Latest news with #datasets
Yahoo
6 hours ago
- Business
- Yahoo
Snorkel AI secures investment from Accenture
Accenture has invested in Snorkel AI, aimed at assisting financial services firms in developing and scaling AI solutions. The financial details of the strategic investment have not been disclosed. This investment, through Accenture Ventures, focuses on enhancing the curation of high-quality datasets essential for training and evaluating AI models. Snorkel AI offers Data Development Platform, which facilitates the evaluation and tuning of specialised AI systems at scale. The platform includes services such as Snorkel Expert Data-as-a-Service and Snorkel Enterprise AI, which expedite the transition from prototype to production by employing advanced data development technology. Snorkel AI co-founder and CEO Alex Ratner said: 'This partnership marks a major milestone in our mission to make data-centric AI the foundation of enterprise innovation. 'As momentum builds around agentic AI, most enterprises still lack the domain-specific data and expertise needed to move from prototype to production.' Originating from the Stanford AI Lab, Snorkel AI's platform is currently utilised by major corporations, including BNY and Experian, as well as by the US government. The platform is designed to address the needs of agentic AI, employing automation and repeatable workflows to convert fragmented data and domain knowledge into datasets for AI model training and evaluation. This data-centric methodology supports quicker and safer deployment of AI systems, particularly in complex regulatory environments. Accenture and Snorkel AI plan to collaborate on creating customised solutions tailored to specific industries, with an initial emphasis on the financial services sector. Additionally, Snorkel AI will participate in Accenture Ventures' Project Spotlight, which is an accelerator programme for data and AI companies. This initiative provides access to Accenture's industry expertise and client network, enabling startups to innovate and realise their technological potential. Accenture Ventures global lead Tom Lounibos said: 'Our clients are looking to harness AI in ways that are fast, secure, and aligned to real business outcomes. 'Snorkel's unique approach solves one of the most persistent pain points in AI development—high-quality datasets for training and evaluation of AI models.' In late July 2025, Accenture announced the acquisition of Maryville Consulting Group, a US-based technology consultancy, to strengthen its technology strategy capabilities. "Snorkel AI secures investment from Accenture" was originally created and published by Verdict, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site.
Yahoo
a day ago
- Business
- Yahoo
Accenture (ACN) Backs Snorkel AI to Help Businesses Build Better AI—Starting With Finance
Accenture plc (NYSE:ACN) is one of the . On August 6, the company announced that it has made a strategic investment, through Accenture Ventures, in Snorkel AI. The investment aims to enable enterprises to build and scale AI solutions rapidly by managing high-quality datasets for training and evaluating AI models. Snorkel's flagship platform uses automation and repeatable workflows to transform data and domain knowledge into high-quality training and evaluating data for AI models. Through its data-centric approach, AI systems can be deployed safely and swiftly, particularly in complex and regulated environments. Accenture and Snorkel AI will also be collaborating to build industry-specific solutions, using best-in-class training data to build AI solutions at scale. Their initial focus is going to be on the financial services industry. 'Our clients are looking to harness AI in ways that are fast, secure, and aligned to real business outcomes. Snorkel's unique approach solves one of the most persistent pain points in AI development—high-quality datasets for training and evaluation of AI models. This investment can help our clients move from experimentation to impact more quickly.' -Tom Lounibos, global lead for Accenture Ventures. Accenture plc (NYSE:ACN) offers strategy and consulting services. While we acknowledge the potential of ACN as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: and . Disclosure: None. Sign in to access your portfolio
Geeky Gadgets
01-08-2025
- Business
- Geeky Gadgets
Use Excel Like a Pro : Fill Blank Cells in Seconds with This Genius Hack
Have you ever found yourself staring at a sea of blank cells in Excel, wondering how to fill them without hours of manual effort? For years, this has been a frustrating bottleneck for professionals working with large datasets. Blank cells disrupt calculations, skew analyses, and create headaches for anyone striving for clean, consistent data. But what if there was a smarter, faster way to handle this? Thanks to Excel's dynamic array functions, the days of tedious, error-prone fixes are over. These innovative tools allow you to automate the process of filling blanks with precision, transforming what was once a chore into a seamless part of your workflow. Excel Off The Grid explain how functions like `SCAN`, `REDUCE`, and `LAMBDA` can transform the way you manage blank cells in Excel. You'll uncover how these tools work together to create a dynamic, reusable solution that adapts to datasets of any size or complexity. Whether you're dealing with a single column or a sprawling multi-column spreadsheet, this method eliminates repetitive formulas and ensures your data remains structured and analysis-ready. By the end, you'll not only understand the mechanics behind these functions but also see how they can simplify your data management process in ways you never thought possible. Sometimes, the smallest changes in approach can lead to the biggest leaps in productivity. Filling Blank Cells Efficiently Why Filling Blank Cells Matters Blank cells in datasets can significantly disrupt calculations, analyses, and reporting. For example, in a dataset where a column contains product