Several architecture frameworks for software, systems, and enterprises have been proposed in the literature. They identified various stakeholders and defined architecture viewpoints and views to frame and address stakeholder concerns. However, the stakeholders with data science and Machine Learning (ML) related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks. Therefore, they failed to address the architecture viewpoints and views responsive to the concerns of the data science community. In this paper, we address this gap by establishing the architecture frameworks adapted to meet the requirements of modern applications and organizations where ML artifacts are both prevalent and crucial. In particular, we focus on ML-enabled Cyber-Physical Systems (CPSs) and propose two sets of merit criteria for their efficient development and performance assessment, namely the criteria for evaluating and benchmarking ML-enabled CPSs, and the criteria for evaluation and benchmarking of the tools intended to support users through the modeling and development pipeline. In this study, we deploy multiple empirical and qualitative research methods based on literature review and survey instruments including expert interviews and an online questionnaire. We collect, analyze, and integrate the opinions of 77 experts from more than 25 organizations in over 10 countries to devise and validate the proposed framework.

Pricing is the most important, least-discussed element of the software industry. In the past, founders could get away with giving pricing short shrift under the mantra, "the best product will ultimately win." In the age of AI-enabled software, pricing and product are linked; pricing fundamentally impacts usage, which directly informs product quality. For the first time in 20 years, there is a compelling argument to make for changing the way that SaaS is priced. For those selling AI-enabled software, it's time to examine new pricing models.

Businesses across almost every industry deploy artificial intelligence to make jobs simpler for staff and tasks easier for consumers. Computer software teaches customer service agents how to be more compassionate, schools use machine learning to scan for weapons and mass shooters on campus, and doctors use AI to map the root cause of diseases. Sectors such as cybersecurity, online entertainment and retail use the tech in combination with wide swaths of customer data in revolutionary ways to streamline services. Though these applications may seem harmless, perhaps even helpful, the AI is only as good as the information fed into it, which can have serious implications. You might not realize it, but AI helps determine whether you qualify for a loan in some cases.

One Georgia school district plans to spend $16.5 million to install artificial intelligence-powered surveillance cameras in its roughly 100 buildings in coming years. Businesses across almost every industry deploy artificial intelligence to make jobs simpler for staff and tasks easier for consumers. Computer software teaches customer service agents how to be more compassionate, schools use machine learning to scan for weapons and mass shooters on campus, and doctors use AI to map the root cause of diseases. Sectors such as cybersecurity, online entertainment and retail use the tech in combination with wide swaths of customer data in revolutionary ways to streamline services. Though these applications may seem harmless, perhaps even helpful, the AI is only as good as the information fed into it, which can have serious implications.

Artificial Intelligence promises to benefit humankind in unprecedented ways. But small states are especially vulnerable to the technology's downside short of strengthening social cohesion and resilience. Artificial intelligence or AI, broadly defined as human-like intelligence and qualities exhibited by machines, has made a huge technological leap since 1956 when the term was first coined. Tech giants like Google and IBM believe that AI will benefit mankind in unprecedented ways. For example, autonomous vehicles are expected to enhance both traffic safety and flow whereas care-bots will aid in areas such as elderly and patient care.