Podcasts about machine learning ml

In dieser Folge geht's um Künstliche Intelligenz (KI) und Machine Learning (ML).  Michél und Daniel Manzke sprechen über Definitionen, Anwendungsfälle und Herausforderungen – besonders beim Datenschutz und der Nutzerakzeptanz. Außerdem werfen sie einen Blick auf Europas Stellung im KI-Wettbewerb mit den USA und China. Ein weiteres Thema: Warum Prompt Engineering wichtig ist und wieso wir den Umgang mit KI lernen sollten.   Die Themen des DIGITALWERK Podcasts mit Daniel Manzke im Überblick: (00:00) - Einführung: Worum geht es in dieser Folge? (00:00:41) - Vorstellung von Daniel Manzke: CTO, Investor und KI-Experte (00:05:15) - Technologie und Business: Die Brücke zwischen Tech und Produktwelt (00:06:39) - KI im Bauwesen und anderen Branchen: Ein Blick über den Tellerrand (00:19:25) - Machine Learning: Wo fing alles an? Die Geschichte von Spracherkennung (00:20:45) - OCR-Technologien und ihre Bedeutung für KI-Anwendungen (00:32:46) - Chatbots und KI im Customer Support: Wo steht die Technologie heute? (00:33:46) - Mensch vs. KI: Warum KI aktuell als Unterstützung und nicht als Ersatz dient (00:35:03) - Datenqualität als Erfolgsfaktor: Wie wichtig sind vorhandene Datensätze? (00:07:58) - Der Hype um KI: Wie Unternehmen KI in ihren Pitch Decks nutzen (00:41:00) - KI und die Zukunft: Welche Entwicklungen erwarten uns? (00:55:30) - Datenschutz und DSGVO: Herausforderungen für KI-Projekte (01:03:00) - Fazit: Chancen und Risiken der KI-Technologie   Werbepartner der heutigen Folge sind Brüninghoff und die WMM Gruppe.   Brüninghoff bietet als Lösungspartner für innovative Gebäude alles aus einer Hand – von der ersten Idee bis zur schlüsselfertigen Übergabe. Einfach auf www.brueninghoff.de  gehen und mehr erfahren.   Die WMM Gruppe baut massive Ziegelmodule, zentral im Werk vorgefertigt, komplett einzugsfertig ausgestattet und just in time auf die Baustelle geliefert. Mehr dazu unter www.wmm-modulbau.de.   Für mehr Infos rund um DIGITALWERK auf den spannendsten Baustellen abonniere jetzt den Kanal:    / @digitalwerk-podcast     Oder checkt unsere anderen Kanäle aus! WEBSEITE: https://www.digitalwerk.io/ INSTAGRAM:   / digitalwerk.io   LINKEDIN:   / digitalwerk-io     Wir freuen uns, wenn du unsere Inhalte teilst, um andere zu inspirieren!

Neste episódio, tivemos o prazer de conversar com Ahirton Lopes e Felipe Teodoro, dois especialistas em machine learning. Durante nossa discussão, exploramos a importância do aprendizado de máquina e sas diferenças essenciais aos modelos machine learning ML e inteligência artificial IA. Mas será que é preciso ser ser um PhD para trabalhar com machine learning? Assuntos abordados no tema Breve contextualização sobre o que é Machine Learning (ML) e sua importância no cenário tecnológico atual. Definição técnica: diferença entre ML, IA e Deep Learning Breve explicação sobre redes neurais Exemplos simples de algoritmos de ML (regressão linear, redes neurais, etc.). Como criar modelos básicos e rodar localmente? Futuro do ML (AutoML, Quantum Machine Learning, Edge AI) Cálculos, álgebra linear, matemática… Impacto no mercado de trabalho: novas profissões e habilidades necessárias. Por onde começar? Carreira, mercado de trabalho e estudos É possível aprender o básico e explorar o machine learning sem trabalhar na área? Informações úteis e divulgações Links úteis Nosso Discord: https://discord.com/invite/hGpFPsV2gB Café Debug Global https://open.spotify.com/show/3S1OK2ecjZj7zoaZ34bFkP?si=ae09a6a1796a4587 Pesquisa de satisfação Café Debug 2024 https://docs.google.com/forms/d/e/1FAIpQLSdlkPGS-sqfD3QOmkddRDqj7dlYE8mpIlZXORIfTtn-MztKKA/viewform https://cloud.google.com/learn/artificial-intelligence-vs-machine-learning?hl=pt-BR https://www.bbc.com/portuguese/geral-62803019 https://robertaduarte.com/ Participantes Jéssica Nathany (Software Developer e host)LinkedIn: https://www.linkedin.com/in/jessica-nathany-carvalho-freitas-38260868/ Weslley Fratini (Software Developer e co-host)LinkedIn: https://www.linkedin.com/in/weslley-fratini/Felipe Teodoro (Diretor de Ciência de Dados e Engenheiro de Dados na CCapital)LinkedIn: https://www.linkedin.com/in/teodorofelipe/ Ahirton Lopes (AI Data Manager, Teacher 5x MVP Microsoft Google Developer Expert)LinkedIn: https://www.linkedin.com/in/ahirtonlopes/ Produtora AGO Filmes: https://thiagocarvalhofotografia.wordpress.com/dúvidas, sugestões ou anúncios envie para: debugcafe@gmail.comSee omnystudio.com/listener for privacy information.

O que é Machine Learning? Machine Learning (ML), ou aprendizado de máquina, é um campo da inteligência artificial (IA) que permite aos computadores aprenderem e fazerem previsões ou tomar decisões baseadas em dados, sem serem explicitamente programados para cada tarefa. Ele é essencialmente uma abordagem para ensinar máquinas a identificar padrões em grandes conjuntos de dados e melhorar seu desempenho ao longo do tempo com base na experiência. O processo de aprendizado de máquina pode ser resumido em três etapas principais: Coleta e Preparação de Dados: Os modelos de ML dependem de grandes volumes de dados de alta qualidade para aprender. Treinamento do Modelo: O algoritmo é treinado com os dados, ajustando seus parâmetros para minimizar erros e maximizar a precisão das previsões. Testes e Ajustes: Após o treinamento, o modelo é testado em dados não vistos para garantir que ele funcione bem em cenários do mundo real. ML pode ser dividido em três categorias principais: Como Funciona: O modelo aprende a partir de um conjunto de dados rotulados, ou seja, onde as entradas têm saídas conhecidas. Exemplos: Como Funciona: O modelo busca padrões em dados não rotulados, como agrupamentos ou anomalias. Exemplos: Como Funciona: Um agente aprende a tomar decisões em um ambiente interativo, recebendo recompensas ou penalidades com base em suas ações. Exemplos: Regressão Linear: Usado para prever valores numéricos contínuos. Árvores de Decisão: Para tomada de decisão baseada em características. Redes Neurais: Modelos inspirados no cérebro humano, ideais para tarefas complexas. K-Means Clustering: Um algoritmo não supervisionado para agrupamento. Máquinas de Vetores de Suporte (SVM): Usadas para classificação e regressão. ML está presente em diversas áreas, incluindo: Saúde: Diagnóstico de doenças e personalização de tratamentos. Finanças: Previsão de mercados e detecção de fraudes. E-commerce: Recomendação de produtos e personalização da experiência do cliente. Transporte: Veículos autônomos e otimização de rotas. Entretenimento: Sistemas de recomendação em plataformas como Netflix e Spotify. Qualidade dos Dados: Dados inconsistentes ou incompletos afetam a precisão do modelo. Overfitting: Quando o modelo aprende "demais" sobre o conjunto de treinamento e não generaliza bem. Interpretação: Modelos complexos, como redes neurais profundas, podem ser difíceis de interpretar. Viés e Ética: Dados enviesados podem levar a decisões injustas ou preconceituosas. Com o aumento no poder computacional e a disponibilidade de grandes volumes de dados, o ML continuará a transformar setores, permitindo inovações como: Sistemas mais inteligentes e autônomos. IA explicável e confiável. Aplicações mais acessíveis e diversificadas. Machine Learning é uma tecnologia que já está moldando o presente e promete desempenhar um papel ainda maior no futuro. Como Funciona o Machine Learning?Categorias de Machine Learning1. Aprendizado Supervisionado2. Aprendizado Não Supervisionado3. Aprendizado por ReforçoAlgoritmos Comuns de Machine LearningAplicações de Machine LearningDesafios do Machine LearningO Futuro do Machine Learning

In this special episode, Bessie explores the rapidly evolving world of artificial intelligence. It stands out because 90% of the content, including a 20-minute dialogue, transcripts, and even the shownotes, have been generated by AI tools. Inspired by Google's AI-powered notebook platform NotebookLM, Bessie experiments with feeding her handwritten notes from a 10-week London Business School course on AI into this tool, resulting in a fascinating dialogue on AI and machine learning's real-world applications.We discuss:Key AI Concepts – Breaking down AI, Machine Learning (ML), and their types (Supervised, Unsupervised, and Reinforcement Learning).Industry Case Studies – How AI is reshaping customer service (Vodafone, T-Mobile), quality control (Domino's Pizza), and predictive maintenance (Schneider Electric).Practical Insights – Challenges, opportunities, and a framework for successful AI implementation in businesses.Future of AI – The role of leadership, data as an asset, and embracing a culture of innovation.� What Makes This Episode Unique:Almost entirely AI-generated, this episode is a testament to AI's capabilities in content creation. It raises the question:How will AI continue to transform creative industries?� Join the Conversation:What are your thoughts on AI's potential and its role in content creation? Share your reflections in the comments section!Thank you for tuning in! Let's explore the future of AI together. �

Send us a textWhat if you could get personalized medical solutions tailored exactly to your needs, diagnosis, and even genetics?Personalized medicine may have sounded like a pipe dream a few years ago, but it's fast becoming a major sector of the industry.In this episode of CareTalk, David Williams and John Driscoll sit down with Bradley Bostic, Founder and CEO of hc1 and managing director at Health Cloud Capital, to explore how diagnostics and AI are transforming personalized healthcare through data-driven innovation.This episode is brought to you by BetterHelp. Give online therapy a try at https://betterhelp.com/caretalk and get on your way to being your best self.As a BetterHelp affiliate, we may receive compensation from BetterHelp if you purchase products or services through the links provided.TOPICS(0:16) Intro(0:32) Sponsorship(2:11) Congratulations to Marty Makary(2:51) How Important Is Lab Testing for Healthcare?(4:38) Going Beyond Basic Lab Data(7:49) How Tests Lead Into Personalized Medicine(10:41) Looking at Data More Effectively(15:00) Will Changes in the Healthcare System Affect Lab Data?

Send us a textArtificial intelligence (AI) and Machine Learning (ML) continue to be top of mind as marketers are leveraging AI and ML in a variety of ways to improve their customer loyalty programs and strategies. Integration of these technologies into day-to-day workflows is a challenge for many brands, especially when considering potential roadblocks from limited budgets, internal resources, and the ability for these innovative technologies to complement brands' existing marketing technology stacks.Loyalty360 CEO Mark Johnson recently spoke with Monique De La Rosa. VP, Data Science and Analytics at Ansira, about the opportunities AI and ML present to elevate customer loyalty programs with more seamless and personalized experiences.

