If competition is an extreme sport, then Amazon is winning. Journalist Brad Stone examines the relentless entrepreneur Jeff Bezos and his company.

Jeff Bezos and Extreme Competition

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

Athlétique et intrépide, Giovanna Amati n’a qu’une idée en tête: devenir pilote automobile. Pour l’heure, la jeune femme de 19 ans vit enchainée dans un caisson obscur et insonorisé de 4m². Daniel Nieto est son geôlier attitré. Une nuit, il entre dans la cage à visage découvert...

Enlèvement à l’italienne
Rome. Fin des années 1970. Daniel Nieto, un braqueur marseillais, est en cavale dans l’Italie des années de plomb où le rapt est une véritable industrie. Il devient kidnappeur et enlève la fille d’un riche industriel du cinéma. Une relation contre-nature se noue entre l’otage et son geôlier. Elle sera fatale à ce dernier.

Une série documentaire de Marine Vlahovic
Enregistrements: Août 2020 à mai 2022 - Entretiens: Marine Vlahovic - Montage : Marine Vlahovic et Sara Monimart - Réalisation : Marine Vlahovic et Samuel Hirsch - Musique originale & mixage: Samuel Hirsch - Illustration : Zaven Najjar - Production : Arte Radio

Enlèvement à l'italienne(4/7): Dans la cage

Après une enfance miséreuse mais pas malheureuse dans la Marseille de l’après-guerre, Daniel Nieto devient un as du hold-up et finit sous les verrous. En 1976, il profite d’une permission pour s’enfuir en Italie. Il gravit les échelons du crime aux côtés du Jacques Mesrine local. Puis embrasse la carrière de kidnappeur.

Enlèvement à l’italienne
Rome. Fin des années 1970. Daniel Nieto, un braqueur marseillais, est en cavale dans l’Italie des années de plomb où le rapt est une véritable industrie. Il devient kidnappeur et enlève la fille d’un riche industriel du cinéma. Une relation contre-nature se noue entre l’otage et son geôlier. Elle sera fatale à ce dernier.

Une série documentaire de Marine Vlahovic
Enregistrements: Août 2020 à mai 2022 - Entretiens: Marine Vlahovic - Montage : Marine Vlahovic et Sara Monimart - Réalisation : Marine Vlahovic et Samuel Hirsch - Musique originale & mixage: Samuel Hirsch - Illustration : Zaven Najjar - Production : Arte Radio

Enlèvement à l'italienne (1/7): Voyage en Italie

<ul> 
<li>Apple is just three weeks away from showing off the iPhone 14.</li> 
<li>Apple workers launch petition over company's reported return-to-office plan.</li> 
<li>Apple's new iPad multitasking system doesn't cut it</li> 
<li>Apple releases iOS, iPadOS and macOS security fixes for two zero-days under active attack.</li> 
<li>Tim Cook, Jony Ive, and Laurene Powell Jobs to speak at Code 2022 conference next month.</li> 
<li>iOS VPNs have leaked traffic for years, researcher claims.</li> 
<li>M2 MacBook Air runs Windows 11 faster than pricier Dell laptop.</li> 
<li>Former Apple engineer accused of stealing automotive trade secrets pleads guilty.</li> 
<li>John Foster: In memoriam.</li> 
<li>10 million watched 'House of Dragon,' HBO says.</li> 
<li>Apple fails in bid for Big Ten Conference streaming package.</li> 
<li>Apple brings Self-Service Repair to the Mac.</li> 
<li>Jason found a funny bug in macOS beta.</li></ul> 
Picks of the Week<ul> 
<li>Alex's Pick: Stellar X2</li> 
<li>Andy's Pick: Anker Nano II 65W GaN Charger</li> 
<li>Jason's Pick: One Thing &amp; Shortery</li></ul> 
Hosts: <a href="https://twit.tv/people/leo-laporte">Leo Laporte</a>, <a href="https://twit.tv/people/alex-lindsay">Alex Lindsay</a>, <a href="https://twit.tv/people/andy-ihnatko">Andy Ihnatko</a>, and <a href="https://twit.tv/people/jason-snell">Jason Snell</a> 
Download or subscribe to this show at <a href="https://twit.tv/shows/macbreak-weekly">https://twit.tv/shows/macbreak-weekly</a>. 
Get episodes ad-free with Club TWiT at <a href="https://twit.tv/clubtwit" rel="payment">https://twit.tv/clubtwit</a>
Sponsors:<ul>
<li><a href="http://zocdoc.com/macbreak" target="_blank" rel="sponsored">zocdoc.com/macbreak</a></li>
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</ul>

MacBreak Weekly 832: A Fine Patina

At Made by Google '22, Google unveiled its latest Pixel lineup with the Pixel 7 and Pixel 7 Pro powered by Google's 2nd-gen mobile chip, the Tensor G2, powering more AI and machine learning features like Super Res Zoom, Photo Unblur, Cinematic Blur. Also announced was Google's first smartwatch, the Google Pixel Watch, with Fitbit's health and fitness expertise, and shared some details on the Pixel Tablet with a charging speaker dock coming in 2023. 
Hosts: <a href="https://twit.tv/people/leo-laporte">Leo Laporte</a> and <a href="https://twit.tv/people/ron-richards">Ron Richards</a> 
Download or subscribe to this show at <a href="https://twit.tv/shows/twit-news">https://twit.tv/shows/twit-news</a>. 
Get episodes ad-free with Club TWiT at <a href="https://twit.tv/clubtwit" rel="payment">https://twit.tv/clubtwit</a>

TWiT News 386: Made by Google 2022

This is a series about what happens when someone who knows nothing about business starts one. It's called StartUp.  In episode one, Alex Blumberg botches his pitch to Silicon Valley billionaire Chris Sacca.

