Podcasts about Natural language processing

Yascha Mounk and David Bau delve into the emerging science of AI interpretability and what we can learn from billions of neural signals. David Bau is Assistant Professor at Northeastern University and Director of the National Deep Inference Fabric, researching the emergent internal mechanisms of deep generative networks in both Natural Language Processing and Computer Vision. In this week's conversation, Yascha Mounk and David Bau discuss how AI models actually produce their results and reflect about problems, whether the “thinking” process that models show users reveals their authentic thought processes, and how researchers can decode the internal representations of neural networks to understand what information they contain and use. If you have not yet signed up for our podcast, please do so now by following ⁠this link on your phone⁠. Email: leonora.barclay@persuasion.community Podcast production by Jack Shields and Leonora Barclay. Connect with us! ⁠Spotify⁠ | ⁠Apple⁠ X: ⁠@Yascha_Mounk⁠ & ⁠@JoinPersuasion⁠ YouTube: ⁠Yascha Mounk⁠, ⁠Persuasion⁠ LinkedIn: ⁠Persuasion Community Learn more about your ad choices. Visit megaphone.fm/adchoices

My conversation with Andrea starts at about 22 minutes in to today's show after headlines and clips Subscribe and Watch Interviews LIVE : On YOUTUBE.com/StandUpWithPete ON SubstackStandUpWithPete Stand Up is a daily podcast. I book,host,edit, post and promote new episodes with brilliant guests every day. This show is Ad free and fully supported by listeners like you! Please subscribe now for as little as 5$ and gain access to a community of over 750 awesome, curious, kind, funny, brilliant, generous soul On YOUTUBE.com/StandUpWithPete ON SubstackStandUpWithPete Andrea Jones-Rooy, Ph.D., is a data and social scientist, science educator, standup comedian, and circus performer. They are a professor and the Director of Undergraduate Studies at the NYU Center for Data Science, where they teach the flagship undergraduate course, Data Science for Everyone, as well as advanced courses on Natural Language Processing. Andrea is also a research consultant and keynote speaker for global Fortune 500 and tech companies of all sizes on how to thoughtfully integrate data science into achieving their goals, especially in the people analytics space. When they aren't doing those things, they perform standup, trapeze, and fire all over the world. Andrea hosts the podcast Majoring in Everything and is working on a book about why focusing on just one thing is overrated. Get in touch after the interview… • @jonesrooy on Twitter, Instagram, and TikTok www.jonesrooy.com  jonesrooy@gmail.com Listen rate and review on Apple Podcasts Listen rate and review on Spotify Pete On Instagram Pete on Blue Sky Pete on Threads Pete on Tik Tok Pete on Twitter Pete Personal FB page Stand Up with Pete FB page Gift a Subscription https://www.patreon.com/PeteDominick/gift Send Pete $ Directly on Venmo All things Jon Carroll  Buy Ava's Art  Subscribe to Piano Tuner Paul Paul Wesley on Substack Listen to Barry and Abigail Hummel Podcast Listen to Matty C Podcast and Substack Follow and Support Pete Coe Hire DJ Monzyk to build your website or help you with Marketing

Stewart Alsop interviews Nizar, CEO of Pixel Robotics, on the Crazy Wisdom Podcast to explore the intersection of AI, robotics, and perception. The conversation covers a wide range of technical topics including how transformers enable multimodal representation across text, images, and voice, the role of world models in predicting physical interactions, the advantages of diffusion models over traditional LLMs for certain applications, and the challenges of achieving real-time processing for robotics applications. Nizar explains Pixel Robotics' work on creating accurate 3D meshes from smartphone cameras for companies like L'Oréal, moving away from specialized sensors to make the technology more accessible through sophisticated algorithms, and discusses the future of robotics as closing the perception-action loop to enable robots to perform real tasks beyond simple demonstrations. To find out more visit Pixel Robotics' website.Timestamps00:00 Stewart welcomes Nizar, CEO of Pixel Robotics, discussing what a pixel is as the smallest visual unit on screens composed of red green and blue colors05:00 Discussion of perception systems and how logarithmic laws help compress signals in both human and artificial systems, exploring normalization layers and sigmoid functions in deep learning10:00 Exploring how transformers unified different data modalities including text voice and images, creating common representations through methods like contrastive learning15:00 Nizar explains transformers as brute force learning systems with room for improvement through focused attention mechanisms and knowledge graphs rather than processing everything20:00 Conversation about loss functions local minima versus global minima and how mixture of experts uses specialized small models instead of one massive generalist network25:00 Discussion of deterministic versus probabilistic systems and how explicitly defined task graphs often outperform orchestrator-based approaches in AI systems30:00 Exploring world models as predictive physics-based systems that learn environmental flows and transformations, complementing rather than replacing language models35:00 Nizar discusses real-time processing challenges for robotics requiring millisecond responses with small memory footprints using vision transformers for faster experimentation40:00 Pixel's work creating three d meshes from smartphone cameras for companies like L'Oreal, moving away from specialized sensors toward accessible software-based solutions45:00 Explanation of different three d representations including voxels point clouds and meshes, with meshes being optimal for manipulation and rendering in applications50:00 Future direction involves closing perception-action loops in robotics, moving beyond dancing toy robots toward practical multimodal systems that perform real tasks55:00 Pixel's goal is democratizing high-quality three d scanning through smartphones, making mesh creation accessible to unlock applications in gaming cinema and virtual showroomsKey Insights1. Pixel Robotics derives its name from combining perception and action in robotics, where the pixel represents the digital perception component and robotics represents the physical action component. The pixel serves as a metaphor for how robots must quantize and digitize continuous analog information from the real world into discrete units that computer systems can process, similar to how pixels are the fundamental building blocks of images on a screen. This quantization process is essential because numerical systems cannot work with truly continuous data and must convert reality into tractable digital representations that algorithms can manipulate.2. The transformer architecture has created a fundamental unification in how different types of data can be represented and processed across multiple modalities. Before transformers, researchers working on natural language processing, computer vision, and audio analysis used completely different approaches and methodologies. The breakthrough of transformers was establishing a common representational framework that could handle text, images, voice, and other data types using similar underlying mechanisms. This unification is what enabled the development of truly multimodal AI systems and represents one of the most significant advances beyond just the language modeling capabilities that initially gained public attention.3. Current transformer-based systems represent a brute force approach to learning that will likely be superseded or enhanced by more efficient algorithms. Despite claims that we have exhausted internet text data for training, significant improvements continue to emerge every few months through algorithmic innovations rather than simply adding more data. Future developments will likely involve more specialized attention mechanisms that focus on relevant information rather than correlating everything with everything, mixture of experts architectures with small specialized models, and approaches inspired by biological systems such as logarithmic compression laws and event-based processing that humans use naturally.4. Diffusion-based language models represent a promising alternative to standard next-token prediction that could produce more accurate outputs through an iterative refinement process. Unlike traditional language models that predict one token at a time and cannot revise earlier outputs, diffusion models treat text generation like image denoising, starting with a noisy representation and progressively refining the entire output across multiple steps. This holistic approach allows the model to reconsider and improve all parts of the response simultaneously, potentially leading to higher quality results, though it may be slower than current autoregressive methods. This represents an important direction for overcoming fundamental limitations in how language models currently generate text.5. For robotics applications, real-time performance and small model size are critical constraints that differ significantly from the requirements of large language models deployed in data centers. Vision transformers are being used as a testbed for developing efficient real-time algorithms because they require far fewer computational resources to train and test compared to large language models, making them more practical for rapid experimentation. The goal is to achieve millisecond-level response times with minimal memory footprint so that robots can react quickly to dynamic environments and run on affordable hardware that can be embedded in actual robotic systems rather than requiring expensive server infrastructure.6. Practical robotics implementation requires moving beyond specialized sensors to software solutions that work with ubiquitous devices like smartphones for tasks such as three-dimensional reconstruction. Pixel Robotics evolved from building specialized scanning hardware to focusing on algorithms that can generate high-quality mesh representations of environments using only smartphone cameras, making the technology far more accessible and practical for real-world deployment. This approach enables applications ranging from industrial robotic arm control to virtual showrooms, and more importantly, it allows anyone to capture three-dimensional data without expensive equipment, which can also help generate larger training datasets for future AI development.7. The next frontier in AI and robotics is closing the perception-action loop to enable robots to perform real practical tasks rather than remaining as demonstration systems or toys. While significant progress has been made in cognitive capabilities through language models and in robotic mobility through mechanical engineering advances, the critical challenge is integrating perception with action through systems like Vision-Language-Action models. The fundamental starting point for learning this integration is simple perception-action exercises, such as programming a camera mounted on servo motors to track and center a colored object, which demonstrates the basic principle of using sensory input to drive physical response that underlies all more sophisticated robotic behaviors.

In this episode of Disruption/Interruption, host KJ sits down with David Trainer, CEO of New Constructs, a financial technology firm using machine learning and natural language processing to expose the accounting distortions buried in corporate filings. David pulls back the curtain on decades of Wall Street corruption — from two sets of earnings numbers (one for retail, one for institutions) to the legal practice of front-running client order flow. He explains how he built a robo-analyst to do what human analysts won't: read every footnote of every filing to reveal the truth about corporate profitability. David also shares how Google Cloud chose New Constructs to build the first-ever AI investing agent, and why he believes clean, transparent data is the best defense against both Wall Street manipulation and future AI bad actors. Four Key Takeaways: The system was designed to serve Wall Street, not investors (4:11) David witnessed firsthand at Credit Suisse how analysts maintained two sets of numbers — artificially low estimates for retail investors to manufacture "beats," and real numbers shared only with institutional clients. Wall Street research analysts don't generate revenue for their firms; they exist to facilitate investment banking relationships, meaning they're incentivized to stay bullish regardless of reality. What's unethical isn't always unlawful (8:37) Regulation Fair Disclosure — the law requiring companies to disclose material information to all investors simultaneously — wasn't enacted until the year 2000, after the tech bubble burst. Before that, selective tipping was perfectly legal. And today, payment for order flow (selling your trade data to firms like Citadel before your order is filled) remains legal — a structural advantage that benefits Wall Street at retail investors' expense. 96% of Wall Street analyst ratings are "buy" or "hold" (11:28) Only about 4% of stocks covered by Wall Street analysts receive a sell rating. Trainer uses this stat to illustrate a core conflict of interest: analysts are paid by bankers to say good things about companies. Expecting honest sell-side research is like expecting a car salesman to talk down their own inventory. New Constructs + Google Cloud built the first AI agent for investing (22:59) Google Cloud selected New Constructs — because of their clean, auditable data — to build Finsights, an AI chatbot that answers sophisticated investing questions: which companies are overstating earnings, which stocks are most likely to miss next quarter, which have the most off-balance-sheet debt. Every data point can be traced back to the original corporate filings. Their Core Earnings Leaders Index outperformed the S&P 500 by 900 basis points in 2025. Quote of the Show (12:24):"Expecting Wall Street to talk bad about a stock is like expecting a car salesman to talk bad about their cars." — David Trainer Join our Anti-PR newsletter where we’re keeping a watchful and clever eye on PR trends, PR fails, and interesting news in tech so you don't have to. You're welcome. Want PR that actually matters? Get 30 minutes of expert advice in a fast-paced, zero-nonsense session from Karla Jo Helms, a veteran Crisis PR and Anti-PR Strategist who knows how to tell your story in the best possible light and get the exposure you need to disrupt your industry. Click here to book your call: https://info.jotopr.com/free-anti-pr-eval Ways to connect with David Trainer:LinkedIn: http://www.linkedin.com/in/davidtrainerCompany Website: https://newconstructs.com How to get more Disruption/Interruption: Amazon Music - https://music.amazon.com/podcasts/eccda84d-4d5b-4c52-ba54-7fd8af3cbe87/disruption-interruptionApple Podcast - https://podcasts.apple.com/us/podcast/disruption-interruption/id1581985755Spotify - https://open.spotify.com/show/6yGSwcSp8J354awJkCmJlDSee omnystudio.com/listener for privacy information.

My conversation with Andrea starts at about 41 minutes in to today's show after headlines and clips Subscribe and Watch Interviews LIVE : On YOUTUBE.com/StandUpWithPete ON SubstackStandUpWithPete Stand Up is a daily podcast. I book,host,edit, post and promote new episodes with brilliant guests every day. This show is Ad free and fully supported by listeners like you! Please subscribe now for as little as 5$ and gain access to a community of over 750 awesome, curious, kind, funny, brilliant, generous soul On YOUTUBE.com/StandUpWithPete ON SubstackStandUpWithPete Andrea Jones-Rooy, Ph.D., is a data and social scientist, science educator, standup comedian, and circus performer. They are a professor and the Director of Undergraduate Studies at the NYU Center for Data Science, where they teach the flagship undergraduate course, Data Science for Everyone, as well as advanced courses on Natural Language Processing. Andrea is also a research consultant and keynote speaker for global Fortune 500 and tech companies of all sizes on how to thoughtfully integrate data science into achieving their goals, especially in the people analytics space. When they aren't doing those things, they perform standup, trapeze, and fire all over the world. Andrea hosts the podcast Majoring in Everything and is working on a book about why focusing on just one thing is overrated. Get in touch after the interview… • @jonesrooy on Twitter, Instagram, and TikTok www.jonesrooy.com  jonesrooy@gmail.com   Listen rate and review on Apple Podcasts Listen rate and review on Spotify Pete On Instagram Pete on Blue Sky Pete on Threads Pete on Tik Tok Pete on Twitter Pete Personal FB page Stand Up with Pete FB page     Gift a Subscription https://www.patreon.com/PeteDominick/gift Send Pete $ Directly on Venmo All things Jon Carroll  Buy Ava's Art    Subscribe to Piano Tuner Paul Paul Wesley on Substack Listen to Barry and Abigail Hummel Podcast Listen to Matty C Podcast and Substack Follow and Support Pete Coe Hire DJ Monzyk to build your website or help you with Marketing    

Many people will have learning another language on their bucket list and with the help of AI that reality is creeping ever closer. Ed Crook, strategy and operation leader at German-based AI translation firm DeepL discusses how the startup is helping business get language nuance right, what it is like to build a startup in Germany and why he thinks there will always be a place for language learning in schools. 