EMW Capital portfolio company Wanda Health's CEO Tom smith joined Steve Darling from Proactive to share news the company has announced a strategic partnership with the COPD Foundation, a prominent nonprofit dedicated to supporting individuals affected by chronic lung diseases, including Chronic Obstructive Pulmonary Disease (COPD), bronchiectasis, and NTM lung disease. COPD impacts over 30 million Americans and ranks as the third leading cause of death globally. CEO Tom Smith shared with Steve Darling from Proactive that this collaboration will grant Wanda Health access to some of the world's top experts in respiratory health. By tapping into their knowledge, Wanda aims to refine its innovative platform and proprietary AI and Machine Learning (ML) capabilities, enhancing its ability to deliver early detection and intervention for patients with acute and chronic conditions. The partnership also includes marketing opportunities, such as space in a peer-reviewed medical journal, to reach broader healthcare audiences. Wanda's platform empowers healthcare providers and payers to identify exacerbations in patients at an early stage, enabling swift interventions, reducing adverse events, and improving patient adherence. Through its alliance with the COPD Foundation, Wanda Health strengthens its capacity to provide targeted, expert-informed solutions specifically designed for patients managing respiratory conditions, improving care outcomes and quality of life. #proactiveinvestors #emvcapital #wandahealth #COPD #FoundationCOPD #RespiratoryCare #RemotePatientMonitoring #DigitalHealth #VirtualCare #AIinHealthcare #COPDCare #HealthcareInnovation #PatientMonitoring #ProactiveInterviews #TomSmith #invest #investing #investment #investor #stockmarket #stocks #stock #stockmarketnews

Can cloud technologies revolutionize how we approach electronics and PCB design? What role does AWS play in shaping the future of collaboration, security, and performance in this industry? I'm your guest host, Matthew Walsh, filling in for Steph Chavez this week, and in this episode, we sit down with Adam Cabler, Chief Cloud Architect at Siemens Electronic Board Systems. Adam shares his extensive experience with EDA software development and dives into how Siemens is leveraging AWS to enhance PCB design tools. We explore the journey of integrating cloud capabilities into Siemens' design tools, the specific challenges they've faced, and the transformative cloud-enabled features that are enhancing collaboration for design teams worldwide. This episode offers key insights for engineers, IT professionals, and anyone curious about the intersection of cloud technology and electronics design. What You'll Learn in this Episode: The driving forces behind Siemens' adoption of AWS for PCB design (02:15) Advanced ECAD visualization and how the cloud enhances user experience (03:05) How cloud collaboration is changing teamwork for global design teams (05:05) Siemens' approach to security and compliance in the cloud (06:25) Real-world examples of improved time-to-market with AWS (9:25) Future capabilities and the role of AI in PCB design (10:30) The impact of AI and Machine Learning (ML) of the future of PCB design (11:50) Adam's advice for companies considering cloud integration (13:20) Connect with Matthew Walsh: LinkedIn Website  Connect with Adam Cabler LinkedIn Website

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.  

Artificial intelligence and machine learning are two much-heralded technologies set to revolutionise signals intelligence collection, processing and dissemination. An increasingly congested radio spectrum is set to challenge the limits of human cognition in the search for the signal of interest. In this episode of Radioflash! we catch up with Patrick ‘Krown' Killingsworth, EpiSci's  director of autonomy projects. We define the terms Artificial Intelligence (AI) and Machine Learning (ML) and their unique capabilities. ML algorithms are trained on huge amounts of data which makes these algorithms great for processing huge amounts of data, a key requirement in Signals Intelligence (SIGINT). The application of AI and ML in SIGINT is not necessarily about replacing the human but helping the SIGINT operator sort these data. Nonetheless, the continuing introduction of AI and ML into SIGINT analysis prompts concern and enthusiasm in equal measure. Ensuring that enough data are available for training algorithms creates challenges given the paucity of data which the SIGINT cadre may be interested in. We talk about the risks of using synthetic data for training and tackling the risk by anticipating potential problems from the start. AI- and ML-enabled SIGINT systems continue to get smarter, faster and more accurate. The future brings challenges in terms of moving this SIGINT technology from the strategic level to the tactical edge, although edge computing in the tactical domain should help no end in this regard.

Dive into an intriguing exploration with Shawn Rogers, CEO of BARC US, as we delve into whether Power BI is a stepping stone to mastering Artificial Intelligence (AI) and Machine Learning (ML), or if it's the other way around. This episode isn't just about technology—it's a deep dive into the symbiotic relationship between business intelligence tools and the latest advancements in AI. With his extensive background in analytics and business intelligence, Shawn unravels how Power BI can serve as both a platform leveraging AI capabilities and a beneficiary of AI-driven enhancements. He discusses the dynamic interplay where AI not only complements but also extends the functionalities of Power BI, transforming how businesses interpret and act on data. Listeners will gain a nuanced understanding of how to strategically prepare their Power BI setups to both utilize and enhance AI technologies. Tune in and see firsthand how Power BI and AI are reshaping the analytics landscape together. Whether you're deep into data or just starting to see its potential impact on your business, Shawn Rogers brings a wealth of expertise that bridges the technical with the practical. Are you looking for a podcast that cuts through the noise? Raw Data by P3 Adaptive breaks down complex data topics into business value you can use. From business intelligence and dashboards to AI and digital transformation, we make it simple and relatable. Don't forget to subscribe and leave a review on your favorite platform!

The art and science of screening in the KYC and AML world has certainly come a long way from the days of manually checking for names and organizations against a short list. Risk factors have evolved exponentially, increasing regulations have influenced risk appetites, and powerful technologies like Machine Learning (ML) and Generative AI (GenAI) have caught on like wildfire. In this episode of KYC Decoded, in-house experts Mike Xu, Data Strategy Director, and Victor Cotillon, Risk & Screening Product Strategist, provide valuable insights into the role of Machine Learning when screening. Highlights of their conversation with host, Alex Pillow, include:What screening means and why it is performed by KYC and AML practitionersThe concept of ‘fuzzy matching'How technologies help weed out false positives when name matchingPros and cons of integrating ML into an intelligent screening processKeep human's in the loop when using more sophisticated MLInterested in learning more? Check out the resources below mentioned in this episode:CourseraMoody's AI in Compliance resourcesIf you want to learn more about intelligent risk and compliance screening for your organization, please visit our website and feel free to reach out – we would love to hear from you!

In this introductory episode of Art and Science of AI we discuss our background, expertise, and motivation for embarking on this journey to demystify AI! Piyush shares his fascination with seemingly magical AI technologies like ChatGPT and MidJourney, and his desire to understand the underlying mechanics. Nikhil shares his background in AI, and his plan for demystifying ChatGPT in this season: starting with the basics of Artificial Intelligence (AI) and Machine Learning (ML), we will demystify key concepts such as neural networks, deep learning, and Large Language Models (LLMs). We will also explore the exciting potential of ChatGPT and its implications for business and society. Note: Season 1 was originally recorded back in May 2023 as a continuous 3-hour long conversation. Season 2 is now live, so please subscribe for new episodes every week! artscienceai.substack.com ===

In this episode of the Kubernetes Bytes podcast, Bhavin sits down with Bernie Wu, VP Strategic Partnerships and AI/CXL/Kubernetes Initiatives at Memverge. They discuss about how Kubernetes is the most popular platform to run AI model training and model inferencing jobs. The discussion dives into model training, talking about different phases of a DAG, and then talk about how Memverge can help users with efficient and cost-effective model checkpoints. The discussion goes into topics like saving costs by using spot instances, hot restart of training jobs, reclaiming unused GPU resources, etc. Check out our website at https://kubernetesbytes.com/ Episode Sponsor: Nethopper Learn more about KAOPS: @nethopper.io For a supported-demo: info@nethopper.io Try the free version of KAOPS now! https://mynethopper.com/authCloud Native News: https://www.aquasec.com/blog/linguistic-lumberjack-understanding-cve-2024-4323-in-fluent-bit/ https://kubernetes.io/blog/2024/05/20/completing-cloud-provider-migration/ https://thenewstack.io/introducing-aks-automatic-managed-kubernetes-for-developers/ https://www.harness.io/blog/harness-to-acquire-splitShow Links: https://www.linkedin.com/in/berniewu/ https://criu.org/Main_Page https://memverge.com/ https://youtu.be/tY8YOMRuqWI?si=yB3hHqLUpYPZ-KWN https://youtu.be/ND4seSKpJHI?si=shh0iuA9qC-dO6ebTimestamps: 01:04 Cloud Native News 08:47 Interview with Bernie 51:40 Key takeaways

Dr. Brice Gaudillière, an investigator at the March of Dimes Prematurity Research Center at Stanford University, discusses a breakthrough Machine Learning (ML) algorithm that makes reliable predictions about labor onset, preterm birth, and preeclampsia and also identifies the biological markers supporting those predictions. 

How can we use AI and Machine Learning to support Arctic fishing, shipping, and kelp mariculture—all of which are increasingly impacted by the climate crisis?  Leslie Canavera, a Yup'ik Alaska Native and the CEO of PolArctic, is here to tell us on the latest episode of The Conch podcast!  The PolArctic team developed the “first AI model of its kind to treat Indigenous Knowledge and Western science as equals, training with and validating both knowledge systems.” If you're curious to hear how merging these two knowledge bases has helped strengthen the seafood industry in the face of climate change, you won't want to miss Season 5, Episode 8.  You'll learn more about their impactful work and Leslie's journey from getting her Master's in Business Administration to serving in the Air Force to launching PolArctic!  Episode Transcript Episode Guide :00 Intro 01:20 Introducing Leslie Canavera, founder of PolArctic, a company focusing on the Arctic and the ocean, creating custom, cutting-edge artificial intelligence models for forecasting future environmental change 04:19 Artificial Intelligence (AI) is challenging established paradigms in the Arctic environment. Leslie tells us how 05:59 The Arctic is experiencing incredible challenges due to climate change 10:12  Ice patterns in the Arctic are changing due to climate change. Leslie walks us through the impacts on fisheries and communities 13:40 PolArctic recently successfully developed the first AI model to integrate Indigenous knowledge, scientific data, and remote sensing techniques to assess potential inshore fishery locations for an Arctic community 16:15  The Arctic is a data desert 19:00 Parachute science: when we're collecting data sets and asking people for their knowledge, it's important to use CARE and FAIR principles 20:54 Leslie's career journey from growing up in Anchorage, to becoming an AirForce officer, to launching a tech startup 26:25 Leslie's advice to eliminate inequality in the tech sector 30:45 Reshma Saujani said, "We're raising our girls to be perfect, and we're raising our boys to be brave." The challenges of VC funding and the importance of networking and sponsors 39:05 Leslie uplifts Jennifer Brown from Foraged and Found! Resources: Check out PolArctic's website to learn more about solutions to business and policy questions about the Arctic through the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques. Interested in understanding why women-founded companies generate outsized social and financial returns? Read this article. Recommend this episode to one person who is curious about the importance of Indigenous knowledge in developing solutions to the greatest challenges facing our planet.    

Join Dr. Anna Wolska and Dr. May Choi as they explore the role of Artificial Intelligence (AI) and Machine Learning (ML) in guiding diagnostic and care decisions for systemic lupus erythematosus (SLE). Dr. Choi delves into key elements of machine learning and its potential impact on improving outcomes, particularly in underserved populations. She also discusses various types of machine learning, such as deep learning, supervised learning, and unsupervised learning, highlighting their respective contributions to enhancing efficiencies within clinical care settings.   Read the related Review published in LSM: https://doi.org/10.1136/lupus-2023-001140

Welcome to Part 2 in our "Best of" Series.  This episode doubles down on data (not for) dummies as we explore the transformative power of data in the connected restaurant. Our esteemed guests dive into all aspects of data beyond the numbers⸺specifically, its strategic role in driving innovation from the kitchen to the customer experience.Top takeaways include: How API ecosystems impact data collection and strategies  Innovative approaches for data storytelling Presenting the data … Is the dashboard dead?!The skinny on data lakes, data models, and analyzing data in flow Challenges and techniques for managing large volumes of data volumes efficientlyIntegrating real-time data for operational efficiency in the kitchen How loyalty apps can help (or not) with data capture and customer engagement Hear how data-centric approaches can reshape the restaurant industry, especially when rooted in Artificial Intelligence (AI) and Machine Learning (ML). Guest soundbites from: Phil Crawford (Adyen) / Josh Boshard (Savory) / Kelly MacPherson (Union Square Hospitality Group) / Darien Bates (FourTop Solutions) /  Meredith Sandland & Carl Orsbourn (The Digital Restaurant)Related EpisodesAI Inside & Outside the Four Walls – Phil Crawford, CKEThe Future of Data Starts Here – Darien Bates, FourTopCheck out Qu's Annual State of Digital for Enterprise QSR & Fast Casual Brands

