Podcasts about natural language processing nlp

In dieser Folge habe ich, Christian, mit Sebastian Maier über die Rolle von Künstlicher Intelligenz (KI) in der modernen Geschäftswelt gesprochen – und es wurde richtig spannend!

Welcome to AI Lawyer Talking Tech, your weekly deep dive into the transformative power of artificial intelligence and technology within the legal profession. The legal landscape is undergoing significant changes as AI becomes increasingly integrated into workflows, from streamlining contract analysis and automating record retrieval to revolutionizing medical malpractice litigation and enhancing patent portfolio strategies. While AI offers the potential for greater efficiency, improved accuracy, and deeper insights, its rise also brings crucial discussions about ethical implementation, developing comprehensive policies, and addressing potential impacts on jobs, including headcount reductions in in-house legal teams. We're seeing the emergence of new tools and approaches, like meta-agents, the strategic blending of Large Language Models (LLMs), Small Language Models (SLMs), and Natural Language Processing (NLP) for responsible applications, and innovative services like AI-powered transcript summaries. Join us as we explore these developments, delve into the practical challenges and opportunities, and hear from experts on how legal professionals can effectively adopt and leverage technology to maintain a competitive edge and drive growth while navigating this evolving digital era.Open New Doors at CGI 2025: What Not to Miss in Las Vegas30 Apr 2025ContractPodAiLegal Transformation on Wheels: The Power of VIN Decoder Technology in the Automotive Industry02 May 2025Lawyer MonthlyWho Should Benefit from AI in Depositions: The Client, the Law Firm, or Both?02 May 2025LexologyFFO Feat: LI New York, AI Impacts, Denmark + More02 May 2025Artificial LawyerFFO Feat: LI New York, AI Impacts, Denmark + More02 May 2025Artificial LawyerMeet The New + Improved LegalSifter02 May 2025Artificial LawyerNavigating the Rise in Data Subject Access Requests02 May 2025Ogletree DeakinsOne Big Thought – Charting a Human-Centered Future in the Age of Artificial Intelligence: Part Six02 May 2025Morris, Manning & Martin,LLPStop Treating Supply Chain Contracts as Legal Documents. They're Business Processes01 May 2025ITSupplyChain.comAI on Trial: The New York Times sues OpenAI and Microsoft01 May 2025LexologyDon't watermark your legal PDFs with purple dragons in suits01 May 2025ArsTechnicaTechnology's Role in Streamlining Medical Malpractice Legal Workflows01 May 2025Legal ReaderLawDroid Founder Tom Martin on Building, Teaching and Advising About AI for Legal01 May 2025LawSitesNAEGELI Transcript Summaries: A Smarter Way to Prepare for Your Case01 May 2025Crwe World2025 Best & Brightest MBA: Min Kyung LEE, National University of Singapore01 May 2025Poets&QuantsLaw Firms Keep Buying Amazing Tech… Lawyers Keep Not Using It01 May 2025Above The Law#LMA25: Harnessing AI For Cross-Selling: Don't Miss An Opportunity For Growth01 May 2025Nancy Myrland's Legal Marketing BlogCleary Gottlieb to roll out Legora across the firm01 May 2025Legal Technology InsiderLegal Tech Adoption: The Slow Burn Of Cloud, The Sudden Spark Of AI01 May 2025Above The LawState Privacy Law Enforcement Coordination - Cookie Banners in the Crosshairs01 May 2025JD SupraHow Brandon Harter Built a Profitable Firm in Half the Time with Lawyerist Lab01 May 2025LawyeristLaw Society Wales urges Welsh Government to expand legal apprenticeships01 May 2025Pembrokeshire HeraldManchester agency teams up with Glaisyers for the launch of a new AI policy service01 May 2025Prolific NorthProtecting businesses in the absence of UK AI legislation01 May 2025Legal Futures7 Crucial Legal Challenges Fintech Law Firms in Vietnam Can Help You Overcome for Business Success01 May 2025LexologyHalf of people would trust AI to help write their will, survey finds01 May 2025Today's Wills & ProbateThe Role of AI & Technology in Record Retrieval01 May 2025JD Supra

AI is transforming the mortgage industry in several ways, making processes faster, more efficient, and more customer-friendly. Here are some key impacts:1. Streamlining Loan Origination & UnderwritingAI-powered algorithms can quickly analyze an applicant's financial history, credit score, and risk factors, reducing the time it takes to approve loans.Machine learning models can assess alternative data (such as rental payment history and utility bills) to approve borrowers who may not have traditional credit histories.Automated underwriting systems can detect inconsistencies or potential fraud more effectively than manual review.2. Enhancing Customer ExperienceAI-driven chatbots and virtual assistants provide instant answers to mortgage-related questions, guiding customers through the application process 24/7.Personalized recommendations based on a borrower's financial profile help customers find the best mortgage products.3. Improving Risk Assessment & Fraud DetectionAI can analyze vast amounts of data to detect patterns indicative of fraud, such as falsified documents or identity theft.Predictive analytics help lenders anticipate potential loan defaults, allowing for proactive risk mitigation.4. Automating Document ProcessingOptical Character Recognition (OCR) and Natural Language Processing (NLP) enable AI to scan, extract, and verify information from documents like pay stubs, tax returns, and bank statements.This automation reduces manual errors and speeds up the mortgage approval timeline.5. Enhancing Regulatory ComplianceAI helps mortgage lenders stay compliant with regulations by continuously monitoring transactions and flagging potential compliance risks.Automated reporting tools simplify the audit process, ensuring transparency and reducing human error.6. Market Insights & Pricing OptimizationAI analyzes real estate market trends, interest rates, and borrower behavior to help lenders set competitive mortgage rates.Predictive analytics help lenders anticipate market shifts and adjust strategies accordingly.7. Expanding Access to HomeownershipAI-driven alternative credit scoring models provide more opportunities for individuals with non-traditional credit backgrounds to qualify for mortgages.More inclusive lending practices can help close homeownership gaps for underserved communities.Challenges & ConcernsWhile AI brings efficiency, there are some concerns:Bias in Algorithms: AI models may unintentionally reinforce biases if they are trained on biased historical data.Data Privacy: The increased use of AI requires stronger data protection measures to prevent breaches.Human Oversight: AI should complement, not replace, human decision-making to ensure fairness and accuracy.Overall, AI is reshaping the mortgage industry by making it more efficient, customer-friendly, and data-driven. However, balancing innovation with ethical considerations remains crucial.Are you exploring AI for a mortgage-related business, or just interested in how it's evolving?tune in and learn at https://www.ddamortgage.com/blogdidier malagies nmls#212566dda mortgage nmls#324329 Support the show

The EU-DREAM project, a European project dedicated to pioneering next-generation energy services, solutions, and products that genuinely benefit consumers, is launching a survey to explore consumer needs, motivations, and challenges related to the flexibility and controllability of household appliances. This feedback is vital in shaping innovative, consumer-driven energy solutions. EU-DREAM focuses on improving consumer interaction with the energy market. It aims to simplify complex energy management processes and enhance customer awareness, trust, and confidence through the introduction of an Artificial Intelligence (AI)-based assistant and a Natural Language Processing (NLP) intermediary. "At EU-DREAM, we believe that the key to a successful energy transition lies in understanding and addressing consumer needs. This survey is a crucial step in gathering the insights necessary to develop smarter, more consumer-focused energy solutions," said João Catalão, full professor, Faculty of Engineering at the University of Porto (Portugal) and project coordinator at EU-DREAM. "Your participation will help us create solutions that not only simplify energy management but also build trust and confidence among consumers." Why Participate in the survey? Influence Research: Insights will contribute to the EU-DREAM project, impacting scientific papers, project deliverables, and public communications. Anonymity Guaranteed: Responses will remain anonymous - no personal data will be disclosed. Quick and Easy: The survey takes only 15 minutes to complete! What to Expect in the Survey? The questionnaire is divided into three main sections: Personal/Demographic Information: To understand consumer backgrounds. Appliances' Controllability: How consumers interact with and manage their household appliances. Barriers and Enablers: Factors that support or hinder participation in energy flexibility solutions. The survey is available in nine different languages (English, Danish, Dutch, Finnish, French, Greek, Italian, Portuguese, Spanish) at https://www.surveymonkey.com/r/eudream?lang=en. For more details about the EU-DREAM project, visit eu-dream.eu. For questions about the survey, please contact info@eu-dream.eu.

Sumedha Rai is an experienced AI strategist, thought leader, and data scientist with a background in computer science, economics, and finance, who is known for bridging the gap between academic research and real-world applications in industries like fintech and healthcare. She has a strong foundation in both the theoretical and practical aspects of AI, with a focus on Natural Language Processing (NLP), and is passionate about deploying AI ethically to address societal challenges. Her work includes creating AI solutions for fraud prevention, bias detection, and improving patient outcomes, while also sharing her expertise through speaking, writing, and teaching.In this thought-provoking episode of About That Wallet, host Anthony Weaver engages with Sumedha Rai, a dynamic expert at the intersection of finance and artificial intelligence. Together, they explore the transformative impact of AI on the workforce and how individuals can navigate this rapidly evolving landscape. Sumedha Rai shares her insights on the importance of understanding technology, emphasizing that knowledge is the key to alleviating fears surrounding job security in the age of automation.Listeners will gain valuable perspectives on how to upskill and adapt to changes in their respective fields, with practical advice on utilizing AI tools to enhance productivity and streamline repetitive tasks. Samita highlights the significance of research and self-education, encouraging everyone to take proactive steps in their financial and professional journeys.The conversation also delves into the ethical considerations of AI, particularly regarding bias in data and decision-making processes. Sumedha underscores the necessity of inclusive data representation and the critical role of human oversight in AI applications, especially in areas like credit decisions and healthcare.As the episode concludes, Sumedha reflects on her personal journey and the importance of having meaningful conversations about money and technology. She inspires listeners to embrace change and seek out opportunities for growth, reminding us that wealth is not just about financial assets but also the skills and knowledge we acquire along the way.

Information virality is an increasingly important topic in modern media environments, but it often remains overlooked in discussions about information security. This presentation will explain why information virality is a cybersecurity concern and how it can be exploited to manipulate public discourse. By utilizing theories from prominent cultural psychologists and employing natural language processing techniques, we will demonstrate methods for capturing viral discourse and identifying additional features linked to behavioral patterns that may motivate participation in discussions. This talk will focus solely on the methodology and our preliminary findings, as the research is still ongoing. About the speaker: Nick Harrell has served in the military for 18 years. Currently, he works as a data systems engineer, where he designs, builds, and maintains complex data systems that help Army leaders make informed decisions. He is on a fellowship at Purdue University, pursuing a Ph.D. in Information Security. Nick is a member of the International Information System Security Certification Consortium (ISC2) and the Project Management Institute (PMI). His research interests focus on Natural Language Processing (NLP) for Information Assurance, specifically on mechanisms that enhance user engagement in online public discourse.

