This paper explores how cloud-native databases enable AI-driven applications by leveraging purpose-built technologies such as vector databases (pgvector), graph databases (AWS Neptune), NoSQL stores (Amazon DocumentDB, DynamoDB), and relational cloud databases (Aurora MySQL and PostgreSQL). It presents architectural patterns for integrating AI workloads with cloud databases, including Retrieval-Augmented Generation (RAG) [1] with LLMs, real-time data pipelines, AI-driven query optimization, and embeddings-based search. Performance benchmarks, scalability considerations, and cost-efficient strategies are evaluated to guide the design of AI-enabled applications. Real-world case studies from industries such as healthcare, finance, and customer experience illustrate how enterprises utilize cloud databases to enhance AI capabilities while ensuring security, governance, and compliance with enterprise and regulatory standards. By providing a comprehensive analysis of AI and cloud database integration, this paper serves as a practical guide for researchers, architects, and enterprises to build next-generation AI applications that optimize performance, scalability, and cost efficiency in cloud environments.

In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, self-driving capabilities for improved system performance, etc. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

The technologies behind artificial intelligence and machine learning keep getting better. From the first AI checkers and chess programs written in 1951 at the University of Manchester to OpenAI's ChatGPT and Google's Bard AI, the history and evolution of artificial intelligence is long and full of breakthroughs. Currently, AI is being used for many purposes across various industries. In the transportation industry, AI is now used in self-driving cars, auto-pilot software for autonomous flying, and software used to help drivers find the most efficient routes to avoid traffic and save time and fuel. In the healthcare industry, AI is now used by doctors to help keep track of symptoms and identify potential diagnoses and used by pharmaceutical scientists to design new drug therapies.

Apple's Siri, Google Assistant, and Alexa are some best examples of AI-powered applications. ChatGPT, an AI chatbot, is the latest AI tool that redefine the use of AI. Artificial intelligence, commonly known as AI, is almost everywhere in the modern world. Services such as ChatGPT to how Google Photos erases the background in a photo are some best examples of the use of AI. With the advancement of AI in electronic products such as speakers, TVs, and more, humans can interact and get work done and answer queries in no time.

This article was published as a part of the Data Science Blogathon. Many a time while driving, we need to adjust things like in-vehicle seat relaxation function, interior lighting, music, air-conditioning, fragrances, etc, manually while attempting to keep our eyes on the road. However, it is very inconvenient and sometimes it can potentially put the driver and the co-passengers at the risk. Furthermore, it becomes even more complex when a previous driver has different preferences for cabin temperature, audio playlist, and so on. In light of this, would not it be convenient if our vehicle's heating, ventilation, illumination, music, visor, and air conditioning (HVAC) system could learn our individual preferences, and automatically make these adjustments for us?

Artificial intelligence has successfully presented itself as a significant driver of healthcare transformation leading the industry to a revamped landscape. Ever since the pandemic began, AI-powered applications have exhibited a high level of competence as compared to conventional healthcare technologies. The advantages of artificial intelligence are spread across various fields of healthcare starting from inventory management to striking off inequalities. For healthcare professionals, it has become a fundamental subject to know how AI in healthcare is operated. Therefore, here are some artificial intelligence courses that will imbibe the knowledge of AI in healthcare in healthcare professionals.

Although artificial intelligence (AI) is getting more advanced due to an exponential rate of development, limitations to this modern technology still exist. So, can synthetic data be the solution for all AI-related concerns? In the fourth industrial revolution, every industry sector has discovered the potential of modern technologies; such as artificial intelligence (AI) and machine learning (ML). Almost every other organization is deploying AI to create more efficient business processes and to ensure better customer satisfaction. But, startups, SOHOs, and small and medium businesses (SMBs) face a major issue while adopting AI- it's called the cold start problem.

Although artificial intelligence (A)I is getting more advanced due to an exponential rate of development, limitations to this modern technology still exist. So, can synthetic data be the solution for all AI-related concerns? In the fourth industrial revolution, every industry sector has discovered the potential of modern technologies; such as AI and ML. Almost every other organization is deploying AI to create more efficient business processes and to ensure better customer satisfaction. But, startups, SOHOs, and small and medium businesses (SMBs) face a major issue while adopting AI- it's called the cold start problem.

The tool is designed for those looking to integrate and run AI and ML technologies across cloud environments. IBM is announcing a new addition to its open-source Cloud-Native Toolkit that will allow developers to integrate their AI and ML applications "to cloud-native environments and optimize scalable, reliable deployments." Saishruthi Swaminathan, Carlos Santana and Sepideh Seifzadeh -- members of the IBM Center for Open-Source Data & AI Technologies team -- explained the effort in a blog post, noting that it was becoming necessary to integrate and run AI and ML technologies across cloud environments. Last year, the team released the Elyra AI toolkit and said the latest launch is a machine-learning, end-to-end pipeline starter kit within the Cloud-Native Toolkit. "Using critical hybrid cloud capabilities including open source and Red Hat OpenShift, developers can use the new toolkit as a starting point to transition their ML and AI-powered applications from Jupyter notebooks to production environments," the IBM team wrote.

Research Tech (ResTech) offers a great avenue to apply AI and Machine Learning the modern context of doing business. As businesses across industries face an uncertain 2021, one thing remains clear: a constantly-shifting public health and economic landscape will affect everything from customers' purchasing decisions, to their likes and dislikes about the products and services they use. Companies looking for more effective ways to keep their finger on the pulse of their customers in 2021 must look beyond multiple-choice survey responses or numerical scores. Instead, they should focus on collecting open-ended survey responses to understand what their customers are saying in their own words--complete with slang, emojis, and misspellings--if they want to truly understand what their customers are thinking. For decades, analyzing these open-ended responses has been a tedious process, with researchers reading and tagging each response to count concepts to quantify concerns and identify representative verbatims.