IBL News

Category: Top News

  • Anthropic Added a Google Docs Integration to Its Claude.ai Assistant

    Anthropic Added a Google Docs Integration to Its Claude.ai Assistant

    IBL News | New York

    Anthropic added a Google Docs integration to its Claude.ai assistant. This feature allows users to access and reason about a document’s content from Google Docs within their chats and Projects.

    This way, Claude can summarize long Google Docs and reference historical context from the files to inform decision-making or help with strategic planning.

    This integration is available on the Claude Pro, Team, and Enterprise plans.

    Another update allows Claude to match users’ communication and preferred way of writing.

    Users can choose from these styles:

    • Formal: clear and polished responses
    • Concise: shorter and more direct responses
    • Explanatory: educational responses for learning new concepts

    Beyond these preset options, Claude can automatically generate custom styles and edit preferences as they evolve.

    OpenAI’s ChatGPT and Google’s Gemini have similar features that allow users to tailor responses based on their writing style and tone. The Writing Tools feature in Apple Intelligence also provides presets with similar styles.

    Creating a custom style is effectively an easy way to automate how you engineer prompts to make responses sound more like your own personal style.
  • OpenAI Released a Course Encouraging K-12 Teachers to Use ChatGPT

    OpenAI Released a Course Encouraging K-12 Teachers to Use ChatGPT

    IBL News | New York

    OpenAI released a free online course titled “ChatGPT Foundations for K-12 Educators,” which encourages teachers to use its tool to create lesson plans, interactive tutorials for students, and other pedagogical practices.

    The course was created in collaboration with the nonprofit organization Common Sense Media. It’s one hour long and has a nine-module program covering the basics of AI and its pedagogical applications.

    OpenAI says the course has already been deployed in “dozens” of schools, including the Agua Fria School District in Arizona, the San Bernardino School District in California, and the charter school system Challenger Schools.

    OpenAI is aggressively going after the education market, which it sees as a critical growth area.

    In September, OpenAI hired former Coursera chief revenue officer Leah Belsky as its first GM of education and charged her with bringing OpenAI’s products to more schools. In the spring, the company launched ChatGPT Edu, a version of ChatGPT that was built for universities.

    According to Allied Market Research, AI in education could be worth $88.2 billion within the next decade.

    However, a poll by the Rand Corporation and the Center on Reinventing Public Education found that just 18% of K-12 educators use AI in their classrooms, reflecting many skeptical pedagogues.

    Late last year, the United Nations Educational, Scientific and Cultural Organization (UNESCO) pushed for governments to regulate the use of AI in education, including implementing age limits for users and guardrails on data protection and user privacy. However, little progress has been made on those fronts, especially on AI policy in general.

  • Perplexity.ai Launched a New AI-Powered Shopping Assistant

    Perplexity.ai Launched a New AI-Powered Shopping Assistant

    IBL News | New York

    Perplexity.ai debuted last month a shopping feature that offers recommendations with search results and allows users to place an order without visiting a retailer’s website.

    This feature, called Buy with Pro, allows paid customers in the U.S. to check for products from select merchants right on the Perplexity website or app.

    With the move, Perplexity is taking on Google and Amazon to capture a portion of shopping search results.

    The shopping recommendations aren’t sponsored, appear tailored to the search, and include products across Shopify.

    To scale e-commerce operations, Perplexity launched a free Merchant Program for large retailers. It provides payment integrations and free API access.

    With startups like Daydream, Deft, and Remark, AI-powered shopping searches have become a growing business area. Amazon debuted its AI assistant Rufus earlier this year.

    These companies are betting that AI will help you find an item quickly, and you won’t need to spend much time looking for something.

  • Udacity Released Its 2025 State of AI at Work Report

    Udacity Released Its 2025 State of AI at Work Report

    IBL News | New York

    Udacity, now an Accenture company, released its 2025 State of AI at Work Report this month. The report details how this technology is reshaping workplaces across industries and where there are the most significant opportunities for upskilling.

    These are the main outcomes:

    • Nearly 90% of workers are eager to build their AI skills through additional training and certifications, but only one in three say their organization provides the resources to do so. Over half of workers report that their employers lack clear AI policies or guidelines.

    • More than half (54%) of Millennials believed that AI could increase revenue or income, while only 24% of Generation Z and 16% of Generation X felt this.

