Everything About the Updates : OpenAI_DevDay
Amidst the technological breakthroughs, OpenAI’s ChatGPT, built on the foundation of GPT-3.5, stands as a landmark in natural language processing. Introduced by OpenAI, it represents a progression from earlier models, showcasing advancements in deep learning and artificial intelligence. ChatGPT underwent iterative improvements, with valuable user feedback received during beta testing, reflecting OpenAI’s dedication to advancing conversational AI capabilities.Operating on a transformer neural network architecture, GPT-3.5 powers ChatGPT, employing unsupervised learning from diverse internet text to generate human-like responses. Trained to grasp patterns, context, and language nuances, it utilizes attention mechanisms for coherent text generation based on input prompts, establishing itself as a formidable conversational AI. Recently, ChatGPT for GPT-4 integrated voice and vision capabilities, including the cutting-edge DALL-E3 image model, a significant leap in visual processing. For enterprise users, ChatGPT Enterprise offers high-end features, ensuring security, expedited GPT-4 access, extended context windows, and tailored enhancements for professional settings, providing a secure, efficient, and feature-rich experience.
With a user base surpassing 2 million developers integrating ChatGPT across diverse applications, the platform records over 100 million weekly active users. Recognizing ChatGPT’s pivotal role in these users’ endeavors, maintaining their loyalty becomes a paramount business objective. This requires a proactive stance to identify and address any shortcomings, placing a central emphasis on elevating user satisfaction. Aligned with the need for ongoing information updates, this strategy acknowledges the evolving expectations of users over time. The unwavering commitment to this continuous improvement process underscores the platform’s dedication to remaining responsive to user needs within a dynamic environment.
What are the updates now?
Throughout its history of model launches, OpenAI has consistently prioritized exclusivity for developers. The newest addition to their lineup, GPT-4 Turbo, comes with six notable upgrades. This latest industry-driven model marks a significant leap forward in AI capabilities, introducing a host of advancements that redefine the landscape. Positioned as a more intelligent iteration in comparison to GPT-4, GPT-4 Turbo distinguishes itself with a range of key features.
Extended Context Length: With an impressive context length of 128,000 tokens, GPT-4 Turbo ensures heightened accuracy, staying up-to-date with information until its knowledge cutoff in April 2023.
Text-to-Speech Model: A new addition allows the generation of remarkably natural audio from text via API, offering six preset voices for users to choose from.
Custom Models: OpenAI collaborates closely with companies to develop exceptional custom models, facilitating diverse use cases through specialized tools.
Token Doubling: GPT-4 Turbo doubles the tokens per minute for all customers, making it easier to achieve more. Users can also request changes to raid limits and quotas directly in their API account settings.
Enhanced Control: Simplified JSON mode API calls empower developers to make multiple calls at once for reproducible outputs.
Improved World Knowledge: GPT-4 Turbo integrates advanced retrieval capabilities, enabling users to import knowledge from external documents or databases and mitigating concerns about outdated information.
New Modalities: Introducing DALL-E 3, GPT-4 Turbo seamlessly integrates vision and a new text-to-speech model into its API. This enables image inputs, generating captions, classifications, and analyses in six different modes, including Whisper v3.
Customization Boom: Building on the success of fine-tuning in GPT-3.5, GPT builders expand to 16k versions, empowering users to create custom models through specialized tools and a tailored RL post-training process.
Higher Rate Limits: GPT-4 Turbo boasts doubled rate limits, enhancing efficiency and responsiveness. This comprehensive suite of improvements establishes GPT-4 Turbo as a transformative force in the realm of artificial intelligence.
Copyright Shield
OpenAI staunchly supports its customers by covering the expenses incurred in legal claims related to copyright infringement, a policy applicable to both ChatGPT Enterprise and API. Despite its advanced capabilities, this model proves to be significantly more cost-effective than GPT-4, with a threefold reduction in input prompt token costs and a twofold decrease in output token costs.
In our pioneering GPT builder business model, customer protection takes center stage as we bear the legal claim defense costs. Our public and private Chat GPTs establish an industry benchmark, finely calibrated for optimal performance. They seamlessly integrate precise instructions, extensive knowledge, and swift actions, delivering an unparalleled user experience. This forward-thinking approach not only safeguards our customers but also harnesses cutting-edge AI technology to ensure efficiency and reliability. We are not merely redefining customer support; we are revolutionizing it, driven by a commitment to excellence and innovative technological solutions.
Does ChatGPT truly oppose Prompt Engineering?
Indeed, ChatGPT doesn’t possess an inherent opposition to prompt engineering; rather, it acknowledges the existence of this practice and the potential influence it can exert on the model’s behavior. OpenAI, the entity responsible for ChatGPT, appreciates the user community’s interest and creativity in experimenting with prompt engineering.
However, OpenAI emphasizes the importance of responsible usage, cautioning against manipulating the system in ways that could generate unsafe or biased outputs. The organization strives to strike a delicate balance between granting users the ability to customize their interactions and ensuring ethical, unbiased, and secure AI experiences.
In this pursuit of balance, OpenAI actively seeks user feedback, recognizing it as a valuable tool for refining the system. By consistently refining the model, OpenAI aims to enhance its behavior, address concerns arising from prompt engineering, and ultimately provide users with a more reliable and responsible AI tool. This collaborative approach underscores OpenAI’s commitment to fostering a community-driven, ethically sound environment for AI development and interaction.
Introducing GPTs: Understanding the potential of GPTs
Enthusiasts are crafting live AI commentators for video games such as League of Legends. In another scenario, a yoga instructor is leveraging image processing through their webcam, employing GPTbuilder to guide and provide real-time feedback during training sessions.
Moreover, GPTs are being employed to create stickers, forming an impressive and dynamic collection used in real-time. GPTs can also generate prompts for specific instructions when utilizing a custom model. Users have the ability to pre-sets a single assistant for a dedicated use case.
Furthermore, the visual capabilities of GPT, coupled with the Text-to-Speech (TTS) API, are harnessed for processing and narrating videos. This integration allows for a seamless blend of GPT’s visual prowess and audio narration, enhancing the overall video experience.
Custom Models
In the realm of GPT Custom models, users have the power to provide tailored instructions. By incorporating conversation starters such as Code interpreter, Web browsing, and DALL-E-3 for image generation, individuals can shape the assistant’s actions. Additionally, users can select specific functionalities within the assistant and have the option to store API data in long-term memory.
Moreover, users are granted the ability to seamlessly integrate external applications into the ChatGPT web interface. This empowers them to construct their own GPT extensions. Furthermore, envision an extension to this capability where multiple GPTs interact with one another. The possibilities are boundless, marking a significant stride towards mass adoption. Over time, the tangible results of this evolution are poised to become increasingly evident.
Summary and Reflection
In the wake of its recent updates, OpenAI is earning widespread acclaim and recognition for the substantial contributions it has made to the technological landscape. This recognition is particularly pronounced among users and, notably, resonates strongly within the developer community. The enhancements and innovations introduced by OpenAI are being hailed for their positive impact, exemplifying the organization’s unwavering commitment to advancing technology and addressing the evolving needs of its user base. This sentiment is especially pronounced among those actively engaged in software development.
The positive reception underscores OpenAI’s influential role as a trailblazer in the field, highlighting its dedication to pushing the boundaries of what is possible in technology. The acknowledgement and applause from the tech community serve as a testament to the effectiveness and relevance of OpenAI’s efforts, further solidifying its position as a leading force in shaping the future of artificial intelligence and related technologies.
“What makes Generative AI the top choice?”
History
Generative AI boasts a history that traces back to the mid-20th century. Initial forays in the 1950s and 60s focused on rule-based systems for text generation. However, a significant leap occurred in the 2010s with the emergence of deep learning. Milestones like the introduction of recurrent neural networks (RNNs) and the breakthrough of long short-term memory (LSTM) networks in 2014 propelled generative AI forward. The release of GPT-3 in 2020 represented a pivotal moment, showcasing increasingly sophisticated models capable of producing human-like text. This revolutionized natural language processing and creative content generation. One sterling example of generative AI’s prowess is OpenAI’s DALL·E. This cutting-edge model crafts images based on textual descriptions, showcasing AI’s ability to generate realistic, novel content. DALL·E underscores OpenAI’s commitment to pushing the boundaries of artificial intelligence, unlocking new creative avenues, and fundamentally reshaping how we interact with and generate visual content in the digital realm.
Mechanism
Generative AI, as demonstrated by GPT-3.5, operates through a sophisticated mechanism encompassing two key phases: training and inference. During the training phase, the model is exposed to an extensive and diverse dataset of text, which it uses to adjust its internal parameters and weights. This process enables it to grasp the intricacies of language, encompassing grammar, semantics, and context. By analyzing vast text samples, the model learns to recognize patterns, associations, and relationships between words and phrases, thereby acquiring a comprehensive understanding of language structure.
In the inference phase, the AI applies its learned knowledge to generate text. When provided with an initial prompt, it predicts the most likely next word or sequence of words based on the context established by the prompt and its internal knowledge. This interplay between training and inference is a dynamic and iterative process that empowers generative AI to produce coherent and contextually relevant content. As a result, it can mimic human-like text generation across a wide range of applications, from natural language understanding to creative content creation and more.
Limitations in its mechanism
Generative AI, while powerful, has notable limitations while producing content.
- It can produce biased or offensive content, reflecting biases in the training data. It may lack creativity, often producing content that mimics existing data. Ethical concerns arise due to its potential to generate deep fakes and misinformation.
- It requires substantial computational resources, limiting accessibility. Long input prompts can lead to incomplete or irrelevant outputs. The models might not fully understand context and produce contextually inaccurate responses.
- Privacy issues may arise when using sensitive or personal data in generative AI applications, necessitating careful handling of information.
Applications
Natural Language Generation (NLG) Generative AI excels at crafting human-like text, automating content creation for news articles, reports, marketing materials, and chatbots. This ensures consistent, high-volume content production.
Computer-Generated Imagery (CGI) Within the realms of entertainment and advertising, generative AI generates realistic graphics and animations, reducing the need for labor-intensive manual design and enabling cost-effective special effects.
Art and Design Artists leverage AI for creating unique artworks, while designers use it for layout recommendations and logo generation, streamlining the creative process.
Healthcare With Generative AI, doctors can instantly access a patient’s complete medical history without the need to sift through scattered notes, faxes, and electronic health records. They can simply ask questions like, ‘What medications has this patient taken in the last 12 months?’ and receive precise, time-saving answers at their fingertips.
Autonomous Systems In self-driving vehicles and drones, AI generates real-time decisions based on sensory input, ensuring safe and efficient navigation.
Content Translation AI bridges language gaps by translating text and speech, facilitating cross-cultural communication and expanding global business opportunities.
Simulation AI generates realistic simulations for training pilots, doctors, and other professionals, providing a safe and effective environment for skill development.
Generative AI is revolutionizing diverse fields by streamlining operations, reducing costs, and enhancing the quality and personalization of outcomes.
Challenges
Generative AI has indeed transformed from a science fiction concept into a practical and accessible technology, opening up a world of possibilities. Yet, it does come with its set of challenges, albeit ones that can be managed with the right approach.
Ethical Concerns The primary challenge revolves around the ethical use of generative AI, which can produce misleading content like deepfake videos. Developers and organizations are actively working to establish ethical guidelines and safeguards to ensure responsible AI application and adherence to ethical standards.
Bias in Generated Content Generative AI models, trained on extensive datasets, can inherent biases present in the data, potentially leading to generated content that reinforces stereotypes or discrimination. To combat this issue, researchers are dedicated to devising techniques for bias reduction in AI models and advocating for more inclusive and varied training data.
Computational Resources Training and deploying generative AI models, especially large ones, requires substantial computational resources. This can be a barrier to entry for smaller organizations or individuals. Cloud-based services and pre-trained models are helping mitigate this challenge, making generative AI more accessible.
In summary, while generative AI poses challenges, it’s an evolving field with active solutions in progress. Staying informed, following ethical guidelines, and utilizing the expanding toolset enables individuals and organizations to effectively tap into generative AI’s creative potential, pushing digital boundaries.
In a nutshell, Generative AI’s horizon is defined by an unceasing progression in creativity, personalization, and effective problem-solving. Envisage the emergence of ever more intricate AI models effortlessly integrated into our daily routines, catalyzing revolutionary shifts in content creation, healthcare, art, and various other domains. This ongoing transformation is poised to fundamentally redefine our interactions with technology and information, ushering in a future where AI assumes an even more central and transformative role in our daily experiences.
Streamlining Digital Transformation with BPM
While the world is getting digitized in diverse domains, why not business processes? How about transforming any manual or semi-automated business processes into digitized and automated services? Why do so? Across diverse businesses, services typically encompass customer interactions, order processing, supply chain management, and internal workflows. By migrating these processes to digital platforms, organizations gain numerous advantages, including heightened efficiency, fewer errors, enhanced data accuracy, and elevated customer satisfaction.
While a variety of BPM tools exist such as IBM Business Process Manager, Appian, Bizagi, Pega, Camunda, Nintex, Bonita, TIBCO BPM, Oracle BPM Suite, and K2, they offer a streamlined approach to modeling, automating, executing, and monitoring business processes in various sectors. These tools provide a visual representation of processes, allowing stakeholders to collaboratively design and optimize. When it comes to converting traditional processes into digital services, BPM tools prove invaluable. The process to streamline businesses remains consistent with following steps
Process Modeling and Design: BPM tools visually define map processes aiding in spotting inefficiencies. Graphical representation fosters collaboration and communication, enhancing stakeholder understanding.
Automation and Integration: BPM tools integrate diverse systems for end-to-end digital services spanning departments and technologies, while automating manual tasks, boosting speed and consistency minimizing errors.
Data-Driven Insights: BPM tools offer analytics and reporting. Monitoring digital services provides data on performance, bottlenecks, and interactions, enabling informed decisions and improved efficiency, satisfying customers.
Flexibility and Agility: BPM tools foster agility by enabling process modeling, testing, and adjustments. This flexibility aids smooth transitions and optimizations, vital in evolving business environments.
Enhanced Customer Experience: Digitalization enhances customer experiences. Converting processes like orders and support to digital offers quick responses, self-service, personalization, elevating satisfaction and loyalty.
Compliance and Governance: BPM tools enable compliance integration in digital services, embedding regulations, security, and approvals. This guarantees adherence to industry standards and organizational policies during process design.
Key Features Signifying the BPM Tools
Lucidchart: Lucidchart is a visual workspace that bridges the communication gap between business and IT teams in BPM by enabling collaborative process modeling and diagramming.
UML (Uniform Modeling Language): UML is a standardized language for visualizing, designing, and documenting software systems. It’s integral in BPM for precise process representation and analysis.
Flowchart Symbols: Flowcharts use symbols and notations to illustrate processes, aiding in BPM by visually conveying steps, decisions, and workflows.
Data Flow and Control Flow: In BPM, data flow and control flow diagrams depict how data moves and how processes are controlled, enhancing clarity in process understanding.
Data Mining: Data mining techniques within BPM uncover insights from process data, enabling data-driven decisions and continuous improvement.
Business Process Analysis: BPM analyzes existing processes to enhance efficiency or governance. It identifies bottlenecks and inefficiencies, enabling informed process enhancements.
Hyper Automation: Hyper Automation, a BPM approach, combines AI, RPA, and other tools to automate complex processes, boosting efficiency and reducing manual effort.
Six Sigma: Six Sigma methodologies, applied in BPM, streamline processes, minimize defects, and enhance overall process quality, aligning with BPM’s efficiency goals.
Application of BPM Tool in Healthcare Industry
In the healthcare industry, the application of BPM tools holds immense potential to revolutionize business operations, streamline patient care processes, and enhance overall efficiency.
Enhancing Patient Journey:
BPM tools enable healthcare providers to map out and optimize patient journeys, from appointment scheduling to discharge. By visualizing the entire process, identifying bottlenecks, and automating routine tasks, hospitals and clinics can improve patient experience, reduce waiting times, and ensure timely care delivery.
Claim and Billing Management:
Efficient claim processing and billing are paramount for healthcare businesses. BPM tools can automate the end-to-end claim process, from submission to reimbursement, minimizing errors, accelerating claims processing, and ensuring accurate billing, which in turn leads to improved revenue cycle management.
Supply Chain Optimization:
In healthcare, an optimized supply chain is crucial for maintaining inventory levels of medications, medical devices, and equipment. BPM tools streamline procurement, tracking, and distribution processes, preventing shortages, reducing costs, and ensuring essential supplies are readily available.
Patient Onboarding and Engagement:
BPM tools can facilitate seamless patient onboarding, enabling electronic consent forms, electronic health record (EHR) integration, and personalized treatment plans. This enhances patient engagement and enables remote monitoring, fostering a patient-centric approach.
Telemedicine Integration:
As telemedicine gains traction, BPM tools can streamline virtual consultations, appointment scheduling, and prescription issuance. Integration with telehealth platforms ensures efficient communication between healthcare professionals and patients.
Risk Management and Patient Safety:
Identifying and mitigating risks is vital in healthcare. BPM tools enable healthcare businesses to assess risks, implement preventive measures, and track incidents. This proactive approach enhances patient safety and reduces medical errors.
Integrating BPM tools in healthcare enhances operations, betters patient results, and cuts expenses. Automation, compliance, and collaboration enable agile navigation through the intricate healthcare ecosystem.
Unleashing the Power of Digital Twins: An Innovation in Telecommunications
Why unleash the power of digital twins in telecommunications? In the fast-paced and ever-evolving telecommunications industry, staying ahead of the curve is a constant challenge. However, Digital twins are a technology that is transforming the way of operations and networking massively. With the power to revolutionize telecommunications, digital twins have emerged in the race to deliver seamless connectivity and exceptional user experiences.
In the dynamic realm of telecommunications, digital twins play a crucial role in simulating and monitoring various elements such as network infrastructure, devices, and even customer experiences. By providing real-time visualization and understanding of intricate systems, digital twins empower telecom operators to maximize network performance, swiftly address issues, and proactively predict potential failures. The possibilities are truly endless when it comes to leveraging digital twins for an optimized and seamless telecommunications experience. Let’s explore this exciting frontier together!
Digital Twins Mechanism
Every individual component can be created in digital space, the way those components interact with each other in the real world and often the environment they exist in, are digitally replicated. Leveraging the power of artificial intelligence, these digital twins simulate and vividly demonstrate the potential impacts that alterations in design, process time, or conditions would have—without the need to subject real-world objects to those same changes. Simply, it’s like having a digital playground where experimentation and optimization can happen swiftly and intelligently!
Let’s explore an example of a digital twin in the field of telecommunications: Imagine a telecommunications company that operates a vast network of cellular towers and antennas to provide wireless connectivity. They create a digital twin that replicates their entire network infrastructure, including the placement and configuration of towers, antennas, and other critical components.
With this digital twin, the company can continuously monitor and optimize its network’s performance. They can simulate various scenarios, such as changes in user demand, network congestion, or the addition of new towers, to predict how the network will behave under different conditions. These insights enable the company to proactively address network bottlenecks, optimize signal strength, and enhance overall service quality.
Digital twins in telecommunications
Digital twins have limitless potential in the field of telecommunications.
1. Network Planning and Optimization: Telecommunication companies can use digital twins to create virtual replicas of their network infrastructure, including towers, switches, routers, and other equipment. This helps in planning and optimizing network capacity, coverage, and performance. Digital twins can simulate real-time traffic patterns, predict network congestion, and identify areas that require additional infrastructure investment.
2. Predictive Maintenance: Digital twins can monitor the health and performance of telecommunication equipment, such as towers, switches, and routers. By analyzing real-time data from these digital twins, companies can identify potential failures or maintenance needs before they occur. This reduces downtime and increases operational efficiency.
3. Customer Experience Management: Digital twins can be created to represent individual customers or user segments. By analyzing data from these digital twins, telecommunication companies can better understand customer behavior, preferences, and usage patterns. This enables them to offer more personalized services, improve customer satisfaction, and optimize marketing strategies.
4. Service Assurance: Digital twins can provide real-time monitoring and analysis of network performance and service quality. By comparing the actual performance with the digital twin’s expected behavior, companies can quickly detect and resolve service issues, minimizing the impact on customers and ensuring a smooth user experience.
In a nutshell, the digital twins empower telecommunications companies to optimize their network operations, predict and prevent disruptions, boost innovation and productivity, and deliver reliability and efficiency. Isn’t it interesting to unleash the power of digital twins to explore better plan capacity, simulate changes, and ensure optimal performance twins in telecommunications!
Top 3 Advantages of Implementing Chatbot with ChatGPT
Why Chatbot again when ChatGPT is ruling over?! Or why not their combination?! ChatGPT, a revolutionary tool stands for a generative pre-trained transformer which is an interactive platform through chat, designed to give comprehensive answers whereas chatbots are plugins using Natural Language Processes for any business or website to interact with.
Chatbots are typically pre-programmed with a limited set of responses, whereas ChatGPT is capable of generating responses based on the context and tone of the conversation. This makes ChatGPT more personalized and sophisticated than chatbots. Both ChatGPT and chatbots are conversational agents designed to interact with humans through chat giving them real experience. However, there are some them in various factors.
Differences between ChatGPT and Chatbot
Efficiency and speed
Chatbots can handle a high volume of user interactions simultaneously with fast responses. They quickly provide users with information or assist with common queries, reducing wait times which improves overall efficiency. In contrast, ChatGPT generates responses sequentially and has limited scalability for handling large user bases.
Task-specific expertise
Chatbots can be built with specialized knowledge or skills for specific industries or domains. For instance, a chatbot in healthcare can provide accurate medical advice or help schedule appointments, leveraging its deep understanding of medical protocols. ChatGPT, while versatile, may not possess such specialized knowledge without additional training.
Control over responses while user interaction
Chatbots offer businesses more control over the responses and images they want to project. As a developer, you can design, curate, and review the responses generated by a chatbot, ensuring they align with your brand voice and guidelines. ChatGPT, although highly advanced, generates responses based on a large dataset and may occasionally produce outputs that are off-topic or not in line with your desires.
Improved conversational capabilities
Integrating ChatGPT into a chatbot, can leverage its advanced natural language processing abilities. ChatGPT excels at understanding context, generating coherent and human-like responses, and handling more nuanced conversations. This can enhance the overall conversational experience for users interacting with the chatbot.
Advantages Chabot with ChatGPT
Richer and more engaging interactions
ChatGPT’s ability to understand and generate natural language responses can make the interactions with the chatbot feel more realistic and engaging. The chatbot can provide personalized and contextually relevant responses, leading to a more satisfying user experience.
Continuous learning and improvement
ChatGPT is designed to learn from user interactions, allowing it to improve its responses over time. Integrating ChatGPT with a chatbot enables the system to continuously learn and adapt based on user feedback. This means that the chatbot can become smarter and more effective at understanding and addressing user needs.
Flexibility and scalability
ChatGPT can be integrated with various chatbot platforms and frameworks, offering flexibility in implementation. ChatGPT is constantly learning, which means that it can improve its responses over time by building a chatbot for customer support, virtual assistants, or other applications.
Integration of ChatGPT into the back end of the chatbot requires to implementation of their combination. Whenever a user enters a message, the chatbot would pass that message to ChatGPT, which would generate a response based on its machine-learning algorithms using the cloud services. The chatbot would then display the response to the user. This approach can result in a more natural and intuitive conversation between the user and the chatbot, as ChatGPT is capable of generating responses that are more human-like.
In summary, ChatGPT is a more advanced and intuitive conversational AI, it may not always have access to real-time data or provide the most up-to-date information on rapidly changing events than traditional chatbots. But it is capable of understanding the nuances of human language, context, and intent, which makes it a more effective tool for customer service, personal assistants, and other applications while generating responses to user input, while the chatbot serves as the interface through which users can interact with the system.
How Kubernetes Helps in Transforming your Business?
Today, the majority of businesses are going through a digital transformation. While the digital journey brings speed, efficiency and mobility solutions to the table, it comes with certain challenges too. As you migrate the existing infrastructure to the cloud, you’ll have to deal with legacy issues. In the cloud, you have to manage a wide range of tools and services. The implementation of DevOps to manage your infrastructure gives you additional responsibilities.
The DevOps environment demands frequent and faster deployments every day. According to the State of the DevOps 2019 report, elite performers make 209 times more deployments than low performers.
They gain 106 times faster lead time from commit to deploy with a 7% change fail rate. The time to recover from incidents is 2,604 times faster than low performers.
Gone are the days when IT development was confined to software companies. Today, every enterprise develops and manages a wide range of software applications.
As such, tracking and managing automated deployments is a challenge. Kubernetes is here to help.
An Overview of Kubernetes
Kubernetes is a popular tool that enables administrators to orchestrate and manage server cluster workloads in a datacenter, including automation and declarative configuration. Also known as K8s, the Kubernetes open-source platform helps you in efficiently managing containerized apps and services. You can simultaneously run and manage multiple servers while providing access to resources 24/7. This is achieved with the implementation of distributed data processing that allows Kubernetes to run multiple servers of different kinds installed at different locations and operating on different platforms.
The Traditional Software Development Environment
In a traditional operations environment, multiple applications are hosted on a single physical server. It resulted in an inefficient allocation of resources as some applications consumed more resources while others were left out to underperform. Installing each application on a separate server consumed huge infrastructure expenses. Moreover, each server was underutilized. Virtual machines came as a solution to this issue. A virtual machine runs the operating system on top of the virtual hardware and contains all the required elements to run the applications. Using virtualization, administrators were able to isolate applications between different VMs and run multiple applications on the same physical machine.
The Advent of Containerization
The container technology takes this system to a next level. A container is a software package that is bundled with all the required components of the app such as binaries, dependencies, libraries and configuration files while isolating it from the underlying hardware. Containers are lightweight and enable administrators to seamlessly deploy workloads across multiple environments. Keeping the base image consistent, developers can run the app in any environment. By sharing the host OS, containers eliminate the need for libraries and boot OS and therefore become lightweight. They are highly scalable, highly portable, offer agility and provide application-centric management.
Today, enterprises simultaneously run hundreds of containers in different environments. As such, managing the deployment of containers, tracking each container and setting up a new container when a container is down is a challenge. Kubernetes helps you to define your deployment patterns and efficiently manage resilience in a distributed systems environment.
Kubernetes and Containerization
Kubernetes is the most popular container-deployment system for the following reasons. Firstly, it enables you to provide access to containers via an IP address or a DNS name. In addition to service discovery, it helps you to perform load balancing for routing traffic to the right container. Secondly, infrastructure management becomes easy as you can define the desired state of a container. At any given point in time, you can easily roll back the system to its desired state and create and terminate containers at your will. It supports self-healing wherein containers are automatically replaced or restarted. Be it local storage or a public cloud datacenter, Kubernetes allows you to mount storage automatically for efficient orchestration of storage.
While managing a cluster of servers, you can define resource allocation (RAM, CPU) for each container and Kubernetes will optimize the infrastructure by rightly fitting containers onto the nodes. It also allows you to securely store and manage secret information such as passwords, keys etc. without exposing them in the configuration of the stack.
What is it for Businesses?
Powered by Cloud Native Cloud Foundation (CNCF), Kubernetes is the fastest-growing open-source software which means you are entitled to use a wide range of open-source tools developed for Kubernetes. Portability is a big benefit of Kubernetes. It means you not only manage the automatic deployment of large clusters of servers but also moves them between multi-cloud environments with ease.
Kubernetes offers high scalability enabling you to run containers or different environments such as a virtual machine, public, cloud, bare metal. Organizations enjoy high availability at the infrastructure level as well as the application level. You can configure it to support multi-node replication or add a storage layer for the high availability of stateful workloads. Kubernetes uses a simple client-server architecture but offers amazing benefits such as auto-scaling, rolling updates, self-healing etc. Backed by Google, you can rest assured of its reliability and credibility.
Businesses hugely benefit from faster time to market, increased productivity, high availability, high scalability, IT cost optimization, seamless migration between multi-cloud environments and many more. Most importantly, it makes your business future-proof.
Are you leveraging the benefits of Kubernetes? If not, CloudTern is here to help!
How can businesses make profits with a low-code/no-code approach?
The year 2021 seems to be the year of low-code / no-code app development. Though the low-code approach is still in its nascent stage, businesses are already reaping benefits from it.
The month of June has already seen some interesting announcements related to low-code / no-code app development.
Mendix Shows the Way
A notable announcement came from Mendix on June 8, 2021, stating that Dutch’s largest insurance company TVM has partnered with Mendix to develop Bumper, a low-code app that accelerates damage claims processes.
When a vehicle meets with an accident and gets damaged, you can instantly add the damage details into the app and get a detailed report of the damage. It helps you to smartly process damage claims while giving you insights into the process in real-time. As such, damage claims are quickly and efficiently processed while delivering high customer satisfaction.
Amazon Web Services (AWS) enters the Low-code Arena
On June 17 2021, Amazon Web Services (AWS) announced the launch of Workflow Studio, a low-code app development tool that enables organizations to quickly build applications with minimal coding skills on its public cloud platform. So, how do businesses benefit from this AWS low-code / no-code offering?
AWS cloud infrastructure accounts for a majority of cloud usage across the globe. As most businesses run their cloud networks on AWS, it becomes easy for them to take advantage of low-code solutions. While developers use this platform to quickly build applications, business teams with zero coding knowledge can create their apps for day to day business activities. Low-code apps offer faster time to market, reduce development costs, HR-related costs, office footprint etc. It also helps businesses in dealing with the shortage of qualified software professionals.
UI / UX gets better with Infragistics
Low-code or no-code app development platforms focus on enabling users to quickly build apps without writing lengthy code. Regardless of the level of coding, users can quickly build apps using drag-n-drop tools. However, User interface (UI) and User experience (UX) has been a concern for businesses. Currently, low code platforms help you to convert your idea into a prototype while not concentrating on the user experience. Infragistics is now filling this gap.
Cross-platform UI/UX toolmaker Infragistics released a new product Infragistics Ultimate 21.1 on 17th June 2021 which aims to deliver the right UI/UX designs while building low-code apps. It helps business teams to build highly intuitive dashboards with the right UI/UX design. Currently, the IT industry is in shortage of experienced UI/UX professionals. The average salary of a UX designer in the US is $96,529 along with a cash bonus of $5000 per year, as reported by Indeed. San Francisco is the highest paying state for UX designers, paying $140,975 per annum. With Infragistics Ultimate 21.1, organizations can incorporate UI/UX designs into the apps using pre-built templates and tools. As such, businesses can save huge amounts on UI/UX professionals’ salaries while also overcoming the shortage of experienced UI/UX professionals. It also expedites software development projects. As such, businesses deliver a much better customer experience.
Looking at the entrance of IT giants into this segment, it becomes evident that low-code is not just a business hype but is delivering results. So, organizations need to tap these business benefits at the earliest.
Here are 5 important areas wherein businesses are making profits with low-code development:
BizDevOps
BizDevOps is a new buzzword in the development circles in recent times. Low-code app development extends DevOps, incorporating business staff into cross-functional teams to develop customer-centric apps. When a team has a clear understanding of the value stream of the project, customer end-to-end lifecycle, company strategy and business objectives, quality products are built faster and with reduced costs. Shadow IT can be effectively controlled.
Accelerate your Microservices Journey
As businesses are moving away from monolithic systems towards a microservices architecture, low-code app platforms accelerate this process by enabling you to quickly re-architect monolith functions into microservices via APIs. You can start with low-risk apps that highly impact your business processes.
Self-serving customer-centric portals
Business teams that are involved with customers know what customers need from a business. Searching for the company services, getting a quote, paying bills, getting an answer is a few of them. As such, business teams without coding knowledge can quickly build a self-serving web portal to address customer-specific needs. In addition, companies can quickly build a mobile app and serve customers.
Optimized Costs
Low-code / no-code app development platforms eliminate the need to hire expensive software engineers. With low-code platforms, you can quickly and cost-effectively build and deploy business applications with ease. Advanced features and integration tasks can be handed over to senior developers. That way, you can reduce the software team size and the office footprint. While it saves operational costs, you don’t have to go through the tedious hiring process. Bonuses, insurances and HR-related compliances can be avoided too.
Customer satisfaction is the key
Apps build on low-code platforms are highly customer-centric as they are built by people who interact with customers. Often, sales guys complain about the inefficient processes designed by IT teams that will make the customer leave away before closing a sale. When the sales guy creates the app, he knows what should be included and what shouldn’t. So, businesses can make more sales and generate revenues. More satisfied customers mean repeat business and new references as well.
Several companies have already started to benefit from low-code app platforms. What about your organization?
Does 5G Technology Cause Corona Virus or other health issues?
Corona Virus has become the latest buzz everywhere in recent times. Be it news, whatsapp messages, facebook posts or phone calls, all you hear is mostly about this pandemic disease. However, not every Corona-related post is true. Recently, people burnt the 5G cell towers in UK amid the fear of 5G radio waves causing corona virus. According to BBC News, three 5G tower masts were burnt down in Melling, Liverpool and Birmigham in the first week of April 2020. As these fake posts are virally forwarded, it is important to know if 5G technology causes Corona or any other health issues.
An Overview of 5G Technology
5G technology is the next generation wireless standard that is redefining the way devices connect and communicate over the network. This fifth generation technology offers high speed internet connectivity of up to 10-20 Gigabits per second with a low latency of 1 millisecond. This is possible because 5G operates across multiple bandwidths to leverage and expand spectrum resources. It operates in both 5G New Radio (NR) sub -6 GHz and 5G NR mmWave (24 GHz – 100 GHz) of the electromagnetic spectrum.
Orthogonal Frequency-Division Multiplexing (OFDM) is the method used by 5G technology wherein it reduces interference with digital signal modulation across multi channels. As such, 5G offers higher internet speeds and accommodates 100x more traffic with greater network efficiency along with a 10x lower latency when compared with 4G. It means you can watch high definition streaming videos, use AR/VR apps and play multi-player HD cloud games in real time.
The availability of multiple sensors and low latency means that you can efficiently monitor operations at a remote industrial plant, assist a surgeon in operation, control logistics, create design and manage smart cities, autonomous cars etc. The list is endless. It allows network slicing wherein you can create subnets and categorize data based on your priorities. While earlier generation wireless standards such as 2G, 3G and 4G operated in the 2.4 GHz – 2.6 GHz spectrum, the new 5G technologies uses both the 2.4 GHz and the 5 GHz spectrum. This broader range of spectrum gives the scalability to accommodate a variety of devices operating at high speeds.
5G and Corona Virus
As 5G operates in a higher spectrum when compared with earlier wireless standards, there has been several theories about the health implications of 5G on humans. Corona Virus is actually named as Severe Acute Respiratory Syndrome Corona Virus 2 (SARS – CoV 2). It is also called New Corona Virus 19 (nCOVID-19). As the name says, nCOVID-19 is a biological virus that spreads from droplets transmitted by humans when they sneeze or cough to nearby people. Once the virus gets into contact with a human, they quickly move into the nasal passage and mucous membrane. These virus particles come with a thronged protein particles on their surfaces that get hooked to human cell membranes. As the genetic material of the virus enter the cell membrane, it will hijack the cell and multiple itself to exponentially reproduce the virus.
On the other hand, 5G technology uses radio waves that have less energy than visible light. While 5G is a physical component, corona virus is a biological component. So, they are two different worlds altogether. As 5G waves cannot carry the virus, it cannot spread the virus. The International Commission On Non-Ionizing Radiation Protection (ICNIRP), the scientific body that sets guidelines for EMF radiation, gave an official statement that 5G technology doesn’t cause Corona Virus.
The Effect of 5G Radiation on Health
The term ‘radiation’ is always frightening as it reminds everyone of nuclear radiation, ultra violet radiation, and gamma radiation. However, 5G technology radiation is not harmful. The electromagnetic spectrum is actually divided into two portions:
Non-ionising radiation: In this type of radiation, the energy per quantum is not enough to ionize molecules or atoms. In the electromagnetic spectrum, 700 MHz to 750 THz is considered as non-ionizing radiation portion.
Ionizing Radiation: The energy of the radio waves here have the ability to ionize an atom or a molecule. As the frequency is high, the energy is high too. Gamma rays, X-Rays, UV Rays fall into this category
5G technology operates within the 2.4 GHz – 5GHz. The possible future 5G air waves are expected to operate between 26 GHz and 66 GHz. As such, the radiation has less energy than light. So, it is not harmful to human health. The ICNIRP body regulates the maximum EMF radiation levels and sets guidelines. As per these guidelines, the current 5G emissions are very minimal. While there are a few scientists who claim that 5G radiation is harmful to human health, they haven’t been able to provide minimal scientific evidence to prove their point.