Podcasts about nlp natural language processing

Qu'est-ce que le RAG ? L'avenir de l'intelligence artificielle générative pour des réponses précises et contextuellesBienvenue dans cet épisode de Digital Feeling ! Aujourd'hui, nous allons plonger dans le monde fascinant du RAG, ou Retrieval-Augmented Generation. Si vous avez déjà entendu des termes comme LM (Language Model), NLP (Natural Language Processing), ou RAG, cet épisode vous aidera à mieux comprendre comment fonctionne le RAG, pourquoi il est prometteur, et comment l'intégrer dans vos projets en intelligence artificielle.Le RAG : une combinaison puissante entre génération et récupération d'informationsLe RAG combine deux techniques principales de l'intelligence artificielle :Récupération d'informations : il va chercher des données spécifiques dans une base de connaissances, qu'il s'agisse d'une base de données interne ou de ressources en ligne.Génération de texte : il utilise des modèles de langage pour générer une réponse en langage naturel.Comment fonctionne un modèle RAG ?Identification de la question : Le modèle reçoit une question de l'utilisateur.Recherche d'informations : Contrairement à un modèle classique qui se base sur sa mémoire, le RAG va consulter des sources externes spécifiques.Récupération des données : Il sélectionne les documents ou extraits les plus pertinents pour répondre à la question.Génération de la réponse : Le modèle intègre ces informations dans une réponse contextuelle et détaillée.Les avantages du RAG pour les entreprises et les utilisateursLe RAG offre une solution idéale pour ceux qui cherchent des informations précises, car il permet d'adapter la réponse aux données les plus récentes. Voici quelques exemples d'applications concrètes :Support client : un assistant virtuel RAG peut accéder aux bases de données en temps réel pour fournir des réponses personnalisées.Éducation : il peut offrir des informations académiques à jour, surpassant les manuels imprimés.Santé et médecine : pour des réponses basées sur les recherches médicales les plus récentes.Finance et droit : il peut intégrer les derniers changements réglementaires, notamment en matière de législation.Pourquoi le RAG est-il essentiel à l'ère de l'intelligence artificielle ?Avec le développement rapide des bases de données et des capacités des modèles d'IA, le RAG devient un outil puissant pour combiner expertise humaine et intelligence artificielle en temps réel. À l'heure où les utilisateurs cherchent des alternatives aux moteurs de recherche traditionnels, le RAG propose une précision accrue et des informations contextualisées, évitant les hallucinations qui peuvent parfois survenir avec des modèles de génération pure.Éthique et confidentialité des donnéesL'utilisation de données externes pose des questions sur la transparence et la gestion des données. Les entreprises devront veiller à adopter des pratiques éthiques et respecter la confidentialité des informations lors de l'utilisation du RAG, tout en assurant un niveau de précision élevé.ConclusionLe RAG, ou Retrieval-Augmented Generation, est une avancée prometteuse pour offrir des réponses précises, contextuelles et mises à jour, transformant notre manière de consulter et d'utiliser les connaissances. Il allie la puissance de la génération de texte à la récupération d'informations, créant un assistant intelligent pour des domaines aussi variés que le support client, l'éducation, la recherche, et les industries réglementées.Merci d'avoir écouté cet épisode de Digital Feeling. Vous avez aimez cet épisode et vous voulez soutenir ce podcast ? Laissez moi un avis sur Apple podcast ou Spotify et parlez-en autour de vous, il pourrait aider quelqu'un d'autre ;)Vous avez une question en marketing digital ou sur l'Intelligence Artificielle Générative ? Posez-moi vos questions sur Linkedin en MP et je me ferai un plaisir d'y répondre lors d'un prochain épisode : https://www.linkedin.com/in/elodiechenolPour rester en contact découvrez ma newsletter : https://substack.com/@elodiechenolA bientôt pour un prochain épisode ;)Et si vous me découvrez à travers ce podcast, je suis Elodie Chenol, formatrice, consultante et conférencière en marketing digital et Intelligence artificielle.A bientôt !Elo

You.com showcases the state of AI today The story of you.com is multi-faceted and telling in many ways. You.com was founded in 2020 by Richard Socher, one of the leading NLP (Natural Language Processing) researchers in the world, to offer a better search experience to users and compete with Google. With a startup exit and a Chief Data Scientist stint at Salesforce, Socher got the experience, network and backing he needed to pursue his long-time ambition of taking on Google. That's something few people have tried, with moderate success. Socher diagnosed early enough that the way to success is by carving a niche for you.com. You.com focuses on serving knowledge workers in "complex informational / action searches": elaborate queries, and queries that are really about accomplishing a task, respectively. In 2022, in the pre-ChatGPT era, Socher set out a course for you.com based on AI, apps, privacy, and personalization. In 2024, you.com is staying the course, but a few things have changed. In the GenAI era the competition is growing, and borrowing pages from you.com's book. Language model providers such as OpenAI and Anthropic now offer services similar to you.com. Upstarts such as perplexity.ai have sprung up, and Google itself is embracing the AI approach to search. You.com is making progress too. Since launching in November 2021, you.com has served 1 billion queries and has millions of active users, including from Fortune 500. The company's ARR has grown by 500% since January 2024. Today, you.com announced a $50 million Series B funding round, as well as a new team plan called Multiplayer AI. We caught up with Socher, talked about the news, and took you.com for a spin. Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2024/09/04/you-com-raises-50m-to-lead-ai-for-knowledge-workers/

Das Thema Künstliche Intelligenz polarisiert. Spätestens seit ChatGPT ist KI im Marketingkontext Teil des Alltags von Marketingfachleuten geworden. Doch wie sieht KI im Marketing konkret aus? Warum lohnt sich der Einsatz und wie wird KI heute schon zielgerichtet eingesetzt?  Themen:  [00:48] Was ist KI im Marketing?  [02:22] Machine Learning [02:59] Deep Learning  [03:21] NLP - Natural Language Processing [03:54] Chancen und Vorteile von KI im Marketing  [05:52] Fazit Feedback? Gerne an podcast-dach@hubspot.com  Mehr über uns unter: https://www.hubspot.de/podcasts/listen-and-grow 

Mark Gordon, Founder and Co-Chairman of Tower Research Capital, brings a New York background and Ivy League education to a discussion about money, technology, and politics. The Wall Street Executive who continues development breakthroughs in AI and NLP (Natural Language Processing) shares the latest developments in technology and benefits of Artificial Intelligence as well as challenges we face as we head toward a brave new world. Fascinating insights!

George Brooks interviews Artem Koren, Co-founder of Sembly.ai. Artem shares the journey of creating Sembly, an AI-powered meeting companion. Artem unveils Sembly.ai's unique team structures for different technological components and emphasizes the flexibility required in handling NLP (Natural Language Processing) and AI development. The interview showcases the pivotal role of product managers, who drive innovation, user experience, and customer interaction. Discover how Sembly.ai transforms meetings with its AI teammate, revolutionizing the way we collaborate.

In this episode, Nader Samii and T.J. Rock from the National Medical Billing Services, discuss the future of revenue cycle management in healthcare. Experience how technology streamlines revenue cycle management processes and improves efficiency. Learn about the roles of RPA (Robotic Process Automation), NLP (Natural Language Processing), computer-assisted coding, and conversation analytics, and how these technologies help automate manual processes, bridge interoperability gaps, improve coding accuracy, and analyze conversations between representatives and payers or patients.Uncover the possibilities of voice RPA, a bot that sounds like a person and can call payers to overturn denials, and emerging technologies like ChatGPT for medical coding. Discover the practical applications of AI, machine learning, and RPA in revolutionizing revenue cycle management.Don't miss this enlightening conversation on the future of revenue cycle management in healthcare.

This latest Today's Conveyancer podcast welcomes Chris Harris on to discuss the current state of the conveyancing market and the challenges firms face in harnessing technology to make transactions run smoother. Familiar to many, Chris has been in and around the conveyancing profession for a number of years as a practicing solicitor and a panel manager and in 2011 he founded Lawyer Checker before selling to Dye & Durham in 2021. His latest venture is a tool to help conveyancers better deal with enquiries. Enquiries has been a major issue for conveyancers in recent years, with many taking to social media to highlight the practice of raising lengthy and at times unnecessary enquiries which serve to delay the transaction. Indeed, says Chris, too many times it appears that we delay the transaction more by arguing over who is going to answer the query rather than going to get the answer in the first place. There is, he says, a fundamental lack of collaboration. And who is driving the increase in enquiries? Is it consumers taking a more active role in their home move and asking more questions about the property (and what role does the estate agent play in answering some of those questions). Or is it conveyancers ensuring they are covering their responsibilities with one eye on PII? For Chris, AI and automation could be a part of the solution. We are already seeing increasingly useful tools being developed which makes sense of data which is held in a standard format and can be transmitted between firms in a uniform way. Chris cites examples such auto-report builders and post completion technology which take structured information and present it back to us in a helpful and usable format. But there are plenty of examples in conveyancing of unstructured data; words on a page without any ability to extract and process individual points.  He points specifically to enquiries where conveyancers will often receive emails raising a list of queries with no way of standardising information contained. This is where AI and NLP (Natural Language Processing) might have a role to play in reading these emails, identifying the queries and creating task lists; in the fullness of time, these tools will be able to fulfil the enquiry. For example if a buyer's conveyancer raises an enquiry for a copy of the EPC, automation has the ability to read it, go off and search the EPC Register, and return the result without any intervention explains Chris. In a fascinating discussion Chris explains that we are only just scratching the surface of what AI has the ability to do in conveyancing and warns firms that unless they are embracing the technology coming down the line, they may get left behind.  The Today's Conveyancer podcast can be found on your preferred podcast provider and also at www.todaysconveyancer.co.uk. Subscribe and listen in for all the latest conveyancing industry news and views.

GPT (Generative Pre-trained Transformer) is a state-of-the-art language model developed by OpenAI. It is an AI-based model that uses machine learning to generate natural language text. GPT is trained on a large dataset of text, which allows it to understand the patterns and structure of human language. It can then use this understanding to generate new text that is similar to the text it was trained on. GPT can be used for a variety of natural language processing tasks such as text generation, language translation, and language summarization. It is widely used in the field of NLP (Natural Language Processing) and can be fine-tuned to specific tasks such as question-answering, language summarization, etc. GPT-3, the latest version of GPT, is one of the most powerful models of its kind and is widely used in research and industry for its ability to understand and generate human-like text. Sign up - https://openai.com/

This episode is about ESG data. We covered things like: - What motivated you to found Sanctify - Sanctify ESG scoring - Automated ESG analysis tool - How are you using NLP(Natural Language Processing) in analysis? - What is the most powerful feature of Sanctify? - The news about Sanctify being available in Bloomberg App Portal --- Guest Profile - https://www.linkedin.com/in/erik-dahlberg-4405a27b/ Webiste - https://sanctify.ai/ Link to open on Bloomberg - https://blinks.bloomberg.com/screens/apps%20sesg ---- Do you want to be a guest on our podcast? I will be waiting for your email at vikram@73bit.com http://www.73bit.com/

Summary Using machine learning in production requires a sophisticated set of cooperating technologies. A majority of resources that are available for understanding how to design and operate these platforms are focused on either simple examples that don’t scale, or over-engineered technologies designed for the massive scale of big tech companies. In this episode Jacopo Tagliabue shares his vision for "ML at reasonable scale" and how you can adopt these patterns for building your own platforms. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you. Your host is Tobias Macey and today I’m interviewing Jacopo Tagliabue about building "reasonable scale" ML systems Interview Introduction How did you get involved in machine learning? How would you describe the current state of the ecosystem for ML practitioners? (e.g. tool selection, availability of information/tutorials, etc.) What are some of the notable changes that you have seen over the past 2 – 5 years? How have the evolutions in the data engineering space been reflected in/influenced the way that ML is being done? What are the challenges/points of friction that ML practitioners have to contend with when trying to get a model into production that isn’t just a toy? You wrote a set of tutorials and accompanying code about performing ML at "reasonable scale". What are you aiming to represent with that phrasing? There is a paradox of choice for any newcomer to ML. What are some of the key capabilities that practitioners should use in their decision rubric when designing a "reasonable scale" system? What are some of the common bottlenecks that crop up when moving from an initial test implementation to a scalable deployment that is serving customer traffic? How much of an impact does the type of ML problem being addressed have on the deployment and scalability elements of the system design? (e.g. NLP vs. computer vision vs. recommender system, etc.) What are some of the misleading pieces of advice that you have seen from "big tech" tutorials about how to do ML that are unnecessary when running at smaller scales? You also spend some time discussing the benefits of a "NoOps" approach to ML deployment. At what point do operations/infrastructure engineers need to get involved? What are the operational aspects of ML applications that infrastructure engineers working in product teams might be unprepared for? What are the most interesting, innovative, or unexpected system designs that you have seen for moderate scale MLOps? What are the most interesting, unexpected, or challenging lessons that you have learned while working on ML system design and implementation? What are the aspects of ML systems design that you are paying attention to in the current ecosystem? What advice do you have for additional references or research that ML practitioners would benefit from when designing their own production systems? Contact Info jacopotagliabue on GitHub Website LinkedIn Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links The Post-Modern Stack: ML At Reasonable Scale Coveo NLP == Natural Language Processing RecList Part of speech tagging Markov Model YDNABB (You Don’t Need A Bigger Boat) dbt Data Engineering Podcast Episode Seldon Metaflow Podcast.__init__ Episode Snowflake Information Retrieval Modern Data Stack SQLite Spark SQL AWS Athena Keras PyTorch Luigi Airflow Flask AWS Fargate AWS Sagemaker Recommendations At Reasonable Scale Pinecone Data Engineering Podcast Episode Redis KNN == K-Nearest Neighbors Pinterest Engineering Blog Materialize OpenAI The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Summary The increasing sophistication of machine learning has enabled dramatic transformations of businesses and introduced new product categories. At Assembly AI they are offering advanced speech recognition and natural language models as an API service. In this episode founder Dylan Fox discusses the unique challenges of building a business with machine learning as the core product. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! Your host is Tobias Macey and today I’m interviewing Dylan Fox about building and growing a business with ML as its core offering Interview Introduction How did you get involved in machine learning? Can you describe what Assembly is and the story behind it? For anyone who isn’t familiar with your platform, can you describe the role that ML/AI plays in your product? What was your process for going from idea to prototype for an AI powered business? Can you offer parallels between your own experience and that of your peers who are building businesses oriented more toward pure software applications? How are you structuring your teams? On the path to your current scale and capabilities how have you managed scoping of your model capabilities and operational scale to avoid getting bogged down or burnt out? How do you think about scoping of model functionality to balance composability and system complexity? What is your process for identifying and understanding which problems are suited to ML and when to rely on pure software? You are constantly iterating on model performance and introducing new capabilities. How do you manage prototyping and experimentation cycles? What are the metrics that you track to identify whether and when to move from an experimental to an operational state with a model? What is your process for understanding what’s possible and what can feasibly operate at scale? Can you describe your overall operational patterns delivery process for ML? What are some of the most useful investments in tooling that you have made to manage development experience for your teams? Once you have a model in operation, how do you manage performance tuning? (from both a model and an operational scalability perspective) What are the most interesting, innovative, or unexpected aspects of ML development and maintenance that you have encountered while building and growing the Assembly platform? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Assembly? When is ML the wrong choice? What do you have planned for the future of Assembly? Contact Info @YouveGotFox on Twitter LinkedIn Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Assembly AI Podcast.__init__ Episode Learn Python the Hard Way NLTK NLP == Natural Language Processing NLU == Natural Language Understanding Speech Recognition Tensorflow r/machinelearning SciPy PyTorch Jax HuggingFace RNN == Recurrent Neural Network CNN == Convolutional Neural Network LSTM == Long Short Term Memory Hidden Markov Models Baidu DeepSpeech CTC (Connectionist Temporal Classification) Loss Model Twilio Grid Search K80 GPU A100 GPU TPU == Tensor Processing Unit Foundation Models BLOOM Language Model DALL-E 2 The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Bonjour et bienvenue dans Du Vent Sous La Robe, le podcast qui vous emmène à la rencontre d'acteurs innovants qui construisent le droit de demain. Pour cette trente-huitième interview, j'accueille Warren Azoulay et on parle d'intelligence artificielle ! Vous entendez parler d'intelligence artificielle, de machine learning et de NLP (Natural Language Processing), mais vous ne savez pas exactement :  - Ce que c'est ;  - A quoi cela peut servir dans le domaine du droit ;  - Quels sont les applications et usages concrets qui existent aujourd'hui ;  - Ce qui relève au contraire du fantasme ;  - Et quels sont les risques qui existent aujourd'hui ?  Ça tombe bien, dans ce dernier épisode de Du Vent Sous La Robe, Warren Azoulay nous explique tout ! Warren est enseignant-chercheur à la Faculté de droit et de sciences politiques d'Aix-Marseille et consultant pour des legaltechs sur les questions liées aux méthodes d'apprentissage automatique (autrement dénommées machine learning) appliquées au droit. Il fait preuve de beaucoup de pédagogie pour expliquer ces notions, alors n'ayez pas peur et foncez l'écouter !     Pour aller plus loin : - Laetitia Jacquier  - Du Vent Sous La Robe  - Geoffrey Delcroix  - Sumi Saint-Auguste  - Open Law* Le droit ouvert  - Le Global Legal Hackathon (à Paris du 25 au 27 mars 2022)  - Warren Azoulay  - « L'intelligence artificielle ne devra pas servir à standardiser la réponse judiciaire », Warren Azoulay  - « Des machines et des hommes. La guerre n'aura pas lieu », Warren Azoulay  - « Droit et enjeu pénal à l'épreuve du renseignement Numérique », Warren Azoulay  - « Justice digitale – Antoine Garapon et Jean Lassègu »  Compte rendu par Warren Azoulay  - « Desire and Disobedience in the Digital Age », Warren Azoulay.  

