The paper asserts that emulating empathy in human-robot interaction is a key component to achieve satisfying social, trustworthy, and ethical robot interaction with older people. Following comments from older adult study participants, the paper identifies a gap. Despite the acceptance of robot care scenarios, participants expressed the poor quality of the social aspect. Current human-robot designs, to a certain extent, neglect to include empathy as a theorized design pathway. Using rhetorical theory, this paper defines the socio-cultural expectations for convincing empathetic relationships. It analyzes and then summarizes how society understands, values, and negotiates empathic interaction between human companions in discursive exchanges, wherein empathy acts as a societal value system. Using two public research collections on robots, with one geared specifically to gerontechnology for older people, it substantiates the lack of attention to empathy in public materials produced by robot companies. This paper contends that using an empathetic care vocabulary as a design pathway is a productive underlying foundation for designing humanoid social robots that aim to support older people's goals of aging-in-place. It argues that the integration of affective AI into the sociotechnical assemblages of human-socially assistive robot interaction ought to be scrutinized to ensure it is based on genuine cultural values involving empathetic qualities.

This paper introduces a novel approach to optimizing portfolio rebalancing by integrating Graph Neural Networks (GNNs) for predicting transaction costs and Dijkstra's algorithm for identifying cost-efficient rebalancing paths. Using historical stock data from prominent technology firms, the GNN is trained to forecast future transaction costs, which are then applied as edge weights in a financial asset graph. Dijkstra's algorithm is used to find the least costly path for reallocating capital between assets. Empirical results show that this hybrid approach significantly reduces transaction costs, offering a powerful tool for portfolio managers, especially in high-frequency trading environments. This methodology demonstrates the potential of combining advanced machine learning techniques with classical optimization algorithms to improve financial decision-making processes. Future research will explore expanding the asset universe and incorporating reinforcement learning for continuous portfolio optimization.

Rapidly Exploring Random Tree (RRT) algorithms, notably used for nonholonomic vehicle navigation in complex environments, are often not thoroughly evaluated for their specific challenges. This paper presents a first such comparison study of the variants Potential-Quick RRT* (PQ-RRT*), Informed RRT* (IRRT*), RRT*, and RRT, in maritime single-query nonholonomic motion planning. Additionally, the practicalities of using these algorithms in maritime environments are discussed and outlined. We also contend that these algorithms are beneficial not only for trajectory planning in Collision Avoidance Systems (CAS) but also for CAS verification when used as vessel behavior generators. Optimal RRT variants tend to produce more distance-optimal paths but require more computational time due to complex tree wiring and nearest neighbor searches. Our findings, supported by Welch`s t-test at a significance level of Alpha = 0.05, indicate that PQ-RRT* slightly outperform IRRT* and RRT* in achieving shorter trajectory length but at the expense of higher tuning complexity and longer run-times. Based on the results, we argue that these RRT algorithms are better suited for smaller-scale problems or environments with low obstacle congestion ratio. This is attributed to the curse of dimensionality, and trade-off with available memory and computational resources.

The AI revolution has arrived. And although the technology is still in its infancy, it promises to radically transform the global economy--impacting human lives, culture, and politics in ways that we can scarcely imagine. A recent article by Forbes Technology Council member Christian Pedersen argued that artificial intelligence will create new opportunities for data scientists, researchers, analysts, and other highly educated technical specialists even as the more easily-automated job functions of low skill workers "fall to the wayside." Pedersen's argument is correct on both counts, but the AI economy is also dependent upon one more ingredient: subject-matter expertise. Participation in the burgeoning AI economy doesn't require an advanced degree in data science or fluency in the latest programming languages.

Recently I chatted with Candace Roulo, managing editor of Advanced Manufacturing Now, about some of the most important trends in medtech and the technologies that are taking the industry to the next level. Click below to listen to the podcast, or read on for select highlights of the conversation – what I consider to be five trends medtech professionals should be talking about. Advanced Manufacturing Now: You mentioned connectivity. With the healthcare sector embracing the interconnectivity of the Internet of Things for more proactive patient management, what are the opportunities and challenges associated with using sensors in medical devices? And if you could, give us a couple examples of those medical devices.

An ERP application has always been an attempt to wrap code around a business process that enables both automation and standardization. Organizations that embrace packaged ERP applications would then write a lot of code to fill in the workflow gaps between business processes. But as ERP applications move into the cloud, providers of ERP applications are starting to significantly increase the number of business processes that can be automated by applying both machine and deep learning algorithms. Case in point is the latest release of SAP S/4 HANA Cloud, which among other new capabilities can now automatically extract payment information from PDF documents. Previously, that task inside most organizations was either performed manually or via a separate application that needed to be developed or acquired.

Sometimes when we write about analytics, machine learning and AI, it's challenging to come up with concrete use cases. That makes it harder than it should be for readers to grasp the power of these technologies. And that's a shame, because it makes AI seem ethereal rather than useful or easily understood. But every so often I am reminded that when one needs use cases, one need look no further than ERP (Enterprise Resource Planning) software. Sometimes ERP is disparaged as mundane.

To help enhance the customer experience across its marketplace platform, eBay has named Jan Pedersen as vp/chief scientist, artificial intelligence. He will lead the company's AI strategy, including computer vision, natural language understanding and machine learning to deliver new customer experiences across the eBay platform. Pedersen was vp/data science at Twitter where he led the company's work in machine learning infrastructure and data analytics. "Jan is a true pioneer in the industry, with over 30 years developing search, deep learning, machine learning and AI technologies at scale," said eBay president and CEO Devin Wenig. "He joins us at a pivotal moment when AI sciences including computer vision and deep learning are now capable of transforming personalized, immersive shopping experiences.

The company announced that it has lured Jan Pedersen from Twitter as its new VP and chief scientist for AI, and he will soon lead the company's strategy across natural language processing, machine learning, and computer vision. He officially starts on February 20. Pedersen has a long and distinguished engineering career in the technology industry according to his LinkedIn profile, serving in various chief scientist roles at AltaVista, Yahoo, and Amazon's A9 before joining Microsoft in 2009. Then in January 2017, he announced he was joining Twitter as VP for data science, where he has spearheaded "Twitter's investment in machine learning infrastructure and data analytics" for the past 12 months. That he has left Twitter so soon may hint at broader problems at Twitter, which has lost a number of key executives in recent years, including COO Anthony Noto just a few weeks ago.

Listen up, payment provider community. Increased digitalization, continued pursuit of integration across channels, ever-increasing complexity, more partnerships and the integration of technologies like the Internet of Things, machine learning and artificial intelligence may be what's needed to propel the payment provider industry forward, at least according to Creditcall's CEO Lars Pedersen. "We must embrace these new technologies and the resulting complexity so we can provide solutions that merchants can readily deploy to increase revenues, reduce costs, and gain greater insight into customer preferences and business logistics," Pedersen said. As a company that was just named to the London Stock Exchange Group's 1,000 Companies to Inspire Britain, all while being 100 percent self-funded, payments provider Creditcall may just have that certain je ne sais quoi. "The main reason we have been able to grow in a self-funded way is that we have a rigorous process for deciding what activity to take on," Pedersen said.