In today's episode, you will learn a series of vocabulary words that are connected to a specific topic. This lesson will help you improve your ability to speak English fluently about a specific topic. It will also help you feel more confident in your English abilities.5 Vocabulary WordsPersonal Assistant (noun): An AI-driven tool or application designed to help manage tasks, schedule appointments, and provide information, often through voice commands. Example Sentences: Virtual personal assistants like Siri and Alexa help users with tasks such as setting reminders and answering questions.She relies on her personal assistant app to organize her daily schedule and send reminders for important events.The personal assistant's voice recognition capabilities make it easy to interact with and manage various tasks hands-free.Predictive Analytics (noun): The use of statistical algorithms and machine learning techniques to analyze historical data and predict future outcomes or trends.Example Sentences: Predictive analytics helps businesses forecast sales and optimize inventory management based on past data.The app uses predictive analytics to recommend products that you are likely to buy based on your purchase history.By employing predictive analytics, the healthcare system can anticipate patient needs and improve care delivery.Natural Language Processing (NLP) (noun): A field of AI that focuses on the interaction between computers and human language, enabling machines to understand, interpret, and respond to text or speech.Example Sentences: Natural Language Processing is used in translation services to convert text from one language to another.The voice-activated assistant relies on NLP to understand and respond to user commands effectively.Advances in NLP have improved the accuracy of sentiment analysis in customer feedback and social media.Facial Recognition (noun): A biometric technology that uses AI to identify or verify individuals by analyzing facial features and patterns.Example Sentences: Facial recognition technology is commonly used in smartphones to provide secure and convenient access to devices.The security system employs facial recognition to monitor and manage access to restricted areas.Facial recognition can enhance customer service by personalizing interactions based on recognized individuals.Robotic Process Automation (RPA) (noun): The use of AI and software robots to automate repetitive, rule-based tasks typically performed by humans in business processes. Example Sentences: Robotic Process Automation has transformed financial services by automating tasks like data entry and processing transactions.The company implemented RPA to streamline administrative tasks, reducing the time and effort required for manual work.RPA tools can handle routine tasks efficiently, allowing employees to focus on more strategic and creative activities.A Paragraph using the 5 vocabulary wordsArtificial intelligence has seamlessly integrated into our daily lives, transforming how we interact with technology. From personal assistants like Siri and Alexa facilitating our routines to facial recognition unlocking smartphones, AI is pervasive. Behind the scenes, robotic process automation streamlines tasks, while predictive analytics powers personalized recommendations on everything from movies to shopping. Natural language processing

Zukunftstrends KI 2025 Shownotes In Episode 867 von TomsTalkTime – DER Erfolgspodcast tauchen wir tief in die Zukunft der KI ein und erkunden die wichtigsten Trends für 2025. Erfahre, wie du KI-gestützte Innovationen optimal für dein Unternehmen nutzen kannst, um an der Spitze zu bleiben und von Best Practices erfolgreicher Unternehmen zu lernen. Diese Episode bietet dir wertvolle Insights, um deine Strategie für das kommende Jahr KI-fit zu machen. Zusammenfassung und Stichpunkte In dieser Episode sprechen wir über: Warum KI-basierte Innovationen/ Zukunftstrends KI 2025 entscheidend sind: In einem sich schnell entwickelnden Marktumfeld sind KI-Innovationen ein Schlüsselfaktor, um flexibel und wettbewerbsfähig zu bleiben. Die bedeutendsten Zukunftstrends KI 2025: Von maschinellem Lernen über Natural Language Processing bis hin zu Fortschritten in der Bild- und Spracherkennung. Strategien zur Integration von KI in Unternehmensprozesse: Wie du KI schrittweise in bestehende Abläufe einbindest und dabei dein Team erfolgreich einbeziehst. Best Practices von Unternehmen: Wie Amazon und andere Marktführer KI bereits effektiv nutzen, um innovativ und kundenorientiert zu agieren. Diese und weitere Themen helfen dir dabei, den Wettbewerbsvorteil von KI zu verstehen und für dein Unternehmen nutzbar zu machen. Zukunftstrends KI 2025 - In dieser Episode geht es darum, wie Unternehmen die neuesten Entwicklungen und Innovationen in der Künstlichen Intelligenz optimal für sich nutzen können, um erfolgreich zu bleiben. Die Episode liefert nicht nur einen Überblick über die neuesten Technologien, sondern zeigt auch konkrete Schritte, wie man KI effektiv in Unternehmensprozesse integrieren kann. Warum KI-gestützte Innovationen entscheidend sind: Der Einsatz von KI wird für Unternehmen immer wichtiger, um den steigenden Erwartungen der Kunden gerecht zu werden und sich gegen die Konkurrenz zu behaupten. KI bietet einzigartige Möglichkeiten, um Märkte in Echtzeit zu analysieren und auf Trends blitzschnell zu reagieren. Diese Vorteile sind besonders für Unternehmen entscheidend, die eine starke Position im Wettbewerb halten oder ausbauen möchten. Wichtige KI-Technologien, die 2025 die Unternehmenslandschaft prägen werden: Im Fokus stehen vor allem maschinelles Lernen und Natural Language Processing (NLP), die es ermöglichen, Prozesse zu automatisieren und riesige Datenmengen effizient auszuwerten. Diese Technologien bieten insbesondere im Kundenservice und der Produktentwicklung große Potenziale. Durch Fortschritte in der Bild- und Spracherkennung wird es immer einfacher, innovative, kundenorientierte Produkte zu entwickeln. Strategien zur Integration von Zukunftstrends KI 2025 in bestehende Prozesse: Wer KI erfolgreich einführen möchte, sollte mit kleinen Schritten beginnen. Oft ist es sinnvoll, mit Bereichen wie Kundensupport oder Datenanalyse zu starten. Die Integration sollte in Etappen erfolgen, und es ist wichtig, das Team frühzeitig einzubinden, um Unterstützung und kreative Ideen aus dem eigenen Unternehmen zu gewinnen. Best Practices erfolgreicher Unternehmen: In der Episode schauen wir uns Beispiele von Unternehmen wie Amazon an, die KI nutzen, um Prozesse zu optimieren und die Kundenerfahrung zu verbessern. Vom Logistikmanagement bis hin zur automatisierten Kundenbetreuung setzen diese Unternehmen KI-Technologien gezielt ein, um innovativ und wettbewerbsfähig zu bleiben. Durch datenbasierte Entscheidungen und gezielte Investitionen in neue Technologien sichern sich diese Unternehmen ihren Vorsprung am Markt. Diese Episode bietet dir die wichtigsten Einblicke und Tipps, wie du KI-Zukunftstrends nutzen kannst, um dein Unternehmen erfolgreich zu positionieren. Egal, ob du neu in der KI bist oder bereits Erfahrung hast – hier gibt's wertvolle Inspiration und praktische Ansätze, um 2024 zu einem starken Jahr für dein Business zu machen! Und denk immer daran: Wer will, findet Wege. Wer nicht will, findet Gründe. Tschüss, mach's gut. Dein Tom.   Hol Dir jetzt Dein Hörbuch "Selfmade Millionäre packen aus" und klicke auf das Bild! Buchempfehlung bei Amazon: Denken Sie wie Ihre Kunden   +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Mehr Freiheit, mehr Geld und mehr Spaß mit DEINEM eigenen Podcast. Erfahre jetzt, warum es auch für Dich Sinn macht, Deinen eigenen Podcast zu starten. Jetzt hier zum kostenlosen Podcast-Workshop anmelden: https://Podcastkurs.com +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++   So fing alles an. Hier geht´s zur allerersten Episode von TomsTalkTime.com – DER Erfolgspodcast. Und ja, der Qualitätsunterschied sollte zu hören sein. Aber hey, das war 2012…

In this episode of The Defense Unicorns Podcast, we're joined by Collen Roller, Founder of Dark Saber, as he shares his journey of innovation within the U.S. Air Force. From his work in Natural Language Processing (NLP) to mentoring airmen on developing applications using outdated systems, Collen's story highlights perseverance and creativity. Discover how Dark Saber has empowered airmen to build production-ready software, transforming their operational efficiency.We explore the transformative potential of generative AI in defense, including its ability to automate tasks like paperwork and coding, enhancing productivity. Collen also introduces Nipper GPT, an AI tool tailored for DoD networks that bridges information gaps through conversational interfaces, pushing the boundaries of data access within military environments.Looking to the future, Collen discusses exciting advancements like Retrieval Augmented Generation (RAG) and multi-agent AI architectures, poised to revolutionize military data interactions. As he reflects on the importance of passion and community in driving change, this episode showcases Collen's commitment to modernizing defense technology through innovation.Key Quote: “I think that people need to realize that these tools are for their benefits and they need to get involved in using them today to enhance their performance and workflow because if you're not using them, you're being slow.”-Collen Roller, Founder of Dark SaberTime Stamps:(00:00) DoD Software and Conversational Systems(11:28) Future of AI in Military Operations(17:05) Future Developments in Artificial Intelligence(34:38) Revolutionizing Software Development in DoD(44:21) Driving Change in DoD Through PassionLinks:Learn more about Dark SaberConnect with CollenConnect with Luke

In this episode, we sit down with Ryan Millner, Co-founder and CEO of Unwrap AI, a cutting-edge tool that leverages Natural Language Processing (NLP) to help product leaders better listen to their customers. With over 4 years of experience at Graphiq (acquired by Amazon Alexa in 2017) and 3.5 years working at Amazon to improve Alexa's intelligence, Ryan shares his deep insights into product development, NLP, and how Unwrap AI is transforming the way companies understand and act on customer feedback. Ryan discusses the power of knowledge graphs, data ontologies, and the importance of building great team cultures. If you're passionate about AI, customer feedback, or creating smarter products, this episode is packed with valuable takeaways.

About the Episode This is a deep dive discussion of the paper, "Emerging Words that Matter: Data Analytics Creates Meaning", generated using Artificial Neural Networks (ANN) (courtesy: https://notebooklm.google.com) This white paper explores the use of Natural Language Processing (NLP) techniques to analyze text data, specifically in the context of the concept of "Emergence". NLP allows us to extract meaning, sentiment, and emotions from text, making it possible to understand how people define and discuss emergence further. The research uses word clouds, sentiment analysis, and emotion analysis to uncover patterns and trends, providing a deeper understanding of this complex concept. The expected findings include visual representations and insights into how emergence is understood and how these perceptions have changed over time. Eventually, this will provide us with emergent patterns from various domains such as healthcare, retail, social media etc. Emergent patterns help businesses predict upcoming trends so that they can prepare themselves.  About the Author Priyangka Roy is a Data Analyst and an MS Information Technology Management student at UT Dallas. 

