If you have been keeping up with the news, you would be aware of the ongoing crisis in Sudan. A deadly power struggle erupted in Sudan in mid-April, between the military factions after the transition to a civilian-led government faltered. Intense clashes are occurring between Sudan’s military and its main paramilitary forces, which have so far killed hundreds of people while thousands are trying to flee the country, running away from a burgeoning civil war that threatens to destabilize the wider region including and around Sudan. That is more or less the gist of what is happening, but to understand more about why all the fighting is taking place and why it is of so much significance, we recommend you catch up on the news.

Now, to rescue the Indians stranded in Sudan, India launched Operation Kaveri, where India sends out flights to the country to rescue as many Indians as possible and bring them back to India. So far quite a lot of people have been brought back home and the Indian Air Force is undoubtedly doing a brilliant job in commanding and executing these rescue missions.

In one such very daring operation, a C-130J heavy-lift aircraft, flown by the Indian Air Force rescued 121 people from a tiny airstrip in Wadi Sayyidna, located about 40km north of the badly violence-hit capital city of Sudan – Khartoum. The operation took place on the intervening night between 27 and 28 April 2023. This operation was carried out especially to rescue people who are unable to reach Port Sudan, which is currently the key exit point from where India is rescuing its people from Sudan.

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.

We will talk about the most recent development in the AI space – GPT-4. A lot of you would have already heard about it, some might even be on the waitlist to get the API, and some might have tried it and tinkered around a bit with it, though we are sure, some are still to discover it. No matter where you stand, we can promise today’s episode should help you get a lot of information about GPT-4.

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.

ChatGPT is known for being a human-like conversationalist. This dialogue format and the training it is backed by empower ChatGPT to answer follow-up questions, challenge premises that might be incorrect, and reject requests that it feels are inappropriate. The key factor here is that ChatGPT is trained and not programmed to do what it does which makes it so much more human. So, what do you think, will ChatGPT take away your job?

Well, a decade ago, in 2013, a study by the University of Oxford found that about 47% of US jobs would be taken away by artificial intelligence in the coming two decades. Halfway through to that point, it doesn’t seem quite likely, and the prediction appears to be quite off the mark. But there are still ten years to go and we know how unpredictable are the times we live in. While millions of jobs may still not get replaced by artificial intelligence in this timeline, as we get more and more exposure to new emerging technologies like ChatGPT, some territory encroachment is bound to happen.

Anu Madgavkar, a partner at the McKinsey Global Institute opines in this regard that human judgment will still need to be applied to these emerging technologies to avoid errors and bias. Tools like ChatGPT can be considered to be productivity-enhancing tools, and may not become complete replacements for all human efforts. In our opinion, ChatGPT does have the potential to disrupt not just blue-collar work but white-collar work as well.

Earlier in March, Infosys Founder – Narayana Murthy was speaking to the press on the sidelines of the NASSCOM Technology and Leadership Forum, where he said that in 1977-78, there was a thing called program generators. At that time, everybody said that the youngsters will now lose all their jobs. But we can see that this did not happen. The human mind is the most flexible instrument, he said, it can adapt very well. And all that happened was people start solving bigger and bigger problems, which these program generators could not handle. Murthy went on to say that we should use ChatGPT as a base and then show our creativity, show our smartness, and show our innovation.

If you keep your skills updated and you keep adding new skills to your repertoire, it makes you super invaluable an asset, and machines will have a run for their money trying to replace you, and despite that, would likely fail! And, for updating your skills, you need to go to www.cognixia.com and check our course offerings, talk to us in the chat window there, we promise it is manned by actual human beings and there is no ChatGPT there, enroll for a live instructor-led online course, and get going on your upskilling journey.

ChatGPT can be integrated with automation development and leveraged to provide personalized customer interactions – from responding to common queries to delivering support for more complex problems. This automation would improve accuracy, reduce response times, and be available & accessible 24x7, all of which are very important factors in the current times. This, in turn, leads to enhanced customer satisfaction and better consistency of responses. So, in this episode, we will explore the different ways that ChatGPT’s capabilities can be leveraged to enable automation development as well as design.

One of the first ways to leverage ChatGPT for fueling automation development & design is to understand other developers’ codes. If you just copy the code, you would like to understand in the ChatGPT window and ask what the particular code will do ChatGPT will give you insights into what can be accomplished by that code. However, as we mention in ChatGPT episode 3 of this series, the last week’s episode, ChatGPT has some data privacy and copyright concerns, so be careful about what code you are sharing with ChatGPT, and that you are not violating anybody’s privacy or intellectual property rights in the process.

we come to the end of this week’s episode and the fourth part in our five-part series on ChatGPT. Next week’s episode will be the last and final part of this special series, and we hope you are enjoying listening to it. ChatGPT can be a valuable tool to have in your arsenal, but take it with a pinch of salt, we’d say, it comes with its unique set of challenges and loopholes too. With time, maybe these challenges will get resolved, and the loopholes will be plugged in, but until then, just be careful!