Join hosts Lois Houston and Nikita Abraham for a special episode of the Oracle University Podcast as they explore the Oracle Analytics AI Assistant. In this episode, you'll discover how Oracle's AI-powered conversational tool empowers users of all backgrounds to interact with business data using simple, natural-language questions. Learn how the assistant interprets queries, surfaces visualizations, and delivers actionable insights in seconds, all within Oracle's secure analytics environment. The episode dives into best practices for data preparation, security and privacy safeguards, how to configure datasets for optimal AI performance, and tips for getting the most relevant results. You'll also hear how synonyms, column indexing, and user permissions make analytics more accessible and accurate.   Visualize Data with the Oracle Analytics AI Assistant: https://mylearn.oracle.com/ou/article-course/visualize-data-with-the-oracle-analytics-ai-assistant/156941/ 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, Anna Hulkower, Kris-Ann Nansen, and the OU Studio Team for helping us create this episode. -------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption Programs with Customer Success Services, and with me is Nikita Abraham, Team Lead: Editorial Services with Oracle University. Nikita: Hi everyone! Today's episode is on the Oracle Analytics AI Assistant, which is all about making business data accessible and useful, no matter your background. Whether you're a seasoned pro or just starting out with Oracle Analytics, you'll want to stick around for this episode because we're covering everything you need to know to unlock powerful, intuitive, and secure data insights. 01:06 Lois: That's right. And full disclosure before we start. We're trying something a little different for this episode. Instead of a live guest, our expert will be an AI-generated voice sharing insights drawn directly from Oracle's official course materials. Think of it as getting a taste of what our training courses are like, with a little help from AI. So, with that, let's kick things off by taking a closer look at what the Oracle Analytics AI Assistant really is. Expert: The Oracle Analytics AI Assistant is an AI-powered tool that provides a conversational interface for data analysis. With this tool, data exploration becomes more intuitive and efficient, helping you access fast, personalized insights. The AI Assistant makes use of Generative AI to process queries, analyze indexed datasets, and create or refine relevant visualizations. It is fully integrated into the Oracle Analytics platform, complementing existing analytic and visualization capabilities. 02:13 Nikita: So, put simply, users have the ability to interact with their data in plain English and receive immediate, visual answers. Expert: Exactly! You can ask natural language questions, such as, "What were my sales in the United States last Tuesday?" or "Show me monthly sales for this year," and the assistant interprets the question, queries the right data, and generates the best visualization. 02:39 Lois: Before we dive deeper, let's ground ourselves in some of the core concepts behind this technology. Here's an overview of the AI technologies powering the assistant. Expert:  - Artificial Intelligence refers to systems or machines that perform tasks which typically require human intelligence, like reasoning, learning, perception, and language understanding. - Large Language Models or LLMs are AI programs trained on very large data sets. LLMs can generate human-like language and perform complex language tasks, such as writing emails or answering questions. - Generative AI is a branch of AI that can create new content, such as text, images, and audio. GenAI includes chatbots and virtual assistants capable of human-like conversations, answering questions, and creating content based on user prompts. - Natural Language Processing or NLP is a subfield of AI, targeting how computers understand and generate human language. 03:42 Lois: Now, let's look at what happens behind the scenes when someone interacts with the Oracle Analytics AI Assistant. Expert: Here is how the process works. You ask a question or make a request in natural language. Oracle Analytics Cloud identifies the most relevant dataset to answer that question, looking at metadata and attribute values. The platform prepares a prompt for the LLM that includes dataset metadata, column names, synonyms, and your question. The LLM and Natural Language Understanding interpret the question, and then translate it into a structured query. Oracle Analytics validates this query against your data model, and then queries your database. Based on the results, the AI Assistant creates the most appropriate visualization, like a chart, table, or similar format, and provides additional natural language insights. 04:36 Nikita: Security and privacy are top priorities for organizations using tools like this, so let's get into Oracle's approach to protecting user data. Expert: At Oracle, your data privacy and security are always top priorities. Specifically, your data is never shared with external model providers or other customers. Pre-trained generative AI models are accessed exclusively within Oracle's secure cloud infrastructure. No customer data is stored or retained by the AI models after processing, and prompt data is not used to train the models. And finally, all data processed is fully isolated and never combined or visible to anyone outside your organization. 05:20 Lois: In other words, users always remain in full control of their own data, with no risk of leakage or exposure to outside parties. Nikita: Yeah, this kind of reassurance is absolutely critical for enterprises. 05:32 Lois: That's right, Niki. Next, let's cover how to get the most accurate and relevant insights from the AI Assistant by following some best practices for prompting. Expert: To get the best answers, you need to be specific. Include key data points, timeframes, or filters. For example, something like: "Show total sales by country for Q2 2024." Keep questions focused, clear, and concise. Refine your request as needed. If you want different details or a simpler trend line, follow up with something like, "Show by quarter," or "Replace product category with customer segment." Avoid complex prompts, like highly nested or multi-step ones. Ask a series of concise questions instead. When typing column names or field values, pause briefly to let the Assistant suggest the correct field. This increases prompt accuracy. Consider the context of the conversation. Filters and refinements made in previous messages persist, so be aware that context builds over the conversation unless reset. 06:36 Nikita: So, you might start with something like, "Show me sales trends for the last 5 years," and then get more granular, like, "Include only technology products," or "Break the results down by product sub-category." Lois: But sometimes, you may just want to start from scratch, so let's discuss how you can reset your session with the AI Assistant. Expert: Just select the "Clear Assistant History" option and you can begin a new analysis. 07:03 Nikita: Language capabilities are another important consideration, so here's an overview of which languages the Assistant currently supports. Expert: Right now, English is the primary language supported. Simple questions in other languages may work, but with less accuracy and fewer features. Talk to your Oracle Analytics administrator if you have multilingual needs. 07:26 Lois: Let's clarify what kinds of questions are beyond the scope of the Assistant. Expert: The Assistant is built for business-oriented, goal-driven queries, not for technical schema questions or database logic. So, don't ask about dataset structures or technical metadata. But do ask about trends, comparisons, breakdowns, and summaries that relate to your business. 07:53 Do you want to fast-track your learning goals? Join us for live events hosted by Oracle expert instructors! Get certification exam tips, learn about new technology, and ask your questions in real time. Take charge of your learning. Visit mylearn.oracle.com and join a live event today!  08:13 Nikita: Welcome back! Now, let's discuss why configuring datasets is crucial for working effectively with the AI Assistant. Expert: Effectively indexing and configuring your dataset can make a huge difference when working with the AI Assistant. When you index a dataset, you're basically creating searchable references. This makes it easier for the AI Assistant to quickly locate the most relevant columns and give accurate responses to natural language questions.  It's important to know that you'll need to manually select which columns to index. For example, if your users are likely to ask about sales in the United States, you'll want to make sure that both the "Country" column and the "Sales" column are included when indexing. That way, the Assistant knows exactly where to look when someone asks a question about U.S. sales figures.  Another thing to remember is that you can make your analytics more user-friendly by resolving ambiguities and assigning synonyms to your dataset columns. For instance, if there's a generic "date" column, clarify whether that refers to the "order date" or the "ship date." It helps to add synonyms as well, so the assistant can handle different ways users might phrase their questions.  So, while it may take a little extra effort upfront, making your dataset easy to search and understand pays off. Your AI Assistant can respond quickly and accurately, and your users get the answers they're looking for with less hassle.  09:43 Lois: Next, we'll outline the steps for configuring and indexing datasets for optimal performance. Expert: First you need to confirm dataset access. You'll need read/write privileges to enable the AI Assistant and index the dataset. Then, on the Search tab, under "Index Dataset For," select "Assistant." Choose your language and, optionally, set an indexing schedule. Carefully pick columns users will likely question, like sales, region, or date. Avoid technical metadata, sensitive data, and high-cardinality columns like Customer IDs. Choose whether to index only column names or names plus data values. Including data values helps with typing suggestions and nuance. Avoid values no one will search on. Importantly, indexed dataset values are never sent to the LLM. They are retrieved from the dataset when visualizations are created. Assign synonyms to attribute names. Oracle Analytics suggests synonyms, but you can also add your own. Finally, save the changes and run indexing to make the dataset searchable by the Assistant. 10:50 Nikita: Now, let's look at how configuring subject areas can further tailor the experience. Expert: You'll need to navigate to the Search Index by going through the Console's Configuration and Settings. Choose your language and indexing schedule. Index folders relevant to business questions; avoid non-relevant or sensitive columns. Select the Index Type: "Index Metadata Only" for high-cardinality columns (like IDs); "Index" for columns and values that users reference. As with datasets, clarify column meanings with user-friendly synonyms. Finalize settings and run the index to prepare your subject area for AI-powered queries. Special care must be taken with date columns. Select and clearly identify the main business date so queries don't become ambiguous. 11:39 Lois: Synonyms play an important role in reducing ambiguity and enhancing results, so let's review the best practices for setting them up effectively. Expert: If your columns use abbreviations, acronyms, or codes—like "custNo" or "Pname"—it's a good idea to provide synonyms to clarify what those attributes actually mean. Think about how people typically refer to those columns in everyday language. So instead of just "custNo," add "Customer Number" as a synonym, and for "Pname," you would use "Product Name."  If you can, actually renaming the column is usually more effective than just adding a synonym. But if that's not possible for some reason, a synonym is the next best thing.  Dates can be another tricky area. Datasets often have several date columns, like "Ship Date," "Order Date," and "Invoice Date." If a user asks, "Show me revenue by date," the system has to decide which date column to use, and it may just pick one for you. If you definitely want "Order Date" to be considered the default date, make sure to assign "date" as a synonym specifically for that column.  There's also the situation where different tables have columns with the same name—like "name" from both a Product table and an Employee table. You'll want to use synonyms for these columns too, to make it clear what each one means.  Adding more than one synonym can help as well. For example, if you have a "Yield" column, maybe also specify "revenue" and "income" as synonyms, so users can ask questions however they naturally would.  Avoid using reserved words or special characters in your synonyms. This means words like "Count," "Year," or anything that's also a SQL function, plus characters like "@" or special symbols. Also, steer clear of Unicode characters and terms that are analytical functions or date formats.  The whole point is to make your columns easy for business users or anyone else to reference naturally, using the terms they're most likely to try in a search.  And finally, just a few rules of thumb: synonyms can be up to 50 characters long, you can use up to 20 synonyms for each column, and you don't need to worry about uppercase or lowercase; column names aren't case sensitive.  Besides the basic setup and using synonyms, you can really improve the quality of answers from the AI Assistant (and the LLM it uses) by prepping and enriching your data. It's easier for the AI to work with words than numbers. Try "binning" numerical values into simple categories people can understand. For instance, instead of showing a long list of sales amounts, split them into groups like "small," "medium," and "large."  LLMs handle words better than blanks. If your data has missing or null values, fill them in with something meaningful, like "Unknown," "Not specified," or "Not available." Skipping this step could cause errors in queries, such as reports missing customers because their country is blank. Incorrect averages or summaries, especially if missing values are ignored. Issues with forecasting, if data gaps throw off trends. The AI Assistant might skip important columns or even generate errors. Ambiguous or duplicate column names confuse both users and the LLM. Make your names clear and consistent.  You can use Oracle Analytics's Transform editor to add even more context. For example, you might extract the day of the week from a date, so you can easily ask, "Show sales for all Fridays in 2026."  By preparing your data with these steps, you help the AI Assistant give you more accurate and insightful answers, making data analysis a lot smoother! 15:27 Nikita: Finally, let's walk through the process of making the Oracle Analytics AI Assistant accessible to end users directly within their workbooks. Expert: Permissions are controlled through application roles. Your administrator must create a specific role enabling access to the AI Assistant. To enable consumer access, open your workbook in edit mode and select Present. From the Workbook tab, toggle it on in the Insights Panel section. Choose tabs like Watch Lists and Workbook Assistant. Decide which data sources in your workbook are available to the consumer. Save, and then use Preview to simulate the user experience. Consumers can access the AI Assistant by selecting Auto Insights at the top of the workbook. They can then type in natural language questions, review visualizations, and follow up. Repeat these steps for each workbook you wish to enable. 16:22 Lois: This really puts agile, self-service analytics at everyone's fingertips, all while keeping data security and integrity front and center. Nikita: And it's not just plug-and-play. To get the best results, you configure your data, enrich it, apply the right synonyms and permissions, and then your team can ask questions and visualize results just by using natural language. Lois: If you're ready to kickstart or deepen your journey with the Oracle Analytics AI Assistant, or you want to review the topics we covered in today's episode in even greater detail, visit mylearn.oracle.com. Nikita: That wraps up this episode. Thanks for spending time listening to us today. Join us next week for another episode of the Oracle University Podcast. Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 17:14 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.  

Is Anthropic late to India's AI party—or perfectly timed? In this episode of The Morning Brief, host Anirban Chowdhury speaks with ET’s Disha Acharya and Puran Choudhary about Anthropic's strategic entry into India's rapidly maturing generative AI market. The conversation explores why the company prioritizes enterprise clients over price-sensitive consumers, how its partnership with Infosys positions it within India's multi-million dollar IT ecosystem, and what makes Claude's focus on Indic language support a genuine differentiator. From hosting developer days and hackathons to building datasets for long-tail languages through nonprofit collaborations, Anthropic’s India stack is significantly large. This episode examines whether India serves as an innovation ground or simply a data mine. As pilots transition to full-scale deployments with governance frameworks solidifying, the episode questions whether widespread IT service integration and public sector adoption will cement Anthropic's leadership.Tune in You can follow Anirban Chowdhury on his social media: X and LinkedinCheck out other interesting episodes like: How Will a Volatile ₹ Impact You in 2026?, How Quick Commerce is Triggering a Health Crisis for Gen Z, India’s Labour Law Reboot, Viral to Valuation: Building Women’s Cricket as a Brand and much more.Catch the latest episode of ‘The Morning Brief’ on The Economic Times Online, Spotify, Apple Podcasts, JioSaavn, Amazon Music and Youtube.Credit @moneycontrolSee omnystudio.com/listener for privacy information.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, a knowledge architect, community builder, and host of the Knowledge Graph Insights podcast. They explore the relationship between knowledge graphs and ontologies, why these technologies matter in the age of AI, and how symbolic AI complements the current wave of large language models. The conversation traces the history of neuro-symbolic AI from its origins at Dartmouth in 1956 through the semantic web vision of Tim Berners-Lee, examining why knowledge architecture remains underappreciated despite being deployed at major enterprises like Netflix, Amazon, and LinkedIn. Swanson explains how RDF (Resource Description Framework) enables both machines and humans to work with structured knowledge in ways that relational databases can't, while Alsop shares his journey from knowledge management director to understanding the practical necessity of ontologies for business operations. They discuss the philosophical roots of the field, the separation between knowledge management practitioners and knowledge engineers, and why startups often overlook these approaches until scale demands them. You can find Larry's podcast at KGI.fm or search for Knowledge Graph Insights on Spotify and YouTube.Timestamps00:00 Introduction to Knowledge Graphs and Ontologies01:09 The Importance of Ontologies in AI04:14 Philosophy's Role in Knowledge Management10:20 Debating the Relevance of RDF15:41 The Distinction Between Knowledge Management and Knowledge Engineering21:07 The Human Element in AI and Knowledge Architecture25:07 Startups vs. Enterprises: The Knowledge Gap29:57 Deterministic vs. Probabilistic AI32:18 The Marketing of AI: A Historical Perspective33:57 The Role of Knowledge Architecture in AI39:00 Understanding RDF and Its Importance44:47 The Intersection of AI and Human Intelligence50:50 Future Visions: AI, Ontologies, and Human BehaviorKey Insights1. Knowledge Graphs Combine Structure and Instances Through Ontological Design. A knowledge graph is built using an ontology that describes a specific domain you want to understand or work with. It includes both an ontological description of the terrain—defining what things exist and how they relate to one another—and instances of those things mapped to real-world data. This combination of abstract structure and concrete examples is what makes knowledge graphs powerful for discovery, question-answering, and enabling agentic AI systems. Not everyone agrees on the precise definition, but this understanding represents the practical approach most knowledge architects use when building these systems.2. Ontology Engineering Has Deep Philosophical Roots That Inform Modern Practice. The field draws heavily from classical philosophy, particularly ontology (the nature of what you know), epistemology (how you know what you know), and logic. These thousands-year-old philosophical frameworks provide the rigorous foundation for modern knowledge representation. Living in Heidelberg surrounded by philosophers, Swanson has discovered how much of knowledge graph work connects upstream to these philosophical roots. This philosophical grounding becomes especially important during times when institutional structures are collapsing, as we need to create new epistemological frameworks for civilization—knowledge management and ontology become critical tools for restructuring how we understand and organize information.3. The Semantic Web Vision Aimed to Transform the Internet Into a Distributed Database. Twenty-five years ago, Tim Berners-Lee, Jim Hendler, and Ora Lassila published a landmark article in Scientific American proposing the semantic web. While Berners-Lee had already connected documents across the web through HTML and HTTP, the semantic web aimed to connect all the data—essentially turning the internet into a giant database. This vision led to the development of RDF (Resource Description Framework), which emerged from DARPA research and provides the technical foundation for building knowledge graphs and ontologies. The origin story involved solving simple but important problems, like disambiguating whether "Cook" referred to a verb, noun, or a person's name at an academic conference.4. Symbolic AI and Neural Networks Represent Complementary Approaches Like Fast and Slow Thinking. Drawing on Kahneman's "thinking fast and slow" framework, LLMs represent the "fast brain"—learning monsters that can process enormous amounts of information and recognize patterns through natural language interfaces. Symbolic AI and knowledge graphs represent the "slow brain"—capturing actual knowledge and facts that can counter hallucinations and provide deterministic, explainable reasoning. This complementarity is driving the re-emergence of neuro-symbolic AI, which combines both approaches. The fundamental distinction is that symbolic AI systems are deterministic and can be fully explained, while LLMs are probabilistic and stochastic, making them unsuitable for applications requiring absolute reliability, such as industrial robotics or pharmaceutical research.5. Knowledge Architecture Remains Underappreciated Despite Powering Major Enterprises. While machine learning engineers currently receive most of the attention and budget, knowledge graphs actually power systems at Netflix (the economic graph), Amazon (the product graph), LinkedIn, Meta, and most major enterprises. The technology has been described as "the most astoundingly successful failure in the history of technology"—the semantic web vision seemed to fail, yet more than half of web pages now contain RDF-formatted semantic markup through schema.org, and every major enterprise uses knowledge graph technology in the background. Knowledge architects remain underappreciated partly because the work is cognitively difficult, requires talking to people (which engineers often avoid), and most advanced practitioners have PhDs in computer science, logic, or philosophy.6. RDF's Simple Subject-Predicate-Object Structure Enables Meaning and Data Linking. Unlike relational databases that store data in tables with rows and columns, RDF uses the simplest linguistic structure: subject-predicate-object (like "Larry knows Stuart"). Each element has a unique URI identifier, which permits precise meaning and enables linked data across systems. This graph structure makes it much easier to connect data after the fact compared to navigating tabular structures in relational databases. On top of RDF sits an entire stack of technologies including schema languages, query languages, ontological languages, and constraints languages—everything needed to turn data into actionable knowledge. The goal is inferring or articulating knowledge from RDF-structured data.7. The Future Requires Decoupled Modular Architectures Combining Multiple AI Approaches. The vision for the future involves separation of concerns through microservices-like architectures where different systems handle what they do best. LLMs excel at discovering possibilities and generating lists, while knowledge graphs excel at articulating human-vetted, deterministic versions of that information that systems can reliably use. Every one of Swanson's 300 podcast interviews over ten years ultimately concludes that regardless of technology, success comes down to human beings, their behavior, and the cultural changes needed to implement systems. The assumption that we can simply eliminate people from processes misses that huma...