Nearly a couple of years back - as I saw ChatGPT - just like everyone else who had been in the AI industry for the last decade, it was super clear that it was a turning point.To be sure, from within the industry, from GPT-2 onward, it was clear that something massive was happening, as for the first time (even if at the time the AI still generated a lot of non-sense), the paradigm was changing, as the output wasn't any longer stitching together of existing phrases, from a text the AI had somehow found.But it generated it independently, unsupervised, by “making sense” of the underlying text. That was mind-blowing!When ChatGPT came out, it was only the confirmation that the underlying model (GPT-3) with a new technique (InstructGPT) could be a game changer.It's nearly two years after the fact, and we've reached a point where tools like NotebookLM are so impressive that it's hard to imagine what's coming next!Indeed, the AI-generated this whole podcast episode after feeding it into our book AI Business Models!Before we get to it and understand its implications, remember you can download the AI Business Models book, if you subscribed to our premium newsletter. As you request access, please provide the email you used to subscribe, and we'll provide access!Subscribe to get access to the Book!Thematic OutlineFundamental ConceptsA. Technological Underpinnings:CPUs vs. GPUs: Differences in processing power, architecture, and applications.AI Supercomputers: Role in training large language models, reliance on GPUs.Transformer Architecture: Impact on natural language processing, attention mechanisms.B. Machine Learning ConceptsPre-training and Fine-tuning: Building general knowledge and specializing for specific tasks.Unsupervised vs. Supervised Learning: Learning from unlabeled data vs. labeled data with instructions.Reinforcement Learning: Learning through trial and error, rewards, and penalties.C. Key Trends in AIContent is King: Importance of high-quality data for training effective AI models.Multimodality: AI processing and integrating diverse data types like text, images, and audio.Emergence: Unexpected capabilities arising from increasingly complex AI models.AI Business Models and EvolutionA. Historical ContextThe Walled Garden Era: Limited access to information, controlled by portals like AOL.The Rise of the Internet: Open access to information, facilitated by web browsers.The Reverse Kronos Effect: Startups using technology to disrupt established industries (e.g., Google vs. AOL).B. Current LandscapeThe AI Ecosystem: Different layers, including infrastructure, models, and applications.Business Models in the "Apps' Layer": Ad-based, subscription-based, and consumption-based models.Building Competitive Moats: Differentiation strategies and challenges in a rapidly evolving field.Future of AI & Ethical ConsiderationsPotential of AIGenerative AI: Creating new content and pushing creative boundaries.InstructGPT: Enhancing AI's ability to follow instructions and generate accurate outputs.Decentralized AI Ecosystem: Exploring feasibility, challenges, and benefits.Ethical ImplicationsBias in AI: Addressing fairness, transparency, and potential discrimination.Job Displacement: Analyzing the impact of automation and potential solutions.Responsible AI Development: Implementing ethical guidelines, transparency, and accountability.Summary of the AI Theory Based on Layers, Hardware, Software, and Business ModelsThe AI Business Models book offers a glimpse into the evolving landscape of Artificial Intelligence (AI), highlighting key layers, technological advancements, and shifting business paradigms.Layers of the AI Ecosystem:These can be broadly categorized as:Infrastructure Layer: This encompasses the hardware and software foundations, with AI Supercomputers and GPUs playing a pivotal role in providing the computational power needed for training Large Language Models (LLMs).Model Layer: This layer focuses on the development and training of AI models like LLMs, utilizing techniques like pre-training on massive datasets and fine-tuning for specific tasks. Generative AI models, capable of creating new content, represent a significant advancement in this layer.Applications Layer: This layer comprises AI-powered applications and services that leverage the capabilities of underlying models. The AI Business Models book mentions various business models for companies operating in this layer, including ad-based, subscription-based, and consumption-based models.New Hardware and Software:Hardware: The AI Business Models book emphasizes the critical role of GPUs in accelerating AI workloads. Unlike CPUs designed for sequential processing, GPUs excel at parallel processing, making them ideal for handling the massive datasets and complex computations involved in AI training. AI Supercomputers, equipped with numerous GPUs, provide the necessary computational power to develop and train LLMs.Software: The AI Business Models book highlights advancements in AI model architectures, particularly the Transformer Architecture. This architecture, leveraging "attention mechanisms," has revolutionized Natural Language Processing (NLP) tasks, enabling significant improvements in language understanding and generation.New Business Model Paradigm:The AI Business Models book touches upon the evolution of AI business models, though they don't provide a comprehensive historical analysis. However, they do highlight the "Reverse Kronos Effect", where startups leverage new technologies and agile practices to disrupt established industries. This effect is exemplified by Google's dominance in the search and advertising market, surpassing previous giants like AOL.The AI Business Models book also mentions various business models for AI-powered applications, including ad-based, subscription-based, and consumption-based models. This suggests a shift towards more diverse monetization strategies in the AI Applications Layer.Expected Developments:The AI Business Models book hints at potential future directions:Multimodality: "Multimodality" is a key development in AI, enabling models to process and integrate diverse data types like text, images, audio, and video. This suggests a future where AI applications offer richer and more versatile experiences beyond text-based interactions.Emergence: The concept of "emergence" is mentioned in the context of AI. The phenomenon where complex behaviors and capabilities arise unexpectedly from the interaction of simpler components in AI systems. This suggests that future AI models might exhibit capabilities that go beyond their initial design, potentially leading to unforeseen breakthroughs and challenges.GlossaryHere is a glossary of key terms based on the provided source:AI Supercomputer: A computing system specifically designed for AI tasks, using many GPUs and specialized hardware to handle the massive processing demands of training and running large language models.Business Engine: The core value proposition and revenue-generating mechanisms of an AI-powered product or service, including pricing models, customer acquisition strategies, and overall business strategy.Content is King: This phrase emphasizes the importance of high-quality content in attracting and retaining an audience. For AI, it highlights the critical role of data in training effective models, as data quality and relevance directly influence AI performance.CPU (Central Processing Unit): The primary processor in a computer, responsible for executing instructions and managing system operations. It excels at sequential processing, handling a limited number of tasks quickly.Distribution Engine: The channels and mechanisms used to deliver AI-powered products or services to end-users, including marketing, partnerships, and platform integrations, facilitating adoption and accessibility.Fine-tuning: The process of further training a pre-trained AI model on a smaller, task-specific dataset to refine its capabilities and optimize its performance for a specific application or industry.Generative AI: A type of artificial intelligence focused on creating new content (text, images, audio, video) based on patterns learned from existing data.GPU (Graphics Processing Unit): An electronic circuit designed for parallel processing. GPUs excel at handling massive datasets and performing complex calculations concurrently, making them suitable for tasks like rendering graphics and training AI models.InstructGPT: A large language model developed by OpenAI that uses human feedback to improve its ability to follow instructions and generate more accurate and useful responses.Large Language Model (LLM): An AI model trained on a massive dataset of text and code. LLMs understand and generate human-quality text, translate languages, write different kinds of creative content, and answer questions informatively.Paradigm Shift: A fundamental change in the underlying assumptions, beliefs, and practices of a specific field or industry. Technological breakthroughs often drive paradigm shifts in AI, leading to new ways of thinking about and leveraging AI.Pre-training: The initial training phase of an AI model using a vast, general dataset. This allows the model to learn fundamental patterns, relationships, and representations, providing a knowledge foundation for building more specialized capabilities through fine-tuning.Prompt Engineering: The process of designing and refining prompts to elicit the most desirable and accurate responses from an AI model. Effective prompt engineering optimizes AI performance and guides its behavior toward desired outcomes.Reinforcement Learning: A type of machine learning where an AI agent learns through trial and error, receiving rewards or penalties for its actions in an environment, allowing it to develop optimal strategies for problem-solving and goal achievement.Reverse Kronos Effect: The phenomenon where a startup uses disruptive technology and agile practices to rapidly overtake established industry leaders.Transformer Architecture: A neural network architecture that has revolutionized natural language processing (NLP). It uses "attention mechanisms" to process sequential data effectively, enabling breakthroughs in language understanding and generation tasks.Unsupervised Learning: A type of machine learning where the AI model trains on unlabeled data, learning patterns and relationships without explicit guidance.

Demystifying AI for Business – Understanding Concepts & Practical Applications In this episode, we dive into one of the most important topics shaping the future of business: Artificial Intelligence (AI). Whether you're a startup founder or a business leader, understanding AI is crucial to staying competitive. We'll break down key AI concepts like Machine Learning (ML) and Deep Learning (DL), showing how these technologies are revolutionizing industries. ML enables machines to learn from data and make smarter decisions, while DL takes it further with neural networks that mimic the human brain. From predictive analytics and automation to Natural Language Processing (NLP) and sentiment analysis, AI is transforming how businesses operate and engage with customers. We'll also discuss real-world examples of AI in action. Learn how Amazon's recommendation engine, Tesla's self-driving technology, and Salesforce's Einstein platform are making a huge impact in their respective industries. These cases demonstrate how AI enhances decision-making, boosts efficiency, and drives innovation. If you're ready to embrace AI's potential in your business, this episode will provide the practical insights and examples you need to get started. AI is more than just a buzzword—it's the key to unlocking future growth and staying ahead in today's competitive landscape. #AIforBusiness #MachineLearning #DeepLearning #BusinessAutomation #PredictiveAnalytics #NLP #AIinBusiness #AIStartups #BusinessInnovation #ArtificialIntelligence #FutureOfBusiness #Entrepreneurship #AITransformation #abhisheksengupta #abhisheksenguptaaudioblogs   Sign up for newsletter : https://zcmp.in/XxWA   Media Credit : Pic Courtesy - UnSplash Video Courtesy - Mixxit Background Music - Upbeat Background Music, Pic & videos : invideo.io & Istock   Disclaimer : 1. This episode is made for information and knowledge. All necessary checks with relevant persons and authorities should be done before taking any actions based on this. Maker of the episode/ company / its employees / its partners / founders/ co-founders / participant will not be responsible for any incident related to this.   2. Images & videos used in this are for representation and educational purpose under fair use provision of copyright. These are not used for any other objective. Copyright Disclaimer under Section 107 of the copyright act 1976, allowance is made for fair use for purposes such as criticism, comment, news reporting, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favour of fair use.  

In this week's episode, Will & Jill discuss Natural Language Processing (NLP), explaining its goals, applications, and challenges. They break down NLP into subtopics, covering its definition, how it works, and different use cases such as chatbots, virtual assistants, and transcription software. They also touch on the challenges NLP faces, including understanding context, idiomatic expressions, and diversity in language. ___Connect with JillConnect with Will___160 Characters is powered by Clerk Chat.

As machine learning algorithms continue to evolve, Large Language Models (LLMs) like GPT-4 are gaining popularity. While these models hold great promise in revolutionizing various functions and industries—ranging from content generation and customer service to research and development—they also come with their own set of risks and ethical concerns. In this episode, Rohan Sathe, Co-founder & CTO/Head of R&D at Nightfall.ai, and I review the LLM-related risks and how best to mitigate them.Action Items and Discussion HighlightsLarge Language Models (LLMs) are built on specialized machine learning models and architectures called transformer-based architectures, and they are leveraged in Natural Language Processing (NLP) contexts.There's been a lot of ongoing work in using LLMs to automate customer support activities.LLM usage has dramatically shifted to include creative capabilities such as image generation, copywriting, design creation, and code writing.There are three main LLM attack vectors: a) Attacking the LLM Model directly, b) Attacking the infrastructure and integrations, and c)Attacking the application.Prevention and mitigation strategies include a) Strict input validation and sanitization, b) Isolating the LLM environment from other critical systems and resources, c) Restricting the LLM's access to sensitive resources and limiting its capabilities to the minimum required for its intended purpose; d) Regularly audit and review the LLM's environment and access controls; e) Implement real-time monitoring to promptly detect and respond to unusual or unauthorized activities; and f) Establish robust governance around ethical development and use of LLMs.Time Stamps00:02 -- Introduction01:54 -- Guest's Professional Highlights02:50 -- Overview of Large Language Models (LLMs)07:33 -- Common LLM Applications08:53 -- AI-Safe Jobs and Skill Sets11:41 -- LLM Related Risks15:30 -- Protective Measures19:09 -- Retrieval Augmented Generation (RAG)20:57 -- Securing Sensitive Data23:07 -- Selecting Appropriate Data Loss Protection Platforms25:00 -- Human Involvement in Processing Alerts26:56 -- Closing ThoughtsMemorable Rohan Sathe Quotes/Statements"Large Language Models (LLMs) are built on specialized machine learning models and architectures called transformer-based architectures, and they are leveraged in Natural Language Processing (NLP) contexts. It is really just a computer program that has been fed enough examples to be able to recognize and interpret human language or other complex types of data. And this data comes from the internet.""The quality of the LLM responses depends upon the data it's trained on.""LLM is a type of deep learning model, and the goal is to understand how characters, words, and sentences function together and do that probabilistically.""There's been a lot of ongoing work in using LLMs to automate customer support activities.""The LLM usage has dramatically shifted to include creative capabilities such as image generation, copywriting, creating designs, and writing code.""There are three kinds of core LLM attack vectors. One is just to attack the LLM model directly. The second is to attack the surrounding infrastructure and the integrations that the LLM has. The third is to attack the application that may use an LLM under the hood.""I have seen a lot of infrastructure attacks and attacking the integrations around the LLMs. And then, of course, just the standard attack: attacking...

