Anthony Chu joins Donovan Brown to show how to deliver live updates from Azure Functions to web, mobile, and desktop apps with Azure SignalR Service.

Learn how to send real-time messages over WebSockets from your serverless apps with a few lines of code.

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In this video, take a deeper look at alternative ledger technologies. We look at developer experiences for Corda using the R3 extension for VS Code.

Additional details and sample code are available on GitHub: https://github.com/corda/samples 

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Lex Fridman interviews William MacAskill, a philosopher, ethicist, and one of the originators of the effective altruism movement.

His research focuses on the fundamentals of effective altruism – the use of evidence and reason to help others by as much as possible with our time and money, with a particular concentration on how to act given moral uncertainty. He is the author of Doing Good Better – Effective Altruism and a Radical New Way to Make a Difference. He is a co-founder and the President of the Centre for Effective Altruism (CEA) that encourages people to commit to donate at least 10% of their income to the most effective charities. He co-founded 80,000 Hours, a non-profit that provides research and advice on how you can best make a difference through your career. This conversation is part of the Artificial Intelligence podcast.

Time Index:

  • 0:00 – Introduction
  • 2:39 – Utopia – the Long Reflection
  • 10:25 – Advertisement model
  • 15:56 – Effective altruism
  • 38:28 – Criticism
  • 49:02 – Biggest problems in the world
  • 53:40 – Suffering
  • 1:01:40 – Animal welfare
  • 1:09:23 – Existential risks
  • 1:19:08 – Existential risk from AGI

Here’s an interesting write up on Deep Learning from Forbes, that provides an overview of the technology for non-practitioners.

“During training, you define the number of neurons and layers your neural network will be comprised of and expose it to labeled training data,” said Brian Cha, who is a Product Manager and Deep Learning evangelist at FLIR Systems. “With this data, the neural network learns on its own what is ‘good’ or ‘bad.’ For example, if you want the neural network to grade fruits, you would show it images of fruits labeled ‘Grade A,’ ‘Grade B,’ ‘Grade C,’ and so on.

The neural network uses this training data to extract and assign weights to features that are unique to fruits labelled good, such as ideal size, shape, color, consistency of color and so on. You don’t need to manually define these characteristics or even program what is too big or too small, the neural network trains itself using the training data. The process of evaluating new images using a neural network to make decisions on is called inference. When you present the trained neural network with a new image, it will provide an inference, such as ‘Grade A with 95% confidence.’”

Here’s an interesting project for those of you with young kids at home looking for ways to find a way to entertain and educate them while under a COVID-19 lockdown.

Using an object detection AI model, a game engine, an Amazon Polly and a Selenium automation framework running on an NVIDIA Jetson Nano to build Qrio, a bot which can speak, recognise a toy and play a relevant video on YouTube. My wife and I have a super curious […]

Deep learning has the potential to answer questions which have been unanswered or unable to be predicted until now.

There are a variety of other machine learning algorithms, which can be used to find insights from data.

The financial sector has been using machine learning techniques for a long time in order to gain business growth through higher profit. Credit card fraud detection, stock market prediction, among others, are some of the popular machine learning approaches in this sector, which the companies have actively adopted to streamline their business operations.

Here’s an interesting read about predicting banking crises with AI.

In this article, we will discuss a deep learning technique — deep neural network — that can be deployed for predicting banks’ crisis. This experiment is based on the African economic, banking and systemic crisis data where inflation, currency crisis and bank crisis of 13 African countries between 1860 to 2014 is given. By predicting through a deep learning model, we will see that this model gives a high accuracy in this task.