It is said that if you have ever written a blog, a product review on any platform, commented on anything online, or just about written anything on the online space, chances are your words were used for training ChatGPT. ChatGPT knows what you wrote, irrespective of whether you wanted it to know about it or not. By now, we have told you a few times about the humongous amount of data that went into training ChatGPT. As a thumb rule, the more data used for training a large language model like ChatGPT, the better the model would be at identifying patterns, predicting what’s next, and delivering plausible text.

ChatGPT is a transformer-based language model that uses deep neural networks to process & understands the text. Now, due to this architecture, ChatGPT can generate more fluent and natural-sounding text than any other models have been able to accomplish previously. Additionally, the ChatGPT model has been trained on a very wide range of content on the internet we could even say that ChatGPT has been trained using just about all the content available on the internet. Due to this, ChatGPT is better equipped to understand and respond to various topics, be it everyday conversations or technical discussions.

Two of the most prominent AI-powered intelligent chatbot tools would be ChatGPT by OpenAI and Sparrow by Google. Both these tools have the potential to change the way we interact with the digital world and digital systems. Both ChatGPT and Sparrow provide users with an effortless and natural communication experience which is, undoubtedly, one of their biggest USPs. 

ChatGPT is a state-of-the-art language model developed by OpenAI, a leading artificial intelligence organization. ChatGPT has been designed to understand and generate human-like text making it a powerful tool for many applications. The tool is based on the original framework for pre-trained models, called the Generative Pre-trained Transformers or GPTs. The tool is trained so widely and effectively that it can generate content that is almost indistinguishable from human-generated content in both the matter and the style! 

To get a bit more technical, ChatGPT is a transformer-based language model that uses deep neural networks to process & understand the text. Now, due to this architecture, ChatGPT can generate more fluent and natural-sounding text than any other models have been able to accomplish previously. 

Sparrow by Google is a chatbot built by DeepMind - the artificial intelligence research group at Google. Like ChatGPT, Sparrow can also understand natural language inputs and respond to them. This makes Sparrow a very valuable tool for customer service and customer support applications. Its ability to process and understand the inputs come from advanced deep-learning algorithms. Sparrow is capable of handling queries in multiple languages as well as dialects, making it useful for a global user base, even in territories where English may not be as commonly spoken. There are multiple areas where Sparrow would score much higher than ChatGPT, and one of the most important areas in that would be data privacy and safety. Did you know? that Sparrow has been designed with data security and privacy in mind, so sensitive information is strongly guarded and the user data is handled very responsibly?

Sparrow uses something called the “transformer-based pre-training”. With this method, Sparrow is able to comprehend the meaning of a sentence, even if Sparrow has never encountered some of the words or terms in the sentence ever before. How cool is that? 

With that, we come to the end of this week’s episode as well as the first episode in our five-part series about ChatGPT. We hope you enjoyed listening to it, and we are really excited about the next episode, which will the second one in this series. Stay tuned for the next episode of the Cognixia podcast, we have a very, very interesting topic lined up for it, and we totally can’t wait to share it with you.

DevOps is a set of practices and guidelines that help improve software development productivity. It has immensely helped shorten the software development lifecycle while delivering high-quality applications with a steady and consistent delivery of code.  

One thing that played a major role in enabling the success of DevOps was containerization. If we had to explain in very simple terms what containerization was, we would say it is the process of placing the code, the environment, as well as its dependencies in an isolated space during the application development process. This, in turn, enhances the speed of deployment, patching, and scaling. Containerization has helped resolve some of the biggest problems that deployment faced and its benefits extend beyond just deployment – from boosting the ability to standardize and automate to enabling people to work across languages and technologies. 

Kubernetes is an open-source system for container orchestration. It is a tool that enables one to manage, scale, and automate the process of software deployment. If we had to describe in very simple terms, how Kubernetes is helpful in DevOps, we would say it helps combine the development and maintenance phases of the software systems which improves agility.

To simplify it further, we would say, containerization using Kubernetes is an efficient and effective way to implement DevOps compared to the traditional monolithic approach. Using Kubernetes, one can create and manage containers on cloud-based server systems. Using Kubernetes, DevOps teams can bring down the burden on the infrastructure by letting the containers operate on a different machine or environment without breaking down either machines or environments, keeping everything functioning smoothly. 

maybe we will just suffice to say that every DevOps engineer must absolutely invest time and effort towards learning and mastering Kubernetes, it is important and it is almost essential. If you are someone who would like to learn DevOps or Kubernetes, we totally recommend checking out our live online instructor-led courses for DevOps as well as Docker & Kubernetes.