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Implications for reverse mortgages as seniors gain comfort with AI

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Data courtesy of University of Michigan’s National Poll on Healthy Aging

The Washington Post explored the topic this week in an article titled, “How America’s seniors are confronting the dizzying world of AI.” The outlet focused on a senior center in Maryland where classes are being offered on a variety of AI subject matter.

“For some older adults, chatbots have become convenient assistants for making travel plans or writing letters and books,” the report explained. “But AI has also upped the potency of scams and misinformation that already target older Americans. They are encountering AI-generated content as it pervades platforms like Facebook and YouTube.”

Seniors may be more insulated from AI-driven scams than some perceive, according to survey data released earlier this year by HomeEquity Bank, the leading reverse mortgage lender in Canada.

But the Post noted that they’re not immune as “fraudsters have used AI tools to fake the voices of family members and real estate agents to scam victims out of thousands of dollars. The technology has also made it easier for criminals to mine the internet for personal information to better target their marks.”

One Maryland senior who spoke to the outlet said she’s been using technology for decades and upgrades her devices as needed. But AI has grabbed her attention due to its sudden and pervasive emergence.

“It feels a little overwhelming, truthfully,” she said. “And that’s why I decided to take this class.”

One of the classes being offered at the senior center was a tutorial on spotting the differences between real and AI-generated images. Others have focused on communicating with tools like ChatGPT and avoiding “unoriginal and predictable” language in AI-generated writing.

How reverse mortgage lenders are meeting this opportunity

The implications of AI for the reverse mortgage industry are still being debated. But lenders that are able to provide these tools to clients in a thoughtful, purpose-driven manner are poised to gain a leg up.

Bill Packer, chief operating officer at Longbridge Financial, told HousingWire’s Reverse Mortgage Daily in June that tailored AI systems for use among seniors will help lenders overcome perceptions of institutional bias. Among other examples, Packer mentioned that AI could offer a “less ageist” approach to appraisal reviews.

“Do I trust a model that unemotionally is looking at house-price appreciation, or the history of the property, the comps that were being used versus other comps?” he asked. “Do I trust that as being less biased than a human being who’s bringing their own thoughts, expectations and experience to the table?”

Andy Peach, chief lending officer for Onity Group — the parent company of PHH Mortgage Corp. and Liberty Reverse Mortgage gave an interview earlier this year in which Onity’s investments in AI took center stage. In February, the company launched LASI, an AI tool for text queries and data extraction.

“It allows clients to search documents and ask unstructured questions about their portfolios,” he said. “It makes it easier for them to oversee the loans we service.”

Last week, at HousingWire’s AI Summit in Dallas, mortgage compliance expert Wendy Lee dove into the use of AI for risk management. She explored a law in Colorado that will regulate the development and deployment of AI systems there.

The Colorado law is expected to set a precedent for other states, Lee said, and consumer privacy protection rights are just one piece of the puzzle to consider.

“Risk assessment in the AI era is fundamentally different from the pre-AI environment,” she said.



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(Policy Address 2025) HK earmarks HK$3B for AI research and talent recruitment – The Standard (HK)

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(Policy Address 2025) HK earmarks HK$3B for AI research and talent recruitment  The Standard (HK)



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Spatially-Aware Image Focus for Visual Reasoning


View a PDF of the paper titled SIFThinker: Spatially-Aware Image Focus for Visual Reasoning, by Zhangquan Chen and 6 other authors

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Abstract:Current multimodal large language models (MLLMs) still face significant challenges in complex visual tasks (e.g., spatial understanding, fine-grained perception). Prior methods have tried to incorporate visual reasoning, however, they fail to leverage attention correction with spatial cues to iteratively refine their focus on prompt-relevant regions. In this paper, we introduce SIFThinker, a spatially-aware “think-with-images” framework that mimics human visual perception. Specifically, SIFThinker enables attention correcting and image region focusing by interleaving depth-enhanced bounding boxes and natural language. Our contributions are twofold: First, we introduce a reverse-expansion-forward-inference strategy that facilitates the generation of interleaved image-text chains of thought for process-level supervision, which in turn leads to the construction of the SIF-50K dataset. Besides, we propose GRPO-SIF, a reinforced training paradigm that integrates depth-informed visual grounding into a unified reasoning pipeline, teaching the model to dynamically correct and focus on prompt-relevant regions. Extensive experiments demonstrate that SIFThinker outperforms state-of-the-art methods in spatial understanding and fine-grained visual perception, while maintaining strong general capabilities, highlighting the effectiveness of our method. Code: this https URL.

Submission history

From: Zhangquan Chen [view email]
[v1]
Fri, 8 Aug 2025 12:26:20 UTC (5,223 KB)
[v2]
Thu, 14 Aug 2025 10:34:22 UTC (5,223 KB)
[v3]
Sun, 24 Aug 2025 13:04:46 UTC (5,223 KB)
[v4]
Tue, 16 Sep 2025 09:40:13 UTC (5,223 KB)



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An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learning


View a PDF of the paper titled RingMo-Aerial: An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learning, by Wenhui Diao and 10 other authors

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Abstract:Aerial Remote Sensing (ARS) vision tasks present significant challenges due to the unique viewing angle characteristics. Existing research has primarily focused on algorithms for specific tasks, which have limited applicability in a broad range of ARS vision applications. This paper proposes RingMo-Aerial, aiming to fill the gap in foundation model research in the field of ARS vision. A Frequency-Enhanced Multi-Head Self-Attention (FE-MSA) mechanism is introduced to strengthen the model’s capacity for small-object representation. Complementarily, an affine transformation-based contrastive learning method improves its adaptability to the tilted viewing angles inherent in ARS tasks. Furthermore, the ARS-Adapter, an efficient parameter fine-tuning method, is proposed to improve the model’s adaptability and performance in various ARS vision tasks. Experimental results demonstrate that RingMo-Aerial achieves SOTA performance on multiple downstream tasks. This indicates the practicality and efficacy of RingMo-Aerial in enhancing the performance of ARS vision tasks.

Submission history

From: Tong Ling [view email]
[v1]
Fri, 20 Sep 2024 10:03:14 UTC (36,295 KB)
[v2]
Mon, 31 Mar 2025 09:07:12 UTC (30,991 KB)
[v3]
Thu, 29 May 2025 14:03:42 UTC (13,851 KB)
[v4]
Tue, 16 Sep 2025 16:47:46 UTC (15,045 KB)



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