Podcast appearances and mentions of kyle polich

Kyle Polich of the Data Skeptic Podcast comes on to talk about the so-called Dead Internet Theory. Is the Internet today Social Media AI serving up content for AI bots and humans are now out of the equation?

We've had this scheduled for a while, but this week AI popped up in the news twice, impersonating George Carlin and Joe Biden, so what better time for a skeptical look at artificial intelligence! Kyle Polich of the long-running Data Skeptic Podcast joins Ben, Celestia and Pascual to talk about different sorts of AI. From generative AI threatening writers and artists to the kind of AI that can help scammers manipulate people -- or even put human lives in peril if it goes wrong. How much of an AI panic are we witnessing right now, and what sort of impact will it really have on our society, our economy . . . and our skepticism?

We celebrate episode 1000000000 with some Q&A from host Kyle Polich.  We boil this episode down to four key questions: 1) How do you find guests 2) What is Data Skeptic all about? 3) What is Kyle all about? 4) What are Kyle's thoughts on AGI?   Thanks to our sponsorsdataannotation.tech/programmers https://www.webai.com/dataskeptic  

First we nosh on some interesting tidbits about a long-dead casino magnate, a mysterious fortune teller with a prophecy about rom-coms, and a prematurely dead Simon Cowell. Then Kyle Polich of the Data Skeptic podcast joins us to talk about the Missing 411, a concept pushed by several books and movies produced by David Paulides. What or who are the Missing 411, and how are Bigfoot and UFOs involved? Is there a coherent theory about what's going on, and is it based on actual happenings or fabrications? And how does misunderstanding (or completely ignoring) data analysis come into play?

Jon talks with Kyle Polich, host of the Software Engineering Daily Podcast, about a variety of topics related to entrepreneurship and how to create a successful organization.   Connect with Jon Dwoskin: Twitter: @jdwoskin  Facebook: https://www.facebook.com/jonathan.dwoskin  Instagram: https://www.instagram.com/thejondwoskinexperience/  Website: https://jondwoskin.com/  LinkedIn: https://www.linkedin.com/in/jondwoskin/ Email: jon@jondwoskin.com  Get Jon's Book: The Think Big Movement: Grow your business big. Very Big!

0:00:00 Introduction Richard Saunders 0:06:42 A Strongly Worded Letter Skeptics receive all sorts of interesting letters from the public, some complimentary, some not. It seems that many people also believe that skeptical organisations wield much more power or influence than we really do. On today's show, Richard Saunders reads one of the more noteworthy examples of frustration from a corespondent. 0:18:16 You Can Count on Adrienne. With Adrienne Hill The Story of 'Lady Ganga', Michele Frazier Baldwin, daughter of Kendrick and Ruth Frazier. When Michele, a 45 year old Mother of three was diagnosed with late-stage cervical cancer, she took this as a call-to-action. She decided that she would travel to India to paddle board over 700 miles down the Ganges River, spreading awareness about this disease that was going to kill her and breaking a World Record in the process. https://www.ladyganga.world LADY GANGA: NILZA'S STORY https://www.youtube.com/watch?v=u5yMCzx0ctU 0:23:34 Rob Palmer at CSICon 2022 Guest reporter Rob roves around the halls at CSICon 2022 in Las Vagas and bumps into Kyle Polich from the Data Skeptic Podcast, Karl Withakay and JD Sword. 0:38:12 A Dive into a Trove A wander through the decades of digitised newspapers on a search for references to Kendrick Frazier. 1989, Nov 25 - Kenosha News 1976, July 26 - Benton Courier 1995, Aug 28 - Clovis News Also Sydney Skeptics in the Pub Special Guests, 24th Nov. https://www.meetup.com/austskeptics/events/287036082 Australian Skeptics National Convention 2022 Science & Skepticism in a changed world 3 - 4 December, National Library of Australia, Canberra https://skepticon.org.au

This episode is hosted by Kyle Polich of the Data Skeptic podcast. We’re glad to welcome Kyle to the Software Daily team. Becoming a contributor to an existing software project can be a daunting task for an engineer. A common convention is to add a README file to your repository to serve as a trailhead

This episode is hosted by Kyle Polich of the Data Skeptic podcast. We’re glad to welcome Kyle to the Software Daily team. Becoming a contributor to an existing software project can be a daunting task for an engineer. A common convention is to add a README file to your repository to serve as a trailhead The post The Missing Readme with Chris Riccomini and Dmitriy Ryaboy appeared first on Software Engineering Daily.

This episode is hosted by Kyle Polich of the Data Skeptic podcast. We’re glad to welcome Kyle to the Software Daily team. Becoming a contributor to an existing software project can be a daunting task for an engineer. A common convention is to add a README file to your repository to serve as a trailhead The post The Missing Readme with Chris Riccomini and Dmitriy Ryaboy appeared first on Software Engineering Daily.