    • AI Writing Assistants are a favorite tool for end users at work.
    AI writing assistants: ChatGPT, Claude, Grammarly, and Jasper AI
    AI image generation: Canva AI, MidJourney, Stable Diffusion, and DALL E
    Machine translation: DeepL Translator, Google Translate, and Microsoft
    Translator Data analysis and visualization: Tableau, Power BI, and DataRobot
    Notetaking and transcription: Zoom AI Assistant, Fathom.video, and Otter.ai

    • Most Commonly Used Categories of AI Technology
    AI frameworks and libraries (e.g., PyTorch, TensorFlow)
    AI models and techniques (e.g., Supervised Learning, Transfer Learning)
    AI tools and platforms (e.g., OpenAI API, Google AI Studio)
    AI applications and use cases (e.g., Image Generation, Chatbots)
    AI Infrastructure and operations (e.g., Vector Databases, MLOps tools)

  • NASA Teams with Microsoft to Create an AI Chatbot for Researchers

    NASA Teams with Microsoft to Create an AI Chatbot for Researchers

    IBL News | New York

    NASA has teamed up with Microsoft to create an AI chatbot called Earth Copilot.

    NASA’s Earth Copilot, which uses Azure OpenAI Service, has condensed NASA’s vast scientific geospatial information and answers questions about Earth. This data can help drive scientific discoveries, inform policy decisions, and support industries like agriculture, urban planning, and disaster response.

    It lets users interact with NASA’s data repository through plain-language queries. They can ask questions such as “What was the impact of Hurricane Ian on Sanibel Island?” or “How did the COVID-19 pandemic affect air quality in the US?” AI will then retrieve relevant datasets, making the process seamless and intuitive.

    An image of NASA’s EARTHDATA VEDA Dashboard.
    NASA’s EARTHDATA VEDA Dashboard.

    The development of this AI prototype aligns with NASA’s Open Science initiative, which aims to make scientific research more transparent, inclusive, and collaborative.

    At the moment, the NASA Earth Copilot is only available to NASA scientists and researchers to explore and test its capabilities.

    After internal evaluations and testing, the NASA IMPACT team said they will explore its integration into the VEDA platform, which already offers access to some of the agency’s data.

     

  • A Report Revealed the Winners and Losers in the New AI Landscape

    A Report Revealed the Winners and Losers in the New AI Landscape

    IBL News | New York

    AI spending surged to $13.8 billion in 2024 from $2.3 billion in 2023 as enterprises embed AI at the core of their business strategies and daily work, according to a study conducted by Menlo Ventures.

    This research, titled “2024 State of Generative AI in the Enterprise Report,” done after surveying 600 U.S. enterprise IT decision-makers, points out that we are still in the early stages of a large-scale transformation.

    This spending will continue: 72% of decision-makers anticipate broader adoption of generative AI tools soon.

    Investments in the LLM foundation model still dominate spending, but the application layer segment to optimize workflows is now growing faster.

    These app layer companies—mostly in highly verticalized sectors—leverage LLM’s capabilities across domains to unlock new efficiencies. Enterprise buyers will invest $4.6 billion in generative AI applications in 2024, an 8x increase from the $600 million invested in 2023.

    The use cases that deliver the most ROI through enhanced productivity or operational efficiency are:

    • Code copilots, such as GitHub Copilot, Cursor, Codeium, Harness, and All Hands.

    • Support knowledge-based chatbots for employees, customers, and contact centers. Aisera, Decagon, Sierra, and Observe AI are some of the examples.

    • Enterprise search, retrieval, data extraction, and transformation to unlock the knowledge hidden within data silos. Solutions like Glean and Sana connect to emails, messengers, and document stores, enabling unified semantic search across systems.

    • Meeting summarization to automate note-taking and takeaways. Examples are Fireflies.ai, Otter.ai, Fathom, and Eleos Health.

    AI-powered autonomous agents capable of managing complex, end-to-end workflow processes are emerging and can transform human-led industries. Forge, Sema4, and Clay are some tools.

    When deciding to build or buy, 47% of solutions are developed in-house, while 53% are sourced from vendors. Often, organizations discover too late that they have underestimated the difficulty of technical integration, scalability, and ongoing support.

    Most customers (64%) prefer buying from established vendors, citing trust.

    The leading vertical AI applications are:

    • Healthcare, with examples like Abridge, Ambience, Heidi, Eleos Health, Notable, SmarterDx, Codametrix, Adonis, and Rivet.

    • Legal, with examples like Everlaw, Harvey, Spellbook, EvenUp, Garden, Manifest, and Eve.

    • Financial Services, with examples like Numeric, Klarity, Arkifi, Rogo, Arch, Orby, Sema4, Greenlite, and Norm AI.