In this episode of the Crazy Wisdom podcast, host Stewart Alsop welcomes Roni Burd, a data and AI executive with extensive experience at Amazon and Microsoft, for a deep dive into the evolving landscape of data management and artificial intelligence in enterprise environments. Their conversation explores the longstanding challenges organizations face with knowledge management and data architecture, from the traditional bronze-silver-gold data processing pipeline to how AI agents are revolutionizing how people interact with organizational data without needing SQL or Python expertise. Burd shares insights on the economics of AI implementation at scale, the debate between one-size-fits-all models versus specialized fine-tuned solutions, and the technical constraints that prevent companies like Apple from upgrading services like Siri to modern LLM capabilities, while discussing the future of inference optimization and the hundreds-of-millions-of-dollars cost barrier that makes architectural experimentation in AI uniquely expensive compared to other industries.Timestamps00:00 Introduction to Data and AI Challenges03:08 The Evolution of Data Management05:54 Understanding Data Quality and Metadata08:57 The Role of AI in Data Cleaning11:50 Knowledge Management in Large Organizations14:55 The Future of AI and LLMs17:59 Economics of AI Implementation29:14 The Importance of LLMs for Major Tech Companies32:00 Open Source: Opportunities and Challenges35:19 The Future of AI Inference and Hardware43:24 Optimizing Inference: The Next Frontier49:23 The Commercial Viability of AI ModelsKey Insights1. Data Architecture Evolution: The industry has evolved through bronze-silver-gold data layers, where bronze is raw data, silver is cleaned/processed data, and gold is business-ready datasets. However, this creates bottlenecks as stakeholders lose access to original data during the cleaning process, making metadata and data cataloging increasingly critical for organizations.2. AI Democratizing Data Access: LLMs are breaking down technical barriers by allowing business users to query data in plain English without needing SQL, Python, or dashboarding skills. This represents a fundamental shift from requiring intermediaries to direct stakeholder access, though the full implications remain speculative.3. Economics Drive AI Architecture Decisions: Token costs and latency requirements are major factors determining AI implementation. Companies like Meta likely need their own models because paying per-token for billions of social media interactions would be economically unfeasible, driving the need for self-hosted solutions.4. One Model Won't Rule Them All: Despite initial hopes for universal models, the reality points toward specialized models for different use cases. This is driven by economics (smaller models for simple tasks), performance requirements (millisecond response times), and industry-specific needs (medical, military terminology).5. Inference is the Commercial Battleground: The majority of commercial AI value lies in inference rather than training. Current GPUs, while specialized for graphics and matrix operations, may still be too general for optimal inference performance, creating opportunities for even more specialized hardware.6. Open Source vs Open Weights Distinction: True open source in AI means access to architecture for debugging and modification, while "open weights" enables fine-tuning and customization. This distinction is crucial for enterprise adoption, as open weights provide the flexibility companies need without starting from scratch.7. Architecture Innovation Faces Expensive Testing Loops: Unlike database optimization where query plans can be easily modified, testing new AI architectures requires expensive retraining cycles costing hundreds of millions of dollars. This creates a potential innovation bottleneck, similar to aerospace industries where testing new designs is prohibitively expensive.

In this episode of Crazy Wisdom, I—Stewart Alsop—sit down with Garrett Dailey to explore a wide-ranging conversation that moves from the mechanics of persuasion and why the best pitches work by attraction rather than pressure, to the nature of AI as a pattern tool rather than a mind, to power cycles, meaning-making, and the fracturing of modern culture. Garrett draws on philosophy, psychology, strategy, and his own background in storytelling to unpack ideas around narrative collapse, the chaos–order split in human cognition, the risk of “AI one-shotting,” and how political and technological incentives shape the world we're living through. You can find the tweet Stewart mentions in this episode here. Also, follow Garrett Dailey on Twitter at @GarrettCDailey, or find more of his pitch-related work on LinkedIn.Check out this GPT we trained on the conversationTimestamps00:00 Garrett opens with persuasion by attraction, storytelling, and why pitches fail with force. 05:00 We explore gravity as metaphor, the opposite of force, and the “ring effect” of a compelling idea. 10:00 AI as tool not mind; creativity, pattern prediction, hype cycles, and valuation delusions. 15:00 Limits of LLMs, slopification, recursive language drift, and cultural mimicry. 20:00 One-shotting, psychosis risk, validation-seeking, consciousness vs prediction. 25:00 Order mind vs chaos mind, solipsism, autism–schizophrenia mapping, epistemology. 30:00 Meaning, presence, Zen, cultural fragmentation, shared models breaking down. 35:00 U.S. regional culture, impossibility of national unity, incentives shaping politics. 40:00 Fragmentation vs reconciliation, markets, narratives, multipolarity, Dune archetypes. 45:00 Patchwork age, decentralization myths, political fracturing, libertarian limits. 50:00 Power as zero-sum, tech-right emergence, incentives, Vance, Yarvin, empire vs republic. 55:00 Cycles of power, kyklos, democracy's decay, design-by-committee, institutional failure.Key InsightsPersuasion works best through attraction, not pressure. Garrett explains that effective pitching isn't about forcing someone to believe you—it's about creating a narrative gravity so strong that people move toward the idea on their own. This reframes persuasion from objection-handling into desire-shaping, a shift that echoes through sales, storytelling, and leadership.AI is powerful precisely because it's not a mind. Garrett rejects the “machine consciousness” framing and instead treats AI as a pattern amplifier—extraordinarily capable when used as a tool, but fundamentally limited in generating novel knowledge. The danger arises when humans project consciousness onto it and let it validate their insecurities.Recursive language drift is reshaping human communication. As people unconsciously mimic LLM-style phrasing, AI-generated patterns feed back into training data, accelerating a cultural “slopification.” This becomes a self-reinforcing loop where originality erodes, and the machine's voice slowly colonizes the human one.The human psyche operates as a tension between order mind and chaos mind. Garrett's framework maps autism and schizophrenia as pathological extremes of this duality, showing how prediction and perception interact inside consciousness—and why AI, which only simulates chaos-mind prediction, can never fully replicate human knowing.Meaning arises from presence, not abstraction. Instead of obsessing over politics, geopolitics, or distant hypotheticals, Garrett argues for a Zen-like orientation: do what you're doing, avoid what you're not doing. Meaning doesn't live in narratives about the future—it lives in the task at hand.Power follows predictable cycles—and America is deep in one. Borrowing from the Greek kyklos, Garrett frames the U.S. as moving from aristocracy toward democracy's late-stage dysfunction: populism, fragmentation, and institutional decay. The question ahead is whether we're heading toward empire or collapse.Decentralization is entropy, not salvation. Crypto dreams of DAOs and patchwork societies ignore the gravitational pull of power. Systems fragment as they weaken, but eventually a new center of order emerges. The real contest isn't decentralization vs. centralization—it's who will have the coherence and narrative strength to recentralize the pieces.

