With AI technology on the rise, Generation Alpha children may never experience a doctor's appointment without the presence of medical artificial intelligence (AI). As an IEEE report revealed, their millennial parents are growing more comfortable with the technology. IEEE's study found that millennial parents across the globe are becoming increasingly comfortable with AI health technology for their Generation Alpha children. Most respondents noted that they would have "at least some trust" in AI tech. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) Globally, 56% of respondents noted that they had a "great deal of trust" in AI technologies for diagnosing and treating their sick children.

Google devices and apps have experienced serious outages that lasted for more than 12 hours and affected millions of users. The issue affected Google Home and Google Home Mini – speakers that respond to voice commands – as well as Chromecast – a device that plugs into a television and allows people to watch video content. Users were angry at both the length of the outage and the lack of information from Google about it, once it had been identified. Google has not given a reason why these devices went down, only apologising for the service problems and identifying a fix for the issues. The bug meant that when some Google Home owners asked a question of their speaker, it responded: "There was a glitch, try again in a few seconds." If they tried to reset the device, it would sometimes fail to reboot.

The young, educated, and tech-savvy population of Brazil stands out as the market having the greatest upside potential, with 59% of our respondents looking forward to buying a device. A general preference for voice interaction and lower levels of concern about online privacy leaves many Asian nations not far behind. In China, more than half (52%) plan to buy one. The story is similar in Vietnam (19% own, 45% plan to), Indonesia (18% own, 49% plan to), and Thailand (15% own, 44% plan to).

The new round is being led by asset manager Fidelity Investments, which has poured some $800 million into Lyft, and includes hedge fund Senator Investment Group LP and others. The investment should help Lyft keep apace of Uber, which raised $1.25 billion in new capital in January from SoftBank Group Corp. and has said it is planning to seek an IPO in next year's second half. Lyft has weighed its own IPO, according to people familiar with the matter, though it may not beat Uber to the punch. With Uber valued recently at $72 billion as part of a settlement granting equity to Alphabet Inc.'s Waymo, its IPO is likely to be one of the largest in recent memory. Both companies are battling for the future of transportation, investing billions in yet unproven self-driving vehicles and snapping up technology and competitors that offer rentable bicycles and scooters for shorter hops within urban centers.

The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.

The government signed a peace accord in late 2016 with the Revolutionary Armed Forces of Colombia (FARC), which was heavily involved in coca production and cocaine trafficking. Since the group demobilized, many new gangs have moved in and taken over its lucrative business and trafficking routes.

China has performed outstandingly well in scientific research and technological innovation among the G20 countries, and the country's scientific and technological strength in the field of AI is seeing rapid growth, second only to the US, a report shows. According to the report, in terms of the overall scientific and technological strength of AI, the US ranks first among the G20 countries followed by China, whose technological strength in AI has increased significantly, especially in the past five years. China has performed outstandingly well in the first three fields among the four branches of AI--machine learning, natural language processing, computer vision, and speech processing. But while the output of the country's scientific research has surpassed that of the US, improvements are needed in terms of the quality of the output. The report focuses on the scale of research output, academic impact, and international cooperation among the G20 countries, as an indication of China's position in the competitive landscape, as well as the challenges it faces.

Although timely sepsis diagnosis and prompt interventions in Intensive Care Unit (ICU) patients are associated with reduced mortality, early clinical recognition is frequently impeded by nonspecific signs of infection and failure to detect signs of sepsis-induced organ dysfunction in a constellation of dynamically changing physiological data. The goal of this work is to identify patient at risk of life-threatening sepsis utilizing a data-centered and machine learning-driven approach. We derive a mortality risk predictive dynamic Bayesian network (DBN) guided by a customized sepsis knowledgebase and compare the predictive accuracy of the derived DBN with the Sepsis-related Organ Failure Assessment (SOFA) score, the Quick SOFA (qSOFA) score, the Simplified Acute Physiological Score (SAPS-II) and the Modified Early Warning Score (MEWS) tools. A customized sepsis ontology was used to derive the DBN node structure and semantically characterize temporal features derived from both structured physiological data and unstructured clinical notes. We assessed the performance in predicting mortality risk of the DBN predictive model and compared performance to other models using Receiver Operating Characteristic (ROC) curves, area under curve (AUROC), calibration curves, and risk distributions. The derived dataset consists of 24,506 ICU stays from 19,623 patients with evidence of suspected infection, with 2,829 patients deceased at discharge. The DBN AUROC was found to be 0.91, which outperformed the SOFA (0.843), qSOFA (0.66), MEWS (0.729), and SAPS-II (0.766) scoring tools. Continuous Net Reclassification Index and Integrated Discrimination Improvement analysis supported the superiority DBN with respect to SOFA, qSOFA, MEWS, and SAPS-II. Compared with conventional rule-based risk scoring tools, the sepsis knowledgebase-driven DBN algorithm offers improved performance for predicting mortality of infected patients in intensive care units.

If you're getting thumb strain from trying to swipe your way to the perfect partner on Tinder, the latest update to the dating app could be the solution for you. Tinder is piloting a new feature, dubbed'Picks', that ditches the need to constantly swipe left or right to trawl through users' profiles on the dating service. Instead, Tinder Picks highlights a handful of fellow lonely hearts that it believes will be a good match, based on similar career, hobbies and interests. Although any Tinder user can see the profiles picked-out for them by the app, only those who subscribe to the Los Angeles-based dating company's £7.49 Tinder Picks will highlight a handful of dating app users who share similar interests, hobbies, and jobs.

In a 2016 address to propaganda cadres and state-run media personnel, Chinese President Xi Jinping expressed dreams of instilling a new international media order "wherever the readers are, wherever the viewers are; that is where propaganda reports must extend their tentacles." As Xinhua News, China's largest state-run news agency, equips itself with "Media Brain," an artificial intelligence (AI) newsroom to assist all stages of reporting, these "tentacles" of propaganda may extend faster. Bringing AI to newsrooms can improve accuracy, enhance data analysis, and increase efficiency. According to a video released by Xinhua in January, the AI newsroom will do everything "from finding leads to news gathering, editing, distribution, and, finally, feedback analysis." Last week, Xinhua announced an update to Media Brain called "MAGIC," which will use machine generated content (MGC) for "fast-speed news production" and can automatically generate a news video in as fast as 10 seconds.