W hen Lunos, an AI startup in New York City, was gearing up for launch, its founder and chief executive, Duncan Barrigan, and his team wanted to make a splash. So they shelled out $3,500 to do the unconventional: hire a horse and a cowboy to lasso the bull of Wall Street. Wearing ranch gear and a western hat stamped with the Lunos logo, he lassoed the bull's horns as invitees and curious passersby watched. He and the horse then circled the statue, handing out cowboy hats and branded stress balls. The goal was simple: deliver Lunos's pitch of "taming the wild west" of accounts receivables in the most literal, public way possible.

Personal AI assistants (e.g., Apple Intelligence, Meta AI) offer proactive recommendations that simplify everyday tasks, but their reliance on sensitive user data raises concerns about privacy and trust. To address these challenges, we introduce the Guardian of Data (GOD), a secure, privacy-preserving framework for training and evaluating AI assistants directly on-device. Unlike traditional benchmarks, the GOD model measures how well assistants can anticipate user needs-such as suggesting gifts-while protecting user data and autonomy. Functioning like an AI school, it addresses the cold start problem by simulating user queries and employing a curriculum-based approach to refine the performance of each assistant. Running within a Trusted Execution Environment (TEE), it safeguards user data while applying reinforcement and imitation learning to refine AI recommendations. A token-based incentive system encourages users to share data securely, creating a data flywheel that drives continuous improvement. Specifically, users mine with their data, and the mining rate is determined by GOD's evaluation of how well their AI assistant understands them across categories such as shopping, social interactions, productivity, trading, and Web3. By integrating privacy, personalization, and trust, the GOD model provides a scalable, responsible path for advancing personal AI assistants. For community collaboration, part of the framework is open-sourced at https://github.com/PIN-AI/God-Model.

Imagine if you instantly could have the answer to anything you once knew, or could recall every detail about your conversations without endless scrolling or searching. Your personal AI is the digital library of you--a treasure trove of your life's information. Whether it's future dinner plans with friends or work meeting recaps, everything is indexed automatically and is entirely discoverable--simply by chatting.

Realiste, a leading AI developer in the real estate market has finally launched in the Middle East, changing the real-estate and housing landscape using the latest advanced technologies within all operations. Realiste is an AI-based real estate market development company committed to providing users with accurate and timely market value appraisals. The company's mission is to digitise the real estate market of every major city across the world, while offering free access to appraisals and personalised recommendations. Since launching in December 2021 it has established operations in Riyadh, Dubai, London, New York, and Moscow and are planning more expansions in the next year. Realiste has successfully fielded an application capable of monitoring the market round-the-clock and displaying the true value of real estate, as well as predicting price movements of properties for the next 6 to 12 months.

Dubai: In the last decade the MENA region has proved to be one of the world's most lucrative fields for the deployment of AI technologies. Realiste, a leading AI developer in the real estate market has finally launched in the Middle East, changing the real-estate and housing landscape using the latest advanced technologies within all operations. Realiste is an AI-based real estate market development company committed to providing users with accurate and timely market value appraisals. The company's mission is to digitize the real estate market of every major city across the world, while offering free access to appraisals and personalized recommendations. Since launching in December 2021 they have established operations in Riyadh, Dubai, London, New York, and Moscow and are planning more expansions in the next year.

Prifina is thrilled to welcome Dr. Peter Cotton as our special guest in the fifth episode of the "Liberty. Peter currently serves as the Senior Vice President and Chief Data Scientist at Intech Investment Management LLC. D. degree in Mathematics from Stanford, he held leadership roles at major U.S. financial institutions. Peter has led data science projects at Morgan Stanley, J.P. Morgan Chase, and several major hedge funds, where he built solutions solving complex data problems. He has extensive experience with crowdsourcing models and helped build one of the first in the world privacy-preserving computation mechanisms at J.P. Morgan Chase. In this podcast we talk about algorithms and innovation, with a focus on financial data. How does a hedge fund normalize messy financial data to build bespoke predictive models? What are the current trends and challenges related to machine learning in the financial services industry? From the innovation point of view, what happens when we cut down the cost of building algorithms to minimum functionality? Is it possible to build personal AI systems for small and mid-cap companies as well as individuals? We also delve into geeky topics, such as how to build a financial probability model for the pricing of vanilla bonds. You can find this podcast on Spotify, Apple Podcasts, Google Podcasts, and SoundCloud. He noted that one of the main areas of focus in his career was to level the playing field in the machine learning (ML) space. He notes that one of the first things to do before building financial prediction, ML, and AI models is create a system that helps clean and normalize data. "It is the most interesting mathematical problem of all because the cleaning of data implies that you understand the market itself.

A couple years ago I took part in a marketing video where I was recorded answering questions about my career path, interests, and goals. The next day, the videographer offered to show me the footage, so I took him up on it. As I watched myself, I became dismayed. My eyes darted from left to right instead of looking into the camera. And my head did this weird, reflexive sort of mini-nod whenever I said something I felt strongly about.

By Dec 22, 2019 0 A couple years ago I took part in a marketing video where I was recorded answering questions about my career path, interests, and goals. The next day, the videographer offered to show me the footage, so I took him up on it. As I watched myself, I became dismayed. My eyes darted from left to right instead of looking into the camera. And my head did this weird, reflexive sort of mini-nod whenever I said something I felt strongly about.

A couple years ago I took part in a marketing video where I was recorded answering questions about my career path, interests, and goals. The next day, the videographer offered to show me the footage, so I took him up on it. As I watched myself, I became dismayed. My eyes darted from left to right instead of looking into the camera. And my head did this weird, reflexive sort of mini-nod whenever I said something I felt strongly about.

We are surrounded by AI, it analyzes our pictures, answers our questions and recommends new shows. The next logical step is an AI that learns directly from us. Something wonderful always happens when a technology reaches the hands of a large amount of people. Digital Photography and Video are great examples. The endless creativity of the global community is always finding new uses, changing lives, having fun and more importantly: empowering people.