This episode is hosted by Kyle Polich of the Data Skeptic podcast. We're glad to welcome Kyle to the Software Daily team. Becoming a contributor to an existing software project can be a daunting task for an engineer. A common convention is to add a README file to your repository to serve as a trailhead The post The Missing Readme with Chris Riccomini and Dmitriy Ryaboy appeared first on Software Engineering Daily.

On this episode of The Artists of Data Science, we get a chance to hear from Kyle Polich, a computer scientist turned data skeptic. He has a wide array of interests and skills in A.I, machine learning, and statistics. These skills have made him a sought after consultant in the data science field. He is also the host of the very popular data podcast, Data Skeptic, which discusses topics related to data science all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches. In this episode, Kyle defines what a data skeptic is, and also goes on to give advice on how to communicate effectively with leaders and executives as a data scientist. Kyle brings a very unique perspective related to all things data, along with actionable advice! WHAT YOU WILL LEARN [00:11:49] Probabilistic data structures [00:15:19] How probabilitistic data structures will change the future [18:55] Is data science more of an art or science? [23:36] Advice for data scientists trapped in a perfectionist mindset [30:43] Important soft skills that you need to succeed [39:40] How to communicate your ideas with executives QUOTES [11:43] "…greatness is achieved by a commitment to your craft and pursuing it." [16:42] "The greatest trick the devil ever pulled was convincing the world he didn't exist. That's what good data science does to me." [24:42] …"being able to fall down but get up fast is important." FIND KYLE ONLINE LinkedIn:https://www.linkedin.com/in/kyle-polich-5047193/ Twitter:https://twitter.com/DataSkeptic Podcast:https://dataskeptic.com/ SHOW NOTES [00:03:01] How Kyle got into data science [00:05:20] What the heck is a data skeptic? [00:07:47] What do you think the next big thing in data science is going to be the next, say, two to five years. [00:11:04] How to be a great data scientist [00:11:49] Kyle gives us a primer on probabilistic data structures [00:15:19] How do you see probabilistic data structures impacting society in the next two to five years? [00:17:19] Data skeptic mission [00:18:39] Kyle answers the question - how do you view data science? Do you think it's more of the art or more science? [00:21:09] We talk about principles and methodologies as it related to art and science [00:21:52] Kyle shares his thoughts on the creative process in data science [00:23:22] Kyle shares his thoughts on being a perfectionist when you're working on a project [00:25:28] Do you have any tips for people who are coming from a non-technical background and they're coming up to these technical concepts face to face for the first time? [00:26:42] We talk about the importance of being a lifelong learner and Kyle shares some advice for aspiring data scientists out there who feel like they haven't learned enough yet to even consider breaking into the field. [00:28:47] What is your advice for data scientists who they feel like they've learned enough, and just don't even need to learn anything else to be successful? [00:30:27] We talk about the soft-skils that candidates should pick-up, and Kyle shares advice for people who are in their first data science roles. [00:31:03] Some insight into the purpose of your resume and how you should leverage that for your job search [00:34:17] We talk about the pursuit of certificates versus the achievement of self-directed learning projects [00:36:18] Tips on finding the right type of project to add to your portfolio [00:39:13] For those people a little further along in their career, Kyle shares tips on how to effectively communicate with executives [00:42:16] We talk about our shared love for Bill Murray [00:43:06] How you should respond when you come across job postings that look like they want the skills of an entire team rolled up into one person. [00:46:22] What's the one thing you want people to learn from your story? [00:47:19] The lightning round. Special Guest: Kyle Polich.

Kyle Polich is the co-host of the incredibly popular Data Skeptic podcast, which he has been churning out since 2014. He studied computer science and focused on artificial intelligence in grad school. His general interests range from areas like statistics, machine learning, data viz, and optimization to data provenance, data governance, econometrics, and metrology.   The Data Skeptic Podcast features conversations on topics related to data science, statistics, machine learning, and artificial intelligence. The podcast breaks down into two different episode formats. One is a short form podcast where Kyle explains complex data science concepts in a way that non-data scientists can understand. In these episodes he’s joined by his co-host and wife, Linh da Tran. The second format is a long form interview format where Kyle interviews experts in various data science and skepticism related arenas about their work.   In this episode of the Data Journeys Podcast, I pick Kyle’s brain for patterns noticed and lessons learned through interviewing and teaching his way through nearly 400 episodes of the Data Skeptic Podcast. Some of the topics covered include:   How the Data Skeptic podcast became the only podcast to be endorsed by the Pope. Kyle’s approach to teaching complex subject matter for entry level comprehension. What patterns and lessons Kyle has taken from interviewing nearly 400 guests on his show over the last four years. Advice for listeners who are considering starting their own podcasts, colored by lessons Kyle has learned in his tenure. Kyle and I get a little meta in trading lessons, best practices, and common experiences learned from their time hosting podcasts.   Enjoy the show!   Show Notes: https://ajgoldstein.com/podcast/ep20 AJ’s Twitter: https://twitter.com/ajgoldstein393/ Kyle’s LinkedIn: https://www.linkedin.com/in/kyle-polich-5047193

