Integrating generative AI (GAI) into higher education is crucial for preparing a future generation of GAI-literate students. Yet a thorough understanding of the global institutional adoption policy remains absent, with most of the prior studies focused on the Global North and the promises and challenges of GAI, lacking a theoretical lens. This study utilizes the Diffusion of Innovations Theory to examine GAI adoption strategies in higher education across 40 universities from six global regions. It explores the characteristics of GAI innovation, including compatibility, trialability, and observability, and analyses the communication channels and roles and responsibilities outlined in university policies and guidelines. The findings reveal a proactive approach by universities towards GAI integration, emphasizing academic integrity, teaching and learning enhancement, and equity. Despite a cautious yet optimistic stance, a comprehensive policy framework is needed to evaluate the impacts of GAI integration and establish effective communication strategies that foster broader stakeholder engagement. The study highlights the importance of clear roles and responsibilities among faculty, students, and administrators for successful GAI integration, supporting a collaborative model for navigating the complexities of GAI in education. This study contributes insights for policymakers in crafting detailed strategies for its integration.

Software architectures for conversational robots typically consist of multiple modules, each designed for a particular processing task or functionality. Some of these modules are developed for the purpose of making decisions about the next action that the robot ought to perform in the current context. Those actions may relate to physical movements, such as driving forward or grasping an object, but may also correspond to communicative acts, such as asking a question to the human user. In this position paper, we reflect on the organization of those decision modules in human-robot interaction platforms. We discuss the relative benefits and limitations of modular vs. end-to-end architectures, and argue that, despite the increasing popularity of end-to-end approaches, modular architectures remain preferable when developing conversational robots designed to execute complex tasks in collaboration with human users. We also show that most practical HRI architectures tend to be either robot-centric or dialogue-centric, depending on where developers wish to place the ``command center'' of their system. While those design choices may be justified in some application domains, they also limit the robot's ability to flexibly interleave physical movements and conversational behaviours. We contend that architectures placing ``action managers'' and ``interaction managers'' on an equal footing may provide the best path forward for future human-robot interaction systems.

After gaining Python and Machine Learning knowledge, it's time to practice. And for that, you need to use Data Science tools like Jupyter and Anaconda. Spend your few hours and play with these tools. Understand what they're for and why you should use them.

"AI systems (will) take decisions that have ethical grounds and consequences." On March 23, 2016, Microsoft released its AI-based chatbot Tay via Twitter. The bot was trained to generate its responses based on interactions with users. But there was a catch. Various users started posting offensive tweets toward the bot, resulting in Tay making replies in the same language.

Devansh Bansal is the VP of the Emerging Tech Business Unit at Damco Solutions. The widespread adoption of chatbots was imminent with the stellar rise and consolidation of instant messaging. However, the accelerated pace at which chatbots have evolved from accepting scripted responses to holding natural-sounding conversations has been unprecedented. According to Google Trends, the interest in AI Chatbots has increased ten-fold over the last five years! With chatbots getting smarter, value-driven, and user-friendly, it has fueled customer-led demand for chatbot-driven interaction at every touchpoint.

A decade back, artificial intelligence was confined to the IT departments of the companies. As digitization became the core of business operations, brands started using technologies like data science and data analytics to get closer to their target audience. With the introduction of DevOps, things have gotten even easier for the tech ecosystem. DevOps has introduced faster release cycles better than ever before and it has also enhanced the dominance of cloud services. Teams are now able to streamline laborious jobs and use advanced tools under the acronym Artificial Intelligence for IT Operations (AIOps).

In the digital world, natural language processing (NLP) is becoming as important as air. With or without knowing, we are using the subset of artificial intelligence at every moment of our life. Starting from AI assistants like Alexa to search engines, everything is powered by NLP technology. Not just simple works, natural language processing can even do big jobs extracting data from documents and enhancing the capabilities of machine learning algorithms. NLP stands as an umbrella term that covers other processes like sentiment analysis, text extraction, machine translation, conversational AI, text summarization, document AI, etc. Owing to the increasing usage of technology, NLP jobs are also becoming popular.

Artificial intelligence had an eventful decade so far. With 2021 bringing more into play, technology has stronghold its place in every ecosystem. Especially, business organizations are enhancing their AI capabilities to streamline routine processes. Starting from attending to customer queries to powering autonomous vehicles, the influence of artificial intelligence is no joke. Besides, critical fields like healthcare, education, and space are adopting AI to sophisticate their existing features and bring in innovation.

In B2B, a buyer is nearly always a group of people working together rather than an individual acting alone. Forrester's 2021 B2B Buying Study revealed that over 60 percent of purchases have more than four people involved–versus just 47 percent in 2017–and they can include different buyer roles and departments. These findings led to Forrester updating its B2B Revenue Waterfall, enabling companies to connect individuals to their buying groups, associate them to specific opportunities and then track the progression of opportunities through the revenue generation process. It shows a tighter alignment between marketing and sales. Lack of agreement on roles and responsibilities, lead qualification, and ways to measure success can impact an organization's ability to increase the volume of opportunities and velocity they move through the Waterfall.

Artificial intelligence is the process of re-creating human knowledge in robots that are designed to think and act like humans. AI has evolved from a once-obscure technology into a standard toolbox for software development and design. Without a question, artificial intelligence (AI) is the way of the future when it comes to automation. Robotization, DevOps phases, the Internet Chabot, and mechanical technology are all examples of upcoming IT advancements using artificial intelligence. Artificial intelligence jobs are a fast-paced, high-risk industry that is rapidly infiltrating our daily lives.