Podcasts about Leaderboard

Thank you to today's sponsors!- The Invasive Species Centre: Protecting Canada's land and water from invasive species- SAIL: The Ultimate Destination for your Outdoor Adventures- J&B Cycle and Marine: Your Home for all things powersports, boats, and equipment- Freedom Cruise Canada: Rent the boat, own the memories- Anglers Leaderboard: Real-time AI angling platform where everyone is welcome, and every catch counts!- Silverwax: Proudly Canadian since 1999The Outdoor Journal Radio crew hits the road again—this time to Air Dale Lodge near Hawk Junction, Ontario! Join Pete, Steve, and Dean as they swap stories over dessert, test out Angler's Leaderboard in a friendly tournament, and battle wild winds in search of walleye, smallmouth, and pike. Hear about the chaos of filming solo in a tin boat, a hilarious three-camera pike sequence, and the top-tier hospitality of Jenn and Martin's team at Air Dale. Plus, listener feedback on burbot fishing, a conservation chat about poaching, and a thoughtful fan question from Frontier Fishing with Ryan Mac. One of the funniest and most fish-filled road episodes yet. 

Talks of a new Leaderboard, 2026 plans, and breakdown of the 2025 Sand Sports Super Show!

Here's a look at the area leaders from Bureau Valley, Hall, Princeton and St. Bede through Week 6 of the 2025 seasonBecome a supporter of this podcast: https://www.spreaker.com/podcast/friday-night-drive--3534096/support.

Want to know how someone with a small email list still landed in the Top 10 of one of the biggest affiliate launches of the year using a single social media network? In this episode, you'll hear directly from a social media pro who leveraged Instagram — not ads, not email — to crush it on the leaderboard. Links Mentioned in this Episode Manu's Instagram – @YourSocialTeam How to Start Your Affiliate Program Get Started with Affiliate Marketing Your First 100 Affiliates  

AI is reshaping industries at a rapid pace, but as its influence grows, so do the ethical concerns that come with it.   This episode examines how AI is being applied across sectors such as healthcare, finance, and retail, while also exploring the crucial issue of ensuring that these technologies align with human values.   In this conversation, Lois Houston and Nikita Abraham are joined by Hemant Gahankari, Senior Principal OCI Instructor, who emphasizes the importance of fairness, inclusivity, transparency, and accountability in AI systems.   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 spoke about how Oracle integrates AI capabilities into its Fusion Applications to enhance business workflows, and we focused on Predictive, Generative, and Agentic AI. Lois: Today, we'll discuss the various applications of AI. This is the final episode in our AI series, and before we close, we'll also touch upon ethical and responsible AI.  01:01 Nikita: Taking us through all of this is Senior Principal OCI Instructor Hemant Gahankari. Hi Hemant! AI is pretty much everywhere today. So, can you explain how it is being used in industries like retail, hospitality, health care, and so on?  Hemant: AI isn't just for sci-fi movies anymore. It's helping doctors spot diseases earlier and even discover new drugs faster. Imagine an AI that can look at an X-ray and say, hey, there is something sketchy here before a human even notices. Wild, right? Banks and fintech companies are all over AI. Fraud detection. AI has got it covered. Those robo advisors managing your investments? That's AI too. Ever noticed how e-commerce companies always seem to know what you want? That's AI studying your habits and nudging you towards that next purchase or binge watch. Factories are getting smarter. AI predicts when machines will fail so they can fix them before everything grinds to a halt. Less downtime, more efficiency. Everyone wins. Farming has gone high tech. Drones and AI analyze crops, optimize water use, and even help with harvesting. Self-driving cars get all the hype, but even your everyday GPS uses AI to dodge traffic jams. And if AI can save me from sitting in bumper-to-bumper traffic, I'm all for it. 02:40 Nikita: Agreed! Thanks for that overview, but let's get into specific scenarios within each industry.  Hemant: Let us take a scenario in the retail industry-- a retail clothing line with dozens of brick-and-mortar stores. Maintaining proper inventory levels in stores and regional warehouses is critical for retailers. In this low-margin business, being out of a popular product is especially challenging during sales and promotions. Managers want to delight shoppers and increase sales but without overbuying. That's where AI steps in. The retailer has multiple information sources, ranging from point-of-sale terminals to warehouse inventory systems. This data can be used to train a forecasting model that can make predictions, such as demand increase due to a holiday or planned marketing promotion, and determine the time required to acquire and distribute the extra inventory. Most ERP-based forecasting systems can produce sophisticated reports. A generative AI report writer goes further, creating custom plain-language summaries of these reports tailored for each store, instructing managers about how to maximize sales of well-stocked items while mitigating possible shortages. 04:11 Lois: Ok. How is AI being used in the hospitality sector, Hemant? Hemant: Let us take an example of a hotel chain that depends on positive ratings on social media and review websites. One common challenge they face is keeping track of online reviews, leading to missed opportunities to engage unhappy customers complaining on social media. Hotel managers don't know what's being said fast enough to address problems in real-time. Here, AI can be used to create a large data set from the tens of thousands of previously published online reviews. A textual language AI system can perform a sentiment analysis across the data to determine a baseline that can be periodically re-evaluated to spot trends. Data scientists could also build a model that correlates these textual messages and their sentiments against specific hotel locations and other factors, such as weather. Generative AI can extract valuable suggestions and insights from both positive and negative comments. 05:27 Nikita: That's great. And what about Financial Services? I know banks use AI quite often to detect fraud. Hemant: Unfortunately, fraud can creep into any part of a bank's retail operations. Fraud can happen with online transactions, from a phone or browser, and offsite ATMs too. Without trust, banks won't have customers or shareholders. Excessive fraud and delays in detecting it can violate financial industry regulations. Fraud detection combines AI technologies, such as computer vision to interpret scanned documents, document verification to authenticate IDs like driver's licenses, and machine learning to analyze patterns. These tools work together to assess the risk of fraud in each transaction within seconds. When the system detects a high risk, it triggers automated responses, such as placing holds on withdrawals or requesting additional identification from customers, to prevent fraudulent activity and protect both the business and its client. 