    • Media and entertainment, with examples like Runway, Captions, Descript, Black Forest Labs, Higgsfield, Ideogram, Midjourney, and Pika.

    Rather than relying on a single provider, enterprises have adopted a multi-model approach, typically deploying three or more LLM in their AI stacks, routing to different models depending on the use case or results.

    To date, close-source solutions underpin the vast majority of usage, with Meta’s Llama 3 holding at 19%, according to the Menlo Ventures research.

    Regarding architectures for building efficient and scalable AI systems, RAG (retrieval-augmented generation) dominates with 51% adoption, while fine-tuning of production molded is only 9%. Agentic architectures, which debuted this year, power 12% of implementations.

    Databases and data pipelines are needed to power RAG. Traditional databases like Postgres and MongoDB remain common, while AI-native vector databases like Pinecone gain ground.

    Menlo Ventures made three predictions for what lies ahead:

    1. Agentic automation will drive the next wave of transformation, tackling complex, multi-step tasks beyond the current systems of content generation and knowledge retrieval. Examples are platforms like Clay and Forge

    2. More incumbents will fall. Chegg saw 85% of its market cap vanish, while Stack Overflow’s web traffic halved. IT outsourcing firms like Cognizant, legacy automation players like UiPath, and even software giants like Salesforce and Autodesk will face AI-native challengers.

    3. The AI talent drought will intensify. AI-skilled enterprise architects will notably increase their salaries. 

    Squint, Typeface…

  • IBM Partnered with Meta to Integrate Llama Into Its AI Platform WatsonX

    IBM Partnered with Meta to Integrate Llama Into Its AI Platform WatsonX

    IBL News | New York

    IBM partnered with Meta to integrate open-source Llama into the WatsonX.ai platform and noticed strong enterprise adoption. One case pertains to Dun & Bradstreet customer operations.

    IBM’s Watsonx.ai provides a next-generation enterprise studio for AI builders worldwide to train, validate, tune, and deploy AI models.

    Other enterprise use cases mentioned are:

    – 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗗𝗮𝘁𝗮: Dun & Bradstreet generates company summaries, revisiting millions of companies quarterly to enhance data pipelines.

    – 𝗦𝗽𝗼𝗿𝘁𝘀: Sevilla FC’s Scout Advisor identifies and evaluates recruits, integrating scouting data and NLP.

    – 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: AddAI’s Q&A chatbot, built, reduced unanswered queries by 50%.

    – 𝗚𝗼𝘃𝗲𝗿𝗻𝗺𝗲𝗻𝘁 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Llama expedites document processing and legal research, reducing case prep time by 50%.

    – 𝗛𝗥 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲: Telecom HR assistants achieved a 75% automation rate and multilingual support.

    – 𝗖𝗹𝗮𝗶𝗺𝘀 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Financial services streamlined call summaries, saving an hour per case and enabling same-day reports.

    – 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻: Local governments developed Q&A assistants with advanced translation capabilities.

    – 𝗦𝗮𝗮𝗦: A SaaS vendor enhanced contract management with watsonx.ai, making AI tools widely accessible.

    – 𝗧𝗮𝗹𝗲𝗻𝘁 𝗔𝗰𝗾𝘂𝗶𝘀𝗶𝘁𝗶𝗼𝗻: AI-driven HR automation cut costs by 90% for a talent firm.

    – 𝗙𝗶𝗻𝗮𝗻𝗰𝗲: Real-time asset valuation and improved transaction monitoring achieved faster insights and analysis.

    – 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Automated document classification sped up archive digitization and data cataloging.

    – 𝗠𝗲𝗱𝗶𝗮: A press agency automated article reviews, translations, and social media edits, saving editors 95% of their time.

    – 𝗧𝗲𝗰𝗵 𝗮𝗻𝗱 𝗜𝗧: AI assistants reduced support ticket resolution times from hours to minutes, achieving 93% accuracy.

  • Nvidia Introduced an AI Model That Modifies Sounds Simply Using Text and Generates Novel Sound

    Nvidia Introduced an AI Model That Modifies Sounds Simply Using Text and Generates Novel Sound

    IBL News | New York

    On Monday, Nvidia showed a new AI model that understands and generates sound as humans do.

    Called Fugatto (Foundational Generative Audio Transformer Opus 1), this model generates or transforms any mix of music, voices, and sounds described with prompts using any combination of text and audio files.

    However, Santa Clara, California-based Nvidia, the world’s largest supplier of chips and software for AI systems, said it is still debating whether and how to release it publicly.

    For example, Fugatto can create a music snippet based on a text prompt, remove or add instruments from an existing song, change the accent or emotion in a voice, and even let people produce sounds never heard.

    Another case can be an online course spoken by any family member or friend.

    Music producers can use Fugatto to prototype or edit an idea for a song quickly, trying out different styles, voices, and instruments. They could also add effects and enhance the overall audio quality of an existing track.

    “This thing is wild, and the idea that I can create entirely new sounds on the fly in the studio is incredible,” said Ido Zmishlany, a multi-platinum producer and songwriter and cofounder of One Take Audio, a member of the NVIDIA Inception program for cutting-edge startups.

    Fugatto is a foundational generative transformer model that builds on Nvidia’s prior work in speech modeling, vocoding, and understanding.

    The full version uses 2.5 billion parameters and was trained on a bank of NVIDIA DGX systems packing 32 NVIDIA H100 Tensor Core GPUs.

    Other players like Runway and Meta have introduced models that generate audio or video from a text prompt.

  • Anthropic Open Sourced a New Standard for Connecting Assistants to AI Models

    Anthropic Open Sourced a New Standard for Connecting Assistants to AI Models

    IBL News | New York

    Anthropic, the creator of the Claude chatbot, open-sourced yesterday a new standard called Model Context Protocol (MCP) for connecting AI assistants to the systems where data lives. The standard aims to produce better, more relevant responses to queries. MCP works for any model, not just Anthropic’s.

    AI assistants have gained mainstream adoption, but even the most sophisticated models are constrained by their isolation from data—trapped behind information silos and legacy systems. Every new data source requires custom implementation, making truly connected systems challenging to scale.

    Anthropic explained that MCP addresses this challenge by providing a universal, open standard for connecting AI systems with data sources, replacing fragmented integrations with a single protocol. “The result is a simpler, more reliable way for AI systems to access the data they need,” the company said.

    The architecture is straightforward: developers can expose their data through MCP servers or build AI applications (MCP clients) that connect to these servers.

    There are three major components of the Model Context Protocol for developers:

    To help developers start exploring, Anthropic shared pre-built MCP servers for popular enterprise systems like Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer.

    Early adopters like Block and Apollo have integrated MCP into their systems, while development tools companies, including Zed, Replit, Codeium, and Sourcegraph, are working with MCP to enhance their platforms.

    This enables AI agents to retrieve relevant information more effectively, understand the context surrounding a coding task more fully, and produce more nuanced and functional code with fewer attempts.

    “Open technologies like the Model Context Protocol are the bridges that connect AI to real-world applications, ensuring innovation is accessible, transparent, and rooted in collaboration,” said Dhanji R. Prasanna, Chief Technology Officer at Block.

    MCP ostensibly solves this problem through a protocol that enables developers to build two-way connections between data sources and AI-powered chatbots and applications. Developers can expose data through “MCP servers” and create “MCP clients” — for instance, apps and workflows — that connect to those servers on command.

    Rivals like OpenAI prefer that customers and ecosystem partners use their data-connecting approaches and specifications. OpenAI has said it plans to bring the capability, called Work with Apps, to other types of apps.

  • Peter Thiel Backed – Mercor AI, Which Uses AI for Job Interviewing, Valued at $250M

    Peter Thiel Backed – Mercor AI, Which Uses AI for Job Interviewing, Valued at $250M

    IBL News | New York

    Mercor, which uses AI to vet and interview job candidates and then match them to open roles, has conducted more than 100,000 interviews and evaluated 300,000 people in less than two years.

    With a workforce of 15 employees, this AI interviewer start-up is now valued at $250 million, following a $32 million round. The business is profitable and has grown 50% month over month.

    Billionaire investor Peter Thiel, Twitter cofounder Jack Dorsey, two OpenAI board directors, Quora CEO Adam D’Angelo, and former Treasury Secretary Larry Summers also personally invested.

    Mercor’s marketplace now depends on its own LLM, which builds on OpenAI and fine-tunes its proprietary data around its job-seeking process.

    Applicants upload their resumes and take a 20-minute video interview with Mercor’s AI. Half that time is spent discussing the candidate’s experience, and the other half is spent responding to a relevant case study.

    The job seeker’s application is then matched against all possible open jobs on Mercor’s marketplace. For more specialized roles, a second, tailored AI interview might follow.

    Mercor promises to quickly connect with employers’ qualified candidates through contracted hourly, part-time, and full-time commitments.

    Mercor’s largest pool of such talent remains in India.

    The roles include engineering, product development, design, operations, and content.

    Mercor faces competition from well-capitalized talent markets, such as startup unicorn Andela.