VeraAI text analysis tools for fact-checking EBU Technology & Innovation
To keep spreadsheet-loving users happy, data teams spend an inordinate amount of time trying to help users export data instead of being able to connect them directly to a data platform. When enterprises begin to migrate their data to modern cloud data platforms, Excel users lose their live analysis capabilities because cloud-based data platforms don’t natively speak MDX as a language. Without native MDX support, Excel cannot perform multidimensional analysis in a direct connection; instead, it forces users to deal with exported snapshots of data that are manual and out-of-date as quickly as they are created. These are related to episodic memories, but are considered distinct, since they do not require revisiting a specific moment. While remembering what attending a great concert was like would count as episodic memory, knowing that it was one’s favorite concert is an example of personal semantic memory.
- While remembering what attending a great concert was like would count as episodic memory, knowing that it was one’s favorite concert is an example of personal semantic memory.
- The company indexes and analyzes 2.5 billion web pages a month to perform this function, which allows for better targeting based on a site’s content rather than the viewer.
- It works with a range of vision-language models, including GPT-4V, Phi-3.5-V, and Llama-3.2-V, making it flexible for developers with a broad range of access to advanced foundation models.
- By combining detection, text extraction, and semantic analysis, OmniParser offers a plug-and-play solution that works not only with GPT-4V but also with other vision models, increasing its versatility.
It maps data such as tables and columns in a cloud-based data source to familiar business terms stored centrally, making data more valuable to the business and simplifying querying for users. The universal semantic layer also provides data modeling, access controls and caching, removing much of the heavy lifting from data teams. Data teams needing to solve the problem are gravitating toward a new solution called modern cloud OLAP. It allows them to connect Excel and other compatible applications directly to cloud data platforms using standard XML for Analysis (XMLA) protocols.
Then, all users can execute sophisticated queries that include slicing, dicing, drilling down and rolling up of data in pivot tables and pivot charts—all without the hassle of data exports and maintaining outdated systems. The information contained in semantic memory ranges from basic facts such as the meanings of words and what colors different kinds of food are to more complex forms of understanding, such as how certain concepts relate to each other. Semantic memory also reflects the abstract details of one’s own life, such as birth date, hometown, or personal characteristics. Legacy OLAP systems were developed over decades, and now modern cloud OLAP offers the best of both legacy and modern approaches, but as with any change, organizations should approach the new data strategy with care.
Semantic Web Company and Ontotext Integrate, Rebrand as Knowledge Graph and AI Powerhouse Graphwise
The European Broadcasting Union is the world’s foremost alliance of public service media, representing over a hundred organizations worldwide. We strive to secure a sustainable future for public service media, provide our Members with world-class content through the Eurovision and Euroradio brands, and build on our founding ethos of solidarity and co-operation to create a centre for learning and sharing. The company indexes and analyzes 2.5 billion web pages a month to perform this function, which allows for better targeting based on a site’s content rather than the viewer. Released relatively quietly by Microsoft, OmniParser could be a crucial step toward enabling generative tools to navigate and understand screen-based environments. Semantic memory is a form of long-term memory that comprises a person’s knowledge about the world. Along with episodic memory, it is considered a kind of explicit memory, because a person is consciously aware of the facts, meanings, and other information that it contains.
Modern cloud OLAP provides native spreadsheet integration, so users get a familiar query experience without reliance on legacy OLAP systems. Just as a GPS system provides accurate routes and prevents wrong turns, knowledge graphs steer AI models in the right direction by organizing and linking data in meaningful ways. The ability to do this has never been so important, as businesses grapple with multiple AI technologies,” said Atanas Kiryakov, president at Graphwise.
Exclusive: Samba TV Acquires Semasio To Beef Up Its Contextual CTV Targeting
You can foun additiona information about ai customer service and artificial intelligence and NLP. Apple has also jumped into the fray with their Ferret-UI, aimed at mobile UIs, enabling their AI to understand and interact with elements like widgets and icons. It works with a range of vision-language models, including GPT-4V, Phi-3.5-V, and Llama-3.2-V, making it flexible for developers with a broad range of access to advanced foundation models. Semantic Web Company brings expertise in knowledge engineering, semantic AI, and intelligent document processing, while Ontotext brings the most versatile graph database engine and state-of-the-art AI models for linking and unifying information at scale.