06:42 Nikita: Wow, interesting. And how is AI being used in the health industry, especially when it comes to improving patient care? Hemant: Medical appointments can be frustrating for everyone involved—patients, receptionists, nurses, and physicians. There are many time-consuming steps, including scheduling, checking in, interactions with the doctors, checking out, and follow-ups. AI can fix this problem through electronic health records to analyze lab results, paper forms, scans, and structured data, summarizing insights for doctors with the latest research and patient history. This helps practice reduced costs, boost earnings, and deliver faster, more personalized care. 07:32 Lois: Let's take a look at one more industry. How is manufacturing using AI? Hemant: A factory that makes metal parts and other products use both visual inspections and electronic means to monitor product quality. A part that fails to meet the requirements may be reworked or repurposed, or it may need to be scrapped. The factory seeks to maximize profits and throughput by shipping as much good material as possible, while minimizing waste by detecting and handling defects early. The way AI can help here is with the quality assurance process, which creates X-ray images. This data can be interpreted by computer vision, which can learn to identify cracks and other weak spots, after being trained on a large data set. In addition, problematic or ambiguous data can be highlighted for human inspectors. 08:36 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. 09:20 Nikita: Welcome back! AI can be used effectively to automate a variety of tasks to improve productivity, efficiency, cost savings. But I'm sure AI has its constraints too, right? Can you talk about what happens if AI isn't able to echo human ethics?  Hemant: AI can fail due to lack of ethics.  AI can spot patterns, not make moral calls. It doesn't feel guilt, understand context, or take responsibility. That is still up to us.  Decisions are only as good as the data behind them. For example, health care AI underdiagnosing women because research data was mostly male. Artificial narrow intelligence tends to automate discrimination at scale. Recruiting AI downgraded resumes just because it had a word "women's" (for example, women's chess club). Who is responsible when AI fails? For example, if a self-driving car hits someone, we cannot blame the car. Then who owns the failure? The programmer? The CEO? Can we really trust corporations or governments having programmed the use of AI not to be evil correctly? So, it's clear that AI needs oversight to function smoothly. 10:48 Lois: So, Hemant, how can we design AI in ways that respect and reflect human values? Hemant: Think of ethics like a tree. It needs all parts working together. Roots represent intent. That is our values and principles. The trunk stands for safeguards, our systems, and structures. And the branches are the outcomes we aim for. If the roots are shallow, the tree falls. If the trunk is weak, damage seeps through. The health of roots and trunk shapes the strength of our ethical outcomes. Fairness means nothing without ethical intent behind it. For example, a bank promotes its loan algorithm as fair. But it uses zip codes in decision-making, effectively penalizing people based on race. That's not fairness. That's harm disguised as data. Inclusivity depends on the intent sustainability. Inclusive design isn't just a check box. It needs a long-term commitment. For example, controllers for gamers with disabilities are only possible because of sustained R&D and intentional design choices. Without investment in inclusion, accessibility is left behind. Transparency depends on the safeguard robustness. Transparency is only useful if the system is secure and resilient. For example, a medical AI may be explainable, but if it is vulnerable to hacking, transparency won't matter. Accountability depends on the safeguard privacy and traceability. You can't hold people accountable if there is no trail to follow. For example, after a fatal self-driving car crash, deleted system logs meant no one could be held responsible. Without auditability, accountability collapses. So remember, outcomes are what we see, but they rely on intent to guide priorities and safeguards to support execution. That's why humans must have a final say. AI has no grasp of ethics, but we do. 13:16 Nikita: So, what you're saying is ethical intent and robust AI safeguards need to go hand in hand if we are to truly leverage AI we can trust. Hemant: When it comes to AI, preventing harm is a must. Take self-driving cars, for example. Keeping pedestrians safe is absolutely critical, which means the technology has to be rock solid and reliable. At the same time, fairness and inclusivity can't be overlooked. If an AI system used for hiring learns from biased past data, say, mostly male candidates being hired, it can end up repeating those biases, shutting out qualified candidates unfairly. Transparency and accountability go hand in hand. Imagine a loan rejection if the AI's decision isn't clear or explainable. It becomes impossible for someone to challenge or understand why they were turned down. And of course, robustness supports fairness too. Loan approval systems need strong security to prevent attacks that could manipulate decisions and undermine trust.  We must build AI that reflects human values and has safeguards. This makes sure that AI is fair, inclusive, transparent, and accountable.  14:44 Lois: Before we wrap, can you talk about why AI can fail? Let's continue with your analogy of the tree. Can you explain how AI failures occur and how we can address them? Hemant: Root elements like do not harm and sustainability are fundamental to ethical AI development. When these roots fail, the consequences can be serious. For example, a clear failure of do not harm is AI-powered surveillance tools misused by authoritarian regimes. This happens because there were no ethical constraints guiding how the technology was deployed. The solution is clear-- implement strong ethical use policies and conduct human rights impact assessment to prevent such misuse. On the sustainability front, training AI models can consume massive amount of energy. This failure occurs because environmental costs are not considered. To fix this, organizations are adopting carbon-aware computing practices to minimize AI's environmental footprint. By addressing these root failures, we can ensure AI is developed and used responsibly with respect for human rights and the planet. An example of a robustness failure can be a chatbot hallucinating nonexistent legal precedence used in court filings. This could be due to training on unverified internet data and no fact-checking layer. This can be fixed by grounding in authoritative databases. An example of a privacy failure can be AI facial recognition database created without user consent. The reason being no consent was taken for data collection. This can be fixed by adopting privacy-preserving techniques. An example of a fairness failure can be generated images of CEOs as white men and nurses as women, minorities. The reason being training on imbalanced internet images reflecting societal stereotypes. And the fix is to use diverse set of images. 17:18 Lois: I think this would be incomplete if we don't talk about inclusivity, transparency, and accountability failures. How can they be addressed, Hemant? Hemant: An example of an inclusivity failure can be a voice assistant not understanding accents. The reason being training data lacked diversity. And the fix is to use inclusive data. An example of a transparency and accountability failure can be teachers could not challenge AI-generated performance scores due to opaque calculations. The reason being no explainability tools are used. The fix being high-impact AI needs human review pathways and explainability built in. 18:04 Lois: Thank you, Hemant, for a fantastic conversation. We got some great insights into responsible and ethical AI. Nikita: Thank you, Hemant! If you're interested in learning more about the topics we discussed today, head over to mylearn.oracle.com and search for the AI for You course. Until next time, this is Nikita Abraham…. Lois: And Lois Houston, signing off! 18:26 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.  

Here's a look at the area leaders from Bureau Valley, Hall, Princeton and St. Bede through Week 4 of the 2025 seasonBecome a supporter of this podcast: https://www.spreaker.com/podcast/friday-night-drive--3534096/support.

subscribe to my yt, i'm more active there. https://youtube.com/@makeshareplaythanks! have a good one

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.  

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.

In this episode, Lois Houston and Nikita Abraham continue their discussion on AI fundamentals, diving into Data Science with Principal AI/ML Instructor Himanshu Raj. They explore key concepts like data collection, cleaning, and analysis, and talk about how quality data drives impactful insights.   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: Hello and welcome to the Oracle University Podcast. I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead: Editorial Services.  Nikita: Hi everyone! Last week, we began our exploration of core AI concepts, specifically machine learning and deep learning. I'd really encourage you to go back and listen to the episode if you missed it.   00:52 Lois: Yeah, today we're continuing that discussion, focusing on data science, with our Principal AI/ML Instructor Himanshu Raj.  Nikita: Hi Himanshu! Thanks for joining us again. So, let's get cracking! What is data science?  01:06 Himanshu: It's about collecting, organizing, analyzing, and interpreting data to uncover valuable insights that help us make better business decisions. Think of data science as the engine that transforms raw information into strategic action.  You can think of a data scientist as a detective. They gather clues, which is our data. Connect the dots between those clues and ultimately solve mysteries, meaning they find hidden patterns that can drive value.  01:33 Nikita: Ok, and how does this happen exactly?  Himanshu: Just like a detective relies on both instincts and evidence, data science blends domain expertise and analytical techniques. First, we collect raw data. Then we prepare and clean it because messy data leads to messy conclusions. Next, we analyze to find meaningful patterns in that data. And finally, we turn those patterns into actionable insights that businesses can trust.  02:00 Lois: So what you're saying is, data science is not just about technology; it's about turning information into intelligence that organizations can act on. Can you walk us through the typical steps a data scientist follows in a real-world project?  Himanshu: So it all begins with business understanding. Identifying the real problem we are trying to solve. It's not about collecting data blindly. It's about asking the right business questions first. And once we know the problem, we move to data collection, which is gathering the relevant data from available sources, whether internal or external.  Next one is data cleaning. Probably the least glamorous but one of the most important steps. And this is where we fix missing values, remove errors, and ensure that the data is usable. Then we perform data analysis or what we call exploratory data analysis.  Here we look for patterns, prints, and initial signals hidden inside the data. After that comes the modeling and evaluation, where we apply machine learning or deep learning techniques to predict, classify, or forecast outcomes. Machine learning, deep learning are like specialized equipment in a data science detective's toolkit. Powerful but not the whole investigation.  We also check how good the models are in terms of accuracy, relevance, and business usefulness. Finally, if the model meets expectations, we move to deployment and monitoring, putting the model into real world use and continuously watching how it performs over time.  03:34 Nikita: So, it's a linear process?  Himanshu: It's not linear. That's because in real world data science projects, the process does not stop after deployment. Once the model is live, business needs may evolve, new data may become available, or unexpected patterns may emerge.  And that's why we come back to business understanding again, defining the questions, the strategy, and sometimes even the goals based on what we have learned. In a way, a good data science project behaves like living in a system which grows, adapts, and improves over time. Continuous improvement keeps it aligned with business value.   Now, think of it like adjusting your GPS while driving. The route you plan initially might change as new traffic data comes in. Similarly, in data science, new information constantly help refine our course. The quality of our data determines the quality of our results.   If the data we feed into our models is messy, inaccurate, or incomplete, the outputs, no matter how sophisticated the technology, will be also unreliable. And this concept is often called garbage in, garbage out. Bad input leads to bad output.  Now, think of it like cooking. Even the world's best Michelin star chef can't create a masterpiece with spoiled or poor-quality ingredients. In the same way, even the most advanced AI models can't perform well if the data they are trained on is flawed.  05:05 Lois: Yeah, that's why high-quality data is not just nice to have, it's absolutely essential. But Himanshu, what makes data good?   Himanshu: Good data has a few essential qualities. The first one is complete. Make sure we aren't missing any critical field. For example, every customer record must have a phone number and an email. It should be accurate. The data should reflect reality. If a customer's address has changed, it must be updated, not outdated. Third, it should be consistent. Similar data must follow the same format. Imagine if the dates are written differently, like 2024/04/28 versus April 28, 2024. We must standardize them.   Fourth one. Good data should be relevant. We collect only the data that actually helps solve our business question, not unnecessary noise. And last one, it should be timely. So data should be up to date. Using last year's purchase data for a real time recommendation engine wouldn't be helpful.  06:13 Nikita: Ok, so ideally, we should use good data. But that's a bit difficult in reality, right? Because what comes to us is often pretty messy. So, how do we convert bad data into good data? I'm sure there are processes we use to do this.  Himanshu: First one is cleaning. So this is about correcting simple mistakes, like fixing typos in city names or standardizing dates.  The second one is imputation. So if some values are missing, we fill them intelligently, for instance, using the average income for a missing salary field. Third one is filtering. In this, we remove irrelevant or noisy records, like discarding fake email signups from marketing data. The fourth one is enriching. We can even enhance our data by adding trusted external sources, like appending credit scores from a verified bureau.  And the last one is transformation. Here, we finally reshape data formats to be consistent, for example, converting all units to the same currency. So even messy data can become usable, but it takes deliberate effort, structured process, and attention to quality at every step.  07:26 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest technology. 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. 08:10 Nikita: Welcome back! Himanshu, we spoke about how to clean data. Now, once we get high-quality data, how do we analyze it?  Himanshu: In data science, there are four primary types of analysis we typically apply depending on the business goal we are trying to achieve.  The first one is descriptive analysis. It helps summarize and report what has happened. So often using averages, totals, or percentages. For example, retailers use descriptive analysis to understand things like what was the average customer spend last quarter? How did store foot traffic trend across months?  The second one is diagnostic analysis. Diagnostic analysis digs deeper into why something happened. For example, hospitals use this type of analysis to find out, for example, why a certain department has higher patient readmission rates. Was it due to staffing, post-treatment care, or patient demographics?  The third one is predictive analysis. Predictive analysis looks forward, trying to forecast future outcomes based on historical patterns. For example, energy companies predict future electricity demand, so they can better manage resources and avoid shortages. And the last one is prescriptive analysis. So it does not just predict. It recommends specific actions to take.  So logistics and supply chain companies use prescriptive analytics to suggest the most efficient delivery routes or warehouse stocking strategies based on traffic patterns, order volume, and delivery deadlines.   09:42 Lois: So really, we're using data science to solve everyday problems. Can you walk us through some practical examples of how it's being applied?  Himanshu: The first one is predictive maintenance. It is done in manufacturing a lot. A factory collects real time sensor data from machines. Data scientists first clean and organize this massive data stream, explore patterns of past failures, and design predictive models.  The goal is not just to predict breakdowns but to optimize maintenance schedules, reducing downtime and saving millions. The second one is a recommendation system. It's prevalent in retail and entertainment industries. Companies like Netflix or Amazon gather massive user interaction data such as views, purchases, likes.  Data scientists structure and analyze this behavioral data to find meaningful patterns of preferences and build models that suggest relevant content, eventually driving more engagement and loyalty. The third one is fraud detection. It's applied in finance and banking sector.  Banks store vast amounts of transaction record records. Data scientists clean and prepare this data, understand typical spending behaviors, and then use statistical techniques and machine learning to spot unusual patterns, catching fraud faster than manual checks could ever achieve.  The last one is customer segmentation, which is often applied in marketing. Businesses collect demographics and behavioral data about their customers. Instead of treating all the customers same, data scientists use clustering techniques to find natural groupings, and this insight helps businesses tailor their marketing efforts, offers, and communication for each of those individual groups, making them far more effective.  Across all these examples, notice that data science isn't just building a model. Again, it's understanding the business need, reviewing the data, analyzing it thoughtfully, and building the right solution while helping the business act smarter.  11:44 Lois: Thank you, Himanshu, for joining us on this episode of the Oracle University Podcast. We can't wait to have you back next week for part 3 of this conversation on core AI concepts, where we'll talk about generative AI and gen AI agents.     Nikita: And if you want to learn more about data science, visit mylearn.oracle.com and search for the AI for You course. Until next time, this is Nikita Abraham…  Lois: And Lois Houston signing off!  12:13 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.

The TeeBox 8-9-25 Craig Rosengarden and Eli Jordan Discuss the Latest Leaderboards and Whether New Golfers Should Get Professionally Fitted for Clubs

