AI Research
Nubank To Continue Leveraging AI To Enhance Digital Financial Services In Latin America

Nubank (NYSE: NU) is reportedly millions of customers across Latin America. Recently, the company’s Chief Technology Officer, Eric Young, shared his vision for leveraging artificial intelligence to fuel Nubank’s global expansion and improve financial services.
During a recent discussion, Young outlined how AI is not just a tool but a cornerstone for operational efficiency, customer-centric growth, and democratizing access to personalized finance.
With a career that includes work at Amazon in the early 2000s, Young brings a philosophy of prioritizing customer experience.
At Amazon, he witnessed firsthand how technology could transform user experiences, a mindset he now applies to Nubank’s mission. “If not us, then who?”
Young posed rhetorically during the videocast, underscoring Nubank’s unique position to disrupt traditional banking.
Founded in Brazil in 2013, Nubank has positively impacted the financial sector by prioritizing financial inclusion and superior customer service, challenging legacy banks with its digital-first approach.
Under Young’s leadership, Nubank’s priorities are clear: enhance agility, expand internationally, and harness AI to serve customers better.
He emphasized the need for cross-functional collaboration, particularly with the product and design teams.
This includes partnering with Nubank’s recently appointed Chief Design Officer (CDO), Ethan Eismann, to iterate quickly on new features.
By fostering a culture of testing and learning, Young aims to deliver products that not only meet but exceed user expectations, ultimately capturing a larger market share.
This involves deepening engagement with existing users, attracting new ones, and venturing into underserved markets where financial services remain inaccessible.
Central to Young’s strategy is AI’s transformative potential.
Nubank’s 2024 acquisition of Hyperplane, an AI-focused startup, marks a pivotal step in this direction.
Young highlighted how advanced language models—such as those powering ChatGPT and Google Gemini—can bridge the gap between everyday users and elite financial advisory services.
These models excel at processing vast amounts of data, including transaction histories, to offer hyper-personalized recommendations.
Imagine an AI that automates budgeting, predicts spending patterns, and suggests investment opportunities tailored to an individual’s financial profile, all without the hefty fees of traditional private banking.
Young drew a parallel to the exclusivity of high-end services.
Historically, AI-driven private banking was reserved for the ultra-wealthy, but Nubank’s vision is to make it ubiquitous.
“We’re democratizing access to hyper-personalized financial experiences.”
By analyzing user data ethically and securely, AI can empower customers from all segments—whether a small business owner in Mexico or a young professional in Colombia—to manage their finances with the precision once afforded only to elites.
This aligns with Nubank’s core ethos of inclusion, ensuring that technology serves as an equalizer rather than a divider.
Looking ahead, Young sees AI as the engine for Nubank’s platformization efforts, enabling scalable solutions that support international growth.
As Nubank eyes further expansion beyond Brazil, Mexico, and Colombia, AI will streamline operations, from fraud detection to customer support chatbots, reducing costs while enhancing reliability.
Yet, Young cautioned that success hinges on responsible implementation—prioritizing privacy, transparency, and human oversight to build trust.
In an era where fintechs aggressively compete for market share, Eric Young’s insights position Nubank not just as a bank, but as a key player in AI-powered financial services.
By blending technological prowess with a focus on the customer, Nubank is set to transform money management, making various services more accessible to consumers.
As Young basically put it, the question isn’t whether AI will change finance—it’s how Nubank will aim to make a positive impact.
AI Research
Will artificial intelligence fuel moral chaos or positive change?

Artificial intelligence is transforming our world at an unprecedented rate, but what does this mean for Christians, morality and human flourishing?
In this episode of “The Inside Story,” Billy Hallowell sits down with The Christian Post’s Brandon Showalter to unpack the promises and perils of AI.
From positives like Bible translation to fears over what’s to come, they explore how believers can apply a biblical worldview to emerging technology, the dangers of becoming “subjects” of machines, and why keeping Christ at the center is the only true safeguard.
Plus, learn about The Christian Post’s upcoming “AI for Humanity” event at Colorado Christian University and how you can join the conversation in person or via livestream:
“The Inside Story” takes you behind the headlines of the biggest faith, culture and political headlines of the week. In 15 minutes or less, Christian Post staff writers and editors will help you navigate and understand what’s driving each story, the issues at play — and why it all matters.
Listen to more Christian podcasts today on the Edifi app — and be sure to subscribe to The Inside Story on your favorite platforms:
AI Research
Beyond Refusal — Constructive Safety Alignment for Responsible Language Models

