We consider parameter estimation and inference when data feature blockwise, non-monotone missingness. Our approach, rooted in semiparametric theory and inspired by prediction-powered inference, leverages off-the-shelf AI (predictive or generative) models to handle missing completely at random mechanisms, by finding an approximation of the optimal estimating equation through a novel and tractable Restricted Anova hierarchY (RAY) approximation. The resulting Inference for Blockwise Missingness(RAY), or IBM(RAY) estimator incorporates pre-trained AI models and carefully controls asymptotic variance by tuning model-specific hyperparameters. We then extend IBM(RAY) to a general class of estimators. We find the most efficient estimator in this class, which we call IBM(Adaptive), by solving a constrained quadratic programming problem. All IBM estimators are unbiased, and, crucially, asymptotically achieving guaranteed efficiency gains over a naive complete-case estimator, regardless of the predictive accuracy of the AI models used. We demonstrate the finite-sample performance and numerical stability of our method through simulation studies and an application to surface protein abundance estimation.

Integrating malicious AI models6 into software supply chains presents a significant and emerging threat to cybersecurity. The attackers aim to embed malicious AI models in software components and widely used tools, thereby infiltrating systems at a foundational level. Once integrated, the malicious AI models execute embedded unauthorized code, which performs actions such as exfiltrating sensitive data, manipulating data integrity, or enabling unauthorized access to critical systems. Compromised development tools, tampered libraries, and pre-trained models are the primary methods of introducing malicious AI models into the software supply chain. Developers often rely on libraries and frameworks to import pre-trained AI models to expedite software development.

Toby: My role here at Darktrace is the Global Head of Threat Analysis. My day-to-day job involves looking at the 100 or so cybersecurity analysts we have spread from New Zealand to Singapore, the UK, and most major time zones in the US. My main role is to evaluate how we can use the Darktrace platform to work with our customers. How can we ensure that our customers get the most out of our cybersecurity expertise and support when using AI to secure their network? The other half of my role at Darktrace is subject matter expertise. This role involves talking to reporters like yourself or our customers who want to hear more about what Darktrace can do to help them from a cybersecurity perspective, discussing the context of current events. That part of my role was born out of a nearly 20-year career in cybersecurity. I first started in government and was one of the founding members of the National Cybersecurity Center here in the UK.

Organizations need to gauge and resolve certain inherent risks associated with AI systems and tools before incorporating them into their daily functioning. Effective management of AI risks by qualified data professionals can allow businesses to exploit the technology's myriad capabilities to the fullest extent. Like every evolving concept, AI's deficiencies are still known only upto a certain extent. Every day, data analysts and IT professionals uncover newer problematic elements in either the technology itself or the way in which it is implemented in various sectors. From what we know about AI already, there are certain clear-cut risks that may present themselves to organizations looking to implement the technology for optimizing their operations.

Organizations are constantly looking to incorporate artificial intelligence (AI) in their daily operations. However, with the large amounts of time and money required for extensive AI integration, organizations must find smarter ways to implement AI, such as using pre-trained AI models. As you probably know, transforming an organization with AI and machine learning can be time-consuming. The efforts and finances required to complete the process depend on the level of automation and digitization being introduced in the various departments of the organization. Deep learning and other components of AI need thousands upon thousands of datasets to improve the competency of automated operations over a given period.