Money is an ancient technology. In this presentation, Andreas M. Antonopoulos examines the historical context of money as a technology and analyses the inflection points that lead to the most recent innovation of peer-to-peer money. This talk is featured in The Internet of Money, which can be purchased in multiple languages in paperback, ebook, or audiobook: Chapters 0:00 Introduction 1:16 How old is the technology of money? 4:16 I think the important insight is that money is a form of communication 5:41 How many different forms of money have we had? 9:33 Now we have Bitcoin, a pretty radical transformation; as radical as the change from precious metals to paper 11:51 Something happened with the invention of the internet 12:56 Bitcoin is the first network-centric, protocol-based form of money 14:35 It should be shocking that, in almost all countries, money is not part of the educational curriculum 15:50 Client-server and master-slave architecture 19:00 In Bitcoin, you don’t owe anyone anything and no one owes you anything 21:37 Aristotle said that the intrinsic value of gold was scarcity. Do you believe that the same thing goes for bitcoin? 25:36 The Banks are now getting into Blockchain. You have nine banks creating a consortium and they’re competing with blockchain ETC. What are your thoughts on this? 28:54 What in your opinion are the biggest threats or problems at the moment in the Bitcoin Ecosystem? 30:39 Talking about public and private, I think that the internet is considered as a common good. [More]
*** Natural Language Processing Course: *** This session on Context Free Grammar will give you a detailed and comprehensive knowledge of context-free grammar and how it is used in Natual Language Processing. It also focuses on Syntax trees and Techniques like Chinking and Chunking. ———————————— About this course : Edureka’s Natural Language Processing with Python course will take you through the essentials of text processing all the way up to classifying texts using Machine Learning algorithms. You will learn various concepts such as Tokenization, Stemming, Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree Parsing and so on using Python’s most famous NLTK package. Once you delve into NLP, you will learn to build your own text classifier using the Naïve Bayes algorithm. ———————————— Meetup: Instagram: Slideshare: Facebook: Twitter: LinkedIn: For more information, please write back to us at or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Some machine learning proponents claim you only have to provide data to get value. However, reality is a bit more complex. On the way to active analytics for business, we have to answer two big questions: What must happen to data before running machine learning algorithms, and how should machine learning output be used to generate actual business value? Jean-François Puget demonstrates the vital role of human context in answering those questions. You’ll discover why human context should be embraced as a guide to building better, smarter systems that people will use, trust, and love. This keynote is sponsored by IBM. Subscribe to O’Reilly on YouTube: Follow O’Reilly on: Twitter: Facebook: Instagram: LinkedIn: