US Customs and Border Protection is paying General Dynamics to create prototype "quantum sensors," to be used with an AI database to detect fentanyl and other narcotics. United States Customs and Border Protection is paying General Dynamics to create a prototype of "quantum sensors" alongside a "database with artificial intelligence " designed "to detect illicit objects and substances (such as fentanyl) in vehicles, containers, and other devices," according to a contract justification published in a federal register last week. "This database and sensor project will integrate advanced quantum and classical sensing technologies with Artificial Intelligence and ultimately deploy proven concepts and end products anywhere in the CBP environment," the justification document reads. "Under this requirement, CBP will take additional steps to enhance its ability to detect, and thus, significantly reduce the harms of illicit contraband entering the United States of America, thus bolstering national security." The document redacts the name of the company developing the prototype; however, contract details included in the federal register entry reveal that the justification is for a $2.4 million General Dynamics contract that has been public since December 2025.

Most Artificial Intelligence (AI) applications are about having the right data at the right time, but also about being able to process them in intelligent ways. The global leaders on AI are already collecting, aggregating, processing and managing very large volume of data. In the future, a proliferating number of enterprises will have to manage very large datasets, as a means of empowering their AI-based processes. This has already a significant impact on the databases of these organizations, which must be more scalable and more intelligent than ever before. However, the relationship between AI systems and modern databases is a two-way one. On the one hand, the quality of the data management infrastructure of an enterprise is a decisive factor for its ability to adopt and fully leverage AI.

ImageNet will remove 600,000 images of people stored on its database after an art project exposed racial bias in the program's artificial intelligence system. Created in 2009 by researchers at Princeton and Stanford, the online image database has been widely used by machine learning projects. The program has pulled more than 14 million images from across the web, which have been categorized by Amazon Mechanical Turk workers -- a crowdsourcing platform through which people can earn money performing small tasks for third parties. According to the results of an online project by AI researcher Kate Crawford and artist Trevor Paglen, prejudices in that labor pool appear to have biased the machine learning data. Training Humans -- an exhibition that opened last week at the Prada Foundation in Milan -- unveiled the duo's findings to the public, but part of their experiment also lives online at ImageNet Roulette, a website where users can upload their own photographs to see how the database might categorize them.

Be sure to share on LinkedIn. As organizations get better at managing and using a wider variety of data, the more they will adopt and make use of AI. IBM General Manager for Data and AI Rob Thomas has said organizations can't have effective AI without sound IA (Information Architecture). And one of the pillars of any IA is data management. In this new era of data, databases are no longer considered the traditional system of record or datastore.

Pragmatic AI is here and the results are real for enterprises that know the ropes. Powerful predictive models, computer vision, and natural language understanding are a few of the applications that enterprises of all kinds can apply to a broad number of use cases. The good news is that much of the work to infuse applications with AI can be performed by data scientists and perseverant developers who are willing to learn new machine learning frameworks like TensorFlow on GPU systems. The incessant challenge is that AI needs powerful and performant data management and processing capabilities to support the full AI development lifecycle and quickly create accurate models. Mike's research focuses on software technology, platforms, and practices that enable technology professionals to deliver prescient digital experiences and breakthrough operational efficiency.