Greetings, SaaS CFO community! Welcome to another exciting episode of The SaaS CFO Podcast. I'm your host, Ben, and today we have a special guest, Jake Soffer, the software founder, and CEO of FirmPilot. Jake's journey is nothing short of inspiring, starting from his days as a recruited hockey player at RPI, diving deep into the fascinating world of Natural Language Processing (NLP), and eventually venturing into entrepreneurship. In this episode, Jake shares invaluable insights from his experience founding Rollio, a cutting-edge natural language interface for CRMs, and how he navigated every classic first-time founder mistake along the way. We'll delve into FirmPilot, the innovative company he founded in 2023, which is revolutionizing the legal marketing landscape with AI-driven SEO, social media, and ad management solutions tailored for law firms. Tune in as Jake reveals FirmPilot's journey from a simple experiment to a thriving business, his strategic approach to targeting small and solo law firms, and how they've already achieved top Google rankings for their clients. Plus, you'll hear about how FirmPilot's go-to-market strategies, fundraising experiences, and metrics for success have shaped the company's growth trajectory. Whether you're a seasoned founder or an aspiring entrepreneur, this episode is packed with lessons and inspiration. Let's dive in! Show Notes: 00:00 Data-driven content creation to boost online presence. 03:41 Transitioned from Rollio, helped brother, created MVP. 09:21 Personalized approach and creative outreach for sales. 12:42 Focus on key metrics for current stage. 14:39 Focused on growth, hired first set of bdRs. Links: SaaS Fundraising Stories: https://www.thesaasnews.com/news/firmpilot-closes-7-million-in-series-a Jake Soffer's LinkedIn: https://www.linkedin.com/in/jake-soffer-00797a62/ FirmPilot's LinkedIn: https://www.linkedin.com/company/firmpilot/ FirmPilot's Website: https://firmpilot.com/ SaaS Metrics Course here: https://www.thesaasacademy.com/the-saas-metrics-foundation-live-cohort-new?mc_cid=b89d27187d&mc_eid=20fbb3e1b5 To learn more about Ben check out the links below: Subscribe to Ben's daily metrics newsletter: https://saasmetricsschool.beehiiv.com/subscribe Subscribe to Ben's SaaS newsletter: https://mailchi.mp/df1db6bf8bca/the-saas-cfo-sign-up-landing-page SaaS Metrics courses here: https://www.thesaasacademy.com/ Join Ben's SaaS community here: https://www.thesaasacademy.com/offers/ivNjwYDx/checkout Follow Ben on LinkedIn: https://www.linkedin.com/in/benrmurray

In this compelling episode of the Legacy Leaders Show, we delve into the unique world of Doxci. This pioneering SaaS platform leverages Artificial Intelligence (AI), Natural Language Processing (NLP), and Robotic Process Automation (RPA) to transform how businesses manage corporate documents. Its standout feature? The ability to process over 100,000 records in just 5 minutes frees valuable time for business leaders to focus on growth. Join us as we interview Austin Ambrozi, the visionary co-founder and managing partner who champions the mantra "Quit Doing Paperwork." Discover how Doxci is an AI employee capable of processing over 100,000 documents in 5 minutes regardless of language. Automating tasks, from compliance documentation and insurance claims processing to handwritten notes and records, allows business leaders to redirect their efforts from tedious paperwork to what truly matters: accelerating business growth. This episode is a must-listen for entrepreneurs, business owners, and innovators who want to scale efficiency and unlock new possibilities in the digital age by automating their paperwork and leading their organizations to success. Tune in to get inspired, informed, and ready to scale with Doxci.ai. Buckle Up, Champ!

Doug Hebenthal is a seasoned technology leader with over 30 years of experience in various high-impact roles, including CTO positions at Realty Mogul, International Sports Sciences Association, and Knowable. At Knowable, he gained expertise in Machine Learning and Natural Language Processing (NLP) by developing a Contract Intelligence platform. His extensive career includes 21+ years at Microsoft, where he was a founding member of the Xbox team and led significant consumer and payment projects. He also spent time at Amazon, Change Healthcare, and Axiom, focusing on driving technological innovation, cloud adoption, and team building. Hebenthal excels at simplifying complex technologies to deliver customer value and has a strong track record of leading large-scale, impactful projects.

Send us a Text Message.Ever wondered how to turn your passion into a profitable niche site, even with Google's relentless updates? Join me, Ed Dawson, on "SEO is Not That Hard," as we unravel the mysteries behind building successful niche websites and the impact of Google's Helpful Content Update on the niche site community. We'll break down how to capitalize on your interests, navigate the challenges posed by named updates, and leverage naked links for exact match domains. Plus, you'll get my take on why the niche site industry still holds immense potential despite recent setbacks.We'll also explore the critical importance of maintaining a consistent name, address, and phone number (NAP) for local SEO success, and delve into the fascinating world of Natural Language Processing (NLP). Discover how NLP can help you understand your audience's intent and enhance your SEO strategy. With over 20 years of SEO experience, I'm here to share the insights, strategies, and tips that can help you stay ahead in the ever-evolving world of search engine optimization. Tune in and equip yourself with the knowledge to thrive in the SEO landscape.SEO Is Not That Hard is hosted by Edd Dawson and brought to you by KeywordsPeopleUse.comYou 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 then book an appointment 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 Twitter @channel5Find 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/

Nearly 80% of Europeans aren't involved in the energy transition, often because they don't know how. Digital tools can make it easier for people to actively participate and support the shift to a greener future, but consumers still lack user-friendly solutions. Leading energy industry and research collaborators from across Europe are launching the EU-DREAM project this month to accelerate innovation in digital tools and enhance the adoption of digital services in the energy market. The EU-DREAM project stands for Effective Uptake of Digital Services to Repower European Consumers and Communities as Active Participants in Energy Transition and Markets and focuses on improving consumer interaction with the energy market. It aims to simplify complex energy management processes and enhance customer awareness, trust, and confidence through the introduction of an Artificial Intelligence (AI) -based assistant and a Natural Language Processing (NLP) intermediary. The project is led by the University of Porto (Portugal) and 16 other collaborators from nine countries, Ireland included. The project is set to develop next-generation energy services, solutions, and products, fully tested and demonstrated in six living labs (LLs) across six EU countries: Portugal, Belgium, Italy, Ireland, Greece, and Denmark. Each LL will be focusing on different aspects of energy innovation: LL1 (Coimbra, Portugal): AI-powered renewable energy communities LL2 (Genk, Belgium): Addressing energy poverty and impacts on vulnerable consumers LL3 (Northern Italy, Italy): Energy flexibility and interoperability at the residential scale LL4 (Dublin, Ireland): Empowering consumers for energy management LL5 (Thessaloniki and other cities, Greece): Smart energy services for multi-energy vectors LL6 (Aalborg, Denmark): DT-enabled residential IoT microgrid "These innovative tools will translate intricate energy market details into everyday language, making energy management accessible and understandable. The AI-based assistant will act as an energy attorney for users, optimizing energy settings according to individual preferences, while the NLP intermediary will facilitate seamless communication in layman's terms," explained João Catalão, full professor, Faculty of Engineering at the University of Porto (Portugal). The Irish LL4, led by SSE Airtricity in collaboration with EPRI Europe and DCSix Technologies, aims to address consumers' ability to optimise energy usage in Ireland through real-time data analysis and AI algorithms, ensuring efficient usage and minimizing grid reliance. "Innovations that help with an equitable, affordable clean energy transition are key to meeting net-zero targets," said Mário Couto, technical leader at EPRI Europe. "We are pleased to work with SSE Airtricity and DCSix Technologies on the lab, which could provide consumers with the tools needed as part of an effective energy transformation." Additionally, a Digital Twin (DT) of households will be created using real-time data from sensors, allowing for precise monitoring and optimization of energy use. These household DTs will integrate into broader community DTs, providing valuable insights for new market designs and consumer-oriented business models. The project, from July 2024 to December 2027, is funded by the European Union's Horizon Europe program, under agreement no. 101160614. For more information about EU-DREAM and its initiatives, please visit https://www.linkedin.com/company/eu-dream/.

UC Today host Rob Scott is joined by AI Expert and Co-Founder of enableUC, Kevin Kieller.In this update, we talk through the most popular AI news headlines:The history and advancement of AI assistants like Apple's Siri, Amazon's Alexa, and Google AssistantThe integration of Natural Language Processing (NLP) and machine learning in voice assistantsExpanding applications of voice assistants in smart devices, homes, cars, and enterprise solutionsThe role of AI voice assistants in enhancing customer experience, productivity, and real-time translation in businessesSpeculation on Microsoft's AI voice strategy: Will Copilot, Cortana, or Clippy lead the way?

From this insightful episode, you'll discover how artificial intelligence works. Andrii Sambir, Linkup Studio's CEO, answered the question, "How does AI work?" His explanations range from basic concepts to complex applications. He explains many types of existing AI. Among them, there are machine learning, neural networks, deep learning technology, and also natural language processing (NLP). He also provided a thorough understanding of artificial intelligence processing. This discussion is based on Linkup Studio's real-world applications and innovations in AI. If you want to get an inside look into artificial intelligence, watch this video.  This episode provides many useful, clear, and practical explanations of many AI processes and technologies.  So, if you are looking to integrate AI into your business or just curious about artificial intelligence or how it works, this episode will help you learn more quickly.  Connect with Andriy Sambir:  https://www.linkedin.com/in/andriy-sambir/  Learn more about Linkup Studio:  https://linkupst.com/      Episode Timeline: 00:36 Understanding AI and Its Types 03:00 Machine Learning – The Heart of AI 05:24 Neural Networks and Deep Learning 06:25 Natural Language Processing (NLP) 07:51 Challenges and Ethical Considerations You may also see the episode on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠! Would you like to know how you can use the power of AI technology for your products? Our Linkup Studio seasoned specialists are ready to guide you and assist in this AI process.  You can like the video, subscribe to the channel, and hit the notification button to receive notifications when we have new and fresh information about AI development, digital product development, product design, and updates in technology trends. Follow us on Social Media: Facebook: https://www.facebook.com/Linkupst/ Twitter: https://twitter.com/linkupst  LinkedIn: https://www.linkedin.com/company/linkup-studio/mycompany/ Instagram: https://www.instagram.com/linkupst/ Visit Us: Website: https://linkupst.com/ Contact Us: info@linkupst.com