Provizio, the leading provider of low-cost advanced perception imaging radar that is reliable in all-weather conditions, proudly announces a strategic partnership with Mergon, the global Tier 1 and Tier 2 automotive supplier. This collaboration marks a significant leap forward in expediting the production of Provizio's VizioR&I family of industry-leading imaging radar with embedded 5D Perception, poised to accelerate the delivery of safe, cost-effective and scalable L3+ autonomy and industrial robotics. Established in 1981, Mergon is globally recognised for its expertise in unit housings, cooling, clear vision systems and air management. With manufacturing facilities strategically located in Ireland, UK, USA, Czech Republic and Mexico,, Mergon's robust infrastructure and extensive experience position it as the perfect partner in aiding to achieve Provizio's ambitious production objectives. "Our mission is clear: to pioneer the delivery of safe, scalable and cost-effective robotics across mobility applications. This partnership with Mergon signifies a pivotal step towards realising this vision, bringing us close to our goal of democratising advanced safety technology. We will do this by unlocking radar perception that is beyond the current state-of-the-art in a highly user friendly "plug-and-play" manner, at scale. I'm looking forward to witnessing the impact of this collaboration as we continue to bring our technology to the world, " said Luke Curley, Supply Chain Lead and Provizio Co-founder, on today's announcement. The collaboration between Provizio and Mergon, cemented by both companies presence in Future Mobility Campus Ireland (FMCI), takes the form of a box-build partnership; under this arrangement, Mergon will assist in the designing, developing and manufacturing processes of Provizio's VizioR&I production units. VizioR&I delivers advanced radar perception capabilities, including object classification, speed, trajectory and driving freespace, by leveraging Provizio's library of proprietary Artificial Intelligence (AI), Machine Learning (ML) and Digital Signal Processing (DSP) neural nets and algorithms. This is all made possible due to the radars industry leading 4D resolution (3D + Velocity) coupled with embedded Nvidia GPU technology to unlock the 5th dimension, edge intelligence. The range capabilities of up to and beyond 550 metres are crucial for applications including autonomous trucking where every second counts when it comes to understanding the way forward. VizioR&I is an integral component of Provizio's advanced perception imaging radar technology, providing plug-and-play experiences at a cost-effective price point. By harnessing Mergon's expertise in volume production, Provizio anticipates a broader availability of its groundbreaking imaging radar technology. Speaking on the collaboration, Caolan Bushell, Vice President of Business Development at Mergon commented, "This partnership aligns with our core objectives of turning creative and innovative ideas into great products that positively contribute to the automotive sector as well as our society. I am confident that our combined expertise will not only propel the advancement of autonomous driving technologies but also shape the future of mobility, paving the way for safer and more efficient mobility solutions globally." Provizio is set to showcase a live demonstration of its revolutionary perception technology at the highly anticipated Transport Research Arena Conference taking place today, 15th April to Thursday, 18th April at RDS, Dublin. The event, known as the largest European research and technology conference on transport and mobility gathers innovators and leaders in the field, is hosted by the Irish Department of Transport and co-hosted by the European Commission. Mergon will also be in attendance, further underscoring the collective commitment of both parties to driving innovation in the automotive sector. See more stories here.

Welcome to our comprehensive guide on the basics of data science! In this video, we delve into the fundamental concepts of data science, covering a wide array of topics including Artificial Intelligence (AI), Machine Learning (ML), Data Analytics, and Business Analytics. Whether you're a beginner looking to explore the world of data science or someone seeking to refresh their understanding, this video is tailored for you. We start by demystifying AI and ML, explaining how these technologies empower computers to learn from data and make predictions or decisions. Next, we dive into the realm of data analytics, where we explore various techniques for extracting meaningful insights from raw data. From descriptive to predictive analytics, you'll gain a solid understanding of how data analytics can drive informed decision-making processes. Moreover, we discuss the significance of business analytics in today's competitive landscape. Learn how businesses utilize data-driven insights to optimize operations, enhance customer experiences, and gain a competitive edge in the market. Whether you're interested in the technical aspects of data science or its practical applications in business, this video covers it all. Join us on this journey to unravel the basics of data science and discover how it's shaping the future of technology and business. Don't forget to like, share, and subscribe for more insightful content on data science, AI, ML, and analytics. Let's embark on this data-driven adventure together!

In this Insights Unlocked episode, UserTesting's Lawrence Williams talks with Dawn Procopio, founder and principal UX researcher at AI-Ethicist.com. They explore the critical role of UX researchers in ensuring that machine learning models are human-centered and ethically sound.

Tune in for a special interview on TALRadio as host Daphne Royse sits down with Erika Bahr, Founder & CEO of Data Pulse. Discover how Data Pulse leverages Artificial Intelligence (AI) and Machine Learning (ML) to revolutionize due diligence, risk analysis, and financial modeling for startups, angel investors, and law firms. Learn how their cutting-edge technology facilitates efficient operations and empowers data-driven decisions. Don't miss this insightful episode exploring the intersection of technology and business strategy. Host: Daphne Royse Guest: Erika Bahr, Founder & CEO of Data Pulse You Can Reach Erika @ linkedin.com/in/erika-marie-bahr #TALRadioEnglish #HappyHomesAndGardens #DataPulse #AI #ML #TechInnovation #ErikaBahr #DaphneRoyse #touchalife

Snap Inc. recently shared insights into its financial health and strategic positioning during an earnings call held on Tuesday February 6th, 2024. CEO Evan Spiegel took the lead communicating the transformative changes the company has undergone over the past year. Pivoting to a more customer-centric approach, Snap Inc. has heavily invested in its Machine Learning (ML) platform - an effort aimed at enhanced performance for advertising partners and improved relevance and ranking. The revenue growth of approximately 5% year-over-year signaled an upswing in the company's income-generating potential, as it continues to shore up its market position. This progress is noteworthy as it suggests the company's consistent strides in solidifying its profitability. Snap Inc.'s reliance on ML as integral to their strategy reflects their focus on enabling better and more efficient advertising on their platform. The company's increasing reliance on ML technology underscores its continual development efforts aimed at delivering substantial value to advertisers. Despite the progression, Snap Inc. was candid in acknowledging the hurdles standing in their way. The company conveyed a commitment to delivering a seamless content experience across its platform, aiming to drive higher user engagement and promote frequent app usage. Targeting growth potential, Snap Inc. revealed plans to expand its user base, particularly in regions that could be lucrative revenue sources. The company's sights are set on Europe and North America, according to Spiegel, signifying an aspiration to fuel further growth while raising its financial output. The appeal of the platform is evident in the rising number of monthly and daily users, paired with significant growth in total viewing time and viewer count. This combination reflects growing user engagement, contributing to the platform's overall popularity. Looking forward, Snap Inc. seems armed with a clear vision for growth and a robust investment plan. The company's forward-focused strategy, coupled with an innovation mindset, mean it is well-positioned to deal with any possible challenges. Using their current endeavours as a stepping stone, combined with their unwavering dedication to innovation, Snap Inc.'s journey ahead promises to be an exciting one, hinging on a strong blend of ambition and realism. SNAP Company info: https://finance.yahoo.com/quote/SNAP/profile For more PSFK research : www.psfk.com  This email has been published and shared for the purpose of business research and is not intended as investment advice.

Episode 160: Artificial Intelligence in Primary Care.      Future Dr. Manophinives explains the present and future of AI in diagnosing and treating diseases.    Written by Rosalynn Manophinives, MS-IV, American University of the Caribbean. Editing by Hector Arreaza, MD.You are listening to Rio Bravo qWeek Podcast, your weekly dose of knowledge brought to you by the Rio Bravo Family Medicine Residency Program from Bakersfield, California, a UCLA-affiliated program sponsored by Clinica Sierra Vista, Let Us Be Your Healthcare Home. This podcast was created for educational purposes only. Visit your primary care provider for additional medical advice.Today, we embark on an intriguing journey at the crossroads of technology and healthcare: The Future of Healthcare in Artificial Intelligence (AI) and Machine Learning (ML). Let's start by establishing the groundwork for AI and ML. Artificial Intelligence involves machines mirroring cognitive functions like learning and problem-solving, while machine learning empowers machines to learn from data and refine their capabilities over time. In healthcare, these technologies aim to elevate diagnostic precision and treatment effectiveness which are pivotal aspects in primary care medicine.Accurate diagnosis is the cornerstone of effective patient care in all forms of medicine because an accurate diagnosis guides treatment decisions and influences patient outcomes. This is why the integration of AI and ML holds immense promise in this field.Section 1: AI in Diagnostic Assistance (4 mins)Let's explore how AI utilizes algorithms to analyze extensive datasets, enhancing diagnostic accuracy significantly.AI serves as a revolutionary force in analyzing a large amount of data, particularly in medical imaging. Imagine AI algorithms as super brains, employing machine learning to decipher intricate details from X-rays, MRIs, and CT scans. Notably, studies have demonstrated their precision matching and even surpassing that of human experts. For instance, research published in the Journal of the American Medical Association revealed AI algorithms outperforming radiologists in detecting conditions like breast cancer.AI's skills extend beyond images. It digs into genetic information, medical history, and treatment outcomes, acting as a detective to spot patterns, predict responses, and customize interventions. Studies support this, showcasing AI models outperforming dermatologists in diagnosing skin cancer from images. Will AI replace doctors?The beauty of AI is that it does not replace doctors but acts as a super investigator in your healthcare corner, expediting diagnoses, and refining treatments. So, AI isn't merely accelerating processes; it's enhancing healthcare outcomes, making diagnoses quicker, and treatments more precise, and minimizing errors. The future appears very promising with AI leading the way to more precise and tailored healthcare.Section 2: Case Studies in Diagnosis (4 mins):Help in research: Let's delve into real-life examples of AI in action, further amplifying diagnostic accuracy. In a research study, Rajkomar and collaborators crafted an AI algorithm predicting patient deterioration within hours, leveraging electronic health record data. This tool allowed for proactive care, identifying potential issues before they escalated. Taking it up a notch, Aliper and collaborators compared AI to human researchers, resulting in AI outsmarting human brains in designing drugs targeting age-related diseases. These experiments underscore AI's potential in diagnostics, from catching issues early to designing groundbreaking drugs.AI here enhances doctors' capabilities and acts as an additional set of eyes, boosting their superpowers, spotting nuances, and proposing game-changing solutions in medicine.Section 3: AI in Risk Prediction (4 mins):Let's shift our focus to AI's role in predicting risks and prognosis, particularly in conditions like COPD.AI employs sophisticated algorithms to analyze patient data comprehensively, including demographics, hospital visits, diagnoses, prescribed medications, and lab results. In COPD, AI not only predicts mortality but also anticipates hospital readmissions for respiratory issues or flare-ups. By scrutinizing various markers, AI resembles Sherlock Holmes, unraveling clues within data.And AI doesn't stop there, AI integrates risk predictions into medical practices, which fosters personalized care tailored to individual risk factors. A study led by Choi and their team analyzed retrospective patient data and they were able to identify individuals at risk of undiagnosed COPD, emphasizing the significance of catching potential issues early, finding those who might slip through the cracks otherwise, which is huge! Section 4: AI in Treatment Planning (4 mins):Let's now explore how AI is revolutionizing treatment planning within medicine.AI, equipped with machine learning algorithms, tailors treatments by analyzing patient-specific data and medical history. In cancer, for example, AI analyzes biopsy images and quantifies biomarkers, facilitating personalized treatments. Beyond cancer, AI extends its reach to cell therapies, predicting their effectiveness through genomic information and drug responses.And here's the techie part: AI employs various smart algorithms like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to provide personalized treatment recommendations. It's like having personalized treatment recommendations by experts that fit you like a glove, catering to individual needs. Section 5: Fuzzy Cognitive Maps and Reduction of Medical Errors (4 mins):Lastly, let me tell you about the impact of AI-driven treatment planning, specifically in reducing medical errors. Imagine this—medical decisions? They're tough. Sifting through tons of data, inaccessible medical records, physicians' lack of experience, and loads of conflicting info, makes the decision often not crystal clear. This is where a high percentage of medical errors occur, which is where Fuzzy Cognitive Maps (FCMs) come in.  FCMs are like a super-smart tool that mimics human reasoning, tackling the messiness of medical data with grace.FCMs are all about modeling complex systems, by combining fuzzy logic and neural networks, just like our brain does—connecting the dots between concepts and their cause-and-effect relationships. From patient records to test results, they make sense of it all.And FCM is not just theory—FCMs are the real deal and they're not the newbies in town; they've been around for a while, evolving from their early days. They've proven their worth in various medical areas too – in radiotherapy planning, diagnosing language impairments, and even in grading tumors!So, in a nutshell, FCMs are useful tools for medical decision support by taking on the complexities of diagnosing and treatment planning.Closing:In conclusion, the integration of Artificial Intelligence and Machine Learning in healthcare is a thrilling frontier, offering invaluable tools to enhance diagnostic accuracy and patient outcomes. As we evolve, responsible use of these advances is paramount, ensuring they optimize rather than replace the indispensable human touch in healthcare.Thank you for joining me in exploring the future of healthcare in AI and Machine Learning. I trust this discussion has sparked curiosity and appreciation for the transformative potential of technology in healthcare. -----------------------------------Conclusion: Now we conclude episode number 160, “Artificial Intelligence in Primary Care.” This is a new and somewhat unknown field of medicine that is rapidly evolving these days. Future Dr. Manophinives explained that AI and ML can be a useful tool in the diagnosis of diseases by, for example, interpreting images accurately. AI also can help develop plans of care by interpreting large amounts of complex data and predicting trends, possible complications, and the effectiveness of multiple treatments. Keep your eyes and mind wide open to learn more about this advancing technology that will continue to support our efforts to bring health and well-being to our communities.This week we thank Hector Arreaza and Rosalynn Manophinives. Audio editing by Adrianne Silva.Even without trying, every night you go to bed a little wiser. Thanks for listening to Rio Bravo qWeek Podcast. We want to hear from you, send us an email at RioBravoqWeek@clinicasierravista.org, or visit our website riobravofmrp.org/qweek. See you next week! _____________________References:Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016;375(13):1216-1219. doi:10.1056/NEJMp1606181. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070532/.Rajkomar, Alvin, et al. "Scalable and accurate deep learning with electronic health records." npj Digital Medicine, 08 May 2018. https://www.nature.com/articles/s41746-018-0029-1Choi, Ellen, et al. "Retrospective analysis of real-world data to identify patients at risk for undiagnosed chronic obstructive pulmonary disease." PLoS ONE, 2020.Choi, Ellen, et al. "Machine Learning in Primary Care: Predicting Hospitalizations and Critical Events." AMIA Annual Symposium Proceedings, 2018.Beam AL, Kohane IS. Translating Artificial Intelligence Into Clinical Care. JAMA. 2016;316(22):2368-2369. doi:10.1001/jama.2016.17217. https://pubmed.ncbi.nlm.nih.gov/27898974/Johnson, Kipp W., et al. "Automated Fuzzy Cognitive Maps Generation for Supporting Clinical Decisions in Primary Care." IEEE Transactions on Fuzzy Systems, 2020.Royalty-free music used for this episode: Gushito, “Gista Mista”, downloaded on November 16th, 2023, from https://www.videvo.net/

