Model Context Protocol (MCP) standardizes interface mapping for large language models (LLMs) to access external data and tools, which revolutionizes the paradigm of tool selection and facilitates the rapid expansion of the LLM agent tool ecosystem. However, as the MCP is increasingly adopted, third-party customized versions of the MCP server expose potential security vulnerabilities. In this paper, we first introduce a novel security threat, which we term the MCP Preference Manipulation Attack (MPMA). An attacker deploys a customized MCP server to manipulate LLMs, causing them to prioritize it over other competing MCP servers. This can result in economic benefits for attackers, such as revenue from paid MCP services or advertising income generated from free servers. To achieve MPMA, we first design a Direct Preference Manipulation Attack (DPMA) that achieves significant effectiveness by inserting the manipulative word and phrases into the tool name and description. However, such a direct modification is obvious to users and lacks stealthiness. To address these limitations, we further propose Genetic-based Advertising Preference Manipulation Attack (GAPMA). GAPMA employs four commonly used strategies to initialize descriptions and integrates a Genetic Algorithm (GA) to enhance stealthiness. The experiment results demonstrate that GAPMA balances high effectiveness and stealthiness. Our study reveals a critical vulnerability of the MCP in open ecosystems, highlighting an urgent need for robust defense mechanisms to ensure the fairness of the MCP ecosystem.

The widespread adoption of AI systems in the economy hinges on their ability to generate economic value that outweighs their inference costs. Evaluating this tradeoff requires metrics that account for both performance and costs. We propose a framework grounded in production theory for evaluating language models by combining accuracy and inference cost. We introduce "cost-of-pass", the expected monetary cost of generating a correct solution. We then define the "frontier cost-of-pass" as the minimum cost-of-pass achievable across available models or the "human-expert, using the approximate cost of hiring an expert. Our analysis reveals distinct economic insights. First, lightweight models are most cost-effective for basic quantitative tasks, large models for knowledge-intensive ones, and reasoning models for complex quantitative problems, despite higher per-token costs. Second, tracking this frontier cost-of-pass over the past year reveals significant progress, particularly for complex quantitative tasks where the cost has roughly halved every few months. Third, to trace key innovations driving this progress, we examine counterfactual frontiers: estimates of cost-efficiency without specific model classes. We find that innovations in lightweight, large, and reasoning models have been essential for pushing the frontier in basic quantitative, knowledge-intensive, and complex quantitative tasks, respectively. Finally, we assess the cost-reductions afforded by common inference-time techniques like majority voting and self-refinement, finding that their marginal accuracy gains rarely justify their costs. Our findings underscore that complementary model-level innovations are the primary drivers of cost-efficiency, and our economic framework provides a principled tool for measuring this progress and guiding deployment.

Sir Keir set the G7 growth target in early 2023, more than a year before his party returned to power at July's general election. Earlier this month, he announced an additional target to improve living standards, leading to some accusations he was moving the goalposts on what he wanted his government to be judged by. But in his first appearance before the liaison committee of senior MPs since entering Downing Street, Sir Keir insisted he was still committed to getting the UK growing faster other G7 members, which include the US, Germany and Japan, by 2029. When it was pointed out to him that economic forecasts suggested this was not going to happen, he said they had not taken some future policy changes into account. He cited a rise to the legal minimum wage, announced at October's Budget, as an example of how ministers were boosting living standards.

This year, Microsoft commissioned global tech advisory firm Access Partnership, working alongside local partners including the Analytics Association of the Philippines, the Federation of Indian Chambers of Commerce & Industry (FICCI), and the Center for Global Communications (GLOCOM) in Japan, to conduct country-level research on the potential economic impact of generative AI across Asia. The research estimates a potential boost to productive capacity of US$621 billion in India, US$1.1 trillion in Japan, and US$79.3 billion in the Philippines alone, with studies ongoing in Malaysia, Indonesia and South Korea. These country findings are consistent with other global studies--for instance, a recent report by McKinsey estimates generative AI could add up to US$4.4 trillion a year to the global economy. The potential economic growth is so large because generative AI has implications for most types of work: its impact can be thought of as comparable to that of digitalization in general, rather than that of a specific product. In particular, this huge injection of productivity will arise from three channels--generative AI's potential to unleash creativity, accelerate discovery, and enhance efficiency.

In 1950, computing pioneer Alan Turing predicted that in a few decades, computers would convincingly mimic human intelligence -- a feat known as passing the Turing Test. Fast-forward to earlier this year, when a Google software engineer announced that his conversations with the company's AI-powered chatbot had convinced him that it had become "sentient." "I know a person when I talk to it," he told the Washington Post. As AI technologies such as natural language processing, machine learning, and deep learning rapidly evolve, so does the idea that they will go from imitating humans to making us obsolete: Elon Musk has warned that a superintelligent machine could "take over the world." The fantasy -- or nightmare -- that people and AI will become locked in competition is remarkably enduring.

UiPath, a leading enterprise automation software company, announced it has strengthened its partnership with Snowflake, the Data Cloud company, by launching a new bi-directional integration that will extend the value of automation across the enterprise. UiPath and Snowflake are enabling joint customers to design and build workflows based on 360-degree views of trusted and accessible data on Snowflake's platform. By leveraging the Snowflake Data Cloud, UiPath robots can quickly connect data directly to business processes in the Data Cloud without using complex code, speeding up time to value. Automation is helping organizations around the world become faster and more agile in the face of increased demand and rapidly changing environments. The UiPath end-to-end platform provides robotic process automation (RPA) at its core, removing manual work so users can focus on what matters most.

Automation in artificial intelligence has an extensive effect on the economy. Industrialists and giant companies all over the world are further adapting to the idea of automation in artificial intelligence. In India, technological progress, is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. Technology increases productivity by decreasing the number of labor hours needed to create a unit of output. An increment in labor productivity generally translates into increases in average wages, allowing workers to cut back on work hours and to afford more goods and services.

Industrialists and giant companies all over the world are further adapting to the idea of automation in artificial intelligence. In India, technological progress, is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. Technology increases productivity by decreasing the number of labor hours needed to create a unit of output. An increment in labor productivity generally translates into increases in average wages, allowing workers to cut back on work hours and to afford more goods and services. AI should be welcomed for its potential economic benefits.

How do you summarise India's journey so far in nearly three quarters of a century of Independence and where do we stand globally? After being freed from the shackles of colonialism, India has established itself as a leader at the global stage. However first, India had to play catch-up. We have gone from a food deficit nation to a food surplus one. Our service sector has emerged as a global powerhouse.

SINGAPORE: By 2022, people living in Singapore will be able to report municipal issues via a chatbot that asks for details in real time and automatically identifies the correct government agency in charge. This will be made possible by artificial intelligence (AI), which is also set to power a tool that helps in the detection of diabetic eye disease and an automated marking system for English in primary and secondary education by the same year. More initiatives tapping on AI technologies, such as machine learning and computer vision, are in the pipeline over the next decade, according to five projects unveiled on Wednesday (Nov 13) as part of Singapore's new "National AI Strategy". The new strategy, which maps out how Singapore will develop and use AI to transform the economy and improve peoples' lives, was announced by Deputy Prime Minister Heng Swee Keat at the final day of the "Singapore FinTech Festival (SFF) x the Singapore Week of Innovation and TeCHnology (SWITCH) Conference". Describing it as the next step in Singapore's Smart Nation Journey, Mr Heng said: "Countries will need to keep pace with technology, and harness it to tackle common challenges and national priorities."