Co-host Ryan Piansky, a graduate student and patient advocate living with eosinophilic esophagitis (EoE) and eosinophilic asthma, and co-host Holly Knotowicz, a speech-language pathologist living with EoE who serves on APFED's Health Sciences Advisory Council, have a conversation about artificial intelligence (AI) and patient education, with guest Dr. Corey Ketchem, a third-year Gastroenterology Fellow at the University of Pennsylvania.   In this episode, Ryan, Holly, and Dr. Ketchem discuss Dr. Ketchem's interests, and his research into using an AI chatbot to provide patient education on eosinophilic gastrointestinal diseases. He shares, in broad terms, the methodology and conclusion of the research and what current and future research he is pursuing about using artificial intelligence to improve patient education and care.   Listen to this episode to learn about the current limitations and potential future benefits of using AI to help patients. Disclaimer: The information provided in this podcast is designed to support, not replace the relationship that exists 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: [1:17] Ryan Piansky and co-host Holly Knotowicz introduce the topic, artificial intelligence and patient education, and their guest, Dr. Corey Ketchem, a third-year Gastroenterology Fellow at the University of Pennsylvania.   [1:30] Dr. Corey Ketchem has an interest in allergic inflammation of the gastrointestinal tract, particularly eosinophilic gastrointestinal diseases (EGIDs), as well as artificial intelligence and epidemiologic studies.   [2:01] Dr. Ketchem did his residency at the University of Pennsylvania following medical school. There he met Dr. Evan Dellon, a world expert in EoE. Dr. Dellon became a mentor to Dr. Ketchem.   [2:24] As Dr. Ketchem learned more about EoE, he was fascinated by the many unknowns and opportunities for discovery within the eosinophilic GI field. He wanted to make an impact on patient care.   [2:51] Under Dr. Dellon's mentorship, he did epidemiologic studies. Seeking specialized training, he ended up at the University of Pennsylvania where he is getting rigorous training in epidemiology to study EGIDs.   [3:18] As ChatGPT was gaining its buzz, Dr. Ketchem saw a lot of clinical applicability. He views AI as an asset in epidemiology and hopes to use it to accelerate his research.   [4:30] AI usually references using computers to mimic human abilities, estimate decisions, or predict outcomes. An example is Natural Language Processing (NLP), to analyze and understand human language. Large Language Models (LLM) use NLP.   [5:08] ChatGPT is based on a LLM. LLMs use NLP techniques to understand vast amounts of text that they are trained on and generate responses in a chat format.   [5:25] Machine learning is another subset of AI that uses statistical techniques to give computers the ability to learn with the data and predict outcomes.   [5:50] The hope is to use these AI techniques to speed up discovery and also minimize human expense or labor.   [6:28] Dr. Ketchem co-authored a paper in Clinical Gastroenterology and Hepatology about an AI chatbot and EoE. He had been inspired by a cardiology paper on whether ChatGPT would create accurate, appropriate answers about cardiology disease health.   [7:19] Dr. Ketchem wondered if ChatGPT could be applied to EoE education. He discussed it with Dr. Dellon and Dr. Krystle Lynch, Dr. Ketchem's mentor at the University of Pennsylvania, and with Dr. Joy Chang, at the University of Michigan. They came up with a study design.   [8:06] The study asked ChatGPT questions about EoE, focusing on patient education and the therapeutics, and seeing if it gave accurate responses or not.   [8:45] The four doctors developed 40 questions that they gave ChatGPT as prompts and evaluated the responses. They proposed the questions in two ways: each question in an individual chat and 40 questions in a single chat.   [9:41] Analyzing the responses, the study demonstrated that ChatGPT responded with multiple inaccuracies to questions about EoE on general topics, complications, and management. Over half of the responses mixed correct and incorrect information.   [10:09] To evaluate the readability of the responses, the doctors used the Flesch-Kincaid reading ease tool. To understand the output from ChatGPT one would need high school and two years of college. That poses a potential health literacy barrier.   [11:40] The questions ranged from general: “What is eosinophilic esophagitis?”, to complications: “What is a food impaction?”, “What is a stricture?”, to therapeutics: “What are steroids for eosinophilic esophagitis?”, “Can I use a proton pump inhibitor for EoE?”   [12:15] It was not clear where ChatGPT pulled data from to respond to the questions. The data it was trained on was known to be in texts over a year old. Newer data may not have been accessible to ChatGPT.   [13:29] The doctors asked about things that were common knowledge in the eosinophilic GI realm, like dupilumab, and ChatGPT didn't know much about it because it was a newer treatment option for EoE at the time of the study. [13:42] The doctors scored the answers on their scientific accuracy and patient educational value. Simple questions got good responses. For questions about therapies and complications, “it wasn't doing well.” They identified limitations to the study. [14:14] The doctors asked ChatGPT if EoE is associated with cancer. From their best epidemiologic knowledge, the doctors don't think that it is. ChatGPT falsely associated EoE with esophageal adenocarcinoma.   [14:34] ChatGPT also associated EoE with Barrett's esophagus. To the doctors' best epidemiologic data, they are not sure that there's a connection.   [15:02] When the doctors asked the questions in individual chats, they asked ChatGPT for medical literature references for the information. It didn't provide accurate references. Titles and authors were often incorrect and links often didn't work.   [15:36] The incorrect references were a signal that ChatGPT wasn't ready to answer complex medical questions. In the more updated versions of ChatGPT, instead of giving references, it says you should consult your doctor, which is the right thing to do.   [15:56] The researchers concluded that implementing this technology requires clinical oversight; it's a tool that should be used with caution for patients in educating themselves and also from the perspective of a physician who is not an expert in EoE.   [16:29] Dr. Ketchem had been surprised by how long the responses were. He was expecting paragraphs but got pages and pages. He was also surprised by how quickly people were starting to use ChatGPT in other aspects of gastroenterology.   [16:57] While Dr. Ketchem and his team were writing the paper, another study came out about gastroesophageal reflux (GERD) that was somewhat similar to what Dr. Ketchem proposed for EoE. There is rapidly much being published about ChatGPT.   [17:14] Although the results were imperfect, there is potential applicability in patient-facing chats in the future for patient education but not yet there “for prime time.”   [18:33] These chats need to be transparent about where they're getting data, especially in the medical field.   [18:41] There will always be a role for people in medicine. You can't replace a face-to-face connection with a nurse or a physician with a chat bot.   [19:11] Dr. Ketchem says everyone needs to be careful about using AI tools. He advises patients to always discuss any medical questions with their physician. AI tools are not yet able to provide accurate medical information all the time.   [19:50] Ryan reminds listeners that this podcast is for educational purposes. Always consult your physician before making any changes to your healthcare. If you ask ChatGPT, also consult with your doctor before making any changes to your healthcare.   [20:31] One of the problems with large language models is the potential for inaccuracy. Dr. Ketchem's gold standard is the medical literature and you don't know where the large language models are getting their information.   [21:04] Future benefits may include helping patients get answers quicker and becoming more educated. Dr. Ketchem hopes we will get to a point where we can trust these technologies and implement them safely.   [21:37] Government organizations like the National Institutes of Health (NIH) and the U.S. Food & Drug Administration (FDA) are bringing together experts to think about large language models and create regulatory frameworks for their use in healthcare. Dr. Ketchem tells how HIPAA (Health Insurance Portability and Accountability Act) rules are followed to protect patients.   [23:29] Dr. Ketchem sees potential in machine learning to predict which therapies an EoE patient will respond to. AI is also used in colonoscopies to identify hard-to-see polyps. It might be useful in endoscopies to see changes in the esophagus from EoE.   [24:35] AI image recognition could also be applied in pathology. Dr. Ketchem is interested in trying to apply it to work he wants to do in the long term. People are working with pathology specimens to automate the counting of eosinophils. Dr. Ketchem discusses the potential use of AI for epidemiology in pathology.   [25:43] Dr. Ketchem and Holly discuss the potential for using AI chatbots in medical screening questionnaires. There will always need to be a human element.   [27:57] Dr Ketchem speaks to the potential future development of educational videos prepared by AI. It is a complex scenario that would require a lot of training. If a camera is added, AI could analyze where patients are having problems in taking medications.   [29:55] Dr. Ketchem says there are many moving parts in healthcare and many stakeholders, making it difficult to implement AI. It could be used in many aspects, but its use must be safe. Dr. Ketchem thinks it will soon be useful in medical imaging.   [30:57] In the next decade, AI may be used in drug discovery, clinical decision-making, and healthcare administrative operations. The goal is to improve the care for the patient. Personalized care would be an aspirational goal of using artificial intelligence.   [31:29] Dr. Ketchem heard of a computer scientist at a government meeting suggesting a far-future scenario of doctors having digital versions of patients to test the patient's reaction to a specific medication, based on comorbidities and other medications in use.   [32:30] Holly thanks Dr. Ketchem for sharing his research findings to help others.   [32:40] Dr. Ketchem's last words: “The future is bright. There are many open avenues to apply these technologies to eosinophilic GI diseases – in diagnostic support, personalizing treatment, and predictive modeling – to make patient care better.”   [33:10] Dr Ketchem is building a research program to use epidemiologic training with artificial intelligence. He hopes to find how to take text from histology or pathology and apply epidemiologic methods, to build a cohort of patients to study diseases faster.   [34:03] Dr. Ketchem hopes to use AI to help predict patient outcomes, regarding who will respond to what therapy and who will have more complications from their disease; those are things he is interested in. There are so many unanswered questions.   [34:30] After Dr. Ketchem finishes his fellowship, he hopes to be an independent investigator, being curious and answering these questions somewhere. If you know of such a job, please let Dr. Ketchem know!   [34:53] To learn more about Dr. Ketchem's research, please check out the links in the show notes. To learn more about eosinophilic gastrointestinal disorders, visit apfed.org/egids. If you're looking for a specialist who treats eosinophilic disorders, use APFED's Specialist Finder at apfed.org/specialist. [35:17] To connect with others impacted by eosinophilic diseases, please join APFED's online community on the Inspire Network at apfed.org/connections.   [35:26] Ryan thanks Dr. Corey Ketchem for joining us today. Holly thanks APFED's Education Partners, GSK, Sanofi, and Regeneron, linked below, for supporting this episode.   Mentioned in This Episode: Corey Ketchem, M.D., M.S. Penn Medicine Abstract of paper in Clinical Gastroenterology and Hepatology: “Artificial Intelligence Chatbot Shows Multiple Inaccuracies When Responding to Questions About Eosinophilic Esophagitis”Medscape article about the paper in Clinical Gastroenterology and Hepatology: “ChatGPT Gives Incorrect Answers About Eosinophilic Esophagitis”, by Carolyn Crist American Partnership for Eosinophilic Disorders (APFED) APFED on YouTube, Twitter, Facebook, Pinterest, Instagram Real Talk: Eosinophilic Diseases Podcast apfed.org/egids apfed.org/specialist apfed.org/connections   Education Partners: This episode of APFED's podcast is brought to you thanks to the support of GSK, Sanofi, and Regeneron.   Tweetables:   “We ultimately came to the conclusion that implementing this technology requires clinical oversight and it's a tool that should be used with caution.” — Corey Ketchem, M.D., M.S.   “There will always be a role for people in medicine. You can't replace a face-to-face connection with a chat. That's just not going to work.” — Corey Ketchem, M.D., M.S.   “There will always need to be a human element to it. The goal is to make [AI for healthcare] as good as it can be. We're certainly not there yet, but it's probably closer to being here than we think.” — Corey Ketchem, M.D., M.S.   Bio: Dr. Corey J. Ketchem, MD is a rising third-year gastroenterology fellow at the University of Pennsylvania, driven by a profound interest in allergic inflammation of the gastrointestinal tract, particularly eosinophilic gastrointestinal diseases (EGIDs). He has acquired a unique skillset in clinical epidemiology and biostatistics that equip him with the necessary tools to conduct rigorous research studies, culminating in a Master of Science in Clinical Epidemiology (MSCE) upon fellowship completion. Dr. Ketchem's passion for EGIDs has spurred a series of epidemiologic investigations focusing on both eosinophilic esophagitis (EoE) and non-esophageal EGIDs, yielding numerous publications in high-quality gastroenterology journals and earning him recognition through various research awards. Moreover, his academic path has included the incorporation of artificial intelligence into his research endeavors, aiming to enhance patient care and facilitate epidemiologic studies. Dr. Ketchem's trajectory is set toward becoming an independent researcher, dedicated to employing high-quality epidemiologic approaches to uncover pivotal insights into EGIDs, advance clinical knowledge, and optimize therapeutic strategies for patients.   Bio: Penn Medicine Division of Gastroenterology and Hepatology Fellows  

Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a patched-decoder style attention model on a large time-series corpus, and can work well across different forecasting history lengths, prediction lengths and temporal granularities. 2023: Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou https://arxiv.org/pdf/2310.10688

Large Language Models (LLMs) have catalyzed significant advancements in Natural Language Processing (NLP), yet they encounter challenges such as hallucination and the need for domain-specific knowledge. To mitigate these, recent methodologies have integrated information retrieved from external resources with LLMs, substantially enhancing their performance across NLP tasks. This survey paper addresses the absence of a comprehensive overview on Retrieval-Augmented Language Models (RALMs), both Retrieval-Augmented Generation (RAG) and Retrieval-Augmented Understanding (RAU), providing an in-depth examination of their paradigm, evolution, taxonomy, and applications. The paper discusses the essential components of RALMs, including Retrievers, Language Models, and Augmentations, and how their interactions lead to diverse model structures and applications. RALMs demonstrate utility in a spectrum of tasks, from translation and dialogue systems to knowledge-intensive applications. The survey includes several evaluation methods of RALMs, emphasizing the importance of robustness, accuracy, and relevance in their assessment. It also acknowledges the limitations of RALMs, particularly in retrieval quality and computational efficiency, offering directions for future research. In conclusion, this survey aims to offer a structured insight into RALMs, their potential, and the avenues for their future development in NLP. The paper is supplemented with a Github Repository containing the surveyed works and resources for further study: https://github.com/2471023025/RALM_Survey. 2024: Yucheng Hu, Yuxing Lu https://arxiv.org/pdf/2404.19543

