Almost every college and university has moved towards e-learning platforms due to the pandemic. The introduction of learning management systems have made it easier for schools, colleges, and universities to reach out to students.
Stats show that the retention rate for students taking classes online is more compared to traditional classroom learning. The learning management system has proved to be an added advantage. With schools and universities cautious about opening up, it seems like e-learning platforms are only going to be getting more popular.
With the implementation of AI, the education world is about to scale to new heights and transform the learning sector like never before. The education system has already seen some of the impacts that artificial intelligence and machine learning have brought about. The aim is to create a new era where every student can understand their full potential and serve the society in the best way possible. Here are the recent advancements in AI that are reshaping the industry.
1. Content Creation
AI has the potential of creating content that is free from errors and of top-notch quality. It can take the burden of creating accurate and unique source material off the institutions, as well as circulating it securely.
Books need not be in the conventional format. With the implementation of voice recognition, audio-books can be created. It is proven that audio-books are more effective than traditional books. For students suffering from dyslexia or who lack reading prowess, these methods prove to be of great help.
2. Smart Mentoring Systems
It is hard to expect a teacher to give equal attention to every student in a classroom. That is where smart mentoring systems have a significant role to play. Based on the capacity of the student and their ability to grasp things, classes can be handled accordingly. Each student has different learning needs. A mentoring system would work on the student’s abilities and design a course according to their individual capabilities.
Plenty of online educational training institutions have opted for such a mode of teaching. Smart mentoring systems will not only help the students but also assist the teachers in equipping themselves according to the capability of the classroom.
3. Augmented Reality
There is a theory that if you create an experience, it stays with you for a lifetime. Augmented reality is based on a similar concept. Implementing this into the education system can genuinely transform the experience of learning. Students get to experience knowledge in the real world rather than just theoretically learning about it.
From ancient history to complex biology, work continues to convert all the academic knowledge into augmented reality. A future awaits us where students get to relive the World War. The hologram is another such method that uses the same principle.
Augmented reality will not only improve the educational standards but will create a future where students will be better equipped to come up with sophisticated solutions to indefinite problems. A global renaissance can be expected with the implementation of this technology.
4. Personalized Learning
Is it reasonable to expect a student to be good at everything? There are still schools where students are asked to be good at subjects they are not interested in. To eradicate this, a new methodology called personalized learning will likely be introduced. Based on the student’s preferences, a virtual mentor would be assigned to handle classes. Just like while binge-watching Netflix, a recommender engine algorithm will be introduced that will help the student choose their preferred subject. Fuelled by this concept, the world can see a rise in specialised Digital Universities where anyone can learn whatever they want.
5. AI-Based Virtual Assistants
Alexa and Siri have vastly impacted our day-to-day lives. Imagine harnessing something like that solely for education. AI-based virtual assistants, equipped with information, can bring everything at the tip of your fingers. They kickstart a whole new era of modern education, where instead of a teacher, a bot would be training the students. The speed, efficiency, and capacity to handle a vast number of students will increase greatly.
Final Thoughts
The future of education is not to be mistaken with a competition between bots and human teachers. The quest behind all these advancements is to empower human teachers and reimagine the educating experience by assisting them with AI. The objective is to not replace but to enhance the experience of gaining and imparting knowledge in an efficient manner and updated with times.
If you ever had a chance to compare the resumes of 2 developers who come from nearly identical backgrounds and skills – languages, frameworks, platforms, methodologies, industries; you may find them practically to be the same person – on paper. However, not both could be equal while comparing their success metrics.
The roadmap to success for a developer is not limited to bearing technical skills and credible experience. It is a long journey of immense hard work and dedication as well as crucial behavioral characteristics and optimal mindset that sets one apart from the crowd. To make a mark in the software development industry, there are a few traits and habits that are widely considered by professionals as value-boosting add-ons that give them an edge over the rest. 1. Write Clean, Readable, And Reusable Code
In the context of software development, everyone can write code, but not everyone can write good code. A successful developer is the one who makes sure their code is clean, robust, flexible, and reusable – not just for themselves in the future but also for anyone else working on it in the future, irrespective of the coding standard or methodology used. While it is easy to write line after line and not pay attention to your function size, you need to pause and remember that you’re going to be spending more time reading and testing it than writing it. It is important to understand that when you go back to read it, you should be able to understand it quickly and know what you’re meant to do. 2. Constantly Expand Your Knowledge Base
Underestimating oneself is dangerous – but so is overestimating. A successful software developer is never complacent; they know there is always something new that they do not know about and are always levelling up their know-how.
Be on the lookout for new improvement opportunities. To keep pace with the advancements, you can join skill enhancement programs, online training, read tutorials, watch videos, etc. Upskilling yourself will be a valuable habit to develop in your career. 3. Be Disciplined And Persistent
A successful software developer is constantly looking to expand their knowledge and sticking their nose into new languages. However, that route is not free of obstacles. To successfully learn something new and gain expertise, one needs to be stubbornly disciplined and not too attached to their comfort zones.
Being disciplined helps finish what you started – and on time – without compromising on quality for speed. Persistence, on the other hand, is what will help you to go beyond the pain-points, beat all the odds and obstacles, and ace your deadlines – both personal and professional. 4. Get Help From Strangers On The Internet
Don’t confuse persistence with pride. Successful developers don’t allow their egos to turn a programming problem into an unnecessary productivity drain—especially not when a solution may be readily available online. Sometimes, asking for help—yes, Google counts—is the most efficient first step toward a solution. Research what the code does and why it solves the problems. Do not just cut/copy and paste. Lack of background knowledge could create problems in your code. 5. Know Your Forte
Not everyone is made for doing everything. You may be good at developing UI and UX but may not be an expert at developing the back-end of an application. Successful software developers know their forte and work around that to succeed in whatever they do. 6. Be Open For New Things
Another habit of successful developers is being open to new things and going the distance in embracing them when it is required. To meet a client’s expectation, a developer may be required to go an extra mile in researching and discovering every aspect of technology. Successful developers are in a constant feat of exploring new technologies, methodologies, and frameworks that help them stay abreast with the latest trending topics. 7. Listen More Than You Speak—Or At Least Listen Before You Speak
If you’re in an office with other developers, listen first, then speak. It’s the quickest way to learn.
This requires humility, especially if you think you’re the smartest person in the room. Great developers have the ability and willingness to admit when they do not know something. Refrain from being that programmer who spews out a bunch of technical jargon instead of acknowledging they might not yet know the answer to a particular question. 8. Pay Attention To The Details
Any successful developer can tell you that to write a perfect code or to complete a project, one needs to pay detailed attention to the client’s requirements and work accordingly. You would not want to work on a piece of code again and again because of a small semicolon that you forgot to add while terminating a line of code. Similarly, be responsible for the objective and the goal that the code you are working on is intended to achieve. Successful developers are always aware of how each line of their code helps to drive the business as a whole. 9. Deeply Focus On The Right Thing
The ability to focus on the shared goal of a particular project without getting side-tracked by nice-to-haves or pie-in-the-sky thinking that isn’t moving you closer to your target is vital. It is understandable to go down rabbit holes or get wrapped up in the edge cases of a particular technical implementation. When you step back a little bit, you might realize that you’re putting a lot of effort into something that is not that crucial to the broader goal that everyone is trying to achieve. Make sure you’re on the same wavelength as the rest of the team. Successful developers understand that innovation is a team sport. 10. Have The End Product In Mind All The Time
You cannot hit a target that you cannot see. You must set clear goals for where you are headed and what you really want to achieve at the end of your project. If you start developing a project keeping this mantra in your mind, there is always a better chance to stand out from the rest.
Source: https://content.techgig.com/7-habits-of-successful-developers/articleshow/72996679.cms
Low-Code and No-Code platforms are going to be game-changer for tech professionals across the world. The increasing number of low-code and no-code machine learning (ML) libraries is making it extremely faster and easier to develop top-notch projects. Here are top no-code and low-code libraries that you should be aware of.
1. Pycaret
This is an open source, low-code machine learning (ML) library in Python which automates ML workflows.
It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive.
You can easily tune the hyperparameters of the various models on GPU.
2. H2O AutoML
Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. AutoML tends to automate the maximum number of steps in an ML pipeline — with minimum amount of human effort — without compromising the model’s performance.
H2O AutoML is an automation tool used as the combined interface for multiple models and algorithms.
It is fully open-source, distributed in-memory machine learning platform with linear scalability.
It supports both Python and R programming languages. For beginners, it helps to automate preprocessing, training, validation and fine-tuning models.
3. Auto-ViML
This low-code library is also known as ‘Auto_ViML’ or “Automatic Variant Interpretable Machine Learning” (pronounced “Auto_Vimal”). It accepts any dataset that is in the form of the Pandas data frame.
One of the library’s unique differentiators is that it performs feature reduction (or feature selection) automatically in order to produce the simplest model which in this case is the model with the least number of features needed to produce reasonably high performance.
This tool performs category feature transformation and simple data cleaning steps such as identifying missing values as “missing” so that they can be best left to the model to decide how to use them.
Auto-ViML provides verbose output to allow for a great deal of understanding and interpretability.
4. Create ML
Create ML is a purely no-code, drag and drop solution developed by Apple. It works on macOS and comes with a bunch of pre-trained model templates.
You can also train models to perform tasks like recognizing images, extracting meaning from text, or finding relationships between numerical values.
Before the training, you can set the iteration count and fine-tune the metrics. For models such as style transfer, Create ML provides real-time results on the validation model.
5. Google Cloud AutoML
Google has created the Apple-like AutoML tool. AutoML by Google Cloud offers various natural language, AutoML translation, and video intelligence products.
Rather than starting from scratch when training models from your data, Google Cloud AutoML implements automatic deep transfer learning and neural architecture search for language pair translation, natural language classification, and image classification.
Google Cloud AutoML helps developers with limited ML expertise to build models specific to their use-case and business needs.
Ambient Intelligence is set to rise in its scope and potential as machine learning continues advancing and the number of IoT devices and sensors continue increasing.
Ambient Intelligence (AmI) is a new paradigm in information technology that’s rapidly transforming the healthcare industry. What started out as merely a concept – by tech company Philips, and European Commission’s Information Society and Technology Advisory Group (ISTAG) – in the 1990s is, today, an amalgamation of two, primary, disruptive technologies – Artificial Intelligence (AI) and Internet of Things (IoT). It is because of AmI that the world has witnessed impressive development in AI assistants like Siri, robotics, sensors and more. With thoughtful use, this technology is on the crux of disrupting healthcare too.
What is Ambient Intelligence?
Ambient Intelligence refers to the combination of IoT sensors, sensor networks, and Human-Computer Interaction (HCI) technologies powered by Pervasive-Ubiquitous Computing, big data and artificial intelligence frameworks. Or in simpler terms, they are physical spaces capable of being sensitive and responsive to the presence of humans. This technology paves the way to a futuristic world where sensors embedded in daily use devices will create an intelligent environment which adapts to its user’s needs and wishes seamlessly.
AmI can be leveraged in a wide range of technologies such as biometrics, affective computing, RFID, Bluetooth low energy, microchip implants, sensors like the thermometer, motion detectors, photo-detectors, proximity sensors, and nano-biometrics. These sensors will gather data, and interpret and analyze it to adjust to or predict user expectations.
Ambient intelligence-powered environments have the following characteristics:
Awareness of individuals’ presence
Recognition of their identities
Awareness of the context (e.g. weather, traffic, news)
Recognition of activities
Adaptation to the changing needs of every individual
How Will It Help Healthcare?
Early applications of AmI could enable more efficient clinical workflows and improved patient safety in ICUs and operating rooms. It can –
Help by recording patient health stats (with patient permission) and update the patient Electronic Medical Record (EMR) to provide a better and more accurate narrative.
Aid health care workers (physicians and nurses) in delivering quality care by analyzing patient information like prior treatments, allergic responses of the patient and more.
Help the elderly by remotely monitoring their health and enables them to have an independent living, in countries with a higher population of senior citizens. (Through Ambient Assisted Living (AAL) technology.)
Enrich overall patient experience, physician satisfaction, and quality of care.
Smart Hospital Rooms
Ambient intelligence can pioneer smart hospital rooms equipped with AI systems that can do a range of things to improve outcomes. The School of Engineering at Stanford University is reportedly exploring how a combination of electronic sensors and artificial intelligence could be installed in hospital rooms and elder care homes to help medical professionals monitor and treat patients more effectively.
It suggests using two types of infrared technologies, i.e. the low-cost active infrared and passive detectors which can be incorporated into the patient environment. The first type of infrared is already being used outside hospital rooms, for instance, to discern whether a person washed their hands before entering and, if not, issue an alert. The second infrared technology, i.e. the passive detectors will help night vision goggles to create thermal images from the infrared rays generated by body heat.
In the hospital setting, a thermal sensor above an ICU bed would enable the governing AI to detect twitching or writhing beneath the sheets, and alert clinical team members to impending health crises without constantly going from room to room. During the research, passive detectors helped the team of researchers avoid relying on high-definition video sensors since capturing video imagery could unnecessarily infringe the privacy of clinicians and patients. Meanwhile, the active infrared helped them in tracking hospital-acquired nosocomial infections. Leveraging such ambient intelligence applications can also help in computer-assisted monitoring of patient mobilization in ICUs, and automating surgical tool counts to prevent objects from being accidentally left in a patient.
Takeaway
Ambient Intelligence is still emerging. Currently, it has already empowered users’ capabilities via the creation of a sensor-based environment which is sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions.
In healthcare, it will help in numerous ways like continuous monitoring, smart hospitals, assisted therapy, etc. Not only that, but Ambient Intelligence is also on the threshold of disrupting businesses and industries like e-commerce, retail and more. With the proliferation of IoT devices, Ambient Intelligence will surge, however, company vendors should be careful about factors like data usage, privacy and overall security.
As a field, computer vision has got a lot of exposure and a good amount of investment.
Computer vision, or the capability of artificially intelligent systems to “see” like people, has been a subject of expanding interest and thorough examination research in the recent decades. As a method of copying the human visual framework, the research in the field of computer vision intends to create machines which can automate tasks that require visual cognition. Notwithstanding, the way towards interpreting pictures, because of the fundamentally, more noteworthy amount of multidimensional information that needs analysis, is considerably more perplexing than understanding different types of binary information. This makes creating AI systems that can perceive visual information more complicated.
Keeping the growth prospects in mind, advances or amalgamation of computer vision with different technologies will dominate the year of 2021. 1. Integration with Edge Computing
Machines that connect with the internet and the cloud can gain from the information accumulated by the network overall and change as need be, optimizing the system’s performance. However, internet connection and the cloud isn’t generally an assurance. That is the place where edge computing comes in.
Edge computing tackles the issue of network accessibility and latency. Presently devices can be set in areas where a network connection is awful or nonexistent without it considering the consequences of the analysis. Moreover, edge computing can counterbalance some of the expenses of utilization and maintenance of cloud computing for data sharing.
For computer vision software, this implies a likelihood to respond better in real-time, and just move significant bits of knowledge to the cloud for further analysis. This component is particularly helpful for autonomous cars.
2. Emotion AI
Emotion AI utilizes computer vision technology to analyze facial appearances and eye developments in photographs and videos, with the point of reading a person’s emotional responses. A London-based emotion AI start-up records the facial expression of a sample crowd who watch a specific promotion through the camera of their PC or cell phone. Realeyes brought $12.4m of funding up in 2019 to help large brands, for example, AT&T, Mars, Hershey’s, and Coca-Cola, distinguish emotions from pictures of facial expressions prior to rating every promotion for attention, emotion, sentiment, etc. Apple additionally gained Emotient in 2016, and Facebook is building up its own products too.
3. Embedded Vision
Embedded vision incorporates image capturing and processing abilities to a single device. This computer vision innovation has discovered its way into numerous modern applications which incorporate arranging systems that demand inspection services. Moreover, providers have even integrated embedded vision into autonomous drones, smartphones and self-driving automobiles.
4. Mixed Reality: VR and AR upgraded
Today, any VR or AR framework establishes a vivid 3D environment, yet it has little to do with the real one the consumer is situated in. Most AR devices can perform basic outputs of the climate (Google ARCore can, for instance, recognize level surfaces and changes in light conditions), and VR frameworks can identify the developments of the customer through head tracking, controllers, and so on, however, their capacities stop there.
Computer vision programming is driving VR and AR into the next phase of advancement that some call Merged Reality (MR). It’s already in the market, however, 2021 will see more developments in this phase.
5. CV as a Service (CVaaS)
CVaaS is a sort of software-as-a-service hosted in the cloud. It permits organizations to lease as opposed to building a computer vision platform. Running computer vision in the cloud has carried innovation to new customers. The on-demand access to algorithms and APIs under a pay-as-you-go model, make the innovation both affordable and scalable.
In the years to come, CVaaS will turn into a vital piece of a business’ automation process. Additionally, computer vision models will progressively run at the edge, permitting them to be embedded in a more noteworthy number of devices.
Today, it is hard to imagine any business without an accompanying software application and that’s the prime reason for the software development industry to forecast a growth of $650B by 2025. This apparently also requires the development community to constantly innovate tools, technologies, and frameworks that can deliver scalable & sustainable solutions with quick time-to-market.
Here, we are going to talk about 10 approaches that simplify the development of a brilliant and scalable software solution.
1. Low Code Development
Low Code development is a modular approach comprising of reusable, component-based architecture that both boosts the delivery cycle and reduces the time-to-market. Low Code development approach makes bug fixing and maintenance easy, improves code reusability, makes tech-stack versatile, simplifies testing, etc.
2. Multi-Platform UI Development
As the name suggests, the Multi-Platform approach is best suited for designing a solution catering to multiple platforms (like iOS, Android, web application, etc.). React Native, Xamarin, and Flutter are some of the popular choices for building cross-platform apps. This approach of frontend development offers reusability and reduced time-to-market and delivery time for all platforms.
3. Serverless Architecture
Serverless architecture is a new lining that allows building scalable applications without having to manage the infrastructure. Applications run on third-party servers such as Amazon AWS, Microsoft Azure, etc. Resources in a Serverless architecture can be scaled up or down as per the need. This frees the engineering team from managing applications, databases, storage systems, and server runtimes, allowing them instead to focus on the core product.
4. AI-First Solutions
Artificial Intelligence has a disruptive effect on almost every industry. Considering the numerous benefits that AI brings in, developers are integrating AI in one form or the other to give an edge to the software applications. Some of the prominent use cases of AI include chatbots, data recognition & classification, object detection, predictive analysis, etc.
5. Microservices And Micro Frontends
These two are modular approaches to backend and frontend development.
Microservices architecture uses independent group components to build a software application. It makes it easy for an app development team to accelerate the development cycle, reduce time-to-market for new features, and keeps an application open for experimentation.
Micro frontends is the microservices approach to frontend development. Herein, an application is broken up by its pages and features, with each feature being owned end-to-end by a single team.
6. Edge Computing
In Edge Computing, an edge server is placed between the cloud and the user at a location within the user’s proximity. This approach is popular because it reduces the workload on the cloud server considerably and, as a result, reduces the data transmission latency.
7. Headless CMS
Headless content management system follows an API-first approach to software development. A headless CMS is a backend-only content management system. It offers backend capabilities for editing, organizing, and storing digital content, irrespective of the platform where it is to be published, displayed or used. Such an approach to development works best when omnichannel experiences are to be developed.
8. Automation Through DevOps
Software development is a collaboration between development and IT operations team. DevOps bring a cultural shift to software development wherein the teams collaborate to automate integration, deployment, delivery, testing, and monitoring of applications.
9. Voice-Enabled Solutions
Voice assistants like Siri, Google Assistant, Alexa introduces a new way to how humans interact with digital devices. With their growing popularity amongst users, especially the millennials, integration of voice assistants with software applications or skill development has become a much adopted trend.
10. Multi-Cloud Strategy
A multi-cloud strategy has two or more cloud services for an application. In this case, a software application utilizes multiple services (SaaS or PaaS) of different service providers which helps to improve efficiency, reduce cost, and offers high resiliency.
Modern day e-commerce needs to be incredibly responsive to user needs. A glitch-free website isn’t enough these days. The website owner needs to continually better the website based on the subsequent user input that is acquired from different engagement mediums.
Given the gargantuan scale at which e-commerce sites have to operate these days, DevOps is the perfect methodology to provide a seamless coding and development platform through which updates and changes can flow.
To give website owners a better perspective of DevOps, we need to look at its two most important categories of automation: Continuous integration (CI) and Continuous deployment (CD). CI and CD are formulated to ease the delivery pipeline by reducing the time and resources required to initiate the development and maintenance processes of a product. A development team needs to have strengthened collaboration in order to fully leverage the benefits that the CI/CD model offers.
If an e-commerce business wants to improve its conversion rates and performance, then the CI/CD model is the best way to do it.
Understanding CI and CD model
Continuous integration (CI) is a process that helps developers in creating, testing and merging their code through to the repositories in an automated manner, thereby reducing the chances of conflict that accompany a wide range of changes being done to the main source code from different development aspects.
In simpler words, CI allows developers to push code with automated testing in order to make sure that once the code is merged with the main website code, nothing breaks or experiences a crash. This reduces the chances of getting integration-related bugs from any external tools that your e-commerce website might have acquired. In CI, the code quality is improved before its released through iterative changes and by tracking of real-time reports.
Continuous delivery (CD) is a major part of this model as it wrests control over the other problematic aspect of code deployment after CI has done its job (i.e., integrating that live code within multiple environments).
With CD, you can release your code at any point in time, without having issues with the state of the product. Together, the CI/CD model automates most of the testing and QA parts of the delivery pipeline resulting in a highly efficient and coherent system that suits modern-day development needs.
Below are some of the advantages of the CI/CD model for e-commerce websites.
1. Faster Development Response Time
The CI/CD model is primed for ensuring that you can test as many types of codes as you want and integrate it within your existing product without running into any major performance issues. This provides e-commerce websites with an incredible opportunity to respond to user demands and needs in real time by pushing everything from updates to feature introductions flawlessly and in a quicker manner than ever before.
You can even use this model to test various versions of your user interface (UI) and the tools on your website so you can align your website with changing market and user trends. This will result in increased optimization of your site’s conversion funnel.
2. Reduced Time To Reach Market-Ready State
Engaging customers before your competition does is crucial toward the success of an e-commerce website. If someone else is able to capture a segment first, then it becomes incredibly difficult for you to carve out your own space there.
Reducing your time to deliver features and tools is a turnaround issue you will continuously encounter in the e-commerce space. Once you leverage the CI/CD model, you will be able to reduce the amount of time between gauging demand and responding to it, thus increasing your market success rate via capturing the space before others do.
3. Better Code Quality and Reduced Costs
One of the major benefits of following the CI/CD model is that your development team can release multiple builds in a very short span of time via automated testing, resulting in a much more robust, glitch-free e-commerce website. E-commerce websites have diverse tools, features and categories, so efficiency and robustness in coding are imperative to deliver a stellar experience to the end user.
This also helps reduce development costs because the same process of releasing new code requires fewer hours. With reduced errors and faster deployment, the overall expense budget allocated to development can be scaled down significantly.
Conclusion
Delivering an exceptional end-user experience is a necessity for modern-day e-commerce web development, and the CI/CD model is the perfect way to achieve it. Integrating this model can help you optimize your conversion rates and increase profitability in a sustainable manner.
Enterprises rely heavily on technology to build scalable, agile and highly available businesses. Event-Driven Architecture is emerging as a key cornerstone enabling modern enterprises to operate in real-time, adapt to changes quickly, and make intelligent business decisions.
“Event-driven architecture (EDA) is a design paradigm in which a software component executes in response to receiving one or more event notifications.”– Gartner
Combining agile methodologies with a platform that can respond rapidly to real-time events empowers businesses to make manual or automated intelligent decisions and achieve true enterprise agility. Events from a variety of sources from Servers to Wearables can then be harnessed to support a number of industrial use cases.
Large organizations that adopt such architectures need to treat Events as first-class citizens of their Platform, along with their APIs and Business Services. Event-Driven Architecture Adoption Patterns
Enterprises that embrace event-driven architecture can capitalize on the benefits of scalable and reliable real-time communication. This can help drive many strategic initiatives across IoT, eCommerce, Data Integration across systems, data and applications, Edge and Financial Fraud Detection, among others.
Companies that add event-driven architecture to their systems and applications can improve the scalability and responsiveness of applications. They can also access the data and context needed for better business decisions, be that stream processing, data integrations or website activity tracking.
Events are captured as they occur from a spectrum of event sources. On a high-level, these events are categorized into Business/Functional or Technical.
True Stories of EDA Driving Business Value
1. Create a new business model through new channels
Emirates NBD uses Red Hat Fuse and Apache Kafka on Red Hat OpenShift to support real-time banking and partner interactions.
2. Incredible improvement in customer experience
A global Financial Services leader implements a real-time campaign solution that delivered a ~60% increase in campaign conversion rates.
3. Measurable and Effective Operational improvement
A global logistics provider uses an events fabric to provide real-time visibility to shipments across its wide global network.
4. In the field of IoT
A global electronic devices manufacturer used proactive event-based monitoring to reduce the number of service maintenance calls by 45%. Event streaming analytics helped to recognize patterns and prioritize responses to alerts.
5. EDA for Inter-microservices communication
A European bank used an event-driven architecture to improve its relationship with customers. A lightweight event bus-based solution helped keep applications in sync and provided a single view of customer data that enabled in streamlining the customer experience.
Crowdsourcing allows you to tap into the collective intelligence of the crowd to improve innovation performance or discover new ideas.
According to a McKinsey survey, 6% of executives are satisfied with their firms’ innovation performance. When it comes to organizational capabilities, lack of skill, talent, and the ability to execute ideas is mentioned frequently as a barrier to innovation. Without the right skills and capabilities, it’s difficult to achieve the desired results. This may be the primary reason why more companies are embracing the crowd.
Organizations leveraging crowdsourcing are some of the world’s most progressive and innovative like NASA, Lockheed Martin, Mozilla, XPrize, and SAP.
Power of Crowdsourcing
Seeking a better way to feed his troops, Napoleon launched a contest that changed the way people eat to this day. Napoleon used crowd-sourcing in 1795 to improve upon the prevailing food preservation methods of the time. Not surprisingly, the purpose was to better feed his army “when an invaded country was not able or inclined to sell or provide food”. Confectioner Nicolas François Appert claimed the prize worth 12000 francs.
Crowdsourcing has emerged as a recent trend in India and various organizations have been employing it for a social, economic, and environmental impact. Recently, Kerala police launched a crowdsourcing campaign for infusing digital technologies for better policing. They will not only get access to diversity and richness of various ideas that exist in the minds of the citizens but also make citizens proud that they are contributing to the nation.
Recently, AB InBev India launched a campaign to build a sustainable supply chain. They crowdsourced solutions to help connect thousands of farmers to technologies and skills, ensure water access and quality in high-stress communities, partner with suppliers to increase recycling content, and add renewable electricity capacity to regional grids.
With such campaigns, the organizations can focus on their core and at the same time capitalize on external crowds to drive innovation in non-core areas.
Framing of Challenges
Framing of challenges is the key to effective crowd-sourcing. The largest problem is that change-makers spend a very little time in framing the challenges. Most challenges do not get solved because nobody frames them.
Examples of two challenges that will be good to frame-
How to build a sustainable and scalable model to help people in rural areas increase their incomes through rural tourism?
Such a challenge, if opened to the crowd, will invite multiple ideas and can lead to the creation of social enterprises and micro-entrepreneurs.
What could be the alternate use for the FasTag that is affixed to your cars?
By now, one would agree that thousands and thousands of such challenges can be framed and be brought to the crowds to seek solutions and monetary awards can be offered for the most innovative and scalable solutions.
Possibilities from Crowdsourcing
The organizations can aim at getting responses in multiple ways from the crowd. Here are a few examples –
1. Getting a situation analysis done: Research on why India is not self-reliant in Aluminium production is an example of a situation analysis. Such research would help in finding the root causes and each root cause could be framed as a challenge for further ideation and solution-finding.
2. Asking people for ideas: How to help farmers increase their income is an example where if you ask 500 people and you get 500 ideas and some ideas could be great to dig deeper.
3. Building new operational models: Some years ago, ITC solved a challenge on how to eliminate the middlemen from the procurement process of farm produces, and as a result they came up with e-Choupals. This can be said as inventing a new operational model.
4. Getting product designs and prototypes: How touchless dispensers could look like for better health and hygiene?
An organization could seek these answers from the crowd.
From inspiration to action, India has a big leap to take. Along with inspirational campaigns such as ‘Atmanirbhar Bharat’ if combined with effective framing of challenges and engaging with crowds through digital means in getting solutions could act as a catalyst and accelerate the development process.
People often confuse coding and programming and use these two terms interchangeably. Let’s just get this out of the way now — they’re not the same thing, and in fact, they are quite different.
“Coding” and “Programming” are the two most important approaches in Software Development Industries. Coding is basically the process of creating codes from one language to another one. It can also be called as a subset of programming since it actually implements the initial steps of programming.
Programming involves not only coding but also other tasks, such as analyzing and implementing algorithms, understanding data structures, solving problems, and more. Programmers are typically technically-minded and have strong analytical skills.
Let’s try to understand the further differences between the two-
1. Difference Between A Coder And A Programmer
There are plenty of coding lessons and tutorials all over the web that allow learners to master basic coding terminology and learn to write simple apps or websites. On the other hand, programmers need a specialized degree and years of experience to obtain the skills and knowledge that are required to write complex programs.
2. Both Have A Different Scope
When coding, you transform the requirements in a human language into a language that your computer will understand. When programming, you give an app or a machine a set of instructions to be carried out. A programmer is typically responsible for an entire software product whereas a coder is responsible for a certain part or parts of the software product.
3. To Be A Programmer, You Have To Create More
Coders typically follow given instructions and requirements to write code, whereas programmers often use their own imagination and analytical skills to solve specific problems. To be a coder, one should know the syntax, but to be a programmer, one should understand complex data structures and algorithms as well as be able to create such algorithms.
4. Programming Involves Higher-Level Thinking
Another difference between programming and coding is that programming involves a lot of complex thinking. Programmers should be able to come up with abstract solutions to a problem before they even start coding. Besides, they should think proactively and be able to predict as many potential issues as possible. Finally, programmers use code to express their thoughts on how to solve a problem.
Programmers often write or ask technical writers to provide technical specifications – documents containing requirements that the final product should meet and problems that it should solve. Coders then follow those specifications to write code, but do not work on specifications themselves. Programmers are also involved in compiling, debugging, testing, and implementation of the code. In other words, the duties of a programmer can go far beyond coding.
5. Programming Is Creating A Book, Coding Is Writing A Chapter
To find the difference in the programming vs coding discussion, we can compare a programming language to a human language such as English. Such comparison shows that a programmer is a person who knows English perfectly and can produce a lengthy work in English. On the other hand, a coder is a person who knows English at a basic level and can write a short story in English.
Credit: Julian Krinsky Camps & Programs
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