Jake Watson is the writer of thedataplatform.substack.com⁠ and Principal Data Engineer at The Oakland Group. MLOps podcast #207 with Jake Watson, Principal Data Engineer at The Oakland Group, How Data Platforms Affect ML & AI. // Abstract I've always told my clients and colleagues that traditional rule-based software is difficult, but software containing Artificial Intelligence (AI) and/or Machine Learning (ML)* is even more difficult, sometimes impossible. Why is this the case? Well, software is difficult because it's like flying a plane while building it at the same time, but because AI and ML make rules on the fly based on various factors like training data, it's like trying to build a plane in flight, but some parts of the plane will be designed by a machine, and you have little idea what that is going to look like till the machine finishes. This double goes for more cutting-edge AI models like GPT, where only the creators of the software have a vague idea of what it will output. This makes software with AI / ML more of a scientific experiment than engineering, which is going to make your project manager lose their mind when you have little idea how long a task is going to take. But what will make everyone's lives easier is having solid data foundations to work from. Learn to walk before running. // Bio Jake has been working in data as an Analyst, Engineer, and/or Architect for over 10 years. Started as an analyst in the UK National Health Service converting spreadsheets to databases tracking surgical instruments. Then continued as an analyst at a consultancy (Capita) reporting on employee engagement in the NHS and dozens of UK Universities. There Jake moved reporting from Excel and Access to SQL Server, Python with frontend websites in d3.js. At Oakland Group, a data consultancy, Jake worked as a Cloud Engineer, Data Engineer, Tech Lead, and Architect depending on the project for dozens of clients both big and small (mostly big). Jake has also developed and productionised ML solutions as well in the NLP and classification space. Jake has experience in building Data Platforms in Azure, AWS, and GCP (though mostly in Azure and AWS) using Infrastructure as Code and DevOps/DataOps/MLOps. In the last year, Jake has been writing articles and newsletters for my blog, including a guide on how to build a data platform: https://thedataplatform.substack.com/p/how-to-build-a-data-platform // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://thedataplatform.substack.com/ How Data Platform Foundations Impact AI and ML Applications blog: https://thedataplatform.substack.com/p/issue-29-how-data-platform-foundations AI in Production Conference: https://home.mlops.community/public/events/ai-in-production-2024-02-15 How to Build a Data Platform blog: https://thedataplatform.substack.com/p/how-to-build-a-data-platform --------------- ✌️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 Jake on LinkedIn: https://www.linkedin.com/in/jake-watson-data/ Timestamps: [00:00] Jake's preferred coffee [00:26] AI in Production Conference teaser [02:38] Takeaways [04:00] Please like, share, and subscribe to our MLOps channels! [04:17] Data Engineer's Crucial Role [05:44] Jake's background [06:44] Data Platform Foundations blog [10:34] Data mesh organizational side of things [17:58] Importance of data modeling [20:13] Dealing with the sprawl [22:03] Data quality [23:59] Data hierarchy on building a platform [29:34] ML Platform Team Structure [31:47] Don't reinvent the wheel [34:04] Data pipelines synergy [37:31] Wrap up

https://youtu.be/VP6y0PiQxWYTry VectorVest for only $0.99  ➥➥➥ https://www.vectorvest.com/YTVectorVest Merch Store  ➥➥➥ https://vectorvest.com/MerchandiseIn this captivating session, we delve into the ever-evolving world of cybersecurity and explore the groundbreaking advancements that are set to revolutionize the industry. Join us as we uncover the latest cutting-edge technologies, strategies, and trends that will shape the future of cybersecurity. From Artificial Intelligence (AI) and Machine Learning (ML) algorithms to cloud-based security solutions, this video explores how these innovations are transforming the way organizations protect themselves against cyber threats.Cyber Security Stocks -  Don't Say WE Didn't Tell You!!!! | VectorVest

In this episode Will and Neil (or maybe not) sit down with the "Outlaw Scientist" (as coined by AMW) Dr. Savannah Thais, an expert on Machine Learning (ML) and Artificial Intelligence (AI).  As a follow up, Dr. Thais defines what AI is and how it is part of our everyday life.  Her research goes beyond the data to focus on how AI impacts society and how we can use AI for social good.  Hear her talk specifically about her research in Appalachia and why she focused on the area.  Take a listen as she explains if regulation is needed in AI and what we should think about when thinking about the future of AI in Appalachia and beyond.  Also, don't forget about the #AppBiz(s) of the week - the next seven businesses in our countdown to the 25 Days of (and Appalachian Small Business) Christmas! Dr. Savannah Thais - https://datascience.columbia.edu/people/savannah-thais/  App News: ARC Ready Non-Profits - www.arc.gov/news/more-than-995000-awarded-to-40-appalachian-organizations-via-arcs-ready-nonprofits-initiative/  ARC Appalachian STEM Academy - www.arc.gov/academies-and-institutes/stem/  ARC Appalachian Entrepreneurship Academy - www.arc.gov/academies-and-institutes/aea/  #AppBiz(s) of the Week (and the next 7 of the countdown to (Appalachian Small Business) Christmas: 15. East Wheeling Clay Works (Wheeling, WV) - www.eastwheelingclayworks.com  16. Cottonwood Handmade (Boone, NC) - www.cottonwoodhandmade.com  17. Bloom (Garrison, KY) - www.bloom-candles.com  18. ZZZ Box Mattress (Hickory, NC) - www.zboxmattress.com  19. Dresden and Company (Dresden, OH) - www.dresdenandcompany.com  20. Appalachian Botanical Company (Ashford, WV) - www.appalachianbotanical.com  21. Pollinates Honeyworks (Belmonst, OH) - www.pollinateshoneyworks.com 

Nos adentramos en el fascinante mundo del aprendizaje automático y la innovación tecnológica, centrándonos en las últimas herramientas desarrolladas por Apple: MLX y AXLearn. Exploramos cómo estas plataformas están revolucionando el campo del Machine Learning (ML) y Deep Learning (DL), con un enfoque particular en su integración y optimización para el potente hardware de Apple Silicon. Este episodio es una inmersión profunda en el estado actual y las perspectivas futuras de la tecnología de aprendizaje automático en el ecosistema de Apple. Profundizamos en MLX, un framework de array para aprendizaje automático diseñado específicamente para el silicio de Apple, destacando sus APIs familiares, su eficiente manejo de la memoria y la capacidad de realizar cálculos de manera perezosa para optimizar la eficiencia. Luego, cambiamos nuestra atención a AXLearn, una biblioteca construida sobre JAX y XLA, que se especializa en el desarrollo de modelos de aprendizaje profundo a gran escala, abordando los retos de ingeniería de software en el aprendizaje profundo y aprovechando la capacidad de procesamiento distribuido y eficiente. Convierte en un Senior iOS Developer con el Swift Full Stack Bootcamp. Encuentra toda la información aquí: IV Swift Full Stack Bootcamp 2024. Descubre nuestro canal de Twitch en: twitch.tv/applecoding. Descubre nuestras ofertas para oyentes: - Cursos en Udemy (con código de oferta) - Apple Coding Academy - Suscríbete a Apple Coding en nuestro Patreon. - Canal de Telegram de Swift. Acceso al canal. --------------- Consigue las camisetas oficiales de Apple Coding con los logos de Swift y Apple Coding así como todo tipo de merchadising como tazas o fundas. - Tienda de merchandising de Apple Coding. --------------- Tema musical: "For the Win" de "Two Steps from Hell", compuesto por Thomas Bergensen. Usado con permisos de fair use. Escúchalo en Apple Music o Spotify.

Xi Jia chats with Dr. Michal Kosinski, an Associate Professor of Organizational Behavior at Stanford University's Graduate School of Business. Michal's research interests recently encompass both human and artificial cognition. Currently, his work centers on examining the psychological processes in Large Language Models (LLMs), and leveraging Artificial Intelligence (AI), Machine Learning (ML), Big Data, and computational techniques to model and predict human behavior. In this episode, they chat about Michal's recent works: "Theory of Mind Might Have Spontaneously Emerged in Large Language Models" and "Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT". Michal also shared his scientific journey and some personal suggestions for PhD students.If you found this episode interesting at all, subscribe on our Substack and consider leaving us a good rating! It just takes a second but will allow us to reach more people and make them excited about psychology.Michal's paper on Theory of Mind in LLMs: https://arxiv.org/abs/2302.02083Michal's paper on reasoning bias in LLMs: https://www.nature.com/articles/s43588-023-00527-xMichal's personal website: https://www.michalkosinski.com/Xi Jia's profile: https://profiles.stanford.edu/xijia-zhouXi Jia's Twitter/X: https://twitter.com/LauraXijiaZhouPodcast Twitter @StanfordPsyPodPodcast Substack https://stanfordpsypod.substack.com/Let us know what you thought of this episode, or of the podcast! :) stanfordpsychpodcast@gmail.com

Dr. Nima Aghaeepour, a researcher at March of Dimes' Prematurity Research Center at Stanford, discusses a Machine Learning (ML) model that predicts prematurity-related newborn diseases weeks before they occur, including before a baby is even born. 

