StatisticMachines @StatisticMach

"In programming, if someone tells you "you’re overcomplicating this," they’re either 10 steps behind you or 10 steps ahead of you." — Andrew Clark (credit: @CodeWisdom)

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Nordic AI Institute @nordicinst

2 years ago

New York Times @nytimes: OpenAI stopped 5 campaigns that used its tech for 'deceptive influence' - Cointelegraph. #industry40 #aiact #aistrategy cointelegraph.com/news/openai-di…

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Researchers at Stanford Propose SleepFM: A New Multi-Modal Foundation Model for Sleep Analysis Researchers from Stanford University and the Technical University of Denmark introduced SleepFM, a groundbreaking multi-modal foundation model for sleep analysis. This model leverages a vast dataset of multi-modal sleep recordings from over 14,000 participants, totaling more than 100,000 hours of sleep data collected between 1999 and 2020 at the Stanford Sleep Clinic. SleepFM utilizes a contrastive learning approach to integrate brain activity, ECG, and respiratory signals. This integration enables the model to capture comprehensive physiological representations, significantly enhancing the accuracy of sleep analysis. SleepFM employs three 1D convolutional neural networks (CNNs) to generate embeddings from each modality (BAS, ECG, and respiratory signals). The architecture of these models is based on a 1D CNN developed for classifying ECG measurements. Each CNN is tailored to handle the specific characteristics of its respective modality: 10 channels for BAS, 2 for ECG, and 7 for respiratory channels. A novel leave-one-out contrastive learning technique is introduced, significantly outperforming the standard pairwise contrastive learning in capturing the synergy between different physiological signals. Full read: marktechpost.com/2024/05/31/res… Paper: arxiv.org/abs/2405.17766 GitHub: github.com/rthapa84/sleep… @Stanford @james_y_zou

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Stanford HAI @StanfordHAI

2 years ago

A new study reveals that even bespoke legal AI tools that promise “hallucination-free” citations still hallucinate an alarming amount of the time. The findings call for a need for rigorous and transparent benchmarking of AI tools in law. stanford.io/3wO4ALo

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Multiplatform.AI @MultiplatformAI

2 years ago

Improving Autonomous Learning Through Automated Data Management: An Innovative Hierarchical K-Means Approach #AI #AItechnology #artificialintelligence #llm #machinelearning #selfsupervisedlearning #SSL multiplatform.ai/improving-auto…

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Marktechpost AI @Marktechpost

2 years ago

Free AI Webinar from our sponsors: 'Using Open-Source CopilotKit for Personalized Banking Applications' Time: June 3, 2024 | 10:00am - 11:00am PDT Register here: pxl.to/89p66jj You’ll learn: ✅ The fundamentals of AI-powered personalized banking applications. ✅How CopilotKit can be leveraged to improve service accuracy and personalization. ✅ Strategies for implementing AI to enhance customer experiences in banking. ✅ Insights into the impact of personalized services on customer loyalty and engagement. ✅ A live demonstration of creating a personalized banking experience using AI technologies. #ArtificialIntelligence

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Marktechpost AI @Marktechpost

2 years ago

MAP-Neo: A Fully Open-Source and Transparent Bilingual LLM Suite that Achieves Superior Performance to Close the Gap with Closed-Source Models Researchers from M-A-P, University of Waterloo, Wuhan AI Research, and 01.AI have released MAP-Neo, a highly capable and transparent bilingual language model with 7 billion parameters, trained on 4.5 trillion high-quality tokens. This model, fully open-sourced, matches the performance of leading closed-source LLMs. The release includes the cleaned pre-training corpus, data cleaning pipeline, checkpoints, and an optimized training and evaluation framework. The comprehensive documentation covers data curation, model architecture, training processes, evaluation codes, and insights into building LLMs, aiming to support and inspire the global research community, especially in non-English regions. The advancement of open-source LLMs is crucial for AI research and applications. Recent efforts focus on enhancing both performance and transparency. MAP-Neo-7B stands out by integrating intermediate checkpoints, a comprehensive data cleaning process, accessible pre-training corpus, and reproduction code, unlike Mistral, LLaMA3, Pythia, Amber, and OLMo models. MAP-Neo-7B excels in benchmarks for Chinese and English understanding (C-EVAL, MMLU), mathematical ability (GSM8K), and coding (HumanEval). It achieves high scores across all tests and sets a new standard for transparency and performance, promoting trustworthiness and collaboration in the research community. Full read: marktechpost.com/2024/05/31/map… Paper: arxiv.org/abs/2405.19327 GitHub: github.com/multimodal-art… Project Page: map-neo.github.io #ArtificialIntelligence #ai @01AI_Yi