Podcasts about why carl

Carl Allen is a world-class entrepreneur, investor, and corporate dealmaker who has worked on more than 330 transactions worth close to $48 billion. In his nearly 30-year career, Carl has analyzed thousands of businesses, big and small, in 17 different countries across nearly every business sector, including technology, pharmaceuticals, transport and logistics, engineering, manufacturing, aerospace, consumer goods and services, business services, retail, professional services, finance, packaging, and corporate clothing.  Carl first earned his reputation during his 16 years on Wall Street, working for Bank of America, Hewlett-Packard, Forrester, and Gartner. There he advised some of the world's largest corporations on investments, mergers, acquisitions, disposals, and restructuring, and helped hundreds of business owners raise both equity and debt financing. Until he almost missed the birth of his second son. That's when Carl quit the rat race and began brokering (and eventually buying) businesses for himself.  Today he is considered one of the world's premier experts on buying and financing small business acquisitions. Carl founded Dealmaker Wealth Society (formerly Ninja Acquisitions) because he believes starting a business from scratch is certifiably crazy! He wanted to use his highly-specialized skill set to help others realize that their dream of self-employment didn't have to be a long, hard, up-all-night slog with a 96% failure rate in 10-years. Carl is currently helping thousands of entrepreneurs all over the world buy existing, profitable small businesses that will immediately put money in their pockets. And best of all, he teaches them how to do this without using a dime of their own capital!What you'll learn about in this episode:Get to know Carl and hear about his journey from Wall Street to Dealmaker Wealth Society.The life-changing moment when Carl's fifth child was born.Insights into what a leveraged buyout is, a strategy Carl commonly used in Wall Street.What Carl learned about small businesses and why he was so drawn to work with them.The two things Carl does today: private equity partner and coaching.Trends Carl has seen with small businesses during COVID; many are flourishing.A story of Carl's friend who pivoted his business and is now thriving.The change that has happened with small business succession: children want to go to college.Why Carl believes that entrepreneurs should not start businesses from scratch.Carl's impetus for starting Dealmaker Wealth Society.Additional Resources:Website: https://dealmakerwealthsociety.com/LinkedIn: https://www.linkedin.com/in/iamcarlallen/Facebook: https://www.facebook.com/dealmakerwealthsociety/Twitter: https://twitter.com/DealmakerWealthYouTube: https://www.youtube.com/channel/UCCcJzIkSSZxECZNtLG7GtegConfessions of A Dealmaker Newsletter: https://dealmakerwealthsociety.com/confessions-sign-up/ Buying Secrets: How to Buy an Established, Profitable Business Using None of Your Own Money:  https://www.amazon.com/Business-Giveaway-Acquire-Established-Acquirer-ebook/dp/B00UC6LRIGDiscount Code: trainwithcarl.com/othersideofpotential

In this episode of Life and Books and Everything, Carl Trueman joins Kevin, Justin, and Collin to discuss his latest book, published by Crossway, which analyzes the development of the sexual revolution as a symptom—rather than the cause—of the human search for identity. You’ll also learn the benefit for Christians of reading Nietzsche and Freud, and what you can say to someone when there isn’t time to debate the philosophy of gender. Timestamps: Thirty-second long book title [00:55 – 1:25] If identity is sexual, then sex is political. [1:25 – 7:37] Behaviors demand toleration; identity demands recognition. [7:37 – 13:39] Grappling with the history of ideas [13:39 – 22:26] Intended audience [22:26 – 24:29] Why Carl wants to be called a bigot [24:29 – 28:32] Should pastors read these non-Christian authors? [28:32 – 34:18] Is Protestantism to blame for sexual identity politics? [34:18 – 44:48] Natural law will help us communicate to younger generations. [44:48 – 50:55] What can you say to the other side when there isn’t time to debate? [50:55 –56:16] Against lament? [56:16 – 57:50] Family shapes theology. [57:50 – 1:03:00] Books and Everything: The Rise and Triumph of the Modern Self: Cultural Amnesia, ExpressiveIndividualism, and the Road to Sexual Revolution, by Carl R. Trueman Civilization & Its Discontents, by Sigmund Freud The Triumph of the Therapeutic, by Philip Rieff Living in God's Two Kingdoms: A Biblical Vision for Christianity and Culture,by David VanDrunen Hands Across the Aisle “The Fury of the Fatherless,” by Mary Eberstadt, First Things

Today we discuss living well by being slower and bolder, and joining us to share his experience and expertise on the subject is Mr. Carl Honoré. Carl is a two-time TED speaker, a bestselling author with books in 35 languages, and an authoritative voice in the slow movement. In this conversation, we get into how the COVID-19 pandemic has brought us an opportunity to slow down and perhaps even pivot into what matters most to us both personally and as societies. Living slower and approaching life with boldness means living life on your terms and having the courage to create, learn, and have a positive impact. It comes from knowing yourself, being present, putting aside the arrogance of speed, and embracing the life you have at any age. Do not miss out on this uplifting conversation with special guest Carl Honoré!Key Points From This Episode: Find out how and when Carl made the purposeful decision to slow down and reconnect. The one-minute bedtime story and other signs that his life was running at an undesired pace. How things in the modern world often appear perfect while rotting away on the inside.The relationship between increasing superficiality and being stuck in fast forward mode.How having extra time has made us more reflective and thus aware of social injustices. Why Carl believes that we might emerge from this crisis with a more humane way of being. The selfishness and arrogance of speed versus the sense of community fostered by slowing down. Focusing on who you are and what you value rather than on what others think you ought to be. The link between creativity and boldness and how slowness creates fertile ground for both. Insights on the U-shaped happiness curve and why older people tend to be more content.Ageism and the role that mindset and expectation around aging plays in how you age. Viewing each new year of life as going up a level and moving forward instead of declining. Recognizing that every age has pros and cons and embracing the stage of life that you are in. Key Messages:1. Slow down and go boldly. 2. Leave behind superficial labels. 3. Live life on your own terms.4. Make the most of life at every level. Quotables:“Slow is about diving deep; it is about getting below the surface to the core, to the heart of the matter.” — @carlhonore [0:07:44] “Having it all is a false god. Having it all is just a recipe for hurrying it all and it is also a very bad deal for the environment.” — @carlhonore [0:10:42] “If you want to go fast, go alone. If you want to go far, go together.” — African Proverb [0:12:53]“Anything worth doing is worth doing slowly.” — Mae West [0:39:00] “Life really begins at forty. Up until then, you are just doing research.” — Carl Jung [0:39:05] Links Mentioned in Today’s Episode:Kristina Hunter Flourishing Carl HonoréCarl Honoré InfoCarl Honoré on TwitterIn Praise of Slow Under PressureThe Slow FixBolder

