As an efficient model for knowledge organization, the knowledge graph has been widely adopted in several fields, e.g., biomedicine, sociology, and education. And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks. In general, knowledge graph embedding techniques aim to learn vectorized representations which preserve the structural information of the graph. And conventional embedding learning models rely on structural relationships among entities and relations. However, in educational knowledge graphs, structural relationships are not the focus. Instead, rich literals of the graphs are more valuable. In this paper, we focus on this problem and propose a novel model for embedding learning of educational knowledge graphs. Our model considers both structural and literal information and jointly learns embedding representations. Three experimental graphs were constructed based on an educational knowledge graph which has been applied in real-world teaching. We conducted two experiments on the three graphs and other common benchmark graphs. The experimental results proved the effectiveness of our model and its superiority over other baselines when processing educational knowledge graphs.

SAN FRANCISCO – SoftBank Group Corp. has put billions of dollars into a laser-based technology that could allow cars to drive themselves and help astronauts land on distant planets. It turns out that same technology makes a pretty good vacuum cleaner. Engineers at SoftBank Robotics have spent years applying lidar, which accurately maps distances in real-time, to carpet cleaning. The result is Whiz, a sort of ultra-high-end Roomba that SoftBank will start selling to companies in the U.S. on Tuesday for $499 a month. Given the high price, offices are the target market. The robot can run for three hours on a charge and clean as much as 15,000 sq.

Artificial intelligence (AI) has been used for years on satellite images – so why all the excitement? The application of AI and machine learning (ML) to Earth observation (EO) data is a huge growth area, demonstrated by the many online competitions, GitHub repositories and entire businesses founded exclusively on this topic. GitHub is a development platform inspired by the way people work, from open source to business. Users can host and review code, manage projects and build software alongside 31 million developers. However, taking the Oxford English Dictionary's definition of AI as'systems able to perform tasks normally requiring human intelligence', AI has been available for many years to address the meatiest challenges in image interpretation.

Researchers at Carnegie Mellon University have demonstrated that people who play a game with a robot suffer in performance when the robot criticizes them. Trash talking has a long and colorful history of flustering game opponents, and now researchers at Carnegie Mellon University have demonstrated that discouraging words can be perturbing even when uttered by a robot. The trash talk in the study was decidedly mild, with utterances such as "I have to say you are a terrible player," and "Over the course of the game your playing has become confused." Even so, people who played a game with the robot ― a commercially available humanoid robot known as Pepper ― performed worse when the robot discouraged them and better when the robot encouraged them. "This is one of the first studies of human-robot interaction in an environment where they are not cooperating."

WITF-FM, a public radio, television, and online news broadcaster in central Pennsylvania, includes the following statement above select online news coverage: "WITF strives to provide nuanced perspectives from the most authoritative sources. We are on the lookout for biases or assumptions in our own work, and we invite you to point out any we may have missed." It's not uncommon for news organizations to invite comments and feedback from their audience; in fact, most encourage it. But WITF has gone above and beyond a general invitation for engagement. This statement highlights the potential for bias in their own reporting -- and their attempt to avoid it.

For some time, financial and technology media have been sounding the alarm about job losses to artificial intelligence (AI) in the not-too-distant future. More recently, general media (e.g., The New Yorker), and even a presidential candidate--Andrew Yang--have jumped on the bandwagon forecasting doom. Obviously, forecasts on the extent of impact vary significantly from one source to another and the estimated amount of job losses depend on the job type. Nonetheless, it is valid to ask whether AI is going to slash clinical engineering/health technology management (CE/HTM) jobs, especially for those who are not planning to retire in the next decade. There is no question that AI can and will affect medical equipment maintenance and management.

Some areas of AI are further along in adoption than others. One of those areas is in recruiting. Already, there are companies that are marketing services to review hundreds (or thousands) of applicants and give each candidate a "score" based on multiple factors.The potential pitfall is that the output from some of these systems may have a disparate impact on a protected group. The most notable example was a system being developed (and rejected) by Amazon that did not like women. Thus, HR needs to have a seat at the table when these systems are being considered.

Just as power system operators are mastering data analytics to optimize hardware efficiencies, they are discovering how the complexities of artificial intelligence tools can do far more, and how to choose which to use. With deployment of advanced metering infrastructure (AMI) and smart sensor-equipped hardware, system operators are capturing unprecedented levels of data. Cloud computing and massive computational capabilities are allowing data analytics to make these investments pay off for customers. But it may take machine learning (ML) and artificial intelligence (AI) to address new power grid complexities. AI is a form of computer science that would make power system management fully autonomous in real time, researchers and private sector providers of power system services told Utility Dive.

MELVILLE, N.Y., November 18, 2019 – Effectively managing today's workforce is crucial for improving customer experience, operational efficiency, and compliance. Yet currently, rising expectations of both customers and employees have made forecasting and scheduling contact center agents and customer engagement resources exponentially more challenging. To give companies a simpler way to manage work across the enterprise, Verint Systems Inc. (Nasdaq: VRNT), The Customer Engagement Company, today announced the newest release of its market-leading Workforce Management (WFM) solution, which leverages artificial intelligence-infused automation and new mobile tools to streamline forecasting and scheduling and improve employee engagement, all easily accessible via the Verint Cloud. "The workforce represents up to 80 percent of overall contact center budgets so accurate and cost-effective scheduling is vital," says Verint's John Goodson, SVP and general manager, Products. "At the same time, today's employees demand easier flex scheduling options, so organizations must balance flexibility and cost to provide superior service. As a pioneer in WFM, we view this new release as one that can invigorate the market to meet the ever-changing demands of today's contact centers and throughout the enterprise."

How often, or little, they're used by employees to increase their efficiency and improve customer satisfaction. For IBM, the only way to make AI a core part of the workflow across entire companies is to take a cue from a decades-old office staple: email. It's dangerous to think of AI as a magic tool, said Daniel Hernandez, VP of data and AI at IBM, speaking at the Gartner IT Symposium/Xpo in Orlando, Florida last week. Instead, it should be seen as a strategy to empower staffers, helping them make more effective decisions through data while boosting employee experience. "Email gets no respect because it's boring," said Hernandez.