The success of agentic AI in financial services depends not just on smarter models, but on an authoritative context data store--one that is accessible, reliable, and governed at scale. Financial services companies have unique needs when it comes to business AI. They operate in one of the most highly regulated sectors while responding to external events that are updated by the second. As a result, the success of agentic AI in financial services depends less on the sophistication of the system and more on the quality, security, and accessibility of the data it relies on. "It all starts with the data," says Steve Mayzak, global managing director of Search AI at Elastic. Agentic AI--systems that can independently plan and take actions to complete tasks, rather than simply generate responses--holds enormous potential for financial services due to its ability to incorporate real-time data and optimize complex workflows.

Biological signals, such as electroencephalograms (EEG), play a crucial role in numerous clinical applications, exhibiting diverse data formats and quality profiles. Current deep learning models for biosignals (based on CNN, RNN, and Transformers) are typically specialized for specific datasets and clinical settings, limiting their broader applicability. This paper explores the development of a flexible biosignal encoder architecture that can enable pre-training on multiple datasets and fine-tuned on downstream biosignal tasks with different formats.To overcome the unique challenges associated with biosignals of various formats, such as mismatched channels, variable sample lengths, and prevalent missing values, we propose Biosignal Transformer (BIOT). The proposed BIOT model can enable cross-data learning with mismatched channels, variable lengths, and missing values by tokenizing different biosignals into unified sentences structure. Specifically, we tokenize each channel separately into fixed-length segments containing local signal features and then rearrange the segments to form a long sentence. Channel embeddings and relative position embeddings are added to each segment (viewed as token) to preserve spatio-temporal features.The BIOT model is versatile and applicable to various biosignal learning settings across different datasets, including joint pre-training for larger models. Comprehensive evaluations on EEG, electrocardiogram (ECG), and human activity sensory signals demonstrate that BIOT outperforms robust baselines in common settings and facilitates learning across multiple datasets with different formats. Using CHB-MIT seizure detection task as an example, our vanilla BIOT model shows 3% improvement over baselines in balanced accuracy, and the pre-trained BIOT models (optimized from other data sources) can further bring up to 4% improvements. Our repository is public at https://github.com/ycq091044/BIOT.

The accelerating growth of scientific publications has intensified the need for scalable, trustworthy systems to synthesize knowledge across diverse literature. While recent retrieval-augmented generation (RAG) methods have improved access to scientific information, they often overlook citation graph structure, adapt poorly to complex queries, and yield fragmented, hard-to-verify syntheses. We introduce SciRAG, an open-source framework for scientific literature exploration that addresses these gaps through three key innovations: (1) adaptive retrieval that flexibly alternates between sequential and parallel evidence gathering; (2) citation-aware symbolic reasoning that leverages citation graphs to organize and filter supporting documents; and (3) outline-guided synthesis that plans, critiques, and refines answers to ensure coherence and transparent attribution. Extensive experiments across multiple benchmarks such as QASA and ScholarQA demonstrate that SciRAG outperforms prior systems in factual accuracy and synthesis quality, establishing a new foundation for reliable, large-scale scientific knowledge aggregation.

Biological signals, such as electroencephalograms (EEG), play a crucial role in numerous clinical applications, exhibiting diverse data formats and quality profiles. Current deep learning models for biosignals (based on CNN, RNN, and Transformers) are typically specialized for specific datasets and clinical settings, limiting their broader applicability. This paper explores the development of a flexible biosignal encoder architecture that can enable pre-training on multiple datasets and fine-tuned on downstream biosignal tasks with different formats.To overcome the unique challenges associated with biosignals of various formats, such as mismatched channels, variable sample lengths, and prevalent missing val- ues, we propose Biosignal Transformer (BIOT). The proposed BIOT model can enable cross-data learning with mismatched channels, variable lengths, and missing values by tokenizing different biosignals into unified "sentences" structure. Specifically, we tokenize each channel separately into fixed-length segments containing local signal features and then rearrange the segments to form a long "sentence". Channel embeddings and relative position embeddings are added to each segment (viewed as "token") to preserve spatio-temporal features.The BIOT model is versatile and applicable to various biosignal learning settings across different datasets, including joint pre-training for larger models.

