Podcasts about MathWorks

The Cybercrime Wire, hosted by Scott Schober, provides boardroom and C-suite executives, CIOs, CSOs, CISOs, IT executives and cybersecurity professionals with a breaking news story we're following. If there's a cyberattack, hack, or data breach you should know about, then we're on it. Listen to the podcast daily and hear it every hour on WCYB. The Cybercrime Wire is brought to you Cybercrime Magazine, Page ONE for Cybersecurity at https://cybercrimemagazine.com. • For more breaking news, visit https://cybercrimewire.com

MathWorks, Creator of MATLAB, Confirms Ransomware Attack Adidas warns of data breach after customer service provider hack Dutch Intelligence Agencies Say Russian Hackers Stole Police Data in Cyberattack Huge thanks to our sponsor, ThreatLocker ThreatLocker® is a global leader in Zero Trust endpoint security, offering cybersecurity controls to protect businesses from zero-day attacks and ransomware. ThreatLocker operates with a default deny approach to reduce the attack surface and mitigate potential cyber vulnerabilities. To learn more and start your free trial, visit ThreatLocker.com/CISO.

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“Laundry Bear” airs dirty cyber linen in the Netherlands. AI coding agents are tricked by malicious prompts in a Github MCP vulnerability.Tenable patches critical flaws in Network Monitor on Windows. MathWorks confirms ransomware behind MATLAB outage. Feds audit NVD over vulnerability backlog. FBI warns law firms of evolving Silent Ransom Group tactics. Chinese hackers exploit Cityworks flaw to breach US municipal networks. Everest Ransomware Group leaks Coca-Cola employee data. Nova Scotia Power hit by ransomware.  On today's Threat Vector, ⁠David Moulton⁠ speaks with ⁠his Palo Alto Networks colleagues Tanya Shastri⁠ and ⁠Navneet Singh about a strategy for secure AI by design.  CIA's secret spy site was… a Star Wars fan page? Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. Threat Vector In this segment of Threat Vector, host ⁠David Moulton⁠ speaks with ⁠Tanya Shastri⁠, SVP of Product Management, and ⁠Navneet Singh⁠, VP of Marketing - Network Security, at Palo Alto Networks. They explore what it means to adopt a secure AI by design strategy, giving employees the freedom to innovate with generative AI while maintaining control and reducing risk. You can hear their full discussion on Threat Vector ⁠here⁠ and catch new episodes every Thursday on your favorite podcast app. Selected Reading Dutch intelligence unmasks previously unknown Russian hacking group 'Laundry Bear' (The Record) GitHub MCP Server Vulnerability Let Attackers Access Private Repositories (Cybersecurity News) Tenable Network Monitor Vulnerabilities Let Attackers Escalate Privileges (Cybersecurity News) Ransomware attack on MATLAB dev MathWorks – licensing center still locked down (The Register) US Government Launches Audit of NIST's National Vulnerability Database (Infosecurity Magazine) Law Firms Warned of Silent Ransom Group Attacks  (SecurityWeek) Chinese Hackers Exploit Cityworks Flaw to Target US Local Governments (Infosecurity Magazine) Everest Ransomware Leaks Coca-Cola Employee Data Online (Hackread) Nova Scotia Power Suffers Ransomware Attack; 280,000 Customers' Data Compromised (GB Hackers) The CIA Secretly Ran a Star Wars Fan Site (404 Media) Want to hear your company in the show? You too can reach the most influential leaders and operators in the industry. Here's our media kit. Contact us at cyberwire@n2k.com to request more info. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Battery management takes center stage in this week's Fish Fry podcast! My guest is Danielle Chu from MathWorks. Danielle and I explore the primary functions of battery management systems and the common challenges faced by engineers when designing electric vehicle battery management systems. We also investigate the role that modeling and simulation play in battery management design, the importance of cell characterization for battery modeling and how MathWorks is encouraging innovation in this arena. 

In this episode, we talk to Lucas from MathWorks about their Safe AI Approach and how it can be used to make money, think about the Pope, and introduce our new US correspondent.

You're great at planning events, but you want to make a bigger impact. Maybe even move up. And that means proving you're more than logistics.In this episode, Sarah Gannon, Director of Corporate Events at MathWorks, shares the mindset and strategies that helped her grow from tactical planner to trusted business partner. With 25+ years in tech events and multiple industry accolades, Sarah breaks down what it takes to earn buy-in, align with sales, and show the true business value of events.You'll hear:✅ Why aligning with sales is the shortcut to C-suite buy-in.✅ How to think about pipeline—not just leads—when planning events.✅ The tools and tactics Sarah uses to fill her calendar before the show starts and how she measures success after.If you're ready to lead with impact—not just logistics—this is the episode for you.----------------------------------Connect with SarahOn her LinkedIn: https://www.linkedin.com/in/sarahehamilton/ Connect with MeOn my LinkedIn: https://www.linkedin.com/in/matt-kleinrock-9613b22b/   On my Company: https://rockwayexhibits.com/   

HighlightsTime at Mathworks (17:27)Being a team leader (18:52)Staying in touch with the technical side (20:04)Advice (27:07)BioSohini Sarkar is a Principal Product Manager and a Senior Team Lead at MathWorks. Her career started with earning a Ph.D. in Civil Engineering and then working on DOE and EPA projects as a postdoctoral researcher. She has held various positions at Dassault Systèmes, ranging from a solution consultant to a market and competitive intelligence analyst, concurrently earning her MBA. Currently at MathWorks, Sohini focuses on established areas such as Math, Statistics, Optimization, and Machine Learning, to more emerging technologies such as Quantum Computing, Large Language Modeling and Generative AI, as well as Scientific Machine Learning.Connect with SohiniSohini Sarkar on LinkedinConnect with UsChisoo Lyons on LinkedInFollow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Louvere Walker-Hannon, application engineer senior team lead at MathWorks, joins us for this episode of Diverse to unpack the concept of weathering — the physical toll chronic stress takes on the human body, especially among women of color. In conversation with host Inaas Darrat, SWE president-elect, Walker-Hannon shares her inspiring journey in STEM, starting with a love for archaeology, and reflects on the challenges she's faced as a woman of color in engineering. Together, they explore the systemic barriers that contribute to health disparities and underrepresentation in STEM fields. Walker-Hannon also delves into the hidden toll of systemic challenges, exploring how adversity can shape resilience and inspire change. She unpacks the ripple effects of chronic stress on health, the power of community awareness, and the transformative potential of advocacy.

We are jumping head first into the world of AI to start 2025! My guest this week is Lucas Garcia, Principal Product Manager for Deep Learning at MathWorks. Lucas and I discuss the biggest challenges surrounding generative AI, the need for verification and validation for AI, the trends surrounding AI-based reduced order models and how AI can transform the world of control design.

In this episode of Embedded Edge, we explore how AI-native technologies are revolutionizing wireless systems. Our guest, Houman Zarrinkoub, principal product manager for wireless communications at MathWorks, discusses the importance of AI in advancing 5G and 6G standards, the benefits over traditional models, and the future potential of digital twins in wireless communication.

AI-based anomaly detection takes center stage in this week's Fish Fry podcast! My guest is Rachel Johnson from MathWorks and we explore how AI can work in tandem with engineers to reduce the incidence of defects and optimize maintenance schedules and the steps involved in designing and deploying an AI-based anomaly detection system; from conceptualization and data gathering to deployment and integration.

My podcast guests this week are Jack Ferrari and Johanna Pingel from MathWorks! We discuss the trends and technologies driving the adoption of edge AI applications, the common challenges associated with edge AI and the roles that the maintenance and upkeep of machine learning models, over the air updates, and on-device training will play for the future of edge AI applications. 

Today, we're joined by Lucas García, principal product manager for deep learning at MathWorks to discuss incorporating ML models into safety-critical systems. We begin by exploring the critical role of verification and validation (V&V) in these applications. We review the popular V-model for engineering critical systems and then dig into the “W” adaptation that's been proposed for incorporating ML models. Next, we discuss the complexities of applying deep learning neural networks in safety-critical applications using the aviation industry as an example, and talk through the importance of factors such as data quality, model stability, robustness, interpretability, and accuracy. We also explore formal verification methods, abstract transformer layers, transformer-based architectures, and the application of various software testing techniques. Lucas also introduces the field of constrained deep learning and convex neural networks and its benefits and trade-offs. The complete show notes for this episode can be found at https://twimlai.com/go/705.

My guest this week is Dr. Houman Zarrinkoub - Principal Product Manager of Wireless Communications at MathWorks. Houman and I investigate why the adoption of AI native technologies is necessary for the development of next-generation wireless standards, the steps involved in designing and integrating an AI-native wireless system and the common hurdles that engineers face when integrating AI into their wireless system design and the solutions to help solve these challenges. Also this week, I examine a new generative AI tool developed by MIT that can let you have a conversation with a potential version of yourself in the future.

Charli has long time friend of the pod, John Brennen, on this week to have a chat the advancement of Industry. John Brennan is the Director of IIoT at A. W. Chesterton, the makers of the Chesterton Connect™ IIoT analytics platform optimized for rotary equipment. John has an extensive background in industrial sales and product marketing from several companies, the most prominent of which was MODICON, now Schneider Electric, where he spent many years selling PLC's and automation equipment. After success in sales at MODICON, he was promoted to product manager to develop and launch the Quantum Automation Series PLC, the most successful PLC from MODICON.  John previously worked with the development team of the Chesterton Connect, was a sales engineer for variable speed drives, sold and serviced industrial flow meters, and spent many years in business development and product marketing in the software business with Cadence Design Systems and The MathWorks.     Read up at EmpoweringPumps.com and stay tuned for more news about EPIC at the Colorado School of Mines Nov 12th and 13th.Find us @EmpoweringPumps on Facebook, LinkedIn,  Instagram and Twitter and using the hashtag #EmpoweringIndustryPodcast or via email podcast@empoweringpumps.com 

Artug Acar is the Chief Operating Officer at Mercury, overseeing sales, marketing, operations, product development, people operations, and IT to drive the company's growth and success. Originally from Izmir, Turkey, Artug began his career as a mechanical design engineer before transitioning into software engineering after earning a Master's degree in Mechanical Engineering from Northeastern University. He has held roles at leading tech companies in the Boston area, including MathWorks, Amazon Robotics, and Symbotic. Artug also holds an MBA from UMass Amherst, an MS in Mechanical Engineering from Bogazici University, and a BS in Mechanical Engineering from Ege University. Mercury is a global logistics company specializing in time- and temperature-sensitive shipping for the biotech, life sciences, clinical trials, diagnostics, and medical device industries. With over 40 years of experience since its founding in 1984, Mercury has built a reputation for reliability and personalized service. Our portal provides clients with a holistic view of their entire shipping operations, driving operational efficiencies and simplifying the logistics process. Unlike other providers, Mercury offers individual attention and proactive support, tracking every shipment and resolving issues, so clients can focus on their core business. We serve over 700 clients and deliver hundreds of thousands of shipments annually, providing flexible pickups, customized invoices, and reports—all without added fees or contracts. At Mercury, our mission is to simplify healthcare and life science shipping, helping companies save time, reduce stress, and operate more efficiently.

