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On this episode of "Speaking of Bitcoin", join hosts Adam B. Levine, Andreas M. Antonopoulos, Stephanie Murphy and Jonathan Mohan for a discussion about loans in the world of crypto.
HODL. It's crypto-land philosophy for not selling your preferred token even when things look their most bleak. But where philosophy intersects with real-world concerns like shelter, food and the niceties of life, tactics have emerged offering both opportunities and risks. From smart contracts to bitcoin collateralized mortgages, the hosts discuss their experiences with loans in the world of crypto.
Credits
Today’s show featured Andreas M. Antonopoulos, Jonathan Mohan, Stephanie Murphy and Adam B. Levine, with editing by Adam B. Levine and music by Gurtybeats.com. Our episode art is a picture provided by The New York Public Library, modified by "Speaking of Bitcoin."
Any questions or comments? Send us an email at adam@speakingofbitcoin.show

SOB #496 The Ups and Downs of Crypto Loans

<a href="https://www.economist.com/leaders/2022/06/15/the-fed-and-the-ecb-turn-on-a-dime" rel="noopener noreferrer" target="_blank">America’s central bank </a>raised rates by .75% yesterday—the biggest increase in almost 30 years. Whether that will help tame rising prices without triggering a recession is unclear. The poor performance of Russian tanks in Ukraine has led some to wonder whether <a href="https://www.economist.com/interactive/international/2022/06/15/does-the-tank-have-a-future" rel="noopener noreferrer" target="_blank">the tank itself</a> is obsolete. And the rousing, darkly humorous defiance of <a href="https://www.economist.com/culture/2022/06/08/ukrainian-musicians-have-a-stark-message-for-the-kremlin" rel="noopener noreferrer" target="_blank">Ukrainian war anthems</a>. For full access to print, digital and audio editions of The Economist, subscribe here <a href="http://www.economist.com/intelligenceoffer" rel="noopener noreferrer" target="_blank">www.economist.com/intelligenceoffer</a> &#10;&nbsp; <hr>See <a style='color: grey; ' target='_blank' rel='noopener noreferrer' href='https://acast.com/privacy'>acast.com/privacy</a> for privacy and opt-out information.

Powell to the people: The Fed raises rates

Wall Street Journal's Josh Zumbrun details how airlines get rid of certain passengers on a flight and how passengers can fight against this issue.

The Science Behind Who Airlines Bump From a Flight—and How to Exploit It

Listen to an excerpt from I'M GLAD MY MOM DIED by Jennette McCurdy, read by the author.

I'M GLAD MY MOM DIED Audiobook Excerpt

Liebe Hörerinnen und Hörer,

gestartet sind Jochen Gebauer und Dom Schott mit der gleichen, heiligen Mission: Einen treuen Kult errichten und vier böse Gottheiten vom Thron stoßen, das erklärte Ziel von Cult of the Lamb. Die Wege aber, die die beiden dafür einschlugen, hätten unterschiedlicher nicht sein können. Und so kommt das Duo nach vielen Stunden wieder zusammen, um über das große Kot-Müsli-Fiasko, Opferrituale, unheilige Festessen, Hamsterräder und süchtig machende Arbeitsroutinen zu sprechen - und ihren Kultführungsstil miteinander zu vergleichen.

Viel Spaß!
Dom & Jochen

Timecodes:

00:00 - Einstieg                         	
04:10 - Spielidee & Grafik               	
22:41 - Aufbau des Kults                 	
37:01 - Rituale                          	
53:28 - Die Runs                         	
1:17:45 - Probleme des Spieles             	
1:29:03 - Fazit

Auf ein Bier

Runde 387: Cult of the Lamb

Getcha podcast ready!

T.O., Rob, Alonzo and Roy are BACK with boxing legend Sugar Ray Leonard on this week's show! The guys as the champ about his training, process of intimidation and whether or not Roy has a shot a boxing career at the Y in Burbank!

Check out more on the podcast over at www.getchapodcastready.com

#22: Sugar Ray Leonard

Carl Frampton v Kiko Martinez II dominates the podcast with the Northern Irishman, Frampton, bidding to win his 19th bout in a row.
Mike Costello and Steve Bunce discuss the fight as well as who Carl Froch's next opponent should be and the future of Anthony Joshua. Sep 04, 2014

5lspecials: 5live Boxing: Frampton v Martinez 04 Sep 14

Daredevil Season 2 is almost upon us and that means a new iteration of The Punisher, one of the most mangled and misrepresented characters in Marvel cinematic history! To help us wade through Frank Castle's movie graveyard, Lon Strickland comes on the show to argue that Dolph Lundgren's 1989 version is the best so far! Beyond that, we talk about Spider-Man dropping in on the 'Captain America: Civil War' trailer, the state of animation in the USA and what it was like for Lon to work at Machinima during their heyday!

