Lex Fridman interviews Ben Goertzel in episode 103 of his AI podcast.

Ben Goertzel is one of the most interesting minds in the artificial intelligence community. He is the founder of SingularityNET, designer of OpenCog AI framework, formerly a director of research at the Machine Intelligence Research Institute, Chief Scientist of Hanson Robotics, the company that created the Sophia Robot. He has been a central figure in the AGI community for many years, including in the Conference on Artificial General Intelligence. This conversation is part of the Artificial Intelligence podcast.

Show outline:

  • 0:00 – Introduction
  • 3:20 – Books that inspired you
  • 6:38 – Are there intelligent beings all around us?
  • 13:13 – Dostoevsky
  • 15:56 – Russian roots
  • 20:19 – When did you fall in love with AI?
  • 31:30 – Are humans good or evil?
  • 42:04 – Colonizing mars
  • 46:53 – Origin of the term AGI
  • 55:56 – AGI community
  • 1:12:36 – How to build AGI?
  • 1:36:47 – OpenCog
  • 2:25:32 – SingularityNET
  • 2:49:33 – Sophia
  • 3:16:02 – Coronavirus
  • 3:24:14 – Decentralized mechanisms of power
  • 3:40:16 – Life and death
  • 3:42:44 – Would you live forever?
  • 3:50:26 – Meaning of life
  • 3:58:03 – Hat
  • 3:58:46 – Question for AGI

Azure digital twins combine traditional business data with a comprehensive model of many different aspects of reality in a single pane of glass driving operations, analytics and simulation.

This episode shows about how Bentley’s iTwin and iModel.js technologies are integrated with Azure Digital Twins in creating a solution for civil infrastructure design and operations that brings “live and time series sensor data” to the design and operations of the roads, bridges and tunnels in a roadway engineering project in Australia.

Learn more about iModel.JS at https://aka.ms/iotshow/iModelJS

ExplainingComputers provides an overview of eight x86 single board computers (SBCs)

X86 SBC overview, including LattePanda, Udoo, Odyssey & Digital Loggers boards. All of the single board computers featured in this video are based on an x86, x86-64 or AMD64 CPU, and have been reviewed in depth on this channel in the following videos

Content index:

  • 00:00 Introduction
  • 01:32 Atomic Pi
  • 03:10 LattePanda V1.0
  • 06:00 UDOO X86 AP
  • 08:02 LattePanda Delta
  • 10:25 Odyssey X86J4105
  • 13:23 LattePanda Alpha
  • 15:57 UDOO Bolt V8

In addition to powerful deep learning frameworks like TensorFlow for Arduino, there are also classical ML approaches suitable for smaller data sets on embedded devices that are useful and easy to understand — one of the simplest is KNN.

One advantage of KNN is once the Arduino has some example data it is instantly ready to classify! We’ve released a new Arduino library so you can include KNN in your sketches quickly and easily, with no off-device training or additional tools required.

In this article, we’ll take a look at KNN using the color classifier example. We’ve shown the same application with deep learning before — KNN is a faster and lighter weight approach by comparison, but won’t scale as well to larger more complex datasets.

Check out how you can manage and secure distributed multi-cloud compute resources in Azure using Azure Arc.

This extends a unified management plan to your virtual machines and physical servers on-premises, including your SQL Servers wherever they are.

Travis Wright, Principal Group Program Manager from the Azure Data Engineering Team, joins host Jeremy Chapman to share the latest updates.

If you’re new to Azure Arc, it simplifies complex and distributed environments across on-premises, edge, and multi-cloud into a unified central management plan in Azure. Now you don’t have to migrate these resources or move them to a common directory service; they simply stay where they are.

Google today announced changes to ML Kit, a toolset for developers to infuse apps with AI, designed to make it easier to use offline.

While the original ML Kit was tightly integrated with the web development platform Firebase, the refreshed ML Kit makes available on-device APIs in a standalone SDK that doesn’t require a Firebase project.

Google notes that more than 25,000 applications on Android and iOS now make use of ML Kit’s features, up from just a handful at its introduction in May 2018. Much like Apple’s CoreML, ML Kit is built to tackle challenges in vision and natural language domains, including text recognition and translation, barcode scanning, and object classification and tracking.