In this episode, hosts Lois Houston and Nikita Abraham, together with Senior Cloud Engineer Nick Commisso, break down the basics of artificial intelligence (AI). They discuss the differences between Artificial General Intelligence (AGI) and Artificial Narrow Intelligence (ANI), and explore the concepts of machine learning, deep learning, and generative AI. Nick also shares examples of how AI is used in everyday life, from navigation apps to spam filters, and explains how AI can help businesses cut costs and boost revenue.   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 Nikita: Hello and welcome to the Oracle University Podcast. I'm Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi everyone! Welcome to a new season of the podcast. I'm so excited about this one because we're going to dive into the world of artificial intelligence, speaking to many experts in the field. Nikita: If you've been listening to us for a while, you probably know we've covered AI from a bunch of different angles. But this time, we're dialing it all the way back to basics. We wanted to create something for the absolute beginner, so no jargon, no assumptions, just simple conversations that anyone can follow. 01:08 Lois: That's right, Niki. You don't need to have a technical background or prior experience with AI to get the most out of these episodes. In our upcoming conversations, we'll break down the basics of AI, explore how it's shaping the world around us, and understand its impact on your business. Nikita: The idea is to give you a practical understanding of AI that you can use in your work, especially if you're in sales, marketing, operations, HR, or even customer service.  01:37 Lois: Today, we'll talk about the basics of AI with Senior Cloud Engineer Nick Commisso. Hi Nick! Welcome back to the podcast. Can you tell us about human intelligence and how it relates to artificial intelligence? And within AI, I know we have Artificial General Intelligence, or AGI, and Artificial Narrow Intelligence, or ANI. What's the difference between the two? Nick: Human intelligence is the intellectual capability of humans that allow us to learn new skills through observation and mental digestion, to think through and understand abstract concepts and apply reasoning, to communicate using language and understand non-verbal cues, such as facial expressions, tone variation, body language. We can handle objections and situations in real time, even in a complex setting. We can plan for short and long-term situations or projects. And we can create music, art, or invent something new or have original ideas. If machines can replicate a wide range of human cognitive abilities, such as learning, reasoning, or problem solving, we call it artificial general intelligence.  Now, AGI is hypothetical for now, but when we apply AI to solve problems with specific, narrow objectives, we call it artificial narrow intelligence, or ANI. AGI is a hypothetical AI that thinks like a human. It represents the ultimate goal of artificial intelligence, which is a system capable of chatting, learning, and even arguing like us. If AGI existed, it would take the form like a robot doctor that accurately diagnoses and comforts patients, or an AI teacher that customizes lessons in real time based on each student's mood, pace, and learning style, or an AI therapist that comprehends complex emotions and provides empathetic, personalized support. ANI, on the other hand, focuses on doing one thing really well. It's designed to perform specific tasks by recognizing patterns and following rules, but it doesn't truly understand or think beyond its narrow scope. Think of ANI as a specialist. Your phone's face ID can recognize you instantly, but it can't carry on a conversation. Google Maps finds the best route, but it can't write you a poem. And spam filters catch junk mail, but it can't make you coffee. So, most of the AI you interact with today is ANI. It's smart, efficient, and practical, but limited to specific functions without general reasoning or creativity. 04:22 Nikita: Ok then what about Generative AI?  Nick: Generative AI is a type of AI that can produce content such as audio, text, code, video, and images. ChatGPT can write essays, but it can't fact check itself. DALL-E creates art, but it doesn't actually know if it's good. Or AI song covers can create deepfakes like Drake singing "Baby Shark."  04:47 Lois: Why should I care about AI? Why is it important? Nick: AI is already part of your everyday life, often working quietly in the background. ANI powers things like navigation apps, voice assistants, and spam filters. Generative AI helps create everything from custom playlists to smart writing tools. And while AGI isn't here yet, it's shaping ideas about what the future might look like. Now, AI is not just a buzzword, it's a tool that's changing how we live, work, and interact with the world. So, whether you're using it or learning about it or just curious, it's worth knowing what's behind the tech that's becoming part of everyday life.  05:32 Lois: Nick, whenever people talk about AI, they also throw around terms like machine learning and deep learning. What are they and how do they relate to AI? Nick: As we shared earlier, AI is the ability of machines to imitate human intelligence. And Machine Learning, or ML, is a subset of AI where the algorithms are used to learn from past data and predict outcomes on new data or to identify trends from the past. Deep Learning, or DL, is a subset of machine learning that uses neural networks to learn patterns from complex data and make predictions or classifications. And Generative AI, or GenAI, on the other hand, is a specific application of DL focused on creating new content, such as text, images, and audio, by learning the underlying structure of the training data.  06:24 Nikita: AI is often associated with key domains like language, speech, and vision, right? So, could you walk us through some of the specific tasks or applications within each of these areas? Nick: Language-related AI tasks can be text related or generative AI. Text-related AI tasks use text as input, and the output can vary depending on the task. Some examples include detecting language, extracting entities in a text, extracting key phrases, and so on.  06:54 Lois: Ok, I get you. That's like translating text, where you can use a text translation tool, type your text in the box, choose your source and target language, and then click Translate. That would be an example of a text-related AI task. What about generative AI language tasks? Nick: These are generative, which means the output text is generated by the model. Some examples are creating text, like stories or poems, summarizing texts, and answering questions, and so on. 07:25 Nikita: What about speech and vision? Nick: Speech-related AI tasks can be audio related or generative AI. Speech-related AI tasks use audio or speech as input, and the output can vary depending on the task. For example, speech to text conversion, speaker recognition, or voice conversion, and so on. Generative AI tasks are generative, i.e., the output audio is generated by the model (for example, music composition or speech synthesis). Vision-related AI tasks can be image related or generative AI. Image-related AI tasks use an image as the input, and the output depends on the task. Some examples are classifying images or identifying objects in an image. Facial recognition is one of the most popular image-related tasks that's often used for surveillance and tracking people in real time. It's used in a lot of different fields, like security and biometrics, law enforcement, entertainment, and social media. For generative AI tasks, the output image is generated by the model. For example, creating an image from a textual description or generating images of specific style or high resolution, and so on. It can create extremely realistic new images and videos by generating original 3D models of objects, such as machine, buildings, medications, people and landscapes, and so much more. 08:58 Lois: This is so fascinating. So, now we know what AI is capable of. But Nick, what is AI good at? Nick: AI frees you to focus on creativity and more challenging parts of your work. Now, AI isn't magic. It's just very good at certain tasks. It handles work that's repetitive, time consuming, or too complex for humans, like processing data or spotting patterns in large data sets.  AI can take over routine tasks that are essential but monotonous. Examples include entering data into spreadsheets, processing invoices, or even scheduling meetings, freeing up time for more meaningful work. AI can support professionals by extending their abilities. Now, this includes tools like AI-assisted coding for developers, real-time language translation for travelers or global teams, and advanced image analysis to help doctors interpret medical scans much more accurately. 10:00 Nikita: And what would you say is AI's sweet spot? Nick: That would be tasks that are both doable and valuable. A few examples of tasks that are feasible technically and have business value are things like predicting equipment failure. This saves downtime and the loss of business. Call center automation, like the routing of calls to the right person. This saves time and improves customer satisfaction. Document summarization and review. This helps save time for busy professionals. Or inspecting power lines. Now, this task is dangerous. By automating it, it protects human life and saves time. 10:48 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. 11:30 Nikita: Welcome back! Now one big way AI is helping businesses today is by cutting costs, right? Can you give us some examples of this?  Nick: Now, AI can contribute to cost reduction in several key areas. For instance, chatbots are capable of managing up to 50% of customer queries. This significantly reduces the need for manual support, thereby lowering operational costs. AI can streamline workflows, for example, reducing invoice processing time from 10 days to just 1 hour. This leads to substantial savings in both time and resources. In addition to cost savings, AI can also support revenue growth. One way is enabling personalization and upselling. Platforms like Netflix use AI-driven recommendation systems to influence user choices. This not only enhances the user experience, but it also increases the engagement and the subscription revenue. Or unlocking new revenue streams. AI technologies, such as generative video tools and virtual influencers, are creating entirely new avenues for advertising and branded content, expanding business opportunities in emerging markets. 12:50 Lois: Wow, saving money and boosting bottom lines. That's a real win! But Nick, how is AI able to do this?  Nick: Now, data is what teaches AI. Just like we learn from experience, so does AI. It learns from good examples, bad examples, and sometimes even the absence of examples. The quality and variety of data shape how smart, accurate, and useful AI becomes. Imagine teaching a kid to recognize animals using only pictures of squirrels that are labeled dogs. That would be very confusing at the dog park. AI works the exact same way, where bad data leads to bad decisions. With the right data, AI can be powerful and accurate. But with poor or biased data, it can become unreliable and even misleading.  AI amplifies whatever you feed it. So, give it gourmet data, not data junk food. AI is like a chef. It needs the right ingredients. It needs numbers for predictions, like will this product sell? It needs images for cool tricks like detecting tumors, and text for chatting, or generating excuses for why you'd be late. Variety keeps AI from being a one-trick pony. Examples of the types of data are numbers, or machine learning, for predicting things like the weather. Text or generative AI, where chatbots are used for writing emails or bad poetry. Images, or deep learning, can be used for identifying defective parts in an assembly line, or an audio data type to transcribe a dictation from a doctor to a text. 14:35 Lois: With so much data available, things can get pretty confusing, which is why we have the concept of labeled and unlabeled data. Can you help us understand what that is? Nick: Labeled data are like flashcards, where everything has an answer. Spam filters learned from emails that are already marked as junk, and X-rays are marked either normal or pneumonia. Let's say we're training AI to tell cats from dogs, and we show it a hundred labeled pictures. Cat, dog, cat, dog, etc. Over time, it learns, hmm fluffy and pointy ears? That's probably a cat. And then we test it with new pictures to verify. Unlabeled data is like a mystery box, where AI has to figure it out itself. Social media posts, or product reviews, have no labels. So, AI clusters them by similarity. AI finding trends in unlabeled data is like a kid sorting through LEGOs without instructions. No one tells them which blocks will go together.  15:36 Nikita: With all the data that's being used to train AI, I'm sure there are issues that can crop up too. What are some common problems, Nick? Nick: AI's performance depends heavily on the quality of its data. Poor or biased data leads to unreliable and unfair outcomes. Dirty data includes errors like typos, missing values, or duplicates. For example, an age record as 250, or NA, can confuse the AI. And a variety of data cleaning techniques are available, like missing data can be filled in, or duplicates can be removed. AI can inherit human prejudices if the data is unbalanced. For example, a hiring AI may favor one gender if the past three hires were mostly male. Ensuring diverse and representative data helps promote fairness. Good data is required to train better AI. Data could be messy, and needs to be processed before to train AI. 16:39 Nikita: Thank you, Nick, for sharing your expertise with us. To learn more about AI, go to mylearn.oracle.com and search for the AI for You course. As you complete the course, you'll find skill checks that you can attempt to solidify your learning.  Lois: In our next episode, we'll dive deep into fundamental AI concepts and terminologies. Until then, this is Lois Houston… Nikita: And Nikita Abraham signing off! 17:05 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.