Geekscape 402: Punishing The Guilty With Lon Strickland!

Le sommier qui grince. Les cris et gémissements de la porte à-côté. Le plafond qui menace de s'écrouler. Pourquoi la vie sexuelle de nos voisins est-elle aussi envahissante ? Comment une activité privée finit-elle par devenir publique ? Ils prennent du Viagra ou des amphétamines ? Et surtout : est-ce que par hasard... on n'aimerait pas entendre - et écouter - nos voisins ?

A retrouver aussi sur la chaîne Youtube ARTE Radio
Enregistrements : avril 18 - Mise en ondes & mix : Arnaud Forest - Texte & voix : Maïa Mazaurette - Illustration : Sjoerd van Leeuwen

Sex and sounds (n°26) : Les voisins bruyants

In this episode of the Data Show, I spoke with Alan Nichol, co-founder and CTO of Rasa, a startup that builds open source tools to help developers and product teams build conversational applications. About 18 months ago, there was tremendous excitement and hype surrounding chatbots, and while things have quieted lately, companies and developers continue to refine and define tools for building conversational applications. We spoke about the current state of chatbots, specifically about the types of applications developers are building today and how he sees conversational applications evolving in the near future.

Using Machine Learning to Improve Dialog Flow in Conversational Applications

In this episode of the Data Show, I spoke with Francesca Lazzeri, an AI and machine learning scientist at Microsoft, and her colleague Jaya Mathew, a senior data scientist at Microsoft. We conducted a couple of surveys this year—“How Companies Are Putting AI to Work Through Deep Learning” and “The State of Machine Learning Adoption in the Enterprise”—and we found that while many companies are still in the early stages of machine learning adoption, there’s considerable interest in moving forward with projects in the near future. Lazzeri and Mathew spend a considerable amount of time interacting with companies that are beginning to use machine learning and have experiences that span many different industries and applications. I wanted to learn some of the processes and tools they use when they assist companies in beginning their machine learning journeys.

Lessons Learned While Helping Enterprises Adopt Machine Learning

In this episode of the Data Show, I spoke with Vitaly Gordon, VP of data science and engineering at Salesforce. As the use of machine learning becomes more widespread, we need tools that will allow data scientists to scale so they can tackle many more problems and help many more people. We need automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection and hyperparameter tuning, as well as monitoring.

I wanted the perspective of someone who is already faced with having to support many models in production. The proliferation of models is still a theoretical consideration for many data science teams, but Gordon and his colleagues at Salesforce already support hundreds of thousands of customers who need custom models built on custom data. They recently took their learnings public and open sourced TransmogrifAI, a library for automated machine learning for structured data, which sits on top of Apache Spark.

Building Tools for Enterprise Data Science

In this episode of the Data Show, I spoke with Alex Wong, associate professor at the University of Waterloo, and co-founder of DarwinAI, a startup that uses AI to address foundational challenges with deep learning in the enterprise. As the use of machine learning and analytics become more widespread, we’re beginning to see tools that enable data scientists and data engineers to scale and tackle many more problems and maintain more systems. This includes automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection, and hyperparameter tuning, as well as tools for data engineering and data operations.

Wong and his collaborators are building solutions for enterprises, including tools for generating efficient neural networks and for the performance analysis of networks deployed to edge devices.

Tools for Generating Deep Neural Networks With Efficient Network Architectures

For the end-of-year holiday episode of the Data Show, I turned the tables on Data Show host Ben Lorica to talk about trends in big data, machine learning, and AI, and what to look for in 2019. Lorica also showcased some highlights from our upcoming Strata Data and Artificial Intelligence conferences.

Trends in Data, Machine Learning, and AI

In this episode of the Data Show, I spoke with Haoyuan Li, CEO and founder of Alluxio, a startup commercializing the open source project with the same name (full disclosure: I’m an advisor to Alluxio). Our discussion focuses on the state of Alluxio (the open source project that has roots in UC Berkeley’s AMPLab), specifically emerging use cases here and in China. Given the large-scale use in China, I also wanted to get Li’s take on the state of data and AI technologies in Beijing and other parts of China.

In the Age of AI, Fundamental Value Resides in Data

Guess Who

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

Using Machine Learning to Improve Dialog Flow in Conversational Applications

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