Co-hosts Ryan Piansky, a graduate student and patient advocate living with eosinophilic esophagitis (EoE) and eosinophilic asthma, and Holly Knotowicz, a speech-language pathologist living with EoE who serves on APFED's Health Sciences Advisory Council, interview Evan S. Dellon, MD, and Elizabeth T. Jensen, PhD, about a paper they published on predictors of patients receiving no medication for treatment of eosinophilic esophagitis. Disclaimer: The information provided in this podcast is designed to support, not replace, the relationship between listeners and their healthcare providers. Opinions, information, and recommendations shared in this podcast are not a substitute for medical advice. Decisions related to medical care should be made with your healthcare provider. Opinions and views of guests and co-hosts are their own.   Key Takeaways: [:52] Co-host Ryan Piansky introduces the episode, brought to you thanks to the support of Education Partners GSK, Sanofi, Regeneron, and Takeda. Ryan introduces co-host Holly Knotowicz.   [1:14] Holly introduces today's topic, predictors of not using medication for EoE, and today's guests, Dr. Evan Dellon and Dr. Elizabeth Jensen.   [1:29] Dr. Dellon is an Adjunct Professor of Epidemiology at the University of North Carolina School of Medicine in Chapel Hill. He is also the Director of the UNC Center for Esophageal Diseases and Swallowing.   [1:42] Dr. Dellon's main research interest is in the epidemiology, pathogenesis, diagnosis, treatment, and outcomes of eosinophilic esophagitis (EoE) and eosinophilic GI diseases (EGIDs).   [1:55] Dr. Jensen is a Professor of Epidemiology with a specific expertise in reproductive, perinatal, and pediatric epidemiology. She has appointments at both Wake Forest University School of Medicine and the University of North Carolina at Chapel Hill.   [2:07] Her research primarily focuses on etiologic factors in the development of pediatric immune-mediated chronic diseases, including understanding factors contributing to disparities in health outcomes.   [2:19] Both Dr. Dellon and Dr. Jensen also serve on the Steering Committee for EGID Partners Registry.   [2:24] Ryan thanks Dr. Dellon and Dr. Jensen for joining the podcast today.   [2:29] Dr. Dellon was the first guest on this podcast. It is wonderful to have him back for the 50th episode! Dr. Dellon is one of Ryan's GI specialists. Ryan recently went to North Carolina to get a scope with him.   [3:03] Dr. Dellon is an adult gastroenterologist at the University of North Carolina at Chapel Hill. He directs the Center for Esophageal Diseases and Swallowing. Clinically and research-wise, he is focused on EoE and other eosinophilic GI diseases.   [3:19] His research interests span the entire field, from epidemiology, diagnosis, biomarkers, risk factors, outcomes, and a lot of work, more recently, on treatments.   [3:33] Dr. Jensen has been on the podcast before, on Episode 27. Holly invites Dr. Jensen to tell the listeners more about herself and her work with eosinophilic diseases.   [3:46] Dr. Jensen has been working on eosinophilic gastrointestinal diseases for about 15 years. She started some of the early work around understanding possible risk factors for the development of disease.   [4:04] She has gone on to support lots of other research projects, including some with Dr. Dellon, where they're looking at gene-environment interactions in relation to developing EoE.   [4:15] She is also looking at reproductive factors as they relate to EoE, disparities in diagnosis, and more. It's been an exciting research trajectory, starting with what we knew very little about and building to an increasing understanding of why EoE develops.   [5:00] Dr. Dellon explains that EoE stands for eosinophilic esophagitis, a chronic allergic condition of the esophagus.   [5:08] You can think of EoE as asthma of the esophagus or eczema of the esophagus, although in general, people don't grow out of EoE, like they might grow out of eczema or asthma. When people have EoE, it is a long-term condition.   [5:24] Eosinophils are a type of white blood cell, specializing in allergy responses. Normally, they are not in the esophagus. When we see them there, we worry about an allergic process. When that happens, that's EoE.   [5:40] Over time, the inflammation seen in EoE and other allergic cell activity causes swelling and irritation in the esophagus. Early on, this often leads to a range of upper GI symptoms — including poor growth or failure to thrive in young children, abdominal pain, nausea, and symptoms that can mimic reflux.   [5:58] In older kids, symptoms are more about trouble swallowing. That's because the swelling that happens initially, over time, may turn into scar tissue. So the esophagus can narrow and cause swallowing symptoms like food impaction.   [6:16] Ryan speaks of living with EoE for decades and trying the full range of treatment options: food elimination, PPIs, steroids, and, more recently, biologics.   [6:36] Dr. Dellon says Ryan's history is a good overview of how EoE is treated. There are two general approaches to treating the underlying condition: using medicines and/or eliminating foods that we think may trigger EoE from the diet.   [6:57] For a lot of people, EoE is a food-triggered allergic condition.   [7:01] The other thing that has to happen in parallel is surveying for scar tissue in the esophagus. If that's present and people have trouble swallowing, sometimes stretching the esophagus is needed through esophageal dilation.   [7:14] There are three categories of medicines used for treatment. Proton pump inhibitors are reflux meds, but they also have an anti-allergy effect in the esophagus.   [7:29] Topical steroids are used to coat the esophagus and produce an anti-inflammatory effect. The FDA has approved a budesonide oral suspension for that.   [7:39] Biologics, which are generally systemic medications, often injectable, can target different allergic factors. Dupilumab is approved now, and there are other biologics that are being researched as potential treatments.   [7:51] Even though EoE is considered an allergic condition, we don't have a test to tell people what they are allergic to. If it's a food allergy, we do an empiric elimination diet because allergy tests aren't accurate enough to tell us what the EoE triggers are.   [8:10] People will eliminate foods that we know are the most common triggers, like milk protein, dairy, wheat, egg, soy, and other top allergens. You can create a diet like that and then have a response to the diet elimination.   [8:31] Dr. Jensen and Dr. Dellon recently published an abstract in the American Journal of Gastroenterology about people with EoE who are not taking any medicine for it. Dr. Jensen calls it a real-world data study, leveraging electronic health record patient data.   [8:51] It gives you an impression of what is actually happening, in terms of treatments for patients, as opposed to a randomized control trial, which is a fairly selected patient population. This is everybody who has been diagnosed, and then what happens with them.   [9:10] Because of that, it gives you a wide spectrum of patients. Some patients are going to be relatively asymptomatic. It may be that we arrived at their diagnosis while working them up for other potential diagnoses.   [9:28] Other patients are going to have rather significant impacts from the disease. We wanted to get an idea of what is actually happening out there with the full breadth of the patient population that is getting diagnosed with EoE.   [9:45] Dr. Jensen was not surprised to learn that there are patients who had no pharmacologic treatment.   [9:58] Some patients are relatively asymptomatic, and others are not interested in pursuing medications initially or are early in their disease process and still exploring dietary treatment options.   [10:28] Holly sees patients from infancy to geriatrics, and if they're not having symptoms, they wonder why bother treating it.   [10:42] Dr. Jensen says it's a point of debate on the implications of somebody who has the disease and goes untreated. What does that look like long-term? Are they going to develop more of that fibrostenotic pattern in their esophagus without treatment?   [11:07] This is a question we're still trying to answer. There is some suggestion that for some patients who don't manage their disease, we very well may be looking at a food impaction in the future.   [11:19] Dr. Dellon says we know overall for the population of EoE patients, but it's hard to know for a specific patient. We have a bunch of studies now that look at how long people have symptoms before they're diagnosed. There's a wide range.   [11:39] Some people get symptoms and get diagnosed right away. Others might have symptoms for 20 or 30 years that they ignore, or don't have access to healthcare, or the diagnosis is missed.   [11:51] What we see consistently is that people who may be diagnosed within a year or two may only have a 10 or 20% chance of having that stricture and scar tissue in the esophagus, whereas people who go 20 years, it might be 80% or more.   [12:06] It's not everybody who has EoE who might end up with that scar tissue, but certainly, it's suggested that it's a large majority.   [12:16] That's before diagnosis. We have data that shows that after diagnosis, if people go a long time without treatment or without being seen in care, they also have an increasing rate of developing strictures.    [12:29] In general, the idea is yes, you should treat EoE, because on average, people are going to develop scar tissue and more symptoms. For the patient in front of you with EoE but no symptoms, what are the chances it's going to get worse? You don't know.   [13:04] There are two caveats with that. The first is what we mean by symptoms. Kids may have vomiting and growth problems. Adults can eat carefully, avoiding foods that hang up in the esophagus, like breads and overcooked meats, sticky rice, and other foods.   [13:24] Adults can eat slowly, drink a lot of liquid, and not perceive they have symptoms. When someone tells Dr. Dellon they don't have symptoms, he will quiz them about that. He'll even ask about swallowing pills.    [13:40] Often, you can pick up symptoms that maybe the person didn't even realize they were having. In that case, that can give you some impetus to treat.   [13:48] If there really are no symptoms, Dr. Dellon thinks we're at a point where we don't really know what to do.   [13:54] Dr. Dellon just saw a patient who had a lot of eosinophils in their small bowel with absolutely no GI symptoms. He said, "I can't diagnose you with eosinophilic enteritis, but you may develop symptoms." People like that, he will monitor in the clinic.   [14:14] Dr. Dellon will discuss it with them each time they come back for a clinic visit.   [14:19] Holly is a speech pathologist, but also sees people for feeding and swallowing. The local gastroenterologist refers patients who choose not to treat their EoE to her. Holly teaches them things they should be looking out for.   [14:39] If your pills get stuck or if you're downing 18 ounces during a mealtime, maybe it's time to treat it. People don't see these coping mechanisms they use that are impacting their quality of life. They've normalized it.   [15:30] Dr. Dellon says, of these people who aren't treated, there's probably a subset who appropriately are being observed and don't have a medicine treatment or are on a diet elimination.   [15:43] There's also probably a subset who are inappropriately not on treatment. It especially can happen with students who were under good control with their pediatric provider, but moved away to college and didn't transfer to adult care.   [16:08] They ultimately come back with a lot of symptoms that have progressed over six to eight years.   [16:18] Ryan meets newly diagnosed adult patients at APFED's conferences, who say they have no symptoms, but chicken gets caught in their throat. They got diagnosed when they went to the ER with a food impaction.   [16:38] Ryan says you have to wonder at what point that starts to get reflected in patient charts. Are those cases documented where someone is untreated and now has EoE?   [16:49] Ryan asks in the study, "What is the target EGID Cohort and why was it selected to study EoE? What sort of patients were captured as part of that data set?"   [16:58] Dr. Jensen said they identified patients with the ICD-10 code for a diagnosis of EoE. Then they looked to see if there was evidence of symptoms or complications in relation to EoE. This was hard; some of these are relatively non-specific symptoms.   [17:23] These patients may have been seeking care and may have been experiencing some symptoms that may or may not have made it into the chart. That's one of the challenges with real-world data analyses.   [17:38] Dr. Jensen says they are using data that was collected for documenting clinical care and for billing for clinical care, not for research, so it comes with some caveats when doing research with this data.   [18:08] Research using electronic health records gives a real-world perspective on patients who are seeking care or have a diagnosis of EoE, as opposed to a study trying to enroll a patient population that potentially isn't representative of the breadth of individuals living with EoE.   [18:39] Dr. Dellon says another advantage of real-world data is the number of patients. The largest randomized controlled trials in EoE might have 400 patients, and they are incredibly expensive to do.   [18:52] A study of electronic health records (EHR) is reporting on the analysis of just under 1,000. The cohort, combined from three different centers, has more than 1,400 people, a more representative, larger population.    [19:16] Dr. Dellon says when you read the results, understand the limitations and strengths of a study of health records, to help contextualize the information.   [19:41] Dr. Dellon says it's always easier to recognize the typical presentations. Materials about EoE and studies he has done that led to medicine approvals have focused on trouble swallowing. That can be relatively easily measured.   [20:01] Patients often come to receive care with a food impaction, which can be impactful on life, and somewhat public, if in a restaurant or at work. Typical symptoms are also the ones that get you diagnosed and may be easier to treat.   [20:26] Dr. Dellon wonders if maybe people don't treat some of the atypical symptoms because it's not appreciated that they can be related to EoE.   [20:42] Holly was diagnosed as an adult. Ryan was diagnosed as a toddler. Holly asks what are some of the challenges people face in getting an EoE diagnosis.   [20:56] Dr. Jensen says symptoms can sometimes be fairly non-specific. There's some ongoing work by the CEGIR Consortium trying to understand what happens when patients come into the emergency department with a food bolus impaction.   [21:28] Dr. Jensen explains that we see there's quite a bit of variation in how that gets managed, and if they get a biopsy. You have to have a biopsy of the esophagus to get a diagnosis of EoE.   [21:45] If you think about the steps that need to happen to get a diagnosis of EoE, that can present barriers for some groups to ultimately get that diagnosis.   [21:56] There's also been some literature around a potential assumption about which patients are more likely to be at risk. Some of that is still ongoing. We know that EoE occurs more commonly in males in roughly a two-to-one ratio. Not exclusively in males, obviously, but a little more often in males.   [22:20] We don't know anything about other groups of patients that may be at higher risk. That's ongoing work that we're still trying to understand. That in itself can also be a barrier when there are assumptions about who is or isn't likely to have EoE.   [23:02] Dr. Dellon says that in adolescents and adults, the typical symptoms are trouble swallowing and food sticking, which have many causes besides EoE, some of which are more common.   [23:18] In that population, heartburn is common. Patients may report terrible reflux that, on questioning, sounds more like trouble swallowing than GERD. Sometimes, with EoE, you may have reflux that doesn't improve. Is it EoE, reflux, or both?   [24:05] Some people will have chest discomfort. There are some reports of worsening symptoms with exercise, which brings up cardiac questions that have to be ruled out first.   [24:19] Dr. Dellon mentions some more atypical symptoms. An adult having pain in the upper abdomen could have EoE. In children, the symptoms could be anything in the GI tract. Some women might have atypical symptoms with less trouble swallowing.   [24:58] Some racial minorities may have those kinds of symptoms, as well. If you're not thinking of the condition, it's hard to make the diagnosis.   [25:08] Dr. Jensen notes that there are different cultural norms around expressing symptoms and dietary patterns, which may make it difficult to parse out a diagnosis.   [25:27] Ryan cites a past episode where access to a GI specialist played a role in diagnosing patients with EoE. Do white males have more EoE, or are their concerns just listened to more seriously?   [25:57] Ryan's parents were told when he was two that he was throwing up for attention. He believes that these days, he'd have a much easier time convincing a doctor to listen to him. From speaking to physicians, Ryan believes access is a wide issue in the field.   [26:23] Dr. Dellon tells of working with researchers at Mayo in Arizona and the Children's Hospital of Phoenix. They have a large population of Hispanic children with EoE, much larger than has been reported elsewhere. They're working on characterizing that.   [26:49] Dr. Dellon describes an experience with a visiting trainee from Mexico City, where there was not a lot of EoE reported. The trainee went back and looked at the biopsies there, and it turned out they were not performing biopsies on patients with dysphagia in Mexico City.   [27:13] When he looked at the patients who ended up getting biopsies, they found EoE in 10% of patients. That's similar to what's reported out of centers in the developed world. As people are thinking about it more, we will see more detection of it.   [27:30] Dr. Dellon believes those kinds of papers will be out in the next couple of months, to a year.   [27:36] Holly has had licensure in Arizona for about 11 years. She has had nine referrals recently of children with EoE from Arizona. Normally, it's been one or two that she met at a conference.   [28:00] Ryan asks about the research on patients not having their EoE treated pharmacologically. Some treat it with food avoidance and dietary therapy. Ryan notes that he can't have applesauce, as it is a trigger for his EoE.   [28:54] Dr. Jensen says that's one of the challenges in using the EHR data. That kind of information is only available to the researchers through free text. That's a limitation of the study, assessing the use of dietary elimination approaches.   [29:11] Holly says some of her patients have things listed as allergies that are food sensitivities. Ryan says it's helpful for the patients to have their food sensitivities listed along with their food allergies, but it makes records more difficult to parse for research.   [30:14] Dr. Dellon says they identify EoE by billing code, but the codes are not always used accurately. Natural Language Processing can train a computer system to find important phrases. Their collaborators working on the real-world data are using it.   [30:59] Dr. Dellon hopes that this will be a future direction for this research to find anything in the text related to diet elimination.   [31:32] Dr. Jensen says that older patients were less likely to seek medication therapy. She says it's probably for a couple of reasons. First, older patients may have been living with the disease for a long time and have had compensatory mechanisms in place.   [32:03] The other reason may be senescence or burnout of the disease, long-term. Patients may be less symptomatic as they get older. That's a question that remains to be answered for EoE. It has been seen in some other disease processes.   [32:32] Dr. Dellon says there's not much data specifically looking at EoE in the older population. Dr. Dellon did work years ago with another doctor, and they found that older patients had a better response to some treatments, particularly topical steroids.   [32:54] It wasn't clear whether it was a milder aspect of the disease, easier to treat, or because they were older and more responsible, taking their medicines as prescribed, and having a better response rate. It's the flip side of work in the pediatric population.   [33:16] There is an increasingly aging population with EoE. Young EoE patients will someday be over 65. Dr. Dellon hopes there will be a cure by that point, but it's an expanding population now.   [33:38] Dr. Jensen says only a few sites are contributing data, so they hope to add additional sites to the study. For some of the less common outcomes, they need a pretty large patient sample to ask some of those kinds of questions.   [33:55] They will continue to follow up on some of the work that this abstract touched on and try to understand some of these issues more deeply.   [34:06] Dr. Dellon mentions other work within the cohort. Using Natural Language Processing, they are looking at characterizing endoscopy information and reporting it without a manual review of reports and codes. You can't get that from billing data.   [34:29] Similarly, they are trying to classify patient severity by the Index of Severity with EoE, and layer that on looking at treatments and outcomes based on disease severity. Those are a couple of other directions where this cohort is going.   [34:43] Holly mentions that this is one of many research projects Dr. Jensen and Dr. Dellon have collaborated on together. They also collaborate through EGID Partners. Holly asks them to share a little bit about that.   [34:53] Dr. Jensen says EGID Partners is an online registry where individuals, caregivers, and parents of children affected with EGIDs can join.   [35:07] EGID Partners also needs people who don't live with an EGID to join, as controls. That gives the ability to compare those who are experiencing an EGID relative to those who aren't.   [35:22] When you join EGID Partners, they provide you with a set of questionnaires to complete. Periodically, they push out a few more questionnaires.   [35:33] EGID Partners has provided some really great information about patient experience and answered questions that patients want to know about, like joint pain and symptoms outside the GI tract.   [36:04] To date, there are close to 900 participants in the registry from all over the world. As it continues to grow, it will give the ability to look at the patient experience in different geographical areas.   [36:26] Dr. Dellon says we try to have it be interactive, because it is a collaboration with patients. The Steering Committee works with APFED and other patient advocacy groups from around the world.    [36:41] The EGID Partners website shows general patient locations anonymously. It shows the breakdown of adults with the condition and caregivers of children with the condition, the symptom distribution, and the treatment distribution.   [37:03] As papers get published and abstracts are presented, EGID Partners puts them on the website. Once someone joins, they can suggest a research idea. Many of the studies they have done have come from patient suggestions.   [37:20] If there's an interesting idea for a survey, EGID Partners can push out a survey to everybody in the group and answer questions relatively quickly.   [37:57] Dr. Dellon says a paper came out recently about telehealth. EoE care, in particular, is a good model for telehealth because it can expand access for patients who don't have providers in their area.   [38:22] EoE is a condition where care involves a lot of discussion but not a lot of need for physical exams and direct contact, so telehealth can make things very efficient.    [38:52] EGID Partners surveyed patients about telehealth. They thought it was efficient and saved time, and they had the same kind of interactions as in person. In general, in-state insurance covered it. Patients were happy to do those kinds of visits again.   [39:27] Holly says Dr. Furuta, herself, and others were published in the Gastroenterology journal in 2019 about starting to do telehealth because patients coming to the Children's Hospital of Colorado from out of state had no local access to feeding therapy.   [39:50] Holly went to the board, and they allowed her to get licensure in different states. She started with some of the most impacted patients in Texas and Florida in 2011 and 2012. They collected data. They published in 2019 about telehealth's positive impact.   [40:13] When 2020 rolled around, Holly had trained a bunch of people on how to do feeding therapy via telehealth. You have to do all kinds of things, like make yourself disappear, to keep the kids engaged and in their chairs!   [40:25] Now it is Holly's primary practice. She has licenses in nine states. She sees people all over the country. With her diagnosis, her physicians at Mass General have telehealth licensure in Maine. She gets to do telehealth with them instead of driving two hours.   [40:53] Dr. Jensen tells of two of the things they hope to do at EGID Partners. One is trying to understand more about reproductive health for patients with an EGID diagnosis. Only a few studies have looked at this question, and with very small samples.   [41:15] As more people register for EGID Partners, Dr. Jensen is hoping to be able to ask some questions related to reproductive health outcomes.   [41:27] The second goal is a survey suggested by the Student Advisory Committee, asking questions related to the burden of disease specific to the teen population.   [41:48] This diagnosis can hit that population particularly hard, at a time when they are trying to build and sustain friendships and are transitioning to adult care and moving away from home. This patient population has a unique perspective we wanted to hear.   [42:11] Dr. Jensen and Dr. Dellon work on all kinds of other projects, too.   [42:22] Dr. Dellon says they have done a lot of work on the early-life factors that may predispose to EoE. They are working on a large epidemiologic study to get some insight into early-life factors, including factors that can be measured in baby teeth.   [42:42] That's outside of EGID Partners. It's been ongoing, and they're getting close, maybe over the next couple of years, to having some results.   [43:03] Ryan says all of those projects sound so interesting. We need to have you guys back to dive into those results when you have something finalized.   [43:15] For our listeners who want to learn more about eosinophilic disorders, we encourage you to visit apfed.org and check out the links in the show notes below.   [43:22] If you're looking to find specialists who treat eosinophilic disorders, we encourage you to use APFED's Specialist Finder at apfed.org/specialist.   [43:31] If you'd like to connect with others impacted by eosinophilic diseases, please join APFED's online community on the Inspire Network at apfed.org/connections.   [43:41] Ryan thanks Dr. Dellon and Dr. Jensen for joining us today. This was a fantastic conversation. Holly also thanks APFED's Education Partners GSK, Sanofi, Regeneron, and Takeda for supporting this episode.   Mentioned in This Episode: Evan S. Dellon, MD, MPH, Academic Gastroenterologist, University of North Carolina School of Medicine   Elizabeth T. Jensen, MPH, PhD, Epidemiologist, Wake Forest University School of Medicine, University of North Carolina at Chapel Hill   Predictors of Patients Receiving No Medication for Treatment of Eosinophilic Esophagitis in the United States: Data from the TARGET-EGIDS Cohort   Episode 15: Access to Specialty Care for Eosinophilic Esophagitis (EoE)   APFED on YouTube, Twitter, Facebook, Pinterest, Instagram Real Talk: Eosinophilic Diseases Podcast apfed.org/specialist apfed.org/connections apfed.org/research/clinical-trials   Education Partners: This episode of APFED's podcast is brought to you thanks to the support of GSK, Sanofi, Regeneron, and Takeda.   Tweetables:   "I've been working on eosinophilic gastrointestinal diseases for about 15 years. I started some of the early work around understanding possible risk factors for the development of disease. I've gone on to support lots of other research projects." — Elizabeth T. Jensen, MPH, PhD   "You can think of EoE as asthma of the esophagus or eczema of the esophagus, although in general, people don't grow out of EoE, like they might grow out of eczema or asthma. When people have it, it really is a long-term condition." — Evan S. Dellon, MD, MPH   "There are two general approaches to treating the underlying condition, … using medicines and/or eliminating foods from the diet that we think may trigger EoE. I should say, for a lot of people, EoE is a food-triggered allergic condition." — Evan S. Dellon, MD, MPH   "I didn't find it that surprising [that there are patients who had no treatment]. Some patients are relatively asymptomatic, and others are not interested in pursuing medications initially or are … still exploring dietary treatment options." — Elizabeth T. Jensen, MPH, PhD   "We have a bunch of studies now that look at how long people have symptoms before they're diagnosed. There's a wide range. Some people get symptoms and are diagnosed right away. Other people might have symptoms for 20 or 30 years." — Evan S. Dellon, MD, MPH   "EGID Partners is an online registry where individuals, caregivers, and parents of children affected with EGIDs can join. EGID Partners also needs people who don't live with an EGID to join, as controls." — Elizabeth T. Jensen, MPH, PhD

Artificial intelligence often struggles with the ambiguity, nuance, and shifting context that defines human reasoning. Fuzzy logic offers an alternative, by modelling meaning in degrees rather than absolutes.In this roundtable episode, ResearchPod speaks with Professors Edy Portmann, Irina Perfilieva, Vilem Novak, Cristina Puente, and José María Alonso about how fuzzy systems capture perception, language, social cues, and uncertainty. Their insights contribute to the upcoming FMsquare Foundation booklet on fuzzy logic, exploring the role of uncertainty-aware reasoning in the future of AI.You can read the previous booklet from this series here: Fuzzy Design-Science ResearchYou can listen to previous fuzzy podcasts here: fmsquare.org

Hasan Rizvi, EVP, Database Engineering, Oracle, talks to Bob Evans in this latest episode of Cloud Wars Live. They explore the launch of Oracle AI Database 26ai, the Autonomous AI Lakehouse, and breakthroughs in multi-cloud deployment. Rizvi also discusses vector search, agentic AI, and how Oracle is simplifying complex architectures for the AI era. It's a compelling look at how Oracle is reshaping enterprise data strategy for the age of AI.Oracle's Next-Gen Data StrategyThe Big Themes:AI Demands a Modern Data Foundation: As AI shifts operations from human scale to machine speed, enterprises must ask: “Is my data foundation ready?” Without intelligent data structures, comprehensive access, real‑time performance, and strong security, organizations will struggle to compete. The introduction of Oracle AI Database 26ai is positioned as that foundation. The urgency of this shift is clear: companies that delay risk being left behind.Agentic AI and Vectors Come to the Enterprise Database: Generative AI and autonomous agents require new data types and workflows. Oracle has built vector data types and vector indexes into the database so enterprises can perform similarity search, retrieval‑augmented generation (RAG) and agent workflows directly on their private data. Further, Oracle is enabling annotations (metadata) so LLMs can understand enterprise data schemas, improving accuracy. Finally, agentic workflows (AI that takes action) are supported within the database, reducing data movement, improving performance and strengthening security.Start‑Ups and Established Enterprises Both Benefit: The case study of Retraced (a fashion supply‑chain company) underscores how smaller, agile firms are using Oracle's autonomous AI database to innovate quickly: multi‑datatype support, agentic AI, automatic scaling, and reduced operational overhead. At the same time, Oracle's heritage in mission‑critical enterprise systems means large companies with massive workloads benefit from the same platform. The point: whether you're a start‑up or a Fortune 500, the difference will be how fast you move.The Big Quote: “We really believe that in in the age of AI, where you have to move much faster, you really don't have a choice but to start simplifying your environment. Otherwise, you're going to get left behind."More from Hasan Rizvi and Oracle:Connect with Hasan on LinkedIn and learn more about Oracle AI Database 26ai. Visit Cloud Wars for more.

Generative artificial intelligence is emerging as a tool to look at how people learn language. University of Arizona professor Gondy Leroy discusses research into how advanced machine learning can help families diagnose autism through the way their children acquire speaking skills. Gondy Leroy spoke with Leslie Tolbert, Ph. D. Regent's professor in Neuroscience at the University of Arizona.

