We introduce DRBench, a benchmark for evaluating AI agents on complex, open-ended deep research tasks in enterprise settings. Unlike prior benchmarks that focus on simple questions or web-only queries, DRBench evaluates agents on multi-step queries (for example, ``What changes should we make to our product roadmap to ensure compliance with this standard?") that require identifying supporting facts from both the public web and private company knowledge base. Each task is grounded in realistic user personas and enterprise context, spanning a heterogeneous search space that includes productivity software, cloud file systems, emails, chat conversations, and the open web. Tasks are generated through a carefully designed synthesis pipeline with human-in-the-loop verification, and agents are evaluated on their ability to recall relevant insights, maintain factual accuracy, and produce coherent, well-structured reports. We release 15 deep research tasks across 10 domains, such as Sales, Cybersecurity, and Compliance. We demonstrate the effectiveness of DRBench by evaluating diverse DR agents across open- and closed-source models (such as GPT, Llama, and Qwen) and DR strategies, highlighting their strengths, weaknesses, and the critical path for advancing enterprise deep research. Code is available at https://github.com/ServiceNow/drbench.

Predicting company growth is crucial for strategic adjustment, operational decision-making, risk assessment, and loan eligibility reviews. Traditional models for company growth often focus too much on theory, overlooking practical forecasting, or they rely solely on time series forecasting techniques, ignoring interpretability and the inherent mechanisms of company growth. In this paper, we propose a machine learning-based prediction framework that incorporates an econophysics model for company growth. Our model captures both the intrinsic growth mechanisms of companies led by scaling laws and the fluctuations influenced by random factors and individual decisions, demonstrating superior predictive performance compared with methods that use time series techniques alone. Its advantages are more pronounced in long-range prediction tasks. By explicitly modeling the baseline growth and volatility components, our model is more interpretable.

OptiSol is a trusted digital transformation partner of global enterprises with expertise in Native Web, Native and Hybrid Mobile Applications, AI & ML services hosted on AWS and Azure Cloud and product implementations. OptiSol is a team of about 475 Agile smart employees with a development centre in India and global offices in the US, UK, Australia, Ireland, Sweden, and Dubai. They have been in operations for about 14 years now and we have built about 500 digital solutions. With 200 happy and satisfied clients across 24 countries, they are a top-rated service provider in peopleperhour and have an excellent review and rated high on Clutch and Guru. Our machine learning and data science practices have helped companies build custom solutions to improve business realities.

It's perhaps fair to say that AI and machine learning still occupy the realm of science-fiction within our minds, yet there's a strong chance that something using artificial intelligence already exists in most of our homes. Voice assistants like Siri and Alexa, while far from truly self-aware, are still clever enough to amaze first-time gadget users with their apparent grasp of language, problem-solving, and even humor. The possible applications of business-oriented AI can be on the spectacular side of things – or downright mundane. Harvard Business Review indicates that artificial intelligence comes in three forms that can aid any kind of company, namely, the automation of processes, pattern recognition in data, and engagement with customers. The latter refers mainly to things like chatbots and non-human customer service agents.

Hyderabad, November 23, 2020 –– Analytics Insight conducted a survey "The Global Artificial Intelligence Trends 2020" to understand the global adoption of Artificial Intelligence (AI) amongst enterprises and recognize the business perceptions of AI across sectors. Analytics Insight reached out to 2,200 professionals online located in different geographic regions across a wide range of industries to explore different views toward AI and its current implications among enterprises. Receiving 256 responses for the survey, Analytics Insight articulated a detailed report, which can be indicative of the market as a whole. Out of the 256 respondents, 48.5% were working at small-scale companies with the company size of fewer than 100 employees. About 29.8% of the respondents were employed at companies which had total employees ranging from 100-1000, while 21.7% of respondents had a company size of over 1000 employees.

Despite recent doom-and-gloom anecdotal reporting, a nationwide survey of 1,001 workers in the United States (U.S.) finds that 70% have an upbeat attitude toward new workplace technologies involving artificial intelligence (AI), such as chatbots, robots and augmented reality. Only 5% say they dislike new technology for putting their jobs at risk today. In fact, 32% of U.S. respondents feel AI will have a positive impact on their job in the next five years, increasing from 26% today. Just 19% of those surveyed express fear that AI/bots could swallow their jobs within the next decade. These findings stem from new research by Genesys (www.genesys.com),

Very interesting data compiled and analyzed by O'Reilly, using statistical models such as Lasso regression to predict salary based on different factors. It reminds me our own analysis based on simulated (but realistic) data, to assess whether having Python or R (or both) commands a bigger salary, and what is the extra boost provided by these skills, individually. The statistical model used was Jackknife regression, and it was designed for tutorial purposes. The O'Reilly survey is much bigger, based on real data, and it includes many factors, as well as factor selection. It uses standard statistical techniques which might be less robust than Jackknife regression.

Can computers understand an individual human's personality (and then, presumably, use that understanding to better target marketing messages)? It turns out that's no longer even a question: if you haven't yet played with CrystalKnows, be prepared for some weirdly accurate insights into yourself and those you know well, based on public Internet information. And, yes, Crystal advises you how to interact with others based on those insights, going so far as to suggest changes to your emails to better fit the style of the recipient. If there's a gap between this and letting a computer just manage the whole relationship without any human involvement, it's almost too small to worry about. So the real challenge isn't having the computer analyze your data; it's having enough data for the computer to analyze.