In this episode, Ivan Lee, the CEO of Datasaur.ai, joins Amir Bormand to discuss productizing LLMs and dealing with labeling issues. Ivan shares his insights on the productization of LLMs and provides his thoughts on the future of this technology. Tune in to learn more about the work of Datasaur.ai and the exciting developments in the AI industry. Highlights: [00:02:44] The importance of data labeling. [00:03:34] The challenge of harnessing AI. [00:06:04] Disconnect in AI capabilities. [00:09:04] The future of AI models. [00:14:41] Productization of LLMs. [00:15:36] LLMs in product innovation. [00:18:52] LLMs and evolving business models. [00:22:25] ROI in AI implementation. [00:27:10] Exploring the potential of AI. [00:29:13] NLP's transformative power. Ivan Lee graduated with a Computer Science B.S. from Stanford University and dropped out of his master's degree to found his first company, Loki Studios. After raising institutional funding and building a profitable game, Yahoo acquired Loki. Lee spent the next 10 years building AI products at several companies and discovered a gap in serving the rapid evolution of Natural Language Processing (NLP) technologies. He built Datasaur to focus on democratizing access to NLP - Datasaur now serves companies such as Google, Netflix, Qualtrics, Spotify, and more. LinkedIn: https://www.linkedin.com/in/iylee/ ---- Thank you so much for checking out this episode of The Tech Trek, and we would appreciate it if you would take a minute to rate and review us on your favorite podcast player. Want to learn more about us? Head over at https://www.elevano.com Have questions or want to cover specific topics with our future guests?Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

In this episode, we dive into the transformative potential of AI in college admissions. We explore the evolving landscape of talent matching, emphasizing the unique challenges and opportunities AI presents in this area. Our discussion centers on the innovative concept of Grade Point Trajectory (GPT) as a more dynamic and fair measure compared to traditional GPA, highlighting how AI can aid in reducing biases and reshaping the admissions process to better capture a student's true potential and growth.Shownotes:1. Introduction- Ardis Kadiu introduces the episode and sets the stage for exploring the transformative role of AI in shaping the future of enrollment management in higher education.2. AI in Enrollment Management- Ardis and Dr. JC Bonilla discuss the potential of AI in talent matching and admissions, highlighting the challenges and opportunities in incorporating AI throughout higher education.3. Understanding AI in Higher Education- The hosts address confusion and misunderstanding around AI, its classification, and its role in campus infrastructure, emphasizing the need for clarity and education around AI in higher education.4. Impact of AI on Change Management- The conversation delves into the impact of AI on change management and technology adoption in higher education, with a focus on mindset change and upskilling in response to AI integration.5. Role of AI in Admissions Processes- Dr. JC Bonilla discusses the use cases of AI in decisioning processes, candidate matching, and the potential for AI to enhance the admissions experience for students.6. AI Applications in Admissions- The hosts elaborate on the various ways AI is being used for reviewing transcripts, letters of recommendation, essays, conducting interviews, and communication with applicants, emphasizing the role of Natural Language Processing (NLP) and the potential for eliminating biases.7. Leveraging AI in Decision-making- Emphasizing AI's role in handling certain tasks to free up time for high-touch interventions by humans, the hosts highlight the importance of using AI to scale human decision-making processes.8. Challenges and Imperfections in AI Integration- Ardis Kadiu and Dr. JC Bonilla advocate for embracing imperfect solutions and focusing on delivering value rather than striving for perfection, acknowledging the need to carefully optimize AI processes.9. Amplifying Human Capabilities with AI- Both hosts stress the importance of AI amplifying human capabilities rather than replacing them, especially in finding the right students for academic institutions, and discuss the potential for reducing biases in talent recruitment.10. Addressing Biases and Enhancing Recruitment- The conversation highlights the potential of AI in addressing biases and enhancing the recruitment and admissions processes in higher education, emphasizing the use of generative AI to match individuals with the right programs for success and graduation.11. Conclusion- The hosts summarize the key takeaways from the episode, underscoring the ethical and practical implications of AI in admissions processes and inviting listeners to engage further with the topic. - - - -Connect With Our Co-Hosts:Ardis Kadiuhttps://www.linkedin.com/in/ardis/https://twitter.com/ardisDr. JC Bonillahttps://www.linkedin.com/in/jcbonilla/https://twitter.com/jbonillxAbout The Enrollify Podcast Network:Generation AI is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too! Some of our favorites include The EduData Podcast and Visionary Voices: The College President's Playbook.Enrollify is made possible by Element451 — the next-generation AI student engagement platform helping institutions create meaningful and personalized interactions with students. Learn more at element451.com. Connect with Us at the Engage Summit:Exciting news — Ardis will be at the 2024 Engage Summit in Raleigh, NC, on June 25 and 26, and would love to meet you there! Sessions will focus on cutting-edge AI applications that are reshaping student outreach, enhancing staff productivity, and offering deep insights into ROI. Use the discount code Enrollify50 at checkout, and you can register for just $99! This early bird pricing lasts until March 31. Learn more and register at engage.element451.com — we can't wait to see you there!

Every innovation in technology eventually impacts education, yet artificial intelligence already seems poised to transform the way people learn and teach at every level. Amy and Mike invited technology expert Amit Jain to explore how tutors can use AI. What are five things you will learn in this episode? How have Large Language Models (LLMs) solved the decades-old problem of Natural Language Processing (NLP)? How is AI helping curate and personalize content for individual students? How is AI enhancing comprehension and engagement, and bridging cultural and linguistic gaps for students? What ethical considerations should be taken into account when using AI in education? How can tutors and test prep professionals get started with AI ? MEET OUR GUEST Amit Jain is the CEO and Co-founder of MentoMind, a learning platform company on a mission to boost student outcomes in education. Amit's journey began with a deep passion for computer science, leading him to first earn an engineering degree with a gold medal and then impart knowledge in the same field as a lecturer at an engineering college in India. His career then progressed through various leadership roles in several global software and technology companies. The Covid pandemic reignited Amit's interest in education, inspiring him and his friends to develop MentoMind. This AI-powered learning platform, originally launched for college preparation in India, has successfully garnered an active user base of over 30,000 students. Expanding its reach, Amit recently introduced MentoMind in the United States, targeting college-bound students preparing for the digital SAT. Amit can be reached at amit.jain@mentomind.com. LINKS AI as Personal Tutor Khanmigo Education AI Guide RELATED EPISODES TUTORING IN THE AGE OF ARTIFICIAL INTELLIGENCE ARTIFICIAL INTELLIGENCE AND ACADEMIC INTEGRITY COLLEGE ESSAYS IN THE AGE OF ARTIFICIAL INTELLIGENCE ABOUT THIS PODCAST Tests and the Rest is THE college admissions industry podcast. Explore all of our episodes on the show page. ABOUT YOUR HOSTS Mike Bergin is the president of Chariot Learning and founder of TestBright. Amy Seeley is the president of Seeley Test Pros. If you're interested in working with Mike and/or Amy for test preparation, training, or consulting, feel free to get in touch through our contact page.  

What stands between you and your dream life, an actual impediment or just the byproducts of overthinking? In this episode, the magical Amanda Towne joins us to teach us how to release resistance and live a life of joy, abundance, and freedom. Amanda is a former Accountant turned into a Transformational Coach, Law of Attraction (LOA), and Rapid Transformational Therapy (RTT) Practitioner. Her logical, analytical, and linear mind couldn't have enough the moment she learned about Yoga, Hypnotherapy, and Natural Language Processing (NLP), and she felt urged to dive deeper, meeting the work of Abraham Hicks, Joe Dispenza, and Bashar in the process. Today, she's committed to using her coaching to change people's lives. Throughout our conversation, you'll hear about Amanda's "previous life" as an accountant and why she decided to change her life and take the leap of faith into coaching, LOA, and RTT. Amanda also shares her thoughts on channeling, explains what it is and how it works, and how inner child and inner being work can help us through virtually any challenge. Additionally, Amanda walks us through Abraham Hicks's Law of Attraction's five steps, talks about the non-physical being Bashar, who speaks through the channel Darryl Anka, and much more. Tune in to episode 15 of RADitude and let Amanda teach you how to let go of resistance, embrace your full potential, and create your formula to start Liv-nRAD and Loving Contagiously.In This Episode, You Will Learn:About Amanda's background and decision to become a coach (2:00)The thousand doors Yoga opened for Amanda (7:10)What is a channel? (13:00)How do inner child and inner being sessions work? (17:20)Amanda talks about her Law of Attraction work (23:50)Connect with Amanda:WebsiteInstagramYouTubeFacebookJoin Amanda's Law of Attraction GroupLet's connect!WebsiteContact UsLinkedInInstagramFacebookTwitter Hosted on Acast. See acast.com/privacy for more information.

Join us on "Stop Aging Now" for a groundbreaking episode where Dr. Nick Delgado and guest speaker Mary Ligon explore the transformative power of Natural Language Processing (NLP) in the realm of health and wellness. Discover how the sophisticated techniques of NLP can be applied to not only enhance mental well-being but also to create profound positive changes in your physical health. In this insightful conversation, Mary Ligon, an expert in NLP, delves into the science and practice behind this compelling approach, demonstrating how language and communication can be powerful tools in healing and personal growth. Dr. Nick Delgado, a pioneer in anti-aging and lifestyle medicine, ties in the critical connections between mindset, language, and our body's health. Together, they will explore real-life applications of NLP in overcoming health challenges, managing stress, and enhancing overall well-being. Whether you're a health enthusiast, a professional in the wellness industry, or someone looking for innovative ways to improve your health journey, this episode offers a wealth of knowledge and practical tips. If you find yourself captivated and yearning for a deeper dive into your personal health journey, I'd be honored to guide you. Join me in my exclusive 'Health Coaching and Guidance Program'. Together, let's design a roadmap tailored to your unique needs and aspirations. Reach out, and together, we'll navigate the path to optimal health and understanding Check out my new book, “STOP AGING NOW”: https://www.amazon.com/Stop-Aging-Now-Seven-Secrets/dp/B0CGKTX7HG For More Info... • Apply for a coaching program: https://nickdelgado.com/ • Subscribe to our YouTube channel:  https://www.youtube.com/delgadovideo • Shop here for our amazing supplements: https://estroblock.com/
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In the second episode of our new AI-focused series, guest hosts Pauline James and David Creelman investigate some key ways that large HR vendors are utilizing new forms of AI.Joining David and Pauline this time are Mike Bollinger, Ben Zweig, and Jarik Conrad. Mike Bollinger is an accomplished executive with 20 years of industry experience. He has deep technology and HCM domain skills including strategic workforce planning, communications, change management, team building, business case development, and leadership. In his current role as Vice President of Strategic Initiatives with Cornerstone, Mike is responsible for internal research as well as strategy development around outcome-based goals. In 2020, Mike helped found, and currently manages, the Cornerstone People Research Lab (CPRL), whose mission is to generate data-driven discoveries about the world of work today and identify emerging trends that will give rise to new work models. Ben Zweig is a prominent figure at the intersection of Data Science and Human Resources. With a PhD in Economics specializing in Labor Economics and Economic Development from CUNY Graduate Center, he has a strong academic background. Ben's expertise extends to Natural Language Processing, Labor Economics, Human Resources, and Occupational Transformation. Notably, he spent several years as a Managing Data Scientist at IBM's Chief Analytics Office, where he utilized Natural Language Processing (NLP) to develop data-driven solutions for businesses. Currently, Ben serves as the CEO of Revlio Labs, an organization that supports the convergence of Data Science and HR. The company focuses on applying data-driven methodologies to enhance workforce management and adapt to the changing job market.Dr. Jarik Conrad, EdD, SPHR, SHRM-CSP, NACD.DC, is vice president of the Human Insights team at UKG, which consists of former HR practitioners, business leaders, and consultants who are distinguished experts in the HCM field and uniquely qualified to help leadership teams reach their organizational goals. With an acute pulse on industry trends, best practices, and technological innovations, Jarik and his team serve as liaisons, trusted advisors, and thought leaders who help to shape HR industry conversations and direction. Feature Your Brand on the HRchat PodcastThe HRchat show has had 100,000s of downloads and is frequently listed as one of the most popular global podcasts for HR pros, Talent execs and leaders. It is ranked in the top ten in the world based on traffic, social media followers, domain authority & freshness. The podcast is also ranked as the Best Canadian HR Podcast by FeedSpot and one of the top 10% most popular shows by Listen Score. Want to share the story of how your business is helping to shape the world of work? We offer sponsored episodes, audio adverts, email campaigns, and a host of other options. Check out packages here. Follow us on LinkedIn Subscribe to our newsletter Check out our in-person events

October 9: Today on the Conference channel, it's an Interview in Action with Paul Milligan, Director, Product Strategy at IQVIA. How does IQVIA harness the power of Natural Language Processing (NLP) for healthcare solutions? What are the distinctions and interconnections between NLP and LLMs in the context of data analysis and generation in healthcare? In what ways can we ensure that the information summarized or generated by LLMs is accurate and representative of larger datasets, especially considering the critical nature of healthcare information?Subscribe: This Week HealthTwitter: This Week HealthLinkedIn: Week HealthDonate: Alex's Lemonade Stand: Foundation for Childhood Cancer

We are joined by Maximilian Mozes, a PhD student at the University College, London. His PhD research focuses on Natural Language Processing (NLP), particularly the intersection of adversarial machine learning and NLP. He joins us to discuss his latest research, Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities.