It's clear that tech billionaires want AI to be the next tech revolution to change society. How it's being developed and used raises serious questions and concerns around the impact on trans people, especially given the key companies developing these products have very close ties to the Trump administration. First, Imara speaks with Karen Hao, a journalist and bestselling author of Empire of AI, a sweeping investigation into the rise of OpenAI and the global power dynamics of machine intelligence. Then, she chats with Eddie Ungless, a recent Phd graduate in Natural Language Processing, about what it might take to make these technologies safer and more accountable. Send your trans joy recommendations to translash_podcast @ translash [dot] org Follow TransLash Media @translashmedia on TikTok, Instagram, Threads, Bluesky, and Facebook.Follow Imara Jones on Instagram (@Imara_jones_), Threads (@imara_jones_), Bluesky (@imarajones.bsky.social), X (@ImaraJones)Follow our guest on social media: Eddie Ungless: Bluesky (@mxeddie.bsky.social)Karen Hao: Bluesky (@karenhao.bsky.social)TransLash Podcast is produced by TransLash Media.The Translash team includes Imara Jones, Oliver K., Aubrey C., Rebekah R., Josephine M., Hillary E., and Morgan A. Lucy L. did the sound editing and engineering for this episode.Theme music composed by Ben D. Hosted on Acast. See acast.com/privacy for more information.

Send us a textThe prompt mentioned in the episode is:You are an expert SEO analyst specializing in Natural Language Processing and entity-based optimization. I will provide you with the text from a competitor's webpage. Your task is to perform a Named Entity Recognition (NER) analysis on this text.Please identify all the significant entities mentioned in the text. For each entity, classify it into one of the following categories: Person, Organization, Location, Product, Event, or Concept (for abstract ideas, theories, or topics).Please present your findings in a simple list or table format, with one column for the entity and one for its category.Please sort the entities in order of importanceHere is the text:[Paste the competitor's text here]Every rival looks unbeatable until you see what Google actually sees: the network of entities that frames their authority. We pull back the curtain on a simple, repeatable method to map competitor concepts, spot gaps, and build content hubs that earn durable rankings.We start by recapping the four-pillar entity audit that anchors your strategy: brand and products, people, core concepts, and audience topics. Then we turn that blueprint outward. Instead of chasing keyword lists, we show how to identify true SERP competitors for your core ideas, pick a representative high-ranking page, and extract its entities using a clean, copy-paste prompt with your favourite LLM. No specialist software, no guesswork — just a structured list of people, organisations, products, locations, events, and concepts that shape the page's topical focus.From there, we translate data into decisions. You'll learn to compare entity saturation against your own audit, find the missing concepts and influential names you should reference, and read structural clues in URLs, headings, and internal links that reveal how competitors build content hubs. We also touch on brand signals and knowledge panels to understand how well Google recognises a site as an entity. The result is a pragmatic roadmap: pick one high-value gap, create a cornerstone guide and supporting pieces, interlink with consistent anchors, and align metadata and naming to signal clear relevance.SEO Is Not That Hard is hosted by Edd Dawson and brought to you by KeywordsPeopleUse.com Help feed the algorithm and leave a review at ratethispodcast.com/seo You can get your free copy of my 101 Quick SEO Tips at: https://seotips.edddawson.com/101-quick-seo-tipsTo get a personal no-obligation demo of how KeywordsPeopleUse could help you boost your SEO and get a 7 day FREE trial of our Standard Plan book a demo with me nowSee Edd's personal site at edddawson.comAsk me a question and get on the show Click here to record a questionFind Edd on Linkedin, Bluesky & TwitterFind KeywordsPeopleUse on Twitter @kwds_ppl_use"Werq" Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/

Humans in AI – Creativity, Wellbeing & Technology in Education a researchers perspective Guest: Dr Rebecca Marrone, Lecturer & Researcher, University of South Australia   In this episode, Dan and Ray welcome Dr Rebecca Marrone to discuss the intersection of AI, creativity, and wellbeing in education. Her research explores how technology, especially AI, is transforming the educational landscape for both teachers and students.   Key Topics AI's Impact on Teacher & Student Wellbeing Creativity & Critical Thinking in the Age of AI Ethical Risks of AI in Education Strategies for digital wellbeing and critical engagement. The evolving role of soft skills alongside technological advancements. Connect & Learn More For more about Dr Rebecca Marrone's research, listeners can reach out directly or explore her work online.   Find Rebecca on Linkedin and read her research publications via   Google Scholar‬     We mentioned Rebecca and Vitomir's critique of the Brainrot study https://theconversation.com/mit-researchers-say-using-chatgpt-can-rot-your-brain-the-truth-is-a-little-more-complicated-259450    In the podcast Rebecca highlighted George Siemens' research and newsletters: Newsletter archive: https://buttondown.com/SAIL/archive/ Google Scholar profile: https://scholar.google.com/citations?user=EtknWk4AAAAJ&hl=en&oi=ao    Research mentioned during this episode   Marrone, R., Taddeo, V., & Hill, G. (2022). Creativity and artificial intelligence—A student perspective. Journal of Intelligence, 10(3), 65. https://www.mdpi.com/2079-3200/10/3/65?trk=public_post_reshare-text    Marrone, R., Zamecnik, A., Joksimovic, S., Johnson, J., & De Laat, M. (2025). Understanding student perceptions of artificial intelligence as a teammate. Technology, Knowledge and Learning, 30(3), 1847-1869. https://link.springer.com/article/10.1007/s10758-024-09780-z    Marrone, R., Cropley, D. H., & Wang, Z. (2022). Automatic Assessment of Mathematical Creativity using Natural Language Processing. Creativity Research Journal, 35(4), 661–676. https://doi.org/10.1080/10400419.2022.2131209    Cropley, D. H., Theurer, C., Mathijssen, A. C. S., & Marrone, R. L. (2024). Fit-For-Purpose Creativity Assessment: Automatic Scoring of the Test of Creative Thinking – Drawing Production (TCT-DP). Creativity Research Journal, 1–16. https://doi.org/10.1080/10400419.2024.2339667 https://www.tandfonline.com/doi/full/10.1080/10400419.2024.2339667       

David Bau is Assistant Professor at Northeastern University and Director of the National Deep Inference Fabric, researching the emergent internal mechanisms of deep generative networks in both Natural Language Processing and Computer Vision.  In this week's conversation, Yascha Mounk and David Bau explore the technology behind AI, why it's concerning that so many computer scientists don't understand how it works, and how to embed morals, values, and alignment. If you have not yet signed up for our podcast, please do so now by following this link on your phone. Email: leonora.barclay@persuasion.community Podcast production by Mickey Freeland and Leonora Barclay. Connect with us! Spotify | Apple | Google X: @Yascha_Mounk & @JoinPersuasion YouTube: Yascha Mounk, Persuasion LinkedIn: Persuasion Community Learn more about your ad choices. Visit megaphone.fm/adchoices

Thomas Wolf is the cofounder and chief science officer of open-source AI platform Hugging Face, which provides access to thousands of pretrained AI models that can be downloaded and run locally. With over 10 million users, getting started on the site can be a daunting task. Thomas explains how the company aims to improve its accessibility through documentation on the company blog as well as community feedback, similar to social media likes and upvoting. Thomas and Sam discuss the benefits and trade-offs of both open-source and closed-source AI models, as well as the evolution of microchips and the future of hardware and software development — as well as the hopes Thomas has for the future of coding with AI, starting with his children's generation. Read the episode transcript here. Guest bio: Thomas Wolf is cofounder and chief science officer of Hugging Face, a collaborative AI platform. Wolf likes creating open-source software (OSS) that makes complex research, models, and data sets widely accessible. He can also be found pushing for open science in research in AI and machine learning, to try lowering the gap between academia and industrial labs through projects like the BigScience Workshop. He also writes and produces education content on AI, machine language, and natural language processing, including the reference book Natural Language Processing with Transformers, The Ultra-Scale Playbook, his blog, and videos. Me, Myself, and AI is a podcast produced by MIT Sloan Management Review and hosted by Sam Ransbotham. It is engineered by David Lishansky and produced by Allison Ryder. We encourage you to rate and review our show. Your comments may be used in Me, Myself, and AI materials.

Today I'm joined by Vishal Vadodaria, CEO of AutoEngage. We dive into the product obsession Vishal learned under Steve Jobs, why a major public dealer group bet early on his AI tech, how Agentic AI is poised to reshape the future of the car business and much more. This episode is brought to you by: 1. CDK Global - CDK SimplePay is the only payment solution that's built into CDK solutions for unrivaled reliability and financial efficiency. To learn more or schedule a demo visit @ ⁠http://www.CDK.com/simplepay⁠ 2. OPENLANE - The world's best online dealer marketplace for used cars, bringing you exclusive inventory, simple transactions, and better outcomes. Learn more at https://www.openlane.com 3. AutoEngage - LISA (Linguistic Intelligence Service Assistant) “proactively” engages service customers in 2-way conversations throughout their ownership lifecycle using the most advanced Natural Language Processing in the market and books the appointment directly in the scheduler with no human assistance to increase retention, drive revenue, reduce cost and maximize customer lifetime value. Learn more @ https://autoengage.ai Need help finding top automotive talent? Get started here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.cdgrecruiting.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in advertising with Car Dealership Guy? Drop us a line here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://cdgpartner.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in being considered as a guest on the podcast? Add your name here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/3Suismu⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Topics: 01:02 How Apple shaped Vishal's approach 06:35 Why switch to automotive? 07:28 Biggest customer retention challenges 10:24 How Auto Engage solves problems? 13:02 Most impressive AI success story? 18:36 Measuring AI ROI how? 23:33 AI improves customer interactions? 30:57 Customizing AI for dealerships? 37:25 Future AI applications in auto? Check out Car Dealership Guy's stuff: CDG News ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://news.dealershipguy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ CDG Jobs ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://jobs.dealershipguy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ CDG Recruiting ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.cdgrecruiting.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ My Socials: X ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠x.com/GuyDealership⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠instagram.com/cardealershipguy/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠tiktok.com/@guydealership⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/company/cardealershipguy⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Threads ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠threads.net/@cardealershipguy⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠facebook.com/profile.php?id=100077402857683⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Everything else ➤ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠dealershipguy.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.

Demetrios chats with Arpita Vats about how LLMs are shaking up recommender systems. Instead of relying on hand-crafted features and rigid user clusters, LLMs can read between the lines—spotting patterns in user behavior and content like a human would. They cover the perks (less manual setup, smarter insights) and the pain points (latency, high costs), plus how mixing models might be the sweet spot. From timing content perfectly to knowing when traditional methods still win, this episode pulls back the curtain on the future of recommendations.// BioArpita Vats is a passionate and accomplished researcher in the field of Artificial Intelligence, with a focus on Natural Language Processing, Recommender Systems, and Multimodal AI. With a strong academic foundation and hands-on experience at leading tech companies such as LinkedIn, Meta, and Staples, Arpita has contributed to cutting-edge projects spanning large language models (LLMs), privacy-aware AI, and video content understanding.She has published impactful research at premier venues and actively serves as a reviewer for top-tier conferences like CVPR, ICLR, and KDD. Arpita's work bridges academic innovation with industry-scale deployment, making her a sought-after collaborator in the AI research community.Currently, she is engaged in exploring the alignment and safety of language models, developing robust metrics like the Alignment Quality Index (AQI), and optimizing model behavior across diverse input domains. Her dedication to advancing ethical and scalable AI reflects both in her academic pursuits and professional contributions.// Related Links#recommendersystems #LLMs #linkedin ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreMLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Arpita on LinkedIn: /arpita-v-0a14a422/Timestamps:[00:00] Smarter Content Recommendations[05:19] LLMs: Next-Gen Recommendations[09:37] Judging LLM Suggestions[11:38] Old vs New Recommenders[14:11] Why LLMs Get Stuck[16:52] When Old Models Win[22:39] After-Booking Rec Magic[23:26] One LLM to Rule Models[29:14] Personalization That Evolves[32:39] SIM Beats Transformers in QA[35:35] Agents Writing Research Papers[37:12] Big-Company Agent Failures[41:47] LinkedIn Posts Fade Faster[46:04] Clustering Shifts Social Feeds[47:01] Vanishing Posts, Replay Mode

Co-hosts Mark Thompson and Steve Little explore OpenAI's groundbreaking ChatGPT Agent, demonstrating how this autonomous tool can research, analyze, and perform complex tasks on your behalf.Next, they address important security concerns to consider in the new world of AI agents, introducing practical guidelines for protecting sensitive family data and avoiding prompt injection attacks.This week's Tip of the Week provides a back-to-basics guide on what AI is and its four core strengths: summarization, extraction, generation, and translation.In RapidFire, they discuss OpenAI's rumored office suite, Microsoft and Google's own efforts to integrate AI into their office suites, and recently announced AI infrastructure investments, including; Meta's Manhattan-sized data center and President Trump's new AI Action Plan.The hosts also announce their new Family History AI Show Academy, a five-week course beginning in October of 2025. See https://tixoom.app/fhaishow/ for more details.Timestamps:In the News:05:20 ChatGPT Agent: Autonomous Research Assistant for Genealogists22:49 Safe and Secure in the Age of AITip of the Week:36:20 What is AI and What is it Good For? Back to BasicsRapidFire:50:57 OpenAI's Office Suite Rumors53:56 Microsoft and Google Bring AI to Their Office Suites60:17 Big AI Infrastructure: Manhattan-Sized Data CentersResource Links:Introduction to Family History AIhttps://tixoom.app/fhaishow/Do agents work in the browser?https://www.bensbites.com/p/do-agents-work-in-the-browserIntroducing ChatGPT agent: bridging research and actionhttps://openai.com/index/introducing-chatgpt-agent/OpenAI's new ChatGPT Agent can control an entire computer and do tasks for youhttps://www.theverge.com/ai-artificial-intelligence/709158/openai-new-release-chatgpt-agent-operator-deep-researchOpenAI's New ChatGPT Agent Tries to Do It Allhttps://www.wired.com/story/openai-chatgpt-agent-launch/Agent demo posthttps://x.com/rowancheung/status/1945896543263080736OpenAI Quietly Designed a Rival to Google Workspace, Microsoft Officehttps://www.theinformation.com/articles/openai-quietly-designed-rival-google-workspace-microsoft-officeOpenAI Is Quietly Creating Tools to Take on Microsoft Office and Google Workspacehttps://www.theglobeandmail.com/investing/markets/stocks/MSFT/pressreleases/33074368/openai-is-quietly-creating-tools-to-take-on-microsoft-office-and-google-workspace-googl/What's new in Microsoft 365 Copilot?https://techcommunity.microsoft.com/blog/microsoft365copilotblog/what%E2%80%99s-new-in-microsoft-365-copilot--june-2025/4427592Google Workspace enables the future of AI-powered work for every businesshttps://workspace.google.com/blog/product-announcements/empowering-businesses-with-AIGoogle Workspace Review: Will it Serve My Needs?https://www.emailtooltester.com/en/blog/google-workspace-review/Tags:Artificial Intelligence, Genealogy, Family History, AI Agents, ChatGPT Agent, OpenAI, Computer Use, AI Security, Prompt Injection, Database Analysis, RootsMagic, Cemetery Records, AI Office Suite, Microsoft 365 Copilot, Google Workspace, Data Centers, AI Infrastructure, Natural Language Processing, Large Language Models, Context Windows, AI Education, Family History AI Show Academy, AI Reasoning Models, Autonomous Research, AI Ethics

