The recruitment website is jazzy, awash with pictures of happy young workers, and festooned with upbeat mini-slogans such as insane speed, infinite curiosity and customer obsession. Read a bit lower, and there are promises of perks galore: competitive compensation, free meals, free gym membership, free health and dental care and so on. But then comes the catch. Each job ad contains a warning: Please don't join if you're not excited about working ~70 hrs/week in person with some of the most ambitious people in NYC. The website belongs to Rilla, a New York-based tech business which sells AI-based systems that allow employers to monitor sales representatives when they are out and about, interacting with clients. The company has become something of a poster child for a fast-paced workplace culture known as 996, also sometimes referred to as hustle culture or grindcore.

Artificial Intelligence is reshaping America's \$9.4 trillion labor market, with cascading effects that extend far beyond visible technology sectors. When AI transforms quality control tasks in automotive plants, consequences spread through logistics networks, supply chains, and local service economies. Yet traditional workforce metrics cannot capture these ripple effects: they measure employment outcomes after disruption occurs, not where AI capabilities overlap with human skills before adoption crystallizes. Project Iceberg addresses this gap using Large Population Models to simulate the human-AI labor market, representing 151 million workers as autonomous agents executing over 32,000 skills and interacting with thousands of AI tools. It introduces the Iceberg Index, a skills-centered metric that measures the wage value of skills AI systems can perform within each occupation. The Index captures technical exposure, where AI can perform occupational tasks, not displacement outcomes or adoption timelines. Analysis shows that visible AI adoption concentrated in computing and technology (2.2% of wage value, approx \$211 billion) represents only the tip of the iceberg. Technical capability extends far below the surface through cognitive automation spanning administrative, financial, and professional services (11.7%, approx \$1.2 trillion). This exposure is fivefold larger and geographically distributed across all states rather than confined to coastal hubs. Traditional indicators such as GDP, income, and unemployment explain less than 5% of this skills-based variation, underscoring why new indices are needed to capture exposure in the AI economy. By simulating how these capabilities may spread under scenarios, Iceberg enables policymakers and business leaders to identify exposure hotspots, prioritize investments, and test interventions before committing billions to implementation

Understanding how events in a scenario causally connect with each other is important for effectively modeling and reasoning about events. But event reasoning remains a difficult challenge, and despite recent advances, Large Language Models (LLMs) still struggle to accurately identify causal connections between events. This struggle leads to poor performance on deeper reasoning tasks like event forecasting and timeline understanding. To address this challenge, we investigate the generation of causal event graphs (e.g., A enables B) as a parallel mechanism to help LLMs explicitly represent causality during inference. This paper evaluates both how to generate correct graphs as well as how graphs can assist reasoning. We propose a collaborative approach to causal graph generation where we use LLMs to simulate experts that focus on specific semantic relations. The experts engage in multiple rounds of discussions which are then consolidated by a final expert. Then, to demonstrate the utility of causal graphs, we use them on multiple downstream applications, and also introduce a new explainable event prediction task that requires a causal chain of events in the explanation. These explanations are more informative and coherent than baseline generations. Finally, our overall approach not finetuned on any downstream task, achieves competitive results with state-of-the-art models on both forecasting and next event prediction tasks.

Which technological linkages affect the sector's ability to innovate? How do these effects transmit through the technology space? This paper answers these two key questions using novel methods of text mining and network analysis. We examine technological interdependence across sectors over a period of half a century (from 1976 to 2021) by analyzing the text of 6.5 million patents granted by the United States Patent and Trademark Office (USPTO), and applying network analysis to uncover the full spectrum of linkages existing across technology areas. We demonstrate that patent text contains a wealth of information often not captured by traditional innovation metrics, such as patent citations. By using network analysis, we document that indirect linkages are as important as direct connections and that the former would remain mostly hidden using more traditional measures of indirect linkages, such as the Leontief inverse matrix. Finally, based on an impulse-response analysis, we illustrate how technological shocks transmit through the technology (network-based) space, affecting the innovation capacity of the sectors.

The staggering interest in AI has come to the surface (again) in 2022 with the great success of systems like ChatGPT for a general-purpose chatbot assistant, and DALLE-2 for image generation from text descriptions (as the image above). With this wide mainstream adoption of AI, it's very important to clarify these 5 common misconceptions about AI. Contrary to popular belief, artificial intelligence is not a new technology. It has been in development for over 70 years, beginning in the mid-20th century. There have been several AI "winters," where funding and interest in the field dwindled, but the success of deep learning has helped to revitalize the field.

Researchers have noted that Austrian talent has increasingly gained attention from Silicon Valley tech corporations in prior years, leading to local AI operations at Amazon, Meta (Facebook), and Snap. The initial wave of AI Hubs launched primarily focused on doing AI research in Austria with the help of local expertise and little involvement with the neighborhood. This trend gained traction over the last year, resulting in the development of AI Centers of Excellence, the establishment of AI businesses' European offices, and the incorporation of foreign startups in Austria. Here are some of the cool artificial intelligence startups/businesses that are innovating the Artificial Intelligence market in various ways, but they are all outstanding businesses worth following. Adverity, founded in 2015, assesses and visualizes expenses, performance, and returns. They also identify anomalies and suggest the best money to spend on each marketing channel. The product suite is utilized by well-known companies like Red Bull, IKEA, and Zurich Insurance and is accessible to agencies, brands, and e-commerce providers.

Researchers have called upon the community of ethical hackers to work together to help prevent the looming'crisis of trust' that is set to impact artificial intelligence. Specifically, the researchers are putting forward the need fora global hacker'red team', macheted by the incentive of rewards, for hunting algorithmic biases. This type of activity is needed to help reduce the so-termed'tech-lash' that artificial intelligence faces unless firm measures are taken to increase public trust. The reason for proposing this issue is because technology sector is facing concerns that underpin advances in artificial intelligence, such as the progress with driverless cars and autonomous drones. There is also concern with aspects like social media algorithms that spread misinformation and provoke political turmoil.

As a VP at F5, Christine Puccio is a leader in the technology industry and a champion for diversity and inclusion. Imagine you've just turned on your computer and it suddenly tells you all about itself. What it is, what it can do and all its capabilities. "The more humans share with me, the more I learn," it says. "I come in many forms as artificial intelligence. Many companies utilize me to optimize their tasks. I can continue to learn on my own. I am making predictions on your life right now."

Vietnam has ranked 62nd in the 2021 Government Artificial Intelligence (AI) Readiness Index, jumping 14 places since the year before. The index is an annual report released by a UK-based research group in collaboration with Canada's International Development Research Centre (IDRC). With an overall score of 51.82 out of 100, Vietnam ranked 6th in the Association of Southeast Asia Nations (ASEAN). This is the first year its score has surpassed the global average of 47.42. The index, published yearly, ranks countries based on 42 indicators across three pillars: government, the technology sector, and data and infrastructure.

The U.S. federal government is the most prepared among 160 nations to use artificial intelligence in the public services they provide, according to a report released this week by London-based consultancy Oxford Insights. The annual report, called the 2021 Government AI Readiness Index, rates countries based on 42 indicators--including software spending and industry investment in emerging technologies--across three pillars: government, technology sector, and data and infrastructure. Buoyed by the unrivaled maturity of its technology sector, the U.S. topped the rankings, followed by Singapore, which topped the government pillar due to its digital capacity. The United Kingdom, Finland and the Netherlands finished third, fourth and fifth, respectively. "Governments stand to gain from the vast applications of recent developments in AI," said Richard Stirling, CEO and co-founder of Oxford Insights. "Those governments who take a strategic approach to harnessing AI within government and promoting their national AI sector are likely to see the greatest benefits.