Cognixia Podcast

Hello everyone and welcome back to the Cognixia podcast. Around 423 years ago, the titular character of Shakespeare’s legendary play – Hamley said “To be or not to be”. Today, four centuries later, we ponder on another such dilemma, and we say, “To Devin or Not to Devin”. Now if that confuses you, allow us to explain, because that is what today’s episode is all about – Devin.ai. So, fasten your seatbelts amigos and amigas, we are in for a ride.

Devin is a revolutionary AI that functions as a software engineer. This groundbreaking technology, created by Cognition under Scott Wu's leadership, can code, debug, and even develop apps and websites. Devin signifies a major advancement in AI's role within software development. Unlike AI advancements that threaten job security, Devin is designed to work alongside humans, boosting productivity rather than replacing them. This AI's ability to learn and adapt is transforming how software engineering tasks are tackled, paving the way for a future of closer collaboration between humans and AI.

Devin.ai isn't your average program. This cool AI tool is like having a whole new kind of engineer on your team – one that can code! Devin understands your instructions like your text commands, to be specific, and can tackle tasks like checking how well an app performs.

The way it works is super nifty. Devin has its own toolbox – a command line, code editor, and even a web browser. Using these tools, Devin can not only access information but also understand it, thanks to its built-in reasoning engine. Plus, it seems to have some serious long-term planning skills, likely powered by fancy reinforcement learning.

So, what can Devin actually do? Well, buckle up! This AI can build websites, find, and fix bugs in code like a champ, deploy applications, and even train other AI models. Sounds pretty impressive, right?

Hello everyone and welcome back to the Cognixia podcast.

The increasing popularity of electric vehicles (EVs) is accompanied by growing concerns about malfunctioning charging stations. This presents challenges for both EV owners and the companies that manage charging infrastructure.

The global shift towards electric vehicles (EVs) has experienced significant momentum in recent years. Governments around the world are actively promoting EV adoption through purchase incentives and investments in charging infrastructure. However, this rapid growth has exposed a critical gap – the lack of a sufficiently developed charging infrastructure. Owing to this, there is significant ‘charging anxiety’ in the market, if we can call it that. People are not as much worried about the range, as they are about reliable, safe charging.

The burgeoning popularity of electric vehicles (EVs) is encountering a significant roadblock: unreliable charging infrastructure. A substantial portion of EV owners experience the frustration of encountering malfunctioning or inoperable charging stations. Industry reports indicate that over 20% of charging attempts fail, with a staggering 72% of these failures attributable to charger issues.

This pervasive problem extends beyond mere inconvenience for users. It translates into a substantial financial burden. Estimates suggest that as much as $20 billion of the global $100 billion investment in EV charging infrastructure is currently rendered ineffective due to non-functional chargers.

Hello everyone and welcome back to the Cognixia podcast. What is CI? Continuous integration (CI) is a fundamental practice within the DevOps methodology. It emerged in the late 1990s as a response to the challenges of traditional software development, where infrequent and large code merges often led to integration issues and bugs. CI automates integrating code changes from developers into a central repository. Every time a developer commits their code, automated builds and tests are triggered, providing immediate feedback on the code's functionality and compatibility with the existing codebase. This continuous integration cycle offers significant benefits to both developers and users. Developers can identify and fix errors early in the development process, leading to faster bug resolution and a more stable codebase. Additionally, CI empowers developers with frequent feedback, allowing them to iterate and improve code quality more efficiently. Ultimately, CI translates to a smoother development process and a more reliable, bug-free final product for users. By enabling faster development cycles and early detection of issues, CI paves the way for quicker delivery of new features and functionalities to users. So, CI has been around for a while now. But then, so has DevOps. DevOps has been around for a while, yeah, but that's a good thing! Many of you haven't experienced the struggles of waterfall development or even know what tools like Visual Test were – that's awesome! While understanding different approaches can be helpful, focusing on the current tools that matter most is key. Today, we have entire DevOps stacks that streamline development, building, testing, and even deployment within an agile environment. These constantly evolve, with new features and capabilities emerging all the time. Remember how everyone used to talk about Application Release Orchestration (ARO)? That term faded away, right? You might even hear it again occasionally, sparking a "wait, is that coming back?" moment. Vendors come and go, products merge, and some disappear entirely. This is a sign of a healthy market – needs evolve, users mature, and better solutions emerge. Sure, having a product vanish can be frustrating, but the prevalence of open source in CI/CD helps soften the blow. As outdated tools fall by the wayside, newer, better options take their place, making the whole ecosystem stronger. Speaking of tools, let's talk about Jenkins. It's widely used, maybe a little too widely used. Usage seems to be dipping, and the developers are making improvements to address things that push organizations toward other solutions. The good thing about Jenkins is that it can do everything. But that is also part of the problem with Jenkins. While Jenkins was once a simpler tool, years of competition and evolving needs have turned it into a bit of a heavyweight. If you're working on a small project or a team using it solo with basic requirements, all you really need is something that gets a quality product out the door fast.

Hello everyone, and welcome back to the Cognixia podcast. how does ITIL 4 help you be a better leader? first, ITIL 4 enables you to have more holistic thinking. ITIL 4 moves away from siloed thinking and encourages leaders to consider the "Four Dimensions of Service Management." These dimensions - Organizations and People, Information and Technology, Partners and Suppliers, and Value Streams and Processes - help you see the big picture and make decisions that benefit the entire ecosystem. Second, ITIL 4 will help you lead with agility and adaptability. The IT landscape is constantly evolving. ITIL 4 emphasizes the importance of being flexible and responsive to change. As a leader, you can foster an agile environment where teams can quickly adapt to new technologies and market demands. Third, ITIL 4 facilitates and encourages collaboration. Gone are the days of the lone wolf leader. ITIL 4 highlights the importance of collaboration across teams and departments. By fostering a collaborative culture, you can leverage the diverse expertise within your organization and achieve better outcomes. Fouth, ITIL 4 emphasizes adopting a customer-centric approach and it inspires you to do the same. ITIL 4 focuses on the importance of aligning IT services with customer needs. As a leader, you can ensure your team focuses on delivering value to customers and meeting their expectations. What is a leader without the ability to innovate and invest in continuous improvement? Fifth, this is what ITIL 4 helps you achieve. ITIL 4 encourages a culture of continuous improvement. As a leader, you can create an environment where innovation is encouraged, and lessons learned are used to constantly improve processes and services. These are just some of the ways that the concepts and frameworks of ITIL 4 can help you be a better leader. When you get down to implementing it, the possibilities will be endless.

Hello everyone and welcome back to the Cognixia podcast! who is a data steward? The data steward role emerged in the early 2000s as organizations grappled with the explosion of digital information. As data became a strategic asset, concerns arose about its quality, security, and compliance with regulations. Enter the data steward: a champion tasked with overseeing the well-being of an organization's data assets. Popularity for this role soared with the rise of data governance initiatives and the implementation of stricter data privacy regulations like GDPR and CCPA. Data stewards became crucial in navigating the complexities of data management, ensuring data accuracy, accessibility, and responsible usage. Their responsibilities are multifaceted, encompassing everything from defining data ownership and access controls to managing data quality and ensuring adherence to data governance policies. They act as a bridge between technical and business teams, translating data needs into actionable plans and ensuring data is used effectively to drive informed decision-making. Data stewards play a critical role in ensuring the quality, security, and responsible use of data within an organization. Their work could revolve around multiple areas, such as: One, data governance and policy. Here, a data steward would be involved in defining and enforcing data governance policies, collaborating with stakeholders, monitoring compliance, etc. Two, data quality and management. The data steward could be actively working on defining data standards & formats, monitoring data quality, and ensuring proper documentation takes place for data origin, transformation, as well as usage to maintain traceability and accountability. Three, data security and access control. A data steward’s role would require defining and enforcing data access controls, implementing security measures, and staying updated on data privacy regulations like GDPR and CCPA, etc. They would work in tandem with other teams to ensure the right data security measures are implemented across the organization. Last and most importantly, communication and collaboration. Data stewards act as an important liaison between the technical and the business teams, acting as a bridge between the technical aspects of data management and the business needs of different departments. They are responsible for promoting data literacy in the organization. Their contributions play a critical role in enabling data-driven decision-making in the organization since they are responsible for translating data insights into actionable plans for various data functions.

Hello everybody and welcome back to the Cognixia podcast! An Azure Administrator would have subject matter expertise in implementing, managing, and monitoring an organization’s Microsoft Azure environment, including virtual networks, storage, compute, identity, security, and governance.

According to Microsoft, as an Azure Administrator, you often serve as a part of a larger team dedicated to implementing an organization’s cloud infrastructure. You also coordinate with other roles to deliver Azure networking, security, database, application development, and DevOps solutions.

An Azure administrator would also be well-versed in operating systems, networking, servers, and virtualization. Besides, they would also have vast experience with PowerShell, Azure CLI, the Azure portal, Azure Resource Manager templates, and Microsoft Entra ID.

With Microsoft Azure's continued growth in cloud computing, the role of a Microsoft Azure Administrator becomes ever more crucial. These individuals require a deep understanding of Azure's infrastructure, services, and management tools. This expertise allows them to effectively design, implement, monitor, and maintain cloud solutions that empower organizations around the world.

The heart of the Microsoft Azure Administrator role lies in navigating the intricacies of Azure's infrastructure. This includes gaining a thorough grasp of virtual machines, networking, storage, and security within the Azure environment. This translates to configuring virtual networks, implementing robust storage solutions, and fortifying defenses with firewalls, encryption, and access controls.

Azure isn't just about infrastructure - it's a dynamic ecosystem of services that address various business requirements. A skilled Azure Administrator effortlessly navigates services like Azure App Service for seamless web hosting, Azure SQL Database for efficient data management, and Azure Active Directory for streamlined identity and access management. This strategic use of services allows administrators to boost operational efficiency, improve scalability, and optimize resources for maximum performance.

Hello everybody and welcome to the Cognixia podcast!

what is a password manager?

Imagine your passwords as tiny secrets, each needing its own locked box. But juggling hundreds of boxes could be such a nightmare! That's where password managers come in - like a master safe for all your secrets, guarded by one super-strong password (your master password). Think of it as Fort Knox for your logins!

But it's not just storage. Password managers are like personal password chefs, whipping up unique, complex passwords that would make even the strongest hacker cry. Plus, they remember them all, so you don't have to. No more scrambling to recall that password you used once in 2012!

And the best part? They're like automatic door openers for your online accounts. Just tap your master password and boom, you're in! This not only saves you time but also protects you from sneaky malware trying to steal your keys.

Worried about losing your secrets if you switch devices? Don't sweat it! Good password managers keep your info synced across everything, from your phone to your laptop, so you're always covered.

So, ditch the mental juggling act and treat your online life to a password manager. It's like giving your digital self a security upgrade and a much-needed vacation from memorizing gibberish!

Now, there are different types of password managers in the market. There are cloud password managers and local password managers. There are different password managers designed for individuals, families, small businesses, and large enterprises. Ideally, organizations should steer clear from the consumer versions of password managers and instead, invest in enterprise-class tools that would offer greater security from an enterprise perspective for all privileged accounts, services, systems, applications, etc.

To understand cloud-based password management, imagine your passwords living rent-free in a super secure online apartment building. That's the cloud! Now, this comes with some pros and some cons. The pros are, one, it is a syncing heaven. You can access your passwords on any device, anywhere, seamlessly. No more scrambling to remember what password you used on your work laptop versus your phone. There is an option for easy disaster recovery. Lost your computer? No sweat! Your passwords are safely backed up in the cloud, ready to be accessed when you need them.

Hello everyone and welcome back to the Cognixia podcast. The renowned UK security think tank, the Royal United Services Institute has published new research detailing the possible effects of ransomware attacks on businesses and staff, society, the economy as well as national security. Interestingly, it also highlights an impact that not a lot of us usually consider when we talk about the cost and effects of ransomware – the impact on physical and mental health. In today’s episode, we talk about these hidden costs of ransomware attacks, so stay tuned.

Who is the Royal United Services Institute?

The Royal United Services Institute or RUSI is the world’s oldest and the UK’s leading defence and security think tank. Its mission is to inform, influence, and enhance the public debate on a safer and more stable world. It is a research-led institute, that produces independent, practical, and innovative analysis to address today’s complex challenges.

Ransomware incidents are a scourge in our society globally. A ransomware attack takes a visible toll on organizations, individuals, the economy, and even national security. But there are some intangible and not so easily visible impacts of ransomware attacks too. These include physical, financial, reputational, psychological, and social harms. Ransomware is a risk to everyone, whether you are an organization or an individual, whether you are a small startup or a gigantic corporation. The financial costs and losses resulting from a ransomware attack can threaten the entire existence of the organization. Besides, it damages the reputation of the individual or organization too, making them look unprofessional or incapable and exposing sensitive data to vulnerabilities and exploitation.

Hello everyone and welcome back to the Cognixia podcast. DevOps is giving way to DevSecOps, but the pace at which this is happening isn’t quite heartening to see. Another development in the space is Security-as-Code, which works in tandem with DevSecOps, and that is what we will be talking about in today’s episode.

While there is no doubt in any of our minds that security is an integral part of the software, we usually tend to leave it for the end of the SDLC, by which time it is already too late to handle such a critical aspect of the software. This will cause delays in your software development timelines. Since we are all conditioned to believe that security is a one-time thing, most times nobody bothered to automate the security testing, so you’re stuck with doing everything manually, which only takes so much more time than automated testing.

To change this scenario, experts suggest two critical steps. First, shift security to the left. Meaning, why should security be added as an afterthought? Why should security be incorporated after everything else is already completed? Instead, move security steps to the left, making it an integral part of the SDLC, and not a step to be taken after the SDLC is complete.

Second, invest in automation. Manual work is often very tedious, time-consuming, prone to errors, and risky. Instead, put in the effort to automate the security aspects and testing. Automate the process of defining policies, security test cases, etc., and save both time and resources, plus, no errors!

Hello everyone and welcome back to the Cognixia podcast. we visit two very popular terms of our times – Platform engineering and DevOps, and we will explore not just what they are but also, more importantly, how they are different from each other because we understand it can get a little confusing at times. In the fast-moving, modern software development and operations world, terms like DevOps, platform engineering, and Site Reliability Engineering are so commonly thrown around, sometimes even used interchangeably, that it can be hard to keep up and navigate these different domains smoothly. The distinction between these terms is quite nuanced and quite critical to understand, we believe. So that is what we will focus on in today’s episode.

In essence, platform engineering, DevOps, and site reliability engineering or SRE are related but they are distinct disciplines by themselves and do not mean the same thing. They all play varying roles in the development and operation of reliable and scalable software systems but they are quite different from each other. Let us first take a quick look at what each of these terms means.

To begin with, DevOps. DevOps is a cultural and collaborative approach to software development and IT operations with a focus on breaking the silos between the two functions while improving communication, collaboration, and efficiency. It is not a specific job role as such but more like a cultural philosophy for teams and organizations to follow. It revolves around the idea of encouraging developers to take on greater operational responsibilities and for operations teams to be more involved in the development process. It involves a lot of different tools and practices like CI/CD pipelines, automated testing, Infrastructure-as-Code, etc.

Hello everyone and welcome back to the Cognixia podcast. we talk about something we all use but don’t pay much attention to these days. At least not as much as we once used to. Today, we talk about anti-virus software. At one point in time, not so long ago, anti-virus software was very highly sought-after and the market was also flooded with pirated copies of popular software brands like McAfee, Norton, Kaspersky, AVG, Avira, etc. The entire process was also quite tedious, buy a CD, run it on your desktop or laptop, and it was all very time-consuming. Sometimes, the installation would fail and you would need to seek help. You knew that if you didn’t do this, the virus lurking everywhere – from the free song download websites to random forwarded emails or even the pen drives you used to share files with friends would strike, and you would need to format your entire system to clean it out, losing all your files and data on the system in the process. Then came mobile phones and with that came antivirus apps, install them on your phone to keep things safe. We have all at some point been annoyed with those apps because they wouldn’t let us uninstall the apps, or change the SIM cards, and it would be very frustrating at times. But in the interest of safety and security, we let it be. We looked for bundles and packages when we bought new phones so we could get a free subscription to the antivirus app with it. Back then, the thought of a rogue virus wreaking havoc on your pixelated screen sent shivers down your spine. Antivirus software was our knight in shining armor, diligently guarding our digital gates against those pesky malware invaders. Fast forward to 2024. Cloud computing, AI-powered devices, and hyper-connected ecosystems have revolutionized our digital realm. So, the question begs: are traditional antivirus software and apps still relevant in this ever-evolving landscape? The answer is a nuanced one. While the nature of threats has shifted, antivirus solutions haven't become obsolete. They've simply adapted and evolved alongside the changing security landscape.

Hello everyone and welcome back to the Cognixia podcast.

According to the official Microsoft documentation, PowerShell is a cross-platform task automation solution made up of a command-line shell, a scripting language, and a configuration management framework. PowerShell runs on Windows, Linux, and MacOS. PowerShell is an object-oriented automation engine and scripting language with an interactive command-line shell developed by Microsoft. It aims to help users configure systems and automate administrative tasks. It is quite a mature tool and has a proven as well as established record as an automation tool for users both within and outside the IT function. Originally, PowerShell was offered as a proprietary software however, now PowerShell is available on all Windows systems by default. In 2016, Microsoft open-sourced PowerShell and made it available both on MacOS as well as Linux systems. So, what does PowerShell do? PowerShell was designed to help users with automating their tasks. For instance, if you are looking to automate your batch processing, PowerShell is the tool to help you with this. If you want to create system management tools for your commonly implemented processes, then PowerShell is the tool for you. Apart from this, PowerShell also serves as a replacement for Microsoft’s Command Prompt which goes back to the times of MS-DOS. PowerShell became the default command-line interface for Windows 10 with the build 14791, which is also how most of us today become acquainted with the PowerShell technology. With that, we can say that PowerShell is a modern command shell that includes the best features of many popular shells, however, unlike a lot of other shells which would only accept and return texts, PowerShell will accept and return .Net objects, making it so much more powerful and versatile compared to other shells.

Hello everyone and welcome back to the Cognixia podcast. we will talk about MongoDB and Amazon CodeWhisperer. Earlier in November 2023, MongoDB and Amazon Web Services made a groundbreaking announcement. MongoDB, Inc. and Amazon Web Servinces, Inc. announced that “The two companies are collaborating to optimize Amazon CodeWhisperer to provide enhanced suggestions for application development & modernization on MongoDB’s industry-leading developer data platform that millions of developers and tens of thousands of customers rely on every day for business-critical applications. Trained on billions of lines of Amazon and publicly available code, Amazon CodeWhisperer is an AI-powered coding companion from AWS that generates code suggestions based on natural-language comments or existing code in developers’ Integrated Development Environments or IDE.” “Working together with AWS, MongoDB provided curated training data for MongoDB use cases and took part in the evaluation of Amazon CodeWhisperer outputs throughout the training process to promote high-quality code suggestions. While Amazon CodeWhisperer already provided support for building applications on MongoDB, developers can now get enhanced suggestions that reflect best practices, allowing developers to ideate more quickly, rapidly prototype new features, as well as accelerate application development,” the Press Release continued. The developer landscape is buzzing with excitement around Generative AI coding companions like Amazon CodeWhisperer. These coding companions are unlocking new ways to work, freeing developers from routine tasks and letting them tackle truly challenging problems. CodeWhisperer already shines when working on common coding and AWS API tasks, and now, thanks to the collaboration with MongoDB, the combined capabilities would be extended to millions more developers who build with MongoDB. With Amazon CodeWhisperer coming into the hands of even more developers courtesy of this collaboration, they would be more empowered than ever before to tap into the transformative power of Generative AI. This isn't just about changing how users interact with apps; it is about changing how developers build them. The partnership between AWS and MongoDB to train Amazon CodeWhisperer on MongoDB is a major step in that direction, giving developers the boost they need to build faster and focus on higher-value tasks.

Welcome to the Cognixia podcast. Earlier in December 2023, the 2023 UN Climate Change Conference convened in Dubai, as part of which some very important meetings of the Conference of the Parties or COP 28 took place. These meetings strived to deliver a successful COP 28 that drives global transformation towards a low-emission and climate-resilient world, fostering ambitious climate change action, and facilitating implementation including related support. The Incoming Presidency announced that the focus of the COP 28 would be four major paradigm shifts:

One, fast-tracking the energy transition and slashing emissions before 2030

Two, transforming climate finance, by delivering on old promises and setting the framework for a new deal on finance

Three, putting nature, people, lives, and livelihoods at the heart of climate action, and

Four, mobilizing for the most inclusive COP ever

In the face of the dire situation we are in climate-wise, all measures are important, and everybody has a role to play. The world is potentially set to breach the 1.5C temperature increase threshold by 2027. 2023 saw some major climate change-driven disasters, such as the wildfires in Hawaii, the cloak of orange haze from the record Canadian fires, July becoming the hottest month on the planet since 1880, heat waves in February and the cyclonic activity at the beginning and end of monsoon in India, the worsening hold of El Niño. The world as a whole is under a lot of pressure to slash its carbon emissions to meet the net zero goals set for 2050.

The time to wake up and ensure sustainability measures are weaved into everything we do was yesterday and the world is waking up to this reality now. While all industries are gradually taking responsibility and driving change in their respective spaces, one sector that is undergoing rapid transformation to meet their sustainability goals and ensure that there is progress without compromising the future of the planet is the Technology space.

The tech industry is responsible for a very large carbon footprint, there is no denying that. The data centers, the coding that goes into keeping them working as well as all the code they handle daily, artificial intelligence, machine learning algorithms, wireless throughputs, etc. come with a very energy-intensive nature, and while they do wonders for changing the world around us for the better, they are not exactly sustainable or planet-friendly from an environmental impact perspective. Just the data centers, a large majority of which are in the United States, and the transmission networks account for about 3% of the global electricity consumption as well as about 3.5% of global greenhouse gas emissions. While these numbers might appear quite small, consider this – these numbers are roughly the same as those for the entire aviation industry. In fact, they are just slightly lesser than those for all the industries of the world that manufacture fertilizers, pharmaceuticals, refrigerants, and oil & gas extraction together, which hold about 3.6% of global carbon emissions. On this front, a Reimagining Futures Study by Ernst & Young finds that more than half the enterprises who participated in the study felt that emerging technologies could play a vital role in accelerating global sustainability.

Hello everyone and welcome back to the Cognixia podcast. Salesforce is an easy-to-use, simple-to-set-up, all-in-one CRM platform that helps you take your business to the next level. It offers a multitude of products under its umbrella for different sizes of businesses and different business needs. It is the world’s most trusted customer relationship management platform that can help an organization’s marketing, sales, commerce, service, as well as IT teams work as one irrespective of where in the world they are all located. Every tea, even every team member could be in different parts of the world and Salesforce would bring them together virtually to ensure operations take place seamlessly, without a hitch. It is used by over 150,000 companies, both big and small to manage and grow their businesses. If you browse their website, you will see how Salesforce helps businesses save up to 20 hours of work each week which can be used more productively in doing important tasks, it helps improve sales productivity by up to 29% while reducing support costs by up to 27%, and even reduces the customer acquisition costs for organizations by up to 27%. Salesforce, after all, is a cloud-based software designed to help businesses connect to their customers in a whole new way, so they can find more prospects, close more deals, and wow more customers with their amazing service. In the recently concluded Salesforce World Tour New York 2023 event, Salesforce announced expanded capabilities of its Generative AI assistant called Einstein Copilot as well as enhanced integration with Apple offerings. The Data Cloud Vector Database is being added to the Salesforce Data Cloud which would allow both the Salesforce Data Cloud as well as Einstein Copilot to handle large amounts of unstructured data. The Data Vector Cloud Database is expected to be a very major enhancement for the Salesforce Data Cloud and the pilot for the same is expected to be launched in February 2024. But what is this Data Vector Cloud Database? It is an integrated vector database support that enables a user to use a wide variety of data types. Using this, unstructured data like PDFs, emails, audio files, social media content, etc. can be combined with structured data such as purchase history, customer support cases, and product inventory. Salesforce also has a Generative AI assistant called Einstein Copilot. Einstein Copilot, once the pilot we just mentioned is launched, will be able to use unstructured data to respond to questions and perform analysis by accessing the data for the same through the Salesforce Data Cloud. Einstein Copilot is also expected to become available to users around February 2024.

Hello everybody and welcome back to the Cognixia podcast. If you remember the headlines from November last year, you will recall that ChatGPT became so popular that it broke all records held by any platforms and apps in the past and reached a mind-blowing one million users in just five days of its launch. This huge surge in user sign-ups was hailed as an undeniable testimony to the growing interest in large language models and as an indicator of the beginning of a new era powered by artificial intelligence. Countless reports of potential job losses and rampant unemployment triggered by AI circulated, while there were also plenty of reports highlighting how the world was going to change for the better thanks to artificial intelligence. From writing marketing copy to creating interesting blogs, generating social media captions writing scripts for videos, writing poetry and prose, ChatGPT has become very commonplace in generating content. It does come with limitations and obstacles, sure, but it does work as a great aid for anyone seeking some help to generate content, hashtags, etc. ChatGPT has also found a great place in the education space. In the past year, educators have used ChatGPT to craft personalized learning experiences for the learners, build interactive exercises, and send personalized feedback for the learners. Students are also leveraging the power of ChatGPT to help with their learning journeys and for self-directed learning and language acquisition. The advent of automation had already begun transforming customer service and user experience. ChatGPT only championed the potential of AI to transform this space further. In the past year, several businesses have adopted ChatGPT to improve how users interact with the brand and their customer service interactions. ChatGPT can answer common customer queries, resolve basic issues, provide 24/7 personalized support, and more without breaking the bank. The interactions can be so human-like that it can often be challenging to identify whether you spoke to an AI or a human! ChatGPT has also helped countless IT professionals – developers, QA testers, coders and programmers, engineers, project managers, and more to accomplish their tasks more efficiently – writing code, checking code, testing code, mapping resources, doing complex calculations, running test scenarios, etc. Some of these individuals were also the initial adopters of ChatGPT and have reaped amazing benefits from using the tool.

Hello everyone and welcome back to the Cognixia podcast. We talk about the recently released “The State of Developer Ecosystem 2023” Report by JetBrains. This report is a culmination of insights gathered from 26,348 developers from all around the globe. The world of developers is vast and diverse, making it an endlessly fascinating realm for exploration & learning. Through yearly research initiatives like this one, our goal is to explore this captivating world, uncover valuable insights about developers and their craft, and then share these facts with the community. The State of the Developer Ecosystem Report analyzes and presents valuable insights on a wide range of topics from programming languages, tools, and technologies to demographics and fun facts.

It also digs deeper into the lifestyle of developers, showcasing the current passions and hobbies among developers. The 2023 State of Developers Report also includes insights on artificial intelligence, a category that has been newly introduced to the Report. The Report showcases the features of AI that developers are commonly using, the challenges they face, and some insights into how the situation is around the adoption of AI-enhanced tools.

Now, this begs the question of who is doing the research for this and how is the research done. Well, the State of the Developer Ecosystem Report is a report brought out by JetBrains. JetBrains is a company that aims to make professional software development a more productive and enjoyable experience. JetBrains helps developers work faster by automating common, repetitive tasks to enable them to stay focused on code design & the big picture. For this, they provide tools to explore and familiarize with code bases faster. JetBrains’ products are designed to help developers take care of quality during all stages of development and spend less time on maintenance tasks.

Hello everyone and welcome back to the Cognixia podcast. When developing applications, one often needs to integrate third-party or open-source dependencies into the applications to meet the intended business requirement or utility. For instance, a food delivery app could be dependent on Google Maps or MapMyIndia for the Map functionality on which a user can track their food. An e-commerce app or website could have a dependency on WhatsApp for enabling a live chat with a customer service bot or a customer service rep to help answer any queries that the customer of the platform may have. These dependencies could be paid for by the using entity to the service/API provider or it could be open-source and play an important role in supporting the efficient functioning as well as any other features that the application might be providing.

The simple logic here was instead of building a whole functionality from scratch, which by no means is an easy feat since it takes looking for and hiring the right people with the right skills and domain knowledge, and a boatload of resources to put together a team, lots of time, and so much more. Instead, one could use the API or functionality built by someone else, something that is already tested and ready to use with established success, just integrate it into whatever you are building, and voila! The functionality creator could get a licensing fee or a royalty payment of sorts, and the user entity has a perfectly functioning feature that would be highly valuable for its users.

Sounds like a great thing, right? It totally is. Then where exactly is the problem?

Well, whenever great innovations have taken place, haven’t the unscrupulous elements always caught up with them sooner or later for their gains while penalizing or harming others in some way?

Dependencies are no exception. Enters Dependency Confusion Attack.

Dependency confusion attacks are relatively new to the world, but in the short time they have been around, they have sent ripples around the world showing the unimaginable levels of harm they can cause. So, who is at risk? How do these attacks function? Can we do something to stop it or fight it once it happens? We are sure you have many questions, and we will try our best to answer as many of these as possible in this podcast episode.

New research by OX Security, a DevOps software supply chain company has revealed that just about every application that has more than 1 billion users and more than 50% of applications with roughly 30 million users are highly vulnerable to dependency confusion attacks. The research also makes a shocking revelation – organizations that are at the most risk would likely have about a whopping 73% of their assets exposed to dependency confusion attacks!

Hello everyone and welcome back to the Cognixia podcast. we talk about something that has been in the news a lot this year and actually makes it to the news for all the undesirable reasons every year for quite some years now. Today, we talk about the AQI or the Air Quality Index. This year, Delhi became the World’s most polluted city in the world, with Mumbai and Kolkatta not far behind in the air quality index. Diwali time makes the air in these cities become absolutely unbreathable, with the rampant air pollution from bursting firecrackers, construction work, industrial pollution, burning the stubbles in farms, etc. and it is affecting the lives and health of everyone in and around these cities.

Air pollution levels in most of the urban areas are a cause of serious concern. It is the right of the people to know the quality of the air they are breathing. However, the data generated by the National Ambient Air Monitoring Network are reported in a form that may not be easily understood by common people. This system of air quality information was found inadequate to facilitate people’s participation in the air quality improvement efforts. With this in mind, the Central Pollution Control Board developed the Air Quality Index for Indian cities as a tool to disseminate information on air quality in qualitative terms, as well as its associated and likely impact on health. The Air Quality Index has six key objectives:

1. Resource allocation

2. Ranking of locations

3. Enforcement of standards

4. Trend analysis

5. Public information, and

6. Scientific research

Let us begin by first understanding what is the Air Quality Index. The Air Quality Index or AQI was introduced in 2015 and aimed at quantifying the severity of air pollution at a particular location. It is measured by recording the levels of multiple pollutants in the air. It is a single composite index that monitors eight important individual pollutants. These eight pollutants are PM10, PM2.5, nitrogen dioxide, sulfur dioxide, carbon monoxide, ground-level ozone, ammonia, and lead. These metrics are measured at different monitoring stations and the metric values are calculated using their average concentration values over a 24-hour span for all metrics except the carbon monoxide and the ground-level ozone metrics, which are measured over 8 hours. The health breakpoint concentration is also taken into consideration when calculating the AQI. Based on this, the worst sub-index value would be taken as the Air Quality Index for the location.

Hello everyone, and welcome back to the Cognixia podcast. we continue our conversation about Generative AI, this time with a focus on different job roles and how managers can use Generative AI to design better job roles for the organization as well as employees. Organizations do their best to improve employee engagement and keep employee turnover as minimal as possible. But, there is one key metric, or rather a very important attribute that often gets ignored or hasn’t been checked much so far employee burnout. Surveys conducted by Gallup in the United States in 2022 showed that 40% of the employees surveyed reported that the job hurt their mental health and around 30% have shared that they regularly face burnout. About 32% have said that they feel engaged at work while 17% have said that they were actively disengaged. On a global level, the employees’ lack of engagement has been estimated to cost employers roughly $7.8 trillion, which is equivalent to a whopping 11% of the global gross domestic product, according to a report in the MIT Sloan Management Review.

Experts opine that a lot of disengagement and work stress employees go through is rooted in how organizations design job roles for the employees. Decades of extensive research by experts have discovered that poor work design leads to negative employee outcomes – mental strain, high turnover, job dissatisfaction, decreased productivity, impaired learning, etc.

The MIT Sloan research goes on to highlight that quite often, the managers in organizations lack the understanding and insight to design high-quality jobs. With the recent advances in technology, this is a very critical area in which artificial intelligence can help organizations. It can help bridge the knowledge gap among managers and enable organizations to design high-quality work which would be a win-win situation for both the organization as well as the employees, current and potential. But before the managers are empowered to do this, they need to be well-versed in the pros and cons of using Generative AI for work design.

Hello everyone, and welcome back to the Cognixia podcast. The ICC Men’s Cricket World Cup is in full swing in India, packed with nail-biting action and some stellar performances by different cricketers and teams. For all of us at home, watching the matches on our televisions, laptops, tablets, and mobile phones, our match-viewing experience has been significantly enhanced in recent times. We can see the analytics and charts clearly on our screen, while chilling on our couches, munching on our favorite chips and dips. Like all sports, cricket has benefitted greatly by embracing technological advances. It has enhanced the experience not just for the players and viewers, but also for the umpires, commentators, coaches, selection committees, and a lot of other people. “Cricket fans across the world will be able to experience and engage with the game like never before through new digital activities, placing fans at the center of the ICC Men’s Cricket World Cup 2023,” says ICC on their website. ICC has shared that it is leveraging the ICC’s first-of-its-kind vertical feed coverage to create highlights tailored for hundreds of millions of fans that access the content on their mobile phones and will be available to access on the ICC app, website, and the ICC Meta channels. ICC is actively leveraging its WhatsApp and Instagram channels to share exclusive news and videos, enabling millions of fans worldwide to stay connected and up-to-date with what’s happening at the World Cup 2023. Exclusive content on both these channels includes match highlights, breaking team news, viral videos, etc.

Hello everyone and welcome back to the Cognixia podcast. To understand what large language models are, we first need to understand what transformer models are. As a human, we see the text as one word at a time and comprehend it accordingly, whereas machines see the text as just a bunch of characters. Machines were usually unable to interpret text like human beings can. However, this began changing when Vaswani et al published a paper establishing something called the transformer model. A transformer model is based on the attention mechanism, which enables the machine to read an entire sentence or even an entire paragraph instead of one character or one word at a time, and once the machine has consumed the entire input text, it will be capable of producing an output based on the input received. This enables the transformer model to understand the context of the input and deliver better outputs. These transformer models are the basis of many other models commonly used in machine learning and generative AI today. They process data by tokenizing the input and simultaneously conducting mathematical equations to.

Large Language Models are more advanced and complex versions of the transformer model in a way. A large language model is a deep learning algorithm that can perform a variety of natural language processing tasks. The large language models use transformer models and are trained using very, very large data sets. This is also why the models are called LARGE language models. Due to the wide training and powerful transformer models at the backbone, the large language models are equipped to recognize, translate, predict, or generate text or other content. From understanding protein structures to writing code, these large language models can be trained to do a very wide range of things. But how are transformer models and other machine learning models able to predict text? According to a very influential and interesting paper by Claude Shannon titled “Prediction and Entropy of Printed English”, the English language has an entropy of 2.1 bits per letter, despite having 27 letters, that is, 26 alphabets and 1 space, hence, 27. If these letters were used randomly, the entropy would be about 4.8 bits per letter, which would make it easier for machine learning models, especially the transformer models to predict what would come next in a human language text. The models keep repeating this process again and again, creating entire paragraphs, word by word, that we then receive as an output.

Also, how does the transformer model or the machine learning model comprehend and deal with grammar? The model sees grammar as a pattern of how different words are used in a sentence or a context. It would be challenging for anyone to list out all the rules of grammar and then teach them to a machine-learning model. Instead, the models are programmed to acquire these grammar rules implicitly using examples. When the transformer model is large enough, as is the case for large language models, the model can be trained to learn a lot more than just the grammar rules, learning to extend these ideas beyond just the examples it has been trained on.

Hello everyone, and welcome back to the Cognixia podcast. With the Search Generative Experience, Google is bringing the power of Generative AI to its search platform to offer its users new ways of finding, visualizing, and creating content. In a recent blog post, Google said, “Search has long been a place where you can find information to help with life’s questions – whether they are big or small. With advances in AI, we have continued to develop simpler and smarter ways to help you uncover useful insights & make sense of information. As we continue to experiment with bringing Generative AI capabilities into Search, we are testing new ways to get more done as you are searching – like creating an image that can bring an idea to life, or getting help on a written draft when you need a starting point.” So, what is the Search Generative Experience? On this, Google elaborates, “There are times when you might be looking for a specific image, but you can’t find exactly what you have in mind. Or maybe you have an idea that you need help visualizing. So beginning today, we are introducing the ability to create images with our Generative AI-powered Search Experience or SGE.” Simply put, Search Generative Experience or SGE enables Google users to generate AI images and text by entering a prompt into the Google Search bar. This is like how the popular Generative AI-powered text-to-image tools like Midjourney and DALL-E 2 function. This capability also rivals Microsoft’s Bing Chat powered by GPT-4. Users can opt-in for the SGE after which they will see an option to create the AI-generated images directly when using Google Images. The model, however, is currently in its trial phase. The function is currently only rolled out in the United States for users aged 18 and above.

Hello everyone and welcome back to the Cognixia podcast. Twenty years ago, Dan Brown published his world-renowned book – the Da Vinci Code. Three years later, a film adapted from it with the same title starring Tom Hanks was released worldwide. The book and the movie both, were super interesting, and make for a great read and a great watch even today. The story takes the viewers and readers through the Louvre in Paris, stars the Mona Lisa and The Last Supper, and so many other renowned paintings, all on a quest for the Holy Grail. If you paid attention to the book or the movie or both, you would have encountered a sequence of numbers mentioned in the plot that was essential to decoding a clue that Professor Langdon is working on with Sophie. This sequence of numbers is usually written in a triangle form and is called the Fibonacci Sequence.

If you are thoroughly lost, let us explain a little more. First, what is the Fibonacci sequence?

The Fibonacci Sequence was first discussed in Europe by Leonardo of Pisa, whose nickname was Fibonacci in the early 13th century. However, the origin or the first mentions of the sequence can be traced back to about 2000 BCE in Indian literature. There is an enormous amount of literature about and using the sequence today, and it has connections to multiple branches of mathematics.

In the Fibonacci sequence, every number is the sum of the preceding two numbers, so the sequence goes 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, and so on. It is a fantastic example of a second-order linear recurrence relation.

Now, why is this Fibonacci sequence important? The Fibonacci sequence is a sort of nature’s secret code. The number of petals in a flower, the seed heads of a flower, countless paintings, plant spirals, pinecones, hurricanes, spiral galaxies in space, and so much more are examples of the Fibonacci sequence. The ratio, shapes, and arrangements formed by the Fibonacci numbers are everywhere – growth patterns of plants, arrangements of leaves, sections of your fingers, spiral shapes of sea shells, how your eye sees things, everywhere!

So, why is this Fibonacci sequence important in Scrum? What can it be used for there?

Scrum teams use the Fibonacci numbers to understand the scope and size of their product backlog items. Here, size is not just a measure of how many people are involved or how long it will take to complete the task. The size of a product backlog item involves two major components: 1. Scope 2. Complexity Agile teams will usually not use size as an indicator of the time, the size of a product backlog item cannot be say, one day. Time is never a good indicator for estimating the size and complexity of a work item. A senior team member could finish a task in say 2 days, while a junior team member or a new team member may take 4 days. This wouldn’t be an accurate representation of size then. The size of a product backlog item should give a shared idea of the scope as well as the complexity of the work, not individual time estimates.

Developers on the scrum would regularly discuss which items to bring into the sprint. When these discussions happen, size is an essential aspect to consider so that different stakeholders get a precise idea of what to expect. So, how does one describe the relative sizes between the different product backlog items? This is where the Fibonacci sequence does the magic.

Hello everyone and welcome back to the Cognixia podcast. The important aspects of project management, that is, conflict management. A project runs on the back of the efforts of its people. These people come from different backgrounds, different teams, different disciplines, and different ideologies, and are required to work together for the success of the project. Often, these people may have never worked together before. To top it off, the project works within the defined constraints of resources, time, and scope. This makes the environment quite conducive for conflicts to arise. Since the project manager leads all the teams and is responsible for ensuring everything runs smoothly, it becomes an important part of their job to address any conflicts that might arise and to resolve them at the earliest in the best possible manner. To equip the project manager with the right tools, the Project Management Body of Knowledge Guide, or the PMBOK Guide for short, recommends some effective strategies and techniques. Conflict management is one of the disciplines covered by the PMBOK Guide and is an important part of the certification exam outline for the Project Management Professional certification by the Project Management Institute. As you must already know, the latest edition of the PMBOK Guide is the 7th edition of this guide. The PMBOK Guide is a flagship publication of the Project Management Institute. The PMBOK Guide – 7th edition, according to the Project Management Institute, “adapts to the changes in the industry and helps you achieve your goals effectively, no matter what they are.” Integrating different disciplines and a diverse range of skills is imperative for the success of a project. Conflicts will always arise; we have slowly understood that. However, the existence of conflict doesn’t have to always be detrimental to the project or the organization. If managed and resolved effectively, conflicts can be beneficial for the project and the organization too. The project manager would undoubtedly play a key role in diffusing such situations while ensuring everyone and everything is on track and there is no adverse impact on the performance of the team or the outcomes of the project. What is a conflict? Anytime there is a difference of perception, opinion, or belief among two or more people, it is a conflict. Simply put, when people disagree, it usually is or leads to a conflict. In a project management situation, the project manager is responsible for creating a culture of harmony and collaboration. Conflict is natural and inevitable when there are multiple people with such diverse backgrounds, opinions, ideas, and realities, come together to collaborate. However, research has increasingly shown that when conflict is effectively managed, it can lead to better performance and more positive outcomes. Gone are the days when conflicts were a bane and a nightmare for any organization. In today’s world, little or no conflict means, little or no innovation, little or no change, and little to no room for improvement for the organization. Now, no organization would want that, would they?

Hello everyone, and welcome back to the Cognixia podcast. First, let us understand what bioengineering is. Bioengineering is the application of the principles and design concepts of engineering to the study & manipulation of biological systems and the development of biological products. It is a broad field of study that includes a very wide range of disciplines from biomedical engineering to agricultural engineering and bioprocess engineering to environmental engineering. Bioengineering is a rapidly advancing field with varied applications. Bioengineering professionals are playing a crucial role in improving human health and well-being for mankind globally.

Innovations in bioengineering, like most fields of science and technology, are driven by academia, often through the startup route. We are seeing more and more companies and startups come up in the bioengineering space, pushing the biomedical world to be more innovative and creative than ever before. The market is huge. According to McKinsey Research, apart from the transformational benefits to human health & well-being as well as a more sustainably managed environment, just about 400 bioengineering use cases, almost all of which are scientifically feasible, could have an economic impact of approximately $2 trillion to $4 trillion per annum from 2030 to 2040.

As technologies evolve, the pace of innovation in bioengineering is also accelerating. According to SynBioBeta – a professional network for biological engineers, investment in synthetic biology companies raised about $4.6 billion in the first quarter of 2021 alone! This is more than four times the investment in the same quarter a year earlier.

Hello everyone and welcome back to the Cognixia podcast. This week, we talk about something that the entire nation came together to celebrate – the Chandrayaan-3. Chandrayaan-3 is the Indian lunar exploration mission under the Indian Space Research Organization’s Chandrayaan program. The Chandrayaan-3 had a lander named Vikram and a rover named Pragyan, very similar to the previous edition of the Chandrayaan, Chandrayaan-2. The Chandrayaan-3 was launched on 14 July 2023 and it entered the lunar orbit on 5 August. On 23 August, the lander touched down on the South Pole of the moon, making India the fourth country to successfully land on the moon after Russia, USA, and China, and the first-ever country to land on the South Pole of the moon.

One technology that has played an important role in the success of this mission is artificial intelligence. Chandrayaan-3 has been able to greatly improve its planning, navigation, data analysis, and overall operational efficiency, thanks to artificial intelligence. Let us look at the information we could find on how artificial intelligence has contributed to the success of Chandrayaan-3.

A lot of algorithms are run to design and develop a space exploration mission vehicle. The Chandrayaan-3 is no exception. Artificial intelligence played a crucial role in developing the algorithms and then designing as well as developing the Vikram lander, the Pragyan rover, and the entire Chandrayaan-3. The design for the Chandrayaan-3 was optimized for weight, performance, and safety with the help of algorithms powered by artificial intelligence too.

Hello everyone and welcome back to the Cognixia podcast. what is data hoarding. Data hoarding is the excessive collection and retention of data. It is also sometimes referred to as digital hoarding. It is a growing challenge in the tech space. Countless organizations have a field time keeping on purchasing more and more storage space to accommodate the growing amount of data. Not just structured data but unstructured data is also growing enormously, necessitating increasing amounts of precious storage space. This is also leading to a major problem of disconnected data silos. Just as hoarding things in real life can be a problem, and mind you, Marie Kondo wrote a whole book about dealing with this, hoarding in the digital world is also a problem. This commonly happens when organizations tend to save data, maybe out of habit, out of the fear of losing it, out of hope that it will be useful someday, or even simply because they didn’t know any better.

A new study has found that 47% of consumers would stop buying from a company that fails to control how much unnecessary or unwanted data it is storing. Compare this to the fact that in this environment, 60% of Gen Z consumers have online accounts that they no longer use, and about 69% have never tried to close these unused accounts. This was found in a study conducted by Veritas Technologies. Useless data about these accounts lies unused in data centers, which keep using up electricity and resources. Experts opine that such useless data could account for roughly 2% of all global carbon emissions. 2% may not sound like a lot but to put this in perspective, take this – this size of the carbon footprint is the same size of the entire airline industry put together, the entire global aviation industry together! Now does that feel huge? Data centers around the world run 24 by 7 and by 2030, they are expected to be using up about 8% of all electricity that gets produced on the planet.

In the middle of that is the fact that most consumers are absolutely unaware of the impact of their own carbon footprint. 44% of the consumers surveyed said that it was wrong for businesses to waste energy and cause pollution by storing unnecessary information online. However, 51% also believe that electronic versions of their account-related statements and other documents stored online do not have any negative environmental impact. The Veritas study also found that 49% of the consumers surveyed thought that it was the responsibility of the organizations that store this data to delete it when it is no longer required or useful.

There is a slightly older study, conducted again, by Veritas Research, which found that about 35% of the enterprise data is dark. Dark data means that the data has an unknown value. It also found that about 50% of the data stored by organizations is redundant, obsolete, or trivial. A good move in this direction is that a lot of companies are now including the environmental impact of their data storage in their corporate environmental, social, and governance reports.

According to a report by the IDC, about 60% of the storage budget is not really spent on storage. Instead, it is spent on secondary copies of data for data protection, such as backups, backup software licenses, replication, and disaster recovery. It also found that about one-third of IT organizations are spending most of their IT storage on this secondary data. This makes it important for organizations to define cold data storage strategies and work on establishing unstructured data management policies. The risk of not identifying and eliminating unnecessary data, basically the risk of not stopping data hoarding is too huge to ignore any longer.

welcome back to the Cognixia podcast. What is digital trust. According to the World Economic Forum, digital trust is an individual’s expectation that digital technologies and services, as well as the organizations that provide them, would protect the interests of the stakeholders, and do their bit to uphold societal expectations & values. Now, we agree, that that is a very heavy definition. Let us try to break it down and simplify it a little bit. According to TechTarget, digital trust is the confidence the users have in the ability of the people, technologies, and processes to create a secure digital world. Now that’s much simpler and easier to comprehend, isn’t it? So, by extension, when an individual or a business decides to use another individual or business’ digital products or services, they confirm that they have digital trust in the business. Why is it important for businesses? Well, the more digital trust a business receives, the more users it is likely to gain, and the more robust would be its top-line numbers. We all understand the importance of online data security at the current time. Protecting digital privacy and safeguarding the users as well as the businesses against cybercrime is indispensable. This necessitates state-of-the-art trust technology that can keep everyone safe and ensure that the valuable digital trust does not break. According to a recent report by the IDC, with digital trust emerging as a paramount concern for organizations and consumers, business leaders and technology suppliers must expand their understanding of trust and its importance to success in the new digital economy. Customers increasingly want to know the companies that they are doing business with or whose products and services they are using, are authentic and worth the trust they are putting in them. Businesses also need to constantly be on their toes to demonstrate their digital trustworthiness and authenticity in business, and one of the most important means to do that is by relying on the best-in-class digital trust technologies.

Hello everyone, and welcome back to the Cognixia podcast.

Google has reported that it will leverage artificial intelligence for several online safety features and share insights with users about whether the information they are seeing could have been posted on the dark web. Google made this announcement at the Google I/O Conference. What are these new features and how are they going to use artificial intelligence? Let’s take a deeper look into this.

1. The first measure revolves around image searches. Google Image Search will begin flagging AI-generated content.
2. Secondly, Google also plans to enhance its safe browsing features by leveraging artificial intelligence to scan dangerous websites and files.
3. Thirdly, if you are a Google One subscriber, you can now also run dark web reports to check if your personal information is listed in any attacker’s target lists
4. Google is enhancing its spam filtering as well as Google Maps security capabilities. These features are not exactly getting an artificial intelligence boost,
5. One of the major developments in this regard from Google is also the integration of the PaLM 2 Large Language Model, which will help make its key products and services much simpler and smarter. PaLM 2 is short for Pathways Language Model

Hello everyone and welcome back to the Cognixia podcast. we are going to talk about a new programming language that is becoming incredibly popular among AI developers, the language is a perfect fit for working with artificial intelligence, it is very simple and user-friendly like Python, and it is as quick as Rust, and has the performance and control like C++. Now, let’s take a second to just appreciate how awesome that is! This is the Mojo language. Mojo achieves high performance through innovative compiler technologies like integrated caching, multi-threading, cloud distribution, etc. It also includes auto-tuning and meta-programming which enables code optimization for various hardware. Let us look at some of the key features of the Mojo language: First, Mojo has a Python-like syntax and dynamic typing ability which makes the Mojo language very easy to learn if you are already familiar with Python. When you are working with modern developments in artificial intelligence and machine learning, then knowing Python is inevitable and with those skills, you can easily work with Mojo too. Second, you can import and use any of the Python libraries in Mojo, easily. Mojo has complete interoperability with Python, so you won’t have to keep wondering how easy your task would be if only you had access to the matplotlib library, just import it into Mojo and get working! Third, Mojo follows a very easy unified programming model that is super beginner-friendly and highly scalable for a wide range of use cases based on accelerators. It accomplishes this by combining dynamic and systems language capabilities. Fourth, the Mojo compiler applies advanced optimizations and GPU/TPU code generation, thereby supporting both Just-in-Time compilation as well as Ahead-of-Time compilation. Fifth, there are zero cost abstractions, so you can take control of the storage by inline-allocating values into the structures. Sixth, Mojo offers language-integrated auto-tuning, which enables you to automatically find the best values for your parameters to make the most of your target hardware. Seventh, you can very easily extend your models with pre- as well as post-processing operations, and you can even replace the operations with custom operations, taking full advantage of kernel fusion, graph rewrites, shape functions, etc. Finally, the Mojo language gives the user full control over the memory layout, concurrency, and other low-level details.

With that, we come to the end of this episode of the Cognixia podcast. We hope you enjoyed listening to us today and learned something new during this episode. If you would like to recommend any topics to us or have any questions, feel free to reach out to us, we would love to hear from you.

Do check out our website, www.cognixia.com to learn more about our live online instructor-led cybersecurity courses.

The ITIL 4 certifications required to be updated only when a new update to the ITIL library was released. For instance, anybody with the ITIL v3 certification was required to take an ITIL 4 Managing Professionals exam after a short re-training to upgrade their credentials to ITIL 4. This way, once the library was updated, one could transition to the latest update to keep their credentials current. However, from January 2023, all ITIL 4 certifications would be issued with a renew-by date that would be three years from the award date. Any certification holders who do not renew their certifications within three years of the award date will still be a part of the Successful Candidates Register but a note will be added to their record to indicate that their certification is not aligned with the current certification requirements.

So, if you are someone who has pursued their ITIL certification in 2023, or are planning to pursue it in the future, your certification comes with a renew-by date which would be exactly three years from the award date of the certification. But what if you already had an ITIL 4 certification, something you earned already before 2023? In that case, this is what you can do:

If you were certified before 30 June 2020, you have until 1 June 2023 to choose to renew your ITIL 4 certification, irrespective of when the certification was issued. If you were certified after June 2020, you will have the option of renewing your certificate within three years of the original award date.

If you are wondering how you can find out what your renew-by date is, allow us to help. You need to log into your PeopleCert account to get all the information about your ITIL 4 certification.

if you are beginning to wonder how the role of a Scrum Product Owner is different from the role of a Scrum Master, fret not, we are going to answer that too.

While both Scrum Master and Scrum Product Owners play very important roles in an Agile environment. However, they work together closely. The Scrum Master is the leader of the Agile team and they support the Scrum Product Owner by sharing updates with team members and employees. In contrast, the Scrum Product Owner manages the product backlog while ensuring the company derives maximum value from the product.

Hello everyone, and welcome back to the Cognixia podcast. We are back with another interesting episode today. Every week, we get together to discuss a new topic from the world of emerging digital technologies – from new developments to hands-on guides, from things you should know to what you can do to embrace new tools and best practices, and so much more.

Did you know that in 2022, the world spent over $150 billion worldwide to stop or fight hackers, without seeing all that much success? The Russian government continues its cyberattacks against Ukraine, ransomware attacks are at an all-time high, especially against hospitals and schools, or even entire governments, crypto hacks continue, and they are getting increasingly expensive, the hackers are getting younger and the hacked are becoming increasingly high-profile.

So, what can we expect in the world of cybersecurity in the coming time?

Remember the cyberattack on ViaSat in Ukraine last year? ViaSat is a US satellite communications company, whose services were being used by civilians as well as troops in Ukraine. The cyberattack on ViaSat caused a major communications breakdown at the start of the Russia-Ukraine war. Since then, there have been countless cyberattacks involving a variety of wiper malware, malicious computer code, and much more. This does account for and in a way contributes to cyber warfare. Cases of cyberespionage are also on the rise and will continue to be so.

While we are on this topic, did you know the difference between cyber warfare and cyber espionage? Let us tell you that.

The biggest difference between cyber warfare and cyber espionage lies in their primary goal. The primary goal of cyber warfare is to disrupt the activities of a country or state or territory. In contrast, the primary goal of cyber espionage is for the attacker to remain hidden for as long as possible and gather maximum possible intelligence. While the former is all about disruption and an outright attack, the latter is more about stealing information and valuable secrets.

With that, we come to the end of this week’s episode of the Cognixia podcast. Hope you found it useful and insightful.

We hope you enjoyed listening to it and learned something new from it. We promise to come back next week with another new, exciting episode of the Cognixia podcast.

we come to the end of this week’s episode of the Cognixia podcast. We hope you enjoyed listening to us today as much as we enjoyed creating & recording this episode

Two main things where technology can be very useful. The first would be to improve the accuracy of the weather forecasts, and the second would be to make the forecast data comprehensible and accessible to stakeholders in different domains. Suppose you are a non-profit organization that works in the disaster relief space, like say you are a part of a disaster-relief team at the Red Cross. You usually need about a month’s lead time to arrange supplies and relief measures for say a flood. Now, if you get the right information in the month of April that there is going to be a major flood and cyclone on the eastern coast in the Odisha – Andhra Pradesh belt in May, you could get your team together, make the necessary arrangements, work with the government to evacuate the people and get them to safer places, the assets can be safeguarded wherever possible, and accomplish a lot more before the cyclone and flood actually hit in a month. Currently, you don’t get information that much in advance, and we also don’t have an accurate idea of where the cyclone will hit, will it fizzle out or get stronger, etc. There are reasonable probabilities involved in every forecast too.

Both ChatGPT and Google Bard are chatbots powered by artificial intelligence that has been trained on large natural language models, so their responses could also be similar. Ask the same question to ChatGPT and Google Bard, and you will get quite a similar response from both, except if you ask something that is more time-specific or requires more current information. In that case, the answers would be separate, since, as we mentioned before, ChatGPT has information till 2021 only, whereas Google Bard is constantly updated since it has full access to the updated search library of Google Search.

Bard is Google’s public tool in the very highly competitive artificial intelligence chatbot space. Another popular player in this field, as we have mentioned before, is OpenAI’s ChatGPT. So, simply put, Google Bard is yet another artificial intelligence-powered chatbot in the market. It was launched in early 2023. Back then, it was an experiment that was fundamentally based on a conversational large language model. You enter a text as a prompt for Bard, and then Bard generates a response to the prompts you have entered. The biggest strength of Google Bard lies in its ability to access the internet and leverage the power of Google search for generating its responses.

What can you use Google Bard for?

You can do a lot of different things using Google Bard. It is a smart, intelligent tool that can be used to explore ideas, compose text, write code, and much more. We’d say, look at Bard like an intelligent friend or colleague, and ask them something accordingly. So, say you want to brainstorm some ideas, or you want to research some topic and want to find some books or related concepts or information related to that topic, or you want to draft an email to someone, or you want to create an outline for say a project or a proposal, or you want to paraphrase or summarize some text, or you want to write some code, or you want to debug some code, Google Bard can do it for you.

Now, because Bard has access to Google Search, one thing Bard can do very well is to streamline the search for you. Imagine, you are planning to buy a new mobile phone. Ideally, you will look for a particular brand and model, then dig up all the specifications and reviews for it across different websites, and then, maybe compile a Google Doc that would contain all the information that can help you do a comparative. You keep doing this for every brand and model you are interested in, till you have all the information and can make the choice that is right for you. But with Google Bard, you don’t need to follow this cumbersome process

Google Search essentially functions on the back of keywords. The links the search delivers in response to the search query are pulled up against how relevant the particular link would be for the user’s search query based on the keywords. There are also featured snippets of information that Google Search feels would answer the user query best.

In contrast, Google Bard responds to natural language prompts. The response to the prompt is a string of interactions, it is like a chat conversation, and you can also expand, clarify, or rephrase that response. You can’t do this in Search. Also, in Search, you would need to click on individual links to get the information you need, it is not directly presented to you like Bard would.

We come to the end of this week’s episode of the Cognixia podcast. We will be back next week with another interesting and inspiring episode of the podcast. Do check out our previous episodes if you haven’t already. You can leave us a review and share our episodes with your friends and colleagues, it would help us a lot.

Hello everyone and welcome to a fresh new episode of the Cognixia podcast. Every week, we come together to discuss something new from the world of emerging digital technologies and strive to inspire our listeners to learn something new and take the next big leap in their careers by ramping up their skills and knowledge.

About two years back, an artist named Beeple sold an NFT-based artwork for a whopping $69 million. In 2021, $69 million was equivalent to an unimaginable INR 5.2 billion. That was two years ago, and the world has gone around twice since. Since then, NFTs have shaken up the world of art, tech, and so many other subcultures. Interestingly, the journey has been quite a rollercoaster. Artists and collectors have built fortunes on the back of NFTs, a horde of brands have plunged into the space, marketing gimmicks galore, the metaverse has risen and gotten somewhat lost in the corridors of tech fads but keeps showing up here and there once in a while, and the space has become so happening.

However, in recent times, NFTs have lost their shine a bit. Their momentum has dropped, the sales of NFTs have cratered considerably and the crypto market has seen an unignorable loss of value. Will the communities that got created during the early days of the NFTs still be maintained? Will those crazy growth days come back or was it just a fad that has now fizzled out? One thing we can say for sure is that the situation is somewhat complicated and things are not as easy as they might seem. But we don’t intend to scare you off, so let us begin simply by first trying to understand what are NFTs.

Some people called “N. F. T.”, some call it “nefts”. NFT stands for non-fungible token. Non-fungible is an attribute of the token implying that the token is unique and cannot be replaced by something else. To give you an example, a bitcoin is a fungible token, you can trade one bitcoin for another, and it will still be the same thing, right? However, NFTs are one-of-a-kind. If you traded an NFT for another NFT, you will have a different NFT in your possession and not the same one you originally had because each NFT is different, which means, it is non-fungible. So, to put it in one line, an NFT is an irreplaceable, unique token. Now, how do the NFTs function?

With that, we come to the end of this week’s episode of the Cognixia podcast. We hope you enjoyed listening to us today as much as we enjoyed creating & recording this episode.

So, you now have some idea of what are electro-optical sensors, how they operate, what they are used for, the technologies that power them, and why they are so important for everyone. And with that, we come to the end of this week’s episode of the Cognixia podcast.

One such area where artificial intelligence is making quite a mark is DevSecOps. We are going to take a quick minute here to tell everybody what is DevSecOps. DevSecOps is the practice of integrating security testing at every stage of the software development process. This would include the tools & processes which facilitate collaboration among developers, security specialists, operations team members, etc. to ensure that the final product, the software, or the application is both efficient and secure. To put it in a very simplified form, DevSecOps adds the element of security to the DevOps culture, weaving it into the process itself, instead of adding measures as an afterthought after the software or the application has been produced.

Going back to our topic for the day, how is artificial intelligence reshaping the roles of a developer in the DevSecOps environment?

The recent Seventh Annual Global DevSecOps Report by GitLab has found that artificial intelligence and machine learning in the software development workflow have found promise but challenges like the complexity of the toolchain and concerns about security continue to remain. According to this research, about 65% of the developers are not using artificial intelligence and machine learning in their code-testing efforts or have plans to do this in the next three years. This is quite a huge step in the automation of the software development process.

This survey by GitLab covers over 5,000 IT leaders, CISOs, and developers across various sectors including financial services, automotive, healthcare, telecommunications, and information technology. The survey focused on understanding the successes, challenges, and priorities for the DevSecOps implementation.

One of the very interesting things this report found, as we mentioned just now, was how artificial intelligence and machine learning is being adopted in the software development process. In 2022, only 55% of the developers were using AI/ML to check their code, while this number is now up to 62%. Also, last year, only 39% of the developers were using bots in the testing process; this number is up to 53% this year.

Some of the top skills, Git Lab reports are considered essential for security professionals are:

1. Artificial intelligence and machine learning

2. Soft skills

3. Subject matter expertise

4. Metrics & quantitative insights

This definitely indicates that all-round expertise and skill set are essential for a successful career in security and overcoming security challenges they would encounter in their work.

To make the most of emerging technologies like artificial intelligence and machine learning in the security and DevSecOps space, enterprises must invest in the right training and tools for their teams to leverage the potential of these technologies in the software development and security workflow processes.

Hello everyone and welcome back to the Cognixia podcast. As of 2022, women earn, on average, about 82% of what men earn which is only a 2% increase from 2002, according to Pew Research. Women are highly underrepresented in STEM fields, even more so in managerial roles, making up only 27% of the workforce in those industries, combined, according to the US Bureau of Labor Statistics.

This trend is not limited to women in tech. It is a fact that women are increasingly and deeply underrepresented in higher-paying positions. Pull up the median pay data for any industry, not just tech, and the problem becomes quite clear. Studies have also found that companies which disclose the pay gaps are more likely to fix them compared to those that do not.

According to a 2021 survey by Hired, men in tech were offered higher salaries than women for the same job title 59% of the time. On average, women in tech were offered salaries 2.5% less than the ones that men were given for the same roles. Women also suffered a greater job loss compared to men during the pandemic. Reports say that about five million women have lost their jobs since February 2020. A slight ray of hope comes from a 2021 survey by MetLife which found that of the women who were considering coming back to the workforce post-pandemic, 8 in 10 said that they would like to change their careers and pursue something in STEM now.

There are also representational issues, which researchers say, has an enormous impact on the paychecks women receive. Women in tech have extremely low representation, which only gets lower as we move up the ladder. This, in turn, pushes average and median salaries for women in tech way below that of men, who are well-represented at all tiers. On the other side, women also negotiate less, and when they do, they are hardly ever as aggressive as men are when negotiating the salaries. Put it all together, and it creates a multiplier effect which collectively pushes salaries down for all women.

GPT-4 is the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model which is of course not as capable as human beings in a lot of real-world scenarios, it can still be said to exhibit human-level performance on various professional and academic benchmarks. If the claims on the OpenAI website are anything to go by, GPT-4 has passed the simulated bar exam with a score around the top 10% of the test takers. OpenAI has taken 6 months iteratively aligning GPT-4 using lessons from the adversarial testing program as well as ChatGPT, delivering the best-ever results on factuality, steerability, and keeping within the guardrails.

GPT-4 is more reliable, creative, and capable of handling a lot more than its predecessors. GPT-4 can accept inputs in the form of text as well as images. This allows users to specify any vision or language task to GPT-4. The outputs would be textual – could be a natural language, could be code, etc. Despite being multi-modal, its results are at par with text-only inputs. GPT-4 can also be augmented with test-time techniques which were intended for text-only language models, such as a few-shot or a chain-of-thought prompting.

We are eager to see how GPT-4 pans out for everyone as more and more artificial intelligence-powered technologies and developments come to the fore to empower everyone. The future is surely going to reveal some very interesting developments, disruptions, and revolutions.