Paola Jaramillo is working at MathWorks as the Technical Manager for the Application Engineering team supporting you and your team of domain experts in the vision creation and implementation of innovative technologies with leading tools and services for professional software development. She is an active participant in high-tech events in Benelux, such as the Smart Systems Industry Summit, Bits & Chips Smart Systems, Dutch Machine Vision Conference, and AutoSens. Her interests are sensor data analytics and autonomous systems, where Signal and Image Processing, Computer Vision, and Deep Learning are commonly used. In her previous experience, she implemented a sensor-based DSP system for Structural Health Monitoring and developed a sensor-based predictive (Machine Learning) system that optimizes energy consumption in office buildings. ONLINE PRESENCE ================

Outline00:00 - Intro01:00 - From Boeing to Planetary Resources08:57 - The origin of control videos17:07 - About teaching style20:52 - The (unnecessary?) math behind controls26:54 - On interdisciplinarity31:35 - How to build knowledge fast48:32 - Resourcium01:00:49 - The map of control theory 01:11:09 - IFAC Cartoons01:15:35 - Fundamentals of control theory book01:24:49 - The role of projects01:34:27 - Future of control education01:43:43 - Advice to future students LinksBrian's website: https://tinyurl.com/DouglasBrian1Boeing: https://tinyurl.com/DouglasBrian2Planetary resources: https://tinyurl.com/DouglasBrian3Khan Academy: https://tinyurl.com/DouglasBrian4Building Knowledge in an Interdisciplinary World: https://tinyurl.com/DouglasBrian5Why Models Are Essential to Digital Engineering: https://tinyurl.com/DouglasBrian6SysML: https://tinyurl.com/DouglasBrian7What Is Robust Control: https://tinyurl.com/DouglasBrian8Algebraic Riccati equation: https://tinyurl.com/DouglasBrian9Resourcium: https://tinyurl.com/DouglasBrian10Map of control theory: https://tinyurl.com/DouglasBrian11Map of mathematics: https://tinyurl.com/DouglasBrian12Brian's Cartoons: https://tinyurl.com/DouglasBrian13Fundamentals of control theory: https://engineeringmedia.com/booksxkcd: https://xkcd.com/what if?: https://xkcd.com/what-if/Computational Control: https://www.bsaver.io/teachingargmin: https://www.argmin.net/The Art of the Realizable: Support the Show.Podcast infoPodcast website: https://www.incontrolpodcast.com/Apple Podcasts: https://tinyurl.com/5n84j85jSpotify: https://tinyurl.com/4rwztj3cRSS: https://tinyurl.com/yc2fcv4yYoutube: https://tinyurl.com/bdbvhsj6Facebook: https://tinyurl.com/3z24yr43Twitter: https://twitter.com/IncontrolPInstagram: https://tinyurl.com/35cu4kr4Acknowledgments and sponsorsThis episode was supported by the National Centre of Competence in Research on «Dependable, ubiquitous automation» and the IFAC Activity fund. The podcast benefits from the help of an incredibly talented and passionate team. Special thanks to L. Seward, E. Cahard, F. Banis, F. Dörfler, J. Lygeros, ETH studio and mirrorlake . Music was composed by A New Element.

Seth Earley and Chris Featherstone are joined by special guest Bob Levy. Bob Levy, Founder and CEO of Immersion Analytics, brings a wealth of experience in technology and data visualization, having worked with top companies such as IBM, Rational Software, and Mathworks. He shares his profound insights on integrating multidimensional visualization technology using virtual and augmented reality to tackle complex data challenges.Bob Levy is Founder & CEO of Immersion Analytics. With extensive experience in R&D and product management at companies like IBM and Rational Software. Bob is an expert in AI and data visualization. He's been a speaker at prestigious events like MIT Technology Review's EmTech Caribbean and Reilly Strata Data Conference, and has won competitions like MIT's Reality Virtually hackathon and Tableau's DataDev Competition.Key Takeaways:- Examples of how visualization tools help investors make more informed decisions based on a multitude of data attributes.- The transformative potential of VR and AR in business settings and educational environments, backed by partnerships with tech giants like Microsoft and Apple.- Visualization technology as a tool for simplifying the understanding of AI-related compliance and emerging standards.- The discussion on the lack of global compliance standards and the need for potential new standards or refinement of existing ones.- Use cases in derivatives trading, financial performance metrics, and real-time pricing data for detecting anomalies and opportunities.- Innovative ways to visualize artificial neural networks and understand the training processes via VR.- Visualization tools for web and enterprise-level applications, including programming languages and hardware requirements.- The crucial role of visualization in making AI systems comprehensible to non-technical stakeholders like regulators.Quote of the Show:"Seeing all the data points and complexity is crucial for understanding the true nature of the data and avoiding misinterpretation." - Bob LevyLinks:LinkedIn: https://www.linkedin.com/in/boblevy/Website: https://www.immersionanalytics.com/Ways to Tune In:Earley AI Podcast: https://www.earley.com/earley-ai-podcast-homeApple Podcast: https://podcasts.apple.com/podcast/id1586654770Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/Stitcher: https://www.stitcher.com/show/earley-ai-podcastAmazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcastBuzzsprout: https://earleyai.buzzsprout.com/ Thanks to our sponsors: CMSWire Earley Information Science AI Powered Enterprise Book

As job markets undergo a significant transformation, traditional higher education-to-employment pathways are increasingly under scrutiny. Digital badging and credentials have emerged as pivotal elements in recognizing and validating skills outside conventional degrees. Amidst a technological revolution that emphasizes skills over degrees, this alternative credentialing could reshape hiring practices and career development. As industries worldwide grapple with skills gaps and a swiftly changing economic landscape, the question of how digital credentials can play a role becomes increasingly relevant.Are digital credentials the future of workforce development and education? Can they provide the flexibility and specificity that employers demand, and help bridge the widening skills gap?In the latest episode of DisruptED, host Ron J Stefanski and guest Peter Janzow, Vice President of Pearson Workforce Skills, delve into the transformative world of digital badges and credentials, exploring their impact on lifelong learning and career pathways.The two discuss...The evolution and growing importance of digital badges in the last decade.How digital credentials facilitate a direct linkage between learning achievements and job market requirements.The role of digital badges in democratizing education and empowering individuals with recognized and verifiable skill sets.Peter Janzow has been at the forefront of the digital credential movement, beginning his journey in the tech and educational sectors over a decade ago. While his extensive experience includes roles at notable companies like Wiley and MathWorks, he has also been a key player in developing Pearson's digital badging platform Acclaim.

Low Earth Orbit satellite communication takes center stage in this week's Fish Fry podcast! Mike McLernon from MathWorks joins me to chat all about the advantages of Low Earth Orbit satellites versus traditional Geostationary Earth Orbit (GEO) satellites, the challenges of this kind of satellite technology, and the tools and practices that engineers can use to overcome these challenges. Also this week, I investigate new research from the University of Helsinki that contends that we can accurately predict LEO satellite movement with the help of weather models.

Today we're joined by Heather Gorr, principal MATLAB product marketing manager at MathWorks. In our conversation with Heather, we discuss the deployment of AI models to hardware devices and embedded AI systems. We explore factors to consider during data preparation, model development, and ultimately deployment, to ensure a successful project. Factors such as device constraints and latency requirements which dictate the amount and frequency of data flowing onto the device are discussed, as are modeling needs such as explainability, robustness and quantization; the use of simulation throughout the modeling process; the need to apply robust verification and validation methodologies to ensure safety and reliability; and the need to adapt and apply MLOps techniques for speed and consistency. Heather also shares noteworthy anecdotes about embedded AI deployments in industries including automotive and oil & gas. The complete show notes for this episode can be found at twimlai.com/go/655.

EDINBURG, Texas - FIRST in Texas, a nonprofit organization dedicated to inspiring K- 12 students in the STEM fields, has teamed up with MathWorks to help FIRST students develop advanced computing skills.MathWorks is an American privately held corporation that specializes in mathematical computing software. Its major products include MATLAB and Simulink, which support data analysis and simulation. FIRST in Texas is a statewide nonprofit that teaches robotics. It has a strong presence in the Rio Grande Valley.“This is hot off the press,” said Jason Arms, executive director of FIRST in Texas. “I am proud to announce FIRST in Texas now has a partnership with an amazing organization called MathWorks. MathWorks are the creators of something called MATLAB, a machine learning and algorithm platform. And that is AI learning. And it's really robust. It's used in a lot of industry. And it's even included in smart cars.”Arms continued: “We're really excited about the MATLAB component. And, exclusively for us here in Texas, our agreement with them is… it will give our robotics teams access to six of their engineers and those six engineers will be holding office hours for any of our teams across this great state to access those brilliant engineers to talk to them about coding, AI, machine learning and how to really advance some of that advanced coding and programming skills using their FIRTS robotics platform.”Arms believes the collaboration will help the FIRST students create better robotics teams and develop the more advanced skills needed to go into the high tech industry.Editor's Note: Here is an audio interview with Jason Arms about the FIRST In Texas-MathWorks collaboration.To read the new stories and watch the news videos of the Rio Grande Guardian International News Service go to www.riograndeguardian.com.

Giorgia Zucchelli, Technical Marketing for RF & Mixed-Signal, and Houman Zarrinkoub, Principal Product Management at MathWorks, talk with Pat Hindle about the different capabilities of MATLAB and Simulink from designing antennas to full system simulation including EMI and signal integrity. MATLAB Simulink 5G Toolbox

Artificial intelligence takes center stage in this week's Fish Fry podcast! My guest Johanna Pingel (MathWorks) and I examine the benefits of using artificial intelligence for low code, the best practices in this arena, what applications would be a good fit for AI for low code and the super cool stuff MathWorks is doing in this space. Also this week, I investigate a new realm of AI exploration in medical applications… can AI ask AI for a second opinion? 

In this episode, we chat with Cleve Moler, a pioneer in numerical mathematics,  creator of MATLAB and co-founder of MathWorks. We cover the birth of MATLAB, along with captivating stories about the origin of the iconic MathWorks logo, the enigmatic "why" command, the concept of "embarrassingly parallel computations," and the mysterious Pentium bug, among other. Outline00:00 - Intro  05:23 - Advice to students 05:45 - Caltech & J. Todd 07:07 - Stanford & G. Forsythe08:27 - The MathWorks logo  11:50 - ETH Zürich & Stiefel16:51 - Householder meetings 19:48 - LINPACK & EISPACK projects  26:10 - The birth of MATLAB 29:42 - Stanford course and the founding of Mathworks 38:40 - Embarrassingly parallel computing39:54 - The pentium bug 43:58 - SIAM and matrix exponentials47:19 - Future of mathematics51:36 - OutroLinksCleve's corner - https://blogs.mathworks.com/cleve/Mathworks - https://mathworks.com/ History of Matlab - https://tinyurl.com/3dupkb7wDatatron computer - https://tinyurl.com/4kmcw95rJ. Todd - https://tinyurl.com/2s432wzcG. Forsythe - https://tinyurl.com/5583cfwxMathWorks logo - https://tinyurl.com/yc4th7sk E. Stiefel - https://tinyurl.com/ys4r2h96 J. Wilkinson - https://tinyurl.com/ye23bkdc LINPACK - https://tinyurl.com/39d7rwxk Computer solutions of linear algebraic systems - https://tinyurl.com/h9z7s342 Argonne Labs - https://www.anl.gov/ J. Dongarra - https://tinyurl.com/juzrw6y6 Embarrassingly parallel - https://tinyurl.com/yck38a4yPentium bug - https://tinyurl.com/4k7dt76p 19 dubious ways to compute the exponential of a matrix - https://tinyurl.com/yeyjy2bw Perron-Frobenius theorem - https://tinyurl.com/fa59dv32 O. Taussky - https://tinyurl.com/yckexuwsSupport the showPodcast infoPodcast website: https://www.incontrolpodcast.com/Apple Podcasts: https://tinyurl.com/5n84j85jSpotify: https://tinyurl.com/4rwztj3cRSS: https://tinyurl.com/yc2fcv4yYoutube: https://tinyurl.com/bdbvhsj6Facebook: https://tinyurl.com/3z24yr43Twitter: https://twitter.com/IncontrolPInstagram: https://tinyurl.com/35cu4kr4Acknowledgments and sponsorsThis episode was supported by the National Centre of Competence in Research on «Dependable, ubiquitous automation» and the IFAC Activity fund. The podcast benefits from the help of an incredibly talented and passionate team. Special thanks to L. Seward, E. Cahard, F. Banis, F. Dörfler, J. Lygeros, ETH studio and mirrorlake . Music was composed by A New Element.

On this episode of The Wantrepreneur to Entrepreneur Podcast, host Brian Lofrumento speaks with storytelling expert Hari Kumar about using storytelling in marketing. They share personal experiences and insights into the power of relatable storytelling, and give tips on how to build and grow storytelling skills. The episode covers various aspects of storytelling, including finding inspiration, adding detail, and using supporting characters. They also discuss the importance of intentionality and practice in developing a story library and becoming a better storyteller over time. If you're interested in using storytelling to enhance your marketing strategy or become a better communicator, this episode is a must-listen!ABOUT HARIHari's bio kinda has a storyteller's journey. You can think of it as being in three parts: Act 1: Hari the engineer -- 10 years as an engineer, with a master's degree in engineering. Act 2: Hari the academic -- 10 years as a humanities scholar and storyteller, with a master's degree in communication. Then Act 3: Hari the sales storytelling coach -- 5 years in the tech industry, working at MathWorks, Slack, and Salesforce.Which brings him to 2022 and Act 4, when he makes the jump to entrepreneurship and starts his own consulting company. There were of course many twists and turns in the story, and certainly more will come ahead. His own roots and routes include India, Yemen, and 26 consecutive winters in New England, where he now lives in the Berkshire mountains of Western Massachusetts.LINKS & RESOURCESFind Hari's website at StoryCoach.ioAdd Hari on Instagram @haritellastory

In this episode, we hear from * PM Anthony Albanese and cybersecurity minister Clare O'Neil about their new national cybersecurity co-ordinator appointment * Optus CEO Kelly Bayer Rosmarin and MD marketing and revenue Matt Williams about the reaction to an appeal court's rejection of the Telstra-TPG regional sharing deal * Optus's MD enterprise Gladys Berejiklian on the company's approach to partnerships, and * a chat with Mathworks' Ruth-Anne Marchant about what 6G might look like

Heather Gorr holds a Ph.D. in Materials Science Engineering from the University of Pittsburgh and a Masters and Bachelors of Science in Physics from Penn State University. Since 2013, she has supported MATLAB users in the areas of mathematics, data science, deep learning, and application deployment. She currently acts a Senior Product Marketing Manager for MATLAB, leading technical marketing content in data science, AI, deployment, and advanced MATLAB and Python programming. Prior to joining MathWorks, she was a Research Fellow, focused on machine learning for prediction of fluid concentrations. Yann Debray is a highly skilled MATLAB Product Manager with a passion for data science and technology computing. He has been with MathWorks, a leading provider of MATLAB software, since June 2020 and has since contributed significantly to the company's success. As a Product Manager, Yann is responsible for ensuring that MATLAB remains at the forefront of the industry by keeping up with the latest trends and technologies. He has a deep understanding of the data science market and uses this knowledge to help MathWorks' customers succeed. —————————————————————————————

Las empresas buscan ser más eficaces y eficientes, pero ¿cuál es el modo adecuado de optimizar procesos? ¿Nos puede ayudar la optimización a luchar contra el hambre, el cambio climático y la desigualdad? ¿Qué sucede cuando, para cumplir nuestros objetivos, perjudicamos a otros? Charlamos con Alejandra Peña, doctora en ingeniería industrial por la Northwestern University de Illinois y desarrolladora de software en el grupo de control de riesgos de MathWorks. --- Send in a voice message: https://podcasters.spotify.com/pod/show/itam-mx/message

Welcome to an episode with a Professor of International Business and Strategy at Northeastern University's D'Amore-McKim School of Business, Ravi Sarathy. What happens if a company or country regulates its own digital currency? In this episode, Ravi Sarathy answers very interesting questions about blockchain and cryptocurrency. He outlines the features and capabilities of a blockchain, the implications of using it, and how organizations can leverage blockchain to their advantage. Ravi Sarathy is the author of Enterprise Strategy for Blockchain, published by MIT Press in Oct. 2022, in which he explains how companies can gain a competitive advantage by developing and deploying blockchain capabilities. Ravi has published in journals such as Journal of International Business Studies, Journal of Management Studies, Long Range Planning, Small Business Economics, and California Management Review. His previous book was Firms within Families: Enterprising in Diverse Country Contexts. His research interests are in global strategy, technology strategy, and family business. Ravi holds a Ph.D., Univ. of Michigan, and is a graduate of the Indian Institute of Management, Ahmedabad. He has taught executive education programs for companies such as BAE Systems, LG Electronics (S. Korea), Masa Shipyards (Finland), Mathworks, EMC, and others. He was a Fulbright scholar, as the Fulbright-Flad Chair in Strategic Management at the Technical University of Lisbon. He has been a Visiting Professor at the University of Michigan, at the Australian Graduate School of Management in Sydney, and other institutions. Get Ravi's book here: Enterprise Strategy for Blockchain: Lessons in Disruption from Fintech, Supply Chains, and Consumer Industries (Management on the Cutting Edge) Enjoying this episode? Get access to sample advanced training episodes here: www.firmsconsulting.com/promo

Welcome to Strategy Skills episode 302, an episode with a Professor of International Business and Strategy at Northeastern University's D'Amore-McKim School of Business, Ravi Sarathy. What happens if a company or country regulates its own digital currency? In this episode, Ravi Sarathy answers very interesting questions about blockchain and cryptocurrency. He outlines the features and capabilities of a blockchain, the implications of using it, and how organizations can leverage blockchain to their advantage. Ravi Sarathy is the author of Enterprise Strategy for Blockchain, published by MIT Press in Oct. 2022, in which he explains how companies can gain a competitive advantage by developing and deploying blockchain capabilities. Ravi has published in journals such as Journal of International Business Studies, Journal of Management Studies, Long Range Planning, Small Business Economics, and California Management Review. His previous book was Firms within Families: Enterprising in Diverse Country Contexts. His research interests are in global strategy, technology strategy, and family business. Ravi holds a Ph.D., Univ. of Michigan, and is a graduate of the Indian Institute of Management, Ahmedabad. He has taught executive education programs for companies such as BAE Systems, LG Electronics (S. Korea), Masa Shipyards (Finland), Mathworks, EMC, and others. He was a Fulbright scholar, as the Fulbright-Flad Chair in Strategic Management at the Technical University of Lisbon. He has been a Visiting Professor at the University of Michigan, at the Australian Graduate School of Management in Sydney, and other institutions. Get Ravi's book here: Enterprise Strategy for Blockchain: Lessons in Disruption from Fintech, Supply Chains, and Consumer Industries (Management on the Cutting Edge) Enjoying this episode? Get access to sample advanced training episodes here: www.firmsconsulting.com/promo

In our latest Electronic Specifier Insights podcast, we spoke to Johanna Pingel, Product Marketing Manager at MathWorks all about why engineers need explainable AI.

AI once again takes center stage in this week's Fish Fry podcast! Johanna Pingel from MathWorks and I discuss the differences between complexity vs. explainability in artificial intelligence, how you will know if explainability is right for your application and how explainability will impact the future of AI. Also this week, I examine how AI could predict extreme wildfire danger.

On this episode of Embedded Insiders, we're joined by Paul Butcher, Senior Software Engineer at AdaCore, to discuss how AI can make fuzz testing even more robust through the integration of techniques like symbolic execution and input-to-state correspondence that optimize test data sets against scenarios a system might encounter in the real world.Next, Brandon heads back into the Industrial Metaverse with part 2 of a Blueprints series – created in partnership with Bosch, Cloud Blue, the MathWorks, NVIDIA, and Siemens – which reveals how the combination of cyber-physical systems, model-based systems engineering, and digital twins can provide a path to solving some of the world's most complex problems.But first, Brandon and Rich express their hesitations about the European Commission's proposed Cyber Resilience Act, which requires manufacturers to protect their IoT and IIoT device from unauthorized access at all stages of the product lifecycle.

My guest for this episode is, Giacomo Gigliarelli, Inside Sales Manager @ MathWorks... Connect with Giacomo - https://www.linkedin.com/in/giacomogigliarelli/ 

Many companies make components and materials for our everyday lives. Sometimes we don't even know the names of those companies that are helping us achieve our goals.    One of those empowering companies is MathWorks, which has software embedded in chips for phones, cars, planes, and even hearing aid implants, just to name a few applications. MathWorks' Director of Creative Services and Web Operations, Ken Hyman, recently saw began an implementation of Agile thinking and processes with his team, and that's why we wanted him to join us for this episode of Modern Business Operations.    Host Briana Okyere starts the conversation by asking Ken to define Agile and the reasons why MathWorks implemented it.   Briana and Ken also discuss: MathWorks' move from being primarily office-based to being fully remote Why Agile leads to more customer-centricity How Agile allows Mathworks to focus on outcomes instead of output   You'll also hear about Ken's use of job satisfaction reports after the implementation of Agile to keep iterating on the best possible version of it for MathWorks.   This episode is brought to you by Tonkean Tonkean is the operating system for business operations and is the enterprise standard for process orchestration. It provides businesses with the building blocks to orchestrate any process, with no code or change management required. Contact us at tonkean.com to learn how you can build complex business processes. Fast.    

Mathworks the US mathematical computing software company is amongst a large number of tech companies that have setup their EMEA offices in Ireland. They recently celebrated their 5th anniversary in Galway and lot has happened in those five years including the pandemic. Ronan talks to Mathworks Ireland MD Richard Haxby about this and more. Richard talks about his background, what Mathworks does, their work with startups, their biggest selling product and their 5th anniversary. Richard also talks about how the pandemic affected Mathworks and their clients, the new staff they are hiring, and the metaverse. More about Mathworks: MathWorks is the leading developer of mathematical computing software for engineers and scientists and, looking ahead to 2022 and beyond, MathWorks Ireland is working towards the establishment of a dedicated Commercial Customer Success Team which will ensure seamless end-to-end customer support for those looking to implement MathWork's products into their workflows.

Viele Entwickler und vor allem Ingenieure bestätigen uns: Die Integration von KI Modellen ist die große Herausforderung für die Industrie. Plug and Play wird oft versprochen, aber kaum eingehalten. MathWorks sieht in diesem Bereich eine Chance für sich. Dr. Alexander Diethert erklärt uns im Interview die Ansätze seines Teams.

There are many applications that require real-time interactions. Automotive certainly fits into that category. But the term “real time” is bothersome to me, as it's not something that's achievable. We can get “near real time,” but that's about as good as it gets. But is that good enough? That's the question I asked Heather Gorr,  Senior Product Marketing Manager for the MATLAB product at Mathworks, in this week's Embedded Executives podcast.

Losing a high-level software development job during the 2009 recession can be devastating. Having the courage to explore your hobbies can lead to new opportunities. Following his love of grilling and food, Ivan works as a “food rescuer” as the Chief Procurement Officer for Lex Eat Together and is a Board member of the non-for-profit Food Link which rescues surplus fresh food and delivers it to community organizations. Ivan is also a life-time Boston Red Sox fan, and a year round tour guide for Fenway Park in Boston. Prior to following these passions, Ivan worked as the Director of Program Management for the software firm Mathworks where he was responsible for software development and project management.

In our latest Electronic Specifier Insights podcast, we spoke to Jos Martin, Director Of Engineering - Cloud Integration and Parallel Computing at MathWorks about the application of AI  

Wie kann Matlab und Simulink im Verpackungsmaschinenbau genutzt werden? Was muss man tun um in einer immer komplexer werdenden Arbeitswelt sich nachhaltig zu positionieren und wie kann der Einstieg im Bereich KI und Deep Learning mit Matlab und Simulink funktionieren? Diesen Fragen stellt sich heute Philipp Wallner. Herr Wallner ist Industry Manager bei MathWorks. MathWorks ist ein Unternehmen, welches sich auf die Software für technische Berechnungen, Multidomain-Simulation und Software für dynamische Systeme spezialisiert hat. Der Matlab YouTube Kanal LinkedIn Philipp Walllner Für weitere Informationen besuchen Sie uns gerne auch hier: Facebook LinkedIn Website

Prasanna Poori (Director Data Science for PepsiCo.) joins the Data Bytes podcast to discuss her experience in Data Architecture. Learn how to design and build a flexible, scalable, and successful data architecture and what do to with old architecture. Prasanna discusses and outlines the best practices for moving your data into the cloud through five points. 1. Hybrid cloud architecture 2. High performing on permanent solutions 3. Parallel and distributed processing 4. Scalability 5. Data accesses. She also shares what are the biggest innovations coming in data architecture. Prasanna actively educates girls in rural areas about science and self development. About Prasanna: Prasanna Poori is currently the Director Data Science for PepsiCo. Data + Analytics Organization. She has over 27 years of IT industry experience, especially in data and analytics space. Played multiple roles as solution architecting, program and delivery management. She has deep experience in handling large data management, data warehousing and analytics programs across Manufacturing, Services, Health Care and Banking domains She believes… as a lead one should become redundant at one point in time to bring their teams to the level of high performing. She treats both success and failure as equal learning opportunities. Prasanna has done Bachelor of Engineering and Masters in Digital Systems from Osmania University. She lives in Hyderabad, India with her husband and younger daughter. Elder daughter works for MathWorks in US. Prasanna enjoys travel, cooking, painting, Carnatic music... She actively participates in educating girls in rural areas about science and self development. Connect with her on LinkedIn: https://www.linkedin.com/in/prasanna-poori-30441547/ Learn more about our mission and become a member here: https://www.womenindata.org/ --- Support this podcast: https://anchor.fm/women-in-data/support

Coffee Sessions #52 with Dave Bergstein, Vector Similarity Search at Scale. //Abstract Ever wonder how Facebook and Spotify now seem to know you better than your friends? Or why the search feature in some products really “gets” you while in other products it feels stuck in the '90s? The difference is vector search— a method of indexing and searching through large volumes of vector embeddings to find more relevant search results and recommendations. Dave Bergstein, the Director of Product at Pinecone, joins us to describe how vector search is used by companies today, what are the challenges of deploying vector search to production applications, and how teams can overcome those challenges even without the engineering resources of Facebook or Spotify. // Bio Dave Bergstein is Director of Product at Pinecone. Dave previously held senior product roles at Tesseract Health and MathWorks where he was deeply involved with productionalizing AI. Dave holds a Ph.D. in Electrical Engineering from Boston University studying photonics. When not helping customers solve their AI challenges, Dave enjoys walking his dog Zeus and CrossFit. --------------- ✌️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 Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Dave on LinkedIn: https://www.linkedin.com/company/pinecone-io/mycompany/

Louvere Walker-Hannon has worked at MathWorks (the company that makes MATLAB) for over 21 years, where she's also a STEM Ambassador. She studied biomedical engineering as an undergraduate at Boston University and did graduate work at Northeastern University in geographic information technology with a specialization in remote sensing.She loved working with MATLAB as an undergraduate and when MathWorks came to the career fair when she graduated, she sought them out, got an interview, and has been working there ever since.She says there are both technical and non-technical skills that are valuable in the field of data science. Technical skills include coding, some programming, a foundation in mathematics, some statistics, and in some cases physics. Non-technical skills are also very important. It's critical to be able to communicate your findings clearly using a variety of techniques. She says stay away from technical jargon and communicate as if you're having a conversation. A second important skill is active listening, to be open to suggestions from others, especially those who are new to the field.She explains that there are also barriers to people entering the field of data science. For some people, coding is a barrier to engagement with data science as many people in STEM professions are not comfortable with coding. MathWorks is doing more development to provide user interfaces or apps to give people a starting point without having to rely on writing code.There are also concerns about model interpretability where it's difficult to get insights into how certain models work. More people are gaining awareness about the topic, and that's leading them to explore how to implement it and ask why it's important. She is noticing that more people are trying to incorporate model interpretability into their data science applications.One of the systemic barriers is implicit bias. People are used to working with and being around people with certain characteristics. And in a work setting, there could be a project coming up, and there are several individuals who could work on this project. Many times, the people selected to work on a project tend to be the same individuals. But then it begs the question, when do others get the chance? There's still a lot of under-representation from various population groups in data science. Even if people from an under-represented group have the skills and education, if they don't feel like they belong, that can impact their productivity. It's important to build a sense of community and have someone guide the person, make them feel welcome and help them become a part of the culture, so they can understand what they can do in order to thrive. Louvere is also a STEM ambassador at MathWorks where she volunteers in STEM advocacy and outreach in schools or on STEM panels. She loves hearing high school age and younger students at science fairs talk about their projects and see how proud they are of their work. This gives her hope that young people are excited about data, about analysis, and communicating their insights to others. RELATED LINKSConnect with Louvere on LinkedIn and TwitterFind out more about MathworksConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

It’s Kubernetes release day! The team that launched v1.22 of everyone’s favourite cluster management software was led by Savitha Raghunathan, Senior Platform Engineer at MathWorks. Savitha joins host Craig Box to talk contribution, containers and cricket. Do you have something cool to share? Some questions? Let us know: web: kubernetespodcast.com mail: kubernetespodcast@google.com twitter: @kubernetespod Chatter of the week Life before smartphones Dark Sky, hyperlocal weather app Karl the Fog Universal Studios Kubeyland 2021 The Simpsons Ride News of the week Kubernetes 1.22 announcement Sign up for the 1.23 release team Linkerd graduates* in the CNCF Cosign 1.0 Episode 152, guest host Dan Lorenc Episode 155, with Priya Wadwha Cloud Native Rejekts CFP Episode 79, with Chris Kühl Introducing Koncrete by the Kalm team Nestybox adds Kubernetes support Curiefense adds NGINX support Replicated announces $50M Series C Episode 143, with Grant Miller Kubernetes platform updates: Deckhouse, by Flant, is GA Red Hat OpenShift 4.8 Rafay adds new features to Kubernetes Management Cloud Carvel Package Manager for Kubernetes Porter and seed funding announcement Links from the interview Chennai Super Kings Stephen Fleming; coach, A/C salesman and Yellow Wiggle Royal Challengers Bangalore MathWorks MATLAB Math vs maths? (Doesn’t actually matter; MATLAB is short for Matrix Laboratory) Savitha’s first contribution Kubernetes GitHub workflow and pull request guide Kubernetes 1.22 release announcement Release Team Loki and WandaVision Enhancements of note: Seccomp by default Rootless Kubelet Pod admission control Node swap support Windows privileged containers 1.21 release interview with Nabarun Pal Do, Delegate and Defer Release lead for 1.23: Rey Lejano In memoriam: Peeyush Gupta Donate to Peeyush’s Family Education Fund Coffee art Amigurumi Savitha’s cat Savitha Raghunathan on Twitter

Learn more: https://www.leostream.com In this episode of Let's Talk, we sat down with Karen Gondoly, CEO at Leostream, a company that leverages open-source technologies to offer vendor-neutral VDI solutions to organizations of all sizes. The pandemic has created a massive demand for remote access to application environments so users can continue to work accessing enterprise applications remotely. In this interview, we talked about how the pandemic has changed the world around and also explored how Leostream helps organizations better manage their resources and also allow them to work with talented people all across the globe without any need for relocation. Guest: Karen Gondoly (LinkedIn, Twitter) Company: Show: Let's Talk Topics: Virtualization, Remote Work, Open Source, Linux About the company: Leostream provides the critical connection-broker technology required for enterprises to achieve successful large-scale VDI, hosted desktop, or hosted application environments in both private and public clouds. About the guest: Karen Gondoly joined Leostream from The MathWorks, Inc., a technical software company where she was a developer for the Control System Toolbox before specializing in usability. Her technical background includes roles as a software developer, GUI designer, technical writer, and usability specialist. Karen holds bachelor's and master of science degrees in aeronautical/astronautical engineering from the Massachusetts Institute of Technology.

Rasagna Holt is the Founder and CEO of KGTiger, a company that serves the in-house recruitment and talent acquisition industry. KGTiger provides solutions that combine human intelligence and machine learning to solve critical talent acquisition problems. Rasagna and her team have serviced companies including MathWorks, Microsoft, Disney, ESPN, Under Armour, and many more. Throughout her career, Rasagna has helped major organizations build talent strategies around diversity hiring, international expansion, talent market research, volume hiring, executive staffing, and talent pipeline. She continues to grow her team and company through new and innovative strategies around talent acquisition. In this episode… Are you finding the right candidates for your team, or are you still hiring employees who are only driven by a paycheck? If you're looking for a better way to grow your business, Rasagna Holt is here to share her expert strategies for finding valuable candidates, promoting diversity, and building teams that contribute to the success of your company. Equity and fairness are at the heart of Rasagna's work. With years of experience in the talent acquisition field, she has helped many organizations expand their talent pools, develop their teams, and effectively grow their businesses. According to Rasagna, your company will thrive with a diverse team of employees whose values match your organization's. So, how can you start attracting the right candidates and developing your team for long-term success? In this episode of Level Up, Nick Araco sits down with Rasagna Holt, the Founder and CEO of KGTiger, to talk about her strategies for hiring — and leading — a great team. Rasagna discusses her three-dimensional approach to talent acquisition, her best practices for hiring and training candidates, and her tips for building a talent pipeline that aligns with your company's mission. Stay tuned!

This week's guest Nausheen Moulana grew up fascinated by the potential of the humble electron. Her parents were wary to send her to the US after hearing some scary stories, but Nausheen made it here in time for grad school. As a MATLAB power-user, Nausheen was thrilled to work at The Mathworks for decades. We talk about when it's time to move on, the importance of financial literacy for software professionals, and the delicate balance of "finding your voice" as a female in engineering.   How I got started in Tech Stealth technology https://en.m.wikipedia.org/wiki/Radar_cross-section#Reduction B-2 Stealth bomber  About Hyderabad Biryani + Charminar + Hyderabadi Pearls Experience coming to the US https://en.m.wikipedia.org/wiki/Dotbusters Career in High tech CASE Career Advice First Break All The Rules StrengthsFinder  

This week’s podcast guest, Johanna Pingel, a Product Manager at MathWorks, said that engineers should be concerned with the AI workflow. Do you agree? Do you even know what that means? I didn’t so I decided to go directly to the source and ask the question. Hear the answer on this week’s Embedded Executives podcast.

In this week’s Embedded Insiders, the Insiders comment on a recent fire that shut down the Renesas Naka semiconductor fab where the company manufactures automotive chips. Later, Rich is joined again by Zane Tsai, Director of the Platform Product Center at ADLINK Technology, to discuss how logistics companies are being affected by the race to deploy AI at the edge, and insights for developers looking to increase the efficiency and productivity of logistics automation systems.The two are also joined by NVIDIA’s Amit Goel, Director of Product management for embedded AI platforms, who examines the complexities of integrating reliable performance into AI-based applications like independent automation. NVIDIA is currently building a hardware platform that will bring greater compute intelligence to autonomous systems at the edge. However, a solid software framework that can streamline the development, deployment, and management of the AI applications that will run on these devices is still critical. The company’s Isaac SDK and DeepStream SDK for AI-based multi-sensor processing, video, audio, and image understanding are positioned to support these workloads across the engineering and operational lifecycles of AI-enabled robots.Finally, Tiera Oliver addresses the evolution of real-world AI. How do we transition from our historical lack of understanding about what’s going on under the hood of complex neural networks, and into an era of AI explainability around how these models operate? Will we ever be able to test, verify, and validate these workloads to the point that they can be heavily relied upon in safety-critical systems? Johanna Pingel and David Willingham, deep learning project managers at The MathWorks, believe we’re already on the way.

What's in This Podcast? Dr. Hiroka Warshauer Talks "Productive Struggle" Texas State Math Camp Mathworks Missy and Dr. Hiroko talk Hong Kong Links Join us on our Marriage Podcast, Purposeful Marriage What the Mother Podcast Listen to Pastor Dan Schiel Preach! Visit Todds' blog and get his take on everything from education to football to food to politics and this year a year of Bible Blogs.  Check it out. Become a PATRON! Download the Marriage Guide for free!

We interview Dr. Hiroko Warshauer, Texas State Professor and Author of Mathworks, All on TTIT 169. The post We Interview Dr. Hiroko Warshauer about Teaching Math and A Great Curriculum, MathWorks, All on TTIT 169 first appeared on TWO TEACHERS IN TEXAS.

Basics Of simulink. How to use interface. Simulink, developed by MathWorks, is a graphical programming environment for modeling, simulating and analyzing multidomain dynamic systems. Its primary inte #CS #CSII #ControlSystemII Join us on social: https://discord.gg/mGBb8xKd Discord https://twitter.com/realthileban Twitter https://www.instagram.com/thileban/ Instagram https://www.facebook.com/Thileban-Nagarasa-103160761083207/ Facebook --- Support this podcast: https://anchor.fm/etexplains/support

https://go.dok.community/slack Abstract of the talk… This talk will walk through how to get started contributing to Kubernetes, combatting imposter syndrome, the many other ways you can get started contributing to K8s other than by writing code, and the benefits to joining a community such as K8s. Bio… Rin is a Technical Community Builder at Camunda. They enjoy discussing all things open source, with a particular focus on diversity in tech, improving hiring pipelines in OSS for those that are neurodivergent, and removing accessibility barriers to learning programming. Rin is also a Member of Kubernetes, a contributor to Spinnaker, involved in the Kubernetes Contributor Experience SIG, and is a Storyteller on the Kubernetes Upstream Marketing Team. When not immersed in all things OSS and cloud-native, they can be found hanging out with their wife and pets, making candles, cooking, or gaming. Savitha is a Senior Platform Engineer at MathWorks. She has been working with container technologies for the past 5 years and use Kubernetes in her day to day job. She also contributes to the Kubernetes ecosystem, currently involved with release, security, mentoring, and documentation efforts. Key take-aways from the talk… How to get started contributing to K8s Why you should contribute to K8s Combatting imposter syndrome And more!

While I personally don’t have the patience for programming, Louvere Walker-Hannon does, as she is a senior applications engineer at Mathworks, the maker of MATLAB. She does speaking, too, and has presented at several conferences. We spoke about her job at Mathworks, her interest in public speaking, any tips she can provide to get better at public speaking. TEACH THE GEEK teachthegeek.com youtube.teachthegeek.com @teachthegeek (FB, Twitter) @_teachthegeek_ (IG)

We talk with Lauren Tabolinsky, Sam Reinsel and Mark Corless join us from Mathworks to talk about MATLAB and Simulink and how those products are used in the automotive industry and in the Autodrive Challenge itself. Check out careers at Mathworks at https://www.mathworks.com/company/jobs/students.html?stid=crnav_ov 
Find more information about MATLAB at https://www.mathworks.com/academia/student-competitions/racing-lounge.html

In this episode of Embedded Insiders, Brandon and Rich review some of the highlights from the 2020 Consumer Electroncis Show (CES). Most notably, many embedded technology companies and organizations continue to embrace the world of open source, both within their product offerings and in the way they deliver products to market.Later, Wensi Jin and Mark Corless of MathWorks take the wheel as the discussion turns to simulation in the automotive sector, where the emergence of AI, ADAS sensors, and autonomous driving technologies are driving more broad testing requirements than ever before.Finally, Jean Labrosse turns his attention to a lack of proper software documentation in "Things That Annoy A Veteran Software Engineer."Tune in.

How did this Pittsburgh girl end up all the way in Holliston?!?Growing up in a suburb about 20 minutes outside of the city, Deb always loved dance (and as you'll learn towards the end of the interview, her daughter is the exact same way). During her Junior and Senior year of high school, she was apart of a kickline that traveled around the country performing at different shows and parades including Disney! Deb had an interest in theater where she describes a hysterical moment where she got a future broad way start to actually break character in rehearsals unintentionally.When she was in high school, Deb took an interest to accounting as she really enjoyed how the numbers matched up and everything balanced out. She left high school pursuing a degree in accounting at Penn State. She did find later on at Penn State though, accounting wasn't for her. She switched to marketing because that enabled her to tap in to her creativity and personable abilities much more than accounting ever could.Shortly after graduating, Deb landed a job with Macy's in management. She later switched to a marketing position with a financial firm to pursue something she was more passion about. Eventually she decided to go get her MBA full-time going down the tract of entrepreneurship. Originally, she thought she was going to open up her own retail shop with her degree, but she had enough self awareness to realize she was very risk adverse. She graduated in 2008, right in the middle of the infamous Stock Market Crash...Needless to say, getting a job was a bit difficult after graduating from her masters program. Her and her boyfriend at the time (now husband and Firewaller), Jeremy, moved out to Illinois because he was pursuing a PhD out that way. Deb quickly found a temporary job at a place called Noodles. She later went on to join an internet marketing company. In the meantime, she acquired some group fitness certifications and took up some positions as a group fitness instructor at her local gym. She also ended up working for an administrative position at a hospital where she was able to apply her marketing talents. A fun fact about her time there is that she actually made a farmer's market within the hospital!Once Jeremy completed his PhD program, his new job took him to none other than Framingham where he worked for MathWorks at the time. He currently works for Bose. Deb started working at a local hospital and in April of 2019, she joined Blue Cross Blue Shield where she works on the quality side of provider groups.In the back half of the interview, Deb opens up to us about how her and Jeremy met. Astonishingly, they were friends for 8 whole years prior to dating! I think you'll really enjoy the story of how they finally started dating after all that time.Deb and Jeremy have now been in Holliston for around two years. They have two kids - Mikayla (5 years old) and Joshua (3 years old).Thank you so much for coming on the show, Deb! We greatly appreciate the time you took to tell us all about your life and everything in it. Please continue to inspire positivity, liveliness and a can-do attitude at Firewall. You're the best!

The following keynote address by Bruce Tannenbaum, manager of technical marketing for vision, AI, robotics, and autonomous applications at MathWorks, was given at Venture Café Cambridge’s annual Robotics and AI Connect night. In this talk, Bruce Tannenbaum presents key factors for successful projects and illustrates the importance of these factors using case studies from diverse vision applications including automated driving and crop harvesting.

Sebastian, Stefan, Manu - das sind wir, die EnergiewendeMACHER. Ein Podcast der dir alle zwei Wochen einen Einblick in die heißen Themen der Energiewelt und ihrem Wandel gibt. Zusammen mit handverlesenen Gästen aus den unterschiedlichsten Bereichen diskutieren wir alle zwei Wochen über Blockchain, AI, Machine Learning, IoT, Digitalisierung, New Work - und was der Buzzword Katalog noch so alles hergibt. Wir freuen uns auf dich! More: www.patreon.com/energiewendeMACHER Sebastian, Stefan, Manu - that's us, the Energiewende Makers. A podcast that every two weeks gives you an insight into the hot topics in the cold energy world. Together with hand-picked guests from various fields we discuss Blockchain, AI, Machine Learning, IoT, Digitalisation , new work - and what else the Buzzword Catalogue has to offer - every two weeks. We are looking forward to seeing you!

Sebastian, Stefan, Manu - das sind wir, die EnergiewendeMACHER. Ein Podcast der dir alle zwei Wochen einen Einblick in die heißen Themen der Energiewelt und ihrem Wandel gibt. Zusammen mit handverlesenen Gästen aus den unterschiedlichsten Bereichen diskutieren wir alle zwei Wochen über Blockchain, AI, Machine Learning, IoT, Digitalisierung, New Work - und was der Buzzword Katalog noch so alles hergibt. Wir freuen uns auf dich! More: www.patreon.com/energiewendeMACHER Sebastian, Stefan, Manu - that's us, the Energiewende Makers. A podcast that every two weeks gives you an insight into the hot topics in the cold energy world. Together with hand-picked guests from various fields we discuss Blockchain, AI, Machine Learning, IoT, Digitalisation , new work - and what else the Buzzword Catalogue has to offer - every two weeks. We are looking forward to seeing you!

The digital twin is a federation of data and models that can be analyzed or put into a simulation to create useful information about the past, present or future of the DTs physical twin. The type of model and the level of model can make or break the analysis or simulation. In this episode of the IoT Business Show, I get down and dirty with Jim Tung, breaking the digital twin down in order to understand this most important tech at both the atomic and system level. Read the rest of the show analysis notes including the transcripts at: http://bit.ly/IoTPodcast87notes This show is brought to you by DIGITAL OPERATING PARTNERS Related links you may find useful: Season 1: Episodes and show notes Season 1 book: IoT Inc Season 2: Episodes and show notes Season 2 book: The Private Equity Digital Operating Partner Training: Digital transformation certification

Originating from western Massachusetts, Mike found his way over to Holliston because of family and work after an incredible upbringing in Chicopee!When he graduated high school, he initially started out commuting to Western New England on a path towards a career in pharmacy because he saw the $$$. He eventually discovered that pharmacy was definitely not his calling. He took a turn and decided to pursue a career in computer science. After his freshman year, he transferred to UMass Amherst where he was able to enjoy the full college experience. Once Mike finished college, he continued with his college summer job at circuit city working as a car audio installation specialist. Eventually, MathWorks reached out to him about a job, and then he proudly illustrates how he has moved up within the company over the past 13 years.Later on we talk about how he met his wife, Kyla, through his cousin who was close friends with Kyla in high school and through college. Kyla expressed interest in Mike during their high school years, but unfortunately Mike was taken at the time and had to respectfully decline. Ultimately, the timing worked out where they were able to finally get together over a spring break during college. Everything happens for a reason!Currently Mike lives with his wife (and Firewall member) Kyla, his two daughters Lily and Lexi, 1 dog - Daisy the beagle, and 4 chickens, which he explains WHY he originally got the chickens. As you'll find out at the end of the interview, in the maybe not so distant future, Mike's number of pets could exponentially increase - and trust me, you'll never guess as to why... (Hint: buzz buzz)As a kid, Mike always played soccer. However as an adult, he fell in love with the sport of hockey! Mike parallels his hockey league with CrossFit meaning people of all different skill levels are playing with each other side by side with endless amounts of support.Be sure to listen the whole way through as Mike absolutely crushes our questions for him at the end!Thank you so much for coming on the show Mike! We greatly appreciated your transparency, honesty, and enthusiasm. Although there was a ton of his life story we didn't even graze over, we hope many were able to get a good idea of Mike's past, present and future.

In this second episode we talk with Sebastian Castro who works at Mathworks, which is well known from Matlab and Simulink. We talk about the latest Matlab software and updates, alongside what his opinion is about the Robocup in general and what his vision is about the Robocup and why it is important for Mathworks to go to these sorts of events. Furthermore Sebastian explains what they do to support their community and how they reach out to the community. Last but not least we talk about the competitors of the Mathworks software and how they even work together with open-source software. Sponsors for this episode Mathworks Kuehne + Nagel

Vinay Seth Mohta is Managing Director at Manifold, an artificial intelligence engineering services firm with offices in Boston and Silicon Valley. Vinay has helped develop Manifold’s Lean AI process to build useful and accurate machine learning apps for a wide variety of customers. During today’s episode, Vinay and I discuss common misconceptions about machine learning. Some of the other topics we cover are: The 3 buckets of machine learning problems and applications. Differences between traditional product development and developing apps with machine learning from Vinay’s perspective. Vinay’s opinion of what will change as a result of growth in the machine learning industry Maintaining a vision of a product while building it Resources and Links: CRISP-DM Ways to Think About Machine Learning by Benedict Evans The Lean AI process Vinay Seth Mohta on LinkedIn Big Data, Big Dupe: A little book about a big bunch of nonsense by Stephen Few Quotes from Vinay on today’s episode: “We want to try and get them to dial back a little bit on the enthusiasm and the pixie dust aspect of AI and really, start thinking about it, more like a tool, or set of tools, or set of ideas that enable them with some new capabilities.” “We have a process we called Lean AI and what we’ve incorporated into that is this idea of a feedback loop between a business understanding, a data understanding, then doing some engineering – so this is the data engineering, and then doing some modeling and then putting something in front of users.” “Usually, team members who have domain knowledge [also] have pretty good intuition of what the data should show. And that is a good way to normalize everybody’s expectations.” “You can really bring in some of the intuition that [clients] already have around their data and bring that into the conversation and that becomes an almost shared decision about what to do [with the data].” Episode Transcript Brian: We got Vinay Seth Mohta on the show today. I’m excited to have you here. Vinay’s maybe a little outside the normal parameters of who we planned to have as a guest on designing for analytics but not entirely. He has an engineering background but he’s done a lot of stuff in the product management space as an executive. Correct me if I’m wrong. You’ve been at MathWorks before, you worked on search at Endeca Technologies, and you were at Kayak, which is one of my favorite sites, actually, for booking travels. I’m sure everybody listening has probably touched Kayak at some point, and you were a product manager there, correct? Vinay: That’s correct, yup. Brian: Okay, and I know you did some healthcare. You were a CTO at Kyruus, and now, you are a Managing Director of Data Platforms at manifold.ai, which is a services company that works on data science, machine learning projects, and artificial intelligence. Is that correct? Vinay: That’s right, yup. Brian: Tell us a bit about what Manifold’s doing and what you’re doing there. Vinay: Sure thing. Manifold, as an organization, is an AI consulting company, as you mentioned. More importantly, we unpack AI into […] really focusing on data engineering, data platforms, getting your data ready, and then also building machine learning models and getting all of that put together into either an internal-facing or an external-facing product. So, I’m looking forward to talking a lot more about that. As a company, we largely work with Global 500 organizations and also a spectrum of organizations. Sometimes, I actually get down to fairly early stage startups, where they’re looking for very specialized help in a particular area like Computer Vision, for example. We are largely a team of experienced product folks and engineering folks who’ve worked at both large organizations like Google and Fullcom as well as venture-backed startups like some of the companies you’ve mentioned in my background. Brian: What kinds of projects are people coming to you guys with? Obviously, the whole AI machine learning thing is a pretty active space right now. Everyone’s trying to jump on to that and you got to invest in this. What kinds of projects are you guys doing? Vinay: That’s a great question in terms of the different places and the different motivations people have when they come to us. I try to demystify AI right from the first conversation. Particularly, when we’re talking to executives, which we often do, we want to try and get them to dial back a little bit on the enthusiasm and the pixie dust aspect of AI, and really start thinking about it more like a tool, or set of tools, or set of ideas that really enable them with some new capabilities that also can be thought of, and what I at least see as some more traditional product development spectrum. That’s really what I like to use to frame where customers are when they come to us. By the product development spectrum, I mean there is a starting point of what are the right questions to ask and what are the right types of business strategy questions I should think about, go to market-type questions that might be relevant to consider. Some customers that we’ve talked to are starting all the way back there. There are folks who’ve answered that question for themselves, and now, they’re actually starting to think more actively about what are the product-related areas I want to invest in based on my overall business strategy, what are some of the technology approaches I can take. Machine learning is not always the right answer for a pretty business problem and then really getting into more of the actual design and architecture pieces, and then the hands-on keyboard of actually building, and then deploying data engineering, related data pipelines, or machine learning models, for example. We’ve really seen clients come to us at all different phases. The parts we generally like to focus on start from the product strategy, technology strategy-type conversations, going all the way to building and delivering software and machine learning models that are going to get deployed into production. So, that’s really our zone of focus. Brian: If I could take it back for one second, you said pixie dust and I thought that was funny. But I also get what you’re saying in there. Do you think, as consultants and service providers working in the space—I work on the design side, you’re working a lot on the engineering side and the data science side—are we propagating the wrong thing when we say artificial intelligence and in the analytic space, the term big data? Stephen Few just wrote a book, I think last year, they called Big Data, Big Dupe. I tend to agree with it. There’s a lot of marketing hype surrounding the term. No one can really even define what makes it big versus regular. Do you think we have to stop using that as that? Does it matter what we call it? I feel kind of silly every time I say “AI” because it has such a loaded meaning to people that maybe don’t know as much about it. What do you think about that? Vinay: I generally agree with the spirit of your question, which is, it’s just good to use words all of us understand that map to things that we can touch when we type with our keyboards and things like that. So, it’s very helpful to talk about software engineering as oppose to AI for example or a machine learning model. I’ve also come to terms with the fact that there is a massive marketing wave that is much larger than what you or I choose to do and I think that creates the context that someone is coming into a conversation with us. When they enter the conversation, they already have some of that context. So, what is more important for us to focus on, as opposed to the specific choice of words, is really taking where people are starting in a known context and then walking them into either a world where we feel we can have a much more real conversation with the types of things that are grounded and the actual work that we do. A lot of people are uncomfortable with terms they don’t understand but they believe they’re supposed to continue using them and they should understand them, et cetera. I also find the other thing that’s nice about taking in marketing term but then really almost using it as an educational opportunity when you’re unpacking those terms. People start to feel more comfortable that, “Oh, okay. These things can be mapped into things I understand,” and then being able to use some much more effectively. At least, in our conversations with them, we have a shared vocabulary. I often bucket those conversations under recognizing that this is a marketing term. “Let’s talk about what you mean by AI and let me unpack what I mean and make sure we have a shared vocabulary.” I think there’s some nice ways to undo the marketing hype in more intimate settings, but at a larger scale, I had found that anytime I try to fight the marketing, the five-year macro trend marketing term, people mostly say, “Oh, you don’t do anything related to that and you do this after-effect.” And it’s like, “What? No, no, no. That’s not what I meant.” I think we have to pick our battles. The other thing which I always have mixed feelings about but it does feel like—and I’ve seen this with several of the major technology trends over the last two to three decades—is that it does motivate organizations that traditionally wouldn’t look at technology as enabling components of their business strategy. It does force them to at least take a look, revisit new ideas that may have been scary before. But now they feel like, “Oh, well, let’s at least take a look because it seems everybody else is getting some value from it.” It does at least stir up things inside organizations where you get some creativity going and people are willing to at least step out of their day-to-day and take a look. I’m definitely not a hype person in general, but it does seem to serve at least some positive purpose in that sense. Brian: I kind of see it—we’ve joked about this in the past offline—like there’s a new hammer at Home Depot and everyone’s racing out to go buy this tool but not everyone knows what it does. It’s just, “I got to have one like everyone else. It does everything.” On that thought, of the ten people, ten clients that come in, what role would your typical client be? And of ten of those, how many of them have either unrealistic expectations of like, “Hey, we want to do this grand project with AI and machine learning to do X,” versus, “Hey, we want to really optimize this one part of our supply chain,” or, “We want to do…” something very specific that’s been thought of in terms of either products or service offering or an internal analytics thing where they want to actually apply an optimization or something like that. How many had fallen to the “educated versus maybe less educated,” in terms of what they’re asking for from you? Vinay: I would probably say order 20% to 30% of folks are in that bucket of, “I have a very targeted need. I know exactly what I want to get out of this state of pipeline. I have this other data pipeline I’d like you to work with to put the whole thing together,” or, “I need a specialized machine learning model that will help me segment some of my customers into more fine grain way for this very particular use case,” things like that. Those tend to be organizations that already have a software engineering capability. There’s some data for other business problems already and they either need more help than they have in house or they need some kind of specialized help. So maybe, they have largely done more structured data marketing-related use cases and now, they want to do more natural language-related or in a different area. They generally have a fairly good feel of the landscape and they know how our work would plug into their work. There is probably roughly 50% of what we get as more where we get people who are VPs of Technology, VPs of Product. They understand operations in a pretty meaningful way. A line of business leader who has a meaningful business case in mind, so they already have one or more business problems in mind that they think will be compelling. They want to know, is this a good fit for a machine learning or not? What would be required to actually get to even trying out machine learning? I would put those folks in the bucket that they have thought through some of the business strategy related, sort of going back to that spectrum idea of starting from business strategy all the way to shipping something to production. I would say they are more in the product and technology strategy bucket where they want to figure out, “I don’t know what I have in the rest of my organization, but I know we have some software, we have some data based on running a website for the last four years, whatever else, or some other kind of operational system. I’d like to figure out if we could use machine learning in some way to do something predictive, for example to improve how a call center handles inbound calls and prioritizes some of the tasks.” There are cases where people have much more thought through use cases in mind, but they don’t have the expertise on: What is the data pipeline? What data do I actually need from machine learning? Have I actually ever built and deployed a model before? They’ve usually not have done that. There’re a lot of folks in that bucket. And then, the third bucket is the remainder, which is really people are starting more in the business strategy side, where they’re saying, “Oh, we’d really like to have an open-ended conversation. Our CEO has a five or ten-year vision around transforming our core business and how we service our customers.” I’ve talked to folks that are in much more traditionally industrial businesses like paper processing, for example, or staffing, or more instrument manufacturing, or other types of manufacturing. Those kinds of areas, there is really this historical model of hardware or some other service that gets provided as opposed to Software as a Service. I think everybody is interested in some kind of move to a subscription model and also some understanding of what is the relevance of these technologies. But they are not at the stage where they’ve identified a particular business case or a use case. Brian: If I’m a product manager or someone that’s in charge of bringing ROI to data within my company, say I’m not a technology company, should I be looking to make an investment in a place where maybe it’s more of a traditional analytics thing or maybe I have humans doing eyeball analysis, making decisions about insights from the data, and then saying, “Okay, what we’d like to do is actually see if we can automate this existing process. So, it’s like A, B, C, D, E, F. We want to swap out stage D with a machine learning solution to free those people to do other work”? Or is more like, “We have this data we’re sitting on. Hey, we could train it and do something with it. We’re not doing anything with it right now.” Is there a strategy or some thinking around one of those maybe being a more successful project to take on, any thoughts? Vinay: I think that’s a great way to pose the question because one of the things I would think about as with any new effort in an organization, is that you want to be successful as the person who’s bringing in some new technology or new approach, whether it’s process or people or technology. I think really having a lower risk, a smaller bite at the apple in some sense to get your first success on the board, and then starting to build on that nucleus would definitely be the way I would think about get it going. There may be different situations where, as a leader of a large organization, you really have a directive to be more transformative and that can be a different type of conversation. But as I’d think about somebody who’s in a product role at—let’s call it just for the sake of brevity—a non-tech organization, I think starting with a smaller project where you can get people used to the idea that you could do more with data, it’s not that scary, it’s like another tool, it’s like buying another piece of software and doing some training around it and those kinds of things, then it gives you a success that you can build on and people around you start to have some familiarity with it, where you get less resistance the next time you go and do some things. I think of the overall change management challenge would frame the choice of project in some ways than not. One of the other frameworks I would use also, Ben Evans from Andreessen Horowitz, recently wrote a really nice blog post about how people can organize their thinking around applications of machine learning. The core of the framework is, there are three buckets in which you can think of the problems and potential applicability of machine learning. The first one, actually, falls very much into exactly the example you gave where I might have an analyst working with existing data, etcetera. That’s ‘a known data, known questions’ bucket. So, you have a set of data already available. You have a set of questions your analysts ask every day. Maybe they’re eyeballing it. Maybe they’re running a simple linear regression or something. What’s nice about applying machine learning in that case is it’s literally like, “Oh, you have a mallet. Here I have a stainless steel hammer. Let’s see what happens if I apply my stainless steel hammer.” It’s relatively easy to get set up to do it. Our organization who knows roughly what’s already involved with that data, the semantics of the data. It’s clean enough that you could probably start working with it. It gives you a relatively easy pathway into trying out machine learning. Just saying like, “Oh, we got 50-basis point lift just by applying this new tool, without really changing anything else.” That’s one bucket. The other two buckets, I definitely encourage folks to read the article, to put in the show notes or something. The other two buckets are ‘unknown data, new questions,’ and then the last one is ‘new data, new questions.’ Just to give you a placeholder for what the last bucket is, those are opportunities that you might be able to apply computer vision or put new sensors in a particular environment. So, gathering entirely novel data streams, unmasking new questions. There’s a handful of organizing ideas like this. We generally suggest a few different articles and I am definitely happy to offer those for the show notes as well, if [you’re looking for 00:17:27] different ways to organize their thinking around approaching machine learning problems. Brian: Great. Yes, I’ll definitely put those links into the show notes. Thanks for sharing those. Also, a follow up to that. Once you’re into a project, what are some of the challenges around for projects that have user interface or some kind of user experience that’s directly accessed? Are there challenges that you see your clients having with getting the design right? Are there challenges about getting the model and the data science part right or getting it into production? I heard a lot about this at Strata Conference that I was at in London, that they’re talking a lot about you can do all this magic stuff with your data sciences in the PhDs. But if they don’t know how to either help the engineers or themselves get that code into a production environment, it’s just sitting in a closet somewhere and it’s never going to really return value. Can you talk about some of the design and the engineering challenges that you might be seeing? Vinay: I’m assuming most people listening to the podcast are familiar with traditional product development processes, design iteration, and so forth. What I’ll offer here is the difference when you start thinking about data and machine learning. We have a process we call Lean AI and what we’ve incorporated into that is this idea of a feedback loop between a business understanding, a data understanding, then doing some engineering—this is the data engineering—then doing some modeling, and then putting something in front of users. The major part here is that, you may have a particular idea around what the ideal user experience might be. But then as we start to get into the data, as we start trying different modeling techniques, we might either surface additional opportunities that there may be something compelling that the user could do in their workflow using what the model has surfaced. Or it may be that the original experience as envisioned is going to have to change because there is not enough predictive power in the data, or a data source that you thought you’d be able to get your hands on is just not going to be available, or things like that. So, there is an additional component to the [iteration 00:19:46] loop that you have to rely on, which is just what is in the data, how much can I get access to, and then some of the more traditional software engineering constraints. If it’s going to take six months to get that particular piece of data cleaned up enough such that we can actually use it, is there something lighter weight that we could at least get started with at something in front of users first, and then continue to refine and iterate over time? That’s probably the big difference in terms of traditional product development that just involves software engineering in apps versus working with the data and machine learning. There’s a little bit of just this science of what is possible inside of the data given the signal inside [00:20:27] datum. The engineering part is definitely, as you said, something that is talked less about historically and it sounds like, based on some of the things you’ve heard at Strata, that is something that is starting to change. What I’ve seen is that a lot of the tutorials, a lot of the content out there has historically been focused on, “Get your first model going,” or, “Take this particular data set and try out building a model or tweaking this or that.” In that sense, there’re also a lot of tools available for doing data science and data science exploration. It’s great that, exactly like you said, Brian, that somebody’s built a model that’s interesting. But one, if we haven’t built the rest of the product around it and then if we haven’t actually got that model to production; as I like to say, if at the end of the day somebody’s not pushing a button differently because of your model or pulling the leverage differently because of your model, it really doesn’t matter that you built it in the first place. That actually goes back to requiring engineering and product development type expertise as opposed to data science type expertise, which I feel a little bit more like traditional on science type disciplines where you’re doing experimentation. Brian: Do you get to the point where you’re midway through a project and just kind of like, “We’re not sure if we can do this,” or “The predictive power is not there”? I imagine you probably try to prevent getting into a situation where that happens. Is there a client training that has to go on if they’re coming to you too early? Like, “We’re ready to build this thing. We want to put a model to do X,” and you’re like, “Whoa.” How do you take them on like, “Come back to us in two months or when you guys have figured this out”? How do you take them on to make sure that doesn’t happen and they don’t spend all this money on hiring data scientist internally to work with you or on their own, or just you and not getting an ROI? How do you educate on that? Vinay: That’s again what we have incorporated into this Lean AI process where we’ve taken the spirit of Agile and some of the ideas around Lean startup, for example. There’s actually an old framework from the late 90s called CRISP-DM—it’s from the data mining community—and really, the idea in all of these things is tackling your big risks early and surfacing them. We take a similar approach where anybody can do this. But it’s getting an understanding of what is the business problem you want to go after and what is the data you have available. We call it a business understanding phase and a data understanding phase. During that phase of the data understanding, it’s really doing a data audit. Particularly, it’s an issue on large organizations. People think they have access to certain data but it may be that somebody in a different organization owns the data and they’re not going to give it to you. You sort of have the human problems that we’ve always had. Then there’s other parts which are, “Is there a predictive power in the data? Is the data clean?” Generally, the first thing we do is just apply a suite of tools that will characterize the data, profile the data, and help us get an understanding of what do we think is there. Usually, we work with clients, team members who have domain knowledge. They generally have pretty good intuition of what should the data show and that oftentimes is a good way to normalize everybody’s expectations. As an example, we’re working on one with an industrial client last year. In addition to sensor data coming off their devices, they also had field notes that people had entered when they were servicing some of the equipment. As we were working with their experts during the data understanding phase, the experts actually said, “You know what? I wouldn’t trust the field notes. People sometimes put them in and sometimes they don’t. The quality varies a lot across who put those notes in and what they put in there. So, let’s just not use that data source.” You can really bring in some of the intuition that people already have around their data and bring that into the conversation. That becomes an almost shared decision about what do we think we can try and get out of this data, what’s in the data, and do you guys agree that this data actually is saying what you think it should say? Those kinds of things. I would say, tackling big risks early is one of the major themes of what we do. The other part really comes from, again, the engineering approach that a lot of us have taken historically from our past experiences. [It’s probably 00:25:48] the best analogy I can do from their product management days is this idea of just doing mockups and doing paper mocks and those kinds of things before you get to higher fidelity mocks. There’s a similar idea in machine learning where we have this idea like, “Okay, get some basic data through your data pipeline. It doesn’t have to be perfect.” Then we build this thing called the baseline model, which is, “Yes, there are 45 different techniques you can use to build a machine learning model. Let’s take one of the simplest ones. Something like random forest where we know that’s not the best performing model for every use case, but it’s really easy to build. It’s really easy to understand at least out of your first version what the model is doing.” You can get some baseline of performance pretty quickly, which is, does it perform at 60% or does it perform at 80%? From there, you can start to have a discussion about, how much more investment do we want to make? Do we need to get more data in here to clean the existing data and transform it in different ways, explore different modeling techniques? Those kinds of things. I draw the analogy to some of the product development processes that we would follow if we were just doing software engineering project, which is, let’s get something built end-to-end then add more functionality over time, things like that and then take it from there. Brian: Regarding the projects you work on, are your clients , most of the time,the actual end users of this service or the direct beneficiaries, or typically, are they building something internally that will be used by other employees or vendors or their customers? How close to that is the person going to benefit from or use the service that you’re building? Vinay: I’m definitely not aware of all of our projects, but the projects I’m aware of and the ones I’m working on right now, they all have enterprise users. None of them are applications that are going to go out to end users. But nonetheless, the enterprise users are folks who are not technology people or not particularly specialized in data or anything like that. They are more folks who are executing on processes as part of a broader workflow. For example, it might be a health coach that is at a particular company, or it might be a call center employee, or it might be the maintenance and repairs center at an industrials company. It’s more internal users or if it’s external users, it’s still again enterprise users who are using a larger product. Brian: Do you ever get direct access to those when you’re working with your clients or typically, is your client the interface to them? How involved do you get with some of these like a call center rep or something like that? Vinay: It actually depends on the type of expertise that our client has. If they have a product owner and a product manager who’s fairly confident about their ability to interface with the end user, we might. Instead of them being part of the user feedback sections, as some of these models go in front of users, there may be at the beginning of a project, having a few conversations to understand the context in which particular operational data was gathered, or the workflow that might surround the model that we’re building, or the data pipeline that we’re building. We might have a few conversations. But again, if they have a strong product function already, we would probably be more isolated from that. If, on the other hand, there isn’t that much of a product function that is familiar with software engineering and product developments, some of these non-tech organizations, product managers, they are maybe much more hardware-oriented or they may not even have a product to roll, depending on the type of operation. There, we would be much closer to the end users understanding the use cases. We also want to partner with whoever is doing the product design and some of the other UX components as well. I would imagine that there’d generally be another partner of some sort. We’re interested in talking to the end users. But we’re definitely not the experts on product design and so forth. We’d expect somebody else to play that role. Either somebody like you where the client is partnered with another organization or individual, or they have capability internally. Brian: One place we think about lots of data, obviously, is in the traditional analytics space for internal companies or even information like SaaS products and information products. Do you see the capabilities of data science and machine learning that have really been enabled in the last few years? Primarily,what I understand is there’s more data availability. There’s more compute power availability. It’s not so much that the science is new. A lot of the science I hear is quite old. The formulas and algorithms have been around. It hasn’t been as feasible to implement them. Now that it is, do you see that traditional analytics deployments over time will start to leverage more and more like predictive capabilities or prescriptive analytics where there’s less report generation, less eyeball analysis? Say, in the next five years, 20% of traditional analytics capabilities will be replaced by more prescriptive and predictive capabilities because of this? Or is it really just it’s going to take a lot longer to do that? I imagine some of it’s just at the mercy of the data you have available. You can’t solve every problem with this, but do you see an evolution happening in that data? Is that making sense? Vinay: Yeah, absolutely. You’ve hit upon a really important idea. I’ll start my answer though taking a slightly different view, which is what is going to stay constant, and then we can talk about what is going to change. The part I found most exciting about business intelligence, analytics reporting, pick your category name, is when you can get it embedded into a workflow. The folks who are actually on the front lines making, running through a workflow, or going through a customer interaction or whatever, they actually have access to that data and they’re able to drive decision-making as part of their process. What we’ve seen in the last order of 20 years, is this continued increase of this notion of a data-driven organization, that people should have more access to data when they’re in these workflows and decision-making. Everything from things you’ve probably heard about, like insurance companies or telco companies, call center folks being able to offer you something if you’re going to turn, for example. An offer pop ups on their screen and they’ll able to give that to you. That’s a nice example where somebody’s actually using the decision-making as part of their production workflow. We’re just generally seeing more of that. So, no matter what, whether it’s prescriptive or descriptive, whatever else, I broadly see continued adoption of analytics and data in more workflows across a whole range of software products. I’m generally excited about that. I wish it would take less time but at least we’re continuing to make progress on that. I think what you hit upon is what’s going to change. I firmly believe we’re seeing this in name today but we’ll see this more in actual. The nature of the work itself in the future, there’s a lot of people who have the business analyst role today and organizations in their supporting different functions. Largely, I think of them as people who have a fairly deep understanding of the business. They generally live in Excel. They’re complete masters of Excel. They can build what-if models, they can do scenario-solving, they can do VLOOKUPs, and do all of those kinds of things in Excel. I think they’re going to get a whole additional set of tools. I tell people this and I’m going to go on the record here and suggest that, I’m almost imagining Excel 2020 is going to have a button that you can hit and you can say, “Here’s my data. Go try out 50 different models or 500 different models.” Excel will go off, ship your data to Azure, it’ll run a whole bunch of different models and come back and tell you, “Here’s the three that seem to fit your data best.” Really, the skill that you need at the end of the day, which is the skill you need today, is understanding the statistics of the data, having some intuition around the business and what’s going on around you, and then really being able to swap ends and these other statistical methods that we group under machine learning, being able to swap those in once those tools are mature enough for broader use in deployment. Because of that, I think yes, in the five-year timeframe, we’ll see the leading edge of more prescriptive analytics entering product workflows just like we’re now. I’d be curious about your opinion on this but I feel like we’re past the earlier doctrine more now in the mainstream phase of descriptive analytics entering some of the different products. Brian: Yes, maybe it’s fed Microsoft a little tip for how to improve their office lead down a couple of years from now. This has been really informative. Thanks for coming on. Do you have any single message or advice you’d give to data product managers or analytics leaders in businesses in terms of how they can design and/or deploy better data products in their organization or for their customers if they are like a SaaS or information provider? Any general tips you’ve seen or something you can offer them? Vinay: Maybe a handful of things just to run through it with different levels of applicability. One of them is that having a good business case, as the way we talked about earlier and taking on something small is definitely very helpful to build some success. Also, maybe squelch some of the visionary enthusiasm that people might have. In general, trying to feed some of the vision component while you’re trying to get a great concrete success on the board, is something just to keep in mind to get people excited about the potential and the future. That’s one bucket. If you have a vision in mind, one of the things your technology teams and your machine learning teams can do, and is something we definitely ask for when we do our engagements, while you’re solving a specific business case and a specific problem, you can do the work in a way that lays the foundation for longer term leverage on the work. So, if we build the data pipeline, we know that you have a specific two-year vision. We can actually start to lay some of the pieces even as part of that project to make investment towards that vision. While you should execute on smaller opportunities, you should also dream big. I think that’s one general thought. Another thing I’ve been starting to form an opinion around is that, to execute successfully on a product and execute data and machine learning component of a product, you have to have a ‘what’ in mind, like, “What is this product going to do with the data?” You need to have a product direction, product sense, product vision, whatever you want to call it to know what’s going to happen in the context of that product. Longer term, when you start to think about the context for these kinds of capabilities you need to think about organizational vision. For this product it may be that you did it with a couple of folks from another team that sat down the hall just to get something out the door. But then, really having an idea in the 18-month timeframe, do you want to build a software engineering organization? Do you want to build a data engineering capability? Do you want to have a data science team? Do you want to work with the finance team to maybe get a couple of business analysts over to a new team? I think really starting to contextualize your product vision with what’s your organizational vision, is important for the longer term picture and having clarity around that even as you tackle on the shorter term opportunities. Those are probably a couple of things that hopefully people find helpful. Brian: Yes, I definitely did. I was actually going to follow this up but it may be an unnecessary question. But one of the services that I’m often asked to come in with clients is to help them either envision a new product, something that they’re working on, and it’s what I call getting from the nothing to something phase where it’s a Word document of requirements or capabilities, features, what have you and getting to that first visual something. It sounds like you still think that that step, even if you don’t bite off the whole thing from an engineering standpoint, having an idea of your goal post about where a service might go that could incorporate some machine learning or AI technology, still is helpful and deploying a small increment of utility into the organization. Would you agree with that still? Vinay: Yes, absolutely. Even for the folks building their models or building your data pipeline to get the data cleaned up and usable, whether it’s for analytics or for your models, it’s really helpful to have that broader context as opposed to having a very narrow window into, “Oh, I need these three fields to be cleaned up and available.” If you can’t provide that broader context, I feel you end up with a lot of disjointed pieces as opposed to something that feels good when you’re done. I would definitely agree with that. Brian: Well, Vinay, thank you so much for coming on. This has been super educational for me and I’m sure for people listening as well. Where can people learn more about what you’re doing? I’ll definitely put the Ben Evans link and your Lean AI process that you talked about. So, send me those links. But where can people learn more about what you do? Vinay: Our website manifold.ai is definitely the best place to start. We have a few things about the type of work we do and some case studies as well as some background of our team. That would be helpful. In terms of my own time, I actually don’t spend that much time on social media. LinkedIn is probably the easiest place to find me. Generally, I post things there occasionally and definitely participate in some conversations there. It would be great to chat with folks there. Brian: All right, great. Well, thanks again and I hope to talk to you soon. Vinay: Thank you, Brian. I really appreciate it. It’s great conversation.

欢迎收听丽莎老师讲机器人，想要孩子参加机器人竞赛、创意编程、创客竞赛的辅导，找丽莎老师！欢迎添加微信号：153 5359 2068，或搜索微信公众号：我最爱机器人。丽莎老师讲机器人之不知不觉渗入生活的AI技术。人工智能正越来越多的渗透入人们的生活，改变人们的生活，从自然语言生成到语音识别、从医疗诊断到商业决策，AI逐渐开始显露出巨大的优势，并且它的脚步不会停止。1.自然语言生成（NLG）自然语言生成是人工智能的一个子学科，它可以将海量的数据转换成人类可读的文本，通过这样的方式实现与人类的交流。目前主要的应用是为客户提供报告生成和市场摘要等服务。通过对数据的分析、挖掘理解，从数据中抽取出有效的信息并总结成文本输出。优秀的AI还能实现自动排版和美化，做到可读性与优良的可视化效果。目前该技术主要由Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, and Yseop等公司提供。2.语音识别提到语音识别，人们第一时间会想到手机里的Siri。你可以直接通过语音告诉它你的想法、需求或者任何其他的东西。通过机器学习算法，它可以将声音序列转化为对应的词语并进行理解，随后给出响应。支撑它最核心的功能就是准确的识别你发出的声音所表达的意思。当然除了Siri还有很多很多的语音识别系统。每天，都有更多的系统都被创造出来，它们可以通过声音识别人类语言，通过语音响应交互系统和移动应用程序服务于成千上万的 人。目前提供语音识别服务的公司包括NICE, Nuance Communications, OpenText 和Verint Systems。3 .虚拟助理虚拟助理就是能够与人类交互的计算机助理或程序。这种技术最常见的例子是聊天机器人。你可以向他们查询天气、餐馆、服务，甚至可以请它们帮忙预订酒店、机票或者是演出的门票，并为你计划好日程。想象一下，早上起来你告诉你的虚拟助理明天要去广州出差，任务结束后想去见见住在深圳的朋友，吃个饭看看电影。它就能帮帮你查好明天的机票、规划好路线行程所用的时间，并预定好出租车。同时还会根据你的喜好和网上信息定好深圳的餐馆，并给你的朋友发去定位，随后寻找一家最适合的电影院来部大片度过一个美妙的夜晚。除此之外、虚拟助理目前正被用于智能家居管理，它可以接入室内的智能设备，只需你一个指令、就能为你开灯、把电视放到喜欢的台，热好牛奶。提供虚拟助理服务的公司包括供Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, and Satisfy4.机器学习平台机器学习(Machine Learning ML)是计算机科学的一个分支学科，也是人工智能的一个分支。它的目标是开发出让计算机能够自己学习的技术。但今天一般的机器学习已经不需要开发人员自己费力耗时的研究编程、有很多的机器学习平台提供了从算法到应用的一系列工具，包括APIs、开发和培训工具、大数据、应用程序甚至运行算法的基础设施云服务。今天机器学习平台正获得越来越多的注意力，企业和个人都可以接入使用。无需特别深厚的学术功底和技术能力，只需要自己的数据就可以实现机器学习应用。甚至，有的平台还提供数据标注服务。一些销售ML平台的公司包括Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree, and Adext。值得一提的是Adext AI是世界上第一个观众管理工具，它将真正的AI和机器学习应用于数字广告，为每一个广告找到可能带来潜在收益的观众或群体。另外深度学习平台是机器学习重要的部分。深度学习包括具有各种抽象层的人工神经回路，这些抽象层可以模拟人脑，处理数据并创建决策模式。它目前主要用于识别模式和分类的应用程序，包括了计算机视觉、自然语言处理、语音处理等非常广泛的应用。除了互联网巨头外，很多初创公司和独角兽都提供了自己对于深度学习独到的解决方案。Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology 和Sentient Technologies都有值得探索的深层学习技术。5 .人工智能优化硬件人工智能使得硬件更加友好。怎样做到的？现在通过专门为执行人工智能的任务而设计和构造的新图形和中央处理单元和处理设备。除了GPU用于加速神经网络和并行计算外，一系列专用芯片也如雨后春笋般出现，像神经计算棒、自动驾驶芯片和健康管理芯片等ASIC逐渐走入了我们的生活，支撑着低成本、高性能、低功耗的产品。这些芯片可以直接插入你的便携式设备和其他地方。目前Alleviate, Cray, Google, IBM, Intel, and Nvidia等公司都在致力于开发下一代为人工智能优化的硬件产品。6.生物测定学这项技术可以识别、测量和分析人类行为以及身体结构和形态的物理特性。比如人体步态、行为分析、特征点检测、声纹、虹膜、指纹甚至签名笔迹等等生物特征都可以利用人工智能进行识别。利用深度学习的强大能力，将人体特征进行量化准确的测定。它令人类和机器之间更自然的互动，包括与触摸、图像、语音和肢体语言识别相关的互动，在市场研究领域中占有重要地位。3VR、factiva、Agnitio、FaceFirst、sensor、Synqera和Tahzoo都是致力于开发这一领域的生物测定公司。7.自动化机器人自动化机器人使用模拟和自动化人工任务的脚本和方法来完成工作流程。当雇用人从事特定工作或任务过于昂贵或效率低下时，自动化机器人就特别有用。除此之外，包括市场营销、文本处理、以及一切重复性、规则性的劳动都可以被自动化机器人取代。这样可以最大限度地利用人力资源，让员工进入到更具战略性和创造性的岗位，使员工的工作能真正影响公司的发展。（注：这里的机器人除了传统工业机器人外，很大程度上指的是自动化处理程序）Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, and WorkFusion也是拥有自动化机器人技术的公司。8．文本分析和自然语言处理（NLP）这项技术通过统计方法和ML来进行文本分析，理解句子的结构，以及它们的含义和意图。文本分析和自然语言处理主要用于分词词性标注、命名实体、情感分析、文本分类等等，而结合文本分析可以实现智能审核、智能采编、自动问答，以及文本的自动生成、知识图谱等等，同时还被用于安全系统和欺诈检测。大量自动助理和应用程序也在使用它们来提取非结构化数据。比如医疗机构病历电子化和合同的法律合规等都是他们的应用范围。这些技术的一些服务提供商和供应商包括Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, 和Synapsify.9.情感识别这项技术允许软件使用高级图像处理或音频数据处理来“读取”人脸上的情感。我们现在已经可以捕捉“微表情”或微妙的肢体语言线索，以及暴露一个人感情的语音语调了。执法人员可以使用这项技术来尝试在审讯中发现更多信息。在市场上还有其他更广泛的应用。越来越多的初创公司致力于该领域。除了口头分析之外，将音频输入，可以用来描述一个人的性格特征，包括他们积极、激动、生气或喜怒无常的等级。同时情感视频分析被用来激发新产品创意，识别升级并增强消费者体验。情感人工智能被用于游戏、无人驾驶、机器人、教育、医疗保健行业和其他领域，利用来自面部和语音的数据进行面部编码和情感分析。10.图像识别图像识别是识别和检测数字图像或视频中的物体或特征的过程，人工智能越来越多地叠加在这一技术之上，产生了巨大的效果。人工智能可以在社交媒体平台上搜索照片，并将其与各种数据集进行比较，以确定哪些照片在与搜索图像最相关。图像识别技术还可以用于检测车牌、诊断疾病、分析客户及其意见，以及进行面部识别来验证用户。同时我们日常生活中接触最多的“刷脸”也属于图像识别的一种。除此之外，电商平台上的图像检索，农作物病虫害识别、安防监控、交通监测和智慧城市等都在大规模使用着图像识别。

In this podcast we talk with Eric Wetjen, Senior Product Marketing Manager at Natick, Massachusetts-based The MathWorks, one of the world’s leading makers of mathematical computing software. The company’s long heritage in areas like embedded computing and predictive maintenance, as well as its broad focus across industries — everything from industrial automation, to smart cities, to healthcare, and even financial engineering — gives it an especially authentic voice when addressing many of the issues that have both spurred and challenged the growth and development of the Internet of Things across so many different and diverse markets. The podcast was recorded in advance of our Connected Things 2018 conference, taking place April 5th at the MIT Media Lab.

In this podcast we talk with Eric Wetjen, Senior Product Marketing Manager at Natick, Massachusetts-based The MathWorks, one of the world's leading makers of mathematical computing software. The company's long heritage in areas like embedded computing and predictive maintenance, as well as its broad focus across industries — everything from industrial automation, to smart cities, to healthcare, and even financial engineering — gives it an especially authentic voice when addressing many of the issues that have both spurred and challenged the growth and development of the Internet of Things across so many different and diverse markets. The podcast was recorded in advance of our Connected Things 2018 conference, taking place April 5th at the MIT Media Lab.

Stealthy sonar spying on cell phones by Ian Woolf, Anh Nguyen, Flavia Ching Lu, Jenna Chan and Teresa Tran from Cerdon College develop space face cream with Cuberider data, Dr Elias Aboutanio from UNSW talks about sending CubeSats into space, Mathworks talks to Raspberry Pi and Arduino so you don't have to. Rynos Theme by Kevin MacLeod Production checked by Charles Willock, Produced and presented by Ian Woolf Support Diffusion by making a contribution btc: 1AEnJC8r9apyXb2N31P1ScYJZUhqkYWdU2

Episode 61 of the Social Business Engine podcast features Alan Belniak, the Principal Social Media Manager at The MathWorks. Alan is a social media strategist and practitioner with years of tech experience that he used to his advantage when transitioning into a social media role. Tune in to our discussion to hear how Alan persuaded the skeptics into hiring him for a social media role with no social media experience and how he has gone on to strategize social media marketing for multiple mid to large multi-national corporations.  Visit our show notes page for more narrative on this podcast episode: http://www.socialbusinessengine.com/podcasts/how-to-charm-the-skeptics-to-embrace-social

Download | SoundCloud | iTunes Kameswarie Nunna, an applications engineer at MathWorks, my friend Janhavi and I went to Pizza Express where we talked about the differences between studying in England and studying in India.     Links/Resources Surely You’re Joking Mr Feynman!   Keep in touch Kameswarie’s Facebook Janhavi’s Facebook   Intro and outro […]

The book comes in two major sections. First you’ll read a series of hand examples. These examples are designed to show you the difference between how a typical no-limit hold’em player thinks and how a professional-level player thinks. Each of these hand examples represents a small improvement you can make in your play. The second section turns the lessons from the first section into missions you can do on your own. These missions are the equivalent of recipes in a book on nutrition or exercises in a workout book. Doing the missions—doing them as often and using as much your brain on them as possible—are what will make you a better poker player. Do the missions, and you’ll see results. Ignore the missions, and you likely won’t." from the Foreword by Ed Miller -Author of Playing the Player, How To Read Hands At No-Limit Hold’em, Small Stakes Hold’em Doug has been found at Foxwoods and Mohegan Sun poker rooms since 2001. When he is not perfecting this craft, he is blacksmithing, woodcarving or developing his permaculture homestead in Massachusetts. He has authored an engineering textbook, co-authored a second and contributed to a third. He has taught countless people to use MATLAB through his blog at MathWorks.com. Reach him at hull@ThreeBarrelBluff.com.

Bill McKeeman, a Fellow at MathWorks and adjunct faculty at the Computer Science Department of Dartmouth College, reveals his goal to create the smallest extendable self-compiling compiler. (March 4, 2009)