Podcasts about Yunus

Apple changed how we use technology. Grameen changed how the poor use money.From one man's frustration in famine-stricken Bangladesh to a global revolution in microcredit, Muhammad Yunus turned a $27 experiment into one of the world's most powerful engines of poverty reduction.He wasn't supposed to succeed. Banks refused him. Bureaucrats mocked him. Critics swore the poor could never repay loans.But Yunus proved everyone wrong.From the paddy fields of Jobra to the slums of Chicago, Grameen spread an idea that dignity, not charity, was the path out of poverty. Along the way, it became more than a bank. It became a movement.This is the story of Grameen Bank. Of how compassion and credit reshaped lives, industries, and the very meaning of wealth.Episode 1: The First DepositIn famine-stricken Bangladesh, a young economist lends $27 to 42 villagers. What happens next sets in motion a financial revolution.Episode 2: Promise to the PoorBanks dismiss his idea as madness. So Yunus signs his name as collateral and invents a radical new kind of banking.Episode 3: Business is WarTo prove microcredit works beyond one village, Grameen expands into Tangail - a district plagued by violence, rumors, and armed revolutionaries.Episode 4: Growth before the Storm Grameen becomes an independent bank. But just as it grows into a national force, one of the deadliest cyclones in history threatens to wipe it all away.Episode 5: Beyond BankingFrom fish ponds to textiles, from cell phones to America's welfare system, Yunus takes the Grameen model global, imagining a future where poverty belongs only in museums.-Want to showcase your brand to listeners with a combined net worth of over $1 billion and a network of 100,000+ employees and industry contacts? Drop us an email: sales@1upmediapodcast.com-We're looking to grow our team! Support our productions by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠buying us a coffee⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠-Want to meet the team? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow me here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠-If you love the style of Empires, and want similar content, check out:

O Alcorão Sagrado por Surata (Árabe + Brasileiro)

Want to make AI work for your business? In today's episode, Lois Houston and Nikita Abraham continue their discussion of AI in Oracle Fusion Applications by focusing on three key AI capabilities: predictive, generative, and agentic.   Joining them is Principal Instructor Yunus Mohammed, who explains how predictive, generative, and agentic AI can optimize efficiency, support decision-making, and automate tasks—all without requiring technical expertise.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! In our last episode, we explored the essential components of the Oracle AI stack and spoke about Oracle's suite of AI services.  Nikita: Yeah, and in today's episode, we're going to go down a similar path and take a closer look at the AI functionalities within Oracle Fusion Applications. 00:53 Lois: With us today is Principal Instructor Yunus Mohammed. Hi Yunus! It's lovely to have you back with us. For anyone who doesn't already know, what are Oracle Fusion Cloud Applications?  Yunus: Oracle Fusion Applications are a suite of cloud-based enterprise applications designed to run for your business across finance, HR, supply chain, sales, services and more, all on a unified platform. They are designed to help enterprises operate smarter, faster by embedding AI directly into business process. That means better forecasts in finance, faster hiring decisions in HR, and optimized supply chains, and more personalized customer experience.  01:42 Nikita: And we know they've been built for today's fast-paced, AI-driven business environment. So, what are the different functional pillars within Oracle Fusion Apps? Yunus: The first one is the ERP, Enterprise Resource Planning, which supports financials, procurements, and project management. It's the backbone of many organizations, or day-to-day operations. HCM or Human Capital Management, handles workforce-related processes such as hiring, payroll, performance, and talent development, helping HR teams operate more efficiently. SCM, the Supply Chain Management, enables businesses to manage their logistics, inventory, and suppliers and manufacturers in the business. It's particularly critical in industries with complex operations like retail and manufacturing. The CX, which is the Customer Experience, covers the full customer life cycle, which includes sales, marketing, and service. These models help the businesses connect with their customers more personally and proactively, whether through the targeted campaigns or responsive support.  03:02 Lois: Yunus, what sets Fusion apart? Yunus: What sets Fusion apart is how these applications work seamlessly together. They share data natively and continuously improve with AI and automation, giving you not just tools, but intelligence at scale.  Oracle applications are built to be AI first, with a complete suite of finance, supply chain, manufacturing, HR, sales, service, and marketing, that is tightly coupled with our industry and data intelligence applications. The easiest and the most effective way to start building your organization's AI muscle is with AI embedded in Fusion applications. For example, if the customer needs to return a defective product, the service representative simply clicks on Ask Oracle for the answers. Since the AI agent is embedded in the application, it has contextual information about the customer, the order, and any special service, contract, or any other feature that is required for this process. The AI agent automatically figures out the return policy, including the options to send a replacement product immediately or offer a discount for the inconvenience, and also expedite shipping. Another AI agent sends a personalized email confirming details of the return, and different AI agent creates the replacement order for fulfillment and shipping. Our AI-embedded Fusion Applications can automate an end-to-end business process from service request to return order to fulfillment and shipping and then accounting.  These are pre-built and tested so that all the worry and hard work is removed from the implementation point of view. They cover the core workflows. Basically, they address tasks that form part of the organization's core workflow. User requires no technical knowledge in the scenarios.  05:16 Lois: That's great! So, you don't need to be an AI expert or a data scientist to get going. Yunus: The outcomes are super fast in business softwares and context is everything. Just having the right information isn't enough. This is about having the information in the right place at the right time for it to be instantly actionable. They are ready from day one and can be optimized over time. They are powerful out of the box and only get better with day-to-day processes and performance. 05:55 Are you working towards an Oracle Certification this year? Join us at one of our certification prep live events in the Oracle University Learning Community. Get insider tips from seasoned experts and learn from others who have already taken their certifications. Go to community.oracle.com/ou to jump-start your journey towards certification today!  06:20 Nikita: Welcome back! So, when we talk about the AI capabilities in Fusion apps, I know we have different types. Can you tell us more about them?  Yunus: Predictive AI is where it all started. These models analyze historical patterns and data to anticipate what might happen next. For example, predicting employee attrition, forecasting demand in supply chain, or flagging potential late payments in finance workflows. These are embedded into business processes to surface insights before action is needed. Then we have got the generative AI, which takes this a step more further. Instead of just providing insights, it creates content, such as auto-generating job descriptions, summarizing performance reviews, or even crafting draft responses to supplier queries. This saves time and boosts productivity across functions like HR, CX, and procurement. Last but not the least, we have got the agentic AI, which is the most advanced layer. These agents don't just provide suggestions, they take actions on behalf of the users. Think of an agent that not only recommends actions in a workflow, but also executes them, creating tasks, filling tickets, updating systems, and communicating with stakeholders, all autonomously but under user control. And importantly, many business scenarios today benefit from a blend of these types. For example, an AI assistant in Fusion HCM might predict employees turnover, which is predictive AI, generates tailored retention plans, which is generative, and it is generative AI, and initiate outreach or next steps, which is done by the process of agents, which is called agentic AI. So, Oracle integrates these capabilities in a harmonious way, enabling users to act faster, personalize at scale, and drive better business outcomes.  08:39 Lois: Ok, let's get into the specifics. How does Oracle use predictive AI across its Fusion apps, helping businesses anticipate what's coming and act proactively.  Yunus: So in HCM, things like recommended jobs, in this, candidates visiting a potential employer's website encountered an improved online experience, whereby if they have uploaded their resumes, they will be shown job opportunities that match their skills and experience mix. This helps candidates who are unsure what to search by showing them roles and titles they may not have considered. Time to hire provides an estimated as to how long it will take for an HR team to fill an open role, but this is really useful not only in terms of planning, recruitment, but also in terms of understanding whether you might need some temporary cover and for how long will it actually take the process to complete. In the process of supply chain management, the predictive AI is leveraged to revolutionize transit time and estimated time of arrival, which is called as the predictive analysis, enhancing efficiency, and optimizing operations. It can flag abnormal patterns in supply or inventory. For example, if a batch of parts is behaving differently in the production line and predict future demands, helping avoid overstocking or stockouts is a process that can be done by using the SCM predictive analysis or predictive AI. In ERPs, where you can audit your expenses, plan for future expenses, and do dynamic discounting for vendors who are likely to accept earlier payments or earlier payment discounts, it can also speed up reimbursements by automated expense entries. In CX, you have the options to go with adaptive intelligence for sales, which helps representatives prioritize the leads and the likelihood that a specific lead will close, helping representatives focus their time and effort. So predictive scheduling and routing in service delivery ensures that the right resource is assigned to the right customer at the right time, boosting operational efficiency and customer satisfaction, also known as fatigue analysis. 11:23 Lois: Now let's shift our focus to generative AI. How does Oracle implement generative AI across HCM, ERP, Supply Chain, and CX? Yunus: So, in HCM, the generative AI can automatically generate performance review summaries from raw data, saving time for HR teams, and can help you in providing candidates with summaries of their interview process, feedback, and next steps, all auto generated. With AI assistance, goal creation for employees can be automated, and the system analyzes performance data and trends to propose meaningful and attainable goals, aligning them with organizational objectives and employee capabilities. In SCM, similarly, the generative AI process helps you in defining drafting summaries of purchase orders. So generative AI can automatically create clear, readable synopses, and can be summarized with complex negotiations and discussions, making it easier for supply chain managers to analyze supplier proposals, track negotiations, processes, and understand key takeaways. With predictive AI embedded, it is helping you to leverage to help generate the repairs of master definitions of summaries, and can generate descriptions for item based on their specification, helping product teams automatically generate catalog contents. With ERPs, you can automate the creation of business reports, offering more insights and actionable narratives, rather than just showing the raw data. The AI can provide context, interpretations, and recommendations. AI can also take raw project data and generate a comprehensive, easy-to-read project status, reports that stakeholders can quickly review. In CX, we have got service request summarization, which can provide these long summaries for the customer services and the tickets that have been requested by the customers, allowing support teams to understand the key points in the fraction of time, and can also create knowledge base articles directly from common service requests or inquiries, which not only improves internal knowledge management but also empowers customers by enabling self-service. So generative AI can automatically generate success stories or case studies from successful opportunities or sales, which can be used as marketing content or for internal knowledge sharing. 14:20 Nikita: And what about Oracle's Agentic AI? What are its capabilities across the different pillars? Yunus: In HCM, Agentic AI handles the end-to-end onboarding experience, from explaining policies to guiding document submissions, even booking orientation sessions, allowing the HR staff to focus on human engagement. It can further support HR teams during performance review cycles by surfacing high potential employees, pulling in performance data, and recommending next actions like promotions or learning paths. It helps manage time with requests by checking eligibility, policy constraints, and suggesting appropriate substitutes, reducing administrative frictions and errors. With SCM, the Agentic AI Fusion Applications act as a real time Assistant to ensure buyers follow procurement policies, and reducing compliance risk and manual errors. It can also support sales representatives with real-time insights and next best actions during the quoting or ordering process, improving customer satisfaction and sales performance. With ERP, you can handle document intake, extraction, and routing, saving significant time on manual document management across financial functions using Fusion Applications. AI automates reconciliation tasks by matching transactions, flagging anomalies, and suggesting resolutions. It helps you in reducing close cycle timelines and continuously analyzes profit margins. And it recommends the pricing adjustments that can be done in your ERPs. In CX, the Agentic AI Fusion Application supports staff by instantly compiling full customer histories, orders, service requests, interactions, and can act like a real-time assistant, summarizing open tickets and resolutions, helping agents take over or escalate without needing to dig through the notes, and dynamically adjust technicals and technician routes based on traffic, priority, or cancelation, increasing the field efficiency and customer satisfaction. 17:04 Lois: Thank you so much, Yunus. To learn more about the topics covered today, visit mylearn.oracle.com and search for the AI for You course. Nikita: Join us next week as we cover how AI is being applied across sectors like healthcare, finance, and retail, and tackle the big question: how do we keep these technologies aligned with human values? Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 17:30 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Apple changed how we use technology. Grameen changed how the poor use money.From one man's frustration in famine-stricken Bangladesh to a global revolution in microcredit, Muhammad Yunus turned a $27 experiment into one of the world's most powerful engines of poverty reduction.He wasn't supposed to succeed. Banks refused him. Bureaucrats mocked him. Critics swore the poor could never repay loans.But Yunus proved everyone wrong.From the paddy fields of Jobra to the slums of Chicago, Grameen spread an idea that dignity, not charity, was the path out of poverty. Along the way, it became more than a bank. It became a movement.This is the story of Grameen Bank. Of how compassion and credit reshaped lives, industries, and the very meaning of wealth.Episode 1: The First DepositIn famine-stricken Bangladesh, a young economist lends $27 to 42 villagers. What happens next sets in motion a financial revolution.Episode 2: Promise to the PoorBanks dismiss his idea as madness. So Yunus signs his name as collateral and invents a radical new kind of banking.Episode 3: Business is WarTo prove microcredit works beyond one village, Grameen expands into Tangail - a district plagued by violence, rumors, and armed revolutionaries.Episode 4: Growth before the Storm Grameen becomes an independent bank. But just as it grows into a national force, one of the deadliest cyclones in history threatens to wipe it all away.Episode 5: Beyond BankingFrom fish ponds to textiles, from cell phones to America's welfare system, Yunus takes the Grameen model global, imagining a future where poverty belongs only in museums.-Want to showcase your brand to listeners with a combined net worth of over $1 billion and a network of 100,000+ employees and industry contacts? Drop us an email: sales@1upmediapodcast.com-We're looking to grow our team! Support our productions by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠buying us a coffee⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠-Want to meet the team? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow me here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠-If you love the style of Empires, and want similar content, check out:

In this episode, Lois Houston and Nikita Abraham are joined by Principal Instructor Yunus Mohammed to explore Oracle's approach to enterprise AI. The conversation covers the essential components of the Oracle AI stack and how each part, from the foundational infrastructure to business-specific applications, can be leveraged to support AI-driven initiatives.   They also delve into Oracle's suite of AI services, including generative AI, language processing, and image recognition.     AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   -------------------------------------------------------------   Episode Transcript:  00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we discussed why the decision to buy or build matters in the world of AI deployment. Lois: That's right, Niki. Today is all about the Oracle AI stack and how it empowers not just developers and data scientists, but everyday business users as well. Then we'll spend some time exploring Oracle AI services in detail.  01:00 Nikita: Yunus Mohammed, our Principal Instructor, is back with us today. Hi Yunus! Can you talk about the different layers in Oracle's end-to-end AI approach? Yunus: The first base layer is the foundation of AI infrastructure, the powerful compute and storage layer that enables scalable model training and inferences. Sitting above the infrastructure, we have got the data platform. This is where data is stored, cleaned, and managed. Without a reliable data foundation, AI simply can't perform. So base of AI is the data, and the reliable data gives more support to the AI to perform its job. Then, we have AI and ML services. These provide ready-to-use tools for building, training, and deploying custom machine learning models. Next, to the AI/ML services, we have got generative AI services. This is where Oracle enables advanced language models and agentic AI tools that can generate content, summarize documents, or assist users through chat interfaces. Then, we have the top layer, which is called as the applications, things like Fusion applications or industry specific solutions where AI is embedded directly into business workflows for recommendations, forecasting or customer support. Finally, Oracle integrates with a growing ecosystem of AI partners, allowing organizations to extend and enhance their AI capabilities even further. In short, Oracle doesn't just offer AI as a feature. It delivers it as a full stack capability from infrastructure to the layer of applications. 02:59 Nikita: Ok, I want to get into the core AI services offered by Oracle Cloud Infrastructure. But before we get into the finer details, broadly speaking, how do these services help businesses? Yunus: These services make AI accessible, secure, and scalable, enabling businesses to embed intelligence into workflows, improve efficiency, and reduce human effort in repetitive or data-heavy tasks. And the best part is, Oracle makes it easy to consume these through application interfaces, APIs, software development kits like SDKs, and integration with Fusion Applications. So, you can add AI where it matters without needing a data scientist team to do that work.  03:52 Lois: So, let's get down to it. The first core service is Oracle's Generative AI service. What can you tell us about it?  Yunus: This is a fully managed service that allows businesses to tap into the power of large language models. You can actually work with these models from scratch to a well-defined develop model. You can use these models for a wide range of use cases like summarizing text, generating content, answering questions, or building AI-powered chat interfaces.  04:27 Lois: So, what will I find on the OCI Generative AI Console? Yunus: OCI Generative AI Console highlights three key components. The first one is the dedicated AI cluster. These are GPU powered environments used to fine tune and host your own custom models. It gives you control and performance at scale. Then, the second point is the custom models. You can take a base language model and fine tune it using your own data, for example, company manuals or HR policies or customer interactions, which are your own personal data. You can use this to create a model that speaks your business language. And last but not the least, the endpoints. These are the interfaces through which your application connect to the model. Once deployed, your app can query the model securely and at different scales, and you don't need to be a developer to get started. Oracle offers a playground, which is a non-core environment where you can try out models, craft parameters, and test responses interactively. So overall, the generative AI service is designed to make enterprise-grade AI accessible and customizable. So, fitting directly into business processes, whether you are building a smart assistant or you're automating the content generation process.  06:00 Lois: The next key service is OCI Generative AI Agents. Can you tell us more about it?  Yunus: OCI Generative AI agents combines a natural language interface with generative AI models and enterprise data stores to answer questions and take actions. The agent remembers the context, uses previous interactions, and retrieves deeper product speech details. They aren't just static chat bots. They are context aware, grounded in business data, and able to handle multi-turns, follow-up queries with relevant accurate responses, and driving productivity and decision-making across departments like sales, support, or operations. 06:54 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest tech. Whether you're starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That's education.oracle.com/race-to-certification-2025. 07:37 Nikita: Welcome back! Yunus, let's move on to the OCI Language service.  Yunus: OCI Language helps business understand and process natural language at scale. It uses pretrained models, which means they are already trained on large industry data sets and are ready to be used right away without requiring AI expertise. It detects over 100 languages, including English, Japanese, Spanish, and more. This is great for global business that receive multilingual inputs from customers. It works with identity sentiments. For different aspects of the sentence, for example, in a review like, “The food was great, but the service sucked,” OCI Language can tell that food has a positive sentiment while service has a negative one. This is called aspect-based sentiment analysis, and it is more insightful than just labeling the entire text as positive or negative. Then we have got to identify key phrases representing important ideas or subjects. So, it helps in extracting these key phrases, words, or terms that capture the core messages. They help automate tagging, summarizing, or even routing of content like support tickets or emails.  In real life, the businesses are using this for customer feedback analysis, support ticket routing, social media monitoring, and even regulatory compliances.  09:21 Nikita: That's fantastic. And what about the OCI Speech service?  Yunus: The OCI Speech is an AI service that transcribes speech to text. Think of it as an AI-powered transcription engine that listens to the spoken English, whether in audio or video files, and turns it into usable and searchable and readable text. It provides timestamps, so you know exactly when something was said. A valuable feature for reviewing legal discussions, media footages, or compliance audits. OCI Speech even understands different speakers. You don't need to train this from scratch. It is pre-trained model hosted on an API. Just send your audio to the service, and you get an accurate timestamp text back in return. 10:17 Lois: I know we also have a service for object detection… called OCI Vision?  Yunus: OCI Vision uses pretrained, deep learning models to understand and analyze visual content. Just like a human might, you can upload an image or videos, and the AI can tell you what is in it and where they might be useful. There are two primary use cases, which you can use this particular OCI Vision for. One is for object detection. You have got a red color car. So OCI Vision is not just identifying that's a car. It is detecting and labeling parts of the car too, like the bumper, the wheels, the design components. This is a critical in industries like manufacturing, retail, or logistics. For example, in quality control, OCI Vision can scan product images to detect missing or defective parts automatically.  Then we have got the image classification. This is useful in scenarios like automated tagging of photos, managing digital assets, classifying this particular scene or context of this particular scene. So basically, when we talk about OCI Vision, which is actually a fully managed, no complex model training is required for this particular service. It's available via API. It is also working with defining their own custom model for working with the environments. 11:51 Nikita: And the final service is related to text and called OCI Document Understanding, right? Yunus: So OCI Document Understanding allows businesses to automatically extract structured insights from unstructured documents like invoices, contracts, recipes, and also sometimes resumes, or even business documents. 12:13 Nikita: And how does it work? Yunus: OCI reads the content from the scanned document. The OCR is smarter. It recognizes both printed and handwritten text. Then determines what type of document it is. So document classification is done. Text recognition recognizes text, then classifies the document. For example, if this is a purchase order, or bank statement, or any medical report. If your business handles documents in multiple languages, then the AI can actually help in language detection also, which helps you in routing the language or translating that particular language. Many documents contain structured data in table format. Think pricing tables or line items. OCI will help you in extracting these with high accuracy for reporting on feeding into ERP systems. And finally, I would say the key value extraction. It puts our critical business values like invoice numbers, payment amounts, or customer names from fields that may not always allow a fixed format. So, this service reduces the need for manual review, cuts down processes time, and ensures high accuracy for your system. 13:36 Lois: What are the key takeaways our listeners should walk away with after this episode? Yunus: The first one, Oracle doesn't treat AI as just a standalone tool. Instead, AI is integrated from the ground up. Whether you're talking about infrastructure, data platforms, machine learning services, or applications like HCM, ERP, or CX. In real world, the Oracle AI Services prioritize data management, security, and governance, all essential for enterprise AI use cases. So, it is about trust. Can your AI handle sensitive data? Can it comply with regulations? Oracle builds its AI services with strong foundation in data governance, robust security measures, and tight control over data residency and access. So this makes Oracle AI especially well-suited for industries like health care, finance, logistics, and government, where compliance and control aren't optional. They are critical.   14:44 Nikita: Thank you for another great conversation, Yunus. If you're interested in learning more about the topics we discussed today, head on over to mylearn.oracle.com and search for the AI for You course.  Lois: In our next episode, we'll get into Predictive AI, Generative AI, Agentic AI, all with respect to Oracle Fusion Applications. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 15:10 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.  

