Inthese methods, for an input, an explanation is in the form of a contrast point differing in very few features from the original input and lying inadifferent class. Otherworks tryto build globally interpretable models likedecision trees and rule lists based onthe datausing actual labels orbased ontheblack-box models predictions.

US District Court Judge Vince Chhabria spent several hours grilling lawyers from both sides after they each filed motions for partial summary judgment, meaning they want Chhabria to rule on specific issues of the case rather than leaving each one to be decided at trial. The authors allege that Meta illegally used their work to build its generative AI tools, emphasizing that the company pirated their books through "shadow libraries" like LibGen. Kadrey v. Meta is one of the dozens of lawsuits filed against AI companies that are winding through the US legal system. While the authors were heavily focused on the piracy element of the case, Chhabria spoke emphatically about his belief that the big question is whether Meta's AI tools will hurt book sales and otherwise cause the authors to lose money. "If you are dramatically changing, you might even say obliterating, the market for that person's work, and you're saying that you don't even have to pay a license to that person to use their work to create the product that's destroying the market for their work--I just don't understand how that can be fair use," he told Meta lawyer Kannon Shanmugam.

Humans are inherently risk-averse: We spend our days calculating routes and routines, taking precautionary measures to avoid disease, danger, and despair. Still, our measures for controlling the inner workings of our biology can be a little more unruly. With that in mind, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better predicting health outcomes: a machine learning model that can estimate, from the electrical activity of their heart, a patient's risk of cardiovascular death. The system, called "RiskCardio," focuses on patients who have survived an acute coronary syndrome (ACS), which refers to a range of conditions where there's a reduction or blockage of blood to the heart. Using just the first 15 minutes of a patient's raw electrocardiogram (ECG) signal, the tool produces a score that places patients into different risk categories.

"To achieve that goal, availability of good data, of the right data, and availability of that to the right people and systems is very, very critical. So that forms the data strategy for any enterprise today," says chief architect for data and AI services at Kyndryl, Sundar Shanmugam. Getting the most out of digital transformation investments means evaluating and optimizing agility throughout an enterprise to drive actionable insights, says Shanmugam. A strong data governance framework also goes a long way in keeping data high-quality. Often data governance primarily serves regulatory requirements.

As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, have different explanation needs. To address these needs, in 2019, we created AI Explainability 360 (Arya et al. 2020), an open source software toolkit featuring ten diverse and state-of-the-art explainability methods and two evaluation metrics. This paper examines the impact of the toolkit with several case studies, statistics, and community feedback. The different ways in which users have experienced AI Explainability 360 have resulted in multiple types of impact and improvements in multiple metrics, highlighted by the adoption of the toolkit by the independent LF AI & Data Foundation. The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.

A technician took X-rays of Gura's chest but couldn't make heads or tails of the resulting images. "We had to wait a few days for the radiologist to come from the big city to diagnose what I had," delaying his treatment, Gura says. "I asked: How come we don't have a centralized reference database of all the X-rays for people like me so that I can just run a computer vision comparison against it and get my own sense of what's going on in my body? That was the seed of the idea." The idea became Zebra Medical Vision, which transforms vast amounts of medical imaging data into actionable insights, allowing doctors to better detect diseases, tumors and fractures while giving patients more information about their health.

Scientists in the US are using artificial intelligence (AI) to gauge a patient's risk of dying from heart disease. A team from the Massachusetts Institute of Technology created a system called RiskCardio. The technology was made for patients with acute coronary syndrome (ACS), which covers a range of conditions that suddenly reduce blood flow to the heart. RiskCardio works off just 15 minutes of a patient's'raw electrocardiogram (ECG) signal', which records the heart's rhythm and electrical activity. It then draws on a sample of ECG data to sort that particular patient into a'risk category'.

Humans are inherently risk-averse: We spend our days calculating routes and routines, taking precautionary measures to avoid disease, danger, and despair. Still, our measures for controlling the inner workings of our biology can be a little more unruly. With that in mind, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better predicting health outcomes: a machine learning model that can estimate, from the electrical activity of their heart, a patient's risk of cardiovascular death. The system, called "RiskCardio," focuses on patients who have survived an acute coronary syndrome (ACS), which refers to a range of conditions where there's a reduction or blockage of blood to the heart. Using just the first 15 minutes of a patient's raw electrocardiogram (ECG) signal, the tool produces a score that places patients into different risk categories.

Humans are inherently risk-averse: We spend our days calculating routes and routines, taking precautionary measures to avoid disease, danger, and despair. Still, our measures for controlling the inner workings of our biology can be a little more unruly. With that in mind, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better predicting health outcomes: a machine learning model that can estimate, from the electrical activity of their heart, a patient's risk of cardiovascular death. The system, called "RiskCardio," focuses on patients who have survived an acute coronary syndrome (ACS), which refers to a range of conditions where there's a reduction or blockage of blood to the heart. Using just the first 15 minutes of a patient's raw electrocardiogram (ECG) signal, the tool produces a score that places patients into different risk categories.

Microsoft seeks to bring artificial intelligence and chatbots as a mainstream part of the workplace and it's taking steps to make it easier for partners to deliver AI capabilities directly into its popular Power BI reporting tool. At an event in San Francisco Wednesday, company officials revealed new AI capabilities coming to its Power BI self-service analytics tool with its new Azure Cognitive Services containers for partners without deep data-science development skills to deliver intelligent apps. Also looking to make it easier for partners to develop bots, Microsoft announced it has agreed to acquire XOXCO, a startup with expertise in conversational AI. Founded in 2013, XOXCO created the first commercial chatbot for Slack, a meeting scheduling app called Howdy. XOXCO is also known for its Botkit developer tool for building chatbot apps and providing native integration with messaging platforms.