This CIO doesn't'hire engineers to write code': 3 AI fundamentals he prioritizes instead Gill Haus explains Chase's approach to AI - and getting the core fundamentals right. Mark Samuels is a business journalist specialising in IT leadership issues. Agentic AI shifts professionals' responsibilities. Secure services and reliable outputs are crucial. Evidence suggests that delivering value from AI is hard.

The reviewer brings up a good point. SNG-DBSCAN recovers these clusters at rates depending on various properties of the density function. We will further clarify these constant factor dependencies.

A principal curve serves as a powerful tool for uncovering underlying structures of data through 1-dimensional smooth and continuous representations. On the basis of optimal transport theories, this paper introduces a novel principal curve framework constrained by monotonicity with rigorous theoretical justifications. We establish statistical guarantees for our monotone curve estimate, including expected empirical and generalized mean squared errors, while proving the existence of such estimates. These statistical foundations justify adopting the popular early stopping procedure in machine learning to implement our numeric algorithm with neural networks. Comprehensive simulation studies reveal that the proposed monotone curve estimate outperforms competing methods in terms of accuracy when the data exhibits a monotonic structure. Moreover, through two real-world applications on future prices of copper, gold, and silver, and avocado prices and sales volume, we underline the robustness of our curve estimate against variable transformation, further confirming its effective applicability for noisy and complex data sets. We believe that this monotone curve-fitting framework offers significant potential for numerous applications where monotonic relationships are intrinsic or need to be imposed.

Small-scale, hyper-efficient living has always appealed to me, so I was overjoyed to step into numerous examples of sustainable tiny homes this week at CES 2025. There were EV RVs, trailers geared for camping and deliverable, turn-key, self-sustaining living pods. I want one of each to create a little eco village somewhere, preferably within walking distance to a bakery, coffee shop and Thai food. While none of these are cheap, some actually fall under what I would expect, compared to the market at large. And the suite of features employed represent some of the best sustainability capabilities available at the moment -- solar power, gray water recycling, atmospheric water generation and boss-level insulation.

Enterprises need to constantly look for ways to improve and expand what they offer to the marketplace. For example, Sameena Shah, managing director of AI research at JPMorgan Chase, says the company's bankers have been looking for new ways to study early-stage startups looking to raise capital. The challenge was, she says, "finding good prospects in a domain that is fundamentally very opaque and has a lot of variability." The solution for JPMorgan Chase was a new digital platform, built off an algorithm that continually seeks out data, and learns to find prospects by triaging its data into standardized representations to describe startups and likely investors. For users, the platform also offers the context of its output, to help them understand the recommendations.

The disparity between digital transformation leaders and laggards stems from a complex web of overlapping factors -- which often speak more to organizational issues than technical difficulties. Considerations in play include corporate history, IT philosophy, the ability to deliver on customer experience and a product vs. project mindset. A particularly important element separating a successful digital business from its competitors is a knack for translating small successes into enterprise-wide benefits. Indeed, overcoming digital transformation challenges at scale is crucial for realizing the promise of technology-infused business models, according to CIOs and industry analysts. Companies playing catch-up in the digital race must first focus on the essentials, such as customer experience, before moving on to more innovative pursuits.

Language recognition system is typically trained directly to optimize classification error on the target language labels, without using the external, or meta-information in the estimation of the model parameters. However labels are not independent of each other, there is a dependency enforced by, for example, the language family, which affects negatively on classification. The other external information sources (e.g. audio encoding, telephony or video speech) can also decrease classification accuracy. In this paper, we attempt to solve these issues by constructing a deep hierarchical neural network, where different levels of meta-information are encapsulated by attentive prediction units and also embedded into the training progress. The proposed method learns auxiliary tasks to obtain robust internal representation and to construct a variant of attentive units within the hierarchical model. The final result is the structural prediction of the target language and a closely related language family. The algorithm reflects a "staircase" way of learning in both its architecture and training, advancing from the fundamental audio encoding to the language family level and finally to the target language level. This process not only improves generalization but also tackles the issues of imbalanced class priors and channel variability in the deep neural network model. Our experimental findings show that the proposed architecture outperforms the state-of-the-art i-vector approaches on both small and big language corpora by a significant margin.

Gill Haus is the Senior Vice President, Retail and Direct Bank Chief Information Officer at Capital One. In that role, he has overseen many of the changes that have made the Bank synonymous with digital innovation. He believes in having his team regularly experiment with the latest technology to judge applicability to the Bank and its objectives. He has overseen the development of innovation labs that further this mission. Haus is familiar with the difficult work that companies that are larger and that have been in business for a generation or more must undertake in order to become digital ready. These include cultural changes, process changes, and technology changes.

We study the problem of estimating the ridges of a density function. Ridge estimation is an extension of mode finding and is useful for understanding the structure of a density. It can also be used to find hidden structure in point cloud data. We show that, under mild regularity conditions, the ridges of the kernel density estimator consistently estimate the ridges of the true density. When the data are noisy measurements of a manifold, we show that the ridges are close and topologically similar to the hidden manifold. To find the estimated ridges in practice, we adapt the modified mean-shift algorithm proposed by Ozertem and Erdogmus [J. Mach. Learn. Res. 12 (2011) 1249-1286]. Some numerical experiments verify that the algorithm is accurate.