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Research conducted by Vlerick Business School has discovered that in the area of AI financial planning, the technology consistently outperforms humans when allocating budgets with strategic guidelines in place. Businesses that use AI for budgeting processes experience substantial improvements in the accuracy and efficiency of budgeting plans compared to human decision-making.
March 2025 has been a pivotal month for Generative AI, with significant advancements propelling us closer to Artificial General Intelligence (AGI). From intelligent model releases like Gemini 2.5 Pro and ERNIE 4.5 to innovations in agentic AI with Manus AI, weve witnessed a rapid evolution in the AI landscape in the last month. All these […] The post March 2025 GenAI Launches: Are We on The Brink of AGI?
Anthropic has provided a more detailed look into the complex inner workings of their advanced language model, Claude. This work aims to demystify how these sophisticated AI systems process information, learn strategies, and ultimately generate human-like text. As the researchers initially highlighted, the internal processes of these models can be remarkably opaque, with their problem-solving methods often “inscrutable to us, the models developers.” Gaining a deeper understanding of t
Large language models (LLMs) are rapidly evolving from simple text prediction systems into advanced reasoning engines capable of tackling complex challenges. Initially designed to predict the next word in a sentence, these models have now advanced to solving mathematical equations, writing functional code, and making data-driven decisions. The development of reasoning techniques is the key driver behind this transformation, allowing AI models to process information in a structured and logical ma
Start building the AI workforce of the future with our comprehensive guide to creating an AI-first contact center. Learn how Conversational and Generative AI can transform traditional operations into scalable, efficient, and customer-centric experiences. What is AI-First? Transition from outdated, human-first strategies to an AI-driven approach that enhances customer engagement and operational efficiency.
Why are AI chatbots so intelligentcapable of understanding complex ideas, crafting surprisingly good short stories, and intuitively grasping what users mean? The truth is, we dont fully know. Large language models think in ways that dont look very human. Their outputs are formed from billions of mathematical signals bouncing through layers of neural networks powered by computers of unprecedented power and speed, and most of that activity remains invisible or inscrutable to AI researchers.
In the latest MLPerf Inference V5.0 benchmarks, which reflect some of the most challenging inference scenarios, the NVIDIA Blackwell platform set records and marked NVIDIAs first MLPerf submission using the NVIDIA GB200 NVL72 system, a rack-scale solution designed for AI reasoning. Delivering on the promise of cutting-edge AI takes a new kind of compute infrastructure, called AI factories.
In the latest MLPerf Inference V5.0 benchmarks, which reflect some of the most challenging inference scenarios, the NVIDIA Blackwell platform set records and marked NVIDIAs first MLPerf submission using the NVIDIA GB200 NVL72 system, a rack-scale solution designed for AI reasoning. Delivering on the promise of cutting-edge AI takes a new kind of compute infrastructure, called AI factories.
The AI model market is growing quickly, with companies like Google , Meta , and OpenAI leading the way in developing new AI technologies. Googles Gemma 3 has recently gained attention as one of the most powerful AI models that can run on a single GPU, setting it apart from many other models that need much more computing power. This makes Gemma 3 appealing to many users, from small businesses to researchers.
RAG, or Retrieval-Augmented Generation, has received widespread acceptance when it comes to reducing model hallucinations and enhancing the domain-specific knowledge base of large language models (LLMs). Corroborating information produced by an LLM with external data sources has helped keep the model outputs fresh and authentic. However, recent findings in a RAG system have underscored the […] The post What is Bias in a RAG System?
This post is co-authored with Joao Moura and Tony Kipkemboi from CrewAI. The enterprise AI landscape is undergoing a seismic shift as agentic systems transition from experimental tools to mission-critical business assets. In 2025, AI agents are expected to become integral to business operations, with Deloitte predicting that 25% of enterprises using generative AI will deploy AI agents, growing to 50% by 2027.
Powered by metronome.com Welcome Interested in sponsorship opportunities? Join the AI conversation and transform your advertising strategy with AI weekly sponsorship aiweekly.co In the News How the U.S. Public and AI Experts View AI The public and experts are far apart in their enthusiasm and predictions for AI. But they share similar views in wanting more personal control and worrying regulation will fall short pewresearch.org Sponsor The proven playbook for launching usage-based pricing Thinki
Today’s buyers expect more than generic outreach–they want relevant, personalized interactions that address their specific needs. For sales teams managing hundreds or thousands of prospects, however, delivering this level of personalization without automation is nearly impossible. The key is integrating AI in a way that enhances customer engagement rather than making it feel robotic.
A new study from the AI Disclosures Project has raised questions about the data OpenAI uses to train its large language models (LLMs). The research indicates the GPT-4o model from OpenAI demonstrates a “strong recognition” of paywalled and copyrighted data from O’Reilly Media books. The AI Disclosures Project, led by technologist Tim O’Reilly and economist Ilan Strauss, aims to address the potentially harmful societal impacts of AI’s commercialisation by advocating
Large language models (LLMs) like Claude have changed the way we use technology. They power tools like chatbots, help write essays and even create poetry. But despite their amazing abilities, these models are still a mystery in many ways. People often call them a “black box” because we can see what they say but not how they figure it out.
With the multimodal space expanding rapidly, thanks to models like Runways Gen-4, OpenAIs Sora, and some quietly impressive video synthesis efforts by ByteDance, it was only a matter of time before Meta AI joined the bandwagon. And now, they have. Meta has released a research paper along with demo examples of their new video generation […] The post MoCha: Metas Movie-Grade Leap in Talking Character Synthesis appeared first on Analytics Vidhya.
One of the most frustrating things about using a large language model is dealing with its tendency to confabulate information , hallucinating answers that are not supported by its training data. From a human perspective, it can be hard to understand why these models don't simply say "I don't know" instead of making up some plausible-sounding nonsense.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
AI agents extend large language models (LLMs) by interacting with external systems, executing complex workflows, and maintaining contextual awareness across operations. Amazon Bedrock Agents enables this functionality by orchestrating foundation models (FMs) with data sources, applications, and user inputs to complete goal-oriented tasks through API integration and knowledge base augmentation.
As the adoption of AI accelerates, organisations may overlook the importance of securing their Gen AI products. Companies must validate and secure the underlying large language models (LLMs) to prevent malicious actors from exploiting these technologies. Furthermore, AI itself should be able to recognise when it is being used for criminal purposes. Enhanced observability and monitoring of model behaviours, along with a focus on data lineage can help identify when LLMs have been compromised.
In a major leap forward for productivity software, Sourcetable has announced a $4.3 million seed round and the launch of what it calls the worlds first autonomous, AI-powered spreadsheet a “self-driving” experience that reimagines how humans interact with data. With backing from top investors including Bee Partners , Julien Chaumond (Hugging Face), Preston-Werner Ventures (GitHub co-founder), Roger Bamford (MongoDB), and James Beshara (Magic Mind), Sourcetable is taking aim at a pro
Recent advancements in reasoning models, such as OpenAI’s o1 and DeepSeek R1, have propelled LLMs to achieve impressive performance through techniques like Chain of Thought (CoT). However, the verbose nature of CoT leads to increased computational costs and latency. A novel paper published by Zoom Communications presents a new prompting technique called Chain of Draft […] The post Chain of Draft Prompting with Gemini and Groq appeared first on Analytics Vidhya.
The DHS compliance audit clock is ticking on Zero Trust. Government agencies can no longer ignore or delay their Zero Trust initiatives. During this virtual panel discussion—featuring Kelly Fuller Gordon, Founder and CEO of RisX, Chris Wild, Zero Trust subject matter expert at Zermount, Inc., and Principal of Cybersecurity Practice at Eliassen Group, Trey Gannon—you’ll gain a detailed understanding of the Federal Zero Trust mandate, its requirements, milestones, and deadlines.
What does it take to get OpenAI and Anthropictwo competitors in the AI assistant marketto get along? Despite a fundamental difference in direction that led Anthropic's founders to quit OpenAI in 2020 and later create the Claude AI assistant, a shared technical hurdle has now brought them together: How to easily connect their AI models to external data sources.