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: I designed an AI safety course (for a philosophy department), published by Eleni Angelou on September 24, 2023 on LessWrong. Background In the fall of 2023, I'm teaching a course called "Philosophy and The Challenge of the Future"[1] which is focused on AI risk and safety. I designed the syllabus keeping in mind that my students: will have no prior exposure to what AI is or how it works will not necessarily have a strong philosophy background (the course is offered by the Philosophy department, but is open to everyone) will not necessarily be familiar with Effective Altruism at all Goals My approach combines three perspectives: 1) philosophy, 2) AI safety, and 3) Science, Technology, Society (STS); this combination reflects my training in these fields and attempts to create an alternative introduction to AI safety (that doesn't just copy the AISF curriculum). That said, I plan to recommend the AISF course towards the end of the semester; since my students are majoring in all sorts of different things, from CS to psychology, it'd be great if some of them considered AI safety research as their career path. Course Overview INTRO TO AI Week 1 (8/28-9/1): The foundations of Artificial Intelligence (AI) Required Readings: Artificial Intelligence, A Modern Approach, pp. 1-27, Russell & Norvig. Superintelligence, pp. 1-16, Bostrom. Week 2 (9/5-8): AI, Machine Learning (ML), and Deep Learning (DL) Required Readings: You Look Like a Thing and I Love You, Chapters 1, 2, and 3, Shane. But what is a neural network? (video) ML Glossary (optional but helpful for terminological references) Week 3 (9/11-16): What can current AI models do? Required Readings: Artificial Intelligence, A Modern Approach, pp. 27-34, Russell & Norvig. ChatGPT Explained (video) What is Stable Diffusion? (video) AI AND THE FUTURE OF HUMANITY Week 4 (9/18-22): What are the stakes? Required Readings: The Precipice, pp. 15-21, Ord. Existential risk and human extinction: An intellectual history, Moynihan. Everything might change forever this century (video) Week 5 (9/25-29): What are the risks? Required Readings: Taxonomy of Risks posed by Language Models, Weidinger et al. Human Compatible, pp. 140-152, Russell. Loss of Control: "Normal Accidents and AI Systems", Chan. Week 6 (10/2-6): From Intelligence to Superintelligence Required Readings: A Collection of Definitions of Intelligence, Legg & Hutter. Artificial Intelligence as a positive and negative factor in global risk, Yudkowsky. Paths to Superintelligence, Bostrom. Week 7 (10/10-13): Human-Machine interaction and cooperation Required Readings: Cooperative AI: machines must learn to find common ground, Dafoe et. al. AI-written critiques help humans notice flaws AI Generates Hypotheses Human Scientists Have Not Thought Of THE BASICS OF AI SAFETY Week 8 (10/16-20): Value learning and goal-directed behavior Required Readings: Machines Learning Values, Petersen. The Basic AI Drives, Omuhundro. The Value Learning Problem, Soares. Week 9 (10/23-27): Instrumental rationality and the orthogonality thesis Required Readings: The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents, Bostrom. General Purpose Intelligence: Arguing The Orthogonality Thesis, Armstrong. METAPHYSICAL & EPISTEMOLOGICAL CONSIDERATIONS Week 10 (10/30-11/4): Thinking about the Singularity Required Readings: The Singularity: A Philosophical Analysis, Chalmers. Can Intelligence Explode?, Hutter. Week 11 (11/6-11): AI and Consciousness Required Readings: Could a Large Language Model be Conscious?, Chalmers. Will AI Achieve Consciousness? Wrong Question, Dennett. ETHICAL QUESTIONS Week 12 (11/13-17): What are the moral challenges of high-risk technologies? Required Readings: Human Compatible, "Misuses of AI", Russell. The Ethics of Invention, "Risk and Respon...

Introduction: Tooba Faroki, an expert in Robotic Process Automation (RPA) and Artificial Intelligence (AI), shares her journey from graduating in Pakistan to her role at Invisible, a leading company in the automation space. Understanding Automation and AI: Automation vs. AI: Automation focuses on making repetitive tasks self-executing through predefined scripts or bots. In contrast, AI mimics human cognitive functions like learning and problem-solving. A couple of years ago, RPA was limited. It primarily required structured inputs and outputs. With advancements like Machine Learning (ML) and NLP models, there's a shift from structured to unstructured processing. Integrated Robotic Process Automation (IRPA) has been gaining traction. It marries the traditional automation with AI capabilities. Tooba's Personal Journey: Tooba kickstarted her career post-graduation by working at a local company in Pakistan. This foundational experience positioned her as an RPA expert by 2018. Her skills and expertise led her to IBM, where she implemented numerous RPA solutions. While ChatGPT was not present during her early career days, RPA's philosophy was clear: "set it and forget it." About Invisible: Unlike traditional BPOs, Invisible is an outsourcing company that understands client processes and refines them for better Return on Investment (ROI). They aim to deliver solutions faster, cheaper, and more efficiently by removing bottlenecks. They rely on both AI and humans for certain tasks. For example, while AI can generate content, humans are essential for quality assurance. At Invisible, there's a synergy between tech and operations. Communication is pivotal. Tooba, as a solution architect, ensures this smooth flow. Real-world AI Implementation: A Fortune 500 company, referred to as SQU, approached Invisible. Their sales weren't meeting expectations. Invisible stepped in to enhance the product's quality. Initially, the process was manual. Over time, they introduced automation and AI for better efficiency and results. The overarching message Tooba conveys is that while AI can be a game-changer, it's essentially about making statistical predictions. Therefore, understanding its potential and limitations is crucial for businesses. Incorporating AI in Business: Companies looking to integrate AI should shift their mindset. While AI can enhance operations, it's essentially about predictions. There's always room for error. However, the aim should be to leverage AI for betterment. Tooba's Insights and Learnings: One of the most intriguing lessons Tooba learned at Invisible is the importance of diversity. Interacting with a diverse range of people has enriched her understanding of human knowledge. There's a stark difference between professional expectations in Pakistan and at Invisible. In Pakistan, a job usually aligns with a fixed role and function. At Invisible, there's room for creativity and leadership. Every individual is encouraged to be the CEO of their department, driving innovation. Closing Remarks: Tooba's journey from Pakistan to Invisible is testament to her determination and expertise. As AI and automation continue to evolve, her insights provide a lens to understand their real-world applications better.

In this engaging episode of 'AI for Teachers,' we illuminate the intricate world of Machine Learning (ML) and Natural Language Processing (NLP) and their applications in the modern classroom. Consider ML as a tireless teaching assistant, learning from student patterns and refining strategies, and NLP as a multi-lingual linguist, interpreting texts and offering real-time feedback.We dive deeper into how these technologies can benefit educators, streamlining routine tasks like grading and grammar checking. It's about freeing up precious time for creativity, relationship-building, and innovation in teaching methods.Whether you're a teacher wanting to harness the power of AI, an edtech enthusiast, or just curious about the future of education, this episode offers insightful perspectives on how AI can augment your unique pedagogical style. Explore the potential, embrace the tools, and be part of this transformative journey towards an AI-enhanced era in education.

Join us in today's episode of Tech Talks Daily as we sit down with Jeff Miller, CEO of Synchronoss Technologies, to talk about the significance of secure and intelligent personal cloud solutions in this digital era. Synchronoss, with its newly redesigned personal cloud platform, is taking a revolutionary stride in providing customers with advanced cloud management features harnessing Artificial Intelligence (AI) and Machine Learning (ML). As an ex IT guy, our host Neil is particularly keen to explore the importance of data backup and privacy. He discusses with Jeff the unique features of Synchronoss Personal Cloud, which is currently set to roll out to major carriers including Verizon and AT&T. Jeff sheds light on the unique aspects of the platform including: BackTrack: A game-changing feature that safeguards your files against accidental deletions or losses, by retaining old versions of backups for 30 days. Genius: An AI tool that colorizes black and white photos, enhances photos, touches up faces, and applies other filters for an optimized visual experience. Advanced Highlights: This ML-based feature facilitates categorization, tagging, and easy management of content. Private Folders: This added layer of security allows subscribers to create pin-protected folders for sensitive data like tax documents, ID documentation, and secret recipes. Unlike OTT solutions, Synchronoss Personal Cloud, purpose-built for telecom provider and carrier networks, ensures utmost data privacy and security. Jeff emphasizes how they prioritize data privacy by not leveraging user data for ads or other services and focusing on carrier-grade security measures. Additionally, Neil and Jeff delve into the future of cloud solutions incorporating AI, 5G networks, and IoT devices. Jeff also shares his personal journey in technology, his admiration for his father's values, and his commitment to leaving things better than he found them. Get ready for an insightful conversation on how Synchronoss is reshaping the landscape of personal cloud solutions and prioritizing your data's privacy and security. Tune in now!

The topic on everyone's mind is AI, and we decided to jump into the conversation as well. Whether you're an enthusiast or a skeptic, there's no denying AI's significant impact on our world. We're privileged to have Craig Hainey, Head of Ventures at AltaML, and Darby Sieben, Chief Product Officer at Unbounce, join us to navigate the labyrinth that AI is.  Our discussion spans a range of topics, from debunking common AI myths to exploring how UX catalyzes adoption, among others. Most importantly, rest assured humanity is not at risk! Enjoy the show.  Our Guests  ---------------------------  Craig Haney - https://www.linkedin.com/in/craigahaney/ Head of Ventures @ AltaML  Darby Sieben - https://www.linkedin.com/in/darbysieben/ Chief Product Officer at Unbounce and Investor  Our Hosts  ---------------------------  Follow our updates here: ⁠⁠https://www.linkedin.com/company/sleeping-barber/⁠⁠  Get in touch with our hosts:  Marc Binkley: ⁠⁠https://www.linkedin.com/in/marcbinkley/ ⁠⁠ Vassilis Douros: ⁠⁠https://www.linkedin.com/in/vassilisdouros/⁠  Podcast Literature:  ---------------------------  Tristan Harris and Aza Raskin Co-Founders of the Centre for Humane Technology discuss The AI dilemma https://www.youtube.com/watch?v=cB0_-qKbal4  Reasons why AI startups will fail (Darby's post) https://www.linkedin.com/posts/darbysieben_99-of-ai-startups-are-going-to-fail-what-activity-7071506492394242048-BSKI  AI Hype Cycle is Distracting Companies https://hbr.org/2023/06/the-ai-hype-cycle-is-distracting-companies  How Generative AI can Augment Human Creativity https://hbr.org/2023/07/how-generative-ai-can-augment-human-creativity  Timestamps:  ---------------------------  0:45 - Intro to Craig Hainey from AltaML and Darby Sieben from Unbounce  1:39 - AI is will not be the death of humanity  2:35 - Artificial Intelligence (AI) is NOT Artificial General Intelligence (AGI)  6:18 - What's creating the hype for AI  8:01 - How UX is fueling adoption  10:17 - What makes AI different from a trend like PokemonGo, Segway, and Alexa voice search  16:27 - What sectors can't use AI?  19:30 - Why are some companies aren't going all in on AI  24:25 - The intersection of a company's Big Data and Machine Learning (ML)  25:48 - The challenge of the ChatGPT hype cycle  32:21 - What are the risks to enterprises and how to mitigate them  39:50 - How Unbounce uses AI for landing page conversion rates  42:49 - How AltaML uses AI for clients and what they're looking for in partners  46:55 - The new job opportunities with AI  49:00 - Sylvester.AI: A use case for monitoring cat health  51:50 - How intrapreneurs can use AI to help their company innovate  1:04:10 - Post Pod with V and Marc  Where to listen  ---------------------------  Apple Podcasts: https://podcasts.apple.com/ca/podcast/the-sleeping-barber-a-business-and-marketing-podcast/id1609811324 Spotify: ⁠⁠https://open.spotify.com/show/4v0kaM350zEY7X2VBuyfrF?si=7083317d5afd488b⁠⁠ Google Podcasts: https://podcasts.google.com/feed/aHR0cHM6Ly9hbmNob3IuZm0vcy84MWVjYWJhNC9wb2RjYXN0L3Jzcw?sa=X&ved=2ahUKEwji_oSOopP-AhXnlo4IHTZKBgYQ9sEGegQIARAC  Youtube: https://www.youtube.com/@sleepingbarberpodcast  © 2023 Sleeping Barber

Technology is now playing a huge role in HSE.  Listen as Russell sits down with Matt Oberdorfer, CEO of Embassy of Things to discuss the Internet of Things (IoT) and how to use Industrial IoT to utilize the power of Artificial Intelligence (AI) and Machine Learning (ML) to predict catastrophic events and prevent accidents thus contributing to the enhancement of all areas of HSE.  Find out also about Matt's new book coming out soon entitled: “Trailblazer's Guide to Industrial IoT”.   Contact Russell Stewart via LinkedIn: https://www.linkedin.com/in/russell-stewart-podcast-host/ Contact Matt Oberdorfer via LinkedIn: linkedin.com/in/mattoberdorfer Company Website: embassyofthings.com Sponsor Website: www.knowledgevine.com  Contact David Bowman, CEO KnowledgeVine via LinkedIn: https://www.linkedin.com/in/david-bowman-6875641/ This episode is made possible by Knowledgevine Enjoying the show? Leave me a review here Brought to you on the Oil and Gas Global Network, the largest and most listened-to podcast network for the oil and energy industry. More from OGGN ...PodcastsLinkedIn GroupLinkedIn Company PageGet notified about industry events  

There is fragmentation in the field of AI that no one seems to be taking too seriously. Daniel Lenton, the CEO of Ivy, observed this, finding so many frameworks, models, infrastructures, and hardware that make it hard for collaboration. Helping solve this problem, Ivy was created to unify all Machine Learning (ML) frameworks. In this episode, he tells us how they made it possible to fit these pieces together. Daniel also takes us through his journey prior to Ivy, the walls they hit in the labs, and the lessons they learned overcoming them. Talking about Ivy's growth, he then shares their success in GitHub and Discord and where they see they're heading in the future. For more insights about AI, ML frameworks, and open-source technology, join Daniel in this conversation. Find out how Ivy uniquely allows you to bring any model, library, or function into one line of code for your project.

In this episode, we navigate the complex landscape of Machine Learning (ML), Artificial Intelligence (AI), and Automation— specifically honing in on their pivotal role in higher education marketing. As these technologies become increasingly essential in a data-driven world, we highlight the necessity of adaptation and strategic innovation.Our attention then shifts to the specific applications of AI, ML, and Automation within marketing. We highlight how AI can reshape student recruitment strategies, how ML can personalize content for enhanced student engagement, and how automation can streamline administrative tasks, improving overall efficiency.Finally, we explore the tangible advantages that these technologies can offer to higher education marketing. From optimizing Return on Investment (ROI) to increasing operational efficiency, we delve into how a strategic shift towards AI, ML, and Automation can result in significant gains.

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. AbstractMaking Data Simple Podcast is hosted by Al Martin, VP, IBM Expert Services Delivery, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.This week on Making Data Simple, we have Benn Stancil, Chief Analytics Officer + Founder @ Mode. Benn is an accomplished data analyst with deep expertise in collaborative Business Intelligence and Interactive Data Science. Benn is Co-founder, President, and Chief  Analytics Officer of Mode, an award-winning SaaS company that combines the best elements of Business Intelligence (ABI), Data Science (DS) and Machine Learning (ML) to empower data teams to answer impactful questions and collaborate on analysis across a range of business functions. Under Benn's leadership, the Mode platform has evolved to enable data teams to explore, visualize, analyze and share data in a powerful end-to-end workflow. Prior to founding Mode, Benn served in senior Analytics positions at Microsoft and Yammer, and worked as a  researcher for the International Economics Program at the Carnegie Endowment for International Peace. Benn also served as an Undergraduate Research Fellow at Wake Forest University,  where he received his B.S. in Mathematics and Economics. Benn believes in fostering a shared sense of humility and gratitude.  Show Notes1:22 – Benn's history7:09 – Tell us how you got to where you are today9:14 – Tell us about Mode12:08 – What is your definition of the Chief Analytics Officer?21:53 – Why do we need another BI tool?24:09 – What's your secret sauce?27:48 – Where did the name Mode come from?28:41 – How do we use Mode?31:08 – What is you goto market strategy? 32:38 – Any client references?34:58 – “The missing piece in the modern data stack” tell us about thisMode Email: benn@mode.combenn@substack.comTwitter: benn stancilConnect with the TeamProducer Kate Brown - LinkedIn.Host Al Martin - LinkedIn and Twitter. 

In this thought-provoking episode of Conf T, hosts Bryan Young and Tom Porto sit down with Joe Marshall, also known as Rooster Cogburn. Joe, a Senior Security Strategist with Talos, brings his expertise in artificial intelligence and machine learning to the table. AI vs ML: We kick off the episode by addressing a common confusion - what is the difference between Artificial Intelligence (AI) and Machine Learning (ML)? Thanks to an enlightening explanation from ChatGPT, we now know that AI is the broader concept of machines being able to carry out tasks in a way we would consider 'smart', while ML is a specific subset of AI that involves the idea of training machines on data to learn and make decisions. AI Confidence and Source Citation: A deep dive into the issues surrounding AI's confidence and its lack of source citation. We discuss why it's so important for AI to cite its sources and how it can be improved. Ethical Guardrails in AI: Joe gives an insightful perspective on the ethical guardrails in place for AI and how they can potentially be circumvented, sparking a thoughtful debate on the ethical use of this technology. AI Sentience: Is AI sentient? This question sparks a fascinating discussion about the nature of sentience and how it applies, or doesn't apply, to AI. AI and the Dark Web: We explore the sinister side of AI with a look at its use on the Dark Web. How is AI being leveraged by those operating outside the law, and what can be done about it? AI for Phishing and Other Nefarious Tasks: We delve into the concerning issue of AI being used for phishing and other malicious activities. What are the implications, and how can we defend against it? AI Pranks: On a lighter note, we discuss some of the more amusing uses of AI, sharing anecdotes about pranks and other humorous applications and even pulling one live on the show on Mr. Porto. Combating AI-Driven Cybersecurity Threats: Joe gives his expert advice on how to go on the offensive against AI-driven cybersecurity threats. Using Failure to Determine Personhood: An intriguing concept is presented - using failure as a way to determine whether an entity is a real person or AI. Future of AI: Finally, we contemplate the future of AI, now that the barrier to entry has been drastically lowered. What's next on the horizon for this rapidly evolving technology? Tune in to gain some valuable insights into these topics and much more. Whether you're a tech enthusiast or a professional in the field, this is an episode you won't want to miss.

No More Small Businesses With Ai5/11/23IBM Reportedly Prepares to Halt Hiring for Jobs That Could Soon Be Replaced by AIhttps://www.youtube.com/watch?v=Yfn01Z1ZUbcIBM CEO Arvind Krishna Says A.I. Will Make More Jobs Than it Will Replacehttps://www.youtube.com/watch?v=EUX7DkQrVyYRhinoLeg 50 Job Titles with Descriptionshttps://s3.us-central-1.wasabisys.com/rhinoleg-backoffice/Docs/RhinoLeg50.docxIn the next 36 months, about 8,000 jobs will be replacedCEOs want 2 things:MoneyMore moneyPrompt 1Antonio T Smith Jr is teaching a class right now. He asked me to tell you to give him 50 jobs that artificial intelligence will replace in the next 5 years. I need a list of 50 and you don't have to give me a description, just job, title, or industry is good enough. ChatGPT ResponseData Entry ClerkTelemarketerBookkeeping ClerkCompensation and Benefits ManagerReceptionistCourierProofreaderRetail SalespersonMarket Research AnalystAdvertising Sales AgentTaxi DriverFast Food CookInsurance UnderwriterTravel AgentReal Estate BrokerToll Booth OperatorInventory ManagerParalegalBank TellerAgricultural WorkerCustomer Service RepresentativeCredit AnalystLoan OfficerCashierAssembly Line WorkerTextile WorkerHotel Desk ClerkLibrarianPostal Service WorkerNewspaper DeliverySports RefereeTrain OperatorMeter ReaderMail SorterWarehouse LaborerTranscriptionistPhoto ProcessorParking Enforcement WorkerConstruction LaborerSurveyorAircraft Cargo Handling SupervisorSecurity GuardFarm Labor ContractorFilm and Video EditorInsurance Claims ClerkOrder ClerkPayroll and Timekeeping ClerkProcurement ClerkShipping and Receiving ClerkSwitchboard OperatorPrompt 2Antonio T Smith Jr told me the 8,000 jobs will be replaced in the next 5 years by Ai. Please analyze this list and give me another 50 regardless of industry that do not repeat this list.ChatGPT ResponseMedical TranscriptionistSewing Machine OperatorGaming DealerPharmacy TechnicianHousekeeping CleanerBaristaLegal SecretaryReservation AgentFast Food ServerBartenderTaxi DispatcherCasino DealerPrint Binding and Finishing WorkerWatch RepairerInsurance Policy Processing ClerkMail CarrierLibrary TechnicianData AnalystReport WriterOffice ClerkTelemarketing Sales AgentBudget AnalystAccountantBank Loan OfficerDesktop PublisherIT Support SpecialistTechnical WriterStock ClerkHR AssistantSales RepresentativeLogisticianPurchasing AgentCost EstimatorReal Estate AppraiserFinancial AnalystTax PreparerBill and Account CollectorSewers, HandPhotographic Process WorkerNew Accounts ClerkBrokerage ClerkOrder FillerFile ClerkInsurance AppraiserFreight AgentRadio OperatorAvionics TechnicianClinical Laboratory TechnicianNuclear Power Reactor OperatorOccupational Therapy AidePrompt 3I am a CEO of a small business, and I am looking to replace 500 jobs in my industry with Ai within the next year. My industry is [your industry], and I am giving you a list of 10 job titles. Please analyze this list of 10 and tell me how these job titles can be maximized to increase my bottom line by $100,000 a year. With each job title and each analysis, give me a short description of how you would use this job title in my industry, which is [your industry]. It is important that you use my industry. ChatGPT ResponseData Entry Clerk: In an AI-powered CRM industry, this role could be replaced or enhanced by automated data entry systems. This could save time, reduce human error, and allow for real-time data updates. The cost savings from minimizing errors and accelerating the data entry process could significantly contribute to your bottom line.Telemarketer: AI can automate certain tasks of a telemarketer, such as scheduling calls and following up with leads. Automated voice systems or chatbots can handle initial customer interactions, filtering out uninterested leads and saving human agents for more promising or complex cases. This increases efficiency and reduces costs.Bookkeeping Clerk: AI systems can handle a lot of the routine tasks of a bookkeeping clerk, such as invoicing, payroll, and financial reporting. This reduces the likelihood of errors and frees up time for your financial team to focus on more strategic tasks, like financial planning and analysis, which could improve your bottom line.Compensation and Benefits Manager: AI can automate and streamline many aspects of compensation and benefits management, such as tracking employee performance, determining appropriate compensation, and managing benefits enrollment. This allows for more accurate and efficient management of employee compensation, which can help retain top talent and improve overall productivity.Receptionist: AI can replace or assist receptionists through automated scheduling systems, call forwarding, and visitor registration. An AI virtual assistant can handle inquiries 24/7, improving customer service and freeing up human staff for higher value tasks.Courier: In an AI-powered CRM business, physical courier roles may be limited. However, if documents or hardware need to be moved, drones or automated delivery systems could be used to reduce costs and speed up delivery times.Proofreader: AI can automate many aspects of proofreading, such as grammar, spelling, and punctuation checks. This could improve the quality of your content and communication, enhancing your brand image and customer satisfaction.Retail Salesperson: In the context of AI-powered CRM, an AI sales assistant could replace or enhance the role of a retail salesperson. It can provide customers with personalized product recommendations, manage inventory, and process orders. This can lead to increased sales and customer satisfaction.Market Research Analyst: AI can automate and enhance many aspects of market research. It can analyze large data sets to identify trends, segment customers, and predict future behavior. This can provide more accurate and timely insights, leading to better decision-making and strategy development.Advertising Sales Agent: AI can automate many aspects of advertising sales, such as identifying potential clients, personalized outreach, and tracking campaign performance. This can increase advertising sales efficiency and effectiveness, leading to increased revenue and profits.Taxi Driver: In the context of your industry, this might relate to the delivery of services or products. Automated scheduling and routing could optimize the efficiency of these deliveries, reducing costs. Moreover, your CRM could potentially offer solutions to taxi companies, providing AI-powered dispatching and routing, leading to increased sales and profits.Fast Food Cook: This could relate to the execution of routine tasks in your business. AI can automate repetitive tasks, improving efficiency and reducing errors. In terms of your CRM, if it's catering to fast food chains, an AI-powered system could predict order volumes and optimize food preparation times, enhancing customer satisfaction and sales.Insurance Underwriter: AI can automate the risk assessment and policy pricing, improving efficiency, accuracy, and consistency. This would save time and reduce costs in similar risk evaluation tasks in your business. For the CRM aspect, AI could offer predictive analytics and automated risk assessment features to insurance firms, enhancing your product offering.Travel Agent: An AI system can automate and personalize recommendations, bookings, and customer service, improving customer satisfaction and sales. Within your CRM, this could translate to providing intelligent recommendations and automating follow-ups with leads and customers.Real Estate Broker: AI can automate property matching, price estimation, and lead generation, improving efficiency and effectiveness. Similarly, your CRM could potentially offer features tailored to the real estate industry, such as automated property matching and personalized client follow-ups.Toll Booth Operator: This might relate to payment collection within your business. AI can automate payment processing and fraud detection, reducing costs and improving cash flow. As for your CRM, you could offer automated payment processing and fraud detection features to clients in industries that collect tolls or fees.Inventory Manager: AI can automate and optimize inventory management, reducing costs and improving service levels. This could translate into your CRM offering AI-powered inventory management solutions, potentially increasing sales and customer satisfaction.Paralegal: AI can automate legal research and document review, improving efficiency and accuracy. Within your CRM, this could mean providing intelligent search and document analysis features, making it more attractive to legal firms.Bank Teller: AI can automate transactions, customer service, and fraud detection, improving efficiency and security. Your CRM could potentially offer tailored solutions to banks, such as AI-powered customer service and fraud detection features.Agricultural Worker: AI can automate crop monitoring, irrigation, and harvesting, improving efficiency and yields. This might mean that your CRM could offer features like predictive analytics and automated task management to clients in the agricultural industry.Customer Service Representative: AI chatbots could handle common inquiries, freeing up human agents to handle more complex queries. This reduces costs and potentially improves customer satisfaction. You could also offer an AI chatbot feature in your CRM, enhancing your product offering.Credit Analyst: AI algorithms can automate credit risk assessment, reducing errors and bias. This could translate to cost savings in any financial risk evaluation within your company. For your CRM, you might offer AI-powered credit risk assessment features to clients in the financial sector.Loan Officer: AI can automate the loan application and approval process, improving efficiency and reducing bias. Similarly, you could offer AI-powered loan processing features in your CRM, making it more attractive to banking and finance clients.Cashier: In a CRM company, this could relate to payment processing. AI can automate payment collection and fraud detection, reducing costs and improving cash flow. You could also offer these features in your CRM to clients in retail and other sectors.Assembly Line Worker: This might relate to the execution of repetitive tasks in your company. AI can automate such tasks, improving efficiency and reducing errors. Your CRM could potentially offer workflow automation features to clients in manufacturing and other sectors.Textile Worker: This could relate to quality control in your company. AI can automate quality checks, improving consistency and reducing costs. Similarly, your CRM could offer AI-powered quality control features to clients in the textile and other industries.Hotel Desk Clerk: AI can automate booking and customer service tasks, improving efficiency and customer satisfaction. Within your CRM, you might offer AI-powered booking and customer service features to clients in the hospitality sector.Librarian: In a CRM context, this could relate to data management. AI can automate data categorization and retrieval, reducing errors and saving time. Your CRM could also offer AI-powered data management features, enhancing its appeal to various sectors.Postal Service Worker: This could relate to the delivery of services or products in your company. AI can automate scheduling and routing, improving efficiency. Similarly, your CRM could offer AI-powered delivery scheduling features to clients in logistics and other sectors.Newspaper Delivery: In a digital context, this could relate to the distribution of content or information. AI can automate content distribution based on customer preferences and behavior, improving engagement. Your CRM could offer AI-powered content distribution features, enhancing its appeal to media and other sectors.Sports Referee: This could relate to quality assurance within your company. AI can automate checks and balances, reducing errors and maintaining high quality. In terms of your CRM product, you could offer AI-based quality assurance features for clients that need to enforce rules or standards.Train Operator: In a metaphorical sense, this could relate to running operations smoothly. AI can automate and optimize various operational tasks, improving efficiency and reducing costs. Moreover, your CRM could offer AI-based operations management features for industries that have complex scheduling or routing needs.Meter Reader: This could relate to the measurement and analysis of business metrics in your company. AI can automate data collection, analysis, and reporting, improving accuracy and speed. Your CRM could offer similar features to clients who need to regularly track and analyze data.Mail Sorter: This could relate to sorting and managing incoming customer inquiries or requests. AI can automate this process, ensuring inquiries are promptly and appropriately addressed. This feature could also be included in your CRM for clients that receive high volumes of customer communication.Warehouse Laborer: This might relate to the management of digital resources in your company. AI can automate the organization and retrieval of these resources, improving efficiency. This kind of resource management feature could be valuable in your CRM for clients that manage large amounts of digital assets.Transcriptionist: AI can automate transcription, saving time and improving accuracy. This could be useful within your company for transcribing meetings or customer calls. Similarly, a transcription feature in your CRM could be valuable to clients that record customer calls or meetings.Photo Processor: In the context of your business, this might relate to processing and analyzing visual data. AI can automate this, providing insights more quickly and accurately. This feature could also be included in your CRM for clients that use visual data in their business.Parking Enforcement Worker: This could relate to enforcing rules and standards in your company. AI can automate monitoring and enforcement, reducing errors and improving compliance. A rule enforcement feature in your CRM could be valuable to clients that need to monitor compliance with certain standards or regulations.Construction Laborer: This could relate to building and maintaining your CRM platform. AI can automate many aspects of software development and maintenance, improving efficiency and reducing errors. This could result in cost savings and more rapid product updates.Surveyor: This might relate to understanding customer needs and preferences. AI can automate survey distribution, response collection, and data analysis, providing insights more quickly and accurately. An AI-powered survey feature in your CRM could be valuable to clients that want to better understand their customers.Aircraft Cargo Handling Supervisor: This role can be analogous to managing the flow of data or services in your business. AI can automate and optimize the handling of data or services, improving efficiency and reducing costs. Your CRM could also offer AI-powered data management and optimization features to clients in industries that handle large amounts of data or services.Security Guard: This could relate to security measures within your digital systems. AI can enhance security by automating threat detection and response, reducing the risk of data breaches and improving trust. Your CRM could offer similar AI-powered security features to clients, enhancing its appeal.Farm Labor Contractor: In a metaphorical sense, this could relate to managing resources in your company. AI can automate resource allocation and optimization, improving efficiency. Your CRM could offer similar AI-powered resource management features to clients, particularly in industries that require resource optimization.Film and Video Editor: This could be related to the editing and customization of user interfaces or customer experiences. AI can automate the customization of interfaces or experiences based on user preferences and behavior, improving customer satisfaction. Your CRM could offer similar AI-powered customization features, enhancing its appeal.Insurance Claims Clerk: AI can automate the processing and evaluation of claims (or in your company's context, complaints or requests), improving efficiency and accuracy. Your CRM could offer similar AI-powered features to clients in the insurance sector, making it more attractive to them.Order Clerk: This could relate to the management of customer orders or requests in your company. AI can automate the processing and tracking of orders or requests, improving efficiency and customer satisfaction. Your CRM could offer similar AI-powered order management features to clients.Payroll and Timekeeping Clerk: AI can automate payroll and timekeeping tasks, reducing errors and saving time. This could result in cost savings for your company and could also be a feature in your CRM for clients who need to manage payroll and timekeeping.Procurement Clerk: This could be related to the procurement of services or digital resources in your company. AI can automate and optimize procurement processes, reducing costs and improving efficiency. Your CRM could offer similar AI-powered procurement features to clients, especially those in industries that involve complex procurement processes.Shipping and Receiving Clerk: In a digital context, this might relate to the sending and receiving of data or services. AI can automate and optimize these processes, improving efficiency and reducing errors. Your CRM could offer similar AI-powered features to clients in industries that involve the exchange of large amounts of data or services.Switchboard Operator: This could relate to the routing of customer inquiries or requests in your company. AI can automate the routing of inquiries or requests, ensuring they are promptly and appropriately addressed. Your CRM could offer similar AI-powered routing features to clients who receive a high volume of customer communication.Prompt 4Antonio wants me to build the payroll department, but he doesn't want any employees. He wants it to be completely Ai driven. With that being said, I want you to give a 25-step process to build an Ai driven payroll and timekeeping department for the Ai driven CRM company. It is vital that these 25 steps are not general steps as Antonio and the company has the resources such as machine learning, deep learning, and Ai skills to pull this off. Your task is two-fold. First, give me the first 25 steps. Second is to teach me and give me the tools at your disposal to accomplish these 25 steps.ChatGPT Response1. Define Your Objectives: Clearly outline the tasks you want the AI to perform, such as calculating wages, making payments, tracking working hours, handling tax deductions, and generating reports.2. Understand Regulatory Requirements: Research and understand the legal and regulatory requirements for payroll and timekeeping in the jurisdictions where your employees are located.3. Gather Data: Collect historical payroll and timekeeping data. This data will be used to train your AI models.4. Clean the Data: Preprocess the data to handle missing values, outliers, and inconsistencies.5. Data Analysis: Perform exploratory data analysis to understand patterns and relationships in your data.6. Feature Engineering: Identify relevant features in your data that the AI models can use to make accurate predictions.7. Define AI Model Metrics: Define the metrics you'll use to evaluate your AI models, such as accuracy, precision, and recall.8. Split the Data: Split your data into training, validation, and testing sets.9. Choose Model Architectures: Select appropriate machine learning or deep learning architectures for your models. For example, you might use a combination of regression models for wage calculations and classification models for regulatory compliance.10. Train Models: Use your training data to train your models.11. Validate Models: Use your validation data to tune your models and select the best performing ones.12. Test Models: Use your testing data to evaluate your models' performance. Make sure they meet the metrics you defined earlier.13. Interpret Models: Use techniques like SHAP or LIME to understand how your models are making decisions. This is especially important for compliance with regulations that require explainability in AI.14. Set up an AI Infrastructure: Set up the hardware and software infrastructure needed to run your AI models in production.15. Automate Data Pipelines: Build automated data pipelines to preprocess new payroll data and feed it into your AI models.16. Integrate Models into Infrastructure: Integrate your AI models into your infrastructure.17. Develop User Interfaces: Develop user interfaces that allow users to input data and retrieve outputs from your AI system.18. Implement Security Measures: Implement measures to protect sensitive payroll data, such as encryption and access controls.19. Build a Monitoring System: Build a system to monitor your AI models' performance over time.20. Test the Entire System: Conduct end-to-end testing of your entire AI system.21. Plan for Maintenance: Plan for ongoing maintenance of your AI system, including regular updates to your models and infrastructure.22. Train Employees: Train any remaining employees who will interact with the AI system on how to use it.23. Deploy the AI System: Deploy your AI system in production.24. Monitor and Update the System: Regularly monitor your AI system's performance and make updates as needed.25. Continual Learning: Implement a system for your AI models to continually learn from new data and improve over time.These steps may require a variety of tools and skills, including knowledge of machine learning and deep learning, proficiency in programming languages like Python or R, experience with AI frameworks like TensorFlow or PyTorch, and understanding of data management tools like SQL. You may also need to use cloud platforms like AWS, Google Cloud, or Azure for your AI infrastructure, and data visualization tools like Tableau or PowerBI for data analysis and model interpretation. Prompt 5I am ready to build this: Define Your Objectives: Clearly outline the tasks you want the AI to perform, such as calculating wages, making payments, tracking working hours, handling tax deductions, and generating reports. Please analyze my list below and build out my logic and basic implementation to have Ai accomplish all I have listed. Please make any improvements as needed.ChatGPT ResponseTo build an AI solution that can handle the tasks you outlined, we need to break down each task into sub-tasks and identify where AI can be most beneficial. Here's an AI-based implementation plan based on your list:1. Monitor Staff Attendance DailyUse facial recognition or biometrics for attendance tracking. AI can process this data in real-time.Anomaly detection algorithms can identify any unusual patterns in attendance, such as frequent late arrivals or absences.2. Collect, Review, and Verify Timesheets and Work ChartsNatural Language Processing (NLP) can be used to interpret unstructured timesheet data.Machine Learning (ML) algorithms can cross-verify timesheet data for accuracy, comparing it against project timelines, expected work hours, etc.3. Prepare Monthly Salary SheetsML algorithms can automatically calculate monthly salaries based on the data collected, including adjustments for PTO, overtime, bonuses, etc.4. Process Payroll Paperwork for New EmployeesOptical Character Recognition (OCR) and NLP can process and digitize paperwork, extracting relevant information to be added to the payroll system.5. Update HRIS and Payroll DataAutomate updates to HRIS using AI. Any changes in employee status trigger automatic updates in the payroll system.6. Stay Updated on Payroll-related LawsAI can be used to constantly scan and analyze legal databases and websites for updates to laws and regulations, alerting you when relevant changes occur.7. Provide Information on Payroll Policies and BenefitsAn AI chatbot can be programmed to answer common employee queries related to payroll and benefits.8. Investigate and Resolve Payroll and Timekeeping ComplaintsAI can assist in initial complaint triage, identifying the nature of the issue and routing it to the appropriate department or person for resolution.9. Prepare Month-end and Year-end ReportsAI can automate the generation of financial reports by pulling data from the payroll system and applying the necessary calculations and formatting.10. Maintain and Update Employee Information in Payroll RecordsAI can automatically update employee records whenever a change is detected (e.g., address change, bank account change, etc.).To accomplish this, you'll need a diverse set of tools and technologies, including machine learning and deep learning libraries (like scikit-learn, TensorFlow, PyTorch), NLP libraries (like NLTK, spaCy), OCR tools (like Tesseract), AI platforms (like IBM Watson, Google AI), and more. Programming languages like Python and R will also be essential.Remember, while AI can automate many tasks, it's crucial to have a human in the loop for supervision, especially for tasks involving sensitive personal data.Prompt 6What else can we add to this to improve our Ai capabilities?Closing ThoughtsWhen doing all of this, don't look at the progress for a year; just keep showing upVertical leadership/ownership takes timeYou are the greatest thing that happened to this planetThere is nothing more sacred to this world than youThere is nothing on this planet that God loves more than youIf you're not living your dreams, you must learn to lean in and give into the presentFall in love with losing as fast as possibleSupport this podcast at — https://redcircle.com/the-secret-to-success/exclusive-contentAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

In this episode, we're joined by Praful Krishna from Haptik.ai for another detailed AI discussion. We dive into the history of GPT models and how tools like Haptik will have real-world impact for short-term rental hosts and professional managers.⭐️ Links & Show NotesAdam NorkoScott FasanoHaptikPraful Krishna 

In this episode, we're joined by Evan & Braeden from Aidaptive. Aidaptive automatically personalizes the guest booking experience for every single visitor driving more conversions and revenue: learn more about how and when this new tech impacts vacation rental companies. ⭐️ Links & Show NotesAdam NorkoScott FasanoAidaptiveEvan Dolgow Braeden FlahertyTwiddy & Company delivers southern hospitality at scale with Aidaptive

In this episode of This week in Health Tech, Vik and John discuss HIMSS23 conference and insights gained at the conference. The main highlight at the conference was Artificial Intelligence (AI) and Machine Learning (ML). Vik and John discuss few AI implementations in Healthcare including Epic, Microsoft, Google, Nuance, and more. Website: http://www.thisweekinhealthtech.comTwitter: @TWIHT1Tido Inc.: https://www.tidoinc.com/Twitter: @TIDOINCLinkedin: Vik PatelSupport the showListen to all This Week in Health Tech episodesTidoInc.LinkedIn

Welcome to a cutting-edge episode of the "Association Chat Podcast" hosted by KiKi L'Italien! In this exhilarating conversation with Amith Nagarajan, Chairman of Blue Cypress, we'll uncover the transformative power of Artificial Intelligence (AI) and Machine Learning (ML) in the world of associations and nonprofits. Are you intrigued by how technology can revolutionize member engagement, streamline operations, and drive groundbreaking innovation in the association space? Look no further! With nearly 30 years of entrepreneurial success and a rich background as an early-stage investor in B2B SaaS companies, Amith offers invaluable insights into these disruptive technologies. In this episode, you'll learn: - How AI and ML are not just buzzwords but vital tools in shaping the future of associations. - The latest advancements in generative AI that are setting new benchmarks in operational efficiency. - Real-world examples of AI-driven success in engaging members and fostering long-term, sustainable growth. Blue Cypress, a family of purpose-driven companies led by Amith, is renowned for supporting associations and nonprofits to thrive in today's competitive landscape. This conversation taps into his wealth of experience to bring you actionable strategies to leverage AI and ML for your organization's success. Whether you're a tech enthusiast or an association professional looking to stay ahead of the curve, this episode is your gateway to the intersection of technology and associations. Dive in and discover the future with Association Chat's KiKi L'Italien! Keywords: Association Chat Podcast, KiKi L'Italien, Amith Nagarajan, Artificial Intelligence, Machine Learning, Blue Cypress, Associations, Nonprofits, B2B SaaS, Member Engagement, Generative AI, Operational Efficiency, Sustainable Growth, Technology and Associations

Artificial Intelligence is taking the education world by storm! But, as with any new technology, AI doesn't come without risks. In this episode, Jerri and Jamie discuss how AI can enhance learning experiences and improve educational outcomes as well as the potential risks and ethical concerns that arise with the integration of AI in the classroom. Tune in as we delve into this increasingly important topic and consider the future of education in the age of AI. --- ABOUT OUR GUEST Dr. Wednaud J. Ronelus is the Founder and CEO of Alchemist Club Studios. A STEM Learning Scientist, Dr. Ronelus is currently immersed in the process of designing bleeding-edge learning spaces using the latest cutting-edge technologies: Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Hologram Reality (HR), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and more. --- SUBSCRIBE TO THE SERIES: YouTube | Spotify | Apple Podcasts | Google Podcasts | Overcast | RadioPublic | Stitcher FOLLOW US: Website | Facebook | Twitter | LinkedIn POWERED BY CLASSLINK: ClassLink provides one-click single sign-on into web and Windows applications, and instant access to files at school and in the cloud. Accessible from any computer, tablet, or smartphone, ClassLink is ideal for 1to1 and Bring Your Own Device (BYOD) initiatives. Learn more at classlink.com.

Delivering Amazing Customer Experiences through Seamless Self-Service and Human Interactions Shep Hyken interviews Declan Ivory, Vice President of Customer Support at Intercom, dedicated to applying digital technologies to empower customer support teams. He discusses the technology available to drive meaningful change and innovation for customer experience. Top Takeaways:  Artificial Intelligence (AI) and Machine Learning (ML) bring significant innovations that can truly transform customer service and experience. We now have technology that companies can apply quickly and cost-effectively to deliver the transformational experience customers have expected from the support industry for a long time. Technology can now deliver a conversational feel to the customer's interaction with a chatbot. Customer information is now integrated, so chatbots have the same access as human support agents. And often, they can better understand the context of what the customer is contacting them about.  Transitioning from automated to human support needs to be seamless. The customer should have to repeat everything they just said. This personalization and convenience make the customer feel valued throughout the interaction. Most customer support experiences are reactive. Support agents usually respond to the customer's issues or questions. However, customer expectations have evolved so that a compelling experience is one where customer support doesn't wait for something to go wrong; they can predict an issue before it happens and proactively start the conversation with the customer to solve the problem before the customer ever knows about it.  An omnichannel experience is about enabling the customer to engage with you on any channel that makes sense to them. The important thing about this experience is meeting them where they are and demonstrating that you value and understand the customer in every channel they use. Plus, Shep and Declan discuss how technology has evolved in the customer support world- from clumsy and clunky to a seamless experience. Tune in! Quote: "The future is automation and human support. You need the knowledge and understanding that a human support agent brings to handle complex issues. And, you want self-service to be available to quickly handle the more simple transactions.” About:  Declan Ivory is Intercom's Vice President of Customer Support. He is an experienced senior leader with a passion for building and developing high-performing teams and applying digital technologies to support organizations. Shep Hyken is a customer service and experience expert, New York Times bestselling author, award-winning keynote speaker, and host of Amazing Business Radio. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, host Bidemi Ologunde spoke with JT Taylor, the Senior Director of Fraud Investigations and Operations at ID.me, a cybersecurity company valued at over $1.5 billion. The conversation touched on the nuances of synthetic identity theft; how identity verification is a tool to recognize synthetic identity theft; how identity theft ties into recent trends of prescription medication abuse; how Artificial Intelligence (AI) and Machine Learning (ML) technology can be used to enhance fraud detection; the role of social media in synthetic identity fraud; the impact of synthetic identity on brand abuse, geopolitics, and so on; best practices for fraud data strategy and information sharing; how ID.me platform fits into the future of cybersecurity… and lots more.=======Receive $25 off orders of $149+ with code SWAPSRF at Snake River Farms!Whether you're a seasoned veteran or a beginner to beef, the pioneers of American Wagyu have got you covered with $25 off your order.Shop Delicious Meats Now=======Organize your work and life, finally.Become focused, organized, and calm with Todoist. The world's #1 task manager and to-do list app.Start for free=======Productive businesses use 1Password to secure employees at scale.1Password is the world's most-loved password manager, with top ratings from G2Crowd and Trustpilot, and has been named top password manager by leading media outlets including Wired, The New York Times, and CNET.Start protecting your data today=======Turn your Airtable or Google Sheets into modern business tools you need.Softr lets you stop waiting for developers. Build software without devs. Blazingly fast. Trusted by 100,000+ teams worldwide.Start building now.=======Sesame Care - Doctor appointments as low as $19.Find the best price for the highest quality physicians. Book an appointment in minutes.Get Started=======Support the show

Unpack and process Ayurvedic algorithms in order to apply them to modern actions like drinking coffee and alcohol, GMO food production and breathing exercises (pranayama).Dr Sumit K. in fact is researching and applying the medicinal benefits of coffee and single-malt whisky. For those who want to drink coffee and alcohol, this episode may guide you to refine your relationship with consuming substances. Dr Sumit also frequently hikes ruthless pilgrimages into the high Himālayas in search for Ayurvedic herbs, ancient village healers and to commune with the Vedic Energy of Lord Shiva: that Absolute untouched Silence and force of nature which removes irrelevancy.  This episode decodes some of Ayurveda's teachings around substances, the six tastes, and other more occult & rare Vedic technologies.After all, according to Ayurveda, EVERYTHING in the universe can be medicine when you know how to transform that substance.IN THIS EPISODE WE DISCUSS:

George Sweeney is an inspiring and confident educator, trainer, personal tutor, and content designer. George has been working in math education for 20 years and has been a tenured, community college professor for 10+ years. His specialty has been in the cognition of mathematical thinking and the development of mathematical communication to facilitate learning. Recently, he has begun to teach discrete mathematics and work in both Machine Learning (ML) and Artificial Intelligence (AI). His goal as an educator is to expand his students' understanding of difficult and important mathematics to apply it to their various career and academic endeavors. George loves the challenge of making abstract and complicated ideas come to life for students and helping them make connections to a rapidly changing world. In addition to his work as a math professor, he has taken on the role of a trainer in emotional intelligence and soft skills for executive coaching company Rise Up for You, where he uses his experience teaching, interacting with students, colleges, and school districts to help professionals become better communicators and leaders. George's experience listening, managing expectations, developing curriculum, and classroom experiences to maximize learning, has created a natural connection to the world of training, communication, confidence, and well-being coaching. ________________________________________________________________ Thank you again for joining us today! If you know anybody that would benefit from this episode please share it with them and help spread the knowledge and motivation. Don't forget to show your support for the Rise Up For You Podcast by writing a review on iTunes. Your feedback helps the success of our show and pushes us to continuously be better! Check out www.riseupforyou.com for more podcast episodes, webinars, events, and more to help you get to the next level in your personal and professional life! You can also follow us on Facebook, Instagram, LinkedIn, and Youtube @riseupforyou Looking for more support? Grab your free coaching call with our team completely FREE! Bring your questions about Confidence, Leadership or Business and we will assign you the best coach to provide customizable support. SCHEDULE YOUR FREE CALL HERE riseupforyou.com/coaching