Episode 18 – Jackie joins us from Seattle WA, and shares what motivated her to develop one of the most unique AI driven, Legal Tech software products on the market today.  You're also in for a treat as Jackie sings a Christmas jingle about what makes Clearbrief so special!  It's a FUN and informative episode with one talented entrepreneur!  John provides Intro on Jackie and shares her Background:   00:37 – 02:22Jackie gives some additional info on her background and shares with us her musical background and how she and her Husband (who is also her partner at Clearbrief) met and still play music together:   02:23 – 03:16 Don't SUCK Fun Fact about Jackie (Jackie is an AMAZING singer, so we wrote her a song about her company and turned it into a Christmas Jingle).  You are not going to believe her ANGELIC voice as we ask her to SING on the spot:   03:17 – 06:25 Jackie shares some insight as to how Clearbrief came to be and how her experience at Casey Family Program coupled with her interest in data influenced her decision to take the leap into the startup world:   06:26 -  10:11  Jackie digs into what it takes from a discovery process as she shares how much customer discovery they did as they researched the market for pain points prior to Clearbrief product development, talking to well over 200 lawyers, paralegals and judges to get a feel for some of their issues and more importantly validate the same patterns she experienced.  She shares the importance of incorporating potential customers into the development and testing process:  10:12 - 14:59Jackie discusses some of the features and functionality of the software and gives a fantastic overview of what the Clearbrief software does:  15:00  -  23:14  Jackie talks about the powerful NLP (Natural Language Processing) and AI behind Clearbrief and its capability to pull all publicly available legal sources for all legal citations since it ID's every site in your document.  She shares the “easy button” functionality and how much time it can save in the process:  23:15 – 26:54Jackie wraps up by providing some vision on where technology, the court systems and Clearbrief are headed in the future: 26:55 – 30:17   31:39 Jackie's Contact Info: LinkedIn – Jackie Schafer:  https://www.linkedin.com/in/jackieschafer/Website:   www.clearbrief.aiEmail:  jackie@clearbrief.ai  Conclusion:  32:40  Bob Miller Intro with Music Fade

Aujourd'hui la crise sanitaire a complètement modifié notre rapport à la Science

Studies show that over 50% of people have experienced sexual harassment at work, and even a bigger amount of people have witnessed it.We can't operate in business without acknowledge this and do our small contribution to addressing this situation, irrespective of our team size.That's why we decided to bring on Eleanor Manley, CTO and co-founder of Metta Space, a deeptech company with the goal to completely eradicate sexual harassment at work. They do so using NLP (Natural Language Processing) and big data.We discussed how sexual harassment happens at work and the cultural differences between countries and cultural backgrounds, and also how being non-native in a given language brings people to pick the wrong words and how this can be solved with technology.From the technology standpoint, we cover their techstack, how they are training and cleaning their dataset (trained with Reddit data

In this episode, you'll learn about language learning from AI (Artificial Intelligence) linguist and English teacher, Yazzy Ares. In this podcast we'll talk about the field of NLP (Natural Language Processing) and AI, and the difference between humans and computers when it comes to language processing and language acquisition. We also talked about biases and discrimination when it comes to language learning and what we can learn about it from computers. And if you are an English student or teacher, you'll also understand how to use this AI to improve your pronunciation! To watch the video interview with Yazzy and see her presentation go here: https://youtu.be/7FP5ikdqlCs Check out Yazzy's website: https://www.yazzyares.com/ Follow her on Instagram: https://www.instagram.com/yazzy.ares/ If you are doing linguistic research and need a hand with your data, you can contact her there.

In this episode, you'll learn about language learning from AI (Artificial Intelligence) linguist and English teacher, Yazzy Ares. In this podcast we'll talk about the field of NLP (Natural Language Processing) and AI, and the difference between humans and computers when it comes to language processing and language acquisition. We also talked about biases and discrimination when it comes to language learning and what we can learn about it from computers. And if you are an English student or teacher, you'll also understand how to use this AI to improve your pronunciation! To watch the video interview with Yazzy and see her presentation go here: https://youtu.be/7FP5ikdqlCs Check out Yazzy's website: https://www.yazzyares.com/ Follow her on Instagram: https://www.instagram.com/yazzy.ares/ If you are doing linguistic research and need a hand with your data, you can contact her there.

In this episode, you'll learn about language learning from AI (Artificial Intelligence) linguist and English teacher, Yazzy Ares. In this podcast we'll talk about the field of NLP (Natural Language Processing) and AI, and the difference between humans and computers when it comes to language processing and language acquisition. We also talked about biases and discrimination when it comes to language learning and what we can learn about it from computers. And if you are an English student or teacher, you'll also understand how to use this AI to improve your pronunciation! To watch the video interview with Yazzy and see her presentation go here: https://youtu.be/7FP5ikdqlCs Check out Yazzy's website: https://www.yazzyares.com/ Follow her on Instagram: https://www.instagram.com/yazzy.ares/ If you are doing linguistic research and need a hand with your data, you can contact her there.

PRophet's CEO, Aaron Kwittken, joins Coruzant Technologies for the Digital Executive podcast. He shares how he has recently launched his AI company to better predict stories. This is done through machine learning using NLP (Natural Language Processing) by looking at past stories to determine best story, pitch and ultimately what type of spread (viral, syndicate, etc.).

In Knowledge Connexions 2020, we had the honor and the privilege of hosting Hamlet Batista, alongside Dawn Anderson, David Amerland, Jason Barnard, and Andrea Volpini This great group of people shared their insights on "Knowledge Graphs and SEO: The next chapter" It is with deep sadness that we have learned that Hamlet Batista passed away in January 2021 Though our encounter was brief, we can only attest to the opinions of everyone who knew him: Hamlet was deeply knowledgeable and a pleasure to work with. We share the insights of this panel on the interplay between semantic technology, SEO, and knowledge graphs with the community, as a tribute to Hamlet Batista's memory ---- Workshop Description --- A talk by Dawn Anderson (Bertey), David Amerland (Davidamerland.com), Jason Barnard (Kalicube), Hamlet Batista (RankSense) & Andrea Volpini (Wordlift). “Knowledge Graph” is an overloaded term. Today Knowledge Graphs are becoming mainstream, and as this happens, more and more people associate Knowledge Graphs with data models, semantics, knowledge management, and ontologies For many other people, however, Knowledge Graphs still mean Google Search Info Boxes, panels, SERPs, and SEO (Search Engine Optimization). They are all right. The term Knowledge Graph was introduced by Google to signify the huge improvement that semantic technology brought to its search engine. Over time, the extended search capabilities and components enabled by semantic technology have become namesakes for Knowledge Graph. While the term Knowledge Graph has more meanings than this, it's useful to return to the source. The evolution of Knowledge Graph-powered Google search now extends to voice, assimilates information from JSON-LD markup beyond Wikipedia, and advances the state of the art in NLP (Natural Language Processing). Let's explore how this influences, and is influenced by, advances in semantic technology, where the evolution of SEO is headed, and what this means for knowledge graphs at large.

According to Messenger-Inquirer, the responding time of service emergency 911 has been reduced, said the 911 director. Unlike before, the dispatcher now can continue collecting the emergency information from the phone call, while the crew keeps moving toward their jobs. Text To Speech is an AI technology that allows the machine can talk in natural human languages. Together with other technologies such as Chatbot and NLP (Natural Language Processing), they can change the future just like Hollywood movies. With these technologies and automation, job processing will reduce the pressure on humans. It also decreases the time of processing jobs, and increases the quality of works. How it works? Normally, we need many people to take care of the call-center 24 hours per day. The general inquiries from the calls are the information of the callers such as name, address, … and the problems they want to report. The Text-To-Speech technology allows a computer voice to dispatch the call while the crew team dispatcher is still talking to the caller at the same time. After the call, the collected pieces of information will be processed at once and get categorized immediately. Without AI processing, we can not reach this level of these speedy jobs, ever!

ThoughtTrace's CEO, Nick Vandivere, joins Coruzant Technologies for the Digital Executive podcast.  He shares how his company leverages AI to read subject matter for a specific domain or use case using NLP (Natural Language Processing) to address business needs at scale.

В 5-м выпуске я общался со специалистом в области NLP (Natural Language Processing) Николаем Ивановым. Николай рассказал про то, как устроена обработка естественного языка, чем хороши модели GPT от OpenAI и как GPT-3 ведет себя в проекте Replika - виртуальном друге довольно хорошего качества. Отдельного внимания заслуживает история появления проекта Replika, от которой у меня появилось ощущение, что именно с нее вдохновлялись авторы одной из серий "Черного зеркала". Приятного прослушивания! Ссылки выпуска: Выпуск подкаста "Подлодка" - интервью с коллегой Николая (https://podlodka.io/176) Как работает GPT-3 (на английском) (https://jalammar.github.io/how-gpt3-works-visualizations-animations/) Виртуальный друг - Replika (https://replika.ai/) Тот самый курс от Andrew Ng (https://www.coursera.org/learn/machine-learning) Буду благодарен за обратную связь! Оставляйте ваши комментарии. Они помогут сделать подкаст лучше! Напишите что вам было понятно, что не очень, какие темы раскрыть, ну, и вообще в какую сторону катить этот подкаст :) Подписывайтесь на телеграм-канал "Стать специалистом по машинному обучению" (https://t.me/toBeAnMLspecialist) Со мной можно связаться по электронной почте: kms101@yandex.ru

Это 4-й выпуск подкаста, посвященного машинному обучению. И в этот раз я записывался не один, а с Иваном Ямщиковым - специалистом, в отличие от меня, по машинному обучению, реализовавшим многие интересные проекты в этой области и продолжающим развивать эту область. Иван работал аналитиком в Яндексе, занимается наукой в области NLP (Natural Language Processing) и является евангелистом компании Abbey. В подкасте мы сначала немного поговорили на отвлеченные темы: про роботов, продление жизни и трансгуманизм, а потом Иван довольно подробно и простым языком рассказал про нейросети и про отдельный их класс - автоэнкодеры. По мне, выпуск получился интересным! Ссылки выпуска: Послушать Нейронную оборону (https://music.yandex.ru/artist/4445922) Открытие YaC 2017, на которой оркестр исполнял музыку в стиле Скрябина, сгенерированную нейросетью (https://youtu.be/5bfI3bhiRa4) Книги, рекомендованные Иваном: 1. Глубокое обучение. Погружение в мир нейронных сетей (Николенко, Кадурин, Архангельская) 2. Глубокое обучение на Python (Ф.Шолле) 3. Глубокое обучение (Ян Гудфеллоу, Бенджио Иошуа) https://towardsdatascience.com/ - блог с большим количеством примеров и туториалов Курс "Введение в машинное обучение" от ВШЭ и Яндекса (https://ru.coursera.org/learn/vvedenie-mashinnoe-obuchenie) Курс "Специализация Машинное обучение и анализ данных" (https://ru.coursera.org/specializations/machine-learning-data-analysis) Статья на медиуме про лучшие бесплатные курсы по машинному обучению (https://medium.com/nuances-of-programming/%D1%82%D0%BE%D0%BF-10-%D0%BA%D1%83%D1%80%D1%81%D0%BE%D0%B2-%D0%BF%D0%BE-%D0%BC%D0%B0%D1%88%D0%B8%D0%BD%D0%BD%D0%BE%D0%BC%D1%83-%D0%B8-%D0%B3%D0%BB%D1%83%D0%B1%D0%BE%D0%BA%D0%BE%D0%BC%D1%83-%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D1%8E-%D0%B2-2020-1e1d870a24b7) Курс fast.ai (https://www.fast.ai/) Буду благодарен за обратную связь! Оставляйте ваши комментарии. Они помогут сделать подкаст лучше! Напишите что вам было понятно, что не очень, какие темы раскрыть, ну, и вообще в какую сторону катить этот подкаст :) Подписывайтесь на телеграм-канал "Стать специалистом по машинному обучению" (https://t.me/toBeAnMLspecialist) Со мной можно связаться по электронной почте: kms101@yandex.ru

W dzisiejszym odcinku naszym gościem jest Piotr Szpakiewicz – Head od SEO w Ringier Axel Springer. Będziemy mieli możliwość porozmawiania o SEO (Search Engine Optimization) – pozycjonowaniu, w kontekście kancelarii prawnych. Jak rozgryźć mistycznego Google i co warto zrobić, aby znaleźć się w wyszukiwarce Google nad swoją konkurencją? - Co jest największym wyzwaniem w pozycjonowaniu kancelarii prawnej? - Jakie są założenia SEO i jak walczyć z konkurencją Google? - Na czym polega sekret Google w kontekście wyszukiwania informacji? - Czym jest „CTR” i w jaki sposób należy analizować ten współczynnik? - Jak działają algorytmy Google i jaki jest ich wpływ na pozycjonowanie? - Na czym polega optymalizacja SEO on-site i off-site? - Czy Google aktualnie bierze pod uwagę tylko treści tekstowe, a pomija wideo i podcasty? - Czym są dane strukturalne i w jaki sposób możemy optymalizować podcasty pod SEO? - Jakie działania powinna podjąć kancelaria prawna, aby wzmocnić pozycjonowanie swojej strony? - Jaką rolę odgrywają słowa kluczowe w kontekście pozycjonowania i jak je odpowiednio dobierać? - Czym jest element „Title” i dlaczego jest najistotniejszy w rozumieniu SEO? - Dlaczego nie warto realizować techniki „Keyword stuffing’u”? - Jakie są czynniki rankingowe Google i jak wpływają na pozycjonowanie stron? - Na czym polega „User Experience” i dlaczego warto zachować naturalność w pozycjonowaniu? - Co to analiza sentymentu oraz jak możemy ją wykorzystać w SEO? - Jak przetwarzanie języka naturalnego wpływa na SEO? - Jak skutecznie stosować nagłówki H1-H6 – jaka jest poprawna hierarchia nagłówków? - W jaki sposób należy zaplanować budżet na pozycjonowanie? - Co to jest „E-A-T” i dlaczego jest tak ważne w kontekście pozycjonowania? - W jaki sposób należy optymalizować witryny pod kątem wyszukiwarek? - Czym są kluczowe wskaźniki efektywności (KPI) i dlaczego są tak ważne? - Jak istotna pod kątem SEO jest sekcja contentowa? - Czy tworząc bloga należy postawić go na własnej domenie, a może lepiej użyć domeny zewnętrznej? - W jaki sposób efektywnie wypromować swój brand korzystając z SEO? - Jak często zmieniają się algorytmy Google i czym jest to spowodowane? - Jakich strategii i technik nie należy stosować korzystając z SEO? - Jaki rolę w pozycjonowaniu odgrywają media społecznościowe? - Czy posiadanie zoptymalizowanej strony mobilnej jest koniecznością w kontekście SEO? - Jakie są prognozy trendów rozwoju Google w kontekście pozycjonowania podcastów? - Jak wyszukiwarka Google będzie się rozwijała w najbliższym czasie? - Dlaczego warto mieć wizytówkę w „Google Moja Firma”? Narzędzia wspierające analizy SEO (widoczność i słowa kluczowe): http://senuto.com/pl https://ahrefs.com/ https://neilpatel.com/ubersuggest/ https://ads.google.com/intl/pl_pl/home/tools/keyword-planner/ Analiza linków przychodzących (Backlinki): https://pl.majestic.com/ https://ahrefs.com/ Analiza ruchu, analityka, komunikaty techniczne, etc. https://search.google.com/search-console/about Crawlery: https://www.screamingfrog.co.uk/ https://www.oncrawl.com/ https://www.deepcrawl.com/ Dane strukturalne (Guide, Podcasty, Artykuł): https://developers.google.com/search/docs/guides/intro-structured-data?hl=pl https://developers.google.com/search/docs/guides/podcast-overview https://developers.google.com/search/docs/data-types/article Próbka możliwości (komercyjnego) rozwiązania Google Cloud w zakresie NLP (Natural Language Processing): https://cloud.google.com/natural-language#natural-language-api-demo Google Moja Firma + blog z poradami: https://www.google.com/intl/pl_pl/business/ https://www.pierwszynamapie.pl/ --- Send in a voice message: https://anchor.fm/pozaprawem/message

INTELLIGENZA ARTIFICIALE - Diagnosticare il coronavirus in 20 secondi: in Cina lo fa un algoritmoQuesto podcast parla di intelligenza artificiale e diagnosi del coronavirus. E in particolare parla di un algoritmo sviluppato da due centri di ricerca di Alibaba, capace di diagnosticare la Covid-19 (la malattia prodotta dal coronavirus) in 20 secondi.L'algoritmo è già in uso nell'ospedale di Zengzhou, una delle zone più colpite in Cina (dopo la provincia di Hubei, dove si trova l'epicentro Wuhan). Mettendo insieme due ambiti specifici del campo intelligenza artificiale, e cioè l’NLP (Natural Language Processing, cioè la comprensione e l’elaborazione del linguaggio naturale) e le Reti Neurali Convoluzionali (qui siamo nello specifico del machine learning), l'algoritmo riesce a velocizzare in modo esponenziale le diagnosi con un accuratezza vicina al 100%, e ad alleggerire tantissimo il lavoro dei medici.Come funziona? Scoprilo nel podcast.Buon ascolto!Altri contenuti su www.radioit.it

INTELLIGENZA ARTIFICIALE - Diagnosticare il coronavirus in 20 secondi: in Cina lo fa un algoritmo Questo podcast parla di intelligenza artificiale e diagnosi del coronavirus. E in particolare parla di un algoritmo sviluppato da due centri di ricerca di Alibaba, capace di diagnosticare la Covid-19 (la malattia prodotta dal coronavirus) in 20 secondi. L'algoritmo è già in uso nell'ospedale di Zengzhou, una delle zone più colpite in Cina (dopo la provincia di Hubei, dove si trova l'epicentro Wuhan). Mettendo insieme due ambiti specifici del campo intelligenza artificiale, e cioè l’NLP (Natural Language Processing, cioè la comprensione e l’elaborazione del linguaggio naturale) e le Reti Neurali Convoluzionali (qui siamo nello specifico del machine learning), l'algoritmo riesce a velocizzare in modo esponenziale le diagnosi con un accuratezza vicina al 100%, e ad alleggerire tantissimo il lavoro dei medici. Come funziona? Scoprilo nel podcast. Buon ascolto! Altri contenuti su www.radioit.it

I had the privilege of being interviewed by Reuben Dreiblatt, editor in chief at Faveeo, on some of the latest trends in Disruptive Technologies. Contact Reuben: LinkedIn: https://www.linkedin.com/in/reuben-dreiblatt-93719558/ Twitter: faveeo Web: https://www.faveeo.com/ Contact Avrohom: Web: https://asktheceo.biz Facebook: AvrohomGottheil Twitter: @avrohomg Instagram: @avrohomg INTERVIEW HIGHLIGHTS: [00:28] How will the 5G rollout affect the Internet Of Things? [01:18] 5G will enable us to scale mass deployment of IoT devices in every sector [02:32] 5G will enable Connected Infrastructure for Smart Cities and Autonomous Vehicles V2X. [03:40] 5G will make human-driven vehicles much safer – not just Autonomous Vehicles! [04:00] Will TeleHealth enable doctors to perform surgery on Mars from Earth? [04:59] We’re going to see the rollout of IoT increase exponentially, as a result of 5G going mainstream. [05:05] Will older devices still function after 5G goes mainstream? [07:20] 5G will influence the Manufacturing of IoT in terms of CyberSecurity. [08:35] We are installing 127 IoT devices per second! With the number of exposed records per Data Breach rising each year, we need to focus on CyberSecurity to reduce our vulnerability. 5G will exacerbate this due to its scalability. [08:59] An IoT device is an entry-point to your network. [09:14] The Industrial IoT is especially vulnerable to Data Breaches due to having hundreds of thousands of Connected Devices on their networks. [09:14] IIoT is extremely vulnerable to Cyberhacking because it’s challenging to apply & keep track of Cybersecurity patches to hundreds of thousands of disparate endpoints on a regular basis. [09:26] Hackers need only a single open door to get in – we need to guard thousands of doors to keep them out! This presents a big CyberSecurity challenge. [09:36] Today’s technology is already obsolete! How do we secure against the threats of tomorrow? [10:50] 5G will influence the adoption of full-lifecycle patch management for the Internet Of Things. [13:24] What excites you most about the future of AI and what do you fear? [13:24] 5G is a gamechanger with regards to CyberSecurity, as billions of IoT devices will be deployed due to the new scalable network. This will require leveraging AI to keep networks secure. [14:15] Frictionless & Fun - In the future Smart City residents will utilize NLP (Natural Language Processing) to query 5G enabled busses to determine the number of available seats or query a Smart Camera on the bacteria level of stored produce - among many other exciting applications! [15:14] An exciting AI application is Conversational Commerce – if you’re out of coffee, your Smart Home coffee maker will replenish it for you! [18:30] For Smart Home manufacturers AI is a gamechanger, as it converts the sale of IoT devices from a transaction, to a recurring revenue stream via a technology called Conversational Commerce. [23:21] How do we mitigate fears of threat actors leveraging AI to cause us harm? [25:00] We need women & minorities to help us fight the CyberSecurity war against evil applications of AI. Diversity and Inclusion is the answer. [27:41] How do we encourage young women to choose careers in CyberSecurity? [32:07] Will AI create more jobs than it destroys? [34:27] You have to be willing to educate yourself if you wish to become impervious to AI Disruption. [38:19] Is there a consensus on the importance of Data Privacy these days? [42:11] Organizations that demonstrate prioritizing their customer’s Data Privacy will have a competitive edge in the marketplace, as they will have earned Consumer Trust. You can watch this episode on YouTube: https://youtu.be/us7Rpoxxx68

Directors Cut: This is another set of marketing topic excerpts from Bill & Ammon's Bogus Hangout where Terry (a regular) has edited the podcast removing the topics that aren't SEO or internet marketing in nature. Topics included: Optimizing for BERT Google Might Discover Entity Relationships Using 3rd Party Sites Majestic SEO Topical Trust Flow Google Taxonomies and Ontologies Is Google Using Neural Networks to Identify Intent Difference between RankBrain and BERT Using Google Trends in Keyword Research Using Conversion to Prioritize Keywords in Keyword Research Using PPC Conversion to Prioritize Keywords The full video: Highlights from Audio Version Introductions: Bill Slawski blogs on SEO By the SEA and is Director of SEO Research at Go Fish Digital and Doc Sheldon owns Search News Central. and Terry Van Horne is the webmaster and content director at SEO Dojo Radio and founder of SEO Pros. 00:10 Optimizing for BERT: Bill discusses BERT and how like RankBrain and Hummingbird you can't optimize for these algorithms and that using NLP (Natural Language Processing) they are extracting questions from Quora and Yahoo! Answers. 00:45 Google Might Discover Entity Relationships Using 3rd Party Q&A Sites: Bill discusses a patent for discovering entity relationships. 05:12 Using Google Image Search to Identify Categories: Bill shares tips for Using Image Search to Identify Categories. Terry added that you can also find site categories using Topical TrustFlow. 06:37 Google Taxonomies and Ontologies: Terry and Bill discuss using taxonomies and ontologies Google has for GMB Listings and Product Feeds. 14:05 Is Google Using Neural Networks to Identify Intent: Bill discusses a paper from Google on understanding intent. 16:21 Difference between RankBrain and BERT: Bill discusses the difference between RankBrain and BERT explaining that BERT uses context and neural matching to re-write queries. 21:38 Using Google Trends in Keyword Research: The panel discusses using Google trends to help in valuing and prioritizing keywords. 23:10 Using Conversion to Prioritize Keywords in Keyword Research: The panel discusses using Google trends to help in valuing and prioritizing keywords. 24:40 Using PPC Conversion to Prioritize Keywords: The panel discusses using PPC to prioritize and value of keywords. 25:20 25:20 Importance of Client Communication: The panel talks about how and when it is important to communicate what you are doing for your clients. iTunes and the Dojo Radio iPhone App!  

Cambridge Social Media Day (7th& 8th Nov): www.cambridgesocial.media/csm-day-2019/ Guest and Episode LinksLenka Koppová https://www.lenkakoppova.com/blog @lenkakopp: https://twitter.com/lenkakopp@lenkakopp: https://www.instagram.com/lenkakopp/ https://www.linkedin.com/in/lenkakoppova/ https://www.facebook.com/lenkakoppova90  Books:Rise of the Youpreneur – Chris Ducker: https://amzn.to/2Zuqtg9 Other Links:Paralleldots.com: https://www.paralleldots.com/sentiment-analysis Enjoy the Episode - Happy Marketing! www.marketingstudylab.co.uk https://www.linkedin.com/in/petersumpton/ https://www.facebook.com/groups/marketingstudylab/ https://twitter.com/cousinp81(@cousinp81) Intro Sentiment analysis. No not the stuff the ends up at the bottom of a river (science facts). Sentiment analysis is a way to analyse social chatter about your brand. And we have a great link where you can try it out for yourself. But more of that later.  Straight onto our guest this week - who is a freelance social media consultant based in Cambridge, UK, this part is important as Lenka Koppová, helps small, local businesses leverage the power of social media and I’m fascinated to understand how this all works, when we are all strapped for time, let alone running a small business as well as trying to drum up that all important social brand presence. Not only that, why is it so important for small (probably well known in the local area) businesses to have a social presence? Lenka is the perfect person to provide this insight for us. Not only does Lenka run local businesses social media, but she also runs a Social Media Conference, yep you guessed it, locally, in Cambridge. This year it’s being help on the 7th and 8th November so there is still time to get your tickets (link as ever, is in the show notes).   Takeaways - Why should small businesses use social media in there Marketing? Lenka believes this can help to build relationships online as well as offline. Think about it, even though you might be local to a butcher, a baker or a… I’ll leave it there, the chances are you will consume some content online – opening hours, special offers etc, which helps build a reputation locally and this can be done on social. - Time strapped businesses should also find the time to do this, but with efficiency. Have a plan, understand what you want to achieve and most important of all don’t procrastinate or fall down that rabbit hole – There is a HUGE difference between being on social and utilising it for business. - And finally, Lenka doesn't have a formal qualification. As she pointed out, there are so many different ways to learn. And this goes for any type of Marketing Qualification or job role. Reading blogs, listening to podcasts, YouTube, conferences, online training, the list goes on. Try using these for your own research and start to expand your own knowledge around a topic you’re passionate about.   Top Tip – Sentiment Analysis  We're going to do a few things here. Firstly explain what sentiment analysis is, secondly what this actually means and finally why we may have to be careful with the results! So firstly, what is sentiment analysis. Put simply it is a from of text analysis that allow us to workout whether the people we are trying to engage with are saying positive, negative or pretty neutral things about us as a company, our products or services.  The science-y bit would tell us that it is a type of data-mining that measures opinion through NLP – Natural Language Processing, text analysis and computational linguistics. This is then analysed and reveals the contextual polarity of a subject, company, product or service. So what could go wrong!?!?! Well, Paralleldots.com (https://www.paralleldots.com/sentiment-analysis) has a neat little tool where you can test your own statements and what sentiment this creates (the link is in the show notes). The issue here is that the analysis finds it difficult to notice the nuances of some human emotions. For example, I typed: That food was amazing. It even came with a free bug! 90.90% Positive. Full disclosure, there was never a bug. Try it for yourself and let me know your results.  Music Featured on this Podcast: Sleepy in the Garde Lobo Loco www.musikbrause.deCreative Commons License

Coffee Time with Coach Peggyis an open forum, coffee-house chill vibe where Coachanswers questions, shares strategies and concepts aboutAll Things Wellness.Once in a while, special guests stop by to chit chat about a variety of subjects. Every week is different. Grab a coffee and join us.This week Jan Malloch stops by sharing with us her Fit Fab at ANY Age approach. She is a Master Practitioner of NLP (Natural Language Processing), a Clinical Hypnotherapist, Fitness Instructor and Nutrition Adviser.She will share her personal transition from living unhealthy in a stressful career in IT and how she applied her knowledge and techniques to improve her own lifestyle to stop binge drinking and smoking. Her current mission is to guide older women to be healthier and to live long, happy and enjoyable lives through a passionate interest in encouraging increased movement. Though she resides in Scotland, her motivation and positive approaches affect clients all over the world.Join us for an uplifting conversation that will motivate you to improve your current fitness level even if you feel you do not have one.

