a computer and a human, trying to distinguish which is which. While Turing didn't intend it as a practical measure of artificial intelligence, the test has become culturally iconic as a benchmark for machine intelligence. The article explores recent claims that modern AI chatbots like GPT-4 have passed the Turing Test. However, it notes that there's little agreement on the criteria for passing, and doubt about whether conversational skills truly indicate intelligence or "thinking." Various interpretations and implementations of the test are discussed, from strict three-participant versions to looser two-participant formats. The author argues that the Turing Test may be becoming obsolete as our understanding of intelligence evolves. She cites examples from AI history where tasks once thought to require general intelligence, like playing chess, were achieved through narrower approaches. Recent neuroscience research is also mentioned, suggesting that language fluency may be separate from other cognitive abilities. The article concludes by questioning whether Turing's prediction about machines thinking will come true, or if our conception of "thinking" itself will change as we gain a more nuanced understanding of intelligence. Overall, it presents a thoughtful examination of the Turing Test's relevance in light of advancing AI technology and our evolving comprehension of cognition.

·??????? A.I. Is Helping to Launch New Businesses - The New York Times (nytimes.com) - This article discusses how artificial intelligence (AI) is accelerating the growth and efficiency of small businesses and startups. It highlights examples of entrepreneurs using generative AI tools like ChatGPT and GitHub Copilot to perform various tasks, from marketing and coding to customer recruitment and legal document interpretation. The technology is helping startups launch more quickly and efficiently, potentially leading to faster profitability and scale. The piece cites several case studies, including a class at Carnegie Mellon University where students used AI to develop startups with unprecedented speed, and individual entrepreneurs who leveraged AI to overcome initial business hurdles. While concrete data on AI's impact on startup success is still limited, early indicators suggest that newer and smaller businesses are more likely to experiment with and benefit from these technologies. The article also touches on the broader economic implications, noting that startups are crucial for job growth and innovation. It suggests that AI could transform the startup landscape by allowing entrepreneurs to do more with fewer resources, potentially leading to more stable and successful new businesses. However, it also cautions that AI tools can sometimes produce unreliable information, emphasizing the need for verification.

·??????? California A.I. Bill Is Tweaked - The New York Times (nytimes.com) - This article discusses recent amendments to California's S.B. 1047 bill, which aims to regulate artificial intelligence safety. The bill, authored by Senator Scott Wiener, would require companies to test the safety of powerful AI technologies before public release and allow California's attorney general to sue companies if their technologies cause serious harm. Key changes to the bill include shifting regulatory duties to an existing agency instead of creating a new one, and limiting company liability to cases where real harm or imminent danger occurs. These amendments came after discussions with tech industry stakeholders, including companies like OpenAI, Meta, and Google. While some view the changes as addressing industry concerns, others still worry about potential negative impacts on AI development, particularly for smaller companies and open-source projects. The bill is expected to pass by the end of August and, if signed by Governor Gavin Newsom, would position California ahead of federal regulations on AI safety. The article notes that this bill has sparked debate in the tech industry, with various stakeholders taking different positions on regulating AI technology. It also mentions that some San Francisco mayoral candidates have expressed concerns about the bill's potential impact on the city's reputation as a tech innovation hub.

PODCASTS

·??????? Eye On A.I.: #202 Raphael Townshend: How AI and RNA Tech is Transforming Drug Discovery (Inside Atomic AI) (libsyn.com) - In this episode of the Eye on AI podcast, we explore the cutting-edge intersection of AI and biotechnology with Raphael Townshend, founder and CEO of Atomic AI. Raphael delves into the revolutionary potential of AI in RNA drug discovery, highlighting Atomic AI's innovative approach. He shares his journey from studying electrical engineering and computer science at UC Berkeley to developing advanced AI models for understanding RNA structures, analogous to DeepMind's AlphaFold for proteins. We dive deep into the intricacies of RNA's role in the human genome and its untapped potential in treating diseases previously considered undruggable. Raphael explains how Atomic AI's core model, Atom 1, is designed to predict RNA shapes with unprecedented accuracy, enabling the design of new drugs that target RNA instead of proteins. He discusses the significance of RNA in the context of mRNA vaccines, particularly the COVID-19 vaccine, and the challenges of making these vaccines more stable and accessible. The conversation also covers the technical aspects of using AI, including transformer-based models and in-house data generation, to enhance RNA drug discovery. Raphael shares insights into the company's progress, from cell testing to upcoming animal trials, and the broader implications of integrating AI in biotechnology. Join us as we uncover the future of RNA-based therapies, the innovative use of AI in drug discovery, and the groundbreaking advancements that could transform the landscape of medicine. Don't forget to like, subscribe, and hit the notification bell for more expert insights into the latest AI innovations.

·??????? Reinventing AI Agents with Imbue CEO Kanjun Qiu - Weights & Biases (wandb.ai) - In this episode of Gradient Dissent, Kanjun Qiu, CEO and Co-founder of Imbue, joins host Lukas Biewald to discuss how AI agents are transforming code generation and software development. Discover the potential impact and challenges of creating autonomous AI systems that can write and verify code and and learn about the practical research involved.

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