Apple changed how we use technology. Grameen changed how the poor use money.From one man's frustration in famine-stricken Bangladesh to a global revolution in microcredit, Muhammad Yunus turned a $27 experiment into one of the world's most powerful engines of poverty reduction.He wasn't supposed to succeed. Banks refused him. Bureaucrats mocked him. Critics swore the poor could never repay loans.But Yunus proved everyone wrong.From the paddy fields of Jobra to the slums of Chicago, Grameen spread an idea that dignity, not charity, was the path out of poverty. Along the way, it became more than a bank. It became a movement.This is the story of Grameen Bank. Of how compassion and credit reshaped lives, industries, and the very meaning of wealth.Episode 1: The First DepositIn famine-stricken Bangladesh, a young economist lends $27 to 42 villagers. What happens next sets in motion a financial revolution.Episode 2: Promise to the PoorBanks dismiss his idea as madness. So Yunus signs his name as collateral and invents a radical new kind of banking.Episode 3: Business is WarTo prove microcredit works beyond one village, Grameen expands into Tangail - a district plagued by violence, rumors, and armed revolutionaries.Episode 4: Growth before the Storm Grameen becomes an independent bank. But just as it grows into a national force, one of the deadliest cyclones in history threatens to wipe it all away.Episode 5: Beyond BankingFrom fish ponds to textiles, from cell phones to America's welfare system, Yunus takes the Grameen model global, imagining a future where poverty belongs only in museums.-Want to showcase your brand to listeners with a combined net worth of over $1 billion and a network of 100,000+ employees and industry contacts? Drop us an email: sales@1upmediapodcast.com-We're looking to grow our team! Support our productions by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠buying us a coffee⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠-Want to meet the team? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow me here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠-If you love the style of Empires, and want similar content, check out:

Yunus, is the 10th chapter (surah) of the Quran with 109 verses. Yunus is named after the prophet Yunus. According to tafsir chronology, it is believed to have been revealed before the migration of the Islamic prophet Muhammed and his followers from Mecca to Medina. As such, it is known as a Meccan surah.If your interested in more topics related to this Deen of ors follow us on Instagram and X @TheHaqqDawahGrpCheck us out on Youtube to see our new line up. We are currently in the middle of the series Targeted: The Imam Jamil Story. New Episodes every Tuesday. Haqq Dawah Media Youtube channel 

How do you decide whether to buy a ready-made AI solution or build one from the ground up? The choice is more than just a technical decision; it's about aligning AI with your business goals.   In this episode, Lois Houston and Nikita Abraham are joined by Principal Instructor Yunus Mohammed to examine the critical factors influencing the buy vs. build debate. They explore real-world examples where businesses must weigh speed, customization, and long-term strategy. From a startup using a SaaS chatbot to a bank developing a custom fraud detection model, Yunus provides practical insights on when to choose one approach over the other.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! Last week, we spoke about the key stages in a typical AI workflow and how data quality, feedback loops, and business goals influence AI success. 00:50 Nikita: In today's episode, we're going to explore whether you should buy or build AI apps. Joining us again is Principal Instructor Yunus Mohammed. Hi Yunus, let's jump right in. Why does the decision of buy versus build matter? Yunus: So when we talk about buy versus build matters, we need to consider the strategic business decisions over here. They are related to the strategic decisions which the business makes, and it is evaluated in the decision lens. So the center of the decision lens is the business objective, which identifies what are we trying to solve. Then evaluate our constraints based on that particular business objective like the cost, the time, and the talent. And finally, we can decide whether we need to buy or build. But remember, there is no single correct answer. What's right for one business may not be working for the other one. 01:54 Lois: OK, can you give us examples of both approaches? Yunus: The first example where we have got a startup using a SaaS AI chatbot. Now, being a startup, they have to choose a ready-made solution, which is an AI chatbot. Now, the question is, why did they do this? Because speed and simplicity mattered more than deep customization that is required for the chatbot. So, their main aim was to have it ready in short period of time and make it more simpler. And this actually lead them to get to the market fast with low upfront cost and minimal technical complexities. But in some situations, it might be different. Like, your bank, which needs to build a fraud model. It cannot be outsourced or got from the shelf. So, they build a custom model in-house. With this custom model, they actually have a tighter control, and it is tuned to their standards. And it is created by their experts. So these two generic examples, the chatbot and the fraud model example, helps you in identifying whether I should go for a SaaS product with simple choice of selecting an existing LLM endpoint and not making any changes. Or should I go with model depending on my business and organization requirement and fine tuning that model later to define a better implementation of the scenarios or conditions that I want to do which are specific to my organization. So here you decide with the reference whether I want it to be done faster, or whether I want to be more customized to my organization. So buy it, when it is generic, or build when it is strategic. The SaaS, which is basically software as a service, refers to ready to use cloud-based applications that you access via internet. You can log into the platform and use the built-in AI, there's no setup requirement for those. Real-world examples can be Oracle Fusion apps with AI features enabled. So in-house integration means embedding AI with my own requirements into your own systems, often using custom APIs, data pipelines, and hosting it. It gives you more flexibility but requires a lot of resources and expertise. So real-world example for this scenario can be a logistics heavy company, which is integrating a customer support model into their CX. 04:41 Lois: But what are the pros and cons of each approach? Yunus: So, SaaS and Fusion Applications, basically, they offer fast deployment with little to no coding required, making them ideal for business looking to get started quickly and faster. And they typically come with lower upfront costs and are maintained by vendor, which means updates, security, support are handled externally. However, there are limited customizations and are best suited for common, repeatable use cases. Like, it can be a standard chatbot, or it can be reporting tools, or off the shelf analytics that you want to use. But the in-house or custom integration, you have more control, but it takes longer to build and requires a higher initial investment. The in-house or custom integration approach allows full customization of the features and the workflows, enabling you to design and tailor the AI system to your specific needs. 05:47 Nikita: If you're weighing the choice between buying or building, what are the critical business considerations you'd need to take into account? Yunus: So let's take one of the key business consideration which is time to market. If your goal is to launch fast, maybe you're a startup trying to gain traction quickly, then a prebuilt plug and play AI solution, for example, a chatbot or any other standard analytical tool, might be your best bet. But if you have time and you are aiming for precision, a custom model could be worth the wait. Prebuilt SaaS tools usually have lower upfront costs and a subscription model. It works with putting subscriptions. Custom solutions, on the other hand, may require a bigger investment upfront. In development, you require high talent and infrastructures, but could offer cost savings in the long run. So, ask yourself a question here. Is this AI helping us stand out in the market? If the answer is yes, you may want to build something which is your proprietary. For example, an organization would use a generic recommendation engine. It's a part of their secret sauce. Some use cases require flexibility, like you want to tailor the rules to match your specific risk criteria. So, under that scenarios, you will go for customizing. So, you will go with off the shelf solutions may not give you deep enough requirements that you want to evaluate. So, you get those and you try to customize those. You can go for customization of your AI features. The other important key business consideration is the talent and expertise that your organization have. So, the question that you need to ask in the organization is, do you have an internal team who is well versed in developing AI solutions for you? Or do you have access to one of the teams which can help you build your own proprietary products? If not, you'll go with SaaS. If you do have, then building could unlock greater control over your AI features and AI models. The next core component is your security and data privacy. If you're handling sensitive information, like for example, the health care or finance data, you might not want to send your data to the third-party tools. So in-house models offer better control over data security and compliance. When we leverage a model, it could be a prebuilt or custom model. 08:50 Oracle University is proud to announce three brand new courses that will help your teams unlock the power of Redwood—the next generation design system. Redwood enhances the user experience, boosts efficiency, and ensures consistency across Oracle Fusion Cloud Applications. Whether you're a functional lead, configuration consultant, administrator, developer, or IT support analyst, these courses will introduce you to the Redwood philosophy and its business impact. They'll also teach you how to use Visual Builder Studio to personalize and extend your Fusion environment. Get started today by visiting mylearn.oracle.com.  09:31 Nikita: Welcome back! So, getting back to what you were saying before the break, what are pre-built and custom models? Yunus: A prebuilt model is an AI solution that has already been trained by someone else, typically a tech provider. It can be used to perform a specific task like recognizing images, translating text, or detecting sentiments. You can think of it like buying a preassembled appliance. You plug it in, configure a few settings, and it's ready to use. You don't need to know how the internal parts work. You benefit from the speed, ease, and reliability of this particular model, which is a prebuilt model. But you can't easily change how it works under the hood. Whereas, a custom model is an AI solution that your organization designs and trains and tunes specifically for their business problems using their own data. You can think of it like designing your own suit. It takes more time and effort to create. It is built to your exact measurements and needs. And you have full control over how it performs and evolves. 10:53 Lois: So, when would you choose a pre-built versus a custom model? Yunus: Depending on speed, simplicity, control, and customization, you can decide on using a prebuilt or to create a custom model. Prebuilt models are like plug and play solutions. Think of tools like Google Translate for languages. OpenAI APIs for summarizing sentiment analysis or chatbots, they are quick to deploy, require low technical effort, great for getting started fast, but they also have limits. Customization is very minimal, and you may not be able to fine tune it to your specific tone or business logic. These work well when the problem is common and nonstrategic, like, scanning documents or auto tagging images. The custom-build model, on the other hand, is a model that is built from the ground up. Using your own data and objectives, they take longer, and they require technical expertise. But they offer precise control, full alignment with your business needs. And these are ideal when you are dealing with sensitive data, competitive workflows, highly specific customer interactions. For example, a bank may build a custom model which can be used for fraud detection, which can be tuned to their exact transaction standards and the patterns of their transactions. 12:37 Nikita: What if someone wants the best of both worlds?  Yunus: We've also got a hybrid approach. In hybrid approach, we actually talk about the adaptation of AI with a strategy which is termed as hybrid strategy. Many companies today don't start by building AI from scratch. Instead, they begin with prebuilt models, like using an API, which can be already performing tasks like summarizing, translating, or answering questions using generic knowledge. This set will help you in getting up and running quickly with a small level results. As your business matures, you can start to layer in your custom data. Think internal policies, frequently asked questions, or customer interactions. And then you can fine tune the model to behave the way your business needs it to behave. Now, your AI starts producing business-ready output, smarter, more relevant, and aligned with your tone, brand, or compliance needs.  13:45 Lois: Ok…let's think of AI deployment in the hybrid approach as following a pyramid or ladder like structure. Can you take us through the different levels?  Yunus: So, on the top, quick start, minimal setup, great for business automation, which can be used as a pilot use case. So, if I'm taking off the shelf APIs or platforms, they can be giving me a faster, less set of requirements, and they are basically acting like a pilot use. Later, you can add your own data or logic so you can add your data. You can fine tune or change your business logic. And this is where fine tuning and prompt engineering helps tailor the AI to your workflows and your language. And then at the end, which is at the bottom, you build your own model. It is reserved for core capabilities or competitive advantages where total control and differentiation matters in building that particular model. You don't need to go all in from one day. So, start with what is available, like, use an off shelf, API, or platform, customize as you grow. Build only when it gives you a true edge. This is what we call the best of both worlds, build and buy. 15:05 Lois: Thank you so much, Yunus, for joining us again. To learn more about the topics covered today, visit mylearn.oracle.com and search for the AI for You course. Nikita: Join us next week for another episode of the Oracle University Podcast where we discuss the Oracle AI stack and Oracle AI services. Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 15:29 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Apple changed how we use technology. Grameen changed how the poor use money.From one man's frustration in famine-stricken Bangladesh to a global revolution in microcredit, Muhammad Yunus turned a $27 experiment into one of the world's most powerful engines of poverty reduction.He wasn't supposed to succeed. Banks refused him. Bureaucrats mocked him. Critics swore the poor could never repay loans.But Yunus proved everyone wrong.From the paddy fields of Jobra to the slums of Chicago, Grameen spread an idea that dignity, not charity, was the path out of poverty. Along the way, it became more than a bank. It became a movement.This is the story of Grameen Bank. Of how compassion and credit reshaped lives, industries, and the very meaning of wealth.Episode 1: The First DepositIn famine-stricken Bangladesh, a young economist lends $27 to 42 villagers. What happens next sets in motion a financial revolution.Episode 2: Promise to the PoorBanks dismiss his idea as madness. So Yunus signs his name as collateral and invents a radical new kind of banking.Episode 3: Business is WarTo prove microcredit works beyond one village, Grameen expands into Tangail - a district plagued by violence, rumors, and armed revolutionaries.Episode 4: Growth before the Storm Grameen becomes an independent bank. But just as it grows into a national force, one of the deadliest cyclones in history threatens to wipe it all away.Episode 5: Beyond BankingFrom fish ponds to textiles, from cell phones to America's welfare system, Yunus takes the Grameen model global, imagining a future where poverty belongs only in museums.-Want to showcase your brand to listeners with a combined net worth of over $1 billion and a network of 100,000+ employees and industry contacts? Drop us an email: sales@1upmediapodcast.com-We're looking to grow our team! Support our productions by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠buying us a coffee⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠-Want to meet the team? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow me here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠-If you love the style of Empires, and want similar content, check out:

Bu hafta Efilli'nin Kurucu Ortağı ve CEO'su Şefik Yunus Özcan'ı ağırladık. 

Join Lois Houston and Nikita Abraham as they chat with Yunus Mohammed, a Principal Instructor at Oracle University, about the key stages of AI model development. From gathering and preparing data to selecting, training, and deploying models, learn how each phase impacts AI's real-world effectiveness. The discussion also highlights why monitoring AI performance and addressing evolving challenges are critical for long-term success.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we spoke about generative AI and gen AI agents. Today, we're going to look at the key stages in a typical AI workflow. We'll also discuss how data quality, feedback loops, and business goals influence AI success. With us today is Yunus Mohammed, a Principal Instructor at Oracle University.  01:00 Lois: Hi Yunus! We're excited to have you here! Can you walk us through the various steps in developing and deploying an AI model?  Yunus: The first point is the collect data. We gather relevant data, either historical or real time. Like customer transactions, support tickets, survey feedbacks, or sensor logs. A travel company, for example, can collect past booking data to predict future demand. So, data is the most crucial and the important component for building your AI models. But it's not just the data. You need to prepare the data. In the prepared data process, we clean, organize, and label the data. AI can't learn from messy spreadsheets. We try to make the data more understandable and organized, like removing duplicates, filling missing values in the data with some default values or formatting dates. All these comes under organization of the data and give a label to the data, so that the data becomes more supervised. After preparing the data, I go for selecting the model to train. So now, we pick what type of model fits your goals. It can be a traditional ML model or a deep learning network model, or it can be a generative model. The model is chosen based on the business problems and the data we have. So, we train the model using the prepared data, so it can learn the patterns of the data. Then after the model is trained, I need to evaluate the model. You check how well the model performs. Is it accurate? Is it fair? The metrics of the evaluation will vary based on the goal that you're trying to reach. If your model misclassifies emails as spam and it is doing it very much often, then it is not ready. So I need to train it further. So I need to train it to a level when it identifies the official mail as official mail and spam mail as spam mail accurately.  After evaluating and making sure your model is perfectly fitting, you go for the next step, which is called the deploy model. Once we are happy, we put it into the real world, like into a CRM, or a web application, or an API. So, I can configure that with an API, which is application programming interface, or I add it to a CRM, Customer Relationship Management, or I add it to a web application that I've got. Like for example, a chatbot becomes available on your company's website, and the chatbot might be using a generative AI model. Once I have deployed the model and it is working fine, I need to keep track of this model, how it is working, and need to monitor and improve whenever needed. So I go for a stage, which is called as monitor and improve. So AI isn't set in and forget it. So over time, there are lot of changes that is happening to the data. So we monitor performance and retrain when needed. An e-commerce recommendation model needs updates as there might be trends which are shifting.  So the end user finally sees the results after all the processes. A better product, or a smarter service, or a faster decision-making model, if we do this right. That is, if we process the flow perfectly, they may not even realize AI is behind it to give them the accurate results.  04:59 Nikita: Got it. So, everything in AI begins with data. But what are the different types of data used in AI development?  Yunus: We work with three main types of data: structured, unstructured, and semi-structured. Structured data is like a clean set of tables in Excel or databases, which consists of rows and columns with clear and consistent data information. Unstructured is messy data, like your email or customer calls that records videos or social media posts, so they all comes under unstructured data.  Semi-structured data is things like logs on XML files or JSON files. Not quite neat but not entirely messy either. So they are, they are termed semi-structured. So structured, unstructured, and then you've got the semi-structured. 05:58 Nikita: Ok… and how do the data needs vary for different AI approaches?  Yunus: Machine learning often needs labeled data. Like a bank might feed past transactions labeled as fraud or not fraud to train a fraud detection model. But machine learning also includes unsupervised learning, like clustering customer spending behavior. Here, no labels are needed. In deep learning, it needs a lot of data, usually unstructured, like thousands of loan documents, call recordings, or scan checks. These are fed into the models and the neural networks to detect and complex patterns. Data science focus on insights rather than the predictions. So a data scientist at the bank might use customer relationship management exports and customer demographies to analyze which age group prefers credit cards over the loans. Then we have got generative AI that thrives on diverse, unstructured internet scalable data. Like it is getting data from books, code, images, chat logs. So these models, like ChatGPT, are trained to generate responses or mimic the styles and synthesize content. So generative AI can power a banking virtual assistant trained on chat logs and frequently asked questions to answer customer queries 24/7. 07:35 Lois: What are the challenges when dealing with data?  Yunus: Data isn't just about having enough. We must also think about quality. Is it accurate and relevant? Volume. Do we have enough for the model to learn from? And is my data consisting of any kind of unfairly defined structures, like rejecting more loan applications from a certain zip code, which actually gives you a bias of data? And also the privacy. Are we handling personal data responsibly or not? Especially data which is critical or which is regulated, like the banking sector or health data of the patients. Before building anything smart, we must start smart.  08:23 Lois: So, we've established that collecting the right data is non-negotiable for success. Then comes preparing it, right?  Yunus: This is arguably the most important part of any AI or data science project. Clean data leads to reliable predictions. Imagine you have a column for age, and someone accidentally entered an age of like 999. That's likely a data entry error. Or maybe a few rows have missing ages. So we either fix, remove, or impute such issues. This step ensures our model isn't misled by incorrect values. Dates are often stored in different formats. For instance, a date, can be stored as the month and the day values, or it can be stored in some places as day first and month next. We want to bring everything into a consistent, usable format. This process is called as transformation. The machine learning models can get confused if one feature, like example the income ranges from 10,000 to 100,000, and another, like the number of kids, range from 0 to 5. So we normalize or scale values to bring them to a similar range, say 0 or 1. So we actually put it as yes or no options. So models don't understand words like small, medium, or large. We convert them into numbers using encoding. One simple way is assigning 1, 2, and 3 respectively. And then you have got removing stop words like the punctuations, et cetera, and break the sentence into smaller meaningful units called as tokens. This is actually used for generative AI tasks. In deep learning, especially for Gen AI, image or audio inputs must be of uniform size and format.  10:31 Lois: And does each AI system have a different way of preparing data?  Yunus: For machine learning ML, focus is on cleaning, encoding, and scaling. Deep learning needs resizing and normalization for text and images. Data science, about reshaping, aggregating, and getting it ready for insights. The generative AI needs special preparation like chunking, tokenizing large documents, or compressing images. 11:06 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest tech. Whether you're starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That's education.oracle.com/race-to-certification-2025. 11:50 Nikita: Welcome back! Yunus, how does a user choose the right model to solve their business problem?  Yunus: Just like a business uses different dashboards for marketing versus finance, in AI, we use different model types, depending on what we are trying to solve. Like classification is choosing a category. Real-world example can be whether the email is a spam or not. Use in fraud detection, medical diagnosis, et cetera. So what you do is you classify that particular data and then accurately access that classification of data. Regression, which is used for predicting a number, like, what will be the price of a house next month? Or it can be a useful in common forecasting sales demands or on the cost. Clustering, things without labels. So real-world examples can be segmenting customers based on behavior for targeted marketing. It helps discovering hidden patterns in large data sets.  Generation, that is creating new content. So AI writing product description or generating images can be a real-world example for this. And it can be used in a concept of generative AI models like ChatGPT or Dall-E, which operates on the generative AI principles. 13:16 Nikita: And how do you train a model? Yunus: We feed it with data in small chunks or batches and then compare its guesses to the correct values, adjusting its thinking like weights to improve next time, and the cycle repeats until the model gets good at making predictions. So if you're building a fraud detection system, ML may be enough. If you want to analyze medical images, you will need deep learning. If you're building a chatbot, go for a generative model like the LLM. And for all of these use cases, you need to select and train the applicable models as and when appropriate. 14:04 Lois: OK, now that the model's been trained, what else needs to happen before it can be deployed? Yunus: Evaluate the model, assess a model's accuracy, reliability, and real-world usefulness before it's put to work. That is, how often is the model right? Does it consistently perform well? Is it practical in the real world to use this model or not? Because if I have bad predictions, doesn't just look bad, it can lead to costly business mistakes. Think of recommending the wrong product to a customer or misidentifying a financial risk.  So what we do here is we start with splitting the data into two parts. So we train the data by training data. And this is like teaching the model. And then we have got the testing data. This is actually used for checking how well the model has learned. So once trained, the model makes predictions. We compare the predictions to the actual answers, just like checking your answer after a quiz. We try to go in for tailored evaluation based on AI types. Like machine learning, we care about accuracy in prediction. Deep learning is about fitting complex data like voice or images, where the model repeatedly sees examples and tunes itself to reduce errors. Data science, we look for patterns and insights, such as which features will matter. In generative AI, we judge by output quality. Is it coherent, useful, and is it natural?  The model improves with the accuracy and the number of epochs the training has been done on.  15:59 Nikita: So, after all that, we finally come to deploying the model… Yunus: Deploying a model means we are integrating it into our actual business system. So it can start making decisions, automating tasks, or supporting customer experiences in real time. Think of it like this. Training is teaching the model. Evaluating is testing it. And deployment is giving it a job.  The model needs a home either in the cloud or inside your company's own servers. Think of it like putting the AI in place where it can be reached by other tools. Exposed via API or embedded in an app, or you can say application, this is how the AI becomes usable.  Then, we have got the concept of receives live data and returns predictions. So receives live data and returns prediction is when the model listens to real-time inputs like a user typing, or user trying to search or click or making a transaction, and then instantly, your AI responds with a recommendation, decisions, or results. Deploying the model isn't the end of the story. It is just the beginning of the AI's real-world journey. Models may work well on day one, but things change. Customer behavior might shift. New products get introduced in the market. Economic conditions might evolve, like the era of COVID, where the demand shifted and the economical conditions actually changed. 17:48 Lois: Then it's about monitoring and improving the model to keep things reliable over time. Yunus: The monitor and improve loop is a continuous process that ensures an AI model remains accurate, fair, and effective after deployment. The live predictions, the model is running in real time, making decisions or recommendations. The monitor performance are those predictions still accurate and helpful. Is latency acceptable? This is where we track metrics, user feedbacks, and operational impact. Then, we go for detect issues, like accuracy is declining, are responses feeling biased, are customers dropping off due to long response times? And the next step will be to reframe or update the model. So we add fresh data, tweak the logic, or even use better architectures to deploy the uploaded model, and the new version replaces the old one and the cycle continues again. 18:58 Lois: And are there challenges during this step? Yunus: The common issues, which are related to monitor and improve consist of model drift, bias, and latency of failures. In model drift, the model becomes less accurate as the environment changes. Or bias, the model may favor or penalize certain groups unfairly. Latency or failures, if the model is too slow or fails unpredictably, it disrupts the user experience. Let's take the loan approvals. In loan approvals, if we notice an unusually high rejection rate due to model bias, we might retrain the model with more diverse or balanced data. For a chatbot, we watch for customer satisfaction, which might arise due to model failure and fine-tune the responses for the model. So in forecasting demand, if the predictions no longer match real trends, say post-pandemic, due to the model drift, we update the model with fresh data.  20:11 Nikita: Thanks for that, Yunus. Any final thoughts before we let you go? Yunus: No matter how advanced your model is, its effectiveness depends on the quality of the data you feed it. That means, the data needs to be clean, structured, and relevant. It should map itself to the problem you're solving. If the foundation is weak, the results will be also. So data preparation is not just a technical step, it is a business critical stage. Once deployed, AI systems must be monitored continuously, and you need to watch for drops in performance for any bias being generated or outdated logic, and improve the model with new data or refinements. That's what makes AI reliable, ethical, and sustainable in the long run. 21:09 Nikita: Yunus, thank you for this really insightful session. If you're interested in learning more about the topics we discussed today, go to mylearn.oracle.com and search for the AI for You course.  Lois: That's right. You'll find skill checks to help you assess your understanding of these concepts. In our next episode, we'll discuss the idea of buy versus build in the context of AI. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 21:39 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Apartheid-Era Trials Unearthed in New Book by Yunus Carrim by Radio Islam

