Recently, large language models (LLM) based generative AI has been gaining momentum for their impressive high-quality performances in multiple domains, particularly after the release of the ChatGPT. Many believe that they have the potential to perform general-purpose problem-solving in software development and replace human software developers. Nevertheless, there are in a lack of serious investigation into the capability of these LLM techniques in fulfilling software development tasks. In a controlled 2 $\times$ 2 between-subject experiment with 109 participants, we examined whether and to what degree working with ChatGPT was helpful in the coding task and typical software development task and how people work with ChatGPT. We found that while ChatGPT performed well in solving simple coding problems, its performance in supporting typical software development tasks was not that good. We also observed the interactions between participants and ChatGPT and found the relations between the interactions and the outcomes. Our study thus provides first-hand insights into using ChatGPT to fulfill software engineering tasks with real-world developers and motivates the need for novel interaction mechanisms that help developers effectively work with large language models to achieve desired outcomes.

The outbreak of COVID- 19 has made jobless a lot of workers, now Artificial Intelligence has started taking over people's jobs. Several industries have already been affected in several sectors by the intrusion of artificial intelligence, and journalism turns out not to be an exception. Microsoft has recently fired dozens of journalists, who were responsible for arranging and editing news stories to be replaced with automatic systems. At a time when Indian news media companies are sacking employees due to COVID related economic downfall, the possibility of robots is now a major threat to our job security. Artificial Intelligence is still in its early stages, but it will be interesting to see how it will change the fourth pillar of democracy, in the era of web journalism and shouting debates on Prime Time TV news.

As companies engage in the implementation of analytics and data science applications, many challenges lie ahead. According to the Harvard Business Review, many data science applications fail due to poor goal definition, a lack of understanding of the key data, or a lack of focus on business value. Allows engineers to use the platform and be part of the analytics process. This article takes a look at each of these elements in further detail and explores why they are important to driving value for your organization. Having a platform built on your subject matter experts' knowledge is the best starting point.