The quality of meeting summaries generated by natural language generation (NLG) systems is hard to measure automatically. Established metrics such as ROUGE and BERTScore have a relatively low correlation with human judgments and fail to capture nuanced errors. Recent studies suggest using large language models (LLMs), which have the benefit of better context understanding and adaption of error definitions without training on a large number of human preference judgments. However, current LLM-based evaluators risk masking errors and can only serve as a weak proxy, leaving human evaluation the gold standard despite being costly and hard to compare across studies. In this work, we present MESA, an LLM-based framework employing a three-step assessment of individual error types, multi-agent discussion for decision refinement, and feedback-based self-training to refine error definition understanding and alignment with human judgment. We show that MESA's components enable thorough error detection, consistent rating, and adaptability to custom error guidelines. Using GPT-4o as its backbone, MESA achieves mid to high Point-Biserial correlation with human judgment in error detection and mid Spearman and Kendall correlation in reflecting error impact on summary quality, on average 0.25 higher than previous methods. The framework's flexibility in adapting to custom error guidelines makes it suitable for various tasks with limited human-labeled data.

Pretrained, large, generative language models (LMs) have had great success in a wide range of sequence tagging and structured prediction tasks. Casting a sequence tagging task as a Seq2Seq one requires deciding the formats of the input and output sequences. However, we lack a principled understanding of the trade-offs associated with these formats (such as the effect on model accuracy, sequence length, multilingual generalization, hallucination). In this paper, we rigorously study different formats one could use for casting input text sentences and their output labels into the input and target (i.e., output) of a Seq2Seq model. Along the way, we introduce a new format, which we show to to be both simpler and more effective. Additionally the new format demonstrates significant gains in the multilingual settings -- both zero-shot transfer learning and joint training. Lastly, we find that the new format is more robust and almost completely devoid of hallucination -- an issue we find common in existing formats. With well over a 1000 experiments studying 14 different formats, over 7 diverse public benchmarks -- including 3 multilingual datasets spanning 7 languages -- we believe our findings provide a strong empirical basis in understanding how we should tackle sequence tagging tasks.

Documents containing business-critical information and data enter the enterprise from multiple channels and in the form of images, PDF, and Word and Excel documents directly or as part of attachments to email, etc. Traditional document processing solutions have tried to automate the extraction of data required operators to build templates. This approach worked like a patchwork, as it could handle documents of a similar format. The system used to fail when a document of a different format from the same or a new vendor entered the system. However, AI-powered IDP solutions can seamlessly extract and process data from a variety of documents in multiple formats. Such IDP products are able to do so by complimenting OCR with AI, which eliminates the painful template creation and management process. AI makes extraction seamless and guarantees high accuracy. AI-powered IDP automates the entire document processing cycle right from extraction to publishing the data into the record-keeping systems.

Peak enrollment period is here once again as group and voluntary benefits providers put their remote work arrangements to the test in what will be an unusually demanding season. This year has been the year of digital transformation in the insurance industry, and 2020's challenges will inspire new approaches and digitization for enrollment. Fortunately, insurers can use AI and predictive analytics to streamline quoting and enrollment, optimize resources, and automate manual tasks. Traditional renewal processes raise several speedbumps. Disconnects between quoting and underwriting as well as unreliable information on past successful plan designs unnecessarily increase quote turnaround time, resulting in missed opportunities and a poorer customer experience.

We see news about Machine learning everywhere. Indeed, there is a lot of potential in Machine learning. According to Gartner's predictions, "Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization" and Transform 2019 of VentureBeat predicted that 87% of AI projects will never make it into production. Why is it like that? Why do so many projects fail?

Basics of Machine Learning and its types Deep Learning and Neural Networks Learn about Tensorflow Lite Generate Tensorflow lite model from Keras model Generate Tensorflow lite model using saved model Generate Tensorflow lite model using concrete function Train and deploy classification and regression models Use datasets available in different formats for model training Learn Python Programming language Learn popular Machine Learning libraries like Numpy,Pandas and Matplotlib Learn Tensorflow 2.0 This course is designed for Android developers who want to learn Machine Learning and deploy machine learning models in their android apps using TensorFlow Lite. This course will get you started in building your FIRST deep learning model and android application using deep learning. We will learn about machine learning and deep learning and then train our first model and deploy it in android application using tenserflow lite . All the materials for this course are FREE. We will start by learning about basics of Python programming language.