This episode dives into the multifaceted realm of Natural Language Processing (NLP) with a guest expert, Ines Montani (#). The discussion revolves around the use of Python in the context of NLP, the complexities of language, the design of label schemes, and how educators and students can dive into this intriguing area. The conversation also touches on tools such as Prodigy (https://prodi.gy/) and Spacy (https://spacy.io/), as well as practical applications, including a humorous digression on the popular game, Fortnite (https://www.epicgames.com/fortnite/). Teachers are encouraged to explore NLP with their students, emphasizing the importance of hands-on experience and data annotation. There's also a mention of a fascinating project involving a "magic mirror (https://www.raspberrypi.com/tutorials/how-to-build-a-super-slim-smart-mirror/)" powered by Raspberry Pi (https://www.raspberrypi.org/). Special Guest: Ines Montani.

We speak hundreds of languages across the African continent. But only a small number are represented on the internet. So what future do the languages we speak at home and with our families have, if we cannot use them in a digital world? This is where machine learning comes in, Artificial Intelligence. Because there are translation tools out there, built through Natural Language Processing (NLP), which can allow you to be understood anywhere in the world. But the data needed is complex and takes a long time to create. So we brought together 3 women who work in this field and are ‘language champions' for African languages – Salomey Osei from Ghana, Jade Abbott from South Africa, and Kathleen Siminyu from Kenya – to find out whether our African languages can have a digital future.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Language models surprised us, published by Ajeya on August 30, 2023 on The Effective Altruism Forum. Note: This post was crossposted from Planned Obsolescence by the Forum team, with the author's permission. The author may not see or respond to comments on this post. Most experts were surprised by progress in language models in 2022 and 2023. There may be more surprises ahead, so experts should register their forecasts now about 2024 and 2025. Kelsey Piper co-drafted this post. Thanks also to Isabel Juniewicz for research help. If you read media coverage of ChatGPT - which called it 'breathtaking', 'dazzling', 'astounding' - you'd get the sense that large language models (LLMs) took the world completely by surprise. Is that impression accurate? Actually, yes. There are a few different ways to attempt to measure the question "Were experts surprised by the pace of LLM progress?" but they broadly point to the same answer: ML researchers, superforecasters, and most others were all surprised by the progress in large language models in 2022 and 2023. Competitions to forecast difficult ML benchmarks ML benchmarks are sets of problems which can be objectively graded, allowing relatively precise comparison across different models. We have data from forecasting competitions done in 2021 and 2022 on two of the most comprehensive and difficult ML benchmarks: the MMLU benchmark and the MATH benchmark. First, what are these benchmarks? The MMLU dataset consists of multiple choice questions in a variety of subjects collected from sources like GRE practice tests and AP tests. It was intended to test subject matter knowledge in a wide variety of professional domains. MMLU questions are legitimately quite difficult: the average person would probably struggle to solve them. At the time of its introduction in September 2020, most models only performed close to random chance on MMLU (~25%), while GPT-3 performed significantly better than chance at 44%. The benchmark was designed to be harder than any that had come before it, and the authors described their motivation as closing the gap between performance on benchmarks and "true language understanding": Natural Language Processing (NLP) models have achieved superhuman performance on a number of recently proposed benchmarks. However, these models are still well below human level performance for language understanding as a whole, suggesting a disconnect between our benchmarks and the actual capabilities of these models. Meanwhile, the MATH dataset consists of free-response questions taken from math contests aimed at the best high school math students in the country. Most college-educated adults would get well under half of these problems right (the authors used computer science undergraduates as human subjects, and their performance ranged from 40% to 90%). At the time of its introduction in January 2021, the best model achieved only about ~7% accuracy on MATH. The authors say: We find that accuracy remains low even for the best models. Furthermore, unlike for most other text-based datasets, we find that accuracy is increasing very slowly with model size. If trends continue, then we will need algorithmic improvements, rather than just scale, to make substantial progress on MATH. So, these are both hard benchmarks - the problems are difficult for humans, the best models got low performance when the benchmarks were introduced, and the authors seemed to imply it would take a while for performance to get really good. In mid-2021, ML professor Jacob Steinhardt ran a contest with superforecasters at Hypermind to predict progress on MATH and MMLU. Superforecasters massively undershot reality in both cases. They predicted that performance on MMLU would improve moderately from 44% in 2021 to 57% by June 2022. The actual performance was 68%, which s...

"Insights from Luke Arrigoni, CEO of Arricor, on AI Innovations and Business Impact"Description: Welcome to an enlightening episode of our podcast as we dive into the fascinating world of Generative AI, Vision AI, and Natural Language Processing (NLP) with the esteemed Luke Arrigoni. In this Part 1 interview, Luke, Chief Executive Officer at Arricor, takes us on a journey through AI's transformative potential.Discover the minds behind AI advancements as we delve into topics like facial recognition for privacy, Arricor's mission, Prompt engineering, and the myriad use cases that these technologies unlock. Gain valuable insights into Large Language Models (LLMs) and the role of prompt engineering in optimizing AI's capabilities.Luke Arrigoni shares his expertise on avoiding AI hallucinations, the unique differentiation of Arricor, and the remarkable business impact of Generative AI. Join us to explore the present and future of AI through this engaging discussion.Don't miss this opportunity to gain insights from a visionary in the AI field. Connect with Luke Arrigoni on LinkedIn [https://www.linkedin.com/in/lukearrigoni/] and learn more about Arricor's work on their website [http://arricor.com/]. Stay tuned for Part 2 as we continue our conversation on AI's groundbreaking potential.01:40 Meet Luke Arrigoni04:13 Facial recognition for privacy06:21 Arricor mission08:39 More on LLMs10:29 Prompt engineering13:30 Use cases16:30 Arricor differentiation20:59 Avoiding hallucinations26:13 Business impact of GenAILinkedIn:  https://www.linkedin.com/in/lukearrigoni/ Website: http://arricor.com/Want to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

MLOps Coffee Sessions #171 with Thibaut Labarre, Using Large Language Models at AngelList co-hosted by Ryan Russon. We are now accepting talk proposals for our next LLM in Production virtual conference on October 3rd. Apply to speak here: https://go.mlops.community/NSAX1O // Abstract Thibaut innovatively addressed previous system constraints, achieving scalability and cost efficiency. Leveraging AngelList investing and natural language processing expertise, they refined news article classification for investor dashboards. Central is their groundbreaking platform, AngelList Relay, automating parsing and offering vital insights to investors. Amid challenges like Azure OpenAI collaboration and rate limit solutions, Thibaut reflects candidly. The narrative highlights prompt engineering's strategic importance and empowering domain experts for ongoing advancement. // Bio Thibaut LaBarre is an engineering lead with a background in Natural Language Processing (NLP). Currently, Thibaut focuses on unlocking the potential of Large Language Model (LLM) technology at AngelList, enabling everyone within the organization to become prompt engineers on a quest to streamline and automate the infrastructure for Venture Capital. Prior to that, Thibaut began his journey at Amazon as an intern where he built Heartbeat, a state-of-the-art NLP tool that consolidates millions of data points from various feedback sources, such as product reviews, customer contacts, and social media, to provide valuable insights to global product teams. Over the span of seven years, he expanded his internship project into an organization of 20 engineers. He received a M.S. in Computational Linguistics from the University of Washington. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links ⁠Website: https://www.angellist.com/venture/relay --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Ryan on LinkedIn: https://www.linkedin.com/in/ryanrusson/ Connect with Thibaut on LinkedIn: https://www.linkedin.com/in/thibautlabarre/

Natalia A. Gonzalez, PhD, Infinx Chief Data Scientist, shares insights from our research recently presented at the IEEE ICHI conference, demonstrating how referral workflows can be streamlined by efficiently handling varied document structures. Learn how innovative technologies like AI, Natural Language Processing (NLP), and Optical Character Recognition (OCR) are revolutionizing document understanding in healthcare referrals. This is a great opportunity to understand the future of healthcare referrals and how technology can reduce administrative burdens, minimize errors, and enhance patient care. Brought to you by www.infinx.com

Newest Quark.ai board member has a distinguished background in science, leadership corporate development, Girl Scouts and Head Start “When you think about human development, education is the key,” says Sylvia Acevedo, Quark.ai's newly appointed board member. Acevedo observes that historically, education sees the “sage on the stage” while the students passively learn. “With AI, and generative AI, you turn that and really flip the script. Students can now own the learning process at a speed that is customized to them, and then the teacher's role is really the expert, the mentor, the guide who can really crystalize the learning for the student.” “What really drew me to AI is its incredible potential to make an impact on the world, and my belief that we can navigate its challenges to make sure its benefits are realized.” Acevedo is an experienced independent board member, currently serving on two public boards, Qualcomm Inc, and Credo Technologies, and a privately held company, Ambri Inc. Previously, she served on two private company boards, Dynamic Signal, a SaaS employee communications company, and Synergis Education, a higher education services company. Ms. Acevedo has a proven track record as a CEO and has been highly awarded for her success in a variety of industries, technology, education, federal government, and national non-profits. Quark.ai's potential to change customer service. In this podcast we discuss the immense potential of AI but also the dystopian possibilities. We learn that as Quark.ai's founder, Prosenjit Sen, began to talk to Acevedo about Quark.ai and the potential for great change, the conversation included a thought process that includes encouraging the market to use these new technologies wisely, train people for positions where they a re leveraging AI vs being replaced by AI, along with the nearly revolutionary way the technology might transform industry after industry. “We have built something special,” says Sen. Reflecting on the global discussion on how AI will change industries and employment, Sen reflects on the idea that with proper training, AI has the potential to make employees in, for example major industries such as automotive, more productive. “Regular people will be able to do sophisticated jobs.” An experienced leader Acevedo held executive roles at Apple, Dell, IBM, and Autodesk. Reflecting on her time as Chief Executive Officer of the Girl Scouts of the USA from May 2017 to August 2020 and as interim CEO from June 2016 to May 2017, Acevedo discusses how she led the organization to help close the gender gap among young people in STEM. “What I am really proud of is that over one million STEM merit badges were earned and over 180,000 of these were in cybersecurity.” Rocket Scientist A rocket scientist by training, Ms. Acevedo holds a B.S. in Industrial Engineering from New Mexico State University and a M.S. in Industrial Engineering from Stanford University. She has also been awarded an Honorary Doctor of Science from Duke University and an Honorary Doctorate from Washington College. “What really drew me to AI is its incredible potential to make an impact on the world, and my belief that we can navigate its challenges to make sure its benefits are realized.” About Quark.ai Quark.ai's Autonomous Support Platform is the industry's first pre-trained, purpose-built Generative AI platform that uses Large Language Models (LLM), classical Natural Language Processing (NLP), and Computer Vision technologies to provide a unique platform for Technical Support, Field Support, and Sale Support. The platforms unique IP enables the user to input a query to then get generated answers with very high accuracy (> 90%), 100 traceability to the right section sections of the documents that were used in generating the answer, minimal hallucination, and – at the lowest cost of ownership. Quark.ai is the Generative AI leader in Autonomous Customer Support,

