Podcasts about machine learning ai

Ces dernières semaines, les actualités sur les startups et l'entrepreneuriat s'enchainent à Taïwan alors ce sera encore une revue de presse. Nous traiterons de manière assez extensive trois sujets : - Le laboratoire d'intelligence artificielle, Taiwan AI labs, qui a été très sollicité lors du salon Machine Learning AI à Prague en République Tchèque. - Le gouvernement qui vient d'annoncer ses dernières mesures pour attirer les talents étrangers, notamment les nomades numériques - La ville de Taipei qui annonce sa volonté de devenir le pôle régional de recherche en matière d'intelligence artificielle au Computex accompagnée de témoignages de différents acteurs du secteur internationaux

Sponsor Salalabs - North America We specialize in complex tech implementations, including: AI & Machine Learning IoT & Smart Device Integration Custom Software Development Scalable Cloud Solutions System Architecture & Optimization What You Can Expect Transparent pricing and timelines Agile, responsive collaboration A team that understands your needs and delivers at scale For more info, email contact@salalabs.com or reach out to Jason Cavness  Go to www.thejasoncavnessexperience.com for the podcast on your favorite platforms Subscribe to the YouTube channel  https://www.youtube.com/channel/UCGsw6kzZE40sSUZgoStVaJw?sub_confirmation=1 Andrie's Bio Yanjun (Andrie) Lin is a veteran, technologist, and community builder, known for her ability to reinvent herself and thrive across vastly different environments. Currently based in Seattle, she is the VP of Program Management at Panda Mobile, where she leads the development of community-focused MVNOs (Mobile Virtual Network Operators) that provide culturally relevant, niche wireless services to underserved markets especially Chinese immigrant communities and university ecosystems. Andrie's life journey is a testament to adaptability, resilience, and the power of starting over. Originally from China, she left everything familiar behind to pursue her education in the U.S. Despite early struggles, she graduated with summa cum laude, won the second place in business plan competition against the entire school, and completed two internships, proving her ability to overcome new challenges quickly. Driven by a desire for citizenship and a new path, she made the bold decision to leave behind her finance background to join the U.S. Army a field she had no prior experience in. Starting from zero, she trained herself to achieve the highest physical fitness award during basic training and excelled in logistics roles, earning several leadership and performance awards throughout her 7.5 years of service and 1 trip of deployment. She further expanded her skills through financial engineering education, security training, and leadership roles. Upon retiring from the military, Andrie once again pivoted, entering the tech world. In just six months, she transitioned from military logistics to completing a successful software engineering internship, mastering new technologies, languages, and delivering impactful projects. In addition to her corporate roles, she is the Founder of the OffChain Global Seattle Chapter, a hub that brings together entrepreneurs, investors, developers, and technologists in emerging tech fields like Web3, AI, AR/VR, and IoT, fostering vibrant, cross-functional communities. We talk about the following and other items Andrie's Journey to Inner Peace Andrie's Military Background Cultural Shocks and Adaptation Andrie's Military Experience Transition to Tech FinTech Leadership Community Building Challenges and Opportunities in Tech Personal Growth Future Predictions Advice for Military Personnel Off Chain Global Seattle Chapter Tea Ceremony Event Personal Network Seattle Tech Startup Scene Funding Challenges Community Technology AI and Machine Learning AI's Impact and Ethical Considerations Personal Experiences with AI and Technology Networking and Personal Development Challenges  Opportunities in Tech  Balancing Work  Personal Life Mentorship Personal Growth  Advice for Young Professionals  Andrie's Social Media Andrie's LinkedIn: https://www.linkedin.com/in/yanjun-linked/ Panda Mobile: https://pandamobile.com/ Andrie's Advice I would say something that helped myself the most is just to stay in peace of mind and focus on the present. Because you can't change the past, and in the past, you have made the best decision for yourself at that moment. It's not fair to judge yourself with the experience and the knowledge that you have now to judge the past of you not making the right decision. But it's not fair, because in the past you, at that moment, with the level of knowledge and experience, made the best decision. So there's no way for you to think the past and regret, and also there's you can plan for the future. But not to get too worried on whether I make a decision now. Would that lead to a different route that I would regret that I didn't take option B, because it's sort of irrelevant too, because you never know what it's going to lead to.  Lock In Early Pricing with CavnessHR As a subscriber to the Jason Cavness Experience, you can lock in early, discounted pricing before our official launch. Pricing Tiers: 1 to 10 employees: Freemium plan available, or upgrade for just $59/month 11 to 19 employees: $99/month  20 to 34 employees: $199/month 35 to 49 employees: $299/month Sign up now to lock in your rate and simplify your HR before we go public! Schedule time to talk about your HR challenges: Book a Meeting with CavnessHR

Machine Learning (AI) Onsite w/ Eddi Weinwurm of Obvious Future   - AZ TRT S06 EP01 (262) 1-5-2025                 What We Learned This Week Obvious Future is building Machine Learning (AI) programs to be used onsite for a business Corporate Data is too sensitive to be in the cloud / internet Business cannot use cloud AI programs like ChatGPT, Google Cloud, etc because of IP and privacy concerns Large Language Models are not necessary, have more data than needed, can have smaller AI programs tailored for business     Guest:  Eddi Weinwurm AI is top of mind for most enterprises…but most don't know the risks especially in the cloud.   https://obviousfuture.com/#    Eddi Weinwurm is a co-founder and CEO of Obvious Future an AI company with a new approach to keeping AI local and secure.   Eddi Weinwurm has many years of experience in both the development of media management software and AI.  As a visionary he formed the company to address critical enterprises in the growing AI market.     ObviousFuture Resident AI: Faster, Safer, and Transforming Enterprise AI   Eddi Weinwurm co-founder and CEO of ObviousFuture is on a mission to make AI safer and faster for enterprises.   ObviousFuture, a trailblazer in secure and private AI solutions, will be unveiling a disruptive AI solution for the enterprise on December 18—Resident AI.    This solution empowers enterprises to harness the full potential of AI while safeguarding their data locally, marking a critical evolution in the AI landscape.   ObviousFuture's Resident AI operates entirely on-premise, solving a $500 billion market problem by addressing vulnerabilities like data privacy risks, compliance challenges, and vendor lock-ins. The company is focused on key sectors such as government, defense, surveillance, medical, and media.   Early adopters, have achieved ROI within just two months of deployment of the Resident AI platform.           Key benefits for Enterprises  Local- Resident AI is an artificial system that resides directly on the enterprises infrastructure reducing risk and latency often associated with edge computing.  ObviousFuture's approach is fully private and operates with very low latency.    Offline Access- Resident AI allows for complete work productivity without a single point of failure and no third-party API reliance.     Not Edge AI-Resident AI delivers full AI capabilities similar to big cloud models and doesn't require a data center.   Reduces Cost-  Substantial lower deployment and operational costs means a higher ROI for businesses.     Learn more about the company at ObviousFuture and sign up for a demo today.         Seg 1    Obvious Future AI CEO, Eddi Weinwurm   Resident AI for companies, storage of sensitive data, not for use with 3rd parties   Predictive editing from media, has Search and use features   No open AI, search documents internally – onsite at your company   Use local servers and data center on site, nothing goes to the Internet or cloud   For business to use AI with sensitive data, it cannot be in the cloud, it must be on site Business needs verticals with AI integration   Examples of companies with sensitive data that cannot be on the Internet, are media, banking, healthcare, companies doing research and development RND   Also dealing in Research, or math, like at universities   Eddie does not want to call this AI, prefers the term machine learning, using a lot of that now and it will grow   Standard cloud AI used in conjunction online with companies like Google or Microsoft,   You can connect to an API, example is chat GPT The AI work is done in the cloud and over the Internet This involves data centers and huge computer networks Parameters of lots of neurons or brain power in computing   Obvious Future is a technology company, looking to engineer a smaller API with the same machine learning intelligence   History of technology, initially things are large and cost a lot of money Over time, it should be smaller and cheaper and faster   How do you perform the same tasks and have great efficiency?   Lots of uses for artificial intelligence, just one example is checking email for spam   Currently, most AI is a large language model, tons of info or put into the machine learning. But a lot of it is not needed, lots of waste   Compounds and routing models can be the future     Seg 2   Chat GPT, is a supermodel, knows everything, but has wasted data To Eddie, this does not make sense   For most AI tasks, you need a specialist, a form of expert knowledge Need to be routed to the right model, compound the route and info   Network of smaller models – then Which expert model to ask a question This will reduce resources and energy use, plus better use, and faster   Alan Tuning said, difficult work requires lots of engineering   Obvious Future has created a machine learning product called Cara One, used for media production and film   Runs on the premises of a business, local, not in the cloud   Not connected to the Internet, in-house, so you can protect your Intellectual Property (IP)   Air gapped setup with hardware, provide AI resources in house   Problem, how to keep data in house Run in a server room on the premises Cap X model for a business where they can own their own AI   Current AI Cloud set up as a subscription model, this a company would own     Seg 3   Set up a server or multiple servers in a closet in a business office on site, but not a super computer   AI hardware equals expensive cost   Math plus technology and you can shrink the tech   Companies cannot even use lots of AI services or ChatGPT right now, because it is open cloud, too much business risk, can't have your IP or information in the cloud   Cloud options for business are limited, and typically a no go, plus regulations with data protection   Obvious Future has their Resident AI product, called Cara One   Even if the Internet is down, you can still use their resident AI product, and it cost less   Huge paradox in technology right now, we have the best AI products and software and yet many companies cannot use it   Resident AI resides on premises. This opens up the business environment for different verticals and markets that need resident AI   Engine for AI, small sized so companies can use   Rate of return will continue to go up as companies invest in this   Hardware, software, chips are all improving and incorporating AI. Like all things in technology. There is constant improvement, and enhancing AI.   Obvious Future dealing in financing rounds for a business opportunity   Verticals of financing include industries like: banks, medical, legal, research data, customer data   All can be potential customers   Obvious Future working on developing the next level of Cara One, Resident AI for all of these potential businesses   US Patent Office would be another example, they can only partially use cloud AI, need resident AI to sift through all the data         Biotech Shows: https://brt-show.libsyn.com/category/Biotech-Life+Sciences-Science     AZ Tech Council Shows:  https://brt-show.libsyn.com/size/5/?search=az+tech+council *Includes Best of AZ Tech Council show from 2/12/2023   Tech Topic: https://brt-show.libsyn.com/category/Tech-Startup-VC-Cybersecurity-Energy-Science  Best of Tech: https://brt-show.libsyn.com/size/5/?search=best+of+tech     ‘Best Of' Topic: https://brt-show.libsyn.com/category/Best+of+BRT      Thanks for Listening. Please Subscribe to the BRT Podcast.       AZ Tech Roundtable 2.0 with Matt Battaglia The show where Entrepreneurs, Top Executives, Founders, and Investors come to share insights about the future of business.  AZ TRT 2.0 looks at the new trends in business, & how classic industries are evolving.  Common Topics Discussed: Startups, Founders, Funds & Venture Capital, Business, Entrepreneurship, Biotech, Blockchain / Crypto, Executive Comp, Investing, Stocks, Real Estate + Alternative Investments, and more…    AZ TRT Podcast Home Page: http://aztrtshow.com/ ‘Best Of' AZ TRT Podcast: Click Here Podcast on Google: Click Here Podcast on Spotify: Click Here                    More Info: https://www.economicknight.com/azpodcast/ KFNX Info: https://1100kfnx.com/weekend-featured-shows/     Disclaimer: The views and opinions expressed in this program are those of the Hosts, Guests and Speakers, and do not necessarily reflect the views or positions of any entities they represent (or affiliates, members, managers, employees or partners), or any Station, Podcast Platform, Website or Social Media that this show may air on. All information provided is for educational and entertainment purposes. Nothing said on this program should be considered advice or recommendations in: business, legal, real estate, crypto, tax accounting, investment, etc. Always seek the advice of a professional in all business ventures, including but not limited to: investments, tax, loans, legal, accounting, real estate, crypto, contracts, sales, marketing, other business arrangements, etc.  

Since three years, Egor is bringing the power of AI to bear at Wise, across domains as varied as trading algorithms for Treasury, fraud detection, experiment analysis and causal inference, and recently the numerous applications unlocked by large language models. Open-source projects initiated and guided by Egor include wise-pizza, causaltune, and neural-lifetimes, with more on the way. Machine Learning, AI Agents, and Autonomy // MLOps Podcast #282 with Egor Kraev, Head of AI at Wise Plc. // Abstract Demetrios chats with Egor Kraev, principal AI scientist at Wise, about integrating large language models (LLMs) to enhance ML pipelines and humanize data interactions. Egor discusses his open-source MotleyCrew framework, career journey, and insights into AI's role in fintech, highlighting its potential to streamline operations and transform organizations. // Bio Egor first learned mathematics in the Russian tradition, then continued his studies at ETH Zurich and the University of Maryland. Egor has been doing data science since last century, including economic and human development data analysis for nonprofits in the US, the UK, and Ghana, and 10 years as a quant, solutions architect, and occasional trader at UBS then Deutsche Bank. Following last decade's explosion in AI techniques, Egor became Head of AI at Mosaic Smart Data Ltd, and for the last four years is bringing the power of AI to bear at Wise, in a variety of domains, from fraud detection to trading algorithms and causal inference for A/B testing and marketing. Egor has multiple side projects such as RL for molecular optimization, GenAI for generating and solving high school math problems, and others. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links https://github.com/transferwise/wise-pizza https://github.com/py-why/causaltune https://www.linkedin.com/posts/egorkraev_a-talk-on-experimentation-best-practices-activity-7092158531247755265-q0kt?utm_source=share&utm_medium=member_desktop --------------- ✌️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 Egor on LinkedIn: https://www.linkedin.com/in/egorkraev/

Dave Colls and David Tan join me to chat about building effective machine learning teams, the challenges they face, the 7 deadly wastes in data and ML, writing a book, and much more.

About 5.4 million Americans live with some form of paralysis. Sometimes that's just a temporary loss of mobility, but for the Americans whose paralysis is caused by a spinal cord injury, that loss of movement is often permanent, as there's no biological way to heal an injured spinal cord. There are efforts to see if technology might be able to help these individuals regain use of their limbs, and that's the focus of this episode of Stats+Stories with guest Dr. David Friedenberg. Dr. Friedenberg is a Principal Data Science and Neurotechnology and the Team Lead for Machine Learning/AI in the Health Analytics group at Battelle. He's the Principal Investigator on several neurotechnology efforts developing new AI-powered technologies to help improve the lives of people living with motor impairments due to neurological injuries like spinal cord injuries and stroke. An experienced data scientist with consulting experience across several disciplines he is passionate about developing AI/ML-driven solutions to challenging problems for the betterment of humanity.

This Week in AI and Machine Learning From November 17th to November 24th 2024:⚡ AI Data Centers Could Outpace City Electricity Usage

Send us a textDiscover how predictive analytics, machine learning, AI, and virtual reality reshape some of the ways we approach design. In this episode, we journey from the origins of predictive analytics to the convergence of big data, IoT, digital twins and more, paving the way for innovative product development. We'll also discuss the potential of virtual reality to enhance collaboration and communication within design processes.This episode isn't just about embracing the latest tech trends; it's about knowing when simpler solutions will suffice and the critical role of data stewardship. This overview will help you to understand the big picture of where these tools fit into your design process. Listen-in so you can better choose when to use them to optimize your design engineering endeavors, or not. Join the conversation by sharing your thoughts on our blog or newsletter.Give us a Rating & Review**NEW COURSE**FMEA in Practice: from Plan to Risk-Based Decision Making is enrolling students now. Visit the course page for more information and to sign up today! Click Here **FREE RESOURCES**Quality during Design engineering and new product development is actionable. It's also a mindset. Subscribe for consistency, inspiration, and ideas at www.qualityduringdesign.com.About meDianna Deeney helps product designers work with their cross-functional team to reduce concept design time and increase product success, using quality and reliability methods. She consults with businesses to incorporate quality within their product development processes. She also coaches individuals in using Quality during Design for their projects.She founded Quality during Design through her company Deeney Enterprises, LLC. Her vision is a world of products that are easy to use, dependable, and safe – possible by using Quality during Design engineering and product development.

I had another great conversation with Agus Sudjianto around machine learning models, LLMs, risk management, model validation, and building culture. Agus recently retired from Wells Fargo and is now enjoying a variety of side projects including working at H2O.ai, teaching model validation at UNC, and I believe a few other projects which may come to light within the next year.Website:https://www.FancyQuantNation.comSupport the channel:https://ko-fi.com/fancyquantQuant t-shirts, mugs, and hoodies:https://www.teespring.com/stores/fancy-quantConnect with me:https://www.linkedin.com/in/dimitri-biancohttps://twitter.com/DimitriBiancoTiny Expeditions - A Podcast about Genetics, DNA and InheritanceExplore the exciting world of genetics in an easy-to-understand way with Tiny Expeditions.Listen on: Apple Podcasts SpotifySupport the show

This is a bonus episode for The Documentary of The Engineers: Intelligent Machines. This year, we speak to a panel of three engineers at the forefront of the 'Machine Learning: AI' revolution with an enthusiastic live audience.Intelligent machines are remaking our world. The speed of their improvement is accelerating fast and every day there are more things they can do better than us. There are risks, but the opportunities for human society are enormous. ‘Machine Learning: AI' is the technological revolution of our era. Three engineers at the forefront of that revolution come to London to join Caroline Steel and a public audience at the Great Hall of Imperial College:Regina Barzilay from MIT created a major breakthrough in detecting early stage breast cancer. She also led the team that used machine learning to discover Halicin, the first new antibiotic in 30 years. David Silver is Principal Scientist at Google DeepMind. He led the AlphaGo team that built the AI to defeat the world's best human player of Go. Paolo Pirjanian founded Embodied, and is a pioneer in developing emotionally intelligent robots to aid child development. Producer: Charlie Taylor (Image: 3D hologram AI brain displayed by digital circuit and semiconductor. Credit: Yuichiro Chino/Getty Images)

Matt Turck has been publishing his ecosystem map since 2012. It was first called the Big Data Landscape. Now it's the Machine Learning, AI & Data (MAD) Landscape.  The 2024 MAD Landscape includes 2,011(!) logos, which Matt attributes first a data infrastructure cycle and now an ML/AI cycle. As Matt writes, “Those two waves are intimately related. A core idea of the MAD Landscape every year has been to show the symbiotic relationship between data infrastructure, analytics/BI,  ML/AI, and applications.” Matt and Tristan discuss themes in Matt's post: generative AI's impact on data analytics, the modern AI stack compared to the modern data stack, and Databricks vs. Snowflake (plus Microsoft Fabric). For full show notes and to read 7+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Explore the dynamic world of retail data analytics with Sravan Vadigepalli, Senior Director of Products and Engineering at Lowe's. In this episode you'll learn about the unique analytics challenges and opportunities in the home improvement space and how Lowe's approaches product development. During the chat Sravan also shares his thoughts on the future of data-driven innovation in retail. Grab some coffee and join host Evan Wimpey as he delves into the intersection of technology and customer-centric solutions. We hope you enjoy the conversation!In this episode you will learn: ⛛ The importance of recognizing and adapting to the diverse needs of customers⛛ Why data initiatives should be aligned with broader business objectives⛛ How Sravan's team uses a unique framework to streamline decision-making processes⛛ The need for a balance between building internal tools and leveraging external solutionsQuote

Links: Mini sound bath: https://www.youtube.com/watch?v=g-lDrcSqcrQ Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

As CEO of Radial North America, a bpost company, Laura Ritchey builds on her extensive experience and leadership, continuing her commitment to innovating and optimizing eCommerce solutions. Prior to this role, Laura served as Radial's COO from 2021 to 2023, where she directed omnichannel, fulfillment, transportation, and customer care solutions with a strategic mindset and client-centric focus. Laura has more than 25 years of experience leading retail, manufacturing, distribution, and business operations with a successful track record of steering transformational initiatives developing high-performing teams and improved financial results. She began her career in finance, worked at L Brands as EVP Operations for Victoria's Secret Beauty after multiple roles in senior leadership and then was Chief Operating Officer at FullBeauty Brands and Centric Brands. Laura holds a BA, MBA and JD from The Ohio State University and currently serves on the Board of Goodwill Manasota.SHOW SUMMARYIn this episode of eCom Logistics Podcast, hosts Ninaad and Nenad are joined by Laura Ritchie. Laura, who brings a wealth of experience from the retail industry, discusses her journey from working with renowned brands to leading a 3PL company and the challenges and opportunities in this transition.The conversation delves into the evolving landscape of e-commerce logistics, the changing dynamics of customer expectations, and the role of 3PL partners in meeting these demands. They explore the concept of omni-channel fulfillment, the impact of emerging marketplaces like TikTok, and the delicate balance between speed, sustainability, and brand experience in shipping.Laura also sheds light on the integration of AI and machine learning in logistics, with a focus on forecasting and labor planning. She emphasizes the importance of automation as an enabler rather than a replacement for human expertise and how it contributes to delivering an ideal customer experience.HIGHLIGHTS[00:00:16] Customer Experience and Packaging: Laura Ritchey emphasizes the importance of packaging and presentation in creating brand loyalty.[00:01:15] Laura Ritchey's Background: She's introduced as the CEO of Radial with extensive experience in leadership and retail.[00:02:49] Transition from Retail to 3PL: Laura discusses her transition and the importance of a branded experience in 3PL.[00:03:26] Importance of Customer Expectations: Meeting expectations is key to building brand loyalty in 3PL.[00:07:47] Challenges in 3PL: Addressing challenges collaboratively is vital for success.[00:13:47] Omni-Channel Experience: Providing a consistent brand experience across various channels is a challenge.[00:22:31] Sustainable Choices: Focus on eco-friendly shipping and sustainable options.[00:27:22] AI and Machine Learning: AI is used for labor forecasting, employee training, and multilingual support.[00:31:24] Efficient Picking: Laura stresses the need for efficient picking and gradual robotic integration to manage high volumes.[00:46:50] Peak Season 2023: Laura foresees a peak season similar to the previous year, with consumer anticipation for discounts.QUOTES[00:03:26] "The 3PL switch wasn't as difficult as you might think, because what you're doing is delivering that same branded experience for your partners."[00:05:48] "You have to view your 3PLs as strategic partners, as an extension of your business, and it can't be a procurement exercise."[00:15:47] "At the end of the day, it's about less friction and making it convenient and fast."[00:23:46] "It's not so easy if your transportation management system is set up a certain way and it basically puts packages, and then you say, 'I want to slow it down.'"Find out more about Laura Ritchey in the link below:LinkedIn: https://www.linkedin.com/in/laura-ritchey-55836a8/Radial's Official Website: https://www.radial.com/?utm_campaign=2023-eCom-Logistics&utm_source=eComLogistics&utm_medium=Referral

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. 

Like all good 80's kids, Drew Stern loves a good Choose Your Own Adventure story. In fact, it's this approach that has led this former musical theater actor turned award-winning technology CEO to such great success. With three high-growth tech exits in the last decade, Drew's innovative spirit and passion for creative success have made him a trailblazer in the world of technology and the performing arts. Discover how Drew embraced chance and combined his two passions—performing and tech—to create Stageyo AI, the world's 1st digital marketplace and AI engine connecting stage performers with their supporters. Learn how he envisions using technology responsibly to foster authentic connections between performers, their art, and their patrons. Don't miss this inspiring story of intentional risk-taking and leadership that has earned Drew recognition as an influential CEO and celebrated entrepreneur. Connect with Drew and Stageyo AI on Instagram and on the web. Make sure to follow this podcast everywhere you find podcasts, leave a rating and a review, and slip into our Instagram DMs at @wasitchance. More about Heather via @heather_vickeryandco on Instagram and Threads, @Braveheather on TikTok, and listen to The Brave Files More about Alan via @theatre_podcast on Instagram and listen to The Theatre Podcast with Alan Seales EPISODE TAKEAWAYS: Drew's career had two phases. He grew up the youngest of 5 and everyone performed constantly. Tech was unexpected.  Tech was a chance Drew embraced. If you want to be a great leader, go put on a play - this creates trust, creativity, team player, and being a great listener. No entrepreneur can do anything on their own. It has to always be a team effort. Getting venture capital investment is like auditioning, frustrating, scary, difficult and most of them don't book. You need to be able to see the future, then make it happen (manifest it), and then move on. Sometimes you have to close a great show. Growth is a combo of intentionality and embracing chance. You have to have the confidence to take the chance. Drew created his own Machine Learning AI platform well before AI was a household name. Stageyo was born from a desire to build things more emotionally aligned for Drew and allowed him to blend his two passions: Performing and Tech. Stageyo is about creatine agency, equity, and access around performing arts. It answers a need for authentic connection between performers, their art, and their patrons.  Technology is the great equalizer between agency, equity, and access. Envision, trust, and know your capabilities. Past successes allow Drew to play, test things out and really explore possibilities. There are two sides to the AI coin. There are great challenges to using it responsibly.  How can you use technology to enable people to enjoy things in real life - this is one of Drew's goals. Learn more about your ad choices. Visit megaphone.fm/adchoices

[קישור לקובץ mp3]פרק מספר 466 של רברס עם פלטפורמה, שהוקלט ב-22 באוגוסט (אוטוטו נגמר החופש הגדול?).אורי ורן שמחים ומתכבדים לארח באולפן את איתמר מחברת Codium כדי לדבר לדבר על AI (ועוד כל מיני Buzzwords, אבל במידה . . . ) ועל מוצר מדליק שחברת Codium מפתחת - אבל בעיקר על האתגרים הטכנולוגיים המעניינים שמאחורי הפיתוח של המוצר הזה, ועל למה זה צריך לעניין מפתחים, כי זה גם מוצר למפתחים. אז יש פה הרבה שאלות של איך לשלב Machine Learning ו-AI במוצרי תוכנה, וזה נושא שהוא די כללי ומעניין להרבה מאוד מהמאזינים.01:38 כמה מילים על איתמר(רן) אז לפני שנצלול פנימה - כמה מילים עליך, איתמר?(איתמר) אז קודם כל, אני אגיד שפעם הייתי שומע את המילים “AI” - וזה היה קצת מבהיל אותי להגיד את המילים האלה.היום זה יותר רגיל - כשאמרת את זה כל כך הרבה פעמים, אז חשבתי על זה, שהיום אני כבר בסדר עם זה.אז: איתמר פרידמן - נשוי פלוס 2.5, גר ברמת גן.(רן) אתה צוחק . . . (איתמר) כן, אני יודע . . . הייתי פעמיים CTO של VC-Backed Startupsאת החברה האחרונה שהיתה לי Alibaba קנתה, והיה לי את התענוג להתחיל את המרכז פיתוח של Alibaba בארץ [… קרא עוד

In this special two-part episode with guest Stefano Marzani, the WW Tech Lead for Software Defined Vehicles from AWS, we discuss the importance of cloud technology as applied to vehicles.  In this Part 2, we discuss  Prototyping, Machine Learning/AI, and ADAS/Autonomous Driving. In, Part 1, published previously (LINK) we meet Stefano, learn about his important role at AWS and cover the first two topics: Data and Compute. Links referenced in the show: AWS Automotive homepage: www.aws.com/automotive AWS All Things Automotive Podcast:  https://aws.amazon.com/architecture/all-in-series/all-things-automotive/?all-in-livestream-cards.sort-by=item.additionalFields.sortDate&all-in-livestream-cards.sort-order=desc&awsf.products=*all&awsf.tech-category=*all AWS Case study with BMW https://aws.amazon.com/solutions/case-studies/bmw-group-case-study/ Part 2 Chapters: 00:00 - Overview 00:40 - Topic 3: Prototyping in the cloud and “Environmental Parity” 02:20 - Running on Arm in the cloud and the vehicle 03:13 - Start software development in the cloud 03:55 - Difficulty of validation for automotive 04:49 - Empowering developers via the cloud 05:18 - Vehicle software will triple in the next five years 07:07 - “Shift left” to accelerate design cycles 07:54 - $40-50 Billion lost to automotive recalls  08:51 - Developing vehicle HMI with the cloud 10:57 - SDV can reduce cost of recalls 11:43 - Consolidation of ECUs  12:22 - Consolidating software effectively without bloat 13:45 - SOAFEE and standards for automotive 15:31 - Topic 4: Machine Learning and Analytics 16:45 - Data collection and annotation with SageMaker 17:22 - Other frameworks for ML in AWS 17:52 - In-vehicle validation of new models 18:30 - Finding edge cases  19:20 - Tools to manage ML workflows  21:55 - Topic 5: ADAS and Autonomous Driving 22:35 - Near-term benefits from ADAS 25:00 - Seeking edge cases  25:30 - Decomposing autonomous driving 27:30 - Collaboration between AWS and Sonatus 29:29 - Importance of using real ECUs 31:04 - Summary

VERSES AI Compared with Open AI According to ChatGPT4  #futureofai #artificialintelligence by Denise Holt VERSES AI has developed a unique methodology that combines Active Inference AI with the Spatial Web Protocol, trained on real-time data within a common network of Intelligent Agents based on real-world events as they occur and unfold over time.   OpenAI administers a multi-modal methodology of generative Machine Learning AI derived from LLMs trained on text/image pairs.   In a mere three question conversation with ChatGPT4, the OpenAI bot provided a solid basis for understanding the greatest issues facing Machine Learning LLMs, the advantages of Active Inference AI over LLMs, and the way in which the next evolution of the internet protocol, the Spatial Web, when combined with Active Inference AI, can function as a nervous system for a company, city, or even larger systems.   ______________________   Special thanks to Dan Mapes, President and Co-Founder, VERSES AI and Director of The Spatial Web Foundation. If you'd like to know more about The Spatial Web, I highly recommend a helpful introductory book written by Dan and his VERSES Co-Founder, Gabriel Rene, titled, “The Spatial Web,” with a dedication “to all future generations.”   Listen to more episodes in my Knowledge Bank Playlist to learn everything you need to know to stay ahead of this rapidly accelerating technology.   Check out more on my blog at https://deniseholt.us   #futureofai #artificialintelligence #spatialweb #activeinference #ChatGPT4

In this week's pod, we were joined by Micah Piippo to discuss Planning Projects: Past, Present & Future. Micah has spent the last 14 years in Project Controls. While he's performed a wide variety of roles, his passion and main focus is Planning and Scheduling. During his career he's tore down nuclear facilities, built large ferries, built data centers all over the world and more recently is helping Intel deliver state of the art fab capabilities. In December 2022, Micah started writing his Zero Float newsletter. In the newsletter Micah explores his favorite topics that he doesn't see getting enough attention. The main topics we discussed on the podcast were as follows: Whilst there are certifications available for schedulers. There is no degree programme that would allow students to learn the fundamentals of construction management, people management, how to use data and learning the project ecosystem. Most planners simply fall into the role The consequence of the lack of certification is a lack of respect for the skills that a good planner can bring to the organization by utilizing their experience A top level planner should have a breadth of experience as well as strong communication skills when dealing upwards and sideways within an organization Technology can help planners and project managers by having finite and usable as-built data Automated capture has helped companies to better define progress and productivity on projects. This was previously a highly subjective area There is a danger that Machine Learning / AI does not capture some of the politics around project schedules. This may affect the quality of data in future There is no mandate for government funded projects to share data despite being funded by taxpayers. This hampers the level of data available to be harnessed by machine learning technology Here are links to some of the topics we discussed: Problems with Construction Project Scheduling - https://www.plannersplace.com/blog/problems-with-construction-projects-scheduling Why Construction Scheduling is Stuck in the 1980s - https://www.linkedin.com/pulse/why-construction-scheduling-stuck-1980s-micah-piippo/ Plan Rich – How to become a Senior Scheduler - https://planrich.beehiiv.com/p/beginner-winner Join us next time when we're joined by Parveen Sharma to discuss BIM and Virtual Design - disrupting the Design and Construction industry. For more information, blogs or to support our charities visit www.projectchatterpodcast.com If you'd like to sponsor the podcast get in touch via our website. You can also leave us a voice message via our anchor page and let us know if there's something or someone specific that you would like on the podcast. Proudly sponsored by: InEight - https://ineight.com/ Stay safe, be disruptive and have fun doing it! #ProjectManagement #PMO #ProjectControls #Leadership #AI Planners' Place Problems with Construction Projects Scheduling Ever wondered why it seems that not much has changed in construction projects scheduling in the last two to three decades? According to Micah Piippo, the poor state of construction projects schedules can be attributed to the six problems he sees with scheduling. linkedin.com Why Construction Scheduling is Stuck in the 1980s TL;DR – The construction industry should be embracing the power of technology to help schedulers build accurate and sophisticated project schedules. Unfortunately, the lack of innovation and motivation has left the construction field decades behind other industries. https://www.linkedin.com/pulse/why-construction-scheduling-stuck-1980s-micah-piippo/ Plan Rich Beginner to Winner Learn, grow, and get rich with a career in Construction Scheduling. (70 kB) https://planrich.beehiiv.com/p/beginner-winner InEight InEight Construction Project Management Software InEight is the leader in construction project management software. Our project management solutions give you the data you need to make better decisions. (422 kB) --- Send in a voice message: https://podcasters.spotify.com/pod/show/project-chatter-podcast/message

This week's guest is co-founder of FXGuide, arguably the first and foremost podcast and blog in our field. He also co-founded FXPhd which was my route into the industry so this was a massive honor for me.We dive deep into Neural Rendering (which Mike has been involved with since 1999) machine learning and practical applications within visual effects and filmmaking.We then discuss the ethics of machine learning  and art, why there won't "a metaverse" and some of the most likely applications of AR and VR.This raises some general points about the application of new technology which it would be well of us to heed.Sometimes you want to see the artists work as they talk. You can watch all our episodes on our YouTube channel. Subscribe & Watch all episodes on our YouTube Channel Visit our website Thank you for your support! We appreciate you!

Spatial Web and the Era of AI - Part 1  #futureofai #artificialintelligence by Denise Holt Deep Learning Language Models vs. Cognitive Science The pioneering goal of Artificial Intelligence has been to understand how humans think. The original idea was to merge intellectual and computer contributions to learn about cognition.   In the 1990's, a shift took place from a knowledge-driven AI approach to a data-driven AI approach, replacing the original objectives with a type of Machine Learning called Deep Learning, capable of analyzing large amounts of data, drawing conclusions from the results.   Deep Learning is a predictive machine model that operates off of pattern recognition. Some people believe that if you simply feed the model more and more data, then the AI will begin to evolve on its own, eventually reaching AGI (Artificial General Intelligence), the ‘Holy Grail' of AI.    This theory, however, is viewed as being deeply flawed because these AI machines are not capable of “awareness” or the ability to “reason.” With Machine Learning/Deep Learning AI, there is no “thinking taking place.”    These predictive machines are void of any actual intelligence.    Scaling into bigger models by adding more and more parameters until these models consume the entire internet, will only prove useful to a point.   A larger data bank will not be able to solve for recognizing toxicity within the data structures, nor will it enable the ability to navigate sensitive data, permissioned information, protected identities, or intellectual property. A larger data bank does not enable reasoning or abstract thinking.   For AI to achieve the ultimate goal of AGI we need to be able to construct cognitive models of the world and map ‘meaning' onto the data. We need a large database of abstract knowledge that can be interpreted by a machine imparting a level of ‘awareness'. Newton vs. Einstein Model Based AI for Active Inference is an Artificial Intelligence methodology that possesses all the ingredients required to achieve the breakthrough to AGI by surpassing all of the fundamental limitations of current Machine Learning/Deep Learning AI.   The difference between Machine Learning AI and Active Inference AI is as stark as the jump from Newton's Laws of Universal Gravitation to Einstein's Theory of Relativity.   In the late 1800's, physicists believed that we had already discovered the laws that govern motion and gravity within our physical universe. Little did they know how naïve Isaac Newton's ideas were, until Albert Einstein opened mankind's eyes to spacetime and the totality of existence and reality.   This is what is happening with AI right now.   It's simply not possible to get to AGI (Artificial General Intelligence) with a machine learning model, but AGI is inevitable with Active Inference.     ______________________   Special thanks to Dan Mapes, President and Co-Founder, VERSES AI and Director of The Spatial Web Foundation. If you'd like to know more about The Spatial Web, I highly recommend a helpful introductory book written by Dan and his VERSES Co-Founder, Gabriel Rene, titled, “The Spatial Web,” with a dedication “to all future generations.”   Listen to more episodes in my Knowledge Bank Playlist to learn everything you need to know to stay ahead of this rapidly accelerating technology.   Check out more at, SpatialWebAI and Spatial Web Foundation   #futureofai #artificialintelligence #spatialweb #intelligentagents #aitools

See the full episode here:Kansas City Chiefs Patrick Mahomes Ready to FACE the Field! NFL Wildcard Predictions with Machine Learning AI. Buffalo Bills vs Miami Dolphins, Cincinnati Bengals vs Baltimore Ravens, Jacksonville Jaguars vs LA Chargers Code RGR21 at https://draftkings.com/sportsbook ** BUY RGR Chiefs Kingdom Merch from http://bit.ly/RGRStore ⚡ JOIN RGR Memberships for all the perks: https://bit.ly/JoinRGR GET NFL Draft & League Substack Signup DISCOUNT!! https://nfl33.substack.com/RGRMembers ** Kansas City Chiefs Kingdom News and Rumors https://twitter.com/RyanTracyNFL ️ Locked on Chiefs - https://bit.ly/LO_Chiefs RGR Craft - https://bit.ly/RGRCraft Chapter Times ⏱️ 06:47 Machine learning Predicts NFL Wildcard Games! #Chiefs #kcchiefs #ChiefsKingdom #ChiefsNews #RGR #Filmroom GradingReid-Wk18

Kicking off the new season of Select* with Santiago, a well-known Machine Learning Engineer and content creator. Questions covered include: Share a bit about who you are / your background and journey into tech What are you working on now? What is bnomial.com and how was it created?Why is machine learning so important today? How can it help organizations, what problems does it solve? What do you predict with machine learning in 5-10 years? What are some of the biggest mistakes when it comes to machine learning? Pieces of advice to avoid headaches? Can you explain bias and variance in simple terms? What technologies or tools or frameworks are you really excited about right now? Santiago is the Director of Computer Vision at Levatas. He has a Master's in Machine Learning from the Georgia Institute of Technology and two decades of experience building software for some of the largest companies in the world. He co-founded bnomial.com, where he publishes daily Machine Learning questions and competitions.

- Video on BitChute: https://www.bitchute.com/video/VuNCMTr8TWfj/ - Video on Rumble: https://rumble.com/v248kim-current-events-jan4-2023-chycho.html - Video on Odysee: https://odysee.com/@chycho:6/Current_Events_Jan4_2023_chycho:e - Intro Video on CensorTube: https://www.youtube.com/watch?v=bLxmkMNjoyE ***SUPPORT*** ▶️ Patreon: https://www.patreon.com/chycho ▶️ Substack: https://chycho.substack.com/ ▶️ Paypal: https://www.paypal.me/chycho ▶️ SubscribeStar: https://www.subscribestar.com/chycho ▶️ Streamlabs at: https://streamlabs.com/chycholive ▶️ ...and crypto, see below. ▶️ Guilded Server: https://www.guilded.gg/chycho PLAYLIST: Podcasts https://soundcloud.com/chycho/sets/chycho SELECT TIMESTAMPS: - CensorTube Introduction (0:00-11:51) - Leftoid Low IQ People's Heads Explode as Elon Musk's Twitter Files Reveals Rollout of Pure Fascism in the United States of America (12:28-15:29) - YouTube Is a Pale Reflection of What It Was: New Tech and Free Speech Platforms Are the Future of the Internet (15:40-20:03) - Low IQ People Are the Ones That Are up to Date with Their Boosters (22:52-24:17) - Art Should Critique & Reflect Society: My ASMR Readings of WikiLeaks Vault 7, Guantánamo Bay Files & OPCW Douma Docs (24:17-27:01) - If You're Journalist, a Content Creator, an Artist, You Can Not Stay True to Yourself If You Only Share Content on Censored Platforms (28:51-29:44) - Metallica vs. Napster: Sharing My Story from 2000 When Metallica Wanted to Sue My Ass (30:04-33:36) - Andrew Tate Is Not Julian Assange: Persecution by Corporate Media of Those Threatening Centralized Power (33:38-40:26) - Update Regarding Long Form Editing Software - Some Random Discussion - Building Societies on Free Speech: Censorship of Thought and Speech Is the New War on Drugs (42:44-44:57) - Conspiracy Theories: Just Imagine What You Don't Know (48:11-53:21) - Censorship of the Internet, Early Days Compared to Now: The Dead Internet Theory (54:32-57:16) - Some Random Discussion - Comic book Haul #70: CGC Graded Model Age Sandman & Rotten to the Core Political Trading Cards [ASMR] (1:04:48-1:27:14) - Sentient A.I. vs. Machine Learning AI (1:28:32-1:33:36) - More Random Discussion LINK: 1989 Eclipse Rotten to the Core Trading Cards https://www.tcdb.com/Checklist.cfm/sid/102207 ***WEBSITE*** ▶️ Website: http://www.chycho.com ***LIVE STREAMING*** ▶️ Twitch: https://www.twitch.tv/chycholive ***VIDEO PLATFORMS*** ▶️ BitChute: https://www.bitchute.com/channel/chycho ▶️ Rumble: https://rumble.com/c/chycho ▶️ Odysee: https://odysee.com/$/invite/@chycho:6 ▶️ YouTube: https://www.youtube.com/@chycho ▶️ Twitch: https://www.twitch.tv/chycholive ***SOCIAL MEDIA*** ▶️ Twitter: https://twitter.com/chycho ▶️ Minds: https://www.minds.com/chycho ▶️ Gab: https://gab.ai/chycho ▶️ Vk: https://vk.com/id580910394 ▶️ Parler: https://parler.com/#/user/chycho ▶️ Bitclout: https://bitclout.com/u/chycho ▶️ Gettr: https://gettr.com/user/chycho ***AUDIO/PODCASTS*** ▶️ SoundCloud: https://soundcloud.com/chycho ***CRYPTO*** ▶️ As well as Cryptocurrencies: Bitcoin (BTC): 1Peam3sbV9EGAHr8mwUvrxrX8kToDz7eTE Bitcoin Cash (BCH): 18KjJ4frBPkXcUrL2Fuesd7CFdvCY4q9wi Ethereum (ETH): 0xCEC12Da3D582166afa8055137831404Ea7753FFd Ethereum Classic (ETC): 0x348E8b9C0e7d71c32fB2a70DcABCB890b979441c Litecoin (LTC): LLak2kfmtqoiQ5X4zhdFpwMvkDNPa4UhGA Dash (DSH): XmHxibwbUW9MRu2b1oHSrL951yoMU6XPEN ZCash (ZEC): t1S6G8gqmt6rWjh3XAyAkRLZSm9Fro93kAd Doge (DOGE): D83vU3XP1SLogT5eC7tNNNVzw4fiRMFhog Peace. chycho http://www.chycho.com

One of the things I like most about being a part of Future Crunch is that I get to meet a lot of very smart people. People who have an innate ability to provide clear, critical and intelligent analysis of big future trends. Today, I was lucky enough to lock down an hour with one of those extraordinary minds for this podcast. His name is Matt Kuperholz and alongside being one of the most innovative minds on the application of machine learning technologies and data analysis for business he is also one of the nicest people I have met. Matt is formally trained in actuarial science and computer science. His technical skills in these fields have been honed and expanded over 30 years of consulting with top-tier companies to make him an expert in planning, executing and communicating the results of advanced data analytics. He has also worked as the Chief Data Scientist, and in the highest echelons of leadership for Australia's most prominent consulting firms. Matt's area of specialization is the application of artificial intelligence and machine learning technologies to detailed and complex data. In 2015, he was honoured by Malcolm Turnbull and Australian Chief Scientist Alan Finkel as one of the Knowledge Nation 100, a group of innovators and entrepreneurs helping to shape Australia's new economy.Now, there is no denying that Matt's CV is impressive, but the best thing about Matt is his innate ability to translate complexity so that leaders looking to better understand data science, machine learning and AI are unable to make sense of what to some may feel like unknown terrain. In today's podcast Matt and I...Get inside the data economy. What is it, and how does it work? We explore how data can solve real-world leadership challenges. We look at predictive analytics alongside ethics and transparency with regard to data. As leaders, what is the best practice for integrating artificial intelligence in the workplace? We explore the intersection between optimism and innovation, with a focus on how machine learning, AI and the Internet of Things is creating positive transformation for people and the planet. So, today, get ready to get OPTIMIZED in the best kind of way with the incredible Matt Kuperholz. Welcome to the podcast. Hosted on Acast. See acast.com/privacy for more information.

Show Notes Christopher Nguyen CEO and Co-Founder of Aitomatic Hacker, Professor, Builder/Founder w/ successful exits, Leader, Knowledge-First ML Creator @h1st_ai, ex-GoogleApps, http://bit.ly/scholar-ctn   ‣ Strategic executive leadership [Google, Panasonic] ‣ CEO/CTO/VP Eng, successful startup+corp experience [Agenda-Asia, Arimo, Aitomatic] ‣ Hands-on software engineering management - built & led teams of '00s ‣ Machine Learning/AI, Extreme Internet-scale, highly available, low-latency service architectures ‣ Quantitative finance, applied statistics   We talk about          What's it like to successful start and exit several companies? What is a Knowledge-First App Engine? How do you train a cyber-security system against something that has not happened yet? What is open-source project Human-First AI. What are some of the top strategic technology trends you are seeing now and believe will be there in 2023 and 2024?     Connect with Christopher Nguyen https://www.linkedin.com/in/ctnguyen/ https://www.aitomatic.com/ ctn@alumni.stanford.org

เดี๋ยวนี้ใครๆ ก็สนใจการนำเอา Machine Learning หรือ AI มาใช้เพื่อเพิ่มประสิทธิภาพและตอบโจทย์ของลูกค้าของเรากันนะครับ แต่การนำเอา Machine Learning มาใช้เฉยๆ อย่างเดียวไม่พอ เราต้องมีกระบวนการในการปรับปรุงตัว model ที่เรานำเอามาใช้ด้วย วันนี้คุณต้น เจษฎากร สมิทธิอรรถกร CEO จากบริษัท data wow จะมาเล่าให้ฟังกับเคสจริงที่มีการนำเอา Machine Learning Operations มาใช้ จะเป็นอย่างไรนั้น ติดตามได้ในตอนนี้ครับ คำถาม การเอา machine learning มาใช้ใน application ของเรานี่ มีขั้นตอนอย่างไรบ้าง เราจะเริ่มใช้ AI อย่างไร Machine Learning Operations หรือ MLOps คืออะไร แชร์ use case ที่เคยทำ เช่น Social Network ตัวอย่างของ content ที่ไม่เหมาะสมเช่นอะไรบ้าง  การ  train model เรา train จากรูปภาพ เรา train อย่างไร ตอนที่เราเตรียม train model เราไปเอารูปจากไหนมาใช้ train  สรุปแนวทางในการนำเอา ML มาใช้ และการเตรียมกระบวนการ MLOps ให้ผู้ฟังที่อาจจะอยากจะเริ่มทำ . . #missiontothemoonpodcast #TechMondayPodcast . . ติดตาม Mission To The Moon Media ได้ที่  . Website: https://bit.ly/3oHFe99 Facebook: https://bit.ly/32Oe4nW Twitter: https://bit.ly/2TyBOH6 Blockdit: https://bit.ly/3jI0pEk YouTube: https://bit.ly/2TyTXVg TikTok: https://bit.ly/35Gq8aX SoundCloud: https://bit.ly/3e4Tzax Podbean: https://bit.ly/3oCqU1g Spotify: https://spoti.fi/37MNajh Apple Podcast: https://apple.co/3oK1JKy Instragram: https://bit.ly/2OMR30a Clubhouse: @mttmclub

'Plankton" consists of phytoplankton (~plants) and zooplankton (-animals). It represents the basis of the ocean food chain and it includes many species; it's a very complex 'multi-species soup' representing a true science frontier hardly tackled, understood or managed yet. Copepods are part of that taxonomic set up and they contribute usually to the majority - up to 70% - of zooplankton abundance in oceans. Using field data of the Italian National Antarctic Program from the 1980s and 1990s here we model-predict in an interdisciplinary international team effort for 26 copepod species at three ocean depth classes (0-10m, 11-70m, 71-750m) the relative index of occurrence (RIO) for the wider study area of the Ross Sea Region Marine Protected Area (a world-record MPA and ocean wilderness area of global size and relevance). This research uses Machine Learning/AI ensembles and Open Source Geographic Information System (GIS) methods to generalize from the Open Access dataset available from the Global Biodiversity Information Facility (GBIF.org) using the 'Macroscope predictors' (see Huettmann et al. 2015 for details, source and use). Further details are provided in Grillo et al. (2022; compare also with Pinkerton et al. 2010). This work matters as a global workflow template and it allows to obtain 3D models in GIS for plankton abundance, e.g. as needed for foraging estimates of marine mammals, penguins and fisheries. It can also be used for life-history research, carbon sequestration work in climate models as well as for baselines in carrying capacity formulas for fisheries and generic predator-prey studies. The relevance of sound harvest models for krill and fish, e.g. in the so-called 'experimental' fisheries work with CCAMLR and the MPA in the Ross Sea has been outlined by Ainley et al. (2012) and others. Here we offer a solution towards sustainability in times of a generic ocean crisis. References (selection; in order of citation) Grillo M, F. Huettmann, L. Guglielmo and S. Schiaparelli (2022) Three-Dimensional Quantification of Copepods Predictive Distributions in the Ross Sea: First Data Based on a Machine Learning Model Approach and Open Access (FAIR) Data. Diversity 14:355. https://doi.org/10.3390/d14050355 Huettmann, F., M.S. Schmid, and G.R.W. Humphries (2015) A First Overview of Open Access Digital Data for the Ross Sea: Complexities, Ethics, and Management Opportunities. Hydrobiologia 2015, 761, 97–119. Pinkerton, M. H., A.N. Smith, B. Raymond, G.W. Hosie, B. Sharp, J.R. Leathwick and J.M. Bradford-Grieve (2010). Spatial and seasonal distribution of adult Oithona similis in the Southern Ocean: predictions using boosted regression trees. Deep Sea Research Part I: Oceanographic Research Papers 57: 469-485. Ainley, D.G., C.M. Brooks, J.T. Eastman and M. Massaro (2012) Unnatural Selection of Antarctic Toothfish in the Ross Sea, Antarctica. In Protection of the Three Poles; Springer: Berlin/Heidelberg, Germany, pp. 53–75.0 (Photo credit: Andrei Savitsky - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=78800127) --- Support this podcast: https://anchor.fm/falk-huettmann/support

Today's guest is Jay Nanduri, Chief Technical Officer at Truveta in Seattle. Founded in 2020, Truveta's aim is to enable researchers to find cures faster, empower every clinician to be an expert and help families make the most informed decisions about their care. Clinical data from their members is de-identified daily and brought together in an unprecedented data platform to enable research on all health conditions. Truveta believe their collective de-identified data can be used to accelerate research, advance health equity and save lives. Jay is a former Microsoft Technical Fellow with over twenty years of experience, 25+ patents and several publications that demonstrate exemplary technical and business leadership. He has a proven track record of managing and delivering mission critical projects from conception to delivery. Jay also has extensive hands-on experience in designing highly scalable distributed systems, big data, big graph, secure internet platforms, fraud prevention, BI analytics, Ecommerce, personalization, workflow systems and Machine Learning/AI platforms. In the episode, Jay will discuss: His background and previous experience with Microsoft, What motivated him to move to Truveta, The work they do at Truveta and defining their mission statement, Their focus on data coverage, data quality, data consumption and community, Using AI for the benefit of health systems, researchers and clinicians, Using Healthcare Data responsibly and Career opportunities within AI in Healthcare

Colonel Lewis, Lt. Colonel Dirk Mathes, and Deputy Branch Head Simon Purton continue to discuss AI Felix, its potential, and the value of AI to the future of the Alliance.

Canadian Royal Air Force Colonel Sean Lewis, Lieutenant Colonel Dirk Mathes of the German Bundeswehr, and Simon Purton, the Deputy Branch Head for ACT's Analysis of Alternatives Branch discuss the development of NATOs first machine-learning tool: Artificial Intelligence Front End Learning Information Execution, otherwise known as AI Felix.

On March 17th, we had the pleasure of hosting Dr. Scott Hawley of Belmont University at the Study Center for a talk titled “Curves and Categories: Machine Learning, AI, and the Nature of Classification.” Dr. Hawley is a Professor of Physics at Belmont, and his research interests include machine learning, neural networks, and the ethics of A.I. He joined us to explore the fascinating and complex nature of classification and what it reveals about intelligence, human and machine. “Machine learning classification techniques,” Dr. Hawley explained, are increasingly applied to fields as diverse as biology, astronomy, the humanities, law, medicine, the entertainment industry, criminal justice, library science, aesthetics, robotics, and more, in an effort to automate human decision-making on massive scales. The problematic socio-political ramifications of this enterprise are becoming increasingly evident, and merit a closer examination of the philosophies and methods of classification from their origins in antiquity up to present large-scale A.I. systems.During the talk, Dr. Hawley made extensive use of slides and you can view those here while you listen. You can also follow Dr. Hawley on Twitter @drscotthawley. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit christianstudycenter.substack.com

Evan Harris, CTO at TermScout, gives us a reality check on what's happening with machine learning and AI. Many companies are talking about developing ML and AI - but what is the reality? What do tech leaders need to know to integrate AI and machine learning into workflows and software products - and what are the strengths - and the limitations of this technology today? Evan's background implementing AI and machine learning as a CTO informs his perspective and provides listeners with interesting insights.

Matt and Joe review amazing Matt Turck's 2021 Machine Learning, AI and Data (MAD) Landscape. They discuss the article and their thoughts on how this affects data engineers in 2021 and beyond. Article: https://mattturck.com/data2021/ Streamed live on LinkedIn and YouTube #data #dataengineering #machinelearning --------------------------------- TERNARY DATA We are Matt and Joe, and we're "recovering data scientists". Together, we run a data architecture company called Ternary Data. Ternary Data is not your typical data consultancy. Get no-nonsense, no BS data engineering strategy, coaching, and advice. Trusted by great companies, both huge and small. Ternary Data Site - https://ternarydata.com LinkedIn - https://www.linkedin.com/company/ternary-data/ YouTube - https://www.youtube.com/channel/UC3H60XHMp6BrUzR5eUZDyZg

In this episode we speak with Mattia Fiorentini, Head of Machine Learning and Quantum Algorithms at Cambridge Quantum Computing. We will introduce quantum machine learning, discuss why it is exciting and where it is predicted that we will see the most advantage. We will also discuss working in a commercial environment and finish with some advice for anyone keen to get into this field. We hope you enjoy !

In this episode of the Americas Series, David Yakobovitch spoke to Orlando Zayas, CEO of Katapult, about how pandemic has changed the way we shop, Katapult's role in the value chain, alternative credit scoring, and BNPL, and on going public with the SPAC Finserv.  The pandemic transformed the way we shop forever: eCommerce rapidly expanded; it would have taken 5-10 years to reach its point today without the pandemic. Online shopping and BOPIS (buy online and pick up in-store) is a purchasing trend expected to become permanent. What people are buying has changed also; people are spending more money on electronics, home goods, and appliances while clothing and apparel decreases. Buying power also changed during the pandemic:Buying power changed overnight, lenders immediately tightened up, and even prime customers were denied financing. Beyond the pandemic, non-prime consumers don't have the same buying power as prime consumers; they need options. For prime consumers, when lenders tighten up, you need to have other options. AI & Machine Learning: AI & ML are forever changing all of the technology, including fintech. We can gather a massive amount of data, and after analyzing, we continue to optimize our behavior model (Katapult's proprietary scoring model). Our Katapult score continues to outperform the fraud point score by a third-party-the-shelf model. The ability to now gather data with AI & ML enhances the user experience and allows lenders to assess risk models better. BNPL is growing as rapidly as eCommerce:BNPL has expanded amid the pandemic as people found they needed additional financing options and retailers needed to capture a new segment of customers. Katapult focuses on the nonprime customer, which is one of our differentiating factors from Affirm, Klarna, etc.   Going public via a merger with SPAC Finserv: The decision to go public now and what the process has been like from a virtual perspective. Orlando's approach to revenue growth: Orlando joined the company in 2017 when revenue was at 17M and closed 2020 at almost 200M. Hiring the right people, make sure you have a CFO you can trust,  keeping the “dumb” list of all the things to stop, having a vision is great, but you need to have a foundation and strategy to achieve your vision. 

Join Seth as he welcomes Shivani Santosh Sambare to talk about Prebuilt Docker Images for Inference in Azure Machine Learning. Jump to:[00:17] Show begins[00:29] Welcome Shivani[00:38] What are the challenges working with ML environments?[01:11] Solutions to ML challenges/environments = Prebuilt Docker Images for Inference[02:12] How do I make this work with other specialized environments?[04:10] Demo: Deploying PyTorch model using Azure ML[04:30] Scoring script [06:50] End demo & recap[09:06] Learn more Learn more:Concept Prebuilt Docker Images: https://aka.ms/AIShow/PrebuiltDockerImages/AzureML/Doc Python Extensibility Solution: https://aka.ms/AIShow/PrebuiltDockerImages/PythonExtSolution Blog post: https://aka.ms/AIShow/PrebuiltDockerImages/TechComm/Blog Zero to Hero Machine Learning on Azure https://aka.ms/ZerotoHero/MLonAzureZero to Hero Azure AI https://aka.ms/ZerotoHero/AzureAICreate a Free account (Azure) https://aka.ms/aishow-seth-azurefreeAzure Machine Learning https://aka.ms/AIShow/AzureMLFollow Seth https://twitter.com/sethjuarezAI Show Playlist https://aka.ms/AIShowPlaylistDon't miss new episodes, subscribe to the AI Show https://aka.ms/AIShowsubscribe

Join Seth as he welcomes Shivani Santosh Sambare to talk about Prebuilt Docker Images for Inference in Azure Machine Learning. Jump to:[00:17] Show begins[00:29] Welcome Shivani[00:38] What are the challenges working with ML environments?[01:11] Solutions to ML challenges/environments = Prebuilt Docker Images for Inference[02:12] How do I make this work with other specialized environments?[04:10] Demo: Deploying PyTorch model using Azure ML[04:30] Scoring script [06:50] End demo & recap[09:06] Learn more Learn more:Concept Prebuilt Docker Images: https://aka.ms/AIShow/PrebuiltDockerImages/AzureML/Doc Python Extensibility Solution: https://aka.ms/AIShow/PrebuiltDockerImages/PythonExtSolution Blog post: https://aka.ms/AIShow/PrebuiltDockerImages/TechComm/Blog Zero to Hero Machine Learning on Azure https://aka.ms/ZerotoHero/MLonAzureZero to Hero Azure AI https://aka.ms/ZerotoHero/AzureAICreate a Free account (Azure) https://aka.ms/aishow-seth-azurefreeAzure Machine Learning https://aka.ms/AIShow/AzureMLFollow Seth https://twitter.com/sethjuarezAI Show Playlist https://aka.ms/AIShowPlaylistDon't miss new episodes, subscribe to the AI Show https://aka.ms/AIShowsubscribe

The title of “Data Scientist” leapt into prominence in 2012 when the Harvard Business Review named it the “sexiest job of the 21st century.” Almost ten years later, what's changed? And what's next? In this episode, Dave Cole is joined by Mike Tamir , Chief ML Scientist and Head of Machine Learning/AI at SIG , to break down the shifting trends in data science, NLP, and ML—and what it all means for leaders in the field. The conversation covers: - The past, present, and future of data science - The different roles and responsibilities within a data science team - New and exciting advancements in NLP - When models are right for the wrong reasons For daily news and insights on all things data science, follow @MikeTamir on Twitter. Tune in on Apple Podcasts , Spotify , our website , or wherever you listen to podcasts. Can't see the links above? Just visit domino.buzz/podcast for helpful links from each episode.

Taraneh (Tara) Khazaei leads the National AI team for Microsoft Canada. She previously worked as an AI Cloud Solution Architect at Microsoft and held Data Science positions at Financial Services Companies in Canada. Tara talks to us about challenges she has seen in the industry around starting Data Science projects in the cloud and how the responsibility needs to be shared between different teams within a company. She also talks about what a day-to-day of a Data Science looks like and how to start on the profession.

From a payments and commerce perspective, India presents a tremendous amount of opportunities for fintechs. We explore this payments landscape with the help of our guest, Nitya Sharma. Nitya is the Co-Foudner and CEO of Simpl, India’s fastest growing Buy Now Pay Later platform, which uses Machine Learning & AI to offer a quick, secure and hassle-free payment experience to its users. Find show notes and more at: https://www.soarpay.com/podcast/

«Самое главное, что требуется от менеджера продукта, — постановка задачи. Если ты работаешь с Data Scientist, ты должен четко понимать, какая проблема, какая гипотеза и под что мы оптимизируем. В зависимости от того, как поставлена задача, Data Scientist будет подбирать правильный алгоритм». «Когда мы уже поставили задачу, поняли, под что мы оптимизируем, второй этап — мы смотрим на данные, которые нам доступны, и выбираем те data points, которые мы хотим использовать для достижения результата». Собеседник: Анна Трушкина, Senior Product Manager, League Inc LinkedIn: linkedin.com/in/anna-trushkina Ведущий подкаста: Юра Агеев ФБ: fb.com/ageev.yuri Подписывайтесь на канал подкаста в Телеграме: t-do.ru/mspodcast О чем говорим: 1:11 — Анна рассказывает о себе 2:46 — Как развивается применение Machine Learning 4:33 — В чем разница между Machine Learning и Artificial Intelligence 5:17 — Как Deep Learning соотносится с Machine Learning 6:16 — Где не стоит применять AI 7:58 — Как использовать AI для улучшения работы агрегатора 10:38 — Как Machine Learning повышает эффективность выдачи агрегатора для разных типов пользователей 14:51 — Насколько глубокие технические знания и навыки нужны менеджеру продукта 18:01 — В каких областях работы с алгоритмами важен вклад менеджера продукта 20:05 — С чего начать погружение в тему AI и Machine Learning В подкасте мы упоминаем: — Курс Эндрю Ына Deep Learning: http://bit.ly/3i4rUIW — Курсы Master the Fundamentals of AI and Machine Learning на lynda.com: http://bit.ly/3oDjj2p — Сообщество Open Data Science: http://bit.ly/3oFvv2r — Книгу Кай-Фу Ли «Сверхдержавы искусственного интеллекта. Китай, Кремниевая долина и новый мировой порядок»: https://bit.ly/3bzEjn4 — Видео «Kai-Fu Lee: AI Superpowers — China and Silicon Valley»: https://bit.ly/2LJfbzk

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In this episode I'm joined by Upkar Lidder from IBM, and we're talking about modern machine learning. Do you process a large amount of data and manually make decisions on it? There's a good chance that you could be leveraging machine learning to reduce your workload and make more accurate decisions. Learn about what machine learning is, how it differs from artificial intelligence and data science, and what you need in order to be successful with it. A brief writeup to this episode can be found via https://www.thepolyglotdeveloper.com/2020/05/tpdp-e36-machine-learning-ai-data-science/

Josh is Sr Director Product Marketing, Global Services, VMware VMware has ~500K customers & partners around the globe. Support is seen as competitive differentiator and growth engine for VMware and that's how they decided to run Support like product team Org structure -- Roughly 2500 folks reporting into Chief Customer Officer (Scott). 3 teams — Support (Global Support Services), Digital Services (Product + Engg + Marketing), Customer Advocacy. Technology Stack -- Salesforce (Ticketing, Knowledge Base), IBM Watson for Machine Learning/AI, Coveo for Search Two internally built products -- Skyline and SupportHub Skyline -- uses telemetry information coming out of VMware products to alert customers /partners about known issues in KB, security vulnerabilities, configuration best practices etc. Roughly 7500 customers on the service, adding roughly 1000 customers every month. Average # of tickets being filed are going down Skyline is included in support subscription and not priced separately SupportHub — Integrated with ticketing system. Simplifies filing experience and reduces customer effort. AI-powered support experience e.g., routing to best engineer. Announced at VM World 2019 in partnership with IBM Watson. Beta with top customers. Releasing to more customers throughout 2020. Vast majority of tickets come from web (myvmware) / Twitter support as well (integrated with Salesforce) Metrics: NPS (51 for Vmware, Average is around 21), CSAT, Customer Effort Recommend listen - Delta CEO Ed Bestian keynote at CES on how they used fit bits to understand customer journey Resources — The Obstacle is the Way by Ryan Holiday, How to Think Like a Roman Emperor, Meditations by Marcus Aurelius, Crucial Conversations

Sibos is one of the biggest events of the year for the transaction banking industry. This episode on the Payments Podcast features Ed Adshead-Grant discussing some of the key points of this year's event, specifically on Open Banking, Machine Learning and Artificial Intelligence (AI) and the future of Banking.

Clearing the confusion between Artificial Intelligence and Machine Learning . Link to free Success Affirmations so that you can experience AI in action. Support this podcast: anchor.fm/rashim-mogha/support . --- Send in a voice message: https://anchor.fm/rashim-mogha/message Support this podcast: https://anchor.fm/rashim-mogha/support

In this Episode of Oil & Gas This Week we have two guest co-host, Paige Wilson, host of Oil and Gas Industy Leaders and Christopher Penn, Co-Founder and Chief Innovator at Trust Insights, Digital Marketer, Bestselling Author, Keynote Speaker, Ninja. Have a question? Click here to ask. Stories: How Machine Learning and AI industry initiatives are shaping the oil and gas industry Japan To Boost Energy Cooperation With Russia Saudi Oil Tanker “Accidentally” Heads To Venezuela How Does Blockchain Technology Fit into Oil and Gas Oil and Gas Employment Starts 2019 Positively New Houston VC, accelerator group partners with energy giants US Shale To Drill And Complete 20,000 Wells This Year What Happened to all the E&P Deal-Making? Weekly Rig Count As of 2/28/2019 – The American Rig count is 1069 active rigs. Monthly Happy Hour Want to sponsor a Happy Hour? Email our project coordinator, Julie McLelland, by e-mail for more information.

Mark is joined by co-host Matt Amberson, Principal at Option Research & Technology Services (ORATS). CTO Interview: Our gest today is Michael Mescher, Founder of Gammon Capital. He discusses: What is Gammon Capital? You've talked a lot about the dangers of selling volatility and how short vol managers aren't worth their fees. What are your thoughts on the maelstrom of the past week? How have your funds fared over the past week? Machine learning is a hot buzzword these days, but what are some of the practical applications that you're seeing in the financial markets right now? Is it ready for prime time or still in the nascent phase? Unsupervised learning/ fundamental strategies? Supervised learning / quantitative strategies? Classification vs. Regression problems Hiring machine learning specialists vs. Products specialists 90% of the world's data was created in the past two years. Is that an opportunity or a terrifying statistic? Testing models on out-of-sample data vs. carefully constructed back tests? Should managers be concerned that the robots are coming to take their jobs and their clients? The Inbox: Listener questions and comments Question from BLAL9 - What do you see as the outlook for AI and machine learning in the financial management space going forward? I see a lot of smoke but is there much fire? Are there many funds currently using it to generate alpha right now? Question from Tonc - What are the best ways to invest in blockchain right now? Question from Mark Kalis - How are comps getting the edge on us in trading? Is it just pure correlation trading? Comment from tDok3 - No debate to be had there. It's a simple & foregone conclusion. Higher fees ==> lower participation perhaps goal for CB market manipulation In response to tweet: Are exchanges shooting themselves in the foot by continually hiking market data fees? Question from See16 - Can you please do a deep dive into smart routers on a future program? They are the secret sauce of the market these days, but no one really seems to understand how they work. Shedding some light on the otherwise dark world of routing would be a great service.