View a PDF of the paper titled Oyster-I: Beyond Refusal — Constructive Safety Alignment for Responsible Language Models, by Ranjie Duan and 26 other authors
Abstract:Large language models (LLMs) typically deploy safety mechanisms to prevent harmful content generation. Most current approaches focus narrowly on risks posed by malicious actors, often framing risks as adversarial events and relying on defensive refusals. However, in real-world settings, risks also come from non-malicious users seeking help while under psychological distress (e.g., self-harm intentions). In such cases, the model’s response can strongly influence the user’s next actions. Simple refusals may lead them to repeat, escalate, or move to unsafe platforms, creating worse outcomes. We introduce Constructive Safety Alignment (CSA), a human-centric paradigm that protects against malicious misuse while actively guiding vulnerable users toward safe and helpful results. Implemented in Oyster-I (Oy1), CSA combines game-theoretic anticipation of user reactions, fine-grained risk boundary discovery, and interpretable reasoning control, turning safety into a trust-building process. Oy1 achieves state-of-the-art safety among open models while retaining high general capabilities. On our Constructive Benchmark, it shows strong constructive engagement, close to GPT-5, and unmatched robustness on the Strata-Sword jailbreak dataset, nearing GPT-o1 levels. By shifting from refusal-first to guidance-first safety, CSA redefines the model-user relationship, aiming for systems that are not just safe, but meaningfully helpful. We release Oy1, code, and the benchmark to support responsible, user-centered AI.
Submission history
From: Ranjie Duan [view email]
[v1]
Tue, 2 Sep 2025 03:04:27 UTC (5,745 KB)
[v2]
Thu, 4 Sep 2025 11:54:06 UTC (5,745 KB)
[v3]
Mon, 8 Sep 2025 15:18:35 UTC (5,746 KB)
[v4]
Fri, 12 Sep 2025 04:23:22 UTC (5,747 KB)
AI Research
Multimodal SAM-adapter for Semantic Segmentation

arXiv:2509.10408v1 Announce Type: cross
Abstract: Semantic segmentation, a key task in computer vision with broad applications in autonomous driving, medical imaging, and robotics, has advanced substantially with deep learning. Nevertheless, current approaches remain vulnerable to challenging conditions such as poor lighting, occlusions, and adverse weather. To address these limitations, multimodal methods that integrate auxiliary sensor data (e.g., LiDAR, infrared) have recently emerged, providing complementary information that enhances robustness. In this work, we present MM SAM-adapter, a novel framework that extends the capabilities of the Segment Anything Model (SAM) for multimodal semantic segmentation. The proposed method employs an adapter network that injects fused multimodal features into SAM’s rich RGB features. This design enables the model to retain the strong generalization ability of RGB features while selectively incorporating auxiliary modalities only when they contribute additional cues. As a result, MM SAM-adapter achieves a balanced and efficient use of multimodal information. We evaluate our approach on three challenging benchmarks, DeLiVER, FMB, and MUSES, where MM SAM-adapter delivers state-of-the-art performance. To further analyze modality contributions, we partition DeLiVER and FMB into RGB-easy and RGB-hard subsets. Results consistently demonstrate that our framework outperforms competing methods in both favorable and adverse conditions, highlighting the effectiveness of multimodal adaptation for robust scene understanding. The code is available at the following link: https://github.com/iacopo97/Multimodal-SAM-Adapter.
Source link
-
Business2 weeks ago
The Guardian view on Trump and the Fed: independence is no substitute for accountability | Editorial
-
Tools & Platforms1 month ago
Building Trust in Military AI Starts with Opening the Black Box – War on the Rocks
-
Ethics & Policy2 months ago
SDAIA Supports Saudi Arabia’s Leadership in Shaping Global AI Ethics, Policy, and Research – وكالة الأنباء السعودية
-
Events & Conferences4 months ago
Journey to 1000 models: Scaling Instagram’s recommendation system
-
Jobs & Careers3 months ago
Mumbai-based Perplexity Alternative Has 60k+ Users Without Funding
-
Podcasts & Talks2 months ago
Happy 4th of July! 🎆 Made with Veo 3 in Gemini
-
Education3 months ago
VEX Robotics launches AI-powered classroom robotics system
-
Education2 months ago
Macron says UK and France have duty to tackle illegal migration ‘with humanity, solidarity and firmness’ – UK politics live | Politics
-
Podcasts & Talks2 months ago
OpenAI 🤝 @teamganassi
-
Funding & Business3 months ago
Kayak and Expedia race to build AI travel agents that turn social posts into itineraries