categories with blank cells between entries, these gaps can lead to incomplete or inaccurate results. Making sure that blank cells are filled is essential for maintaining data continuity and consistency, which are critical for accurate analysis and decision-making. Dynamic array functions in Excel provide a powerful way to automate this process. By eliminating the need for manual adjustments, these functions ensure that your data remains reliable and ready for analysis. Whether you're working with a single column or a multi-column dataset, this approach offers a scalable and efficient solution to a common data management challenge. How to Fill Blank Cells Dynamically To dynamically fill blank cells with the value from the cell above, you can use a formula that propagates the last non-blank value downward. This method is particularly useful for: Maintaining data consistency in reports and analyses. in reports and analyses. Handling datasets with irregular structures or missing values. Reducing manual intervention and minimizing errors. For multi-column datasets, the process can become more complex. However, by combining dynamic array functions, you can create a formula that efficiently handles blank cells across multiple columns. This ensures that your data remains structured and complete, regardless of its size or complexity. Fill Blank Cells in Excel Using Dynamic Array Functions Watch this video on YouTube. Expand your understanding of Excel functions with additional resources from our extensive library of articles. Key Functions That Power the Solution Several dynamic array functions are integral to building a robust formula for filling blank cells. Each function plays a specific role in making sure the solution is dynamic, scalable, and efficient. Here's an overview of the key functions: SCAN: Iterates through an array, identifying blank cells and replacing them with the previous value. This function ensures that blank cells are dynamically filled as the formula processes the dataset. Iterates through an array, identifying blank cells and replacing them with the previous value. This function ensures that blank cells are dynamically filled as the formula processes the dataset. REDUCE: Handles multi-column datasets by combining results from individual columns into a unified array. This makes it ideal for processing datasets with multiple variables. Handles multi-column datasets by combining results from individual columns into a unified array. This makes it ideal for processing datasets with multiple variables. LAMBDA: Encapsulates the formula into a reusable custom function, simplifying future applications and reducing the need for repetitive formulas. Encapsulates the formula into a reusable custom function, simplifying future applications and reducing the need for repetitive formulas. HSTACK and CHOOSECOL: Stack and select columns to manage datasets with varying structures effectively, making sure flexibility in handling different data layouts. Stack and select columns to manage datasets with varying structures effectively, making sure flexibility in handling different data layouts. LET: Breaks complex formulas into manageable steps, improving both readability and performance by reducing redundancy in calculations. By combining these functions, you can create a dynamic solution that adapts to the specific needs of your dataset, making sure accuracy and efficiency. Overcoming Common Challenges While the concept of filling blank cells dynamically is straightforward, certain challenges may arise during implementation. Here's how to address them effectively: Blank First Row: If the first row of a column is blank, the formula must account for the absence of a starting value. Using `SCAN` with conditional logic ensures that blank cells are only filled when a preceding value exists, preventing errors in the output. If the first row of a column is blank, the formula must account for the absence of a starting value. Using `SCAN` with conditional logic ensures that blank cells are only filled when a preceding value exists, preventing errors in the output. Multi-Column Datasets: Processing multiple columns requires handling each column individually and then combining the results. The `REDUCE` function simplifies this by iterating through columns and merging their outputs into a single array. Processing multiple columns requires handling each column individually and then combining the results. The `REDUCE` function simplifies this by iterating through columns and merging their outputs into a single array. Reusability: To avoid rewriting the formula for each dataset, encapsulate the logic into a `LAMBDA` function. This allows you to define a custom function that can be reused across different datasets and workbooks, saving time and effort. By addressing these challenges, you can ensure that your formula is both reliable and adaptable, making it suitable for a wide range of applications. Building a Custom Function The cornerstone of this solution is creating a custom function using `LAMBDA`. This function encapsulates the formula into a reusable tool, simplifying its application across different datasets. For instance, you can define a function called `FXFillDown` that accepts an array as input and returns the processed array with blank cells filled. Here's why creating a custom function is advantageous: It eliminates the need for repetitive formulas, streamlining your workflow. It ensures consistency across datasets and workbooks, reducing the risk of errors. It saves time, especially when working with large or complex datasets that require frequent updates. Once defined, the custom function can be applied to any dataset, regardless of its size or structure. This makes it a versatile tool for data analysts, financial professionals, and anyone who regularly works with Excel. Real-World Applications The custom function you create is not only efficient but also highly practical. It can be applied in various real-world scenarios to streamline workflows and