We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several "root cause" nodes. We show that it is possible to identify a short list of potential root causes provided that the perturbation is sufficiently strong and propagates according to the same structural equations as in the normal mode. This shortlist consists of the true root causes together with those of its parents lying on a cycle with the root cause. Notably, our method does not require prior knowledge of the causal graph.

Recent advancements in tool-augmented large language models have enabled them to interact with external tools, enhancing their ability to perform complex user tasks. However, existing approaches overlook the role of personalisation in guiding tool use. This work investigates how user preferences can be effectively integrated into goal-oriented dialogue agents. Through extensive analysis, we identify key weaknesses in the ability of LLMs to personalise tool use. To this end, we introduce TAPS, a novel solution that enhances personalised tool use by leveraging a structured tagging tool and an uncertainty-based tool detector. TAPS significantly improves the ability of LLMs to incorporate user preferences, achieving the new state-of-the-art for open source models on the NLSI task.

RStudio on Amazon SageMaker is the industry's first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. In conjunction with tools like RStudio on SageMaker, users are analyzing, transforming, and preparing large amounts of data as part of the data science and ML workflow. Data scientists and data engineers use Apache Spark, Hive, and Presto running on Amazon EMR for large-scale data processing. Using RStudio on SageMaker and Amazon EMR together, you can continue to use the RStudio IDE for analysis and development, while using Amazon EMR managed clusters for larger data processing.

Amazon SageMaker Studio can help you build, train, debug, deploy, and monitor your models and manage your machine learning (ML) workflows. Amazon SageMaker Pipelines enables you to build a secure, scalable, and flexible MLOps platform within Studio. In this post, we explain how to run PySpark processing jobs within a pipeline. This enables anyone that wants to train a model using Pipelines to also preprocess training data, postprocess inference data, or evaluate models using PySpark. This capability is especially relevant when you need to process large-scale data.

Alibaba Cloud, Alibaba Group's digital technology and intelligence backbone, today unveiled its latest large language model, Tongyi Qianwen. The new AI model will be integrated into Alibaba's various business units in the near future to improve user experience. The company's customers and developers will have access to the model to cost-effectively build custom AI functions. Alibaba Cloud also announced lower-cost options for key cloud products, including Elastic Compute Service (ECS) and Object Storage Service (OSS), by introducing new ECS instances, OSS Reserved Capacity (OSS-RC), and OSS Anywhere Reserved Capacity ( OSS-RC). The move will make computing more accessible and affordable for companies looking to unlock new opportunities in China's new AI era.

Gary Fowler is a serial AI entrepreneur with numerous startups and an IPO. He is CEO and cofounder of GSDVS.com and Yva.ai. If you've checked any social media platforms, forums or publishers recently, you've likely seen how the entire media world has been inundated with ChatGPT reviews, explanations and use cases. The generative AI technology from OpenAI is taking the world by storm--and there is no stopping it. Now, you might ask me, "Gary, AI has been in the news for almost a decade now.

Warner Bros. made a point of releasing all its 2021 movies on HBO Max, and the service's next big movie is an appropriate nod to the digital world. The studio has released the first trailer for Space Jam: A New Legacy, and it's more of an ode to sci-fi than you might think. The movie has LeBron James whisked into a "Matrix hell" where he has to play basketball against a supervillain-like Goon Squad to rescue his son. That involves enlisting Bugs Bunny and friends for his squad, of course, but the references go further than that. James can count on help from The Iron Giant's namesake robot, for starters.

In this article, I will walk you through loading your data to S3 and then spinning up a Jupyter notebook instance on Amazon Sagemaker for running deep learning jobs. The method I'm about to review is not the only method for running deep learning in the cloud (in fact it's not even the recommended method). But this method is a nice way to get started. Side note: there is a way to auto-shutdown using the "bring your training to Sagemaker" method, but it requires some additional coding. This is an option you can explore if you want.

Gradually yet steadily, technology has taken over all aspects of our life. And the financial services sector is no exception. Financial Services spanning investments, lending and management of assets are a fundamental part of fund management for individuals as well as corporations. One of the nuances of this sector is the volatility associated with it owing to factors such as prevailing market conditions, political scenario, performance of stocks, taxation norms, etc. Since this condition is a given, companies dealing with such financial instruments need to glean reams of data before counselling clients on the right investment choice or the right kind of loan to opt, for instance.

There are lots of applications of text classification in the commercial world. However, the vast majority of text classification articles and tutorials on the internet are binary text classification such as email spam filtering (spam vs. ham), sentiment analysis (positive vs. negative). In most cases, our real world problem are much more complicated than that. Therefore, this is what we are going to do today: Classifying Consumer Finance Complaints into 12 pre-defined classes. The data can be downloaded from data.gov.