In this episode, Teri talks to doctors Matt Cybulsky and Reid Maclellan, who were both with him at the Voice of Healthcare Summit in Boston in the summer.The three interviewed each other in more of like a fireside chat about the incredible things going on at the intersection of voice technology and healthcare, and in particular the great stuff that they heard when they were at the Voice of Healthcare Summit.Matt Cybulsky is the founder and chief consultant of IONIA Healthcare Consulting, where they offer organizations with support addressing strategic and operational innovative projects. His expertise focuses on technology and healthcare delivery.Reid Maclellan is a physician and adjunct professor at Harvard Medical School and Boston Children’s Hospital. He’s also an instructor of surgery at Harvard Medical School as well as the founder and CEO of Asclepius, an artificial intelligence company with the mission to restore the care in healthcare and improve quality of life for patients and physicians. Key Points from the Discussion!Conversations at the Voice of Healthcare Summit around building applicable tools, ethics and privacy, and expanding tech for voice-first.Key Takeaways from the SummitCompared to the summit in 2018, the number of people doing incredible things in the voice space and healthcare more than doubled. This is evidence that there is a lot of continuously increasing interest.The breadth of different speakers who were representing so many organizations also increased.There were so many different topics in voice and healthcare.We are going to see a skyrocket in wonderful applications that are going to assist physicians and patients in healthcare.Tangible Builds for VoiceThere’s been excellent research in the integration of voice technology into EMRs.The idea of vocal biomarkers is a fascinating area with great potential.There are different voice applications, like from the Mayo Clinic, which has been growing their applications beyond just being informational skills to integrating patient care with voice technology.Beyond Pilot Projects2019 has been called the year of the pilot.Most revolutionary applications that could impact healthcare on a massive scale are still in pilot stage.Some of the organizations, hospitals, and clinicians that have their own pilot projects that they want to grow will need to hasten their efforts in coming out with tangible tools, because some of the larger firms with more capital will start introducing voice tools and interactive features that may be a little more advanced. One of the biggest barriers to launching health-oriented voice applications at scale has been the protection of users’ privacy. With the recent HIPAA Compliance for Alexa for certain institutions, we are going to see some of the healthcare applications come online.The Mayo Clinic has been giving patients a lot of information through voice applications.AI in GeneralA user can converse with an AI type bot in different ways.AI is not intelligent until the creator makes it that way. That’s why a lot of AI was originally formed based off of image pattern recognition because it was easy to give the computer a bunch of data (all the same images), and then a few that were different, for it to learn.With the advent of NLP (Natural Language Processing), AI can understand conversations and words.There will be a cultural change for physicians to really adopt conversational AI.Notable Speakers at the SummitNathan Treloar of Orbita is a heavyweight boxer in the voice first world. Orbita has tools that are tangible and being used in the market. They’ve been able to achieve great milestones.Stuart Patterson of LifePod opened people’s eyes in as far as what can be done in home health, keeping families informed about loved ones who are ill, care for the elderly, and so much more.Punit Soni of Suki talked about the concept of being able to utilize voice while you’re hands-on with the patient and not being distracted by the keyboard and screen.Bianca Phillips of e-Health Consultants talked about the ethics and law of voice technology in health.Other interesting speakers included Michael Antaran of CARROT Pass, Henry O'Connell of Canary Speech, Audrey Arbeeny of Audiobrain, and others.Voice as a Potential Vital SignThere’s a study that was done by Mayo Clinic and BeyondVerbal to look at patients who were going in for a coronary angiogram and they had those patients read different statements. They then looked at the outcomes of the coronary angiograms and found a statistically significant correlation between the way someone speaks and the risk of coronary heart disease.They also did a study to look at people with congestive heart failure. They followed those people over time and had them read or describe an experience. They found a statistically significant correlation between the way someone spoke and the risk of death.Links and Resources in this EpisodeThe Comprehensive Flash Briefing Formula CourseVoice of Healthcare Podcast See acast.com/privacy for privacy and opt-out information.

ברוכים הבאים! תכני הפרק (חוץ מהקדמה לפודקאסט כולו, מהיכן השם ומה האג'נדה): - אתגר המוסד - כמה קל לפרוץ לאתרים רבים בגלל רשלנות בסיסית של המתכנתים - הנושא המרכזי: NLP - Natural Language Processing - הבוט של דונאלד טראמפ - BOTUS

The human voice is helping to drive the future of automation. Systran and Adobe veteran Shivali Goel joins Dirk and Jon to explain how Natural Language Processing, or NLP, is powering the AI of today and tomorrow. Natural Language Processing researcher Shivali Goel joins Dirk and Jon to go deep into this crucial technology. We learn about what Natural Language Processing, or NLP, is, how it powers software, AI, and automation, and discover some of the cutting-edge applications that NLP is driving today.   Memorable Quotes "For example, if I say 'Kids make nutritious snacks', what do you understand? Are the children cooking something nutritious? Or that they themselves are a nutritious snack?" "Instant voice translation is making language no longer a barrier. I can talk to you in Hindi and you can hear the same content in English." "NLP is basically math and statistics with elements of linguistics." "A person got arrested in Iraq because his Facebook translated his 'good morning' to 'attack them'."   Who You'll Hear Dirk Knemeyer, Social Futurist and Producer of Creative Next (@dknemeyer) Jonathan Follett, Writer, Electronic Musician, Emerging Tech Researcher and Producer of Creative Next (@jonfollett) Shivali Goel, Natural Language Processing Researcher (@shivugoel)   Join The Conversation Website & Newsletter: www.creativenext.org Twitter: @GoCreativeNext Facebook: /GoCreativeNext Instagram: @GoCreativeNext   Sponsors GoInvo, A design practice dedicated to innovation in healthcare whose clients are as varied as AstraZeneca, 3M Health Information Services, and the U.S. Department of Health and Human Services. www.goinvo.com  Design Museum Foundation, A new kind of museum, they believe design can change the world. They’re online, nomadic, and focused on making design accessible to everyone. Their mission: bring the transformative power of design everywhere. You can learn about their exhibitions, events, magazine, and more. www.designmuseumfoundation.org BIF, As a purpose-driven firm, BIF is committed to bringing design strategy where it is needed most - health care, education, and public service to create value for our most vulnerable populations. www.bif.is

This Episode will provide you with a short and crisp description of NLP (Natural Language Processing) and Text Mining. You will also learn about the various applications of NLP in the industry. #NLPin10minutes #NLPtutorial #NLPtraining #Edureka

https://www.awesomers.com/136 Top 8 ChatBot Questions for Amazon FBA Sellers 1) What are chatbots? Chatbots are pieces of technology that help respond LIVE to customer interactions. The engagement between a chatbot and a user is very high and the opportunity to create unique and wonderful experiences exist right now. 2) How can chatbots improve customer service? Chatbots will be able to answer basic customer inquiries and even supply transaction related data on a LIVE and INTERACTIVE basis. Additionally chatbots can be triggered using marketing flows to help qualify and segment customers which brings higher custom experiences to each user which can then lead to more sales and happy customers. 3) What are the best chatbot tools? For Amazon FBA Sellers the clear advantage goes to SellerChatBot with ManyChat also offering some high functionality. A specialized, yet unlimited solution can be had for less than 70USD per month.There are other platforms including chatfuel, Aivo, Botsify, ChattPeople, Flow XO, Imperson, and more.. Before you know what the best solution is you must first ask yourself what is the purpose of your chatbot. If you need AI features or NLP (Natural Language Processing) to help manage large volume on your own website you should look for more specialty types of solutions. Enterprise level chatbots can cost 2,000 - 20,000+ per month for the highest levels of volume and technology. 4) Do Chatbots work with Zapier? Manychat is one example of using the zapier integration to help it bridge data gaps that may exist. For example if you are an Amazon seller and you want to launch a new product using chatbots you may choose to distribute one time use coupons so that customers can get a special offer. Manychat does not have this function built in so you must use zapier to connect to a google spreadsheet and then past the codes into the spreadsheet and let zapier distribute those back to manychat. SellerChatBot which offers Amazon FBA Seller features has coupon code distribution and keyword distrbution built in. 5) Do Chatbots work for eCommerce? Some livechat driven software that allow customer service agents to chat with customers also have a programmable chatbot mode to allow some questions to be answered automatically. The best systems integrate with the CRM to track all customer correspondence with the chatbot so that your marketing and sales teams can see the interactions between customers and humans or artificial intelligence. 6) What are some chatbot examples? For the super secret KevinandSteve.com project we created a bot flow that allows customers to get into the bot list so they can stay informed as we start to share the details about this important project. To check out that facebook messenger bot flow go here: https://m.me/2511879378853219?ref=5c8831a52d74ce0a039e6db9 7) What can ChatBots do? 8) What is the best chatbot for an Amazon Seller (or FBA marketplace seller)?

It’s an odd number episode so we’re back to Bits, episodes focussed on a technology. Today we talk about Natural Language processing, a subset field of Machine learning. Show notes: NLP: Machine learning Guide: Example of a Corpus: Quora: Where can I get a large corpus of news articles? MODERN Business Intelligence: NLP: It’s not a Feature, it’s the Future! Tableau: Natural language humanizes your data Feedback welcome on Twitter to Ravi at @scribblr_42 or Tim at @tableautim - or e-mail us, at datumpodcast@gmail.com

Ilya Eckstein is an AI Researcher at Google and co-founder of Robin Labs, a startup that creates open, expandable, and teachable voice-operated personal assistants. Recap During this podcast, we discuss: Ilya’s startup, Robin Labs, and the inspiration for starting it The future of NLP* (Natural Language Processing) and the industries that may be most impacted … The post Interview: Google AI Researcher Ilya Eckstein on what AI actually is and its impact on humanity appeared first on A.I. For Anyone.