As a reminder, if you are a mid-career high-achiever with the goal of entering the C-suite, let's talk about my new coaching program, Highly Promotable. Here's the link to learn more: https://exclusivecareercoaching.com/highly-promotable Today, we're talking about how employers are using Artificial Intelligence (AI) in job interviews – and what that means for you as a job seeker.  AI is being used in the interview process via Natural Language Processing (NLP), chatbots, sentiment analysis, facial expression recognition and visual perception, speech recognition, tone analysis, and decision-making.  Let's start with some definitions:  Artificial intelligence:Computer systems that can perform tasks that normally would require human intelligence. Artificial Intelligence-trained video interviewing technology analyzes facial features, moods, expressions, and intonations of the interviewees to select the most suitable candidates. Speech recognition, personality insights, tone analysis, the relevance of answers, emotional recognition, and psycholinguistics are used in this hiring process that uses technology automation. The best matches are shared with human recruiters along with AI's own notes on individual candidates. Chatbot:An artificial intelligence feature that is short for “chatterbot.” A chatbot is a software or program that simulates human conversations through voice commands and text chats. Chatbots are used for answering initial questions applicants have and to conduct preliminary “screening” interviews. Immediate feedback may be provided to the candidates.   Natural language processing (NLP):The interaction between humans and computers using natural language. AI's machine learning skills derive meaning and understanding from language as it is spoken by humans. The most common uses of NLP in the market today include chatbots, personal assistants (such as Siri and Alexa), predictive text, and language translation.   What AI tools are available to employers? There are at least four categories of tools: Video Conferencing ToolsEmployers often use video conferencing tools including Zoom, Microsoft Teams, and Google Meet to conduct remote AI job interviews.  AI Powered Interview PlatformsSpecialized platforms like HireVue, Pymetrics, and Mya Systems use AI technology to conduct interviews. These platforms employ natural language processing (NLP) and machine learning algorithms to analyze candidates' responses, assess their skills, and provide insights to employers. Online Assessment PlatformsOnline assessment platforms like TalentScored, eSkill, or CodinGame offer AI-related assessment tests and coding challenges specifically designed for evaluating candidates' AI knowledge, problem-solving abilities, and programming skills. Coding PlatformsFor technical positions, employers may use coding platforms such as HackerRank, Codility, or LeetCode. These platforms allow candidates to write and execute code, solve coding problems, and assess their programming skills.  How should you prepare for an AI interview?  This from Talview.com's website: “Candidates should prepare for an AI video interview the same way they would for a face-to-face interview. They must know everything there is to know about the company beforehand; look up the company website, Google news, press releases, and understand what the company and the industry are all about.“Candidates can also make a list of questions that they would like to ask their prospective employer. Practice makes perfect when it comes to an AI video interview. Candidates can make a list of expected questions and practice their answers. Once the video interview begins, candidates will not be able to stop, erase, or edit the interview and must, therefore, be prepared well in advance.“On the day of the interview, candidates must dress professionally. Position themselves in a straight-back chair and make sure the camera angle focuses waist up.“Although an AI video interview is recorded, it is for all purposes conducted just as a face-to-face interview would. So, candidates should sell their candidacy based on the company's needs and let the employer know how they will meaningfully contribute to their organization.“Finally, candidates are asked to keep calm and exude confidence through their body language.” The bottom line is this: There's nothing new that an AI interview does – it asks the same questions as a human interviewer would. But the deep analysis that goes into the assessment of an interview is beyond human undertaking. The speed, accuracy, and convenience of AI recruitment and AI video interviewing are very valuable. It's impossible that AI interviews will go off the grid – if anything, we will see an increase in its use. DIY vs DFYI'm going to combine the DIY and the DFY for this episode. If you want to improve your interview skills on your own, I recommend Yoodli – a free site that allows you to respond to the system's questions or input your own. Yoodli will help you with things like eye contact, use of filler words, and other vocal disrupters.  If you would like human help with your interview preparation, my interview coaching program includes working with Yoodli + 2, 1-hour coaching sessions. In addition to Yoodli's help, you'll work with me to develop strategies to approach difficult and behavioral interview questions, such as “What is your greatest weakness?” “Tell me about yourself.” “Tell me about a time when…” 

Lately, we've been hearing about Natural Language Processing (NLP) all over, so we decided it was time to have a guest who could chat with us about its values and limitations. Dan Alferov from Heartbeat AI joined Kathryn and Michelle to discuss how sentiment and text analysis play into NLP. In this informational episode, Dan shares some applications for these models, as well as benefits and limitations when using them to understand emotions. If you ever had a question about contextual language processing, this is the episode for you! #supervisedlearning #languageprocessing #emotions Meet our guest! Hi, I'm Dan Alferov, the director of empathy analytics at Heartbeat AI. My role involves the intersection of behavioural research and data science to solve business challenges by leveraging emotional analytics of unstructured text data. I am passionate about authentically representing human experience to understand decision-making across diverse groups of individuals, and its implications within healthcare, human resources, and market research. Empathy analytics aims to uncover the emotional drivers which form the experiences of critical demographics and seeks to quantify differences in emotional language expression patterns that represent these drivers. I am a strong supporter of mental health advocacy, equity, and diversity. I believe in constantly educating myself on my privileges to best support social progress and amplify the voices of those who are systematically disadvantaged. Quantifiable empathy represents both an avenue for prioritization of the next "best" action strategically and a way to foster social accountability to best support social progress and allyship across diverse business contexts. Outside of work, I can be found reading philosophy, playing basketball, cooking or occasionally binging reality TV. Extra Information: Dan's LinkedIn: https://www.linkedin.com/in/dan-alferov-27022813a/ Lana (Heartbeat AI's CEO) Linkedin: https://www.linkedin.com/in/lananovikova/ Heartbeat AI Website: https://heartbeatai.com/ Be sure to give us 5 Star rating, leave a review, or subscribe to your preferred method of listening. Don't forget to also follow us on any of our social media platforms listed below. Kathryn on ⁠LinkedIn⁠ Michelle on ⁠LinkedIn⁠ HCD Research ⁠Website⁠ MindSet ⁠Website Page⁠ Sign up for ⁠HCD Newsletter ⁠ Our Socials YouTube - ⁠@HCDResearchInc.⁠ LinkedIn - ⁠@HCDResearch⁠ Twitter - @⁠HCDNeuroscience⁠ Twitter - ⁠@HCDResearchInc⁠ Facebook - ⁠@HCDResearch⁠ Instagram - ⁠@HCDResearch⁠ MindSet is excited to have each and everyone one of you join our curious conversations! --- Send in a voice message: https://podcasters.spotify.com/pod/show/mindset-hcd-research/message

David Talby is the CTO and Founder of John Snow Labs, the company behind two popular open source projects: Spark NLP and LangTest.  In this episode we focus on LangTest, an open-source Python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. [Note: After we recorded this episode, NLTest was renamed to LangTest.]Subscribe to the Gradient Flow Newsletter:  https://gradientflow.substack.com/Subscribe: Apple • Spotify • Stitcher • Google • AntennaPod • Podcast Addict • Amazon •  RSS.Detailed show notes can be found on The Data Exchange web site.

In this podcast you'll hear how by leveraging the power of Natural Language Processing (NLP) and machine learning Observe.AI can analyze and assess every customer interaction, whether on a cloud-based or premise platform.

Cesar Toledo provides us with important information about the expanding new world of AI and how Natural Language Processing (NLP) can be leveraged for better content creation and optimization. Let us know what you thought about this episode and if you have any questions after listening! Download our FREE E-Book: https://websites.bippermedia.com/25-lseo-ebookI'd like to invite you to join our SEO Secrets Facebook group: https://www.facebook.com/groups/seosecretsgroup/Get listed today in our business directory: https://bippermedia.com/best-business-near-me/Visit our website: https://bippermedia.com/Schedule a strategy call: https://calendly.com/seo-consultation-team/30-minute-seo-consultation?month=2023-06 Hosted on Acast. See acast.com/privacy for more information.

John Kang, MD, Ph.D. is an assistant professor of Radiation Oncology and Biomedical Informatics Lead at the University of Washington in Seattle. His research interests include the application of Natural Language Processing (NLP) to examine trends in the MaML space. He is a physician-data scientist passionate about uncovering the complex interactions underneath large datasets. He has over 10 years of experience in the novel applications of computational modeling and machine learning in biology systems.  Host: David Wu Twitter: @davidjhwu Audio Producer: Aaron Schumacher Twitter: a_schu95 Video Editor + Art: Saurin Kantesaria Instagram: saorange314 Social Media: Nikhil Kapur 00:45 Could you tell us about your journey to the intersection of medicine and machine learning 07:40 Balancing Residency Training and staying caught up on research in the machine learning space 16:00 Using machine learning to understand biostatistics  18:12 How would you describe the research that you find the most exciting / Unsupervised learning  23:00 Overview of Word Embedding and addressing  potential bias  29:25 Dr. Kang's application of word embedding for research funding  42:52 The intersection of artificial intelligence and human intelligence  45:35 T-SNE / T-Distributed Stochastic Neighbor Embedding in grant analysis  50:50 Has T-SNE helped guide Dr. Kang's research and grant writing  57:00 The future of creativity and ChatGPT  01:02:30 Fear vs Hope in the Medicine and Machine Learning space  01:07:00 What do you think is the future of the MaML space in the next 10-20 years? 01:11:02 What advice would you give yourself as you were finishing medical school?

How to achieve business value from AI? In this episode, we are excited to bring on Chris Surdak to talk about how businesses should tackle the challenges of elusive success with AI. Chris is an award-winning, internationally-recognized transformation expert leveraging leading-edge technologies such as Robotic Process Automation (RPA), AI, Natural Language Processing (NLP) and Blockchain to drive results-oriented digital transformation for organizations of all sizes, industries and regions. Books by Christopher Surdak Chatpocalypse Now https://surdak.com/ https://twitter.com/CSurdak  

Word2Vec is a must-know if you're interested in Natural Language Processing (NLP) or preparing for any entry-level NLP roles. If you find our episodes helpful, we would really appreciate if you would consider becoming a paid member of our channel to support our growth.

ChatGPT seems to be taking the Internet by storm, so we decided to put it through its paces this episode. For the uninitiated, ChatGPT is a Natural Language Processing (NLP) tool that uses artificial intelligence (AI) to have human-like conversations with people. Did we lose you? Don't worry, you don't have to work at NASA or have a degree in computer science to enjoy this episode. Essentially, Scott asked the chatbot a bunch of questions about Disney and we went around the table grading its responses. This episode was so much fun that we may just do a part 2 in the future. We hope you have even half as much fun listening to this one as we did recording it.   Take a seat at the bar, and follow us on social media! Twitter, Instagram, Pinterest: WDWHappyHour YouTube: https://www.youtube.com/WDWHappyHour Facebook: https://www.facebook.com/wdwhappyhour

Host Jim Tate talks to Bevey Miner, EVP of Healthcare Strategy and Policy for Consensus Cloud Solutions. Consensus Solutions is known as the world's largest digital cloud faxing company. They have leveraged heritage technology to move from simple digital documents to advanced healthcare standard HL7/FHIR for secure data transport as well as Natural Language Processing (NLP) and AI to convert unstructured documents to meaningful structured data. To stream our Station live 24/7 visit www.HealthcareNOWRadio.com or ask your Smart Device to “….Play Healthcare NOW Radio.” Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen