Page 9 – Advance Aii

Microsoft’s Bold AI Bet with OpenAI

Microsoft’s Bold AI Bet with OpenAI Microsoft’s Bold AI Bet with OpenAI is redefining the competitive landscape of enterprise technology. As businesses around the world accelerate their digital transformation, Microsoft has made a calculated decision to embed artificial intelligence at the foundation of its future strategy. Led by CEO Satya Nadella, the company’s integration of OpenAI models into Azure, Microsoft 365, GitHub, and the Power Platform reflects not just an aspiration but a full-scale deployment of AI into real-world workflows. With rival tech firms like Google, Amazon, and Meta charting their own AI paths, Microsoft is positioning itself as the enterprise leader through innovation, versatile tools like Copilot, and its unique cloud-AI fusion. Key Takeaways Microsoft is heavily investing in responsible AI through strategic integration with OpenAI and its Azure cloud infrastructure. Copilot tools across GitHub, Microsoft 365, and Power Platform are increasing productivity and reshaping developer and enterprise workflows. Satya Nadella’s AI vision emphasizes accessibility, security, and broad deployment across Microsoft’s tech stack. Microsoft stands apart from Google, Amazon, and Meta by operationalizing AI faster with real use cases and enterprise reach. Also Read: Nvidia’s Bold Investment in Robotics and AI Microsoft’s AI Strategy: Built for Scale and Trust...

Kushal Kedia (left) and Prithwish Dan (right) are members of the development team behind RHyME, a system that allows robots to learn tasks by watching a single how-to video. By Louis DiPietro Cornell researchers have developed a new robotic framework powered by artificial intelligence – called RHyME (Retrieval for Hybrid Imitation under Mismatched Execution) – that allows robots to learn tasks by watching a single how-to video. RHyME could fast-track the development and deployment of robotic systems by significantly reducing the time, energy and money needed to train them, the researchers said. “One of the annoying things about working with robots is collecting so much data on the robot doing different tasks,” said Kushal Kedia, a doctoral student in the field of computer science and lead author of a corresponding paper on RHyME. “That’s not how humans do tasks. We look at other people as inspiration.” Kedia will present the paper, One-Shot Imitation under Mismatched Execution, in May at the Institute of Electrical and Electronics Engineers’ International Conference on Robotics and Automation, in Atlanta. Home robot assistants are still a long way off – it is a very difficult task to train robots to deal with all the potential scenarios...

Artificial Intelligence (AI) continues to transform industries with its speed, relevance, and accuracy. However, despite impressive capabilities, AI systems often face a critical challenge known as the AI reliability gap—the discrepancy between AI’s theoretical potential and its real-world performance. This gap manifests in unpredictable behavior, biased decisions, and errors that can have significant consequences, from misinformation in customer service to flawed medical diagnoses. To address these challenges, Human-in-the-Loop (HITL) systems have emerged as a vital approach. HITL integrates human intuition, oversight, and expertise into AI evaluation and training, ensuring that AI models are reliable, fair, and aligned with real-world complexities. This article explores the design of effective HITL systems, their importance in closing the AI reliability gap, and best practices informed by current trends and success stories. Understanding the AI Reliability Gap and the Role of Humans AI systems, despite their advanced algorithms, are not infallible. Real-world examples: Incident Error Type Potential HITL Intervention Canadian airline’s AI chatbot gave costly misinformation Misinformation / Incorrect Response Human review of chatbot responses during critical queries could catch and correct errors before they impact customers. AI recruiting tool discriminated based on age Bias / Discrimination Regular audits and human oversight in screening decisions...

Podcast: Play in new window | Download | Embed Subscribe: I have now been vegan for almost 9 years and people keep asking me how I feel and why I did it. So let me share a quick health update as well as the original 6 reasons why I went vegan. Since going vegan I have lost and kept off 25 pounds or about 12 kgs. My body fat has gone down from about 22% to about 14%. My blood pressure went down from about 130 over 90 to about 115 over 75. My resting heart rate has dropped 5 beats to about 52. My BMI dropped almost 2 points to 22. My iron went up. My calcium went up. My triglycerides went down. My overall cholesterol went down. My sugar is stable at 5.1. Even my urologist said that my PSA is like that of a teenager. Now, to be fair, not everything in my bloodwork is perfect and I am still very much struggling with and testing different nutritional protocols to get my LDL marker into the optimal range. However, at 48, I feel subjectively better, people often tell me that I look better, and my bloodwork...

Keywords are no science but an art. There is no such thing as ‘the right keyword,’ as we’re talking about a core concept incorporated into a piece of content in the broadest form. Texts don’t necessarily need to contain an exact keyword. For example, if the term ‘European Union’ is used several times, ‘European Commission’ may be a suitable keyword even though the writer never uses the term. Despite this fluid definition, keywords should be understandable to those who try to find the right ones. That’s where automated labelling comes in. Why should you use automated labelling? When teachers and students use keywords to find specific materials on the internet or in a learning repository, a full-text search doesn’t always suffice. But if content is labelled at a very granular level, keywords might do the trick. This type of labelling can only be done in an automated way, as you’re not simply attributing keywords to a book. Rather, you’re labelling paragraphs so your target audience can search content in an easy, accessible way. It’s impossible to guarantee consistency when tackling such a detailed, refined task manually. Keyword extraction and automated labels In the case of CEFR, you use data that...

object(WP_Post)#8429 (24) { [“ID”]=> int(37459) [“post_author”]=> string(2) “36” [“post_date”]=> string(19) “2025-02-10 22:15:40” [“post_date_gmt”]=> string(19) “2025-02-10 22:15:40” [“post_content”]=> string(2714) “ AI is reshaping the beauty industry, forcing brands to navigate a delicate balance between innovation and authenticity. While AI-driven tools offer hyper-personalised experiences and new creative possibilities, they also raise concerns about unrealistic beauty standards, ethical transparency, and audience trust. Brands must carefully craft their messaging—lean too far into AI, and they risk alienating audiences who crave innovation but are increasingly wary of artificiality. The challenge lies in meeting audience demand for cutting-edge beauty solutions while maintaining credibility in an industry where authenticity is everything. By analysing industry trends and online discussions, we uncover the shifts driving the next era of communications and visualisation, but through the lens of an industry that is one of the first to tackle AI intervention head-on. Key insights include: How AI is redefining beauty standards and personalisation The ethical considerations shaping trust and brand decisions The role of social media in amplifying AI beauty trends Get your copy now Download now ” [“post_title”]=> string(63) “How AI is transforming beauty conversations & communication” [“post_excerpt”]=> string(107) “Explore how journalism shapes trust, audience engagement, and media influence in...

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel artificial intelligence model inspired by neural oscillations in the brain, with the goal of significantly advancing how machine learning algorithms handle long sequences of data. AI often struggles with analyzing complex information that unfolds over long periods of time, such as climate trends, biological signals, or financial data. One new type of AI model, called “state-space models,” has been designed specifically to understand these sequential patterns more effectively. However, existing state-space models often face challenges — they can become unstable or require a significant amount of computational resources when processing long data sequences. To address these issues, CSAIL researchers T. Konstantin Rusch and Daniela Rus have developed what they call “linear oscillatory state-space models” (LinOSS), which leverage principles of forced harmonic oscillators — a concept deeply rooted in physics and observed in biological neural networks. This approach provides stable, expressive, and computationally efficient predictions without overly restrictive conditions on the model parameters. “Our goal was to capture the stability and efficiency seen in biological neural systems and translate these principles into a machine learning framework,” explains Rusch. “With LinOSS, we can now reliably learn long-range interactions,...

Space Engineers community, I have good news to share: Space Engineers 2 will use Steam Workshop as its primary platform for user-generated content. This decision comes directly from listening to our community, particularly our content creators, who have expressed a strong preference for Steam Workshop. Our initial plan to use Mod.io was driven by the goal of providing a unified backend for both PC and console platforms. However, your feedback made it clear that Steam Workshop’s established ecosystem and features are crucial for our PC community. When our community speaks with such a clear voice, it’s important for us to listen and adapt our plans accordingly. Steam Workshop support will be added in our first major update (VS 1.1) after the Early Access launch on January 27. For our console players, we are evaluating the best approach to provide them with UGC access and will share more details as we progress. Thank you for your passionate feedback. Your input continues to help shape Space Engineers 2’s development. Best regards, Marek Rosa CEO & Game DirectorKeen Software House

Skip to content AIRoboticsPro@gmail.com AI Robotics pro We don’t renovate spaces,we transform them HOME BLOG CONTACT Home Blog Contact Are you ready for more? We are here to serve your needs. And if you’d like to learn more, let’s get started. I’m Ready About Our Vision We can make Robots as smart as a human by using a cloud brain.Helpful humanoid robots will be affordable for homes by 2025. This will be achieved by cloud-connected robots,where diverse models of robots share a brain hosted on a cloud platform. Your robot will have access to an ever-growing number of skillssimilar your smart phone’s access to apps today. Our Mission Operating Smart Robots for People. We make helpful robot services possible; and to make them safe, secure and affordable. Our mission is to implement the Vision. As breakthroughs continue along the way to the Vision becoming reality, AIRoboticsPro is preparing to be an operator of diverse models robotsfor people with a wide range of interests and needs. We Make Robots SmarterTM Have a robot? We can make it smarter. Have AI skills? We can integrate them into ever-expanding cloud brains. AIRoboticsPro is the creator of an emerging fabric to connect a...

With the rapid advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs), many believe OCR has become obsolete. If LLMs can “see” and “read” documents, why not use them directly for text extraction? The answer lies in reliability. Can you always be a 100% sure of the veracity of text output that LLMs interpret from a document/image? We put this to test with a simple experiment. We asked colleagues to use any LLM of their choice to extract a list of passenger names (10) from a sample PDF flight ticket. 💡 The results were quite interesting – Claude 3 Opus couldn’t read the PDF at all, Claude 3.5 Sonnet missed one passenger’s name, the output from ChatGPT o3-mini was completely made up (100% hallucination), ChatPDF missed half of the passengers on the list. Only NotebookLM and Deepseek got the list of names completely right! While LLMs can interpret and summarize documents, they lack the precision and structured output required for critical business applications where 100% data accuracy is crucial. Additionally, LLMs require significant computational resources, making them costly and impractical for large-scale document processing, especially in enterprise and edge deployments. OCR, on the other hand, is optimized for efficiency, running...