Proposto #harder per l'attacco del #milan (in alternativa a #hojlund ), ma il #calciomercato rossonero non è finito qui. Yunus #musah piace all' #atalanta , anche se #allegri vorrebbe tenerloDiventa un supporter di questo podcast: https://www.spreaker.com/podcast/radio-rossonera--2355694/support.

Elif Özsoy'un hazırlayıp sunduğu Enerjisini Üreten Fabrikalar programına Goldstein Renewable Satış ve Finans Direktörü Yunus Kaan Arslan konuk oldu.

Elif Özsoy'un hazırlayıp sunduğu Enerjisini Üreten Fabrikalar programına Goldstein Renewable Satış ve Finans Direktörü Yunus Kaan Arslan konuk oldu.

Sandro Calvani, Giuliano Rizzi"Protopia"Edizioni Città Nuovawww.edizionicittanuova.itLa “protopia”, definita da alcuni autori come la speranza dei laici, è l'insieme globale e integrato di ogni forma di partecipazione e responsabilità nei processi generativi socioeconomici a fronte della policrisi in atto. È di fatto la via alternativa sia a decine di distopie della convivenza disordinata di centinaia di popoli sia allo sviluppo sostenibile e inclusivo che sembra a molti un'irraggiungibile utopia. Una terza via, dunque, che milioni di persone hanno scelto nel mondo per sperimentare la felicità nella vita quotidiana, rifiutando l'incuria e l'apatia del comune tentativo di autogiustificazione: “e io che ci posso fare?”. Prefazione di Muhammad Yunus.Sandro Calvani è presidente dell'Istituto per il diritto internazionale della pace Giuseppe Toniolo, docente di Sviluppo sostenibile al Master ESG M. Yunus dell'Asian Institute of Technology. È stato capo missione delle Nazioni Unite e della Caritas in 135 Paesi. Membro del World Economic Forum, Global Agenda Council on Poverty. Specializzato in gestione delle emergenze, dello sviluppo e dei conflitti, si occupa con passione di innovazione sociale, nuove economie circolari e cittadinanza attiva. Autore di 31 libri e oltre 900 articoli, coautore di 27 libri.www.sandrocalvani.itGiuliano Rizzi è presidente dell'Istituto pace sviluppo innovazione delle ACLI del Trentino ed è da tempo impegnato nel volontariato sociale. Ingegnere e sociologo, ha lavorato nel campo dell'analisi dati e delle applicazioni della ricerca ai temi ambientali, nella cooperazione internazionale e nella pubblica amministrazione. Come formatore professionista si occupa di sostenibilità, cooperazione allo sviluppo e globalizzazione.IL POSTO DELLE PAROLEascoltare fa pensarewww.ilpostodelleparole.itDiventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.

Ayşete Yavaş'ın hazırlayıp sunduğu Endüstri 4.0'ın Kalbi: Yapay Zeka İle Üretim programına Dataguess CEO Yunus Altunbıçak konuk oldu.

Ayşete Yavaş'ın hazırlayıp sunduğu Endüstri 4.0'ın Kalbi: Yapay Zeka İle Üretim programına Dataguess CEO Yunus Altunbıçak konuk oldu.

Reopening of Johannesburg central library - Yunus Chamda by Radio Islam

Recent events have made it increasingly clear that India is utterly alone. I call this the Abhimanyu Syndrome, in honor of the teenaged Abhimanyu, who only knew how to enter the impregnable Chakravyuha, and who was then murdered, alone, against all Dharmic principles, by several powerful Maharathis.India's possible rise as an economic and military power is being resisted tooth and nail by the incumbent major powers. This is natural: all 'insurgent' powers face this problem. India's rise will need some paradigm shifts, which may or may not happen.China's rise was helped by two things: an industrial policy and inadvertent help by the US's managers and Wall Street who were seduced by the short-term appeal of the lower-cost 'China price'.The ruthless industrial policy is a result of China's civil services being full of engineers who understand the near term. In the long run, it may or may not succeed because of second-order effects such as population implosion, environmental ruin and skewed investment decisions.The US is now facing decline (and the EU has already begun a steep decline) for a variety of reasons, including natural cycles and the loss of comparative advantage in weaponry.In this context, I wish I knew what paradigm shift, if any, will propel India to a G3 slot, but the general global churn, new technologies, etc might create an opportunity. In the meantime, India has to struggle alone, against a mass of hostile powers.I read an article in a Sri Lankan newspaper that painted India as a tyrant, but China as a benevolent friend dispensing largesse. Yes, the same China that has grabbed Hambantota! And it called for a new SAARC, one minus India. This is the kind of propaganda that China and Pakistan are rolling out in the Indian subcontinent.China has every reason to want to kneecap India, the only Asian power that can (and hopefully will) challenge its hegemonistic pretensions. Even the US doesn't: Obama anointed China as the guardian/manager, if not owner, of 'South Asia'. Spheres of influence, you see.The US, in general, has been a disappointment. Many Indians expected, after Biden's antics with Yunus in Bangladesh, that Trump would be more in tune with Indian interests, partly because the US and India both need to keep China in check, and Trump wants to move more manufacturing out of China.But that has clearly not happened. Instead, India has been in Trump's gunsights over trade (although India's surplus is small, and agricultural items that the US wants to sell are a serious no-no for India). Their embrace of Pakistan during and after Operation Sindoor has been inexplicable unless Trump has adopted pure Deepstate policy.India cannot be a Chinese vassal (although it is in BRICS) and it doesn't want to be an American vassal though it's in the Quad (its ties with Russia and strategic autonomy are too important). Thus India is squeezed; for instance, the recent threat by some NATO muckity-muck to impose Russia-related sanctions was sinister.This could be both good and bad. Let's face it, nobody likes a rising power (see Thucydides Trap). While the Sri Lankan paper glorifies China, let us remember that China has territorial disputes with literally every one of its neighbors, and most of ASEAN is deathly scared of them. That comes from hard power. If SAARC or G20 or somebody is worried about India, that is a good sign that home-grown military power is noticeable.Everyone complains that India is not loyal to them. BRICS boosters grumble that India is a Western ‘mole' that is preventing them from toppling the dollar and making the US irrelevant. Conversely, the Anglosphere complains that India is not sufficiently committed to them, as in not toeing their Ukraine line. This is as it should be: multi-alignment means India is not beholden to anybody, but will pursue its selfish interests first and foremost.This is qualitatively different from the late lamented ‘non-alignment' of Nehruvian days, which meant everybody disliked India for its moral posturing. Multi-alignment means India will engage with everybody, on its own terms. With the US, for technology and trade. With Russia, for weapons and oil. Even with China, despite China being India's staunchest enemy, for electronic components. And even with perfidious Britain, as in the just-concluded FTA, which I personally consider pointless.Thus the splendid isolation is a back-handed compliment: the rest of the world is anticipating the rise of India as a superpower; and superpowers have no friends, only interests. And remember, Abhimanyu died, yes, but his side won overwhelmingly.780 words, 22 Jul 2025 updated 25 Jul 2025 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit rajeevsrinivasan.substack.com/subscribe

A KARO NA FARKO A KAFOFIN YADA LABARAI, LABARIN MALAMIN DA AKA TSARE A GIDAN YARI NA TSAWON SHEKARA 3 A KAN TUHUMAR BOKO HARAM, KUMA ZA KU JI ABINDA YA FARU DAGA BAKIN SA.