improve data quality. Here are some examples: Sales Reports: Automatically fill blank cells in product categories or regions to ensure data completeness and accuracy in reporting. Automatically fill blank cells in product categories or regions to ensure data completeness and accuracy in reporting. Financial Analysis: Maintain continuity in datasets with missing values, such as revenue or expense categories, to support accurate financial modeling. Maintain continuity in datasets with missing values, such as revenue or expense categories, to support accurate financial modeling. Data Cleaning: Prepare datasets for analysis by filling gaps without manual intervention, making sure that the data is ready for use in tools like Power BI or Tableau. For instance, consider a sales report where product categories are listed in one column, but blank cells disrupt the flow of data. By applying the custom function, you can instantly fill these gaps, making sure the report is complete and ready for analysis. This not only saves time but also enhances the reliability of your results, allowing better decision-making. Streamlining Data Management with Dynamic Array Functions Filling blank cells in Excel no longer needs to be a manual or error-prone process. By using advanced dynamic array functions like `SCAN`, `REDUCE`, and `LAMBDA`, you can create a custom formula that automates this task with precision and efficiency. The resulting solution is dynamic, reusable, and adaptable to datasets of any size or structure. Whether you're a data analyst, financial professional, or Excel enthusiast, this approach can significantly streamline your workflow, making sure that your data is always ready for analysis and decision-making. Media Credit: Excel Off The Grid Filed Under: Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. 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The Guardian
17-07-2025
- Business
- The Guardian
Meta argues its AI needs personal information from social media posts to learn ‘Australian concepts'
Meta has urged the Australian government not to make privacy law changes that would prevent the company using personal information taken from Facebook and Instagram posts to train its AI, arguing the AI needs to learn 'how individuals discuss Australian concepts'. In a submission to the Productivity Commission's review on harnessing data and digital technology, published this week, the parent company of Facebook, Instagram and WhatsApp argued for a 'global policy alignment' in the Albanese government's pursuit of privacy reform in the AI age. Meta said generative AI models 'require large and diverse datasets' and cannot rely on synthetic data – data generated by AI alone. The company said available databases, such as Australian legislation, were limited in what they could offer AI compared to datasets containing personal information. 'Human beings' discussions of culture, art, and emerging trends are not borne out in such legislative texts, and the discourse that takes place on Meta products both represents vital learning on both how individuals discuss Australian concepts, realities, and figures, as well as, in particular, how users of our products engage,' Meta said. 'This means that authentic and effective learning to ultimately power meaningful products of communication is best realised from training that includes those discussions and artefacts themselves.' Meta has been training its AI, Llama, on publicly-accessible Facebook and Instagram posts since last year. The company was ordered to stop training its data on users' posts for those based in Europe, and Meta ultimately gave users in the EU an opt-out option. Guardian Australia reported last year that such an option was not available to Australian users because the opt-out option in Europe was 'in response to a very specific legal frame'. Meta said in the Productivity Commission submission it was 'concerned that recent developments are moving Australia's privacy regime to be out of step with international norms, impose obligations on industry that conflict with broader digital policy objectives to promote age appropriate and safe experiences online, and disincentivise industry investment in AI in Australia or in pro-consumer outcomes'. Hardware giant Bunnings, which is appealing a privacy commissioner finding last year against the company's trial of facial recognition technology in select stores, also took aim at Australia's privacy laws. The company told the Productivity Commission that while it was 'committed to protecting customer privacy' it believed 'every team member deserves to feel safe at work, and every customer should be able to shop without fear of harm'. 'Privacy must be considered in the context of an employer's strict liability and an occupier's legal obligations to maintain a safe place of work and business,' it said. Woolworths said it supported privacy reform but said 'the proposals as presently structured could pose unnecessary challenges in how we serve our customers who increasingly expect personalised engagement and a single, frictionless shopping experience'. Tech giant Google said there was too much regulatory uncertainty in Australia around AI, including the proposed AI guardrails, and the company repeated its call for changes to copyright law to allow its AI to be trained without breaching copyright.
Bloomberg
16-05-2025
- Business
- Bloomberg
Data Spotlight 2025 Report
As data becomes more granular and complex, its value in shaping investment strategies, and market demand for it, continues to grow. Increasingly, investors rely on large-scale datasets to construct theses, monitor assets, assess risks, and respond to market-moving events like inflation, supply chain disruptions, and environmental shifts. In this special report, Bloomberg Enterprise Data's Quant and Data Science Team presents selected examples drawn from over 8,000 datasets, offering financial professionals a deeper view into how enterprise data can inform strategy and drive timely analysis.