Visual Studio Code (VSCode) is a powerful, free source-code editor that makes it easy to write and run Python code. This guide will walk you through setting up VSCode for Python development, step by step. Prerequisites Before we begin, make sure you have: Python installed on your computer An internet connection Basic familiarity with your computer’s operating system Step 1: Download and Install Visual Studio Code Windows, macOS, and Linux Go to the official VSCode website: [link] Click the
Amazon has introduced Nova Act, an advanced AI model engineered for smarter agents that can execute tasks within web browsers. While large language models popularised the concept of agents as tools that answer queries or retrieve information via methods such as Retrieval-Augmented Generation (RAG), Amazon envisions something more robust. The company defines agents not just as responders but as entities capable of performing tangible, multi-step tasks in diverse digital and physical environments.
Imagine a world where Artificial Intelligence (AI ) is not only driving cars or recognizing faces but also determining which government jobs are essential and which should be cut. This concept, once considered a distant possibility, is now being proposed by one of the most influential figures in technology, Elon Musk. Through his latest venture, the Department of Government Efficiency (DOGE) , Musk aims to revolutionize how the U.S. government operates by using AI to streamline federal operation
Speaker: Alexa Acosta, Director of Growth Marketing & B2B Marketing Leader
Marketing is evolving at breakneck speed—new tools, AI-driven automation, and changing buyer behaviors are rewriting the playbook. With so many trends competing for attention, how do you cut through the noise and focus on what truly moves the needle? In this webinar, industry expert Alexa Acosta will break down the most impactful marketing trends shaping the industry today and how to turn them into real, revenue-generating strategies.
Imagine an AI that can write poetry, draft legal documents, or summarize complex research papersbut how do we truly measure its effectiveness? As Large Language Models (LLMs) blur the lines between human and machine-generated content, the quest for reliable evaluation metrics has become more critical than ever. Enter ROUGE (Recall-Oriented Understudy for Gisting Evaluation), a […] The post ROUGE: Decoding the Quality of Machine-Generated Text appeared first on Analytics Vidhya.
Foundation models (FMs) and generative AI are transforming enterprise operations across industries. McKinsey & Companys recent research estimates generative AI could contribute up to $4.4 trillion annually to the global economy through enhanced operational efficiency, productivity growth of 0.1% to 0.6% annually, improved customer experience through personalized interactions, and accelerated digital transformation.
The Nintendo Switch 2, unveiled April 2, takes performance to the next level, powered by a custom NVIDIA processor featuring an NVIDIA GPU with dedicated RT Cores and Tensor Cores for stunning visuals and AI-driven enhancements. With 1,000 engineer-years of effort across every element from system and chip design to a custom GPU, APIs and world-class development tools the Nintendo Switch 2 brings major upgrades.
Ant Group is relying on Chinese-made semiconductors to train artificial intelligence models to reduce costs and lessen dependence on restricted US technology, according to people familiar with the matter. The Alibaba-owned company has used chips from domestic suppliers, including those tied to its parent, Alibaba , and Huawei Technologies to train large language models using the Mixture of Experts (MoE) method.
The guide for revolutionizing the customer experience and operational efficiency This eBook serves as your comprehensive guide to: AI Agents for your Business: Discover how AI Agents can handle high-volume, low-complexity tasks, reducing the workload on human agents while providing 24/7 multilingual support. Enhanced Customer Interaction: Learn how the combination of Conversational AI and Generative AI enables AI Agents to offer natural, contextually relevant interactions to improve customer exp
Retrieval-Augmented Generation (RAG) is an approach to building AI systems that combines a language model with an external knowledge source. In simple terms, the AI first searches for relevant documents (like articles or webpages) related to a users query, and then uses those documents to generate a more accurate answer. This method has been celebrated for helping large language models (LLMs) stay factual and reduce hallucinations by grounding their responses in real data.
Can AI generate truly relevant answers at scale? How do we make sure it understands complex, multi-turn conversations? And how do we keep it from confidently spitting out incorrect facts? These are the kinds of challenges that modern AI systems face, especially those built using RAG. RAG combines the power of document retrieval with the […] The post Top 13 Advanced RAG Techniques for Your Next Project appeared first on Analytics Vidhya.
The internet: a once wacky world of strange forums and obscure memes, a tool to harness the sum total of human knowledge at a moment's notice. At least, that was before AI slop ruined everything. To feed data-hungry AI models, companies and individuals are deploying a growing army of AI "web crawlers," bots tasked with sifting the internet for text, pictures, and other data.
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