#1 How Not to Pitch a Billionaire

Firewatch stand auf unserer Liste der meisterwarteten Spiele 2016. Jetzt ist die Zeit der Abrechnung gekommen: Ist das Indie-Abentuer wirklich unsere Vorfreude wert gewesen, oder verdammen wir es wie eifersüchtige Ex-Freundinen?

Runde #46: Firewatch (Some people just want to watch the world burn)

Doug shares the audio from the opening moments of The Benson Movie Interruption of "Willy Wonka And The Chocolate Factory."

P.S. I Love You

In this episode of the Data Show, I spoke with Jason Dai, CTO of big data technologies at Intel, and co-chair of Strata + Hadoop World Beijing. Dai and his team are prolific and longstanding contributors to the Apache Spark project. Their early contributions to Spark tended to be on the systems side and included Netty-based shuffle, a fair-scheduler, and the “yarn-client” mode. Recently, they have been contributing tools for advanced analytics. In partnership with major cloud providers in China, they’ve written implementations of algorithmic building blocks and machine learning models that let Apache Spark users scale to extremely high-dimensional models and large data sets. They achieve scalability by taking advantage of things like data sparsity and Intel’s MKL software. Along the way, they’ve gained valuable experience and insight into how companies deploy machine learning models in real-world applications.

Deep Learning for Apache Spark

Thanks to freeways, cities became something to get through instead of something to get to. Now, as the next transportation revolution -- from rivers to trains to cars to autonomous cars -- promises to change the face of our cities, what happens to car culture, infrastructure, and more?

Who owns what, who pays? And what about the design -- and product management -- challenges, whether it's designing for user trust, city adoption, or an ever-moving target thanks to constantly evolving tech?

This episode of the podcast (in conversation with Sonal) covers all this and more, featuring: a16z's Frank Chen, who recently shared 16 questions about autonomous cars; Taggart Matthiesen, director of product at Lyft who covers the core platform as well as development/strategy for autonomous vehicles; and Carl Pope, former executive director and chairman of the Sierra Club -- and author (with former NYC mayor Mike Bloomberg) of the upcoming book Climate of Hope: How Cities Businesses and Citizens Can Save the Planet. Will curb space be the new shelf space? When we value the "iPhone-ness" over the "carness" of cars, what changes? And... will we all drive less, walk more?

a16z Podcast: Cars and Cities, the Autonomy Edition

In this episode of the Data Show, I spoke with Francisco Webber, founder of Cortical.io, a startup that is applying tools based on Hierarchical Temporal Memory (HTM) to natural language understanding. While HTM has been around for more than a decade, there aren’t many companies that have released products based on it (at least compared to other machine learning methods). Numenta, an organization developing open source machine intelligence based on the biology of the neocortex, maintains a community site featuring showcase applications. Webber’s company has been building tools based on HTM and applying them to big text data in a variety of industries; financial services has been a particularly strong vertical for Cortical.

Natural Language Analysis Using Hierarchical Temporal Memory

In this episode of the Data Show, I spoke with Aurélien Géron, a serial entrepreneur, data scientist, and author of a popular, new book entitled "Hands-on Machine Learning with Scikit-Learn and TensorFlow." Géron’s book is aimed at software engineers who want to learn machine learning and start deploying machine learning models in real-world products.

Becoming a Machine Learning Engineer

In this episode of the Data Show, I spoke with Reza Zadeh, adjunct professor at Stanford University, co-organizer of ScaledML, and co-founder of Matroid, a startup focused on commercial applications of deep learning and computer vision. Zadeh also is the co-author of the forthcoming book TensorFlow for Deep Learning (now in early release). Our conversation took place on the eve of the recent ScaledML conference, and much of our conversation was focused on practical and real-world strategies for scaling machine learning. In particular, we spoke about the rise of deep learning, hardware/software interfaces for machine learning, and the many commercial applications of computer vision.

Scaling Machine Learning

In this episode of the Data Show, I spoke with David Ferrucci, founder of Elemental Cognition and senior technologist at Bridgewater Associates. Ferrucci served as principal investigator of IBM’s DeepQA project and led the Watson team that became champion of the Jeopardy! quiz show. Elemental Cognition (EC) is a research group focused on building an AI system that will be equipped with state-of-the-art natural language understanding technologies. Ferrucci envisions that EC will ship with foundational knowledge in many subject areas, but will be able to very quickly acquire knowledge in other (specialized) domains with the help of “human mentors.”

Having built and deployed several prominent AI systems through the years, I also wanted to get Ferrucci’s perspective on the evolution of AI technologies, and how enterprises can take advantage of all the exciting recent developments.

Language Understanding Remains One of AI's Grand Challenges

Doug reads your message. Yes, one message.

American Pie

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.

Natural Language Analysis Using Hierarchical Temporal Memory

O'Reilly Radar

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