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The government is funding basic research in AI, too. When big technologies like mobile phones, 5G networks and e-commerce arrive, it’s important to get in on the ground floor. That’s why the US government is establishing 12 new research centers, funded with hundreds of millions of dollars, to boost artificial intelligence and quantum computing. By Stephen Shankland @cnet.com.

The Department of Energy’s five quantum computing centers, housed at US national laboratories, are funded by a five year, $625 million project bolstered by $340 million worth of help from companies including IBM, Microsoft, Intel, Applied Materials and Lockheed Martin. The funds came from the $1.2 billion allocated by the National Quantum Initiative Act, which President Donald Trump signed in 2018, but the private sector contributions add some new clout.

The article then deals with:

• Challenging US ‘adversaries’
• Private sector already eager
• Quantum sensors, networks and error correction

The idea is to link government, private and university research to accelerate key areas in the US. It’s the same recipe used for earlier US technology triumphs like the Manhattan Project to build the atomic bomb in World War II, the Apollo program to send humans to the moon and the military-funded effort to establish what became the internet. Interesting!

AI tools are shaping next-generation theorem provers, and with them the relationship between math and machine. By Stephen Ornes.

A proof is a step-by-step logical argument that verifies the truth of a conjecture, or a mathematical proposition. (Once it’s proved, a conjecture becomes a theorem.) It both establishes the validity of a statement and explains why it’s true. A proof is strange, though. It’s abstract and untethered to material experience.

Computers are useful for big calculations, but proofs require something different. Conjectures arise from inductive reasoning – a kind of intuition about an interesting problem – and proofs generally follow deductive, step-by-step logic.

Another gripe is that if they want to use theorem provers, mathematicians must first learn to code and then figure out how to express their problem in computer-friendly language.

Mathematicians, logicians and philosophers have long argued over what part of creating proofs is fundamentally human, and debates about mechanized mathematics continue today, especially in the deep valleys connecting computer science and pure mathematics.

“Computers have done amazing calculations for us, but they have never solved a hard problem on their own Until they do, mathematicians aren’t going to be buying into this stuff.”

– Kevin Buzzard, a mathematician at Imperial College London

Recently, Simon DeDeo together with Scott Viteri, a computer scientist now at Stanford University, reverse-engineered a handful of famous canonical proofs (including one from Euclid’s Elements) and dozens of machine-generated proofs, written using a theorem prover called Coq, to look for commonalities. Very interesting read!

Building micro-frontends to speed up and scale our web development process. By Jonathan Saring.

At Bit, we build tools for over 100,000 developers working with components. Our tools help developers build, reuse, and collaborate on independent components to speed up development and improve application quality.

Micro front-ends are a way to split the monolith front-end codebase into smaller, more manageable pieces. As a result, front-end teams can enjoy similar benefits to those of microservices: maintainable codebases, autonomous teams, independent releases, and incremental upgrades.

Source: https://martinfowler.com/articles/micro-frontends.html/

The articles then pays attention to:

• So what are micro front-ends, really?
• Our process
• Autonomous teams
• Simple, decoupled codebases
• Independent release pipelines
• More incremental upgrades
• Infinite component reuse and collaboration

Some say micro front-ends is nothing but a “good component model”. For their team, it was a good component model as much as it was a better way to build our own team, improve the way we work, build better modular software, and deliver it faster and more often.

In recent years, microservices allowed backend architectures to scale through loosely coupled codebases, each responsible for its own business logic and exposes an API, each independently deployable, and each owned and maintained by a different team.

Tailwind CSS is one of the rising stars in the CSS framework world. It’s especially popular in the Laravel and Vue.js community. By Markus Oberlehner.

In this article, we learn how to set up Tailwind CSS to work with a Vue CLI powered application. Because Tailwind CSS is a utility-first CSS framework which provides a lot of utility classes out of the box, its file size without any optimizations is pretty massive. But luckily, we can use PurgeCSS to improve the final bundle size of our application tremendously.

The article focuses on the following:

• Using Tailwind with Vue CLI
• Customizing the Tailwind configuration
• Reducing file size with PurgeCSS
• Writing purgeable Vue components

You will also get code examples and plenty of links to further reading. Although setting up Tailwind CSS to work with Vue.js is rather straightforward, things become a little more tricky after also adding PurgeCSS into the mix. Short and sweet!

Cloud technology is one of the most talked-about topics in today’s date, and in the past few years, it has revolutionized the way startups and small businesses operate and store their data. It not only helps automate a number of operations in your business but is also a lot more cost-effective. Published on securityboulevard.com

Cloud technology has a number of applications besides just storing your data. If you want your startup to shine in this competitive market, you should make optimum utilization of the cloud technology. In this blog, author will discuss 5 effective ways of using cloud technology that can give you startup an edge over your competitors:

• Setting up remote workplaces
• Adding extra security to the network
• Providing round the clock access to data
• Offering scalability
• Enabling disaster recovery

A majority of the successful small businesses are using cloud technology to their advantage. It is affordable and offers flexibility, security and scalability. Nice one!