SEO is Not Dead: Why SEO is Still a Crucial Part of Digital Marketing A successful SEO campaign can deliver more traffic, increase customer and leads as well as help your business get to the next level. Today’s Awesomers guest is Jason Berkowitz, founder of Break The Web and SEO Services New York. Jason has managed marketing campaigns for some of the world’s most significant brands. Here are more key points on today’s episode: What is NLP (Natural Language Processing). How to keep up with Google's ever changing algorithm. Why Amazon can be following a similar path to Google in regards to customer and search experience. What to do if your website is not ranking. So listen to today’s episode and learn expert insights about SEO and Digital Marketing and how it can help your business. Welcome to the Awesomers.com podcast. If you love to learn and if you're motivated to expand your mind and heck if you desire to break through those traditional paradigms and find your own version of success, you are in the right place. Awesomers around the world are on a journey to improve their lives and the lives of those around them. We believe in paying it forward and we fundamentally try to live up to the great Zig Ziglar quote where he said, "You can have everything in your life you want if you help enough other people get what they want." It doesn't matter where you came from. It only matters where you're going. My name is Steve Simonson and I hope that you will join me on this Awesomer journey. SPONSOR ADVERTISEMENT If you're launching a new product manufactured in China, you will need professional high-resolution Amazon ready photographs. Because Symo Global has a team of professionals in China, you will oftentimes receive your listing photographs before your product even leaves the country. This streamlined process will save you the time money and energy needed to concentrate on marketing and other creative content strategies before your item is in stock and ready for sale. Visit SymoGlobal.com to learn more. Because a picture should be worth one thousand keywords. You're listening to the Awesomers podcast. 01:53 (Steve introduces today’s guest, Jason Berkowitz.) Steve: You're listening to the Awesomers.com podcast episode number 48 in our growing series. Our daily drops are just keep adding up, don't they? Our tradition as we've established over these last 48 episodes. All you have to do is go to Awesomers.com/48 to see relevant show notes and details and perhaps even a link or twos that we throw in. Sometimes we're forgetting the links. If you notice that by the way just go to our page and comment, leave us the contact form and leave us a comment tell us what we did right or tell us what we did wrong. Either way we welcome your feedback. Now today my special guest is Jason Berkowitz. And I really enjoyed my conversation with Jason because we got into some of the nitty-gritty and some of the history behind SEO. Now a little background on Jason, little context. Since 2009 Jason has been an active leader in the New York City Digital Marketing Community. Founding two successful firms, Break The Web and SEO services New York. Since the inception, Jason has managed marketing campaigns for some of the most significant brands of the world. And he carries a unique philosophy when it comes to campaign success. In his spare time Jason could be found falling from the sky as an amateur skydiver. This is not a chicken little story. We definitely are not worried about anything fallen from the sky. And we talked about in the episode SEO is alive and well. And I believe that some of the techniques that the Jason shares and some of the concepts that he shares within this will be very instructive to any digital marketer or E-commerce entrepreneur or really any offspring who wants to understand the internet in general out there. Hello Awesomers, it’s Steve Simonson and I'm back again.

Using Data to Improve Sales Performance, Management, and Leadership Rob Käll is the CEO and Co-Founder of Cein, Inc.   Rob is passionate about data, machine learning, natural language processing, and using these technologies to improve organizations.   We met Rob through a listener of the Catalyst Sale podcast, and we think you will enjoy getting to know him as well.  On this episode, we discuss measuring things that matter, using data to gather insight and improve outcomes, how Machine Learning + Human Intelligence improve performance, and many other topics. Questions Addressed Why is data important to Rob? What does it mean to measure what matters? What is Natural Language Processing? How can AI, Data, NLP, be used for personal development, coaching, and management? What does the future look like when considering AI? How do we differentiate between the signal and the noise when it comes to AI and Machine Learning? What are some of the things Rob knows now that he wishes he would have known 5 years ago? Key Takeaways Replace hunches with facts See beyond KPIs Measure what matters - some numbers go up, some go down, but what do they really mean? Use AI to measure & reduce gaming We know that data is never perfect, it is likely incomplete, inconsistent, delayed, etc. Use data as a signal. Call John, Talk to John, John Reached Out to Me - all may relate, NLP (Natural Language Processing) allows you to connect these things, correlate, and identify patterns. NLP can be used for coaching - coaching in context. You cannot apply your playbook to every situation. Humans are excellent at picking up patterns - but we are also subject to our own biases. The combination of the computer analysis with human intelligence you can very quickly realize a new level of importance. Artificial Intelligence and Data Sciences will fundamentally change how we work within the next 10 years, across all industries. Small improvements can yield significant impact long term. Rob - "we use simple measurements of success.  Baseline, and illustrate changes in performance." Improving sales productivity can be the ultimate medicine for your business. You are going to feel like you are failing as you build your business.  If you are making progress, and you keep solving problems, you will have success. Continue to deliver something of value. Business partners are important, they can help with focus. You have to say No. Organizations work when you have a solid culture, and everyone is working toward the same goals. Communication is critical Don't forget about the importance of 1:1 communication, throughout the organization - know your people "As a startup founder, it is your obligation to care about every member of your team" Training should be more than an afterthought - as a salesperson, you need to know your industry. Nothing is more expensive than replacing a great salesperson. Show Links LinkedIn Twitter Cien.ai Measure What Matters Everything a Sales Leader Needs to Know About AI Call to Action How are you using AI to improve your craft?  Let us know via twitter @catalystsale or email us directly. Please share your stories with us @catalystsale on twitter or via hello@catalystsale.com  ---------------------- Thank you Ratings & reviews help others discover the podcast - thank you for helping us get the message out to the community. Please send listener questions and feedback to hello@catalystsale.com or contact us directly on twitter, facebook or LinkedIn. Catalyst Sale Service Offerings Growth Acceleration - Plateau Breakthrough Product Market Fit Catalyst Sale - Sales Fundamentals Training Program ---------------------- Subscribe to the Catalyst Sale Podcast Subscribe via iTunes Subscribe via Google Play Catalyst Sale In every business, in every opportunity, there is someone who can help you navigate the internal challenges and close the deal.  There is a Catalyst.  We integrate process (Catalyst Sale Process), technology and people, with the purpose of accelerating revenue. Our thoughtful approach minimizes false starts that are common in emerging markets and high-growth environments. We continue to evolve our practice based on customer needs and emerging technology. We care about a thinking process that enables results versus a process that tells people what to do.   Sales is a Thinking Process.

if someone is going on Vacation, Finding if a hotel is good or bad takes time and frustrating process. Here is a simple NLP(Natural Language Processing) powered solution.

Speech to text and vice-versa is a technology that is driving a lot of AI innovation. One of which is virtual interactive assistants like Siri, Alexa and Google Assistant. Aimilios Chalamandaris, CEO and Co-founder of Innoetics shares with us their 10-years journey, starting an NLP (Natural Language Processing) spin-off out of Athena Research and Innovation Centre up to the point of getting acquired by Samsung Electronics. He shares with us the main factors that made the acquisition possible and what are the next steps for Innoetics. If you are a researcher and trying to productise and commercialize your project or idea then this is an interview you should listen to, as Aimilios shares his insights on what skills you need to develop to become a businessman and an entrepreneur. Interviewed by Sakis Triantafyllakis for Tech Talks Central.

Wie verstehen Computer Sprache und welche Anwendungsfälle gibt es dafür? Von Siri über Alexa bis hin zu Chatbots werden die Möglichkeiten für Computer und Sprache immer größer. Deshalb sprechen wir gemeinsam mit Tina Klüwer über Spracherkennung und NLP – Natural Language Processing. Die Gründerin von Parlamind hat nicht nur ein Unternehmen entwickelt, das sich Herausforderungen im Kundendienst mit entsprechender Technologie widmet, sondern auch lange zum Thema geforscht. Du erfährst... 1) …wie Computer Sprache verstehen können 2) …Anwendungsbeispiele für computerbasierte Spracherkennung 3) …was Natural Language Processing ist und wie es funktioniert 4) …wofür Chatbots geeignet sind und wie sie trainiert werden