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 is obsolete.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 Harish Doddi, co-founder and CEO of Datatron, a startup focused on helping companies deploy and manage machine learning models. As companies move from machine learning prototypes to products and services, tools and best practices for productionizing and managing models are just starting to emerge. Today’s data science and data engineering teams work with a variety of machine learning libraries, data ingestion, and data storage technologies. Risk and compliance considerations mean that the ability to reproduce machine learning workflows is essential to meet audits in certain application domains. And as data science and data engineering teams continue to expand, tools need to enable and facilitate collaboration.

As someone who specializes in helping teams turn machine learning prototypes into production-ready services, I wanted to hear what Doddi has learned while working with organizations that aspire to “become machine learning companies.”

Simplifying Machine Learning Lifecycle Management

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 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 Andrew Burt, chief privacy officer and legal engineer at Immuta, a company building data management tools tuned for data science. Burt and cybersecurity pioneer Daniel Geer recently released a must-read white paper (“Flat Light”) that provides a great framework for how to think about information security in the age of big data and AI. They list important changes to the information landscape and offer suggestions on how to alleviate some of the new risks introduced by the rise of machine learning and AI.

We discussed their new white paper, cybersecurity (Burt was previously a special advisor at the FBI), and an exciting new Strata Data tutorial that Burt will be co-teaching in March.

How Machine Learning Impacts Information Security

In this episode of the Data Show, I spoke with Maryam Jahanshahi, research scientist at TapRecruit, a startup that uses machine learning and analytics to help companies recruit more effectively. In an upcoming survey, we found that a “skills gap” or “lack of skilled people” was one of the main bottlenecks holding back adoption of AI technologies. Many companies are exploring a variety of internal and external programs to train staff on new tools and processes. The other route is to hire new talent. But recent reports suggest that demand for data professionals is strong and competition for experienced talent is fierce. Jahanshahi and her team are building natural language and statistical tools that can help companies improve their ability to attract and retain talent across many key areas.

Using Machine Learning and Analytics to Attract and Retain Employees

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.

Lessons Learned While Helping Enterprises Adopt Machine Learning

O'Reilly Radar

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