DAKAR (Thomson Reuters Foundation) - Data analyst Fabrice Sonzahi enrolled in a course on artificial intelligence (AI) in Dakar, hoping to help struggling farmers improve crop yields in his home country of Ivory Coast. He is part of an inaugural batch of students at a new AI programming school in Senegal, one of the first in West Africa. Its mission is to train local people in using data to solve pressing issues like the impact of climate change on crops. The Dakar Institute of Technology (DIT), which opened in September, is running its first 10-week boot camp with nine students in partnership with French AI school VIVADATA. "I am convinced that by analyzing data we can give (farmers) better solutions," said Sonzahi, 30.

As is the case with many technological developments, 'deepfakes' -- videos in which someone who did not originally appear in the clip is rendered into it using artificial intelligence (AI) -- largely started in the world of pornography. Viewers, should they so desire, can now watch videos of their favourite musicians and film stars "in action," although that celebrity was never in that video. In these cases, increasingly sophisticated tools are used to put the musicians and film stars' faces onto pre-existing pornographic videos. There can obviously be a sinister, non-celebrity side to this too. The recent Sam Bourne novel, To Kill The Truth, features a protagonist Maggie Costello who appears in such a video as part of a cruel plot to undermine her.

Google today awarded $25 million in grants to a range of organizations to help them apply machine learning to fight some of the world's biggest challenges. Recipients range from New York City's fire department, which wants to find ways to reduce emergency call response time, to an experiment to track air quality with sensors attached to mopeds in Uganda, information that may shape public policy. The program is also an extension of Google's AI for Social Good program, which provides flood forecasting to communities in India and is researching how to provide speech recognition for more people with disabilities. More than 2,600 applications were received since the contest was announced in October from 119 countries around the world, Google.org The news was announced today onstage at the Google I/O developer conference by CEO Sundar Pichai and AI head Jeff Dean.

Cloud Machine Learning Market report offers detailed analysis and a five-year forecast for the global Cloud Machine Learning industry. Cloud Machine Learning market report delivers the insights which will shape your strategic planning as you estimate geographic, product or service expansion within the Cloud Machine Learning industry.. The Cloud Machine Learning market accounted for $XX million in 2018, and is expected to reach $XX million by 2024, registering a CAGR of YY% from 2019 to 2024. The global Cloud Machine Learning market is segmented based on product, end user, and region. Region wise, it is analyzed across North America (U.S., Canada, and Mexico), Europe (Germany, UK, Italy, Spain, France, and rest of Europe), Asia-Pacific (Japan, China, Australia, India, South Korea, Taiwan, and, rest of Asia-Pacific) and EMEA (Brazil, South Africa, Saudi Arabia, UAE, rest of EMEA). Ask more details or request custom reports to our experts at https://www.proaxivereports.com/pre-order/53269 Moreover, other factors that contribute toward the growth of the Cloud Machine Learning market include favorable government initiatives related to the use of Cloud Machine Learning.

The ubiquity of mobile devices and wearable sensors offers unprecedented opportunities for continuous collection of multimodal physiological data. Such data enables temporal characterization of an individual's behaviors, which can provide unique insights into her physical and psychological health. Understanding the relation between different behaviors/activities and personality traits such as stress or work performance can help build strategies to improve the work environment. Especially in workplaces like hospitals where many employees are overworked, having such policies improves the quality of patient care by prioritizing mental and physical health of their caregivers. One challenge in analyzing physiological data is extracting the underlying behavioral states from the temporal sensor signals and interpreting them. Here, we use a non-parametric Bayesian approach, to model multivariate sensor data from multiple people and discover dynamic behaviors they share. We apply this method to data collected from sensors worn by a population of workers in a large urban hospital, capturing their physiological signals, such as breathing and heart rate, and activity patterns. We show that the learned states capture behavioral differences within the population that can help cluster participants into meaningful groups and better predict their cognitive and affective states. This method offers a practical way to learn compact behavioral representations from dynamic multivariate sensor signals and provide insights into the data.

This article is about the cognitive science of visual art. Artists create physical artifacts (such as sculptures or paintings) which depict people, objects, and events. These depictions are usually stylized rather than photo-realistic. How is it that humans are able to understand and create stylized representations? Does this ability depend on general cognitive capacities or an evolutionary adaptation for art? What role is played by learning and culture? Machine Learning can shed light on these questions. It's possible to train convolutional neural networks (CNNs) to recognize objects without training them on any visual art. If such CNNs can generalize to visual art (by creating and understanding stylized representations), then CNNs provide a model for how humans could understand art without innate adaptations or cultural learning. I argue that Deep Dream and Style Transfer show that CNNs can create a basic form of visual art, and that humans could create art by similar processes. This suggests that artists make art by optimizing for effects on the human object-recognition system. Physical artifacts are optimized to evoke real-world objects for this system (e.g. to evoke people or landscapes) and to serve as superstimuli for this system.

Wikipedia defines Human in the Loop (HITH) as a model that requires human interaction. HITL is associated with modeling and simulation (M&S) in the live, virtual, and constructive taxonomy. HITL models may conform to human factors requirements as in the case of a mockup. In this type of simulation a human is always part of the simulation and consequently influences the outcome in such a way that is difficult if not impossible to reproduce exactly. HITL also readily allows for the identification of problems and requirements that may not be easily identified by other means of simulation.

The graph represents a network of 2,353 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 14 November 2019 at 19:32 UTC. The requested start date was Monday, 11 November 2019 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 5-day, 13-hour, 33-minute period from Tuesday, 05 November 2019 at 11:26 UTC to Monday, 11 November 2019 at 01:00 UTC.

Asus Japan announced this week that it'll show off two new single-board computers at the upcoming ET & IoT Technology 2019 event kicking off November 20 in Yokohama, Japan. The latest Tinker Edge T and Tinker Edge R are designed specifically for IoT (Internet of Things) and edge AI applications. The Tinker Edge T measures 85 x 56mm, which is around the size of a credit card. Both single-board computers depend on a small heatsink with an accompanying cooling fan to stay cool during operation. The system also relies on the Vivante GC7000 Lite 3D graphics engine and Google's Coral Edge tensor processing unit (TPU), which is optimized for Tensorflow Lite and boasts performance up to 4 tera operations per second (TOPS). The Tinker Edge R employs a Rockchip RK3399 Pro system on chip (SoC) that boasts a three-in-one design.