Titus gives his thoughts on a recent debate between Loso and Yunus on the best NOME debuts and why the answer isn't as simple as it seems. Hosted on Acast. See acast.com/privacy for more information.

A version of this essay was published by firstpost.com at https://www.firstpost.com/opinion/shadow-warrior-west-asia-hostilities-will-resume-again-only-question-is-when-13903341.html West Asia is again on the boil. Well, to be precise, it has been on the boil for a very long time, but we have the additional spectacle of the Iran-Israel war. Despite the ceasefire, which I hope does hold, there is a lot here that should concern all of us based on the geopolitical and geo-economic fallout.There are at least three issues of interest: the geopolitics, the war tactics, and the impact on the rest of the world. GeopoliticsIt would be fair to say that much of the turmoil in the region dates back to British (and to a lesser extent French) meddling in the 20th century, for instance the Sykes-Picot Act, or the antics of TE Lawrence. Britain's broader actions—contradictory promises (Balfour), repressive mandates, oil-driven interference, and botched withdrawals—sowed division, resentment, and conflict that shaped the region's 20th-century chaos. Many of these issues, like sectarianism in Iraq and the Israeli-Palestinian conflict, persist today.The nations Britain created with arbitrary lines marked on a map made no sense because they ignored ethnic, tribal, and religious realities, sowing seeds for future conflicts. Indians know all about this: the same sort of random map-making in the Indian subcontinent led to extraordinary misery (the Radcliffe Line, created in just five weeks, created East and West Pakistan with little attention paid to ground realities, using outdated maps and census data).The British Deep State (let us call it Whitehall for short) has lost much of its clout, but it has been leading the American Deep State by the nose in what I referred to as a “master-blaster” relationship. And the latter has a rather clear SoP: there needs to be constant wars to feed the Military Industrial Complex, and so they will arrange for wars, which will lead to a complex money-laundering operation, with petrodollars being whitewashed through the IMF etc and ending up in the coffers of Raytheon, Lockheed Martin, Palantir, and friends. It is notable that one of President Trump's main claims to fame in his first Presidency was that he scrupulously avoided going to war, in sharp contrast with his predecessors over the last several decades, all of whom had started or indulged in one war or the other. It appears that this time, though, the US Deep State has managed to co-opt Trump into its warmaking agenda, which, incidentally does not disqualify him for a Nobel Peace Prize: see Kissinger or Obama.What has happened in this 12-day war is that it became a stalemate, for all practical purposes. Neither Israel nor Iran can fully defeat the other, as neither has the resources to continue. A good metaphor is a boxing match, where evenly matched pugilists are both exhausted, covered and blinded with blood, and can hardly stand on their feet. The referee calling a halt is a blessing for both of them.Iran has, for years, shouted hair-raising slogans about obliterating Israel, although it is not clear how much of this was rhetoric, considering Uncle Sam's support for the latter makes the latter quite powerful. This sloganeering was supplemented by proxy allies, Hamas, Hezbollah and the Houthis, all of whom have been capable of mischief. Plus there is the nuclear bomb.Israel set out to tame Iran on all these fronts. Their goals were to deprecate, if not destroy, Iran's nuclear capability, defang the proxies, and impose a regime-change on the country. Let us remember the Stuxnet incident of 2010 when a computer virus was introduced into the Iranian centrifuges that are used for uranium enrichment, causing many of them to disintegrate. The assaults on Nataz, Fordow and Ispahan (much like Israel's raid on Iraq's Osiraq reactor long ago) were intended to stop Iran's nuclear weapons program altogether.With the US' help, it appears as though there has been serious damage to Iran's weapons capabilities, although there are rumors that 400 kg of highly enriched uranium was smuggled out just before the bunker-buster strikes via B-2 bombers on the fortified, underground sites. Among Iranian proxies or force-multipliers, its so-called Axis of Resistance, Hamas has been severely degraded, with top commanders eliminated (notably Yahya Sinwar and Ismail Haniyeh) and its tunnel network in Gaza largely inoperable. Hezbollah leader Hasan Nazrallah and several key aides have been targeted and killed. The Houthis have escaped relatively unscathed, although the Americans were bombing them.On the other hand, it may not be possible to effect regime change in Iran. There seems to be a standard playbook of so-called ‘Color Revolutions', wherein a ruler is replaced by someone close to the West through what is portrayed as a “popular uprising”. The Ukraine Maidan Revolution that placed Zelenksy in power, the Bangladeshi coup that brought Yunus to power, and the “Velvet Revolution” are examples.But one of the earliest examples was the CIA/MI6 coup in Iran that overthrew Mohammed Mossadegh in 1953 and brought Shah Reza Pahlavi back to monarchical power. And the reason: Mossadegh had nationalized the Iranian oil industry, and freed it from the clutches of British Petroleum. The 1979 coup by the mullahs succeeded because the Shah was unpopular by then. Iranians, despite widespread opposition to Khameini, probably don't want the Shah dynasty back, or for that matter someone else chosen to rule them by outsiders.There was also a fairly strange set of events: just as it is said the Iranians were allowed to spirit their uranium away, the Iranians seem to have given notice of their attacks on US bases in Qatar etc. (allowing the US to move their aircraft and personnel), and, strangest of all, a social media post by Trump that appeared to approve sanctions-free Iranian supply of oil to China!Thus there are some pantomime/shadow-boxing elements to the war as well, and some choreography that is baffling to the impartial observer. Geopolitics is a complex dance.War tacticsThe Israeli assault on Iran started with shock and awe. In the first phase, There was a massive aerial bombing campaign, including on Natanz. But more interestingly, there was a Mossad operation that had smuggled kamikaze drones into a covert base near Teheran, and they, as well as anti-tank missiles degraded Iranian air defenses. Mossad also enabled successful decapitation strikes, with several top commanders and nuclear scientists assassinated.This phase was a big win for Israel, and reminded one of the continuing importance of human intelligence in a technological age. Patiently locating and mapping enemy commanders' movements, managing supply chains and using psychological tactics were reminiscent of how Mossad was able to introduce the Stuxnet worm, and use pagers as remote explosive devices. In the second phase, the two were more evenly matched. Israel's Iron Dome was unable to deal with sustained barrages of Iranian missiles, as no anti-missile system can be more than 90% effective. Both began to suffer from depleted stocks of arms and ammunition. Thus the metaphor of two grievously wounded boxers struggling to stay on their feet in the ring. It took the bunker-busting US B-2 bombers in the third phase to penetrate deep underground to the centrifuges, but there is still the possibility that Iran managed to ship out its fissile material.We are now in a fourth phase: both parties are preparing for the next round of kinetic warfare.The lessons here were once again the remarkable rise of UCAVs or drones as weapons of war, and the continued usage of high-quality human intelligence. It is rumored that Israeli agents had penetrated to high levels in the Iranian military hierarchy, and there was allegedly a high-level mole who was spirited away safely out of Iran.Both of these are important takeaways for India. The success of India's decoy drones in the suppression of Pakistani air defenses will be hard to repeat; the Ukrainian drone strike against Russia's strategic TU-44 and other strategic bombers, which were sitting ducks on the ground, shows us what drones can do: India has to substantially advance its drone capability. India's counterintelligence and human intelligence suffered grievous blows when various personalities, including a Prime Minister, a Vice President, and the head of RA&W all turned hostile, with the result that India's covert presence in Pakistan will have to painfully recreated again. Perhaps India also does not have a policy of decapitation strikes. Should it?Impact on the rest of the world, especially IndiaIn general terms, it's hard to declare an outright non-loser in this war, except possibly China, because it is the one player that seems to be quite unaffected: its saber-rattling on Taiwan continues unabated. Russia lost, because it had been viewed as being an ally of Iran; it was unable to do much, enmeshed as it is in the Ukraine mess. Israel and Iran both came out, in the end, looking weakened, as neither could deliver a fatal blow.The US got kudos for the B-2 bombers and the bunker-busters, but it is not entirely clear if there was some kind of ‘understanding' which meant that Iran is still not that far away from being able to build its nuclear bomb. Indians will remember how President Reagan winked at Pakistan's efforts to nuclearize with Chinese help, and issued certificates of innocence.Pakistan in particular, and the Islamic Ummah in general, took a beating. Instead of expressing Islamic solidarity with Iran, it turns out Pakistan was quite likely opening up its air bases for possible US strikes on Iran. That would explain why Indian strikes on Pakistan's Nur Khan air base alarmed the Americans, who may have been bulking up their presence there partly as a way of opening a new front against Iran.None of the other Islamic powers, with the possible exception of Turkey, paid more than lip service to Iran's troubles, which was interesting to note. The Sunni-Shia schism holds. The worst outcomes were averted: the nightmare scenarios, in order of seriousness, would have been a) World War 3, b) nuclear bombs being dropped on one or more of the belligerents, c) a broad war in West Asia, c) the closing of the Straits of Hormuz and a serious spike in energy prices.From the point of view of a nation like India, it demonstrated, yet again, that superpowers have their own rationale of amoral transactional relationships with other countries. India, as an aspiring superpower needs to internalize the fact that foreign policy is the pursuit of war by other means, and there are only permanent interests, not permanent friends. Instead of the highfalutin' moralizing of the Krishna Menon and Jawaharlal Nehru days, what India needs is the pursuit of its own national interests all the time.In this context, both Israel and Iran are useful to India. There is a billion-dollar arms trade between Israel and India (and Israel long ago offered to destroy Pakistan's Kahuta nuclear reactor with India's help, but shrinking-violet India refused). Today India is Israel's biggest arms buyer, with products ranging from Phalcon AWACS to Barak missiles to Harop and other drones, with Hermes 900 drones co-produced in India and exported to Israel.As for Iran, India's investment in Chabahar port is a strategic counter to China's CPEC and Gwadar port in Pakistan. It enables India to avoid Pakistan in its trade to Afghanistan and Central Asia. It is also a node on the International North South Transport Corridor, using which India can connect to Russia and Europe. It cuts time and cost of shipping to Europe by 30% as compared to the Suez Canal. India has invested more than a billion dollars in Chabahar.Besides, India used to be a big customer for Iranian oil, but that has been cut to near-zero from 20+ million tons a year because of US sanctions on Iran. If and when sanctions are lifted, India will have an interest in buying Iranian oil again. India has interests in both Israel and Iran, and it should continue to maintain its good relations with both. Nevertheless, West Asia remains a tinderbox. Hostilities will resume again, the only question is when. Iran will not give up on its nuclear ambitions, and as with Pakistan, some nuclear power will proliferate to it sooner or later, quite possibly China. The grand ambition to topple Iran's mullahs is not likely to come to fruition. Israel will continue to be beleaguered. Status quo ante, after the current round of noise dies down.2075 words, 1 Jul 2025The AI-generated podcast in Malayalam from notebookLM.google.com This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit rajeevsrinivasan.substack.com/subscribe

Akademisyen Yunus Emre Erdölen, ABD'nin New York kentindeki Demokrat Parti'nin belediye başkanlığı ön seçimlerinde birinci olan New York Temsilciler Meclisi Üyesi Zohran Mamdani'nin hikayesini anlattı.