Co-hosts Mark Thompson and Steve Little explore how AI-based transcriptions are revolutionizing genealogical research across the industry. They examine FamilySearch's Full-Text Search expansion to over 1.25 billion records and Ancestry's new document transcription tool.The episode features an exclusive interview with David Ouimette from FamilySearch, who reveals how they're making previously unsearchable handwritten documents accessible through AI technology.This week's Tip of the Week demonstrates Dan Maloney's innovative Genealogy Assistant HTR tool for transcribing multiple documents simultaneously.In RapidFire, they discuss Grok 4's anticipated release, Apple's shift to integrate ChatGPT and Claude rather than develop their own AI, and Google Photos' new natural language search capabilities powered by Gemini.Timestamps:In the News:03:23 AI Transcriptions Everywhere: The New Normal in Genealogy25:33 Building AI Apps with ClaudeInterview:29:34 David Ouimette on FamilySearch's AI JourneyRapidFire:49:27 Grok 4 Release Announcement51:35 Apple Pivots to Third-Party AI Integration56:02 Google Photos Adds Natural Language SearchResource LinksExpanded Access to Full-Text Searchhttps://www.familysearch.org/en/blog/full-text-search-experimental-aiAncestry announces the Beta launch of its new document transcription featurehttps://www.ancestry.com/c/ancestry-blog/ancestry-news/document-transcription-featureSnagit Screen Capturehttps://www.techsmith.com/snagitWindows Snipping Toolhttps://support.microsoft.com/en-us/windows/use-snipping-tool-to-capture-screenshots-00246869-1843-655f-f220-97299b865f6bScreenshots or screen recordings on Machttps://support.apple.com/en-hk/guide/mac-help/mh26782/macDan Maloney's AI Handwritten Text Recognition Toolhttps://www.genea.ca/htr-tool/Anthropic: Build and share AI-powered apps with Claudehttps://www.anthropic.com/news/claude-powered-artifactsAnthropic now lets you make apps right from its Claude AI chatbothttps://www.theverge.com/news/693342/anthropic-claude-ai-apps-artifactGrok 4 release livestream on Wednesday at 8pm PThttps://x.com/elonmusk/status/1942325820170907915Elon Musk's AI chatbot Grok launches into antisemitic rant amid updateshttps://www.washingtonpost.com/technology/2025/07/08/elon-musk-grok-ai-antisemitism/Apple's Original AI announcement on June 10, 2024https://www.apple.com/ca/newsroom/2024/06/introducing-apple-intelligence-for-iphone-ipad-and-mac/Apple's Integrates ChatGPT into iPhoneshttps://support.apple.com/en-ca/guide/iphone/iph00fd3c8c2/iosApple Weighs Using Anthropic or OpenAI to Power Siri in Major Reversalhttps://www.bloomberg.com/news/articles/2025-06-30/apple-weighs-replacing-siri-s-ai-llms-with-anthropic-claude-or-openai-chatgptGoogle Resumes Rollout of AI-Based 'Ask Photos' Image Searchhttps://petapixel.com/2025/06/27/google-resumes-rollout-of-ai-based-ask-photos-image-search/Google Product Manager says Ask Photos paused for quality issueshttps://x.com/jamieasp/status/1929895255844745284Ask Photos coming to more Google Photos usershttps://blog.google/products/photos/updates-ask-photos-search/Ask Photos' Original Announcement May 14, 2024https://petapixel.com/2024/05/14/gemini-powered-ask-photos-brings-an-ai-assistant-to-google-photos/TagsArtificial Intelligence, Genealogy, Family History, AI Transcription, FamilySearch, Ancestry, Document Analysis, Handwritten Text Recognition, Full-Text Search, Screen Capture Tools, Natural Language Processing, Machine Learning, OCR Technology, Dan Maloney, Genealogy Assistant, API Keys, Claude AI, Anthropic, Google Photos, Apple Intelligence, Grok AI, David Ouimette, Truth Sets, Research Tools

Law professor Daniel Ho says that the law is ripe for AI innovation, but a lot is at stake. Naive application of AI can lead to rampant hallucinations in over 80 percent of legal queries, so much research remains to be done in the field. Ho tells how California counties recently used AI to find and redact racist property covenants from their laws—a task predicted to take years, reduced to days. AI can be quite good at removing “regulatory sludge,” Ho tells host Russ Altman in teasing the expanding promise of AI in the law in this episode of Stanford Engineering's The Future of Everything podcastHave a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu.Episode Reference Links:Stanford Profile: Daniel HoConnect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / FacebookChapters:(00:00:00) IntroductionRuss Altman introduces Dan Ho, a professor of law and computer science at Stanford University.(00:03:36) Journey into Law and AIDan shares his early interest in institutions and social reform.(00:04:52) Misconceptions About LawCommon misunderstandings about the focus of legal work.(00:06:44) Using LLMs for Legal AdviceThe current capabilities and limits of LLMs in legal settings.(00:09:09) Identifying Legislation with AIBuilding a model to identify and redact racial covenants in deeds.(00:13:09) OCR and Multimodal ModelsImproving outdated OCR systems using multimodal AI.(00:14:08) STARA: AI for Statute SearchA tool to scan laws for outdated or excessive requirements.(00:16:18) AI and Redundant ReportsUsing STARA to find obsolete legislatively mandated reports(00:20:10) Verifying AI AccuracyComparing STARA results with federal data to ensure reliability.(00:22:10) Outdated or Wasteful RegulationsExamples of bureaucratic redundancies that hinder legal process.(00:23:38) Consolidating Reports with AIHow different bureaucrats deal with outdated legislative reports.(00:26:14) Open vs. Closed AI ModelsThe risks, benefits, and transparency in legal AI tools.(00:32:14) Replacing Lawyers with Legal ChatbotWhy general-purpose legal chatbots aren't ready to replace lawyers.(00:34:58) Conclusion Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook

#497 Data Optimized Training with TriDot's Jeff Booher    Welcome Welcome to Episode #497 of the 303 Endurance Podcast. We're your hosts Coaches Rich Soares and April Spilde. Thanks for joining us for another week of news, coaching tips and discussion.   Show Sponsor: UCAN UCAN created LIVSTEADY as an alternative to sugar based nutrition products. LIVSTEADY was purposefully designed to work with your body, delivering long-lasting energy you can feel. Whether UCAN Energy Powders, Bars or Gels, LIVSTEADY's unique time-release profile allows your body to access energy consistently throughout the day, unlocking your natural ability to finish stronger and recover more quickly!   In Today's Show Announcements and News  Ask A Coach: Interview with Jeff Booher TriDot Workout of the Week: Smooth Swim Fun Segment: What about an “AI Training Fact or Falsehood” game?   Announcements and News:   Our Announcements are supported by VESPA Power today. Endurance athletes—what if you could go farther, faster, and feel better doing it? With Vespa Power Endurance Nutrition, you can unlock your body's natural fat-burning potential and fuel performance without the sugar crash. Vespa helps you tap into steady, clean energy—so you stay strong, focused, and in the zone longer.  Vespa is not fuel, but a metabolic catalyst that shifts your body to use more fat and less glycogen as your fuel source.   Less sugar. Higher performance. Faster recovery.  Home of Vespa Power Products | Optimizing Your Fat Metabolism Use discount code - 303endurance20   Independence Day Pikes Peak Ride Jul 4, 2025 Join us for an invigorating bike ride from Santa's Workshop at 7700 feet to the top of Pikes Peak at 14,111. 6800 feet of climbing in 18 miles.  Garmin Course - https://connect.garmin.com/modern/course/369763602 https://www.facebook.com/share/197vnpxkbc/   TriDot Pool School July 26-27. https://www.tridotpoolschool.com/component/eventbooking/pool-school/tridot-pool-school-20250726-844-986-401-167-857/94?Itemid=762   Ask A Coach Sponsor: G2G Endurance Training alone is tough. Training smart? That's where we come in. Grit2Greatness Endurance + TriDot gives you optimized training, the data, and the support to crush your goals—without burning out. Try it FREE for 2 weeks through our TriDot links below, then roll into your best season yet for as low as $14.99/month. With the right tools, you're unstoppable. Go to the show notes. Click the link. Let's do this together! Website - Grit2Greatness Endurance Coaching Facebook page @grit2greatnessendurance   Coach April Spilde April.spilde@tridot.com TriDot Signup - https://app.tridot.com/onboard/sign-up/aprilspilde RunDot Signup - https://app.rundot.com/onboard/sign-up/aprilspilde    Coach Rich Soares Rich.soares@tridot.com Rich Soares Coaching TriDot Signup - https://app.tridot.com/onboard/sign-up/richsoares RunDot Signup - https://app.rundot.com/onboard/sign-up/richsoares   Ask A Coach: Interview with TriDot CEO Jeff Booher  As a triathlete you want to have a **growth mindset**, here are **10 introspective and growth-oriented questions** ###

In this episode of the Confessions of a B2B Entrepreneur, host Tom Hunt chats with Mehdi Tehranchi from KnowledgeNet.ai, an AI expert and seasoned entrepreneur. They discuss how AI is transforming businesses by improving efficiency , turning knowledge into actionable insights , and optimising sales processes by combining internal and external data. Mehdi also highlights the crucial role of human critical thinking when utilising AI's power. The conversation delves into AI's ability to surface the right information at the right time and its use internally within businesses to get better results and improve business outcomes.

In this episode of Software Engineering Radio, Abhinav Kimothi sits down with host Priyanka Raghavan to explore retrieval-augmented generation (RAG), drawing insights from Abhinav's book, A Simple Guide to Retrieval-Augmented Generation. The conversation begins with an introduction to key concepts, including large language models (LLMs), context windows, RAG, hallucinations, and real-world use cases. They then delve into the essential components and design considerations for building a RAG-enabled system, covering topics such as retrievers, prompt augmentation, indexing pipelines, retrieval strategies, and the generation process. The discussion also touches on critical aspects like data chunking and the distinctions between open-source and pre-trained models. The episode concludes with a forward-looking perspective on the future of RAG and its evolving role in the industry. Brought to you by IEEE Computer Society and IEEE Software magazine.

Mark Ericksen, creator of the Elixir LangChain framework, joins the Elixir Wizards to talk about LLM integration in Elixir apps. He explains how LangChain abstracts away the quirks of different AI providers (OpenAI, Anthropic's Claude, Google's Gemini) so you can work with any LLM in one more consistent API. We dig into core features like conversation chaining, tool execution, automatic retries, and production-grade fallback strategies. Mark shares his experiences maintaining LangChain in a fast-moving AI world: how it shields developers from API drift, manages token budgets, and handles rate limits and outages. He also reveals testing tactics for non-deterministic AI outputs, configuration tips for custom authentication, and the highlights of the new v0.4 release, including “content parts” support for thinking-style models. Key topics discussed in this episode: • Abstracting LLM APIs behind a unified Elixir interface • Building and managing conversation chains across multiple models • Exposing application functionality to LLMs through tool integrations • Automatic retries and fallback chains for production resilience • Supporting a variety of LLM providers • Tracking and optimizing token usage for cost control • Configuring API keys, authentication, and provider-specific settings • Handling rate limits and service outages with degradation • Processing multimodal inputs (text, images) in Langchain workflows • Extracting structured data from unstructured LLM responses • Leveraging “content parts” in v0.4 for advanced thinking-model support • Debugging LLM interactions using verbose logging and telemetry • Kickstarting experiments in LiveBook notebooks and demos • Comparing Elixir LangChain to the original Python implementation • Crafting human-in-the-loop workflows for interactive AI features • Integrating Langchain with the Ash framework for chat-driven interfaces • Contributing to open-source LLM adapters and staying ahead of API changes • Building fallback chains (e.g., OpenAI → Azure) for seamless continuity • Embedding business logic decisions directly into AI-powered tools • Summarization techniques for token efficiency in ongoing conversations • Batch processing tactics to leverage lower-cost API rate tiers • Real-world lessons on maintaining uptime amid LLM service disruptions Links mentioned: https://rubyonrails.org/ https://fly.io/ https://zionnationalpark.com/ https://podcast.thinkingelixir.com/ https://github.com/brainlid/langchain https://openai.com/ https://claude.ai/ https://gemini.google.com/ https://www.anthropic.com/ Vertex AI Studio https://cloud.google.com/generative-ai-studio https://www.perplexity.ai/ https://azure.microsoft.com/ https://hexdocs.pm/ecto/Ecto.html https://oban.pro/ Chris McCord's ElixirConf EU 2025 Talk https://www.youtube.com/watch?v=ojL_VHc4gLk Getting started: https://hexdocs.pm/langchain/gettingstarted.html https://ash-hq.org/ https://hex.pm/packages/langchain https://hexdocs.pm/igniter/readme.html https://www.youtube.com/watch?v=WM9iQlQSFg @brainlid on Twitter and BlueSky Special Guest: Mark Ericksen.

What can film reviews tell us about gender bias in the movie industry?Dr Wael Khreich from the American University of Beirut explores this question with Genderly, a custom-built AI tool that analyses the language of 17,000 professional reviews. His findings reveal that female-led films are far more likely to be judged through a biased lens—subtly and overtly reinforcing stereotypes. This research sheds light on how language shapes perception, influences careers, and contributes to broader societal inequalities.Read the original research: doi.org/10.1371/journal.pone.0316093

Send us a textEver wondered why AI can be simultaneously brilliant and bewilderingly clueless? Reed Coke, Director of Engineering at KUNGFU.AI, offers a beautifully simple explanation: AI is like singing along to songs in a language you don't know. You recognize patterns and can predict what comes next without truly understanding the meaning.Reed brings his unique background as a linguist-turned-AI engineer to explain natural language processing in terms kids (and adults) can grasp. Growing up bilingual in Dutch and English sparked his passion for languages, eventually leading him to discover programming as a powerful tool for studying human communication. This intersection became his specialty - building AI systems that interact with language.The conversation explores fascinating territory, from Reed's work on an AI system that could detect cancer risks five years before diagnosis to the amusing challenges of teaching computers to understand context (like distinguishing complaints about counterfeit products from reviews of Halloween "fake doctor" costumes). Reed breaks down complex concepts like supervised learning without sacrificing accuracy, making them accessible for young listeners.Reed offers practical advice on getting started with AI through resources like Code Academy, Code Combat, kidspython.com, and Black Girls Hack, emphasizing that community-based learning through classes and camps often provides the best support. Most importantly, he encourages approaching AI development with empathy - considering who created the training data, what experiences it represents, and how systems will affect real people.Ready to dive deeper into the world of AI? Subscribe to our podcast, have your parents leave us a review, and sign up for our weekly AI newsletter at aidigitales.com/newsletter. Support the showHelp us become the #1 podcast for AI for Kids.Buy our new book "Let Kids Be Kids, Not Robots!: Embracing Childhood in an Age of AI"Social Media & Contact: Website: www.aidigitales.com Email: contact@aidigitales.com Follow Us: Instagram, YouTube Gift or get our books on Amazon or Free AI Worksheets Listen, rate, and subscribe! Stay updated with our latest episodes by subscribing to AI for Kids on your favorite podcast platform. Apple Podcasts Amazon Music Spotify YouTube Other Like our content, subscribe or feel free to donate to our Patreon here: patreon.com/AiDigiTales...

Welcome back to our series on AI for the clinician! Large language models, like ChatGPT, have been taking the world by storm, and healthcare is no exception to that rule – your institution may already be using them! In this episode we'll tackle the fundamentals of how they work and their applications and limitations to keep you up to date on this fast-moving, exciting technology. Hosts: Ayman Ali, MD Ayman Ali is a Behind the Knife fellow and general surgery PGY-3 at Duke Hospital in his academic development time where he focuses on data science, artificial intelligence, and surgery. Ruchi Thanawala, MD: @Ruchi_TJ Ruchi Thanawala is an Assistant Professor of Informatics and Thoracic Surgery at Oregon Health and Science University (OHSU) and founder of Firefly, an AI-driven platform that is built for competency-based medical education. In addition, she directs the Surgical Data and Decision Sciences Lab for the Department of Surgery at OHSU.  Phillip Jenkins, MD: @PhilJenkinsMD Phil Jenkins is a general surgery PGY-3 at Oregon Health and Science University and a National Library of Medicine Post-Doctoral fellow pursuing a master's in clinical informatics. Steven Bedrick, PhD: @stevenbedrick Steven Bedrick is a machine learning researcher and an Associate Professor in Oregon Health and Science University's Department of Medical Informatics and Clinical Epidemiology. Please visit https://behindtheknife.org to access other high-yield surgical education podcasts, videos and more.  If you liked this episode, check out our recent episodes here: https://app.behindtheknife.org/listen

I, Stewart Alsop, welcomed Alex Levin, CEO and co-founder of Regal, to this episode of the Crazy Wisdom Podcast to discuss the fascinating world of AI phone agents. Alex shared some incredible insights into how AI is already transforming customer interactions and what the future holds for company agents, machine-to-machine communication, and even the nature of knowledge itself.Check out this GPT we trained on the conversation!Timestamps00:29 Alex Levin shares that people are often more honest with AI agents than human agents, especially regarding payments.02:41 The surprising persistence of voice as a preferred channel for customer interaction, and how AI is set to revolutionize it.05:15 Discussion of the three types of AI agents: personal, work, and company agents, and how conversational AI will become the main interface with brands.07:12 Exploring the shift to machine-to-machine interactions and how AI changes what knowledge humans need versus what machines need.10:56 The looming challenge of centralization versus decentralization in AI, and how Americans often prioritize experience over privacy.14:11 Alex explains how tokenized data can offer personalized experiences without compromising specific individual privacy.25:44 Voice is predicted to become the primary way we interact with brands and technology due to its naturalness and efficiency.33:21 Why AI agents are easier to implement in contact centers due to different entropy compared to typical software.38:13 How Regal ensures AI agents stay on script and avoid "hallucinations" by proper training and guardrails.46:11 The technical challenges in replicating human conversational latency and nuances in AI voice interactions.Key InsightsAI Elicits HonestyPeople tend to be more forthright with AI agents, particularly in financially sensitive situations like discussing overdue payments. Alex speculates this is because individuals may feel less judged by an AI, leading to more truthful disclosures compared to interactions with human agents.Voice is King, AI is its HeirDespite predictions of its decline, voice remains a dominant channel for customer interactions. Alex believes that within three to five years, AI will handle as much as 90% of these voice interactions, transforming customer service with its efficiency and availability.The Rise of Company AgentsThe primary interface with most brands is expected to shift from websites and apps to conversational AI agents. This is because voice is a more natural, faster, and emotive way for humans to interact, a behavior already seen in younger generations.Machine-to-Machine FutureWe're moving towards a world where AI agents representing companies will interact directly with AI agents representing consumers. This "machine-to-machine" (M2M) paradigm will redefine commerce and the nature of how businesses and customers engage.Ontology of KnowledgeAs AI systems process vast amounts of information, creating a clear "ontology of knowledge" becomes crucial. This means structuring and categorizing information so AI can understand the context and user's underlying intent, rather than just processing raw data.Tokenized Data for PrivacyA potential solution to privacy concerns is "tokenized data." Instead of providing AI with specific personal details, users could share generalized tokens (e.g., "high-intent buyer in 30s") that allow for personalized experiences without revealing sensitive, identifiable information.AI Highlights Human InconsistenciesImplementing AI often brings to light existing inconsistencies or unacknowledged issues within a company. For instance, AI might reveal discrepancies between official scripts and how top-performing human agents actually communicate, forcing companies to address these differences.Influence as a Key Human SkillIn a future increasingly shaped by AI, Sam Altman (via Alex) suggests that the ability to "influence" others will be a paramount human skill. This uniquely human trait will be vital, whether for interacting with other people or for guiding and shaping AI systems.Contact Information*   Regal AI: regal.ai*   Email: hello@regal.ai*   LinkedIn: www.linkedin.com/in/alexlevin1/

