Data is the fuel behind the AI revolution -- the foundational building block for the new technological world order. But data is immaterial, difficult to organize, and subject to an ever-growing mountain of walled gardens and regulatory decrees. Businesses seeking to harness AI, therefore, often struggle to make the most of their data, this most vital of resources. At its annual Snowflake Summit user conference, the company announced the release of Openflow, a new service designed to integrate businesses' data into a single, unified, and intelligible channel. Like disparate streams flowing into a single river, Openflow takes the whole of a company's data -- structured, unstructured, batch, and streaming -- and collects them in such a way that they can be more easily visualized and leveraged.

Ukraine managed to not only humiliate the Kremlin by boasting of taking out more than a third of all Russian missile carriers in a spectacular drone attack but also to rewrite the rules of modern warfare, analysts say. Despite being outnumbered and outgunned, Kyiv used inexpensive drones at the weekend to destroy Russian nuclear-capable bombers worth billions of dollars in an operation carried out after months of planning. "Spider's Web" dealt a blow to Russia more than three years after its invasion of Ukraine, and the operation will now be studied closely by militaries around the world as a new strategy in asymmetric warfare.

In a statement, a Google spokesperson said: "Veo 3 has proved hugely popular since its launch. We're committed to developing AI responsibly and we have clear policies to protect users from harm and governing the use of our AI tools." Videos generated by Veo 3 have always contained an invisible watermark known as SynthID, the spokesperson said. Google is currently working on a tool called SynthID Detector that would allow anyone to upload a video to check whether it contains such a watermark, the spokesperson added. However, this tool is not yet publicly available.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. In this latest interview, we hear from Debalina Padariya and hear about her work on Privacy-Preserving Generative Models, why this is such an interesting area for study, the different projects she's been involved in so far during her PhD, and her experience at the Doctoral Consortium at AAAI 2025. I am currently pursuing a PhD at De Montfort University, UK, supported by the prestigious Alan Turing Institute and Accenture Strategic Partnership Program. My research primarily focuses on Privacy-Preserving Generative Models, while designing a framework to quantify the privacy/utility trade-offs in generative model-driven synthetic datasets. Although Synthetic Data Generation (SDG) is one of the emerging use cases of generative AI, potential privacy attacks associated with generative models emerge as critical issues.

Ukraine's surprise Sunday attack on Russian offensive weapons caches may be a good time for the U.S. to reflect on its own weaknesses, should one of its adversaries attempt a similar strike. Col. Seth Krummrich, a retired Army Special Forces commander and vice president at the Virginia-based security firm Global Guardian, warned that the U.S. remains vulnerable to drone attacks. "Interestingly, it is not a technological gap, it is a policy/authority process to engage and deny drone attacks," Krummrich said. "I assess it will take a major drone strike in the U.S. to change policy." Even civilian operations have a tough time getting approval for drone-interception-authority protections, the NFL excepted, he said.

Artificial intelligence startup Anthropic has hit 3 billion in annualized revenue, marking a 200% increase in just five months, according to a Friday report from Reuters. Anthropic's annualized revenue -- or its total projected earnings over the course of the year, assuming its current rate of income continues -- was close to 1 billion in December, according to the Reuters report, which cited anonymous sources close to the matter. It crossed the 2 billion threshold in late March and reached 3 billion last month. Also: Anthropic's free Claude 4 Sonnet aced my coding tests - but its paid Opus model somehow didn't Founded in 2021 by siblings Dario and Daniela Amodei, both former OpenAI employees, Anthropic has built its business model around its Claude family of generative AI chatbots. The company has also positioned itself as a leader in the responsible deployment of powerful AI tools.

Training and inference with large machine learning models that far exceed the memory capacity of individual devices necessitates the design of distributed architectures, forcing one to contend with communication constraints. We present a framework for distributed computation over a quantum network in which data is encoded into specialized quantum states. We prove that for models within this framework, inference and training using gradient descent can be performed with exponentially less communication compared to their classical analogs, and with relatively modest overhead relative to standard gradient-based methods. We show that certain graph neural networks are particularly amenable to implementation within this framework, and moreover present empirical evidence that they perform well on standard benchmarks. To our knowledge, this is the first example of exponential quantum advantage for a generic class of machine learning problems that hold regardless of the data encoding cost. Moreover, we show that models in this class can encode highly nonlinear features of their inputs, and their expressivity increases exponentially with model depth. We also delineate the space of models for which exponential communication advantages hold by showing that they cannot hold for linear classification. Communication of quantum states that potentially limit the amount of information that can be extracted from them about the data and model parameters may also lead to improved privacy guarantees for distributed computation. Taken as a whole, these findings form a promising foundation for distributed machine learning over quantum networks.

We introduce a fairness-aware dataset for job recommendation in advertising, designed to foster research in algorithmic fairness within real-world scenarios. It was collected and prepared to comply with privacy standards and business confidentiality. An additional challenge is the lack of access to protected user attributes such as gender, for which we propose a solution to obtain a proxy estimate. Despite being anonymized and including a proxy for a sensitive attribute, our dataset preserves predictive power and maintains a realistic and challenging benchmark. This dataset addresses a significant gap in the availability of fairnessfocused resources for high-impact domains like advertising - the actual impact being having access or not to precious employment opportunities, where balancing fairness and utility is a common industrial challenge. We also explore various stages in the advertising process where unfairness can occur and introduce a method to compute a fair utility metric for the job recommendations in online systems case from a biased dataset. Experimental evaluations of bias mitigation techniques on the released dataset demonstrate potential improvements in fairness and the associated trade-offs with utility.

Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. Despite its broad applications in fields such as computer vision, graph learning, and natural language processing, the development of a data projection model that can be effectively used to visualize data in the context of FL is crucial yet remains heavily under-explored. Neighbor embedding (NE) is an essential technique for visualizing complex high-dimensional data, but collaboratively learning a joint NE model is difficult. The key challenge lies in the objective function, as effective visualization algorithms like NE require computing loss functions among pairs of data.

Image restoration has experienced significant advancements due to the development of deep learning. Nevertheless, it encounters challenges related to ill-posed problems, resulting in deviations between single model predictions and ground-truths. Ensemble learning, as a powerful machine learning technique, aims to address these deviations by combining the predictions of multiple base models. Most existing works adopt ensemble learning during the design of restoration models, while only limited research focuses on the inference-stage ensemble of pre-trained restoration models. Regression-based methods fail to enable efficient inference, leading researchers in academia and industry to prefer averaging as their choice for post-training ensemble.