İran-İsrail savaşı dünyada yeni bir dönemi başlatıyor. Peki bu savaşta kim, ne istiyor? ABD Başkanı Trump savaşa nasıl bakıyor? ABD savaşa katılır mı? Akademisyen Yunus Emre Erdölen İran-İsrail savaşını mercek altına aıyor ve Trump'ın büyük açmazını tartışıyor.

Rajini Vaidyanathan, BBC News presenter and correspondent, speaks to Muhammad Yunus, interim leader of Bangladesh.The 84-year-old is perhaps one of the world's best-known Bangladeshis. Described as the banker to the world's poor, he gained international recognition as a Nobel prize-winning economist, who founded the Grameen microfinance bank, which delivered small loans to economically deprived people.It was a model applauded by many and is one which now operates across more than 100 countries worldwide. But it's also a model which was criticised by his political rival - Bangladesh's former Prime Minister Sheikh Hasina, who accused Mr Yunus of sucking blood from the poor. Her government made a series of allegations against him, including embezzlement, all of which he denies.And it was the overthrow of Sheikh Hasina's government last August that saw Muhammad Yunus thrust into the political spotlight.Student-led protests saw thousands take to the streets in an uprising against her Awami league government, which was accused of election rigging, human rights abuses and jailing critics.When Ms Hasina fled to neighbouring India, student leaders picked Muhammad Yunus to unite a divided nation. As well as promising to stamp out corruption, he's also faced the challenges of managing a refugee crisis on his doorstep and navigating cuts to foreign aid.So, after nearly a year in the job, how is he faring? The Interview brings you conversations with people shaping our world, from all over the world. The best interviews from the BBC. You can listen on the BBC World Service, Mondays and Wednesdays at 0700 GMT. Or you can listen to The Interview as a podcast, out twice a week on BBC Sounds, Apple, Spotify or wherever you get your podcasts.Presenter: Rajini Vaidyanathan Producer: Ben Cooper Editor: Nick HollandGet in touch with us on email TheInterview@bbc.co.uk and use the hashtag #TheInterviewBBC on social media.(Image: Muhammad Yunus. Credit: Reuters/Yves Herman)

Told largely through Moulana's (rahimahullah) own words, learn about two Hajj journeys undertaken with meagre means, yet fulfilled in astonishing ways by the boundless grace of Allah SWT.

Hajj Crossover: “Hajj Starts Long Before Makkah” — Shaykh Yunus Dudhwala unpacks the UK Hajj Journey by Radio Islam

“Bilesiniz ki, Allah'ın dostlarına hiçbir korku yoktur. Onlar üzülmeyeceklerdir de.” (Yunus 62)“Onlar iman etmiş ve Allah'a karşı gelmekten sakınmış olanlardır.” 63“Korku ancak gelecekle ilgili olur, yani ileride korkutan bir şeyin meydana gelmesinden dolayı korkulur. Hüzün ise ancak geçmişte olan birşeyle ilgili olur. Bu, ya geçmişte insanın hoşuna gitmeyen birşeyin meydana gelmiş olmasından ötürü, yahut da arzu edip sevdiği bir şeyi elde edememiş olmasından dolayı olur.Bazı muhakkikler şöyle demişlerdir: "Veliler için, korku ve hüznün olmamasının söylenmesi, ya onlar bu dünyada iken olur, yahut ahirette iken olur. Birincisi, şu sebeplerden ötürü olamaz;Bu, dünyada olmaz. Çünkü burası, korku ve keder yurdudur. Hele mü'min, Hz. Peygamber (s.a.s)'in şuhadislerinde de buyurduğu gibi, bundan hiç kurtulamaz: "Dünya, mü'minin (adetâ) hapishanesi, kâfirin de cennetidir"“İman etmek" kelimesi nazarî kuvvetin {tefekkür kuvvetinin) mükemmelliğine, "takvaya ermek" tabiri de amelî kuvvetin mükemmelliğine işarettir. Burada bir başka husus da, imanın, itikad ve amelin toplamına hamledilmesidir. Sonra biz "velî"yi, bütün bu hususlarda ittikâ sahibi olarak tavsif ederiz. Takva, ilim hududunda olur ve o hududu aşar. Çünkü Allah'ın celâli, beşer aklının ihata edip kavrayamayacağı derecede yücedir. Binâenaleyh sıddîk, Allah Teâlâ'yı, celâl sıfatlarından bir sıfatla tavsif ettiğinde, Allah'ın kemâl ve celâlinin, kendisinin bildiğine münhasır olmasından tenzih eder. Yine o, Allah'a ibadet ettiğinde Allah'ı,böylesi bir hizmet ve ibadete layık olmaktan tenzih eder. (Yani O'nun pek çok mükemmel tarzda yapılacak ibadetlere müstehak olduğunu düşünür.) Böylece o kimsenin devamlı olarak havf ve takva makamındaolmuş olduğu sâbıt olur.Hz. Ömer (r.a), Hz. Peygamber (s.a.s)'in: "Onlar, aralarında bir akrabalık ve alıp-verecekleri bir malolmadığı halde, birbirlerini Allah için seven kimselerdir. Allah'a yemin olsun ki onlann yüzleri nurdur ve insanlar korkup hüzünlendikleri zaman, onlar korkup hüzünlenmezler" dediğini ve bu ayeti okuduğunu rivayet etmiştir.Yine, Hz. Peygamber (s.a.s)'in: "Onlar öyle insanlardır ki, onları görenler Allah'ı hatırlarlar" buyurduğu rivayet edilmiştir. Bunun sebebi şudur: Onlarda görülen, huşu ve huzû alâmetlerinden ötürü, bir de Hak Teâlâ onlar hakkında, "Secde izinden nişanları yüzlerindedir" (Fetih, 29) buyurduğu için, onların bütün bakıp müşahede edişleri, ahireti hatırlamaya yöneliktir.Herşeyin "velî"si, ona yakın olan demektir. Allah'a mekân ve cihet bakımından yakın olmak imkânsızdır. O halde ona yaklaşmak, ancak insanın kalbi, Hak Teâlâ'yı bilmenin nuruna garkolduğunda olur. Bu kimse, baktığında, Allah'ın kudretinin delillerini görür; dinlediğinde Allah'ın ayetlerini dinler; konuştuğunda, Allah'ı sena eder; hareket ettiğinde, Allah'a kulluk ve hizmet için hareket eder, çalışıp çabaladığında, Allah'a taat için çalışıp çabalar. İşte bu şekilde de, Allah'a son derece yaklaşmış olur. İşte bu şahıs, Allah'ın velîsidir.İnsan böyle olduğunda, Allah da onun dostu ve velîsi olur. Nitekim Hak Teâlâ, "Allah imân edenlerin velîsi (yardımcısı)dır. Onları karanlıklardan nura çıkarır" (Bakara 257)Bu müjdeden maksad, sâlih rüyadır. Hz. Peygamber (s.a.s)'den şöyle dediği rivayet edilmiştir: "Büşrâ (müjde), müslümanın kendisinin gördüğü veya senin, onun için gördüğün salih (güzel) rüyadır," Yine Hz. Peygamber (s.a.s) “Peygamberlik gitti (bitti), geriye mübeşşirât (müjdeci rüyalar) kaldı.”Bil ki ayetteki, "büşrâ" tabirini "sâdık rüya" manasına aldığımızda, ayetin zahiri bu halin ancak veliler için söz konusu olmasını gerektirir. Akı! da buna delalet eder. Çünkü Allah'ın velisi, kalbi ve ruhu zikrullaha gömülmüş kimsedir. Binâenaleyh kim böyle olur ise, uyurken de ruhunda sadece marifetullah bulunur.Marifetullah'ın ve Allah'ın celâlinin nurunun da, ancak hakkı ve doğruluğu göstereceği malumdur. Ama fikri, bu bulanık ve karanlık âlemin hallerine dağılmış kimse, uyuduğu zaman da böyle dağınık kalır.

बांग्लादेश में संसद खत्म, सड़कों पर सेना और सत्ता एक ऐसे शख्स के हाथ में, जिसे जनता ने चुना ही नहीं, नोबेल विजेता मोहम्मद यूनुस. शेख़ हसीना नज़र नहीं आ रहीं. तो क्या यूनुस सत्ता में लाए गए हैं? अगर हां, तो अमेरिका, चीन या किसी और के इशारे पर? भारत क्यों चुप है लेकिन बेचैन भी? इस एपिसोड में जानिए बांग्लादेश की मौजूदा राजनीति, अमेरिका-चीन की चालें और भारत की चिंता, सुनिए 'पढ़ाकू नितिन' में. Disclaimer: इस पॉडकास्ट में व्यक्त किए गए विचार एक्सपर्ट के निजी हैं.

“Time for an Awakening” with Bro.Elliott & Bro.Richard, Sunday 5/25/2025 at NEW TIME 6:00 PM (EST) guests was Afro-descendant Nation Ambassador, Dr. Tauheedah Sabree, and Black Panther Movement Senior Coordinator of Pan-Afrikan Affairs, Elder Mmoja Ajabu. Dr. Sabree updated us on the Afro-descendant Nation, the Sixth Region, which includes us in the global struggle for reparations. In the second portion of the program, Elder Ajabu informed us of the press release about South African President Cyril Ramaphosa meeting with Trump, the meeting with the ambassador of Burkina Faso and staff, and the revolution in the Sahel, and joining us in conversation from Burkina Faso was Activist, Organizer, Ramzu Yunus. Also, Open Forum dialogue on topics that affect Black people locally, nationally, and internationally.