0:00:00 Introduction Richard Saunders   0:04:35 The Raw Skeptic Report.... with Heidi Robertson Heidi heads to the city of Brisbane, Queesland to aid the stand of Light for Riley at the Pregnancy Babies & Children’s Expo. After a wander around to snoop out any woo, Heidi chats to Catherine Hughes about her ongoing campaign to protect babies in the name of Riley Hughes. Light for Riley https://www.facebook.com/lightforriley   0:25:45 Fair Trading takes beef with unsubstantiated cancer cure A Queensland business has been ordered to pay more than $11,000 by the Brisbane Magistrates Court today (13 June 2018) for failing to substantiate claims, following an Office of Fair Trading (OFT) investigation. https://tinyurl.com/y9cuklg7   0:30:05 The Data Skeptic - An interview with Kyle Polich Are you skeptical of data, computers, numbers and data in general. We chat to Kyle Polich who might just have the podcast for you! https://dataskeptic.com/   Also... Australian Skeptics National Convention 2018 https://convention.skeptics.com.au    

Medical imaging is a highly effective tool used by clinicians to diagnose a wide array of diseases and injuries. However, it often requires exceptionally trained specialists such as radiologists to interpret accurately. In this episode of Data Skeptic, our host Kyle Polich is joined by Gabriel Maicas, a PhD candidate at the University of Adelaide, to discuss machine learning systems that can be used by radiologists to improve their accuracy and speed of diagnosis.

Data Skeptic is a podcast about machine learning, data science, and how software affects our lives. The first guest on today’s episode is Kyle Polich, the host of Data Skeptic. Kyle is one of the best explainers of machine learning concepts I have met, and for this episode, he presented some material that is perfect The post Machine Learning with Data Skeptic and Second Spectrum at Telesign appeared first on Software Engineering Daily.

In this week’s episode, Kyle Polich interviews Pedro Domingos about his book, The Master Algorithm: How the quest for the ultimate learning machine will remake our world. In the book, Domingos describes what machine learning is doing for humanity, how it works and what it could do in the future. He also hints at the possibility of an ultimate learning algorithm, in which the machine uses it will be able to derive all knowledge — past, present, and future.

This week, our host Kyle Polich is joined by guest Tim Henderson from Google to talk about the computational complexity foundations of modern cryptography and the complexity issues that underlie the field. A key question that arises during the discussion is whether we should trust the security of modern cryptography.

In this week's episode, host Kyle Polich interviews author Lance Fortnow about whether P will ever be equal to NP and solve all of life’s problems. Fortnow begins the discussion with the example question: Are there 100 people on Facebook who are all friends with each other? Even if you were an employee of Facebook and had access to all its data, answering this question naively would require checking more possibilities than any computer, now or in the future, could possibly do. The P/NP question asks whether there exists a more clever and faster algorithm that can answer this problem and others like it.

In this episode, Professor Michael Kearns from the University of Pennsylvania joins host Kyle Polich to talk about the computational complexity of machine learning, complexity in game theory, and algorithmic fairness. Michael's doctoral thesis gave an early broad overview of computational learning theory, in which he emphasizes the mathematical study of efficient learning algorithms by machines or computational systems. When we look at machine learning algorithms they are almost like meta-algorithms in some sense. For example, given a machine learning algorithm, it will look at some data and build some model, and it’s going to behave presumably very differently under different inputs. But does that mean we need new analytical tools? Or is a machine learning algorithm just the same thing as any deterministic algorithm, but just a little bit more tricky to figure out anything complexity-wise? In other words, is there some overlap between the good old-fashioned analysis of algorithms with the analysis of machine learning algorithms from a complexity viewpoint? And what is the difference between strategies for determining the complexity bounds on samples versus algorithms? A big area of machine learning (and in the analysis of learning algorithms in general) Michael and Kyle discuss is the topic known as complexity regularization. Complexity regularization asks: How should one measure the goodness of fit and the complexity of a given model? And how should one balance those two, and how can one execute that in a scalable, efficient way algorithmically? From this, Michael and Kyle discuss the broader picture of why one should care whether a learning algorithm is efficiently learnable if it's learnable in polynomial time. Another interesting topic of discussion is the difference between sample complexity and computational complexity. An active area of research is how one should regularize their models so that they're balancing the complexity with the goodness of fit to fit their large training sample size. As mentioned, a good resource for getting started with correlated equilibria is: https://www.cs.cornell.edu/courses/cs684/2004sp/feb20.pdf Thanks to our sponsors: Mendoza College of Business - Get your Masters of Science in Business Analytics from Notre Dame. brilliant.org - A fun, affordable, online learning tool.  Check out their Computer Science Algorithms course.

Over the past several years, we have seen many success stories in machine learning brought about by deep learning techniques. While the practical success of deep learning has been phenomenal, the formal guarantees have been lacking. Our current theoretical understanding of the many techniques that are central to the current ongoing big-data revolution is far from being sufficient for rigorous analysis, at best. In this episode of Data Skeptic, our host Kyle Polich welcomes guest John Wilmes, a mathematics post-doctoral researcher at Georgia Tech, to discuss the efficiency of neural network learning through complexity theory.

In this episode, Microsoft's Corporate Vice President for Cloud Artificial Intelligence, Joseph Sirosh, joins host Kyle Polich to share some of the Microsoft's latest and most exciting innovations in AI development platforms. Last month, Microsoft launched a set of three powerful new capabilities in Azure Machine Learning for advanced developers to exploit big data, GPUs, data wrangling and container-based model deployment. Extended show notes found here. Thanks to our sponsor Springboard.  Check out Springboard's Data Science Career Track Bootcamp.

Last year, the film development and production company End Cue produced a short film, called Sunspring, that was entirely written by an artificial intelligence using neural networks. More specifically, it was authored by a recurrent neural network (RNN) called long short-term memory (LSTM). According to End Cue’s Chief Technical Officer, Deb Ray, the company has come a long way in improving the generative AI aspect of the bot. In this episode, Deb Ray joins host Kyle Polich to discuss how generative AI models are being applied in creative processes, such as screenwriting. Their discussion also explores how data science for analyzing development projects, such as financing and selecting scripts, as well as optimizing the content production process.

Recommender systems play an important role in providing personalized content to online users. Yet, typical data mining techniques are not well suited for the unique challenges that recommender systems face. In this episode, host Kyle Polich joins Dr. Joseph Konstan from the University of Minnesota at a live recording at FARCON 2017 in Minneapolis to discuss recommender systems and how machine learning can create better user experiences. 

Thanks to our sponsor Springboard. In this week's episode, guest Andre Natal from Mozilla joins our host, Kyle Polich, to discuss a couple exciting new developments in open source speech recognition systems, which include Project Common Voice. In June 2017, Mozilla launched a new open source project, Common Voice, a novel complementary project to the TensorFlow-based DeepSpeech implementation. DeepSpeech is a deep learning-based voice recognition system that was designed by Baidu, which they describe in greater detail in their research paper. DeepSpeech is a speech-to-text engine, and Mozilla hopes that, in the future, they can use Common Voice data to train their DeepSpeech engine.

In this episode, Tony Beltramelli of UIzard Technologies joins our host, Kyle Polich, to talk about the ideas behind his latest app that can transform graphic design into functioning code, as well as his previous work on spying with wearables.

Engineers have plenty to be skeptical about. We look to data sets to give us something resembling objective truth. Some areas of research have so many variables that it is hard to isolate facts. Kyle Polich hosts the popular data science show Data Skeptic, where he examines problems and solutions around data, and he is The post Skepticism Roundtable with Ammon Bartram and Kyle Polich appeared first on Software Engineering Daily.

  In this week's episode of Data Skeptic, host Kyle Polich talks with guest Maura Church, Patreon's data science manager. Patreon is a fast-growing crowdfunding platform that allows artists and creators of all kinds build their own subscription content service. The platform allows fans to become patrons of their favorite artists- an idea similar the Renaissance times, when musicians would rely on benefactors to become their patrons so they could make more art. At Patreon, Maura's data science team strives to provide creators with insight, information, and tools, so that creators can focus on what they do best-- making art. On the show, Maura talks about some of her projects with the data science team at Patreon. Among the several topics discussed during the episode include: optical music recognition (OMR) to translate musical scores to electronic format, network analysis to understand the connection between creators and patrons, growth forecasting and modeling in a new market, and churn modeling to determine predictors of long time support. A more detailed explanation of Patreon's A/B testing framework can be found here Other useful links to topics mentioned during the show: OMR research Patreon blog Patreon HQ blog Amanda Palmer Fran Meneses

In today’s modern world, where everything seems dependent on technology, millions if not billions of data are being processed worldwide in a small fraction of time. That’s why professionals in the field of computer science who specialized in making sense of these data are vital in making all these processes possible and seamless. Our guest for this week’s edition of …

Kyle Polich of the Data Skeptic Podcast comes on to talk about The Bible Code.