Category: Blog Software

We are propelling into an ever-more digitally dependant universe, and the demand for immediacy and convenience grows with it. While Fritz Lang’s 1927 silent movie Metropolis portrays an eerie future run by machines, 2020 is actually far more exciting and innovative – with the science of Artificial Intelligence ubiquitous and prevalent in our daily lives.

Through its ability to deploy complex deep learning models, AI holds the capacity to transform our quality of living. As such, it comes as no shock that the future of programming hinges on this science.

So, from a programmer’s perspective, what are the best AI frameworks for creating the space for such deep learning? We’ve discovered the top ten.

1. TensorFlow

Jumping straight in with the big dog, TensorFlow is an open-source framework that combines a selection of tools, libraries, and resources that allow programmers the ability to produce holistic results in one environment. With an emphasis on simplification and streamlined development, programmers from some of the most influential brands in the world employ TensorFlow to produce AI frameworks.

2. Amazon SageMaker Neo

Amazon SageMaker Neo is a fresh service feature of Amazon’s machine learning platform. It allows programmers to train machine learning models and to operate them anywhere in the cloud. Amazon SageMaker Neo really comes into its own for real-time object detection, and with the prevalence of edge computing devices and Internet of Things (IoT) sensors (such as car cameras and surveillance systems), this AI framework technology is revolutionary.

3. Microsoft Cognitive Toolkit

This example prides itself on being multilingual, robust, and owning the strength to generate extensive and advanced machine learning datasets. This AI framework can be used as part of a project or as an independent tool by means of BrainScript, its own model description language.

4. Keras

Versatility is a strong point for this Python-written framework. Keras can be layered on top of other AI frameworks to produce even more intelligent algorithms, and it provides a space for rapid experimentation. In addition, the focus on the developer experience makes Keras a yet more attractive framework for rapid prototyping new apps.

5. Theano

For developers who prefer a Python library, Theano is another fantastic example. It entwines deep-learning abilities with NumPy to produce an evaluation of complex mathematical expressions.

6. Accord.Net

This AI framework holds value for programmers who require access to libraries with an aptitude for audio and image processing. Accord.Net is a .NET-based AI framework written in #C and focusses on comprehensive yet speedy applications to accomplish a variety of AI problems.

7. Scikit-learn

Another in the Python-written repertoire of AI frameworks, Scikit-learn helps programmers conquer widespread AI issues, as well as providing space for data mining and analysis. As an open-source platform, it offers users convenience and versatility. It holds solid foundations on NumPy, SciPy, and matplotlib, and is particularly effective for personalized recommendations and predictive analysis.

8. Apache Mahout

This framework has been created by Apache and succeeds by offering programmers the opportunity to quickly implement their own algorithms via distributed linear algebra and mathematically expressive Scala DSL. It too has the capacity to overhaul some of the major AI problems.

9. Torch

Torch holds the edge for prioritizing GPU-led machine learning algorithms. Straightforward, efficient, and wholly flexible, Torch offers programmers an ecosystem of packages to fulfill all demands of deep machine learning applications.

10. Caffe

For programmers seeking greater expression, speed, and modularity, Caffe provides an answer. The advantage of Caffe is the power to collaborate with a community of subscribers offering a communal vibe for AI machine learning tasks. It is adaptable, quick, and supports the training of models without writing code.

To Summarize…

For the growing requirement for AI technology application, it is fair to say there is an equal number of competent AI framework hosts to fulfill the most intricate of programming tasks. Depending on your language, system, and library preferences, at least one of these suggestions will embody a programmer’s requirements… Stretch Your Mind

Blockchain offers huge potential in a large variety of markets and industries. Even outside of cryptocurrencies, blockchain technology makes decentralized consensus possible, which is a very useful property.

In this article, we’ll look at a few of the most significant market areas that will be disrupted by blockchain technology in the near future.

Healthcare: Secure Provider Data

The entire American healthcare system and industry is antiquated. Other countries might have better healthcare, but the way that healthcare data is processed and transferred is suboptimal all over the world. The systems used for accessing and transferring data do a poor job of maintaining security and privacy. Plus, centralization makes data loss likely.

Blockchain can help. By distributing and agreeing upon decentralized, distributed, encrypted data, different healthcare providers can access and share healthcare information with a much smaller chance of a data loss or corruption, not to mention privacy issues.

This isn’t purely theoretical, either. UnitedHealthcare, a major healthcare organization, launched an initiative using blockchain technology to revolutionize healthcare provider data storage and transfer.

Government: Foolproof Online Voting

Online voting systems are rife with fraud and technology challenges. Voting with a blockchain ledger means that votes can only be counted once. A central authority issues voter ID numbers and the blockchain makes sure that each ID number corresponds to at most one vote.

Banking: Easy, Fast Monetary Transactions

By far the largest and most well-tested industry utilizing blockchain is the banking industry. Bitcoin, the original cryptocurrency, uses the blockchain to send and receive money efficiently with no fees and no regulatory issues.

Many of the issues that plagued cryptocurrencies early on are largely being resolved. Environmental concerns, as a result of the high energy costs of proof-of-work algorithms, are made obsolete through new techniques like proof of consensus. The volatility of cryptocurrency is decreasing as it becomes more standardized and regulated.

Supply Chain Management: Guaranteed History

Because the blockchain ledger is irrevocable and distributed, each step in the supply chain can result in one transaction on the ledger. This means that the supply chain can be verified and secured in a way that was previously impossible, utilizing clever cryptography.

Companies in the food supply chain have already started using blockchain technology to efficiently and securely trace each batch back to its ingredients. This data is hugely useful in quality control and supply chain security.

Takeaways

For a number of years, blockchain was mostly just a buzzword. However, it’s proven its worth in a wide range of markets and industries. Because blockchain makes it possible for thousands of computers to simultaneously agree on a ledger, lots of industries and areas that need security and traceability benefit. From secure, distributed healthcare records to tracing the origins of food to voting online, blockchain offers lots of potential.

The digital landscape is a continuously transitioning environment, which means developers are kept on the edge of their seats. Maintaining an awareness of the advancements in the industry takes a particular set of skills.

A passionate Dev requires the ability to be both adaptable and multilingual across many coding languages, as they become responsible for both the frontend and backend creation of a website. Beginners must combat the intimidating task of becoming a fully-fledged developer by approaching the many hurdles with drive and commitment.

Let’s take a broad look at the efficient ways to become a full developer today.

Frontend and Backend Web Development

In order to become a fully-fledged, you must understand how a website is made from both the frontend (which is viewable by the user) and the backend (how the system functions behind the scenes). A full-stack is created when the frontend and backend are blended together to generate an operable digital experience – from website to app.

The backend stack consists of a web framework, a programming language, a database, a web server, and an operating system. One of the more popular examples is a LAMP stack (Linux – operating system, Apache – HTTP Server, MySQL – the database system, and PHP – the programming language), however, there are a multitude of models out there.

By shaping the backend elements, full developers will direct how a server translates the functionality. The frontend aspect of website development combines HTML, CSS, and JavaScript to formulate the viewable and interactive components. By fusing knowledge of each end, a full-stack developer will craft a comprehensive online experience.

Start From The Ground Up

In order to thrive in a full developer role, it’s crucial that you learn the fundamentals. Enter; HTML, CSS and JavaScript. These provide the backbones of any website today (if your goal is to deliver a more interactive and enhanced user experience).

Start with HTML, which forms the vertebrae (or structure) of a website. HyperText Markup Language is translated by the browser into a viewable page for a user. Next, get yourself up to scratch with CSS which molds the style functions of a website. Using Cascading Style Sheets, developers can bring a page to life with distinct fonts, colors, and designs.

Refining Your Developer Skills

Once you have these competencies under your belt, it’s time to hone in on the more rigorous and necessary skills required.

Development Frameworks

Frameworks inject efficiency in full-stack development projects. JavaScript is used to build the underlying technology stack that forms the frontend framework (Vue.js, React.js and Angular.js).

The backend frameworks are composed through Node.js and Django.

Primary Backend Languages

You will need to have knowledge and understanding of:

● PHP

● Python

● Ruby

● Java

Such languages build upon the foundations of HTML and CSS (which you already know now, right?).

Web Architecture And Design Skills

Building and operating the extensive software functions to create a digital output is a complex task, therefore, a full developer must be confident in their abilities to complete and manage a website’s architecture.

While full development requires the implementation of technical abilities, someone looking to excel in this environment will also be creative – after all, you will provide the user-orientated digital experience.

Mastery Of Web Protocols

A full dev will understand HTTP and REST as vital protocols in website creation. HTTP (or the more secure HTTPS) performs communication between server and client. REST produces an interconnection between frontend and backend and generates the ultimate digital experience.

A real Developer In 2021

Alongside these technical abilities, a great Dev should be incredibly patient, meticulous, analytical, and hungry for evolving industry knowledge. It’s not for the faint-hearted, but for those who are dedicated to becoming a full-stack developer, such a career is limitless, especially in 2021.

Even as a deeply technical profession, computer programming requires lots of human interaction. Most projects are done in teams, so you have to be good at working with other people. Plus, in many cases, communicating your ideas is just as important as implementing them in code.

In addition to communication, your personality traits also fall under the umbrella of soft skills. Working hard, being persistent, and adapting to changes are all soft skills as well.

Having great soft skills will make you better at interviewing and better at your job, so it’s worth spending some time improving them. In this article, we will take a look at some of the specific soft skills that every programmer should have for success.

Writing

From Git commit messages to technical API documentation, every programmer also needs to write prose. You don’t need to be the next Hemingway, but you should be able to communicate your ideas clearly. Many of the same characteristics that make for great code also make for great English—concision, clarity, and logical structure, for example.

Adaptability

Technologies change, companies boom and bust, and your career progresses. To succeed at your job and career in the face of constant change, you have to be able to adapt. Your adaptability is more than just going with the flow; it means continuously reevaluating the world and staying on top of changes so that you won’t be left behind.

Work will naturally throw you curveballs as you move through your career. Your success isn’t only defined by what comes your way—your reactions matter even more.

Empathy

Whether it’s helping a coworker through a tough time in their life or understanding a client’s frustrations, good empathy can set you apart. Everyone appreciates someone who understands them, so being there for people and understanding their concerns will make you appreciated.

Mentorship

Although less frequently discussed than other soft skills, your ability to mentor and nurture talent on your team is hugely important as well. As you progress into the senior ranks, you’ll have the opportunity to significantly improve the experience of your less experienced colleagues. You’ll improve your own understanding of fundamental technology while giving a more junior developer the help they need.

Take Soft Skills Seriously

Software engineering is widely considered one of the more meritocratic fields. Compared to other engineering and product development fields, programming has few formal requirements. That said, in addition to having great programming skills, effective software developers are also great at soft skills.

They can work in teams, nurture and improve the abilities of their less experienced teammates, empathize with their clients and coworkers, adapt to changes, and communicate their ideas in writing. These skills can’t be tested with a LeetCode-style whiteboard interview, but that doesn’t mean that you should ignore them. As you progress in your career, soft skills will become even more important as distinguishing factors between you and other experienced developers.

Data has become ubiquitous in our digitally-advancing world. Gaining momentum in early-2000, ‘Big Data’ refers to the huge amounts of data that is available to a business. Big Data is defined by Doug Laney’s three V’s; Volume, Velocity, and Variety. The scale of this data is so enormous that it is almost impossible to access and process through traditional means. Big Data Analytics is a term used to describe such vast datasets.

A Big Data Framework, then, is a structure that businesses employ to develop and expand on the data it retrieves. It offers an organized approach to data handling, and frameworks are malleable to the organization’s needs (regardless of its use of technology, specialization, or tools). It provides a space for consistency through a common reference model, and in its ability to apply deep learning, can identify ways that an organization can evolve and develop successfully over time.

Ultimately, the importance of Big Data isn’t about owning vast amounts of information, but rather how the information is used to accomplish economical benefits, time-efficiency, optimized product development, and a smarter approach to all-round processes and quality checks. Big Data coupled with analytic tools allows businesses to accomplish specific business needs, for example, the ability to identify potential fraudulent behavior, or root causes of failures or defects. It even has the power to generate coupons based on customer’s buying habits. Big Data is an incredibly powerful business tool, and a framework provides a space for the data to work.

Big Data Framework Structure

The structure of a Big Data Framework is combined between six core capabilities, each bolstering the structural integrity of the whole. This holistic approach to the structuring process of a Big Data Framework ensures that attention is spread evenly across each element. These six core capabilities are;

● Big Data Strategy

● Big Data Architecture

● Big Data Algorithms

● Big Data Processes

● Big Data Functions

● Artificial Intelligence (AI)

Frameworks Depending On Project Needs

While a framework is adaptable, it’s important to recognize the best approach to accomplish business needs. While this doesn’t necessarily mean that one framework will do the trick, two or more will set you up nicely. Here are the most popular:

Hadoop

The Hadoop framework is easy-to-use, and has the capacity to store and distribute massive datasets in real-time. It was originally designed for massive scalability. It is cost-effective, fast (via its unique cluster-style storage method), and flexible. It is an open-source batch processing framework, and operates under the premise that hardware will fail. It is effective for its ability to supply a massive storage area for a variety of datasets, meanwhile, offering the processing power to handle tasks simultaneously.

Apache Spark

The Apache Spark framework is more of a hybrid. While it is similar in its approach to data storage, it is also the leading SQL platform for batch processing, stream processing, and machine learning (machine learning is one of the primary attributes of Spark). It can distribute data sets of varying sizes across a multi-computer network single handedly or alongside alternative distributing tools, and at lightning speeds. Its ability to harness machine-learning, and to be operational from a single machine makes Spark such an effective framework.

Apache Storm

Another example of an open-source framework, but one that delivers real-time stream processing. Apache Storm is versatile in the way it can be used with any programming language. It is also a framework that transforms data using a topology model; it demystifies small and discrete operations into useable data. Rather than batch processing, Apache Storm offers a reliable and unbounded stream approach to data processing. Easy to set up and operate, the addition of fault tolerance, high-speed reactivity, and scalability are additional bonuses of Apache Storm.

Samza

Like Storm, Samza delivers and processes data in real-time, therefore, through continuous computation and output (as opposed to batch processing), provides a framework for stateful applications, fault tolerance, and resource management. This multitasking framework offers distributed stream processing with limited disruption to original streams. It is also incredibly agile when working in tandem with alternative frameworks.

Flink

Flink could be considered the perfect all-rounder as an open-source hybrid framework that accommodates both stream processes and batch tasks. Written in Java and Scala, Flink offers users the ability to fuse batch and stream data processing. The Flink framework allows users to create programs written in Java, Scala, Python, and SQL. The stream processing model employed by Flink includes immutable streams, the ability to layer operator functions (to create other streams), sources as entry points for streams, and sinks – as an area for streams to flow out of the system. This allows users a high throughput and fault tolerance. Flink’s downfall is that it does not offer a dedicated storage system, however, the silver lining is its compatibility with Hadoop.

Discovering Your Ideal Big Data Framework

While each framework presents a fantastic solution to certain requirements, oftentimes it makes most sense to use two or more in conjunction. This way, you reap the advantages of different Big Data frameworks. However, it’s important to note that there are varying costs associated with different frameworks. Ensure the one you employ reflects the goals of the business. It could even be a good time investment to play with a couple of frameworks to see which is the best fit. In any case, the Big Data framework/s a business implements should be considered an investment for all the potential returns it guarantees to deliver.

Since its inception in 1956, Artificial Intelligence (AI) has progressively transformed industrial processes – it’s application now ubiquitous in 2021. In this data-centric era, the movement of Industrial AI is pivotal for growth in its ability to employ deep-learning to autonomously solve and develop technology and processes, and produce meticulous results.

Why Industrial AI Is Crucial In 2021

Industrial AI is crucial for development; it enhances time-efficiency while simultaneously delivering consistency and reliability – all contributing to the functions of successful business output. With competition and innovation running rife in all industrial sectors (and the added layer of social distancing in 2020) Industrial AI fulfills the need for a more complex approach to operations, advancing the necessity for progress and development.

Broad Examples Of Industrial AI Applications To Transform Processes Offering sophistication and a smarter tomorrow, Industrial AI reconstructs the way all manner of processes are executed in an industrial context.

Accelerate Design Validation Processes

Industrial AI optimizes the time-consuming design process of new products and systems by discovering potential and concealed issues. This is effective for avoiding future unforeseen obstacles later down the line.

Automation Of Material Movement Jobs

Industrial AI allows for consistent material handling and movement across various warehouses; from aerospace to e-commerce, automobile to perishables. Self-driving vehicles loaded with AI technology navigate their environment while distributing materials from A to B, allowing consistent industrial movement.

Enhanced Predictive Maintenance

Applying Industrial AI systems to maintenance processes ensures such essential tasks are achieved to a superior standard. Predictive maintenance is a combination of AI-driven measures and data from sensors and the Internet of Things (IoT) which allow manufacturers to foresee and desist future machine failures. Not only does this streamline the manufacturing process in real-time, but it offers a more economical and time-efficient future.

Improved Growing Conditions

As the world’s population skyrockets, its crucial that food harvesting is working at an optimum level. With the developing popularity in both indoor and vertical farming (each optimizing space), Industrial AI is used in combination with other digital technology to deliver the perfect growing conditions for plants.

Optimized Supply Chain Network

Hindrances in any widespread supply chain can present disruptions in both cost-efficiency and productivity. Industrial AI commands the supply chain by employing predictive and complex systems to optimize each level of process; from logistic planning to producing inventories. This practical approach removes the potential obstacle of human error and sustains a dynamic supply chain.

The Top 10 Cases Of Real-World Industrial AI

Predictive Maintenance

As explored above – through Machine Learning, data readings, and sensors, Industrial AI systems can interpret and predict the requirement for maintenance of one or many assets in an industrial workspace.

Quality Checking

Industrial AI is used in conjunction with optical technology to perform automated optical inspection which allows manufacturers to spot any detrimental flaws in a system or product.

Manufacturing Process Optimization

Autonomous machines replicate tasks usually carried out by humans and achieve a level of productivity that is both consistent and reliable. Through Machine Learning, robots learn their dedicated task without human interaction.

Supply Chain Optimization

When it comes to a well-oiled supply chain, predictive inventory management is one of the most widespread applications of Industrial AI. It is a vital aspect for manufacturers to generate both economic efficiency and productivity.

Cybersecurity And Privacy

In our data-driven, GDPR-aware society, it is necessary to apply AI-powered cyber threat detection and response. This is critical for maintaining safety in the digital landscape.

Autonomous Physical Security

Big Brother is watching! AI-surveillance systems ensure safety measures are continually present by identifying potential threats and hazards in the workplace (and beyond).

Data Management

Accessing data is a tricky task at the best of times. Industrial AI systems have the capacity to trawl and access stored data with a systematic approach. Such procedures as data management, data acquisition, and data filtering (for example) are achieved in real-time speed and accuracy.

Smart Assistants

There’s no escaping the likes of Siri and Alexa. In the industrial environment, voice assistants enhance the experience of monitoring systems by removing the need for inputting commands or printing long status reports. Instead, workers can request insights in real-time.

AI R&D

Electronic design automation (EDA), or computer-aided design, is a primary application of AI that allows manufacturers to achieve multiple versions of a design that overcome certain pre-set constraints.

Resource Exploration

In more volatile, hazardous working environments, Industrial AI increases levels of safety and productivity by analyzing and processing a scope of images (captured via radar, satellite, and/or drone) to decipher an optimal point for resource extraction. This is most effective in Mining, Oil, and Gas industries.

Industrial AI; All Systems Go

From food manufacture to artificial assistants, AI paves the way to a smarter, more consistent, more productive future. Its place in an industrial context is permanent and evolving – the capacity for AI development is limitless. Optimizing output, efficiency, and productivity and safety measures, AI is a prerequisite in the future of all levels of industry.

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2021 is a great time to learn programming or improve your skills. With more time at home as a result of the coronavirus pandemic, you probably have lots of opportunity to learn more about programming and write fun or useful software.

Whether it’s your first language or your twentieth, learning in-demand programming languages is a great way to expand your knowledge and become more useful to employers. Some are extremely easy to learn, while others focus on speed of execution or runtime safety.

Every language makes a slightly different tradeoff in some dimension, so adding more languages to your toolbox is always a good thing. Plus, most concepts transfer between languages, so the second or third will be far easier than the first.

In this article, you’ll learn a little bit about some of the most popular and useful programming languages in existence and what distinguishes each one from the rest.

Python: Simple, Elegant, Powerful

Python is a hugely popular programming language—quite possibly the most popular in the world. It’s commonly used for tasks ranging from data science to backend web applications. Python’s claim to fame is its ease of use, libraries for every task, and huge community.

Regardless of what you’re doing with Python, expect to find a warm and welcoming community of people from around the world working on a similar thing. The job market for Python is large and thriving, so learning it can be a great financial decision as well.

JavaScript: The Power of the Browser

Unlike Python, JavaScript has one primary niche: frontend web development. While JS can also run server-side via Node.js, it’s mostly used in code running in users’ browsers.

JavaScript’s community is of a similar size as Python’s. Additionally, practically every computer user has a JavaScript runtime installed (in the form of their browser), so showing off your creations couldn’t be easier. On NPM, the package registry for JavaScript, there is a package for nearly every imaginable need.

C# and Java: Fast, Safe Backend Code

C# and Java aren’t directly related, but they fill a similar niche and are similar languages, so they go together. Compared to C++, Java and C# are simple to learn and nearly as fast for most use cases. Backend web apps are the most common use cases for these languages, but nearly anything is possible with these two languages. They’re mature and well-supported on every major operating system.

Java runs on the Java Virtual Machine, while C# runs on the Common Language Runtime. In both languages, you can compile a single executable and run it on any system with the JVM or CLR installed.

What’s Next?

The four programming languages we looked at today are just a few of the hundreds or even thousands of languages in existence. However, knowing a few of these languages well will provide you with a foundation to build all sorts of software and learn any other language you could come across throughout your programming career.

A manifesto is defined as a declaration of set policies and purposes of a particular movement – a rule book if you will. The Agile Manifesto is a response to software development product delivery and is combined of four values and twelve principles. From humble beginnings as a swift and adaptable working model to software development, the Agile Manifesto is universally agreed to dictate a straightforward, more productive approach to product delivery, now, within a variety of industries.

With an understanding of its history and its purpose, it is possible to examine why Agile is relevant in the modern workplace, specifically, while the world is encouraged to work from home during the COVID-19 pandemic.

What Is The Agile Methodology?

The Agile Manifesto was born from frustration caused by a misalignment between a client software order (to overcome a particular business need) and the amount of time to deliver the technology. During the time lag, client business requirements evolved, meaning the final product, based upon the original blueprint, was automatically outdated.

Many projects were canceled. Traditional software development led by the Waterfall model (whereby communication between client and developer was limited) didn’t take advantage of the pace of technological advancement, nor did it achieve the call for speed – it simply didn’t work efficiently enough. In 2000, a group of tech pioneers conceived the set of values and principles that would become The Agile Manifesto procedure for software development. In a nutshell, the Agile Methodology prioritizes speed, innovation, and elevated customer focus in the workplace.

How Does The Agile Manifesto Fit In The Modern Workplace?

Agile remains a crucial component in work methodologies. In the context of 2021, it provides an easy-to-manage platform on which to develop and deliver effective work strategies. However, The Agile Manifesto now encompasses far more than software development. Organizations that maintain the priorities of Agile incorporate the functions throughout the business.

Along with many other realizations, COVID-19 has demonstrated that we can never be fully prepared for a sudden slap in the face. More than ever, the Agile approach is called upon to fulfill service and product demand. After all, its principles are based on quick shifts, reactivity, proactivity, and adaptability; the clue is literally in the name.

The Agile Mindset And The Influence Of COVID-19

The spontaneous shifts and movements of the industrial landscape demand reactivity from organizations – COVID-19 has pushed all working systems to the limits. While those who are able to are encouraged to work from home, Agile provides the backbones for team collaboration and project success; this is particularly beneficial for aiding the obstacle of physical distance. The methodology, to break a working timeline into manageable bitesize pieces, makes it easier for an organization to achieve current expectations of a project at each leg of the journey. With a dissected work journey, Agile provides a gap for team members to provide an immediate and real-time response to such changes, as a result, ensuring the ultimate product is based upon in-the-moment necessities.

Though COVID-19 has disrupted our familiar working system, Agile provides us with a few tricks up our sleeve. Part of the Agile Manifesto is to award power to the individual. Decisions can be made without managerial consent, and this preserves a steady momentum throughout a workflow. While team members are restricted to certain working locations, members are able to implement decisions without drawing out the confirmation process.

Keeping Business Buoyant

The ‘new normal’ molded by the pandemic means that businesses, clients, and individuals alike have been put to the test. Social norms are altered for the foreseeable, meanwhile, working systems have required urgent reformation.

Nevertheless, The Agile Manifesto presents a space for industries to grip on to a sense of stability, despite any temporary downturns. Agile is reuniting us with its technological roots. Through videoconferences, instant messaging, and the plethora of digital tools at our fingertips, organizations have the opportunity to adapt and survive by means of the Agile working model – let’s take advantage.