More information can be found at www.socialchangeleaders.net In the wake of the murder of George Floyd and the subsequent protests and reactions in the Twin Cities and around the world, we wanted to invite Carl Young back to the podcast. Carl is a social change leader, a mental wellness professional who does a lot of community-based work and a black man living in Minneapolis.  In this episode with Carl we talk about: Carl’s experience growing up as a black man in the south contrasted with living in Minnesota for over several decades The historical context of the relationship between black people with the Minneapolis police department and the U.S. police force in general Why Carl believes the George Floyd incident was different than former police murders and why it ignited a global response? What Carl thinks about the increase in white people joining in the antiracist movement  Carl’s advice for our listeners who want to bring about positive social change.  What needs to be done on a personal, micro and macro level? More about Carl Young: Carl Young, MS, co-founder of ILC4Y,Increasing Life Chances 4 You  Connect with Carl on Facebook and catch some of his live videos where he shares his thoughts and perspectives  Carl Young, MS, is the founder of Increasing Life Chances 4 You.   Carl specializes in working with survivors of trauma, PTSD, substance abuse, neglect, mental/physical abuse, domestic abuse, those struggling with life transitions, anxiety and depression, and those needing just that little "push and guidance" to help them move forward toward accomplishing their personal and professional goals.  Carl is dedicated to addressing issues of diversity and equity in the mental health system and advocates for individuals who may need culturally competent life coaching and mental health support.  His passion is to improve individuals’ success professionally, personally, in school and at home; thereby increasing their life chances.    ******** Do you want to live a life of impact that allows your work to align with your values, fit into your lifestyle, generate revenue and create social changes?  But, just like so many people, you struggle with worrying about taking that leap?  Will I have enough time?  Can I make money?  What do I do first? This is exactly why we created our Turn Your Passion Into Impact course for you. Our passion is supporting social change leaders just like you. We know that unleashing the creativity, skills and talents of people like you we are making our contribution to a better world for our kids.  The course is designed for you to take at your own pace.  Video lessons and worksheets will be released weekly for the five weeks of the course.  Lessons launch June 29, 2020. Learn more here and sign up today! About Social Motion:  Social Motion helps leaders to have greater impact in their professional and personal lives so they can have greater impact on our community and world.  www.socialmotion.co About Genuine Impact:  Genuine Impact Consulting and Coaching works with social entrepreneurs and social enterprises to bring clarity and focus so they can make a greater impact.  www.genuineimpact.net

On today's episode of Authentic Influence, Adam Conner is joined by Carl Daikeler, the CEO and Co-Founder of Beachbody. Beachbody is a platform offering home workout videos and nutrition guides. Today, you'll learn: Why Carl founded Beachbody (which includes the admission that he, himself, isn't a fitness guy) The growing importance of fitness at home in the current context (and how it's been reflected in Beachbody's growth) Standout stories of Beachbody consumers, plus how these individual stories are leveraged by the brand The balance between mega-influencers and regular people when it comes to being truly influential. In particular, how: "An influencer with millions of followers is not going to be nearly as influential as the person who's got 50, but has a real relationship with them." The outlook for Beachbody going forward As always, advice on how to build a more authentic brand: specifically, how it's okay to be flawed Be sure to stay subscribed for more content and thought leadership like this, and do please leave a rating and review on iTunes if you like what you hear: https://podcasts.apple.com/us/podcast/authentic-influence/id1440872576. Be sure to follow our LinkedIn page to catch all of our content there: https://www.linkedin.com/showcase/authentic-influence-podcast/. Reach out to Adam Conner on LinkedIn at https://www.linkedin.com/in/adamjconner/ or via email at adam.conner@govivoom.com with suggestions for guests, content, or general interest/feedback. Find more at https://www.podcast.vivoom.co/. Enjoy! Music: "Streetview" by Jahzzar is licensed under a Attribution-ShareAlike License (CC BY-SA 4.0)

Carl Scaramuzza founded his company, Credit Blueprint, with one simple goal: helping people use their credit to build real wealth and increase their net worth. So, of course, I had to bring him on the show to talk to all of you Mommy Millionaires and Mommy Millionaires-to-be! You Will Hear About: [6:00] How Carl made the best of the worst situation he’s been in [8:45] Should you have a credit card? [11:45] Why do people need a high credit score? [16:25] What can you do if you have bad credit? [33:20] Having a partner in your relationship and business [36:10] Why Carl doesn’t invest in real estate – he invests in himself [40:00] Being shameless to build your business Resources: Learn more at https://www.creditblueprint.net Podcast: The Power of Credit with Carl Scaramuzza Instagram: https://www.instagram.com/p/By_AbctFsAW/ LinkedIn: https://www.linkedin.com/in/carl-scaramuzza-09315a39/ Are you enjoying the show? I want to know! (http://getpodcast.reviews/id/1370982175) . Mommy Millionaire is a production of Crate Media

My guest today is Carl Hoffman, the CEO of Basis Technology, and a specialist in text analytics. Carl founded Basis Technology in 1995, and in 1999, the company shipped its first products for website internationalization, enabling Lycos and Google to become the first search engines capable of cataloging the web in both Asian and European languages. In 2003, the company shipped its first Arabic analyzer and began development of a comprehensive text analytics platform. Today, Basis Technology is recognized as the leading provider of components for information retrieval, entity extraction, and entity resolution in many languages. Carl has been directly involved with the company’s activities in support of U.S. national security missions and works closely with analysts in the U.S. intelligence community. Many of you work all day in the world of analytics: numbers, charts, metrics, data visualization, etc. But, today we’re going to talk about one of the other ingredients in designing good data products: text! As an amateur polyglot myself (I speak decent Portuguese, Spanish, and am attempting to learn Polish), I really enjoyed this discussion with Carl. If you are interested in languages, text analytics, search interfaces, entity resolution, and are curious to learn what any of this has to do with offline events such as the Boston Marathon Bombing, you’re going to enjoy my chat with Carl. We covered: How text analytics software is used by Border patrol agencies and its limitations. The role of humans in the loop, even with good text analytics in play What actually happened in the case of the Boston Marathon Bombing? Carl’s article“Exact Match” Isn’t Just Stupid. It’s Deadly. The 2 lessons Carl has learned regarding working with native tongue source material. Why Carl encourages Unicode Compliance when working with text, why having a global perspective is important, and how Carl actually implements this at his company Carl’s parting words on why hybrid architectures are a core foundation to building better data products involving text analytics Resources and Links: Basis Technology Carl’s article: “Exact Match” isn’t Just Stupid. It’s Deadly. Carl Hoffman on LinkedIn Quotes from Today’s Episode “One of the practices that I’ve always liked is actually getting people that aren’t like you, that don’t think like you, in order to intentionally tease out what you don’t know. You know that you’re not going to look at the problem the same way they do…” — Brian O’Neill “Bias is incredibly important in any system that tries to respond to human behavior. We have our own innate cultural biases that we’re sometimes not even aware of. As you [Brian] point out, it’s impossible to separate human language from the underlying culture and, in some cases, geography and the lifestyle of the people who speak that language…” — Carl Hoffman “What I can tell you is that context and nuance are equally important in both spoken and written human communication…Capturing all of the context means that you can do a much better job of the analytics.” — Carl Hoffman “It’s sad when you have these gaps like what happened in this border crossing case where a name spelling is responsible for not flagging down [the right] people. I mean, we put people on the moon and we get something like a name spelling [entity resolution] wrong. It’s shocking in a way.” — Brian O’Neill “We live in a world which is constantly shades of gray and the challenge is getting as close to yes or no as we can.”– Carl Hoffman Episode Transcript Brian: Hey everyone, it’s Brian here and we have a special edition of Experiencing Data today. Today, we are going to be talking to Carl Hoffman who’s the CEO of Basis Technology. Carl is not necessarily a traditional what I would call Data Product Manager or someone working in the field of creating custom decision support tools. He is an expert in text analytics and specifically Basis Technology focuses on entity resolution and resolving entities across different languages. If your product, or service, or your software tool that you’re using is going to be dealing with inputs and outputs or search with multiple languages, I think your going to find my chat with Carl really informative. Without further ado here’s my chat Mr. Carl Hoffman. All right. Welcome back to Experiencing Data. Today, I’m happy to have Carl Hoffman on the line, the CEO of Basis Technology, based out of Cambridge, Massachusetts. How’s it going, Carl? Carl: Great. Good to talk to you, Brian. Brian: Yeah, me too. I’m excited. This episode’s a little but different. Basis Tech primarily focuses on providing text analytics more as a service as opposed to a data product. There are obviously some user experience ramifications on the downstream side of companies, software, and services that are leveraging some of your technology. Can you tell people a little bit about the technology of Basis and what you guys do? Carl: There are many companies who are in the business of extracting actionable information from large amounts of dirty, unstructured data and we are one of them. But what makes us unique is our ability to extract what we believe is one of the most difficult forms of big data, which is text in many different languages from a wide range of sources. You mentioned text analytics as a service, which is a big part of our business, but we actually provide text analytics in almost every conceivable form. As a service, as an on-prem cloud offering, as a conventional enterprise software, and also as the data fuel to power your in-house text analytics. There’s another half of our business as well which is focused specifically on one of the most important sources of data, which is what we call digital forensics or cyber forensics. That’s the challenge of getting data off of digital media that maybe either still in use or dead. Brian: Talk to me about dead. Can you go unpack that a little bit? Carl: Yes. Dead basically means powered off or disabled. The primary application there is for corporate investigators or for law enforcement who are investigating captured devices or digital media. Brian: Got it. Just to help people understand some of the use cases that someone would be leveraging some of the capabilities of your platforms, especially the stuff around entity resolution, can you talk a little bit about like my understanding, for example, one use case for your software is obviously border crossings, where your information, your name is going to be looked up to make sure that you should be crossing whatever particular border that you’re at. Can you talk to us a little bit about what’s happening there and what’s going on behind the scenes with your software? Like what is that agent doing and what’s happening behind the scenes? What kind of value are you providing to the government at that instance? Carl: Border crossings or the software used by border control authorities is a very important application of our software. From a data representational challenge, it’s actually not that difficult because for the most part, border authorities work with linear databases of known individuals or partially known individuals and queries. Queries may be the form manually typed by an officer or maybe scan of a passport. The complexity comes in when a match must be scored, where a decision must be rendered as to whether a particular query or a particular passport scan matches any of the names present on a watch list. Those watch list can be in many different formats. They can come from many different sources. Our software excels at performing that match at very high accuracy, regardless of the nature of the query and regardless of the source of the underlying watch list. Brian: I assume those watch lists may vary in the level of detail around for example, aliases, spelling, which alphabet they were being printed in. Part of the value of what your services is doing is helping to say, “At the end of the day, entity number seven on the list is one human being who may have many ways of being represented with words on a page or a screen,” so the goal obviously is to make sure that you have the full story of that one individual. Am I correct that you may get that in various formats and different levels of detail? And part of what your system is doing is actually trying to match up that person or give it what you say a non-binary response but a match score or something that’s more of a gray response that says, “This person may also be this person.” Can you compact that a little bit for us? Carl: Your remarks are exactly correct. First, what you said about gray is very important. These decisions are rarely 100% yes or no. We live in a world which is constantly shades of gray and the challenge is getting us close to yes or no as we can. But the quality of the data in watch lists can vary pretty wildly, based on the prominence and the number of sources. The US border authorities must compile information from many different sources, from UN, from Treasury Department, from National Counterterrorism Center, from various states, and so on. The amount of detail and the degree of our certainty regarding that data can vary from name to name. Brian: We talked about this when we first were chatting about this episode. Am I correct when I think about one of the overall values you’re doing is obviously we’re offloading some of the labor of doing this kind of entity resolution or analysis onto software and then picking up the last mile with human, to say, “Hey, are these recommendations correct? Maybe I’ll go in and do some manual labor.” Is that how you see it, that we do some of the initial grunt work and you present an almost finished story, and then the human comes in and needs to really provide that final decision at the endpoint? Are we doing enough of the help with the software? At what point should we say, “That’s no longer a software job to give you a better score about this person. We think that really requires a human analysis at this point.” Is there a way to evaluate or is that what you think about like, “Hey, we don’t want to go past up that point. We want to stop here because the technology is not good enough or the data coming in will never be accurate enough and we don’t want to go past that point.” I don’t know if that makes sense. Carl: It does makes sense. I can’t speak for all countries but I can say that in the US, the decision to deny an individual entry or certainly the decision to apprehend an individual is always made by a human. We designed our software to assume a human in the loop for the most critical decisions. Our software is designed to maximize the value of the information that is presented to the human so that nothing is overlooked. Really, the two biggest threats to our national security are one, having very valuable information overlooked, which is exactly what happened in the case of the Boston Marathon bombing. We had a great deal of information about Tamerlan and Dzhokhar Tsarnaev, yet that information was overlooked because the search engines failed to surface it in response to queries by a number of officials. And secondly, detaining or apprehending innocent individuals, which hurts our security as much as allowing dangerous individuals to pass. Brian: This has been in the news somewhat but talk about the “glitch” and what happened in that Boston Marathon bombing in terms of maybe some of these tools and what might have happened or not what might have happened, but what you understand was going on there such that there was a gap in this information. Carl: I am always very suspicious when anyone uses the word ‘glitch’ with regard to any type of digital equipment because if that equipment is executing its algorithm as it has been programmed to do, then you will get identical results for identical inputs. In this case, the software that was in use at the time by US Customs and Border Protection was executing a very naive name-matching algorithm, which failed to match two different variant spellings of the name Tsarnaev. If you look at the two variations for any human, it would seem almost obvious that the two variations are related and are in fact connected to the same name that’s natively written in Cyrillic. What really happened was a failure on the part of the architects of that name mentioning system to innovate by employing the latest technology in name-matching, which is what my company provides. In the aftermath of that disaster, our software was integrated into the border control workflow, first with the goal of redacting false-positives, and then later with the secondary goal of identifying false negatives. We’ve been very successful on both of those challenges. Brian: What were the two variants? Are you talking about the fact that one was spelled in Cyrillic and one was spelled in a Latin alphabet? They didn’t bring back data point A and B because they look like separate individuals? What was it, a transliteration? Carl: They were two different transliterations of the name Tsarnaev. In one instance, the final letters in the names are spelled -naev and the second instance it’s spelled -nayev. The presence or absence of that letter y was the only difference between the two. That’s a relatively simple case but there are many similar stories for more complex names. For instance, the 2009 Christmas bomber who successfully boarded a Northwest Delta flight with a bomb in his underwear, again because of a failure to match two different transliterations of his name. But in his case, his name is Umar Farouk Abdulmutallab. There was much more opportunity for divergent transliterations. Brian: On this kind of topic, you wrote an interesting article called “Exact Match” Isn’t Just Stupid. It’s Deadly. You’ve talked a little bit about this particular example with the Boston Marathon bombing. You mentioned that they’re thinking globally about building a product out. Can you talk to us a little about what it means to think globally? Carl: Sure. Thinking globally is really a mindset and an architectural philosophy in which systems are built to accommodate multiple languages and cultures. This is an issue not just with the spelling of names but with support for multiple writing systems, different ways of rendering and formatting personal names, different ways of rendering, formatting, and parsing postal addresses, telephone numbers, dates, times, and so on. The format of a questionnaire in Japanese is quite different from the format of a questionnaire in English. If you will get any complex global software product, there’s a great deal of work that must be done to accommodate the needs of a worldwide user base. Brian: Sure and you’re a big fan of Unicode-compliant software, am I correct? Carl: Yes. Building Unicode compliance is equivalent to building a solid stable foundation for an office tower. It only gets you to the ground floor, but without it, the rest of the tower starts to lean like the one that’s happening in San Francisco right now. Brian: I haven’t heard about that. Carl: There’s a whole tower that’s tipping over. You should read it. It’s a great story. Brian: Foundation’s not so solid. Carl: Big lawsuit’s going on right now. Brian: Not the place you want to have a sagging tower either. Carl: Not the place but frankly, it’s really quite comparable because I’ve seen some large systems that will go unnamed, where there’s legacy technology and people are unaware perhaps why it’s so important to move from Python version 2 to Python version 3. One of the key differences is Unicode compliance. So if I hear about a large-scale enterprise system that’s based on Python version 2, I’m immediately suspicious that it’s going to be suitable for a global audience. Brian: I think about, from an experience standpoint, inputs, when you’re providing inputs into forms and understanding what people are typing in. If it’s a query form, obviously giving people back what they wanted and not necessarily what they typed in. We all take for granted things like this spelling correction, and not just the spelling correction, but in Google when you type in something, it sometimes give you something that’s beyond a spelling thing, “Did you mean X, Y, and Z?” I would think that being in the form about what people are typing into your form fields and mining your query logs, this is something I do sometimes with clients when they’re trying to learn something. I actually just read an article today about dell.com and the top query term on dell.com is ‘Google,’ which is a very interesting thing. I would be curious to know why people are typing that in. Is it really like people are actually trying to access Google or are they trying to get some information? But the point is to understand the input side and to try to return some kind of logical output. Whether it’s text analytics that’s providing that or it’s name-matching, it’s being aware of that and it’s sad when you have these gaps like what happened in this border crossing case where a name spelling is responsible for not flagging down these people. I mean, we put people on the moon and we get something like a name spelling wrong. It’s shocking in a way. I guess for those who are working in tech, we can understand how it might happen, but it’s scary that that’s still going on today. You’ve probably seen many other. Are you able to talk about it? Obviously, you have some in the intelligence field and probably government where you can’t talk about some of your clients, but are there other examples of learning that’s happened that, even if it’s not necessarily entity resolution where you’ve put dots together with some of your platform? Carl: I’ll say the biggest lesson that I’ve learned from nearly two decades of working on government applications involving multi-lingual data is the importance of retaining as much of the information in its native form as possible. For example, there is a very large division of the CIA which is focused on collecting open source intelligence in the form of newspapers, magazines, the digital equivalent of those, radio broadcast, TV broadcasts and so one. It’s a unit which used to be known as the Foreign Broadcast Information Service, going back to Word War II time, and today it’s called the Open Source Enterprise. They have a very large collection apparatus and they produce some extremely high quality products which are summaries and translations from sources in other languages. In their workflow, previously they would collect information, say in Chinese or in Russian, and then do a translation or summary into English, but then would discard the original or the original would be hidden from their enterprise architecture for query purposes. I believe that is no longer the case, but retaining the pre-translation original, whether it’s open source, closed source, commercial, enterprise information, government-related information, is really very important. That’s one lesson. The other lesson is appreciating the limits of machine translation. We’re increasingly seeing machine translation integrated into all kinds of information systems, but there needs to be a very sober appreciation of what is and what is not achievable and scalable by employing machine translation in your architecture. Brian: Can you talk at all about the translation? We have so much power now with NLP and what’s possible with the technology today. As I understand it, when we talk about translation, we’re talking about documents and things that are in written word that are being translated from one language to another. But in terms of spoken word, and we’re communicating right now, I’m going to ask you two questions. What do you know about NLP and what do you know about NLP? The first one I had a little bit of attitude which assumes that you don’t know too much about it, and the second one, I was treating you as an expert. When this gets translated into text, it loses that context. Where are we with that ability to look at the context, the tone, the sentiment that’s behind that? I would imagine that’s partly why you’re talking about saving the original source. It might provide some context like, “What are the headlines were in the paper?” and, “Which paper wrote it?” and, “Is there a bias with that paper?” whatever, having some context of the full article that that report came from can provide additional context. Humans are probably better at doing some of that initial eyeball analysis or having some idea of historically where this article’s coming from such that they can put it in some context as opposed to just seeing the words in a native language on a computer screen. Can you talk a little bit about that or where we are with that? And am I incorrect that we’re not able to look at that sentiment? I don’t even know how that would translate necessarily unless you had a playing back of a recording of someone saying the words. You have translation on top of the sentiment. Now you’ve got two factors of difficulty right there and getting it accurate. Carl: My knowledge of voice and speech analysis is very naive. I do know there’s an area of huge investment and the technology is progressing very rapidly. I suspect that voice models are already being built that can distinguish between the two different intonations you used in asking that question and are able to match those against knowledge bases separately. What I can tell you is that context and nuance are equally important in both spoken and written human communication. My knowledge is stronger when it comes to its written form. Capturing all of the context means that you can do a much better job of the analytics. That’s why, say, when we’re analyzing a document, we’re looking not only the individual word but the sentence, the paragraph, where does the text appear? Is it in the body? Is it in a heading? Is it in a caption? Is it in a footnote? Or if we’re looking at, say, human-typed input—I think this is where your audience would care if you’re designing forms or search boxes—there’s a lot that can be determined in terms of how the input is typed. Again, especially when you’re thinking globally. We’re familiar with typing English and typing queries or completing forms with the letters A through Z and the numbers 0 through 9, but the fastest-growing new orthography today is emoticons and emoji offer a lot of very valuable information about the mindset of the author. Say that we look at Chinese or Japanese, which are basically written with thousand-year-old emoji, where an individual must type a sequence of keys in order to create each of the Kanji or Hanzu that appears. There’s a great deal of information we can capture. For instance, if I’m typing a form in Japanese, saying I’m filling out my last name, and then my last name is Tanaka. Well, I’m going to type phonetically some characters that represent Tanaka, either in Latin letters or one of the Japanese phonetic writing systems, then I’m going to pick from a menu or the system is going to automatically pick for me the Japanese characters that represent Tanaka. But any really capable input system is going to keep both whatever I typed phonetically and the Kanji that I selected because both of those have value and the association between the two is not always obvious. There are similar ways of capturing context and meaning in other writing systems. For instance, let’s say I’m typing Arabic not in Arabic script but I’m typing with Roman letters. How I translate from those Roman letters into the Arabic alphabet may vary, depending upon if I’m using Gulf Arabic, or Levantine Arabic, or Cairene Arabic, and say the IP address of the person doing the typing may factor into how I do that transformation and how I interpret those letters. There’s examples for many other writing systems other than the Latin alphabet. Brian: I meant to ask you. Do you speak any other languages or do you study any other languages? Carl: I studied Japanese for a few years in high school. That’s really what got me into using computers to facilitate language understanding. I just never had the ability to really quickly memorize all of the Japanese characters, the radical components, and the variant pronunciations. After spending countless hours combing through paper dictionaries, I got very interested in building electronic dictionaries. My interest in electronic dictionaries eventually led to search engines and to lexicons, algorithms powered by lexicons, and then ultimately to machine learning and deep learning. Brian: I’m curious. I assume you need to employ either a linguist or at least people that speak multiple languages. One concern with advanced analytics right now and especially anything with prediction, is bias. I speak a couple of different languages and I think one of the coolest things about learning another language is seeing the world through another context. Right now, I’m learning Polish and there’s the concept of case and it doesn’t just come down to learning the prefixes and suffixes that are added to words. Effectively, that’s what the output is but it’s even understanding the nuance of when you would use that and what you’re trying to convey, and then when you relay it back to your own language, we don’t even have an equivalent between this. We would never divide this verb into two different sentiments. So you start to learn what you don’t even know to think about. I guess what I’m asking here is how do you capture those things? Say, in our case where I assume you’re an American and I am to, so we have our English that we grew up with and our context for that. How do you avoid bias? Do you think about bias? How do you build these systems in terms of approaching it from a single language? Ultimately, this code is probably written in English, I assume. Not to say that the code would be written in a different language but just the approach when you’re thinking about all these systems that have to do with language, where does that come in having integrating other people that speaks other languages? Can you talk about that a little bit? Carl: Bias is incredibly important in any system that tries to respond to human behavior. We have our own innate cultural biases that we’re sometimes not even aware of. As you point out, it’s impossible to separate human language from the underlying culture and, in some cases, geography and the lifestyle of the people who speak that language. Yes, this is something that we think about. I disagree with your remark about code being written in English. The most important pieces of code today are the frameworks for implementing various machine learning and deep learning architectures. These architectures for the most part are language or domain-agnostic. The language bias tends to creep in as an artifact of the data that we collect. If I were to, say, harvest a million pages randomly on the internet, a very large percentage of those pages would be in English, out of proportion to the proportion of the population of the planet who speaks English, just because English is common language for commerce, science, and so on. The bias comes in from the data or it comes in from the mindset of the architect, who may do something as simple-minded as allocating only eight bits per character or deciding that Python version 2 is an acceptable development platform. Brian: Sure. I should say, I wasn’t so much speaking about the script, the code, as much as I was thinking more about the humans behind it, their background, and their language that they speak, or these kinds of choices that you’re talking about because they’re informed by that person’s perspective. But thank you for clarifying. Carl: I agree with that observation as well. You’re certainly right. Brian: Do you have a way? You’re experts in this area and you’re obviously heavily invested in this area. Are there things that you have to do to prevent that bias, in terms of like, “We know what we don’t know about it, or we know enough about it but we don’t know if about, so we have a checklist or we have something that we go through to make sure that we’re checking ourselves to avoid these things”? Or is it more in the data collection phase that you’re worried about more so than the code or whatever that’s actually going to be taking the data and generating the software value at the other end? Is it more on the collection side that you’re thinking about? How do you prevent it? How do you check yourself or tell a client or customer, “Here’s how we’ve tried to make sure that the quality of what we’re giving you is good. We did A, B, C, and D.” Maybe I’m making a bigger issue out of this than it is. I’m not sure. Carl: No, it is a big issue. The best way to minimize that cultural bias is by building global teams. That’s something that we’ve done from the very beginning days of our company. We have a company in which collectively the team speaks over 20 languages, originate from many different countries around the world, and we do business in native countries around the world. That’s just been an absolute necessity because we produce products that are proficient in 40 different human languages. If you’re a large enterprise, more than 500 people, and you’re targeting markets globally, then you need to build a global team. That applies to all the different parts of the organization, including the executive team. It’s rare that you will see individuals who are, say, American culture with no meaningful international experience being successful in any kind of global expansion. Brian: That’s pretty awesome that you have that many languages going in the staff that you have working at the company. That’s cool and I think it does provide a different perspective on it. We talk about it even in the design firm. Sometimes, early managers in the design will want to go hire a lot of people that look like they do. Not necessarily physically but in terms of skill set. One of the practices that I’ve always liked is actually getting people that aren’t like you, that don’t think like you, in order to intentionally tease out what you don’t know, you know that you’re not going to look at the problem the same way they are, and you don’t necessarily know what the output is, but you can learn that there’s other perspectives to have, so too many like-minded individuals doesn’t necessarily mean that it’s better. I think that’s cool. Can you talk to me a little bit about one of the fun little nuggets that stuck in my head and I think you’ve attributed to somebody else, but was the word about getting insights from medium data. Can you talk to us about that? Carl: Sure. I should first start by crediting the individual who planted that idea in my head, which is Dr. Catherine Havasi of the MIT Media Lab, who’s also a cofounder of a company called Luminoso, which is a partner of ours. They do common sense understanding. The challenge with building truly capable text analytics from large amounts of unstructured text is obtaining sufficient volume. If you are a company on the scale of Facebook or Google, you have access to truly enormous amount of text. I can’t quantify it in petabytes or exabytes, but it is a scale that is much greater than the typical global enterprise or Fortune 2000 company, who themselves may have very massive data lakes. But still, those data lakes are probably three to five orders of magnitudes smaller than what Google or Facebook may have under their control. That intermediate-sized data, which is sloppily referred to as big data, we think of it as medium data. We think about the challenge of allowing companies with medium data assets to obtain big data quality results, or business intelligence that’s comparable to something that Google or Facebook might be able to obtain. We do that by building models that are hybrid, that combine knowledge graphs or semantic graphs, derived from very large open sources with the information that they can extract from their proprietary data lakes, and using the open sources and the models that we build as amplifiers for their own data. Brian: I believe when we were talking, you have mentioned a couple of companies that are building products on top of you. Difio, I think, was one, and Tamr, and Luminoso. So is that related to what these companies are doing? Carl: Yes, it absolutely is related. Luminoso, in particular, is using this process of synthesizing results from their customers, proprietary data with their own models. The Luminoso team grew out of the team at MIT that built something called Constant Net, which is a very large net of graph in multiple languages. But actually, Difio as well is also using this approach of federating both open and closed source repositories by integrating a large number of connectors into their architecture. They have access to web content. They have access to various social media fire hoses. They have access to proprietary data feeds from financial news providers. But then, they fuse that with internal sources of information that may come from sources like SharePoint, or Dropbox, or Google Drive, or OneDrive, your local file servers, and then give you a single view into all of this data. Brian: Awesome. I don’t want to keep you too long. This has been super informational for me, learning about your space that you’re in. Can you tell us any closing thoughts, advice for product managers, analytics practitioners? We talked a little about obviously thinking globally and some of those areas. Any other closing thoughts about delivering good experiences, leveraging text analytics, other things to watch out for? Any general thoughts? Carl: Sure. I’ll close with a few thoughts. One is repeating what I’ve said before about Unicode compliance. The fact that I again have to state that is somewhat depressing yet it’s still isn’t taken as an absolute requirement, which is today, and yet continues to be overlooked. Secondly, just thinking globally, anything that you’re building, you got to think about a global audience. I’ll share with you an anecdote. My company gives a lot of business to Eventbrite, who I would expect by now would have a fully globalized platform, but it turns out their utility for sending an email to everybody who signed-up for an event doesn’t work in Japanese. I found that out the hard way when I needed to send an email to everybody that was signed up for our conference in Tokyo. That was very disturbing and I’m not afraid to say that live on a podcast. They need to fix it. You really don’t want customers finding out about that during a time of high stress and high pressure, and there’s just no excuse for that. Then my third point with regard to natural language understanding. This is a really incredibly exciting time to be involved with natural language, with human language because the technology is changing so rapidly and the space of what is achievable is expanding so rapidly. My final point of advice is that hybrid architectures have been the best and continue to be the best. There’s a real temptation to say, “Just grow all of my text into a deep neural net and magic is going to happen.” That can be true if you have sufficiently large amounts of data, but most people don’t. Therefore, you’re going to get better results by using hybrids of algorithmic simpler machine learning architectures together with deep neural nets. Brian: That last tip, can you take that down one more notch? I assume you’re talking about a level of quality on the tail-end of the technology implementation, there’s going to be some higher quality output. Can you translate what a hybrid architecture means in terms of a better product at the other end? What would be an example of that? Carl: Sure. It’s hard to do without getting too technical, but I’ll try and I’ll try to use some examples in English. I think the traditional way of approaching deep nets has very much been take a very simple, potentially deep and recursive neural network architecture and just throw data at it, especially images or audio waveforms. I throw my images in and I want to classify which ones were taken outdoors and which ones were taken indoors with no traditional signal processing or image processing added before or after. In the image domain, my understanding is that, that kind of purist approach is delivered the best results and that’s what I’ve heard. I don’t have first-hand information about that. However, when it comes to human language in its written form, there’s a great deal of traditional processing of that text that boosts the effectiveness of the deep learning. That falls into a number of layers that I won’t go into, but to just give you one example, let’s talk about what we called Orthography. The English language is relatively simple and that the orthography is generally quite simple. We’ve got the letters A through Z, an uppercase and lowercase, and that’s about it. But if you look inside, say a PDF of English text, you’ll sometimes encounter things like ligatures, like a lowercase F followed by a lowercase I, or two lowercase Fs together, will be replaced with single glyph to make it look good in that particular typeface. If I think those glyphs and I just throw them in with all the rest of my text, that actually complicates the job of the deep learning. If I take that FI ligature and convert it back to separate F followed by I, or the FF ligature and convert it back to FF, my deep learning doesn’t have to figure out what those ligatures are about. Now that seems pretty obscure in English but in other writing systems, especially Arabic, for instance, in which there’s an enormous number of ligatures, or Korean or languages that have diacritical marks, processing those diacritical marks, those ligatures, those orthographic variations using conventional means will make your deep learning run much faster and give you better results with less data. That’s just one example but there’s a whole range or other text-processing steps using algorithms that have been developed over many years, that simply makes the deep learning work better and that results in what we call a hybrid architecture. Brian: So it sounds like taking, as opposed to throw it all in a pot and stir, there’s the, “Well, maybe I’m going to cut the carrots neatly into the right size and then throw them in the soup.” Carl: Exactly. Brian: You’re kind of helping the system do a better job at its work. Carl: That’s right and it’s really about thinking about your data and understanding something about it before you throw it into the big brain. Brian: Exactly. Cool. Where can people follow you? I’ll put a link up to the Basis in the show notes but are you on Twitter or LinkedIn somewhere? Where can people find you? Carl: LinkedIn tends to be my preferred social network. I just was never really good at summarizing complex thoughts into 140 characters, so that’s the best place to connect with me. Basically, we’ll tell you all about Basis Technology and rosette.com is our text analytics platform, which is free for anybody to explore, and to the best of my knowledge, it is the most capable text analytics platform with the largest number of languages that you will find anywhere on the public internet. Brian: All right, I will definitely put those up in the show notes. This has been fantastic, I’ve learned a ton, and thanks for coming on Experiencing Data. Carl: Great talking with you, Brian. Brian: All right. Cheers. Carl: Cheers.

Think there's no room in your business or industry for a podcast? Think again! Truth is there are limitless podcast themes out there, and the digital world is waiting for yours. So many entrepreneurs and small business owners are not embracing this marketing tool. Why? Carl explores why podcasting is perhaps the best way to raise your profile and position yourself a leader in your industry.

Carl Safina (@carlsafina) is author of various books and many other writings about how the ocean is changing, lives of free-living animals, and the human relationship with the natural world. His books include among others the award-winning Song for the Blue Ocean and Eye of the Albatross, as well as The View From Lazy Point; A Natural Year in an Unnatural World and Beyond Words; What Animals Think and Feel.Carl is founding president of the Safina Center, and an endowed research professor at Stony Brook University where he is active both in ocean sciences and co-chair of the Alan Alda Center for Communicating Science.He hosted the 10-part PBS series Saving the Ocean with Carl Safina and his writing appears in The New York Times, Audubon, Orion, and other periodicals and on the Web at National Geographic News and Views, Huffington Post, and CNN.com.You can listen right here on iTunesIn our wide-ranging conversation, we cover many things, including: * Why we're much less different from animals than we think * How overfishing could lead to a complete ocean die off * Why Carl's so worried about climate change and unforeseen consequences * What animals can teach us about ourselves * The reason consciousness isn't only limited to people * Why so many animals and superhuman abilities * The reason a vegetarian like Carl is excited about clean meat * Why there probably won't be any commercially viable fish in the ocean by 2050 * Why kids are becoming less creative * The harmful effects without nature * What might an alien or artificial intelligence actually look like * The why we actually love dogs, its not what you think * Why wind and solar could possibly save our speciesMake a Tax-Deductible Donation to Support The DisruptorsThe Disruptors is supported by the generosity of its readers and listeners. If you find our work valuable, please consider supporting us on Patreon, via Paypal or with DonorBox powered by Stripe.Donate

Carl Allen is an entrepreneur, investor, and corporate dealmaker who has worked on transactions worth over $50 billion, which includes over 250 acquisitions and sales, together with more than 100 capital fundraising projects. Carl is one of the world's premier experts on buying and financing small business acquisitions and coaches more than 1000 entrepreneurs all over the world to buy small businesses rather than starting new ones. What you'll learn about in this episode: How Carl Allen got started in his career as a business professional, and entrepreneur and mentor specializing in small business acquisition Why Carl believes you should be buying businesses in industries and specialties you understand and are passionate about Why Carl focuses on buying functioning businesses from distressed sellers who are looking to get out What process Carl uses to connect buyers to businesses and originate deals, and how he finds “off-market” deals that are sector agnostic. Why buying an established business is more reliable and involves less risk than starting your own business, and how you can be the distressed business owners' life line Which steps Carl recommends business owners take in the first three months, whether they are buying an existing business or starting their own venture How Carl and his business work with his coaching students to partner on bigger, more complex deals Where and how to park the funds you earn from real estate. Which business books Carl recommends as essential reading for entrepreneurs and business professionals Why Carl believes it's important to listen to your heart and do what you are passionate about Resources: SmartRealEstateCoachPodcast.com/GetLeverage SmartRealEstateCoachPodcast.com/BusinessBuyingAccelerator SmartRealEstateCoachPodcast.com/NinjaAcquisitions SmartRealEstateCoachPodcast.com/webinar SmartRealEstateCoachPodcast.com/termsbook SmartRealEstateCoachPodcast.com/ebook SmartRealEstateCoachPodcast.com/QLS Carl Allen's Recommended Reading List: The One Thing by Gary Keller: http://a.co/d/gU2qsdb The E-Myth Revisited by Michael E Gerber: http://a.co/d/bM6rYaN Be The Man by Garrett J White: http://a.co/d/4K3TF80 Barbarians at the Gate by Bryan Burrough and John Heylar: http://a.co/d/aJTS7kQ    Traction by Gino Wickman: http://a.co/d/i3nZUCt Expert Secrets by Russell Brunson: http://a.co/d/9RoTLBS DotCom Secrets by Russell Brunson: http://a.co/d/gnYK90x Pre-Suasion by Robert Cialdini: http://a.co/d/dmbjkVk Think and Grow Rich by Napoleon Hill: http://a.co/d/2yspspA Steve Jobs by Walter Isaacson: http://a.co/d/hpq2Srm Red Card by Ken Bensinger: http://a.co/d/fJjogMv

Your diet and lifestyle may be killing you slowly. What’s even worse is the doctors who are supposed to help you are only making you sicker.     If you want to know the truth about how to restore glowing health, you can’t afford to miss Episode 261 with Carl Lanore.   Once a 300 lb. man, Carl discovered that proper nutrition and exercise are the keys to good health. His transformation became the catalyst that drove him to help others.   He is now on a mission to help people regain their health and vitality. Through his podcast, Super Human Radio, he exposes the harsh realities of the food and medical systems we live with and gives people information that has the power to change their lives.   Listen as we discuss: Why Carl was more concerned with adding weight to the bar than losing pounds on the scale when he decided to improve his health.  [3:40] How his newfound health and physique transformation led him to start a radio show.  [6:03] Where did he learn about training and nutrition and who were his biggest influences when he started out?  [9:10] Should your diet be based on your heritage and evolution? [14:02] The truth behind why grains and legumes destroy your gut health. [18:35] Why it’s important to know where your food comes from. [22:26] Are probiotics actually wrecking your gut health? [23:12] What are the simplest, most effective ways to restore your gut health? [29:31] The supplements he recommends and why you were meant to have sex.       [30:52] Should all men (and women) over 35 be on hormone replacement therapy?       [32:56] The problems with standard medical advice and why you should choose a doctor like you choose a mechanic. [37:51] The three “S’s” that will improve your health, protect you from disease, and give you a longer life. [41:30] Simple ways to improve your quality of sleep and why snoring is a sign something isn’t right. [44:29] Why Carl takes a more instinctive approach to strength training these days.       [48:03] His podcast, “Super Human Radio” and the topics he covers. [51:06] His favorite music and the surprising artists on his gym playlist. [52:57]   This episode is brought to you by Omax Health. Their Omax3 Ultra-Pure is a next generation omega-3 supplement.    Omax3 Ultra-Pure helps alleviate joint pain, reduces inflammation and muscle soreness, promotes recovery from intense training, supports optimal heart health, and improves mood, memory and focus. Go to https://www.tryomax.com/jay to get your free box of Omax3 Ultra-Pure with your first purchase.   This episode is also brought to you by Athletic Greens. With 75 ingredients working together to help with 11 different areas of health, Athletic Greens helps detoxify your body, boost your energy, and strengthen your immune system.   Get all your vital nutrition in 30 seconds or less at www.AthleticGreens.com/Jay to claim your Renegade Radio special offer.  

Click Here To Leave A Review (takes 2 min) Take the FREE Winning Leader Assessment www.WinningLeader.io   In this episode of the Coach Your Best Podcast I have an insightful conversation with Coach Carl Valle of spikesonly.com. Here in part one we discuss all things 'testing and performance' including: Variables of testing a 30m sprint What does Carl mean by 'Constellations' when testing athletes? How many tests are needed to see a trend? Difference between global vs specific testing What about coaches who are slave to numbers and feel pressure for job preservation? Carl's thoughts on testing for recovery and fatigue Carl's approach to physical and physiological testing data The relationship between athlete monitoring and the high school athlete What is meant by the term 'athlete monitoring'? Can high school athlete's differentiate between distribution of effort? Why 'presence' is a simple data metric  Carl's rule for creating a wellness questionnaire 3 Things a Coach MUST do when getting data from an athlete Why Carl calls 'blood testing' for elite athletes TRUTH SERUM Plus more! 

This week’s guest is Carl Valle of Spikesonly.com. Carl is, amongst other sprint and hurdle coaching talents, a speed technology expert.  A current trend in athletic performance is jump testing, both as a means of assessing physical progression over the course of a training year, as well as a way of seeking to balance out athletes for the prevention of injury, and potential optimization of performance. The vertical jump is the greatest display of power that an athlete can generally produce, so it does make good sense to have an adequate knowledge regarding testing and assessing the movement.  After all, “what you measure, you improve”. There are a lot of opinions on the best way to test power through the vertical jump, so Carl and I cover a lot of ground on this topic in this podcast. Today’s episode is brought to you by SimpliFaster, supplier of high-end athletic development tools, such as the Freelap timing system, kBox, Sprint 1080, and more. Key Points: The basics of using vertical jumping as a means of assessing athletic preparation Considerations with the novelty factor in jump training Guidelines in deciding which jump monitoring system to utilize Why Carl likes repeated jumps vs. single efforts in testing Pro’s and con’s of accelerometer based testing The advantage of jump testing with added barbell load Embedding tests as a part of the warmup How and when to test jumping for team sports vs. track and field Utilizing jump testing in context of strengths vs. weaknesses, and “balancing” the force-velocity jump profile of athletes How to determine what is necessary to train for, and what data one should collect over time The relationship between athletic structure, sprint testing and vertical jump performance Why Carl likes a plethora of jump tests in training and assessment Quotes: “The first time you get on a force plate or jump mat, you’re excited, and want to know your numbers…. but you start to get bored, and want something different or not at all… jump testing requires an all-out performance to be valid” “When there’s lines (for testing), people aren’t learning and moving and getting better” “You should be video taping jump testing” “Accelerometers, because they’re cheap, you can make devices really easily.  We’re going to see less physical pads or plates, and a lot more sensors” “(knowing wattage) resets people’s perception of the numbers” “In sports preparation, you always want to contrast what they’re not getting.  So basketball players, there is a joke that it is the “no barbell association”, testing vertical jump with a barbell reinforces the cultural side of what we’re trying to do” “My rule is to make sure the tests are embedded (as part of the warmup)” “Rate of force development, you have to be careful, because you have to do a lot of teasing out to get there” “If you don’t think it’s going to improve, don’t test it” “Training data trumps monitoring every single time” “The most humbling experience you can do, is have athletes to do a 2-point leaning stance, and time them, because you won’t get the artificial benefit of learning to accelerate better from a mechanically efficient position” “Maximum velocity is the most wonderful test; I’ve seen guys with awesome 20 meter tests, and the look great, get cleared, but then they get hurt” “You need to start profiling with greater precision than “what’s your 20 time?”  (This is where a device like the 1080 sprint really shines) About Carl Valle: Coach Valle has coached Track and Field at every level, from high school to the Olympic level in the sprints and hurdles. He has had the privilege of working with great athletes that have been All-American and school record holders. A technology professional, Coach Valle has expertise in performance data as well as an understanding for practical application of equipment and software.