In this episode we are going to discuss Model Context Protocol (MCP) in context of Agentic architecture with Mona - Gen AI Specialist Solutions Architect at AWS.We will cover the challenges of implementing Agents and how MCP can help. We will try to simplify it with an example of MCP with Agents and further delve deep into LangGraph adapters such as FastMCP and FastAPI.You will also learn about implementing MCP architecture on AWS.Apart from her daily job, Mona has published two books Natural Language Processing with AWS AI Services: Derive strategic insights from unstructured data with Amazon Textract and Amazon Comprehend and Google Cloud Certified Professional Machine Learning Study Guide. She has authored multiple on AI/ML and cloud technology and a co-author on a research paper on CORD19 Neural Search. She is also a frequent speaker at multiple conferences such as ISMB 2022, AWS Re:Invent. Links https://www.amazon.com/Natural-Language-Processing-AWS-Services/dp/1801812535https://a.co/d/7rlkZwnBlog - Implement MCP in SageMaker AI https://aws.amazon.com/blogs/machine-learning/extend-large-language-models-powered-by-amazon-sagemaker-ai-using-model-context-protocol/Blog - Implement MCP with Bedrock Agents https://aws.amazon.com/blogs/machine-learning/harness-the-power-of-mcp-servers-with-amazon-bedrock-agents/AWS Hosts: Nolan Chen & Malini ChatterjeeEmail Your Feedback: rethinkpodcast@amazon.com

In this episode of AI + a16z, Sesame Cofounder and CTO Ankit Kumar joins a16z general partner Anjney Midha for a deep dive into the research and engineering behind their voice technology. They discuss the technical challenges of real-time speech generation, the trade-offs in balancing personality with efficiency, and why the team is open-sourcing key components of their model. Ankit breaks down the complexities of multimodal AI, full-duplex conversation modeling, and the computational optimizations that enable low-latency interactions. They also explore the evolution of natural language as a user interface and its potential to redefine human-computer interaction.Plus, we take audience questions on everything from scaling laws in speech synthesis to the role of in-context learning in making AI voices more expressive.Key Takeaways:How Sesame AI achieves natural voice interactions through real-time speech generation.The impact of open-sourcing their speech model and what it means for AI research.The role of full-duplex modeling in improving AI responsiveness.How computational efficiency and system latency shape AI conversation quality.The growing role of natural language as a user interface in AI-driven experiences.For anyone interested in AI and voice technology, this episode offers an in-depth look at the latest advancements pushing the boundaries of human-computer interaction.Learn more:The Maya + Miles demoCrossing the uncanny valley of conversational voiceSesame CSM 1B modelFollow everybody on X:Ankit KumarAnjney Midha Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

Dr. Emily Alsentzer joins hosts Raj Manrai and Andy Beam on NEJM AI Grand Rounds to discuss the evolution of natural language processing (NLP) in medicine. A Stanford faculty member and expert in clinical AI, Emily shares her journey from pre-med to biomedical AI, the role of language models in medical decision-making, and the ethical considerations surrounding bias in AI. The conversation explores everything from the early days of rule-based NLP to the modern era of large language models, the challenges of evaluating AI in clinical settings, and what the future holds for open-source medical AI. Transcript.

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

Next in Creator Media spoke with Paul Greenberg, CEO of Butterworks, on how his company uses AI to help brands make more successful social video content, and why so far, the technology has been a net positive.  Still, Greenberg talked about the dangers of the proliferation of AI slop and why it's going to become challenging for consumers and brands to sort through what's real, what's not, and what kind of attention is most valuable.

Tobi Lütke is the founder and CEO of Shopify, a $130 billion business that powers over 10% of all U.S. e-commerce. Starting as a snowboard shop in 2004, Shopify has become the leading commerce platform by consistently approaching problems differently. Tobi remains deeply technical, frequently coding alongside his team, and is known for his unique approach to leadership, product development, and company building. In our conversation, we discuss:• Why complexity kills entrepreneurship• How to develop and leverage your unique talent stack• How specifically Tobi approaches thinking from first principles• The importance of focusing on unquantifiable qualities like joy and delight• Why Tobi works backward from a 100-year vision• Why metrics should support decisions, not make them• The power of following your curiosity• What Tobi believes it takes to be a great product leader• Much more—Brought to you by:• Sinch—Build messaging, email, and calling into your product• Liveblocks—Ready-made collaborative features to drop into your product• Loom—The easiest screen recorder you'll ever use—Find the transcript at: https://www.lennysnewsletter.com/p/tobi-lutkes-leadership-playbook—Where to find Tobi Lütke:• X: https://x.com/tobi• LinkedIn: https://www.linkedin.com/in/tobiaslutke/• Website: https://tobi.lutke.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Welcome and introduction(04:17) The Tobi tornado(07:10) Maximizing human potential(11:05) Education and personal growth(16:47) Operating without KPIs(25:00) First-principles thinking(40:04) Remote work(45:59) Why Tobi never stopped coding(54:46) Embracing disagreement(01:01:27) The 100-year vision(01:09:29) Balancing tactics and positioning(01:17:15) Encouraging entrepreneurship(01:19:34) The power of good UX(01:28:42) The talent stack and unique opportunities(01:34:30) The role of passion in product development(01:36:39) Final thoughts and farewell—Referenced:• How Shopify builds a high-intensity culture | Farhan Thawar (VP and Head of Eng): https://www.lennysnewsletter.com/p/how-shopify-builds-a-high-intensity-culture-farhan-thawar• Breaking the rules of growth: Why Shopify bans KPIs, optimizes for churn, prioritizes intuition, and builds toward a 100-year vision | Archie Abrams (VP Product, Head of Growth at Shopify): https://www.lennysnewsletter.com/p/shopifys-growth-archie-abrams• The ultimate guide to performance marketing | Timothy Davis (Shopify): https://www.lennysnewsletter.com/p/performance-marketing-timothy-davis• Brandon Chu on building product at Shopify, how writing changed the trajectory of his career, the habits that make you a great PM, pros and cons of being a platform PM, how Shopify got through Covid: https://www.lennysnewsletter.com/p/brandon-chu-on-what-its-like-to-build• IRC: https://en.wikipedia.org/wiki/IRC• Goodhart's law: https://en.wikipedia.org/wiki/Goodhart%27s_law• Glen Coates on LinkedIn: https://www.linkedin.com/in/glcoates/• How Shopify builds product: https://www.lennysnewsletter.com/p/how-shopify-builds-product• The Last Dance on Netflix: https://www.netflix.com/title/80203144• Autoregressive Models for Natural Language Processing: https://medium.com/@zaiinn440/autoregressive-models-for-natural-language-processing-b95e5f933e1f• Archimedean property: https://en.wikipedia.org/wiki/Archimedean_property• Tabula rasa: https://en.wikipedia.org/wiki/Tabula_rasa• Daniel Weinand on LinkedIn: https://www.linkedin.com/in/danielweinand/• World of Warcraft: https://worldofwarcraft.blizzard.com• Harley Finkelstein on LinkedIn: https://www.linkedin.com/in/harleyf/• Monorepo: https://en.wikipedia.org/wiki/Monorepo• The Sarbanes Oxley Act: https://sarbanes-oxley-act.com/• Shopify builds Shopify Balance with Stripe to give small businesses an easier way to manage money: https://stripe.com/customers/shopify• Stanford marshmallow experiment: https://en.wikipedia.org/wiki/Stanford_marshmallow_experiment• Brian Armstrong on LinkedIn: https://www.linkedin.com/in/barmstrong/• We are the Web: https://link.wired.com/public/32945405—Recommended books:• Finite and Infinite Games: https://www.amazon.com/Finite-Infinite-Games-James-Carse/dp/1476731713• The Infinite Game: https://www.amazon.com/Infinite-Game-Simon-Sinek/dp/073521350X/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Já não faltavam evidências sobre os malefícios dos vapes e cigarros eletrônicos em 2019, quando falamos pela primeira vez sobre o assunto. Mas quais as novas evidências? E o seu consumo já se configura um problema de saúde pública?Este episódio é apresentado pela ACT Promoção da Saúde, organização não governamental que atua na promoção e defesa de políticas de saúde pública, especialmente nas áreas de controle do tabagismo, alimentação saudável, controle do álcool e atividade física. Esse trabalho é realizado por meio de ações de advocacy, que incluem incidência política, comunicação, mobilização, formação de redes e pesquisa, entre outras. Conheça mais em https://actbr.org.br/Confira o papo entre o leigo curioso, Ken Fujioka, e o cientista PhD, Altay de Souza.>> OUÇA (57min 41s)*Naruhodo! é o podcast pra quem tem fome de aprender. Ciência, senso comum, curiosidades, desafios e muito mais. Com o leigo curioso, Ken Fujioka, e o cientista PhD, Altay de Souza.Edição: Reginaldo Cursino.http://naruhodo.b9.com.br*APOIO: ACTEste episódio é apresentado pelo Sebrae Rio. Sabe aquelas pessoas que a gente admira pela criatividade, pela capacidade de liderar projetos ou de transformar ideias em realidade? Você pode ser uma dessas pessoas com o apoio do Sebrae Rio, desenvolvendo habilidades com a educação empreendedora, que não é só pra quem quer abrir um negócio: essas habilidades são superimportantes pra qualquer profissional.E se você é gestor ou professor de uma instituição de ensino, você pode levar a Educação Empreendedora para os seus alunos.É de graça e ainda emite certificado!Saiba mais, acessando atitude.sebraerj.com.br. E compartilhe suas habilidades empreendedoras nas redes sociais com a hashtag #TáNaSuaAtitude.Sebrae Rio: empreender tá na sua atitude.*REFERÊNCIASRethink Vape: Development and evaluation of a risk communication campaign to prevent youth E-cigarette usehttps://www.sciencedirect.com/science/article/pii/S0306460320307942?casa_token=stt22CU9-6AAAAAA:YPkZZ53Ftu3nkkekilolsWuJNKUbryiRjeLSIDReCt7I_VzpUe7m00pMu7x8ekXPen_tBRSmplYImpact of messages about scientific uncertainty on risk perceptions and intentions to use electronic vaping productshttps://www.sciencedirect.com/science/article/pii/S0306460318312140?casa_token=cLYGPqH_5ycAAAAA:ENqaVvNiFavJdpveZm6twD9JcfZP-EziEL0Vzt9gTE6wY4TLGguWJSDbG0-qZvIyTnMnkIyh3oIComics and Morals: Communicating the Risks of Vaping to Young Adults Through Moralized Graphic Comicshttps://www.proquest.com/openview/30046a092e0b52154768a5774baf4607/1?pq-origsite=gscholar&cbl=18750&diss=yHealth Messaging Strategies for Vaping Prevention and Cessation Among Youth and Young Adults: A Systematic Reviewhttps://www.tandfonline.com/doi/full/10.1080/10410236.2024.2352284Nicotina é até seis vezes maior em quem fuma cigarro eletrônico do que 20 cigarros comuns por diahttps://jornal.usp.br/ciencias/nicotina-e-ate-seis-vezes-maior-em-quem-fuma-cigarro-eletronico-do-que-20-cigarros-comuns-por-dia/Vaping in Youthhttps://jamanetwork.com/journals/jama/article-abstract/2822166?casa_token=AOMeZZluas0AAAAA:sLpdsaUTGQ6B9626AzCUq92sKEiOiQb4ZukceE2Z_lWxzYOfJ69UkK2sLlCNLiN9ulGOk1OzkJE&casa_token=j41vokSLcaUAAAAA:N7nCcnNEPuRTSdhY5abaMDWnmHMatAyw265mnYE3YUj1DOzb8Bt_VVuVMuPLwDh-amcoVdJ6_J8Trends in long term vaping among adults in England, 2013-23: population based studyhttps://www.bmj.com/content/386/bmj-2023-079016.shortA Systematic Review of Predictors of Vaping Cessation Among Young Peophttps://academic.oup.com/ntr/advance-article/doi/10.1093/ntr/ntae181/7717604A Vaping Cessation Text Message Program for Adolescent E-Cigarette Usershttps://jamanetwork.com/journals/jama/article-abstract/2822082?casa_token=jFrwYbTuE00AAAAA:cjSPTgP0FeIYTFS13Uli6akYcN37xjahDcnuCGSEXrgJQMpxExcD2GExrwPO4gNPdb2HqQ9Nyqc&casa_token=TrYwGgae_xEAAAAA:XCLLhI4Ku1KjcxxJ1tIi74OJmwW2Y1eNjq60LVYbJ7B8M2TNh7GPdQwBQIBjDefqVwlkmcaW7TUSmoking and vaping alter genes related to mechanisms of SARS-CoV-2 susceptibility and severity: a systematic review and meta-analysishttps://publications.ersnet.org/content/erj/64/1/2400133.abstractThe Impact of Vaping on the Ocular Surface: A Systematic Review of the Literaturehttps://www.mdpi.com/2077-0383/13/9/2619Drug Use Frequency Variation and Mental Health During the COVID-19 Pandemic: an Online Surveyhttps://pmc.ncbi.nlm.nih.gov/articles/PMC8404543/Vaping among adults in England who have never regularly smoked: a population-based study, 2016–24https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(24)00183-X/fulltextAssociation of vaping with respiratory symptoms in U.S. young adults: Nicotine, cannabis, and dual vapinghttps://www.sciencedirect.com/science/article/pii/S009174352400330XTobacco Harm Reduction: The Industry's Latest Trojan Horse?https://exposetobacco.org/wp-content/uploads/tobacco-harm-reduction-cop10.pdfU.S. retail sales data show 86% of e-cigarette sales are for illegal productshttps://truthinitiative.org/research-resources/tobacco-industry-marketing/us-retail-sales-data-show-86-e-cigarette-sales-are?trk=feed_main-feed-card_feed-article-contentThe Normalization of Vaping on TikTok Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis: Mixed Methods Studyhttps://www.jmir.org/2024/1/e55591/From Smoking to Vaping: The Motivation for E-Cigarette Use at the Neurobiological Level – An fMRI Studyhttps://academic.oup.com/ntr/advance-article/doi/10.1093/ntr/ntae273/7906109Vaping and Smoking Cue Reactivity in Young Adult Nonsmoking Electronic Cigarette Users: A Functional Neuroimaging Studyhttps://academic.oup.com/ntr/advance-article-abstract/doi/10.1093/ntr/ntae257/7863347Impact of Electronic Cigarettes on the Cardiovascular Systemhttps://pmc.ncbi.nlm.nih.gov/articles/PMC5634286/Naruhodo #207 - Vape e cigarro eletrônico são seguros? (2019)https://www.youtube.com/watch?v=Raa9CUrIFbsNaruhodo #85 - Por que é tão difícil parar de fumar?https://www.youtube.com/watch?v=SPkIT0ehoisNaruhodo #49 - O que causa o vício?https://www.youtube.com/watch?v=--Z_ylPXIWcNaruhodo #94 - O que é o Teorema de Bayes? (E o que horóscopo tem a ver com isso?)https://www.youtube.com/watch?v=BE5fpsfPerwNaruhodo #328 - Existem "gatilhos mentais"?https://www.youtube.com/watch?v=fxBQJlin8Z4Naruhodo #419 - Maconha faz mal? - Parte 1 de 2https://www.youtube.com/watch?v=cvLTh2bKPiQNaruhodo #420 - Maconha faz mal? - Parte 2 de 2https://www.youtube.com/watch?v=F7wVcGvpoGANaruhodo #267 - O que é dissonância cognitiva?https://www.youtube.com/watch?v=1xJwqmir5UwNaruhodo #268 - O que é dissonância cognitiva? - Parte 2 de 2https://www.youtube.com/watch?v=--OHlHmOQTM*APOIE O NARUHODO PELA PLATAFORMA ORELO!O podcast Naruhodo está no Orelo: bit.ly/naruhodo-no-oreloE é por meio dessa plataforma de apoio aos criadores de conteúdo que você ajuda o Naruhodo a se manter no ar.Você escolhe um valor de contribuição mensal e tem acesso a conteúdos exclusivos, conteúdos antecipados e vantagens especiais.Além disso, você pode ter acesso ao nosso grupo fechado no Telegram, e conversar comigo, com o Altay e com outros apoiadores.E não é só isso: toda vez que você ouvir ou fizer download de um episódio pelo Orelo, vai também estar pingando uns trocadinhos para o nosso projeto.Então, baixe agora mesmo o app Orelo no endereço Orelo.CC ou na sua loja de aplicativos e ajude a fortalecer o conhecimento científico.bit.ly/naruhodo-no-orelo