By using knowledge graphs, enterprises get more accurate, context-rich insights from their data, which is essential as they look to adopt AI to drive decision-making enterprises, according to the vendors. But video content is much more complex for AI models to analyze than text is, with more contextual factors at play. A travel ad makes a lot more sense juxtaposed against a culture-specific cooking show, for example, so only showing food-related ads on food-related channels would miss out on that possible connection point. Under a typical audience targeting model, a user might go to an online tech publication and see an ad for a travel company that could feel out of place based on the site’s content, even if the user themselves is personally interested in travel. OmniParser is essentially a powerful new tool designed to parse screenshots into structured elements that a vision-language model (VLM) can understand and act upon.
As LLMs become more integrated into daily workflows, Microsoft recognized the need for AI to operate seamlessly across varied GUIs. The OmniParser project aims to empower AI agents to see and understand screen layouts, extracting vital information such as text, buttons, and icons, and transforming it into structured data. At its core, OmniParser is an open-source generative AI model designed to help large language models (LLMs), particularly vision-enabled ones like GPT-4V, better understand and interact with graphical user interfaces (GUIs).
Modern cloud OLAP is also vital if your organization wastes resources supporting legacy OLAP systems for spreadsheet users and cloud-based data platforms. Organizations depend on Microsoft Excel for so much semantic analysis of their strategic planning and decision making. With a universal semantic layer that supports multidimensional analysis and direct spreadsheet connectivity, organizations can enable modern cloud OLAP.
- Just as a GPS system provides accurate routes and prevents wrong turns, knowledge graphs steer AI models in the right direction by organizing and linking data in meaningful ways.
- Implementing a universal semantic layer involves onboarding a new technology, which inevitably comes with resource and time commitments.
- Under a typical audience targeting model, a user might go to an online tech publication and see an ad for a travel company that could feel out of place based on the site’s content, even if the user themselves is personally interested in travel.
- We also ran the video through Contrails.AI’s tool, created by the Bengaluru-based AI startup working on detecting AI-generated content.
With more developers contributing to fine-tuning these components and sharing their insights, the model’s capabilities are likely to evolve rapidly. One ongoing challenge is the accurate detection of repeated icons, which often appear in similar contexts but serve different purposes—for instance, multiple “Submit” buttons on different forms within the same page. According to Microsoft’s documentation, current models still struggle to differentiate between these repeated elements effectively, leading to potential missteps in action prediction.
Together, Graphwise delivers the critical knowledge graph infrastructure enterprises need to realize the full potential of their AI investment. Semantic Web Company and Ontotext announced that the two organizations are merging to create a knowledge graph and AI powerhouse, Graphwise. Samba is already developing its own AI tools to better analyze video content, as Navin demonstrated during the IAB NewFronts in May. Contextual targeting that’s based on content, in contrast, would instead serve up ads more relevant to the tech publication’s purview. However, the AI community is optimistic that these issues can be resolved with ongoing improvements, particularly given OmniParser’s open-source availability.
Additionally, an OCR module extracts text from the screen, which helps in understanding labels and other context around GUI elements. By combining detection, text extraction, and semantic analysis, OmniParser offers a plug-and-play solution that works not only with GPT-4V but also with other vision models, increasing its versatility. While the concept of GUI interaction for AI isn’t entirely new, the efficiency and depth of OmniParser’s capabilities stand out.
OmniParser’s presence on Hugging Face has also made it accessible to a wide audience, inviting experimentation and improvement. Microsoft Partner Research Manager Ahmed Awadallah noted that open collaboration is key to building capable AI agents, and OmniParser is part of that vision. Adding to the challenge, multidimensional analyses, like pivot tables and reporting, operate using Multidimensional Expressions (MDX), a language designed for querying and managing multidimensional data structures.
In 2022 the company was purchased by Fyllo, a cannabis-specific ad compliance platform, which rebranded to Fyllo|Semasio earlier this year, then dropped the Fyllo branding a few months later. Samba TV must have found this pitch pretty convincing, because on Thursday the TV measurement company announced its acquisition of audience data and contextual targeting solution Semasio. The release of OmniParser is part of a broader competition among tech giants to dominate the space of AI screen interaction. Recently, Anthropic released a similar, but closed-source, capability called “Computer Use” as part of its Claude 3.5 update, which allows AI to control computers by interpreting screen content.
Semantic memory ability seems to develop earlier in childhood than episodic memory (the memory for personal experiences). Collected over each person’s lifetime of learning, the information in semantic memory—facts, relationships between objects or concepts, ChatGPT and many more abstract details—is invaluable to everyone from kindergartners to gameshow contestants. What differentiates OmniParser from these alternatives is its commitment to generalizability and adaptability across different platforms and GUIs.
