Large Language Model Multi-Agent Systems (LLM-MAS) have greatly progressed in solving complex tasks. It communicates among agents within the system to collaboratively solve tasks, under the premise of shared information. However, when agents' collaborations are leveraged to perform multi-person tasks, a new challenge arises due to information asymmetry, since each agent can only access the information of its human user.

Data quality monitoring is a core challenge in modern information processing systems. While many approaches to detect data errors or shifts have been proposed, few studies investigate the mechanisms governing error generation. We argue that knowing how errors were generated can be key to tracing and fixing them. In this study, we build on existing work in the statistics literature on missing values and propose MechDetect, a simple algorithm to investigate error generation mechanisms. Given a tabular data set and a corresponding error mask, the algorithm estimates whether or not the errors depend on the data using machine learning models. Our work extends established approaches to detect mechanisms underlying missing values and can be readily applied to other error types, provided that an error mask is available. We demonstrate the effectiveness of MechDetect in experiments on established benchmark datasets.

OpenAI's Atlas Browser Takes Direct Aim at Google Chrome The new ChatGPT-powered web browser is OpenAI's boldest play yet to reinvent how people use the web. OpenAI announced on Tuesday it's rolling out a new internet browser called Atlas that integrates directly with ChatGPT . Atlas includes features like a sidebar window people can use to ask ChatGPT questions about the web pages they visit. "We think that AI represents a rare, once a decade opportunity to rethink what a browser can be about," OpenAI CEO Sam Altman said during a livestream announcing Atlas. "Tabs were great, but we haven't seen a lot of browser innovation since then."

Breakthroughs, discoveries, and DIY tips sent every weekday. Face tattoos, typically reserved for rappers and rockers, could one day be used to make sure everyday office workers aren't overexerting themselves. This week, researchers from the University of Texas unveiled a new, removable, sticker-like electronic face wearable that uses electroencephalography (EEG) and electrooculography (EOG) to constantly monitor changes in the wearer's mental strain as they complete tasks. In theory, the e-tattoo could ensure air traffic controllers or other workers in similarly high-stress environments stay in the "Goldilocks zone" of mental exertion: not so much that it causes errors, but not so little that it leads to boredom and distraction. And unlike regular face tats, these can come off at the end of the day.

Large Language Model Multi-Agent Systems (LLM-MAS) have greatly progressed in solving complex tasks. It communicates among agents within the system to collaboratively solve tasks, under the premise of shared information. However, when agents' collaborations are leveraged to perform multi-person tasks, a new challenge arises due to information asymmetry, since each agent can only access the information of its human user. To address this, we propose a new MAS paradigm termed iAgents, which denotes Informative Multi-Agent Systems. In iAgents, the human social network is mirrored in the agent network, where agents proactively exchange human information necessary for task resolution, thereby overcoming information asymmetry.

ChatGPT-like AI systems will impact 80 percent of US jobs, with personal financial advisors and brokers, insurers and data processors at the top of the list. The warning comes from researchers at OpenAI and the University of Pennsylvania, who investigated whether the technology could complete tasks faster than humans. The team found that about 15 percent of all worker tasks could be completed significantly faster by AI and with the same level of quality. The warning comes from researchers at OpenAI and the University of Pennsylvania, who investigated whether the technology could complete tasks faster than humans. 'Exposure' means how much a job will be impacted by AI Fears of software eliminating human jobs have recently made waves across the globe following the launch of ChatGPT in November and its ability to perform eerily-human professional tasks such as writing emails and resumes.

In today's market, automation in banking has become a crucial factor for banks to remain competitive. Banks are leveraging automation to offer personalised services to customers, reduce operational costs, and improve the speed and accuracy of their processes. Automation technologies like Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML) are being used to automate repetitive and time-consuming tasks such as data entry, account opening, loan processing, and customer service. One of the significant benefits of automation in banking is improved customer experience. Banks are using chatbots and virtual assistants to provide 24/7 customer service, allowing customers to get instant assistance without having to wait for a human representative.

Google researchers believe that natural language processing and AI will enable robots to create their code to take action against new instructions. Google is currently testing a system that allows robots to write their code, follow instructions, and complete tasks. This is a way to streamline reprogramming policies for every new task. It can be tedious and time-consuming, and it requires domain experts. Staff could interact with robots on smart factory floors using simple commands without needing to write complicated code. Google researchers developed language modeling programs called Code as Policies (CaP). This code-writing AI system can generate new code for new instructions. Google robotics scientists stated in a blog post that “Given natural languages, instructions, current language models can write not only generic code, but, as we have discovered, code capable of controlling robot actions.” Google researchers have combined large language models with everyday robots to better respond to complex and abstract human requests. According to the Google team, CaP will enable a single system

Artificial intelligence (AI), which stands for systems or machines that resemble human intellect to complete tasks and may iteratively improve themselves depending on the information they acquire, is most commonly used to describe such systems or machines. AI may appear in a variety of ways. Applications that carry out complicated activities that formerly needed human input, such as playing chess or chatting with clients online, have come to be known as artificial intelligence (AI). The phrase and its subfields, such as machine learning and deep learning, are frequently used interchangeably. For instance, machine learning focuses on creating systems that develop new skills or enhance existing ones based on the data they ingest.

Are you an IT leader feeling stuck in your digital transformation goals? One of the most challenging questions in digital transformation is how to go from vision to execution. You may not be as far behind as you think. You simply need to adopt a better approach. One approach that will make the whole process easier for you to achieve your digital transformation goals is called hyperautomation.