India Shot Down Several F-16 | Yunus to Resign | Pakistan get J-35 | Rajiv Narayanan, PR Shankar

CutTheClutter: Showdown buzz in Bangladesh as Army Chief rejects Rakhine corridor, presses Yunus for early polls

Yunus to Resign after Bangladesh Army Puts him on Notice? | Muhammad Yunus Plans to Flee?

The chief adviser met China's envoy to Bangladesh Saturday to review outcomes of the former's visit to China last month. Ties between India & Bangladesh cratered sharply over last week.----more----https://theprint.in/diplomacy/yunus-initiates-mango-diplomacy-in-bid-to-strengthen-ties-with-china-50-yr-water-sharing-plan-soon/2596931/

Bangladesh Kicks Pakistan in the Back | एक कंगले ने दूसरे भिखमंगे से पैसे माँगे | Yunus Miya Cries

Modi s Masterstoke has Choked Bangladesh | China Ditches Yunus | Tahawwur Rana | Sumit Peer

Yunus Crying After Modi's Kick | Tahawwur Rana makes Congress Nervous | China Checkmated |AadiAchint

Trump's Tariff War on the World is Advantage India | Yunus Put in His Place | Pathikrit Payne

India's eastern corridor, a historically calm region, is back into focus after Bangladesh's new-found bonhomie with China, highlighted by Muhammad Yunus's remarks on India's Northeast during a visit to Beijing. In episode 1635 of #CutTheClutter, Editor-in-Chief Shekhar Gupta takes a close look at the threats abound in Bangladesh, China and Myanmar, explains why it's time for India to pivot towards the East, and the role BIMSTEC can play. --------------------------------------------------------------------------------------------- Ad-lite experience, exclusive content, special privileges & more – Subscribe to ThePrint for Special benefits: https://theprint.in/subscribe/ -------------------------------------------------------------------------------------------- Apply here for ThePrint School of Journalism : https://tinyurl.com/48hdbx9d ---------------------------------------------------------------------------------------------

This is the Catchup on 3 Things by The Indian Express and I'm Ichha Sharma.Today is the 4th of April and here are the headlines.The Trump administration's new tariffs on global trade partners, including a 27% duty on imports from India, will affect sectors like diamonds, smartphones, solar PV modules, and apparel. While certain products like critical minerals, energy products, and pharmaceuticals may be exempt, most of India's top exports, including pharmaceuticals worth $8 billion, are not. Petroleum oils derived from bituminous minerals could benefit, but tariffs on steel, aluminum, and automobiles remain unchanged. These measures could reshape trade relations with India, despite limited relief for some sectors.After nearly 12 hours of debate in the Lok Sabha, the Waqf (Amendment) Bill moved to the Rajya Sabha for further discussion yesterday. BJP MPs accused the Opposition of spreading misinformation, while TMC MP Mohammad Nadimul Islam criticized the Bill as cultural vandalism, accusing the Centre of seeking excessive control over Waqf. The Lok Sabha passed the Bill with 288 votes in favor and 232 votes against. The amendment now awaits a decision in the Rajya Sabha after being tabled by Kiren Rijiju.The Indian Army accused Pakistan of violating the ceasefire along the Line of Control in Poonch's Krishna Ghati sector. According to Army PRO Lt Colonel Suneel Bartwal, Pakistani forces intruded across the LoC and triggered a mine blast on April 1, followed by unprovoked firing. Indian forces responded in a "controlled and calibrated manner," keeping the situation under close watch. Tensions have risen despite the ceasefire agreement, with the Army asserting that India's borders remain secure and that further developments are being monitored.Assam Chief Minister Himanta Biswa Sarma strongly condemned the statement by Bangladesh interim government advisor Muhammad Yunus, who claimed that Bangladesh is "the only guardian of the ocean" for Northeast India. Yunus had stated that Bangladesh holds a unique position to benefit from the economic possibilities of the region. Sarma called the remark offensive, highlighting its connection to India's vulnerable "Chicken Neck" corridor, which connects the Northeast with the rest of India, emphasizing India's territorial integrity and strategic concerns.Bajinder Singh, a self-proclaimed Christian pastor, was sentenced to life imprisonment in a rape case dating back to 2018 by a Mohali court. Singh had been convicted for luring a woman under the pretext of helping her settle abroad, only to rape her and threaten to post a video of the act online. The court convicted him under IPC sections related to rape, voluntary hurt, and criminal intimidation. Singh had been arrested at Delhi airport in 2018 and released on bail until his recent conviction.This was the Catch Up on 3 Things by The Indian Express

By inviting the Chinese companies to the Teesta River Comprehensive Management and Restoration Project, Yunus may have added an extra friction point in ties with India.  

This is the Catchup on 3 Things by The Indian Express and I'm Flora Swain.Today is the 1st of April and here are the headlines.Domestic Stock Market Crashes Ahead of Trump's Tariff AnnouncementOn Tuesday, Indian stock markets experienced a sharp decline due to massive selling, driven by uncertainty over the upcoming US President Donald Trump's reciprocal tariffs set to be implemented on April 2. The BSE Sensex plunged by 1.69%, losing 1,313 points, while the Nifty fell by 1.41%, down 332 points. Investors are awaiting details on how these tariffs will impact various sectors globally, with market reactions likely hinging on the specifics of the tariff announcement tomorrow, said VK Vijayakumar, Chief Investment Strategist at Geojit Investments.Assam CM Condemns Bangladesh Official's Statement on Northeast IndiaAssam Chief Minister Himanta Biswa Sarma strongly condemned the statement by Bangladesh interim government advisor Muhammad Yunus, who claimed that Bangladesh is "the only guardian of the ocean" for Northeast India. Yunus had stated that Bangladesh holds a unique position to benefit from the economic possibilities of the region. Sarma called the remark offensive, highlighting its connection to India's vulnerable "Chicken Neck" corridor, which connects the Northeast with the rest of India, emphasizing India's territorial integrity and strategic concerns.Ruckus in Lok Sabha Over Question Hour AdjournmentA heated scene unfolded in the Lok Sabha after the Question Hour concluded, with Samajwadi Party MPs protesting the decision and demanding that Akhilesh Yadav be allowed to speak. Despite Speaker Om Birla assuring that Yadav would get time during Zero Hour, the MPs continued with slogans and placards, leading to the adjournment of the House until 2 pm. In the Rajya Sabha, Congress leader Mallikarjun Kharge called for an immediate decadal census and caste census, highlighting the delay's impact on welfare schemes. The Waqf (Amendment) Bill is scheduled for discussion tomorrow.Mohali Court Sentences Pastor to Life for 2018 Rape CaseBajinder Singh, a self-proclaimed Christian pastor, was sentenced to life imprisonment in a rape case dating back to 2018 by a Mohali court. Singh had been convicted for luring a woman under the pretext of helping her settle abroad, only to rape her and threaten to post a video of the act online. The court convicted him under IPC sections related to rape, voluntary hurt, and criminal intimidation. Singh had been arrested at Delhi airport in 2018 and released on bail until his recent conviction.Trump's Reciprocal Tariffs Could Lead to Uncertainty in Global TradeAs trade tensions escalate, all eyes are on the US President's announcement of reciprocal tariffs scheduled for April 2. Trump's plan to impose tariffs matching those charged by other countries has raised concerns about how the levies will be rolled out. With over 200 trading partners and thousands of tariff categories, the US could face an administrative nightmare. The initial focus is likely on the 20 countries the US has trade agreements with, with some speculating a blanket tariff could simplify the process but complicate global trade relations.That's all for the today. This was the CatchuUp on 3 Things by The Indian Express

Who's in, who's out, whether Poch sees Yunus as a midfielder, why Brendo is missing, what the coach expects from Gio, and Greg and Belz give preferred lineups versus Panama and the second opponent. First match kicks off in a week.MORE DETAIL ON THE ROME TRIP IN MAY: https://earthlydelights.notion.site/Italia-25-with-Scuffed-1a3f3f1e0145804793e8e83e982168fdThere are still a few slots available: https://docs.google.com/forms/d/e/1FAIpQLSfp2O1bIr5KNSymt3ayP3ctERCooDo3ADM5Kf_kSHpmr6ITMg/viewform Subscribe to Scuffed on Patreon! Patrons get a private ad-free feed for all episodes that go out on the public feed, plus the Monday Review every week with Watke and Vince. Patrons also get access to some video of clips we discuss on the show, our private Discord server, live call-in shows, and the full catalog of historic recaps we've made: https://www.patreon.com/scuffedAlso, check out Boots on the Ground, our USWNT-focused spinoff podcast headed up by Tara and Vince. They are cooking over there, you can listen here: https://boots-on-the-ground.simplecast.com

Also Celtic is out, but makes a good show. Sanjay Sujanthakumar joins the Monday Review crew for a fun, rambling episode. The Weah banger, Vince thinks most of Juve is a bunch of frauds, Theo's madness interrupts a Yunus masterclass, does Joao disrespect Pulisic? The trip to Rome: https://docs.google.com/forms/d/e/1FAIpQLSfp2O1bIr5KNSymt3ayP3ctERCooDo3ADM5Kf_kSHpmr6ITMg/viewform Subscribe to Scuffed on Patreon! Patrons get a private ad-free feed for all episodes that go out on the public feed, plus the Monday Review every week with Watke and Vince. Patrons also get access to some video of clips we discuss on the show, our private Discord server, live call-in shows, and the full catalog of historic recaps we've made: https://www.patreon.com/scuffedAlso, check out Boots on the Ground, our USWNT-focused spinoff podcast headed up by Tara and Vince. They are cooking over there, you can listen here: https://boots-on-the-ground.simplecast.com

Watke, Belz and Vince on Pepi's massive goal and assist, that work of art from Gio off the bench, Musah's meltdown and Pulisic's goal in Milan's loss, and a few other odds and ends. Subscribe to Scuffed on Patreon! Patrons get a private ad-free feed for all episodes that go out on the public feed, plus the Monday Review every week with Watke and Vince. Patrons also get access to some video of clips we discuss on the show, our private Discord server, live call-in shows, and the full catalog of historic recaps we've made: https://www.patreon.com/scuffed

PREVIEW - RUSSIA Colleague John Hardie of FDD (Foundation for Defense of Democracies) explains the limitations of Russian Deputy Defense Minister Yunus-Bek Yevkurov, who reportedly rushed to the Kursk Salient during a Kyiv counterattack. More details to follow. 1900 Kyiv