Analyzing financial transactions is crucial for ensuring regulatory compliance, detecting fraud, and supporting decisions. The complexity of financial transaction data necessitates advanced techniques to extract meaningful insights and ensure accurate analysis. Since Transformer-based models have shown outstanding performance across multiple domains, this paper seeks to explore their potential in understanding financial transactions. This paper conducts extensive experiments to evaluate three types of Transformer models: Encoder-Only, Decoder-Only, and Encoder-Decoder models. For each type, we explore three options: pretrained LLMs, fine-tuned LLMs, and small proprietary models developed from scratch. Our analysis reveals that while LLMs, such as LLaMA3-8b, Flan-T5, and SBERT, demonstrate impressive capabilities in various natural language processing tasks, they do not significantly outperform small proprietary models in the specific context of financial transaction understanding. This phenomenon is particularly evident in terms of speed and cost efficiency. Proprietary models, tailored to the unique requirements of transaction data, exhibit faster processing times and lower operational costs, making them more suitable for real-time applications in the financial sector. Our findings highlight the importance of model selection based on domain-specific needs and underscore the potential advantages of customized proprietary models over general-purpose LLMs in specialized applications. Ultimately, we chose to implement a proprietary decoder-only model to handle the complex transactions that we previously couldn't manage. This model can help us to improve 14% transaction coverage, and save more than \$13 million annual cost.

Mixed Reality (MR) could assist users' tasks by continuously integrating virtual content with their view of the physical environment. However, where and how to place these content to best support the users has been a challenging problem due to the dynamic nature of MR experiences. In contrast to prior work that investigates optimization-based methods, we are exploring how reinforcement learning (RL) could assist with continuous 3D content placement that is aware of users' poses and their surrounding environments. Through an initial exploration and preliminary evaluation, our results demonstrate the potential of RL to position content that maximizes the reward for users on the go. We further identify future directions for research that could harness the power of RL for personalized and optimized UI and content placement in MR.

AI is becoming a part of our daily lives, from approving loans to diagnosing diseases. AI model outputs are used to make increasingly important decisions, based on smart algorithms and data. But if we can't understand these decisions, how can we trust them? One approach to making AI decisions more understandable is to use models that are inherently interpretable. These are models that are designed in such a way that consumers of the model outputs can infer the model's behaviour by reading the parameters of the model. Popular inherently interpretable models include Decision Trees and Linear Regression.

The rapid growth of machine learning has spurred legislative initiatives such as ``the Right to be Forgotten,'' allowing users to request data removal. In response, ``machine unlearning'' proposes the selective removal of unwanted data without the need for retraining from scratch. While the Neural-Tangent-Kernel-based (NTK-based) unlearning method excels in performance, it suffers from significant computational complexity, especially for large-scale models and datasets. Our work introduces ``Fast-NTK,'' a novel NTK-based unlearning algorithm that significantly reduces the computational complexity by incorporating parameter-efficient fine-tuning methods, such as fine-tuning batch normalization layers in a CNN or visual prompts in a vision transformer. Our experimental results demonstrate scalability to much larger neural networks and datasets (e.g., 88M parameters; 5k images), surpassing the limitations of previous full-model NTK-based approaches designed for smaller cases (e.g., 8M parameters; 500 images). Notably, our approach maintains a performance comparable to the traditional method of retraining on the retain set alone. Fast-NTK can thus enable for practical and scalable NTK-based unlearning in deep neural networks.

JPMorgan Chase & Co. is restricting employees from using ChatGPT, according to a person familiar with the matter. The bank didn’t restrict usage of the popular artificial-intelligence chatbot because of any particular incident, the person said. It couldn’t be determined how many employees were using the chatbot or for what functions they were using it.

Reid Simmons has stopped trying to guess what students will come up with next. As head of Carnegie Mellon University's undergraduate program in artificial intelligence, Simmons watches what some of the most creative minds are doing with AI, and they never cease to amaze him. And now as the director of the newly opened JPMorgan Chase & Co. AI Maker Space, Simmons will have a front row seat for collaborative and transformative developments. "We want students from all over the university -- from engineering, business and fine arts -- to come and use their creativity to make interesting things happen," Simmons said. "Giving students the freedom to let their imaginations run wild is really what this space is all about."

The Corporate & Investment Banking Production Management Artificial Intelligence Operations has a mission to change how we support/manage the environment by leveraging new technology like AI/ML. The team applies AI to solve open ended problems that align state of the art AI solutions with enterprise scale challenges. In doing so, the team builds software systems, AI models, technology process and intelligent frameworks that minimize the technology risk, increase operational efficiency and increase the investment efficacy in general. In this particular instance, we are looking for a Data Scientist to join the team whose mission is to combine advanced analytical and quantitative techniques with technology business acumen to serve the technology portfolio of solutions. As a junior level Machine Learning Software Engineer in the Corporate & Investment Bank, you will be responsible for modeling complex problems, discover insights, manipulate terabytes of data and build cutting edge hybrid AI products that solve high impact and big scale problems through statistical modeling, machine learning, visualization and story telling that increases the operational value of our technology portfolio.

Reid Simmons has stopped trying to guess what students will come up with next. As head of Carnegie Mellon University's undergraduate program in artificial intelligence, Simmons watches what some of the most creative minds are doing with AI, and they never cease to amaze him. And now as the director of the newly opened JPMorgan Chase & Co. AI Maker Space, Simmons will have a front row seat for collaborative and transformative developments. "We want students from all over the university -- from engineering, business and fine arts -- to come and use their creativity to make interesting things happen," Simmons said. "Giving students the freedom to let their imaginations run wild is really what this space is all about."

Infrastructure Lifecycle management (ILM) is responsible for managing the Line of business Infrastructure requirements for server and storage products end to end from Demand management to Decommissioning. The main functions involved are the Supply Chain Operations, Customer Engagement and Experience, Infrastructure Build, Refresh and Decommissioning. As a Demand Planner you'll call on your experience to understand customer infrastructure requirements and verify that projects have been effectively planned for. As a team, you and your peers will be responsible for collecting and analyzing requirements data with service owners, tracking infrastructure provisioning through the supply chain process. And while you will be part of a tight-knit team of engineers who share your passion for technology, you'll also gain access the best minds in the business-both as part of the JPMorgan Chase & Co. global technology community, and through our partnerships with some of the most important tech firms in the world.

One of the most troubling trend narratives on the rise today is that of the direction intersection of tech algorithms and machine learning with that of housing and credit checks. Whether we use our residences as a much-needed retreat after work or that which is leveraged for both home and work, this area is critical because it provides our very foundation, literally and figuratively. Concerns around housing, whether redlining or other barriers, have always been issues in this country, however the volume on the topic is growing because we are relying more and more emerging technology such as artificial intelligence to make very final decisions on crucial life plays. Such usage is important to examine because technology never exists in vacuum. Tech intersects with cultural trends, various business agendas, subconscious and conscious cultural bias, therefore, it can and is taking harmful shape across a growing number of demographics.