The past few decades have been marked by the ever-increasing presence of digital technology. This growth, often called digital transformation, places a heavy burden on our environment. We are now facing a potential new phase of digital transformation,6 represented by the emergence of generative AI (GenAI), a subfield of artificial intelligence focused on generating content, such as human-like text, code, and images.14 In particular, the deployment of GenAI as a service, such as ChatGPT or Stable Diffusion, is raising questions around sustainability. The sustainability of any computing technology, however, cannot be addressed without a way to evaluate its environmental impact.

China's Baidu has announced that "Ernie Bot", its rival to ChatGPT, has racked up more than 200 million users, roughly double as many as in December. Baidu CEO Robin Li also said Ernie Bot's application programming interface (API) is being used 200 million times every day, meaning the chatbot was requested by its user to conduct tasks that many times a day. The number of enterprise clients for the chatbot reached 85,000, Li said at a conference in Shenzhen on Tuesday. In February, he told analysts that Baidu was starting to generate revenue from Ernie and in the fourth quarter, the company had earned several hundred million yuan using AI to improve its advertising services and help other companies build their own models. Last March, Ernie Bot was the first locally developed ChatGPT-like chatbot to be announced in China but it only won approval for public release in August, becoming one of the first eight AI chatbots Beijing authorised.

With the widespread adoption of advanced generative models such as Gemini and GPT, there has been a notable increase in the incorporation of such models into sociotechnical systems, categorized under AI-as-a-Service (AIaaS). Despite their versatility across diverse sectors, concerns persist regarding discriminatory tendencies within these models, particularly favoring selected `majority' demographics across various sociodemographic dimensions. Despite widespread calls for diversification of media representations, marginalized racial and ethnic groups continue to face persistent distortion, stereotyping, and neglect within the AIaaS context. In this work, we provide a critical summary of the state of research in the context of social harms to lead the conversation to focus on their implications. We also present open-ended research questions, guided by our discussion, to help define future research pathways.

This study presents a method for implementing generative AI services by utilizing the Large Language Models (LLM) application architecture. With recent advancements in generative AI technology, LLMs have gained prominence across various domains. In this context, the research addresses the challenge of information scarcity and proposes specific remedies by harnessing LLM capabilities. The investigation delves into strategies for mitigating the issue of inadequate data, offering tailored solutions. The study delves into the efficacy of employing fine-tuning techniques and direct document integration to alleviate data insufficiency. A significant contribution of this work is the development of a Retrieval-Augmented Generation (RAG) model, which tackles the aforementioned challenges. The RAG model is carefully designed to enhance information storage and retrieval processes, ensuring improved content generation. The research elucidates the key phases of the information storage and retrieval methodology underpinned by the RAG model. A comprehensive analysis of these steps is undertaken, emphasizing their significance in addressing the scarcity of data. The study highlights the efficacy of the proposed method, showcasing its applicability through illustrative instances. By implementing the RAG model for information storage and retrieval, the research not only contributes to a deeper comprehension of generative AI technology but also facilitates its practical usability within enterprises utilizing LLMs. This work holds substantial value in advancing the field of generative AI, offering insights into enhancing data-driven content generation and fostering active utilization of LLM-based services within corporate settings.

As AI technology advances rapidly, concerns over the risks of bigness in digital markets are also growing. The EU's Digital Markets Act (DMA) aims to address these risks. Still, the current framework may not adequately cover generative AI systems that could become gateways for AI-based services. This paper argues for integrating certain AI software as core platform services and classifying certain developers as gatekeepers under the DMA. We also propose an assessment of gatekeeper obligations to ensure they cover generative AI services. As the EU considers generative AI-specific rules and possible DMA amendments, this paper provides insights towards diversity and openness in generative AI services.

Amazon Web Services (AWS), announced today that it is expanding its generative AI services in a bid to make the technology more available to organizations in the cloud. Among the new AWS cloud AI services is Amazon Bedrock, which is launching in preview as a set of foundation model AI services. The initial set of foundation models supported by the service include ones from AI21, Anthropic, and Stability AI as well as a set of new models developed by AWS known collectively as Amazon Titan. In addition, AWS is also announcing the general availability of Amazon EC2 Inf2 cloud instances powered by the company's own AWS Inferentia2 chips, which provide high performance for AI. Rounding out the updates, the Amazon CodeWhisperer generative AI service for code development is now generally available, with AWS making it free for all individual developers.

With ChatGPT unveiled in November, the tech industry has been working tirelessly to come up with their own version of "generative" artificial intelligence (AI). Google, Elon Musk, Meta, everyone is working on the Open AI tool killer. Now Alibaba, one of the largest e-commerce and cloud computing companies in the world, has recently unveiled its own generative artificial intelligence (AI) model, named Tongyi Qianwen. The model is similar to ChatGPT, the popular AI platform developed by OpenAI that can generate natural language texts based on user inputs. Alibaba plans to integrate Tongyi Qianwen into all its business applications soon, starting with its smart speaker Tmall Genie and its workplace messaging platform DingTalk.

Generative AI is a cutting-edge technological advancement that utilises machine learning and artificial intelligence to create new forms of media, such as text, audio, video, and animation. With the advent of advanced machine learning capabilities like large language models, neural translation, information understanding, and reinforcement learning, it is now possible to generate new and creative short and long-form content, synthetic media, and even deepfakes with simple text, also known as prompts. Top technology companies, like Microsoft, Google, Facebook, and others, have commercial AI labs researching and publishing academic papers to accelerate these AI innovations. In recent years, we have seen investments in GANs (Generative Adversarial Networks), LLMs (Large Language Models), GPT (Generative Pre-trained Transformers), and Image Generation to experiment and, in some cases, create commercial offerings like DALL-E for image generation and ChatGPT for text generation. For example, ChatGPT can write blogs, computer code, and marketing copies and even generate results for search queries.