Need to get up to speed on Cassandra and learn how it can benefit your software development practices? These are the essentials that cover the basics. By Rebecca Mills, Developer Relations @ DataStax.

Apache Cassandra is an extremely powerful NoSQL database used by Apple, CERN, GitHub, Instagram, Netflix, Reddit and over 1500 more companies with large, active data sets. Recent developments have made it much easier for developers to use, including the creation of Kubernetes Operators and “as a service” options.

In this article, we explain Cassandra’s approach, architecture, key concepts, and primary use cases – and demonstrate the reasons why so many people want to adopt it.

The article is split into these sections:

• Cassandra as a NoSQL database
• Distribution provides power and resilience
• Want more power? Add more nodes
• Introducing Partitions
• Replication ensures reliability and fault tolerance
• Tuning your consistency

Ultimately, Cassandra is deployment agnostic. It doesn’t care where you put it – on prem, a cloud provider, multiple cloud providers. You can use a combination of those for a single database. Nicely done!

Run your CI jobs as AWS Fargate tasks with GitLab Runner and the Fargate Driver. Autoscaling GitLab Runner is a unique value proposition for teams that run their self-managed build agents on cloud-hosted virtual machines. By Darren Eastman.

Source: https://about.gitlab.com/blog/2020/05/11/introducing-autoscaling-gitlab-runners-on-aws-fargate/

GitLab Runner autoscaling responds to demand by provisioning new cloud-hosted virtual machines with Docker and GitLab Runner. When demand is lower, any additional virtual machines above the configured minimum size are de-provisioned. However, while this model of automatically provisioning and terminating virtual machine instances continues to be useful for a vast plethora of use cases, customers also want to take advantage of the capabilities of cloud container orchestration solutions for executing GitLab CI/CD jobs.

The article then walks you through:

• So how does it work? (Custom executor)
• So how does it work?
• Limitations
• Getting Started

With the release of the GitLab Runner AWS Fargate driver, we provide the most diverse set of options in the industry for executing CI pipeline jobs in an autoscaling configuration. These options now include cloud-delivered virtual machines, AWS EC2, Google GCP, Azure Compute, and container orchestration platforms: AWS EKS, AWS ECS + Fargate, and Google Kubernetes. Excellent read!

How to succeed with microservices through principles, domain driven design concepts, and coding best practices.By Tiffany Jachja.

For organizations working with microservices, success can be varied and gaining the benefits of the cloud can be a costly journey. This blog post will share how to succeed with microservices through microservices principles, domain driven design concepts, and considerations around coding best practices.

Microservices are small, autonomous services that work together. Loosely coupled and high cohesion are the two concepts that refer to microservices. Cohesion is how we group related code together, and coupling refers to how different services depend on each other.

The article is split into these sections:

• The foundational layers of getting to the most of Kubernetes / container-native benefits
• The nature of systems design
• Microservices
• The seven principles of microservices

Many organizations investing into Kubernetes initiatives are looking to succeed with microservices. This blog post peels back the layers of the onion to show how to succeed with microservices. Ggood read!

The world of artificial intelligence assistants is growing with such velocity that it doesn’t surprise us anymore and it’s about to be ubiquitous. Written by Maria Meheden.

Most of us already engage with AI algorithms on a daily basis: search engines, transportation, healthcare, personal finance, aviation, and many more. Most of the development in AI is associated with benefits to humanity, everything from face and voice recognition to the latest self-driving cars.

Source: https://assist-software.net/blog/how-create-custom-alexa-skill-using-aws-lambda-and-serverless-framework

Conversational interfaces (CI) became one of the main digital marketing trends of last years. In the words of Ev Williams, CEO of Medium, a partner at Obvious Ventures and co-founder of Twitter, “the future operating system for humanity is the conversation.” Conversational interfaces are not just a fun thing, but a great opportunity for brands to be in close communication with their target audience.

The article deals with:

• Artificial intelligence assistants introduction
• Creating an Amazon Alexa Skill
• Building the Lambda function

You will get all the code for lambda function and also charts explaining some concepts. Once you start interacting with Alexa, it becomes so comfortable that you start wanting more from it and you realize that a voice interface isn’t something that’s foreign or not to be adopted. A pathway to grow your business. Well written!

In this two part series, we’ll be taking a deep dive into what exactly the Actor Model is, how exactly the Actor Model is implemented in Elixir/Erlang and how we can leverage this pattern in pragmatic way from within our applications. By Alex Koutmos.

To really understand these concepts, we will be writing a Phoenix application that relies on GenServers as the primary datasource that powers our business logic.

The article then reads about:

• What is the Actor Model?
• How does the Actor Model work on the BEAM?
• Create a new Phoenix project and install dependencies
• Writing a data scraper using Floki
• Seeding our database with sample data

The Actor Model is a programming model that aims to provide a mechanism for dealing with and reasoning about concurrent programs. There are many paradigms and methodologies that exist to provide a pattern for concurrent programming and the Actor Model is just one of those. There are other concurrency models like CSP (Communicating Sequential Processes) and shared memory concurrency (using threads and locks). Plenty of code examples and great number of links to further reading. Good job!