Responsibly deploying artificial intelligence (AI) / machine learning (ML) systems in high-stakes settings arguably requires not only proof of system reliability, but also continual, post-deployment monitoring to quickly detect and address any unsafe behavior. Methods for nonparametric sequential testing -- especially conformal test martingales (CTMs) and anytime-valid inference -- offer promising tools for this monitoring task. However, existing approaches are restricted to monitoring limited hypothesis classes or ``alarm criteria'' (e.g., detecting data shifts that violate certain exchangeability or IID assumptions), do not allow for online adaptation in response to shifts, and/or cannot diagnose the cause of degradation or alarm. In this paper, we address these limitations by proposing a weighted generalization of conformal test martingales (WCTMs), which lay a theoretical foundation for online monitoring for any unexpected changepoints in the data distribution while controlling false-alarms. For practical applications, we propose specific WCTM algorithms that adapt online to mild covariate shifts (in the marginal input distribution), quickly detect harmful shifts, and diagnose those harmful shifts as concept shifts (in the conditional label distribution) or extreme (out-of-support) covariate shifts that cannot be easily adapted to. On real-world datasets, we demonstrate improved performance relative to state-of-the-art baselines.

As AI technology advances, it is driving innovation across industries, increasing the demand for scalable AI project deployment. However, deployment remains a critical challenge due to complex environment configurations, dependency conflicts, cross-platform adaptation, and debugging difficulties, which hinder automation and adoption. This paper introduces AI2Agent, an end-to-end framework that automates AI project deployment through guideline-driven execution, self-adaptive debugging, and case \& solution accumulation. AI2Agent dynamically analyzes deployment challenges, learns from past cases, and iteratively refines its approach, significantly reducing human intervention. To evaluate its effectiveness, we conducted experiments on 30 AI deployment cases, covering TTS, text-to-image generation, image editing, and other AI applications. Results show that AI2Agent significantly reduces deployment time and improves success rates. The code and demo video are now publicly accessible.

Professionals increasingly use Artificial Intelligence (AI) to enhance their capabilities and assist with task execution. While prior research has examined these uses separately, their potential interaction remains underexplored. We propose that AI-driven training ("tutor" effect) and AI-assisted task completion ("tool" effect) can be complementary and test this hypothesis in the context of lung cancer diagnosis. In a field experiment with 336 medical students, we manipulated AI deployment in training, in practice, and in both. Our findings reveal that while AI-integrated training and AI assistance independently improved diagnostic performance, their combination yielded the highest accuracy. These results underscore AI's dual role in enhancing human performance through both learning and real-time support, offering insights into AI deployment in professional settings where human expertise remains essential.

Yet, difficulty successfully deploying generative AI continues to hamper progress. Companies know that generative AI could transform their businesses--and that failing to adopt will leave them behind--but they are faced with hurdles during implementation. This leaves two-thirds of business leaders dissatisfied with progress on their AI deployments. And while, in Q3 2023, 79% of companies said they planned to deploy generative AI projects in the next year, only 5% reported having use cases in production in May 2024. "We're just at the beginning of figuring out how to productize AI deployment and make it cost effective," says Rowan Trollope, CEO of Redis, a maker of real-time data platforms and AI accelerators.

While these prognostications may prove true, today's businesses are finding major hurdles when they seek to graduate from pilots and experiments to enterprise-wide AI deployment. Just 5.4% of US businesses, for example, were using AI to produce a product or service in 2024. Moving from initial forays into AI use, such as code generation and customer service, to firm-wide integration depends on strategic and organizational transitions in infrastructure, data governance, and supplier ecosystems. As well, organizations must weigh uncertainties about developments in AI performance and how to measure return on investment. If organizations seek to scale AI across the business in coming years, however, now is the time to act.

In the words of J.R.R. Tolkien, "shortcuts make long delays." I get it, we live in an age of instant gratification, with Doordash and Grubhub meals on-demand, fast-paced social media and same-day Amazon Prime deliveries. But I've learned that in some cases, shortcuts are just not possible. Such is the case with comprehensive AI implementations; you cannot shortcut success. Operationalizing AI at scale mandates that your full suite of data–structured, unstructured and semi-structured get organized and architected in a way that makes it useable, readily accessible and secure.

In the financial services industry, banks, insurers, asset managers and fintech companies are increasing the speed at which they deploy artificial intelligence (AI)-enabled applications, confident that AI will help them assess risk more accurately, enable operational efficiencies, and reduce costs, results from a new study by American tech firm Nvidia show. The 2023 State of AI in Financial Services report, released on February 02, 2023, draws on a survey of nearly 500 global financial services professionals that sought to understand AI trends in the sector, as well as the opportunities perceived and challenges faced by the industry. Results from the study show that the adoption of AI in the finance sector is accelerating at a fast pace, with over half of the respondents indicating having deployed three or more of the 21 different AI-enabled use cases analyzed by the survey. A fifth of respondents said they had six or more use cases in market. Accelerated adoption of AI in the sector comes on the back of increased awareness of the imperative among executive leadership teams.

In recent years, AI has become more powerful and its applications to business have increased dramatically. As a result, companies that hadn't seriously considered using AI are taking a fresh look. The appeal is obvious: different forms of AI can enhance performance through, prediction, automation of routines, identification of images essential to operational activities, or the identification of key words, phrases and patterns in voice and text for information management. Where organizations often struggle is in knowing where to invest in an AI project that will really pay off. But if AI hasn't been a part of your company before, it can be hard to know where the real potential -- and risks -- lie.

Companies have been investing heavily in artificial intelligence (AI) systems, but all that work is not paying dividends. The digital giants, including the cloud giants and others such as Apple, Facebook, and Netflix, are able to convert data science to business value, but other large enterprises can't. They want to do AI, but they don't know how to get something out of it. What are the problem areas? Are companies new to AI, or do they use basic AI? Are there too high expectations from this niche technology?

Rapid advancements in AI require keeping high ethical standards, as much for legal reasons as moral. During a session at this year's AI & Big Data Expo Europe, a panel of experts provided their views on what businesses need to consider before deploying artificial intelligence. The first question called for thoughts about current and upcoming regulations that affect AI deployments. As a lawyer, De Boel kicked things off by giving her take. De Boel highlights the EU's upcoming AI Act which builds upon the foundations of similar legislation such as GDPR but extends it for artificial intelligence.