Dive into this week's jam-packed episode where we cover all the hottest news across the Peloton and fitness world. Here's what we're talking about today:Peloton halts sales of Guide What's next for this device, and why it's being pulled from shelves?Peloton Repowered rollout The nationwide debut brings refurbished equipment to more members.New features for Peloton Teams Here's how they'll elevate your community spirit and workouts!Peloton Production Assistants go Instagram-official Find out how these behind-the-scenes stars are shining online.Peloton earnings call date announced Mark your calendars and prep for key insights.NYRR partners with iHeart A 3-year deal that'll amplify both running and fitness culture.Where instructors go after Peloton An inside look at what's next for those who move on.Leaderboard deep-dive from TCO Want to know how it works? Tune in for the breakdown.Peloton wins Bike+ trademark case Victory at last! We're spilling the juicy details.Cody Rigsby on Tread rumors Is the gossip true? Cody has something to say.Aditi Shah celebrates *The Shift* What is The Shift and why it matters!Athlete Ally Awards hosted by Matty & Tunde Advocacy meets star power at this standout event.Adrian Williams' car accident Updates and support for this beloved trainer.Echelon mishap alert The competition has equipment issues, and we are here for the tea!Your Top Five classes of the week Listener faves you won't want to miss (as chosen by you!).This Week at Peloton A full rundown of the week's news, happenings, and must-dos.Robin Arzon teases HYROX program What's this hybrid fitness competition, and how is Robin involved?Christine D'Ercole's Reflection Rides Big news! They're now part of an official collection.Denis Morton launches *Sample That Ride* Explore the beats and energy of this exciting new class.Team Peloton X mileage challenge Are you up for the challenge? Details on how to join in.Peloton Birthdays Jayvee Nava (8/2), Marion Roaman (8/3), Jess Sims (8/5), and Alex Toussaint (8/6) Press play now and catch up on all things Peloton! Don't forget to subscribe, leave a review, and share the episode with your fitness-loving crew.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Sarah is joined by 16-game super-champion Scott Riccardi to discuss his Jeopardy! journey, securing his spot on the Leaderboard of Legends, and how he is preparing for the upcoming Tournament of Champions. Inside Jeopardy! is sponsored by Shopify. Visit Shopify.com/jeopardy to sign up for a $1 per month trial period. Host: Sarah Foss Production Support: Alexa Macchia & Carlos Martinez Follow Jeopardy! Instagram: @jeopardy Twitter: @jeopardy Subscribe on YouTube: www.youtube.com/jeopardy Website: www.jeopardy.com Learn more about your ad choices. Visit podcastchoices.com/adchoices

On episode 704 of the 40 Plus Fitness podcast, Coach Allan sits down with Dr. Kyra Bobinet, author of Unstoppable Brain: The New Neuroscience That Frees Us from Failure, Eases Our Stress, and Creates Lasting Change. Dr. Bobinet brings nearly three decades of expertise in neuroscience and behavior change, and together they explore the concept of "failure disease"—that sense of being stuck or losing motivation, especially when it comes to health and fitness over 40. In their conversation, Dr. Bobinet reveals the groundbreaking science behind what really drives our habits and why so many of us feel like we're destined to fail—spoiler alert: it's not just a lack of willpower! You'll learn about the role of the brain's “motivation kill switch,” the habenula, and get practical tools for working with your brain instead of against it. Whether you struggle with all-or-nothing thinking, motivation loss, or the weight of past failures, this episode is packed with actionable strategies to help you break the cycle and create lasting change. Time Stamps: 05:35 Learned Helplessness Misunderstood 06:49 "Learned Helplessness in Elephants" 12:30 Addiction Swap Ineffectiveness 15:40 Status and Ego on the Leaderboard 18:07 "SMART Goals and Workplace Deception" 21:07 Marginal Gains in Elite Sports 24:05 "Rethinking Failure in Goal Setting" 28:32 Understanding Brain Patterns and Negativity 33:07 Adapting and Iterating with AI 35:33 Idea Generation & Iteration Tool 38:11 Introducing Change Through Friction & Spice 41:23 Keys to Iterative Success https://drkhirabobinet.com

TSN senior golf reporter Bob Weeks joins us on Mail it in Friday to discuss Scottie Scheffler's rise up the leaderboard at The Open. Weeks shares his thoughts on whether Scheffler can go for the career Grand Slam. He gives his take on the frustration shown by Shane Lowry and Jon Rahm

We talked about the British Open in our second hour as we were joined by PGA Rules Official Mark Wilson. He and Huge talked about the Leaderboard and who they think could win it all on Sunday, and more. We were then joined by Bill Hobson from Michigan Golf Live so we could continue the conversation. He gave us his thought's on who is most likely to win it on Sunday, and more. We were then joined by Josh Garvey from Doeren Mayhew. He told us about their partnership with the Lindsey Hunter Foundation, talked about the Detroit Tigers, looked ahead to the Lions season, and more. We were then joined by Steve Goff from the Lansing Sports Network. He and Huge went through the Spartan Football schedule and played the win/loss game, and more.See omnystudio.com/listener for privacy information.

We talked about the British Open as we were joined by PGA Rules Official Mark Wilson. He and Huge talked about the Leaderboard and who they think could win it all on Sunday, and more. See omnystudio.com/listener for privacy information.

Today on the show, we're talking about the Detroit Lions, Detroit Tigers, the British Open, and more as we were joined by some of our great guests. We kicked off the show talking about the Lions as Scott Bischoff from the Detroit Lions Podcast joined us. He and Huge talked about the Lions and Tate Ratledge finalizing a deal, talked about all of the injuries on the team before Training Camp even starts, and more. We then played Bill's interview with David Gregory in regards to the House vs. NCAA settlement. We were then joined by one of our Tigers insiders Greg Heeres so he and Huge could preview the series against the Rangers. He and Huge also talked about what they expect from the pitching staff moving forward, and more. We wrapped up the hour with a "Moving Ferris Forward" interview as Huge spoke with Sam Stark, who is Ferris State's Head and Man's Women's Golf Coach. He and Huge talked about the balance between Academic and Athletic success, talked about the Women's team reaching the DII Championship for the first time in 16 years, and much more. We talked about the British Open in our second hour as we were joined by PGA Rules Official Mark Wilson. He and Huge talked about the Leaderboard and who they think could win it all on Sunday, and more. We were then joined by Bill Hobson from Michigan Golf Live so we could continue the conversation. He gave us his thought's on who is most likely to win it on Sunday, and more. We were then joined by Josh Garvey from Doeren Mayhew. He told us about their partnership with the Lindsey Hunter Foundation, talked about the Detroit Tigers, looked ahead to the Lions season, and more. We were then joined by Steve Goff from the Lansing Sports Network. He and Huge went through the Spartan Football schedule and played the win/loss game, and more. In our final hour we were joined by Jeremy Reisman from Pride of Detroit so he and Huge could talk about the Lions. They discussed Ratledge finally signing his deal, gave their thought's on all of the injuries on the team in the off-season, and more. We then played Bill's earlier conversation with Scott Bischoff in regards to the Lions. We were then joined by Nate Wangler who is one of the voices of the West Michigan Whitecaps. He updated us on how Clark and McGonigle have looked in AA Erie, gave his thought's on what the Tigers need to do to bounce back, and more. See omnystudio.com/listener for privacy information.

Bernhard Fercher entwickelt die App Sidekik. Diese fördert auf spielerische Weise die Motivation fürs Fliegen. Was steckt dahinter? +++ Es gibt so unterschiedliche Fliegertypen. Für die einen ist Airtime „alles“. Sie hängen sich stundenlang wie festgenagelt am Prallhang in den Talwind. Hauptsache sie haben keinen Boden unter den Füßen. Andere wiederum haben nur die XC-Punkte im Blick, gehen ständig „auf Strecke“, auch wenn sie dabei das immergleiche Dreieck zum x-ten Mal abreiten. Und dann gibt es diese Pilotinnen und Piloten, die es irgendwie in die Lüfte treibt. Dort gondeln sie eine Weile eher ziellos umher, fallen dann irgendwann mangels Fokus aus der Thermik, stehen bald wieder am Boden, wo sie sich dann, sehnsuchtsvoll in den Himmel schauend, fragen: Wieso bin ich eigentlich nicht weitergeflogen? Vor allem dieser dritten beschriebenen Gruppe kann geholfen werden. Seit Anfang 2025 gibt es die App Sidekik. Programmiert hat sie der österreichische Pilot Bernhard Fercher. Er will damit auf spielerische Weise die Motivation fürs Fliegen fördern. Die App hilft dabei, Ziele zu setzen, um zum aktiveren und letztendlich auch irgendwie besseren Piloten zu werden. Der Weg dorthin führt über Gamification, also der Einführung von spielerischen Elementen in den Flugalltag. Was es dabei mit Flieger-Leveln, Expert-Points, Gipfel- und Segment-Challenges, Leaderboards und Trophäen auf sich hat, und wie sogar ganze Clubs in dieses Spiel mit einbezogen werden können, das erzählt Bernhard Fercher in dieser 165. Folge von Podz-Glidz. +++ Wenn Du Podz-Glidz und den Blog Lu-Glidz fördern möchtest, so findest Du alle zugehörigen Infos unter: https://lu-glidz.blogspot.com/p/fordern.html +++ Musik dieser Folge: Track: Summer Dawn | Künstler: Freedom Trail Studio Youtube Audio Library https://youtu.be/vWWyx5oQegs?si=nBk6KBwRCpZPHVtS +++ Lu-Glidz Links: + Blog: https://lu-glidz.blogspot.com + Facebook: https://www.facebook.com/luglidz + Instagram: https://www.instagram.com/luglidz/ + Whatsapp-Kanal: https://whatsapp.com/channel/0029VaBVs05CHDynzdlJlU34 + Youtube: https://youtube.com/@Lu-Glidz + Soundcloud: https://soundcloud.com/lu-glidz + Spotify: https://open.spotify.com/show/6ZNvk83xxGHHtfgFjiAHyJ + Apple-Podcast: https://itunes.apple.com/de/podcast/podz-glidz-der-lu-glidz-podcast/id1447518310?mt=2 + Linktree: https://linktr.ee/luglidz +++ LINKS zu Berhard Fercher: + Website Sidekik: https://www.sidekik.cloud

Shane Gillis at the ESPY's, favourite golf major, battling the elements at The Open and the NHL schedule is released.

On the worst day of the baseball calendar, the Thursday after the ASB, Lou Blasi and Sky Dombroske take a look at some interesting names on the EV Leaderboard as play resumes. They talk about Oniel Cruz, James Wood, Addison Barger, Ben Rice, Nick Kurtz, Jesus Sanchez, Alejandro Kirk, Ryan McMahon, and Kyle Stowers, among others ...

Have you ever wondered why you can spend hours playing video games but struggle to work on important life goals for even 30 minutes? What if you could harness that same engagement for your most meaningful pursuits?  In this captivating conversation with gamification pioneer Yu-Kai Chou, we uncover the hidden psychology that makes games so irresistible and learn how to apply these same principles to transform our work, habits, and lives.  Yu-Kai shares his remarkable journey from being a self-described "nerdy student" who spent thousands of hours leveling up game characters to becoming a world-renowned expert who has helped organizations like Google, Tesla, and the World Bank drive billions in business results through behavioral design.

When it's time to promote your next leader, how do you choose? How do you ensure you're putting the right person in the role? Is your best executor also your best next leader, or do you need someone more well rounded? Tune in to find out how to go from Leaderboard to Leadership. Artwork: a.p.e.e.z.y Music: bensound.com License code: OTNWOKL5UBLF0KRN

Welcome back to another episode of Bubba (@bdentrek) and the Bloom (@RyanBHQ). On BATB 256, the guys will check out the Last 30-day leaderboards for hitters and pitchers.

Threads gets DMs! It's only on the web, and it's only 1:1 - but you can start using it! Additionally President Trump says he has a buyer for TikTok, and the YouTube team shares some updates. After the music, I do Wednesday Waffle.Are you ready to dive into the latest social media updates that could transform your marketing strategy? Join host Daniel Hill as he navigates through the evolving landscape of social media in this episode of The Instagram Stories - Social Media News, where we explore exciting updates from Threads, TikTok, and YouTube.In this episode of The Instagram Stories - Social Media News, Daniel shares crucial insights into the new DM feature on Threads, allowing users to engage in one-on-one conversations that enhance their Instagram relationships. This innovative addition is set to change how we interact on social media, making it essential for anyone looking to refine their Instagram DM strategies. Furthermore, we delve into the latest TikTok updates, including President Trump's announcement about a potential US buyer for TikTok and the company's unexpected layoffs in its e-commerce division, despite rising sales figures. This juxtaposition of growth and restructuring highlights the social media trends that are shaping the future of platforms like TikTok.Additionally, we break down YouTube's exciting updates, featuring a new AI-powered search feature for premium users and enhanced audience insights for creators. These updates are vital for anyone in the creator economy looking to optimize their content and engage their audience more effectively. As Daniel discusses these platform updates, he also shares a personal story inspired by a podcast he recently listened to, illustrating the profound impact of connections and relationships in unexpected ways.This episode is packed with valuable information that you won't want to miss. From Instagram features and updates to the latest social media insights, Daniel provides a comprehensive overview of the current state of social media marketing. Whether you're interested in Instagram edits, YouTube captions, or DM automation, this episode is your go-to guide for staying ahead in the ever-changing world of social media.Don't forget to tune in to The Instagram Stories - Social Media News for all the latest Instagram news updates, TikTok trends, and effective social media strategies that can elevate your brand and enhance your engagement. Join us as we uncover the latest developments and equip you with the knowledge to navigate the dynamic landscape of social media! Show Notes: Sign Up for The Weekly Roundup: NewsletterLeave a Review: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Me on Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@danielhillmedia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Threads: Threads has DMs! (Threads)TikTok: President Trump says he has a buyer for TikTok (Social Media Today)TikTok: TikTok Cuts More Workers from its US Shop Division (Bloomberg)YouTube: Audience Segments, Leaderboard for Top Fans, "Most relevant" Comment Filter, and MORE!  (YouTube) Wednesday Waffle:Resilience, Community & Legacy: Ridgewood Life-Coach Jim Stroker's Inspiring Journey (YouTube)

Reporting live from Brazil, part-time cohost and WSL commentator Mitchell Salazar rejoins Dave on The Lineup to break down all the action, drama, and implications from the 2025 VIVO Rio Pro presented by Corona Cero. California's Cole Houshmand and Australia's Molly Picklum claimed breakthrough victories at Stop No. 9 of the 2025 Championship Tour, but the event was anything but predictable. Dave and Mitch dive deep into the biggest Winners and Losers from Saquarema–from Griffin Colapinto's title surge to Filipe Toledo's late-season stumble, and from the brilliance of Molly Picklum to the heartbreak for Brazilian fans hoping for a hometown champion. They break down the complexity of forecasting the wave, the challenges of Saquarema backwash, and the mental game of adapting on the fly. They check in on the Vissla CT Shaper Rankings, where Matt Biolos and …Lost Surfboards have all but cemented a three-peat as Shaper of the Year after a dominant Rio showing. The duo also answer Fantasy League and Instagram questions from fans around the world, including whether the women's surfing is officially more exciting than the men's, and drop hints about the final stretch of the CT season leading into J-Bay and beyond. Follow Mitch here. Play WSL CT Fantasy contest and join The Lineup Podcast Mega League for a chance to win! Terms and Conditions apply. Get the latest merch at the WSL Store! Watch the highlights from the VIVO Rio Pro Presented by Corona Cero. Catch the next generation of surfers compete for a spot on the CT at our second Challenger Series event of the year, the Ballito Pro Presented by O'Neill, June 30 - July 6. Stay tuned for CT Stop No. 10, the Corona Open J-Bay Presented by O'Neill, July 11 - July 20th. Join the conversation by following The Lineup podcast with Dave Prodan on Instagram and subscribing to our YouTube channel. Get the latest WSL rankings, news, and event info. **Visit this page if you've been affected by the Los Angeles wildfires, and would like to volunteer or donate. Our hearts are with  you.** Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome to the SHIRO! SHOW! news updates! This week, we'll be discussing: - Leaderboards Added to Yaba Sanshiro on Mobile - Super Mario 64 Dreamcast Port Update: Renewed Effort Making Great Strides - The Mysterious World of El Hazard #BestOfSaturn - City Connection Launches Farland Saga 1 & 2 Saturn Tribute - The SHIRO! Community Battles Through the Eurasian Conflict This July! Follow us on our social media sites: Facebook: https://www.facebook.com/PlaySegaSaturn Twitter: https://mobile.twitter.com/playsegasaturn Website: https://www.segasaturnshiro.com/ Buy our merch at: https://segasaturnshiro.threadless.com/ Buy issue #1 of SHIRO Magazine: https://www.segasaturnshiro.com/shiro-magazine/ Support us on our Patreon at: https://www.patreon.com/shiromediagroup Join our Discord to discuss translation patches, Saturn obscurities, and all things SEGA Saturn!: https://discord.gg/SSJuThN

CEO 2025 experiences, Arcsys reveals Marvel Tokon, EVO registration numbers, and the promising outlook for fighting games through 2026.

Trevor, Hunter, and Konner keep you up to date on everything going on in the disc golf world! Subscribe ► https://youtube.com/@GripLocked?sub_confirmation=1 Check out the Store: http://foundationdiscs.com Patreon: http://patreon.com/foundationdiscgolf Foundation Disc Golf: http://youtube.com/foundationdiscgolf 0:00 - Intro 0:55 - USWDGC Recap 24:20 - Trophy Talk 33:18 - Trevor's Trivia 49:23 - All Women's Sports Network 58:37 - Silas Selects

This is an episode full of Friday whimsy, covering the Chicago Cubs, Wyndham Clark's antics, Sheriff Scottie's department expanding, and more. Andy and Brendan run through an "apology" from Wyndham in the aftermath of destroying a locker at Oakmont and how he turned this moment into a plea for a spot on the Ryder Cup team. The two also discuss Scottie Scheffler's comments from Wednesday's press conference at the Travelers regarding what he considers a "fair test" on the PGA Tour. Speaking of the Travelers, Jordan Speith withdrew with a new injury and Adam Schefter took over Thursday's broadcast with some insane PGA-NFL comparisons. Leaderboard updates are provided for the Women's PGA Championship and Champs Tour at Firestone, where PJ's pick of Thomas Bjorn is fighting for dead last. To wrap up this episode, Brendan chats with Viktor Hovland about Brian Rolapp, Jay Monahan, Oakmont, and his favorite fruit.

Over the last eight years I have promoted many affiliate launches and always placed in the top ten and also won some incredible prizes including a private mastermind with Russell Brunson.How? In this episode I decided to just give you all my secrets. You're welcome! :) Want to work directly with me to help you get unlimited traffic on YouTube? Join the Gold Mastermind - https://www.iServeFirst.com/ Listen to this Podcast on all available players - https://www.TrafficTubeSecrets.com/

Andy and Brendan went live in front of a studio audience at Local Remedy Brewing in Oakmont to recap the second round of the 2025 U.S. Open. They discuss an... interesting... leaderboard heading into the weekend, headlined by Sam Burns after a Friday 65. The two share some worry about the current situations unfolding and debate what the best-case and worst-case scenarios are come Sunday night. They two then run through the big names who won't see the weekend, including Bryson DeChambeau, Shane Lowry, and Ludvig Åberg. There's some scuttlebutt from the grounds, some live audience interaction, and much more whimsy on this Friday the 13th recording.

Legendary sports shock jock Scott Ferrall takes the gaming world by storm with his “in your face” style, previewing the evening slate of games going over lines, totals and props, keeping you out of harms way and on the right side of the line. Ferrall and the crew are back for an all new episode of Coast to Coast! On this episode, Ferrall and Carver recap game 4 of the Stanley Cup Finals, take a look at the latest updates from the U.S. Open, and more. Plus, Gabe Morency joins to share some best bets for tonight's action.

Dan Evans talks MLB Baseball and Jeff Fisher talks Arena Football!

Carl and Mike briefly talk with one of the Father's Day winners, Kelly, who will be bringing her husband to our Father's Day dinner. They then share thoughts on the U.S. Open as some of the top golfers, including Scottie Scheffler and Rory McIlroy are struggling and are not atop the leaderboard.

Ike, Spike and Fritz wrap up their short show by discussing another accolade for Saquon Barkley and some recent Sixers draft news as well.

Peloton launches its own secondary store . A game-changer for anyone looking to find quality refurbished equipment straight from the source. Big update Peloton Programs will no longer be available on some devices . Don't get caught off guard; find out what this means for you. Outsourced customer service? Peloton's team had an “interesting” suggestion, and we have thoughts.  Leaderboard warriors, rejoice! Rankings by distance are now official . Who's ready to climb to the top? Tunde does it again. She walked the Sports Illustrated Swimsuit runway  like an absolute queen. Callie Gullickson fans, mark your calendars! Her new book is available for pre-order . Robin Arzon and Tunde are partnering up for Hyrox . Could this energy duo get any cooler? Turn up the 80s vibes! The newest artist series features Cyndi Lauper . Time after time, Peloton delivers. Equinox slapped with a $600K fine . What's the tea? iFit breaks ground by forming a Science Council . Innovation in fitness is here to stay. TCO's weekly scoop! The Top Five favorite classes from Clip Out listeners are revealed . This Week at Peloton updates you on everything going on at Peloton . Don't miss this week's happenings. Peloton celebrates love and diversity ! Check out the lineup of Pride classes to get moving with purpose. Jess Sims' Strength for Basketball series hits the court . Start building those basketball muscles! Camila Ramon's strength split is here . A methodical approach to strength that's worth a look. Peloton celebrates Global Running Day . Lace up and hit the tread in style. Cliff Dwenger combines cardio and strength training  for an all-in-one workout. The new Strength Plus program, “Build Then Burn,” lets you sweat smarter . Mark your calendars for Peloton birthdays ! Celebrate Cody Rigsby (06/08) and Assal Arian (06/09). Learn more about your ad choices. Visit megaphone.fm/adchoicesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Peloton launches its own secondary store . A game-changer for anyone looking to find quality refurbished equipment straight from the source. Big update Peloton Programs will no longer be available on some devices . Don't get caught off guard; find out what this means for you. Outsourced customer service? Peloton's team had an “interesting” suggestion, and we have thoughts.  Leaderboard warriors, rejoice! Rankings by distance are now official . Who's ready to climb to the top? Tunde does it again. She walked the Sports Illustrated Swimsuit runway  like an absolute queen. Callie Gullickson fans, mark your calendars! Her new book is available for pre-order . Robin Arzon and Tunde are partnering up for Hyrox . Could this energy duo get any cooler? Turn up the 80s vibes! The newest artist series features Cyndi Lauper . Time after time, Peloton delivers. Equinox slapped with a $600K fine . What's the tea? iFit breaks ground by forming a Science Council . Innovation in fitness is here to stay. TCO's weekly scoop! The Top Five favorite classes from Clip Out listeners are revealed . This Week at Peloton updates you on everything going on at Peloton . Don't miss this week's happenings. Peloton celebrates love and diversity ! Check out the lineup of Pride classes to get moving with purpose. Jess Sims' Strength for Basketball series hits the court . Start building those basketball muscles! Camila Ramon's strength split is here . A methodical approach to strength that's worth a look. Peloton celebrates Global Running Day . Lace up and hit the tread in style. Cliff Dwenger combines cardio and strength training  for an all-in-one workout. The new Strength Plus program, “Build Then Burn,” lets you sweat smarter . Mark your calendars for Peloton birthdays ! Celebrate Cody Rigsby (06/08) and Assal Arian (06/09). Learn more about your ad choices. Visit megaphone.fm/adchoices

Dave is joined once again by part-time co-host and WSL commentator Mitch Salazar to break down all the drama, triumph, and heartbreak from Stop No. 7 of the 2025 Championship Tour—the Western Australia Margaret River Pro. With Jordy Smith and Gabriela Bryan each securing their second wins of the season, both now sit atop the rankings and inch closer to their first World Titles. Dave and Mitch go deep on the event's biggest winners and most surprising losers—from Lakey Peterson's clutch performance to save her season, to the top 5 men faltering at a crucial stretch of the calendar, to a sobering mid-season cut for Channel Islands' stacked roster. The duo also takes stock of the updated Vissla CT Shaper Rankings, highlighting which board builders are leading the charge and which brands are left regrouping post-Margarets. They answer burning fan questions (Is power surfing the key to a world title?) and give updates on the wildly competitive Lineup Fantasy League. Follow Mitch here. Play WSL CT Fantasy contest and join The Lineup Podcast Mega League for a chance to win! Terms and Conditions apply. Get the latest merch at the WSL Store! Relive all the action from the Western Australia Margaret River Pro. Catch the first Challenger Series of 2025 the Burton Automotive Newcastle SURFEST Presented by Bonsoy, June 2 - 8th. Stay tuned for the Lexus Trestles Pro Presented by Outerknown, June 9 - 17th. Join the conversation by following The Lineup podcast with Dave Prodan on Instagram and subscribing to our YouTube channel. Get the latest WSL rankings, news, and event info. **Visit this page if you've been affected by the Los Angeles wildfires, and would like to volunteer or donate. Our hearts are with  you.** Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome back to another episode of Bubba (@bdentrek) and the Bloom (@RyanBHQ). On BATB 246, the guys will dig into a LIVE Leaderboard discussing starting pitchers over the last 30 days.  

Eno, DVR, and Jed discuss the new tools and leaderboards at Baseball Savant offering more public-facing data than ever for bat paths and attack angles. Plus, they talk about the eventual arrival of ABS for regular season games, and their 2025 predictions they want to use a mulligan on. Rundown1:02 New Toys at Baseball Savant -- Swing Path & Attack Angle Leaderboards9:12 Looking for Ideal & Unusual Combinations16:00 Is Swing Path Tilt the Most Difficult Thing to Change?25:09 Do Flatter Swing Path Hitters Have Higher Floors?34:40 ABS Getting Closer to Become a Reality in MLB Games Beyond Spring?52:03 Which 2025 Prediction(s) Do You Want a Mulligan On?Baseball Savant's Swing Path & Attack Angle Leaderboards: https://baseballsavant.mlb.com/leaderboard/bat-tracking/swing-path-attack-angleFollow Eno on Bluesky: @enosarris.bsky.socialFollow DVR on Bluesky: @dvr.bsky.sociale-mail: ratesandbarrels@gmail.comJoin our Discord: https://discord.gg/FyBa9f3wFeSubscribe to The Athletic: theathletic.com/ratesandbarrelsHosts: Derek VanRiper & Eno SarrisWith: Jed LowrieExecutive Producer: Derek VanRiper Hosted on Acast. See acast.com/privacy for more information.

Legendary sports shock jock Scott Ferrall takes the gaming world by storm with his “in your face” style, previewing the evening slate of games going over lines, totals and props, keeping you out of harms way and on the right side of the line. Ferrall and the crew are back for an all new episode of Coast to Coast! On this episode, Ferrall and Carver recap last night's NBA Playoff action, discuss the latest updates from the PGA Championship, and more. Plus, Gabe Morency joins to share some best bets for tonight and the upcoming weekend action.

The boys get together on Thursday night of the PGA championship to discuss the first round, what the leaderboard looks like, guys from LIV we do and don't miss, and more.

In The PenRick Graham (@IAmRickGraham) and Jake Crumpler (@jakecrumpler) break down the advanced statistics leaders early in the 2025 season. Join: PL+ | PL ProProud member of the Pitcher List Podcast Network

Peloton's latest “Find Your Push. Find Your Power” campaign is featured on Bandt.com. What's the buzz all about?

Peloton's latest “Find Your Push. Find Your Power” campaign is featured on Bandt.com. What's the buzz all about?

After a short break, The Lineup with Dave Prodan, along with part-time cohost and commentator-extraordinaire Mitchell Salazar, is back and firing as we dive into all things Rip Curl Pro Bells Beach Presented by Bonsoy and gear up for the Bonsoy Gold Coast Pro Presented by GWM at Burleigh Heads. Dave and co-host Mitch Salazar reunite to unpack the highs and lows of Event No. 5 on the 2025 WSL Championship Tour, with big shoutouts to Isabella Nichols and Jack Robinson for ringing the Bell. From moody Easter weather and a tough go for men's rookies, to the surprising stat that not a single CT goofyfooter improved their ranking—this episode breaks it all down. The crew highlights winners like Isabella's late-season surge, Kanoa's fresh spark on JS boards, and Jordy Smith's emphatic rebuttal to any kind of “washed” narrative. We also check the pulse of the Vissla CT Shaper Rankings, with ...Lost still out front. Plus, we answer fan questions, drop fantasy league updates, and tease what's ahead for Gold Coast. Follow Mitch here. Play WSL CT Fantasy contest and join The Lineup Podcast Mega League for a chance to win! Terms and Conditions apply. Get the latest merch at the WSL Store! Relive the Rip Curl Pro Bells Beach Pres. by Bonsoy. Stay tuned to the Bonsoy Gold Coast Pro Presented by GWM, May 3 - 13.. Join the conversation by following The Lineup podcast with Dave Prodan on Instagram and subscribing to our YouTube channel. Get the latest WSL rankings, news, and event info. **Visit this page if you've been affected by the Los Angeles wildfires, and would like to volunteer or donate. Our hearts are with  you.** Learn more about your ad choices. Visit megaphone.fm/adchoices

In The PenRick Graham (@IAmRickGraham) and Jake Crumpler (@jakecrumpler) break down the early-season Stuff+ and PLV leaderboards to uncover 2025's nastiest relievers. Join: PL+ | PL ProProud member of the Pitcher List Podcast Network