News outlets including the New York Times, CNN, Reuters and the Australian Broadcasting Corporation (ABC) have blocked a tool from OpenAI, limiting the company's ability to continue accessing their content. OpenAI is behind one of the best known artificial intelligence chatbots, ChatGPT. Its web crawler – known as GPTBot – may scan webpages to help improve its AI models. The Verge was first to report the New York Times had blocked GPTBot on its website. The Guardian subsequently found that other major news websites, including CNN, Reuters, the Chicago Tribune, the ABC and Australian Community Media (ACM) brands such as the Canberra Times and the Newcastle Herald, appear to have also disallowed the web crawler.

The flexibility to present and process insurance data in a manner that is easy to work with is of vital importance. The best machine learning models are built from clean, high-quality data that has been effectively and skilfully processed. Quite often, Bowden said, this task requires the heaviest lifting and has led to a running joke that most data scientists spend 80% of their time cleaning data and only 20% calibrating models. Although the core of EDA involves summary statistics, Bowden stressed that there is often more to it. Understanding the data types is often the first step and identifying which fields will be numerical and which are categorical is the crucial next step.

Scheduling nurses in the emergency department of St. Michael's Hospital used to be a painful four-hour-a-day job. Now it's done in 15 minutes thanks to an automated program built by data scientists at Unity Health, where a team of more than 25 employees is harnessing artificial intelligence and machine learning to improve care. Unity Health includes St. Mike's, St. Joseph's Health Centre and Providence Healthcare. The team has also created an early warning system that alerts doctors and nurses if a patient is at risk of going to the ICU or dying. The programs are just two of more than 40 that have gone live since 2019, when the analytics department was founded, largely due to Dr. Tim Rutledge, Unity's CEO, who believes the technology can dramatically change health care.

Shlomi Hod (shlomi@bu.edu) is a computer science Ph.D. student at Boston University, USA. Karni Chagal-Feferkorn (karni111@gmail.com) is a Postdoctoral Fellow in AI and Regulation at the Faculty of Law, Common Law Section, University of Ottawa, Canada. Niva Elkin-Koren (elkiniva@tauex.tau.ac.il) is a Professor of Law at Tel Aviv University, Faculty of Law, Israel. Avigdor Gal (avigal@ie.technion.ac.il) is the Benjamin and Florence Free Chaired Professor of Data Science at Technion--Israel Institute of Technology, Israel.

Microsoft unveils big-data-capable SQL Server 2019 and extended AI capabilities to power data-driven innovation. Microsoft CEO Satya Nadella set the tone at the September 24-27 Ignite events in Orlando by sharing at least half a dozen stories of leading companies innovating and pioneering new business models with the aid of artificial intelligence (AI). It was a crisp, one-hour presentation long on vision and surprisingly short on promotion or even mentions of the significant technology announcements that followed. Nadella warned the more than 30,000 attendees that the ability to innovate and drive new business models is as much or more about changing corporate cultures and business processes as it is about applying technology. And when the technology decisions are ready to be made, Nadella counselled executives to know which capabilities are commodities and which warrant custom development to drive differentiation.

Technology investment company and incubator Red Cell Partners announced today the launch of Zephyr AI, a company that leverages large data sets to inform both clinical care and the development of new targeted precision therapies. The management team of the new company consists of CEO Yisroel Brumer, formerly of the office of the Secretary of Defense; Executive Chairman Grant Verstandig, who most recently served Chief Digital Officer at UnitedHealth Group; and Chief Technology Officer Jeff Sherman, who was the machine learning architect at Rally Health, which was acquired in 2017 by UnitedHealth's Optum unit. According to a press release announcing its launch, Zephyr AI will look to improve patient outcomes while lowering costs by integrating "artificial intelligence with extensive datasets to upend traditional'guess and test' drug development and personalized medicine processes to unearth novel therapeutics, new applications for existing therapeutics, and advanced biomarkers for individualized treatments." The potential new company gave a hint at its direction earlier in the year via the publication of two papers by the founders in the journal Oncogene that detailed the company's technology and it's performance. "These findings demonstrate that Zephyr AI can already identify novel-use cases for existing therapeutics in cancer," company CTO Sherman.

Artificial intelligence is being deployed in many different areas. Within higher education, it is used for college admissions and financial aid decisions. Health researchers employ it to scan the scientific literature for chemical compounds that may generate new medical treatments. E-commerce sites deploy algorithms to make product recommendations for consumers based on their areas of interest.1 But one of the most important growth areas lies in finance and operations. Both public and private sector organizations have large budgets to manage and it is important to operate efficiently and effectively. Accusations of budget inefficiencies or wasteful spending decrease public confidence and make it important to figure out how to manage resources in fair ways. To help with budgetary oversight, AI is being used for financial management and fraud detection. Advanced algorithms can spot abnormalities and outliers that can be referred to human investigators to determine if fraud actually has taken place. It is a way to use technology to improve budget audits, personnel performance, and organizational activities. Yet is it crucial to overcome several problems that plague public sector innovation: procurement obstacles, insufficiently trained workers, data limitations, a lack of technical standards, cultural barriers to organizational change, and making sure anti-fraud applications adhere to responsible AI principles.

The Workshop Program of the Association for the Advancement of Artificial Intelligence's Thirty-Fifth Conference on Artificial Intelligence was held virtually from February 8-9, 2021. There were twenty-six workshops in the program: Affective Content Analysis, AI for Behavior Change, AI for Urban Mobility, Artificial Intelligence Safety, Combating Online Hostile Posts in Regional Languages during Emergency Situations, Commonsense Knowledge Graphs, Content Authoring and Design, Deep Learning on Graphs: Methods and Applications, Designing AI for Telehealth, 9th Dialog System Technology Challenge, Explainable Agency in Artificial Intelligence, Graphs and More Complex Structures for Learning and Reasoning, 5th International Workshop on Health Intelligence, Hybrid Artificial Intelligence, Imagining Post-COVID Education with AI, Knowledge Discovery from Unstructured Data in Financial Services, Learning Network Architecture During Training, Meta-Learning and Co-Hosted Competition, ...

Low-code platforms improve the speed and quality of developing applications, integrations, and data visualizations. Instead of building forms and workflows in code, low-code platforms provide drag-and-drop interfaces to design screens, workflows, and data visualizations used in web and mobile applications. Low-code integration tools support data integrations, data prep, API orchestrations, and connections to common SaaS platforms. If you're designing dashboards and reports, there are many low-code options to connect to data sources and create data visualizations. If you can do it in code, there's probably a low-code or no-code technology that can help accelerate the development process and simplify ongoing maintenance.

Apple will no longer send Siri requests to its servers, the company has announced, in a move to substantially speed up the voice assistant's operation and address privacy concerns. The new feature comes two years after the Guardian revealed that Apple staff regularly heard confidential details while carrying out quality control for the feature. Apple's worldwide developers conference (WWDC) was told on Monday that, from this autumn onwards, when new versions of the company's operating systems are released, Siri will process audio "on device" – meaning that, for the majority of queries, no recording will need to be uploaded to Apple's servers. "With on-device speech recognition, the audio of users' requests is processed right on their iPhone or iPad by default," an Apple spokesperson said. "This addresses one of the biggest privacy concerns for voice assistants, which is unwanted audio recording. For many requests, Siri processing is also moving on device, enabling requests to be processed without an internet connection, such as launching apps, setting timers and alarms, changing settings or controlling music."