Fundamental Analysis
OmniParser isn’t limited to specific environments, such as only web browsers or mobile apps—it aims to become a tool for any vision-enabled LLM to interact with a wide range of digital interfaces, from desktops to embedded screens. Notably, advertising based on content doesn’t require user data to work, making it more privacy compliant than previous models – and more actionable for the CTV landscape, where identity resolution was less robust even before the rise of signal loss. From there, Samba TV plans to integrate its video data fully into Semasio’s platform, allowing clients like Acxiom and National Media to access better contextual relevance across digital, mobile and CTV. This acquisition isn’t the first time Semasio’s changed owners, in a strictly technical sense.
The base of knowledge contained in semantic memory is accumulated through many moments of learning, from picking up the basics of language in early childhood to grasping complex ideas and systems in class, in conversations, or while reading books. While few of these moments of learning will remain with us as scenes in episodic memory, our brains collect the abstract insights to help us answer questions, communicate, and solve problems in the future. The universal semantic layer should support native spreadsheet integration so the organization can migrate from legacy OLAP platforms, such as Microsoft SSAS, Oracle Essbase and SAP HANA. With a universal semantic layer that supports native MDX integration, anyone can connect to and reuse trusted data assets and perform multidimensional analysis on governed data using a live connection—no exports are needed. The data remains in the data platform to avoid creating data silos, and the universal semantic layer enables the live multidimensional connection so Excel can query the data in real time.
Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications – Nature.com
Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications.
Posted: Sun, 03 Nov 2024 12:33:15 GMT [source]
We also ran the video through Contrails.AI’s tool, created by the Bengaluru-based AI startup working on detecting AI-generated content. AdExchanger is where marketers, agencies, publishers and tech companies go for the latest information on the trends that are transforming digital media and marketing, from data, privacy, identity and AI to commerce, CTV, measurement and mobile. Our vision is that AI-generated data and insights will yield better-performing and better-quality ads,” Navin said of the trend. Current Semasio CEO Jeff Ragovin will be handing off leadership of this combined iteration of Semasio, which includes Fyllo’s technology, to the contextual company’s General Manager Zac Pinkham during the transition.
Of course, business users tasked with FP&A and other critical processes could care less about the intricacies that make analysis possible. They want to use Excel’s familiar interface to get the job done, and data teams are responsible for helping them do so. Because cubes need to be pre-calculated and refreshed, traditional OLAP technologies limit the size, complexity and scale of data while creating governance headaches, operational complexity and duplicative data silos. The announcement is significant for the graph industry, as it elevates Graphwise as the most comprehensive knowledge graph AI organization and establishes a clear path towards democratizing the evolution of Graph RAG as a category, according to the vendors.
Previous models often struggled with screen navigation, particularly in identifying specific clickable elements, as well as understanding their semantic value within a broader task. Microsoft’s approach uses a combination of advanced object detection and OCR (optical character recognition) to overcome these hurdles, resulting in a more reliable and effective parsing system. Unlike episodic memory, which reproduces the subjective impressions of past experiences, semantic memory contains information that is context-free—not grounded in a particular time and place. A person who started learning the alphabet on a particular afternoon in childhood doesn’t need to revisit that moment to remember (thanks to semantic memory) that the letter P comes after M.
Implementing a universal semantic layer involves onboarding a new technology, which inevitably comes with resource and time commitments. However, instead of having staff maintain legacy systems, they can focus on moving toward modern cloud OLAP. Be sure to take a phased approach, prioritize departments and datasets, define a change management strategy and train users to connect to and find data for analysis in the universal semantic layer. The first step is implementing a universal semantic layer, or a layer of abstraction, that provides a consistent way of interpreting data.
Fundamental analysis is one of the cornerstones of investing, and gives you tools to help determine the value of different investments. This enables models like GPT-4V to make sense of these interfaces and act autonomously on the user’s behalf, for tasks that range from filling out online forms to clicking on certain parts of the screen. This is the third in a series of monthly webinars about the veraAI project’s innovative research on AI-based fact-checking tools. A video of billionaire and Tesla Motors CEO Elon Musk ChatGPT App talking about a cryptocurrency giveaway is being shared on his social media platform X (formerly Twitter), in which he can be heard saying that he will giveaway 5,000 Bitcoin and 100,000 Ethereum. Moreover, the OCR component’s bounding box precision can sometimes be off, particularly with overlapping text, which can result in incorrect click predictions. These challenges highlight the complexities inherent in designing AI agents capable of accurately interacting with diverse and intricate screen environments.