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United States Customs and Border Protection is asking tech companies to send pitches for a real-time facial recognition tool that would take photos of every single person in a vehicle at a border crossing, including anyone in the back seats, and match them to travel documents, according to a document posted in a federal register last week. The request for information, or RIF, says that CBP already has a facial recognition tool that takes a picture of a person at a port of entry and compares it to travel or identity documents that someone gives to a border officer, as well as other photos from those documents already "in government holdings." "Biometrically confirmed entries into the United States are added to the traveler's crossing record," the document says. An agency under the Department of Homeland Security, CBP says that its facial recognition tool "is currently operating in the air, sea, and land pedestrian environments." The agency's goal is to bring it to "the land vehicle environment."

Large Language Model (LLM)-empowered multi-agent systems extend the cognitive boundaries of individual agents through disciplined collaboration and interaction, while constructing these systems often requires labor-intensive manual designs. Despite the availability of methods to automate the design of agentic workflows, they typically seek to identify a static, complex, one-size-fits-all system, which, however, fails to dynamically allocate inference resources based on the difficulty and domain of each query. To address this challenge, we shift away from the pursuit of a monolithic agentic system, instead optimizing the \textbf{agentic supernet}, a probabilistic and continuous distribution of agentic architectures. We introduce MaAS, an automated framework that samples query-dependent agentic systems from the supernet, delivering high-quality solutions and tailored resource allocation (\textit{e.g.}, LLM calls, tool calls, token cost). Comprehensive evaluation across six benchmarks demonstrates that MaAS \textbf{(I)} requires only $6\sim45\%$ of the inference costs of existing handcrafted or automated multi-agent systems, \textbf{(II)} surpasses them by $0.54\%\sim11.82\%$, and \textbf{(III)} enjoys superior cross-dataset and cross-LLM-backbone transferability.

Due to the increased number of parameters and data in the pre-trained model exceeding a certain level, a foundation model (e.g., a large language model) can significantly improve downstream task performance and emerge with some novel special abilities (e.g., deep learning, complex reasoning, and human alignment) that were not present before. Foundation models are a form of generative artificial intelligence (GenAI), and Model-as-a-Service (MaaS) has emerged as a groundbreaking paradigm that revolutionizes the deployment and utilization of GenAI models. MaaS represents a paradigm shift in how we use AI technologies and provides a scalable and accessible solution for developers and users to leverage pre-trained AI models without the need for extensive infrastructure or expertise in model training. In this paper, the introduction aims to provide a comprehensive overview of MaaS, its significance, and its implications for various industries. We provide a brief review of the development history of "X-as-a-Service" based on cloud computing and present the key technologies involved in MaaS. The development of GenAI models will become more democratized and flourish. We also review recent application studies of MaaS. Finally, we highlight several challenges and future issues in this promising area. MaaS is a new deployment and service paradigm for different AI-based models. We hope this review will inspire future research in the field of MaaS.

Maidaan is a'Blockchain Property-Technology Startup' on the verge of incubating various innovative real estate products, which facilitates the vast needs and demands of the fast-paced real estate sector of Pakistan. BaaS is a cloud-based decentralized infrastructure and management platform for digitizing the record keeping process for the real estate sector. Maidaan's BaaS platform will act as a web-3 host, running the back-end operation when it comes to authenticating transactions. Whenever a transaction or transfer is performed through the BaaS network, it is stored on a decentralized transparent digital ledger for all the stakeholder authentication. Maidaan's Rating as a Service (RaaS) helps make investing in real estate an ease for all by making it transparent for investors to make ethical data driven and vetted investment decisions.

This article continues a series of articles tackling the most frightening idea in the world of production ML: putting the damn thing in production. In previous stories, we saw two different approaches to designing a Machine Learning (ML) powered application. First, we examined why you'd want to keep your model within your web server and why you should not do it. Keeping your model side-by-side with your core business logic is a must as you experiment with different ideas and want quick feedback from a trusted circle of testers, but it falls short when deploying in production. The first solution we discussed is a very simple technique that permits us to separate the model from our web server.

Wejo, a global leader in Smart Mobility for Good and cloud and software solutions for connected, electric, and autonomous vehicles, announced it has joined the MONET Consortium, an organization actively promoting collaboration and innovation for mobility services in Japan. As part of the MONET Consortium, Wejo will have the opportunity to work with companies selected from the hundreds of diverse and industry-leading members to drive forward mobility innovation and the mobility-as-a-service (MaaS) market, which could be worth $61 billion in 2030, according to Yano Research Institute. Bringing its groundbreaking solutions to the table, Wejo will provide new perspectives and ideas to the collective conversation while extending its influence in Japan which has the 3rd largest economy in the world and a high level of urbanization, making it naturally incentivized to develop smart mobility innovations. Japan, according to Statista's "Automobile Sector in Japan" report, also produces 8.1 million vehicles per year, which aligns with Wejo's aim to make a more globalized impact with its connected vehicle data and Smart Mobility for Good technology. "With the anticipated growth of MaaS offerings in Japan, we see the potential for a nearly three-billion-dollar addressable market by 2030 for Wejo Smart Mobility for Good products and services," said Richard Barlow, founder and CEO at Wejo. "We're honored to be a part of the MONET Consortium and be part of the conversations that will help accelerate mobility innovation in Japan."

The development and deployment of "educational pathways and materials" for healthcare staff on the use of AI is the main recommendation from an NHS report. The'Understanding Healthcare Workers' Confidence in AI' report is the first of two reports to be released in light of the Topol Review in 2019 which recommended the use of digital technologies such as AI and robotics to achieve digital transformation. The report, which was developed by Health Education England and NHS AI Lab, explores the confidence healthcare workers have in AI and what could drive that to help support the further implementation of AI within the NHS. It suggests that clinicians require training and education opportunities to help manage the gap between their opinion or intuition on a patient's condition and the recommendations made by AI technology. "The main recommendation of this report is therefore to develop and deploy educational pathways and materials for healthcare professionals at all career points and in all roles, to equip the workforce to confidently evaluate, adopt and use AI," the report states.

Artificial intelligence and Machine Learning are taking more and more relevance in businesses to make critical decisions, and create innovative products. Let's face it; AI already exists in our daily routine for every action we do. Think about the lovely conversations with your Siri or Alexa to automatize your home devices, translate from one language to another, set reminders, listen to updated weather forecasts, and, eventually, make coffee for you. Just imagine the massive impact AI and Machine Learning have on our lives and jobs. As our demands towards technology grow and change, Machine Learning and AI are arranged to surprise us with exciting new trends.

Lucy, the virtual being AI from Fable Studio's Wolves in the Walls virtual reality experience, is getting around. Now she has busted the fourth wall and moved into the real, or virtual world, of the virtual Sundance Film Festival. This week, Lucy appeared as a guest at Sundance. She appeared in Zoom sessions with other attendees, and they were able to quiz her. As an artificial intelligence character, she responded with her own comments and views on watching expressionist movies at Sundance.