On New Year’s Eve 2009, I stood on the precipice of a new decade. I had just completed my first book on Silverlight and, more importantly, my wife had just given birth to our first child.

Now, on New Year’s Eve 2019, I ponder what the job market will look like and how lifelong learning will be an essential skill going forward. \

In this article I wrote on LinkedIn, I explain why AI is the Night King and how to fight him.

In the middle of the New Mexico desert lies Spaceport America, a glittering, alien structure advertised as the very first purpose-built commercial spaceport.

It’s home to Virgin Galactic, a space startup that promises to send tourists into orbit as early as next year.

But even if that milestone happens, it will follow years of delays, setbacks, and even tragedy. Local residents in the nearby town of Truth or Consequences were told to expect big things when New Mexico joined the private space economy, but many now wonder if the dream of a space industry will ever materialize. 

Verge Science explores.

Learn how VISEO (https://aka.ms/iotshow/viseo) is analyzing large amounts of data (1 GB to 1 TB) collected from drones and other vehicles flying over 32,000 km of railway tracks in France for Altametris, a subsidiary of SNCF Réseau.

VISEO’s Vincent Thavonekham, Head of Smart Factory, and Igor Leontiev, Chief Cloud Solution Architect, show three demos in this amazing episode. Vincent and Igor demonstrate how VISEO processes 42 billion laser dots and other data collected from 450 km of railroad tracks from Paris to Lyon.

Using VISEO’s custom data model and Azure IoT Edge, data at the edge is now prepared for upload to the Cloud in days instead of weeks. From the Cloud, Altametris analyzes the data to track and maintain its railroad assets remotely reducing expenses and increasing safety.

Donald Knuth is one of the greatest and most impactful computer scientists and mathematicians ever. He is the recipient in 1974 of the Turing Award, considered the Nobel Prize of computing.

He is the author of the multi-volume work, the magnum opus, The Art of Computer Programming. He made several key contributions to the rigorous analysis of the computational complexity of algorithms. He popularized asymptotic notation, that we all affectionately know as the big-O notation.

He also created the TeX typesetting which most computer scientists, physicists, mathematicians, and scientists and engineers use to write technical papers and make them look beautiful.

Lex Fridman interviews him in this video.

The Art of Computer Programming (book): https://amzn.to/39kxRwB

0:00 – Introduction
3:45 – IBM 650
7:51 – Geeks
12:29 – Alan Turing
14:26 – My life is a convex combination of english and mathematics
24:00 – Japanese arrow puzzle example
25:42 – Neural networks and machine learning
27:59 – The Art of Computer Programming
36:49 – Combinatorics
39:16 – Writing process
42:10 – Are some days harder than others?
48:36 – What’s the “Art” in the Art of Computer Programming
50:21 – Binary (boolean) decision diagram
55:06 – Big-O notation
58:02 – P=NP
1:10:05 – Artificial intelligence
1:13:26 – Ant colonies and human cognition
1:17:11 – God and the Bible
1:24:28 – Reflection on life
1:28:25 – Facing mortality
1:33:40 – TeX and beautiful typography
1:39:23 – How much of the world do we understand?
1:44:17 – Question for God

Bitcoin’s emergence as a global digital currency has been as revolutionary as it has been erratic. But while fledgling investors obsess over every fluctuation in the cryptocurrency market, nation-states are more interested in the underlying blockchain technology and its ability to revolutionize how business is done on the internet and beyond.

VICE News’ Michael Moynihan travels to Russia with Vitalik Buterin, inventor of the ethereum blockchain, to get a front-row seat to the geopolitical tug of war over Internet 3.0.

It’s that time of year for “year in review” posts, but this year is extra special: it’s the dawn of a new decade and the twilight of another.

Here’s an interesting look at what has changed in the consumer hardware space in the past ten years.

Lex Fridman interviews Melanie Mitchell in the latest edition of his AI Podicast.

Melanie Mitchell is a professor of computer science at Portland State University and an external professor at Santa Fe Institute. She has worked on and written about artificial intelligence from fascinating perspectives including adaptive complex systems, genetic algorithms, and the Copycat cognitive architecture which places the process of analogy making at the core of human cognition. From her doctoral work with her advisors Douglas Hofstadter and John Holland to today, she has contributed a lot of important ideas to the field of AI, including her recent book, simply called Artificial Intelligence: A Guide for Thinking Humans. This conversation is part of the Artificial Intelligence podcast.

AI: A Guide for Thinking Humans (book) – https://amzn.to/2Q80LbP
Melanie Twitter: https://twitter.com/MelMitchell1

0:00 – Introduction
2:33 – The term “artificial intelligence”
6:30 – Line between weak and strong AI
12:46 – Why have people dreamed of creating AI?
15:24 – Complex systems and intelligence
18:38 – Why are we bad at predicting the future with regard to AI?
22:05 – Are fundamental breakthroughs in AI needed?
25:13 – Different AI communities
31:28 – Copycat cognitive architecture
36:51 – Concepts and analogies
55:33 – Deep learning and the formation of concepts
1:09:07 – Autonomous vehicles
1:20:21 – Embodied AI and emotion
1:25:01 – Fear of superintelligent AI
1:36:14 – Good test for intelligence
1:38:09 – What is complexity?
1:43:09 – Santa Fe Institute
1:47:34 – Douglas Hofstadter
1:49:42 – Proudest moment