Meredith Whittaker is the president of Signal – the not-for-profit secure messaging app. The service, along with WhatsApp and similar messaging platforms, is opposing the UK government's online safety bill which, among other things, seeks to scan users' messages for harmful content. Prior to Signal, Whittaker worked at Google, co-founded NYU's AI Now Institute and was an adviser to the Federal Trade Commission. After 10 years at Google you organised the walkout over the company's attitude to sexual harassment accusations, after which in 2019 you were forced out. How did you feel about that?

Instead of writing myself a bland, general introductory paragraph about what generative AI and ChatGPT are, I asked ChatGPT to explain that to me. Generative AI refers to artificial intelligence systems that are able to generate new content that is similar in style or content to a given input. This can include tasks such as generating text, images, or audio. Generative AI systems are fascinating because they have the potential to create new and creative content that can be used in a variety of applications, such as generating news articles, creating art, or even generating realistic-sounding dialogue for virtual assistants. One of the main milestones in the development of generative AI has been the development of large language models, such as GPT-3 (short for "Generative Pre-trained Transformer 3").

A decade of big data investments, combined with cloud scalability, the rise of more cost effective processing and the introduction of advanced tooling, has catapulted machine intelligence to the forefront of technology investments. No matter what job you have, your operation will be AI powered within five years and machines may be doing your job in the future. Artificial intelligence is being infused into applications, infrastructure, equipment and virtually every aspect of our lives. AI is proving to be extremely helpful at controlling vehicles, speeding medical diagnoses, processing language, advancing science and generally raising the stakes on what it means to apply technology for business advantage. But business value realization has been a challenge for most organizations because of a lack of skills, complexity of programming models, immature technology integration, sizable up front investments, ethical concerns and lack of business alignment. Mastering AI technology and a focus on features will not be a requirement for success in our view. Rather, figuring out how and where to apply AI to your business will be the crucial gate.

Does Ai solve more problems than it creates? Listen to the experts and vendors discuss the state of artificial intelligence these days, and one can be forgiven for feeling confused about what it takes to bring AI to the table in a realistic way. Is it a complex undertaking that requires profound planning, or something that is becoming inherent in just about every solution now available? Is it too hard to find talent to create AI, or is AI filling talent gaps? Is AI driving digital transformation, or does digital transformation spur AI adoption?

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. We love stories of dramatic breakthroughs and neat endings: The lone inventor cracks the technical challenge, saves the day, the end. These are the recurring tropes surrounding new technologies. Unfortunately, these tropes can be misleading when we're actually in the middle of a technology revolution. It's the prototypes that get too much attention rather than the complex, incremental refinement that truly delivers a breakthrough solution.

In the age of digital transformation, technology is nearly impossible to keep up with. While bringing new technology solutions into every facet of your business can be enticing, budgets and resources rarely allow for it. In addition to this, customer support is considered to be a cost function instead of a growth function. After the pandemic, companies are now recognizing that customer support experience and customer support employee experience are imperative to the success of the organization. While this awareness is just starting up, leaders are bound by many different kinds of technologies.

Can you relate to this problem? I can't find anything to play on iOS right now. Now don't worry mobile developers--it's not you, it's me. I don't go for puzzle games or narrative titles because I want something more mindless before I sleep, or while I'm on the bus. I just have a different problem: I've played you already.

There is still a significant gap between expectations and the successful adoption of AI to innovate and improve businesses. Due to the emergence of deep learning, AI adoption is more complex as it often incorporates big data and the internet of things (IoT), affecting data privacy. Existing frameworks have identified the need to focus on human-centered design, combining technical and business/organizational perspectives. However, trust remains a critical issue that needs to be designed from the beginning. The proposed framework is the first to expand from the human-centered design approach, emphasizing and maintaining the trust that underpins the whole process. This paper proposes a new theoretical framework for responsible artificial intelligence (AI) implementation. The proposed framework emphasizes a synergistic business-technology approach for the agile co-creation process. The aim is to streamline the adoption process of AI to innovate and improve business by involving all stakeholders throughout the project so that the AI technology is designed, developed, and deployed in conjunction with people and not in isolation. The framework presents a fresh viewpoint on responsible AI implementation based on analytical literature review, conceptual framework design, and practitioners' mediating expertise. The framework emphasizes establishing and maintaining trust throughout the human-centered design and agile development of AI. This human-centered approach is aligned with and enabled by the "privacy-by-design" principle. The creators of the technology and the end-users are working together to tailor the AI solution specifically for the business requirements and human characteristics. An illustrative case study on adopting AI for assisting planning in a hospital will demonstrate that the proposed framework applies to real-life applications. Keywords Technology management, Artificial intelligence, Responsible AI, Business adoption framework, Human-centered design, Agile methodology 1. Introduction Despite the rapid advancement and growth of global investment in Artificial Intelligence (AI) technologies, a survey with more than 3000 business executives has recently revealed that only a fifth have incorporated AI in their processes (Ransbotham et al. 2017). The lack of adoption shows a significant gap between ambition and execution for implementing AI to innovate and improve businesses. Meanwhile, due to the rapid growth of AI-enabled automation, people become more worried about losing jobs and control over their data.

Too many technologist, in every generation of technology, state that management need to think more like programmers. Rather, the technology professionals need to learn to speak to management. "The Business Case for AI," by Kavita Ganesan, PhD, is a good overview for managers wishing to understand and control the complexities of implementing artificial intelligence (AI) systems in businesses. Usually that means the books just aren't that good. However, sometimes, especially in non-fiction, it means that publishers are clueless about the subject and hesitant to work with people who aren't "names."

After decades of extravagant promises and frustrating disappointments, artificial intelligence (AI) is finally starting to deliver real-life benefits to early-adopting companies. Retailers on the digital frontier rely on AI-powered robots to run their warehouses--and even to automatically order stock when inventory runs low. Utilities use AI to forecast electricity demand. A confluence of developments is driving this new wave of AI development. Computer power is growing, algorithms and AI models are becoming more sophisticated, and, perhaps most important of all, the world is generating once-unimaginable volumes of the fuel that powers AI--data.