Imagine standing at a crossroads, juggling countless possibilities yet needing to choose just one path.That's what most early-stage founders struggle with. And for me, that's picking the right course towards the ever-elusive Product-Market fit.Today, I'll share how I tackle this challenge and what I do to show my best customers the highest possible value of my product as early as possible.This episode is sponsored by Paddle.com — if you're looking for a payment platform that works for you so you can focus on what matters, check them out.The blog post: https://thebootstrappedfounder.com/product-market-fit-time-to-first-value/The podcast episode: https://tbf.fm/episodes/360-product-market-fit-time-to-first-valueCheck out Podscan to get alerts when you're mentioned on podcasts: https://podscan.fmSend me a voicemail on Podline: https://podline.fm/arvidYou'll find my weekly article on my blog: https://thebootstrappedfounder.comPodcast: https://thebootstrappedfounder.com/podcastNewsletter: https://thebootstrappedfounder.com/newsletterMy book Zero to Sold: https://zerotosold.com/My book The Embedded Entrepreneur: https://embeddedentrepreneur.com/My course Find Your Following: https://findyourfollowing.comHere are a few tools I use. Using my affiliate links will support my work at no additional cost to you.- Notion (which I use to organize, write, coordinate, and archive my podcast + newsletter): https://affiliate.notion.so/465mv1536drx- Riverside.fm (that's what I recorded this episode with): https://riverside.fm/?via=arvid- TweetHunter (for speedy scheduling and writing Tweets): http://tweethunter.io/?via=arvid- HypeFury (for massive Twitter analytics and scheduling): https://hypefury.com/?via=arvid60- AudioPen (for taking voice notes and getting amazing summaries): https://audiopen.ai/?aff=PXErZ- Descript (for word-based video editing, subtitles, and clips): https://www.descript.com/?lmref=3cf39Q- ConvertKit (for email lists, newsletters, even finding sponsors): https://convertkit.com?lmref=bN9CZw

Stephen Solka, CTO and co-founder of Standd.io, joins Elixir Wizards Owen and Charles to share the journey of building an AI-native deal intelligence and due diligence platform. Designed to streamline document analysis and text generation for venture capital firms, Standd.io leverages large language models and AI tools to address key customer pain points in document workflows. Stephen explains how Elixir and Phoenix LiveView enabled rapid UI iteration and seamless integration between the front-end and back-end. The conversation also explores the human side of startup life. Stephen reflects on balancing tech debt with customer demands, the value of accelerators in building networks and securing funding, and the challenges of pricing in early-stage startups. He emphasizes the importance of validating ideas with potential customers and learning from the hurdles of growing a business. Tune in for insights on leveraging AI in Elixir, solving real-world problems, and navigating the journey from concept to company. Topics discussed in this episode: The journey from self-taught programmer to CTO The perks of Phoenix LiveView for rapid UI development Integrating front-end and back-end technologies AI tools for code generation How early adopters balance functionality with product polish Validating ideas and understanding customer needs The impact of accelerators on networking and fundraising Approaches to managing pricing strategies for startups Balancing technical debt with feature development The role of telemetry and error reporting in product development Creating collaborative and supportive tech communities Educating users on AI's capabilities and limitations The broader implications of AI tools across industries Links Mentioned Contact Stephen & Julie at Standd: founders@standd.io https://www.standd.io/ https://www.digitalocean.com/community/tutorials/gangs-of-four-gof-design-patterns https://www.thriftbooks.com/w/code-completesteve-mcconnell/248753/item/15057346/ https://aws.amazon.com/sagemaker/ https://www.anthropic.com/ https://getoban.pro/ https://kubernetes.io/ https://www.apollographql.com/ https://aws.amazon.com/startups/accelerators https://accelerate.techstars.com/ https://aider.chat/ https://github.com/Aider-AI/aider https://neovim.io/ https://ui.shadcn.com/ https://tailwindui.com/ https://www.ycombinator.com/ https://www.thriftbooks.com/w/close-to-the-machine-technophilia-and-its-discontentsellen-ullman/392556 Special Guest: Stephen Solka.

Co-hosts Mark Thompson and Steve Little discuss how Anthropic's Claude 3.5 Sonnet upgrade has made Claude an even better AI writer. This make's it even easier to write a great research report. They move on to explore OpenAI's new desktop apps and Advanced Voice Mode, discussing how voice interaction could transform genealogical research, particularly for those who prefer speaking to typing. Combining search and AI in the same tool will be a huge timesaver for genealogy research. This week's Tip of the Week reveals the best ways to leverage free AI tools for family history research. There's no need to pay for AI tools if you only need these features a few times a day.In RapidFire, they examine the future of AI agents, Apple's cautious AI rollout in iOS 18.1, and Meta's strategic content partnership with Reuters.Timestamps:In the News:02:20  Claude 3.5 Sonnet (new): AI Writing Reaches New Heights06:50 ChatGPT Desktop Apps: Now Available for Both Macs and PCs14:00 AI Search Gets Real: AI-Enhanced Search From ChatGPTTip of the Week:19:20 Free AI Tools: How To Get Premium AI Results For FreeRapidFire:26:50 AI Agents: The Future of Computer Interaction31:20 Apple Intelligence: IOS 18.1 Starts the Rollout of AI for Apple40:26 Meta's Reuters Deal: The First (of Many?) Content Partnerships for FacebookResource Links:ANTHROPICClaude 3.5 Sonnethttps://www.anthropic.com/claude/sonnetProjects featurehttps://www.anthropic.com/news/projectsOPENAIChatGPThttps://chatgpt.com/Desktop Appshttps://openai.com/chatgpt/desktop/Advanced Voice Modehttps://help.openai.com/en/articles/8400625-voice-mode-faqGPT Searchhttps://openai.com/index/introducing-chatgpt-search/APPLEiOS 18.1https://www.apple.com/ca/newsroom/2024/10/apple-intelligence-is-available-today-on-iphone-ipad-and-mac/Apple Intelligencehttps://www.apple.com/apple-intelligence/Sirihttps://www.apple.com/siri/Apple Photo Searchhttps://9to5mac.com/2024/09/25/photos-search-in-ios-181-actually-works-thanks-to-apple-intelligence/METAMeta AIhttps://ai.meta.com/meta-ai/Reuters Partnershiphttps://www.reuters.com/technology/artificial-intelligence/meta-platforms-use-reuters-news-content-ai-chatbot-2024-10-25/MISCPerplexityhttps://www.perplexity.aiMicrosoft Copilothttps://www.microsoft.com/microsoft-365/copilotTags:Artificial Intelligence, Technology, Cloud Computing, Machine Learning, Family History, Genealogy, AI, Mobile Technology, Natural Language Processing, Large Language Models, Generative AI, AI Search, Voice Assistants, AI Agents, Research Tools, Content Creation, Digital Writing, Document Analysis, Privacy, Data Protection, AI Ethics, Claude 3.5 Sonnet, ChatGPT, Apple Intelligence, Meta AI, Perplexity, Microsoft Copilot, iOS 18.1, Siri, AI Writing, Desktop Applications, Mobile Apps, Photo Organization, Email Tools, Content Partnerships, Source Citation, Voice Interaction, User Experience, Cloud Services, Free Tools, Premium Features, Tool Comparison, Workflow Optimization, Family Research, Genealogy Tools, Research Reports, Narrative Writing, Genealogists, Family Historians, Tech Writers, Researchers, Digital Creators

Angel Vossough, the brains behind BetterAI, is shaking up the AI game by focusing on making wine searches and recommendations more personalized through her cool project, VinoVoss. Angel and the team blend their skills and vast wine knowledge to craft an AI solution that opens the gateway for everyone to become a better wine expert as well as guide choices to lesser known quality brands. A big advocate for diversity and inclusivity, Angel also discusses the significance of diverse AI training data and backs initiatives like "returnship" to empower women in tech, ensuring their perspectives shape more effective AI systems. So pour your favorite red, white or sparkling beverage and take in this episode. Highlights of our conversation: - BetterAI is transforming the wine industry by using data science and AI to simplify wine selection through their VinoVoss project, ensuring a comprehensive and unbiased shopping experience for consumers. - The Smart Sommelier feature partners with wine shops to offer personalized wine recommendations through conversational AI, allowing customers to make purchases directly through the platform for a seamless experience. - Angel stresses the importance of diverse perspectives, including women's opinions, in training data for AI systems to create inclusive and effective solutions that cater to diverse consumer needs. - BetterAI's focus on high-quality data is evident in their manual review process involving 37,000 wine experts, ensuring precise recommendations and simplifying the wine selection process for consumers. Angel Vossough is the CEO and Co-Founder of BetterAI, a Silicon Valley-based AI service provider headquartered in Silicon Valley. The company is uniquely leveraging advanced AI technologies such as Machine Learning, Generative AI, Natural Language Processing, and Computer Vision to create this transformative solution that is revolutionizing the relationship between wine and digital platforms. She is also Co-Founder & Managing Partner at Caspian Capital, an early-stage investment firm focusing on deep tech, biotech, and AI; and was Co- Founder of OpenCovidScreen, a non-profit focused on driving innovation in low-cost, accessible COVID-19 testing. In her role as BetterAI CEO, and with a strategic focus on VinoVoss as one of its primary products, Angel oversees the direction and growth of the company's innovative AI applications in the wine industry. This includes setting the overall strategic direction for BetterAI and VinoVoss; ensuring company objectives align with market needs and her company's vision; building and maintaining relationships with key stakeholders, partners, and investors to support and advance BetterAI's business goals; and ensuring the alignment of VinoVoss's development with BetterAI's broader technological advancements and business strategy. Connect with Angel: LinkedIn: https://www.linkedin.com/in/vossough/ Website: http://www.vinovoss.com Website: https://www.betterai.io Connect with Allison: Feedspot has named Disruptive CEO Nation as one of the Top 25 CEO Podcasts on the web and it is ranked the number 10 CEO podcast to listen to in 2024! https://podcasts.feedspot.com/ceo_podcasts/ LinkedIn: https://www.linkedin.com/in/allisonsummerschicago/ Website: https://www.disruptiveceonation.com/ Twitter: @DisruptiveCEO #CEO #brand #startup #startupstory #founder #business #businesspodcast #podcast Learn more about your ad choices. Visit megaphone.fm/adchoices

I have another amazing CRE guest using AI to help real estate pros up their game to introduce you to this week, Thomas Foley, co-founder and CEO of Archer.re   If you're tired of the long, slow grind of underwriting deals, you'll love this episode.   Archer.re is leveraging AI in the deal underwriting process, taking it from taking hours down to just a few minutes.   Imagine pulling in all your data, dropping it into your model (yes, your own model – no forced software here), and watching Archer make it happen.   Whether you're in multifamily or another asset class, Thomas's platform will open your eyes to what's possible.   Plus, you'll see how Archer is using AI to automate not just underwriting but also deal sourcing.   Picture this: predictive factors alerting you when an off-market property is likely to hit the market before it's listed.   Skeptical? Well, see how Archer has clients like Marcus & Millichap and Starwood using the platform.   Here's what Archer.re helps you do: Data-Driven Insights: Access deep, AI-powered analysis for smart property and investment decisions. Market Trends & Predictions: Stay ahead of trends with advanced forecasting for commercial real estate (CRE) markets. Automated Valuations: Get instant, accurate valuations on target assets, saving you time and increasing precision. Portfolio Optimization: Manage, track, and optimize your own assets in real time. Streamlined Decision-Making: Make faster, data-backed investment decisions with customizable reports and real-time analytics. Archer is another company that will cause you to rethink everything you know about deal analysis – and help you find, evaluate, and buy deals before they even come to market. ***** The only Podcast you need on for raising capital in real estate - enhanced by AI.   Learn how other real estate pros are using AI to get ahead of their competition.   Get early notice of hot new game-changing AI real estate apps.   Walk away with something you can actually use in every episode.   PLUS, subscribe to my free newsletter and get: • practical guides, • how-to's, and • news updates Get immediately actionable strategies and tools to raise more capital, connect with accredited investors, and scale faster—enhanced by AI to stay ahead of the competition. All in just 5 minutes, for free. https://gowercrowd.com/subscribe

Angel Vossough is the CEO and Co-Founder of BetterAI, a Silicon Valley-based AI service provider headquartered in Silicon Valley. The company is uniquely leveraging advanced AI technologies such as Machine Learning, Generative AI, Natural Language Processing, and Computer Vision to create this transformative solution that is revolutionizing the relationship between wine and digital platforms. She is also Co-Founder & Managing Partner at Caspian Capital, an early-stage investment firm focusing on deep tech, biotech, and AI; and was Co-Founder of OpenCovidScreen, a non-profit focused on driving innovation in low-cost, accessible COVID-19 testing.Angel, an esteemed data scientist, holds dual bachelor's degrees in Mathematics and Computer Engineering, as well as master's degrees in Software Engineering from San Jose State University, and Data Science from UC Berkeley where she graduated with honors. Angel was previously a Senior Network Engineer at Cisco Systems, specializing in Network Architecture for major telecommunications companies including Verizon Wireless.BetterAI's products include “VinoVoss” (www.VinoVoss.com), a semantic search and recommendation system creating a virtual wine sommelier, and “BetterMed,” a generative AI-based medical diagnostic assistant. Angel is a technology leader and serial entrepreneur. She also founded DiverseUp, a public-benefit corporation building a professional community for technical & scientific women. In her role as BetterAI CEO, and with a strategic focus on VinoVoss as one of its primary products, Angel oversees the direction and growth of the company's innovative AI applications in the wine industry. This includes setting the overall strategic direction for BetterAI and VinoVoss; ensuring company objectives align with market needs and her company's vision; building and maintaining relationships with key stakeholders, partners, and investors to support and advance BetterAI's business goals; and ensuring the alignment of VinoVoss's development with BetterAI's broader technological advancements and business strategy.Listen to this informative Sharkpreneur episode with Angel Vossough about transforming wine selection with natural language processing. Here are some of the beneficial topics covered on this week's show:- How women's needs in the workplace are dynamic and change with different life stages. - How BetterAI aims to make win accessible to all by simplifying the wine selection process.- Why BetterAI focuses on specific datasets instead of using large language models. - How regulation, ethical AI, user privacy, and data privacy is vital in AI development. - How entrepreneurs should focus on solving real problems, hiring smarter people, and maintaining agility and flexibility. Connect with Angel:Guest Contact InfoLinkedInlinkedin.com/company/betterai-ioLinks Mentioned:betterai.io Learn more about your ad choices. Visit megaphone.fm/adchoices

Angel Vossough is the CEO and Co-Founder of BetterAI, a Silicon Valley-based AI service provider headquartered in Silicon Valley. The company is uniquely leveraging advanced AI technologies such as Machine Learning, Generative AI, Natural Language Processing, and Computer Vision to create this transformative solution that is revolutionizing the relationship between wine and digital platforms. She is also Co-Founder & Managing Partner at Caspian Capital, an early-stage investment firm focusing on deep tech, biotech, and AI; and was Co-Founder of OpenCovidScreen, a non-profit focused on driving innovation in low-cost, accessible COVID-19 testing. Angel, an esteemed data scientist, holds dual bachelor's degrees in Mathematics and Computer Engineering, as well as master's degrees in Software Engineering from San Jose State University, and Data Science from UC Berkeley where she graduated with honors. Angel was previously a Senior Network Engineer at Cisco Systems, specializing in Network Architecture for major telecommunications companies including Verizon Wireless. BetterAI's products include “VinoVoss” (www.VinoVoss.com), a semantic search and recommendation system creating a virtual wine sommelier, and “BetterMed,” a generative AI-based medical diagnostic assistant. Angel is a technology leader and serial entrepreneur. She also founded DiverseUp, a public-benefit corporation building a professional community for technical & scientific women. In her role as BetterAI CEO, and with a strategic focus on VinoVoss as one of its primary products, Angel oversees the direction and growth of the company's innovative AI applications in the wine industry. This includes setting the overall strategic direction for BetterAI and VinoVoss; ensuring company objectives align with market needs and her company's vision; building and maintaining relationships with key stakeholders, partners, and investors to support and advance BetterAI's business goals; and ensuring the alignment of VinoVoss's development with BetterAI's broader technological advancements and business strategy. Listen to this informative Sharkpreneur episode with Angel Vossough about transforming wine selection with natural language processing. Here are some of the beneficial topics covered on this week's show: - How women's needs in the workplace are dynamic and change with different life stages. - How BetterAI aims to make win accessible to all by simplifying the wine selection process. - Why BetterAI focuses on specific datasets instead of using large language models. - How regulation, ethical AI, user privacy, and data privacy is vital in AI development. - How entrepreneurs should focus on solving real problems, hiring smarter people, and maintaining agility and flexibility. Connect with Angel: Guest Contact Info LinkedIn linkedin.com/company/betterai-io Links Mentioned: betterai.io Learn more about your ad choices. Visit megaphone.fm/adchoices