Public perceptions of Robotics and Artificial Intelligence (RAI) are important in the acceptance, uptake, government regulation and research funding of this technology. Recent research has shown that the public's understanding of RAI can be negative or inaccurate. We believe effective public engagement can help ensure that public opinion is better informed. In this paper, we describe our first iteration of a high throughput in-person public engagement activity. We describe the use of a light touch quiz-format survey instrument to integrate in-the-wild research participation into the engagement, allowing us to probe both the effectiveness of our engagement strategy, and public perceptions of the future roles of robots and humans working in dangerous settings, such as in the off-shore energy sector. We critique our methods and share interesting results into generational differences within the public's view of the future of Robotics and AI in hazardous environments. These findings include that older peoples' views about the future of robots in hazardous environments were not swayed by exposure to our exhibit, while the views of younger people were affected by our exhibit, leading us to consider carefully in future how to more effectively engage with and inform older people.

In public media as well as in scientific publications, the term \emph{bias} is used in conjunction with machine learning in many different contexts, and with many different meanings. This paper proposes a taxonomy of these different meanings, terminology, and definitions by surveying the, primarily scientific, literature on machine learning. In some cases, we suggest extensions and modifications to promote a clear terminology and completeness. The survey is followed by an analysis and discussion on how different types of biases are connected and depend on each other. We conclude that there is a complex relation between bias occurring in the machine learning pipeline that leads to a model, and the eventual bias of the model (which is typically related to social discrimination). The former bias may or may not influence the latter, in a sometimes bad, and sometime good way.

Companies looking for ways to cut costs as they brace for a coronavirus-induced economic slowdown should consider their patent portfolio. It's like a cupboard in desperate need of a spring clean. Businesses spend over $40bn on maintaining their patent portfolio each year, according to a new study from the UK intellectual property (IP) startup Aistemos and media platform IAM, but less than 20% of companies believe they have the right portfolio. Large companies hold tens of thousands of patents aimed at protecting the company's business from copying and legal issues. Around 4.5% of a company's revenues, on average, are vulnerable to patent litigation, according to the US consultancy Analysis Group.

The next 10 years look just as topsy-turvy. Artificial intelligence and machine learning promise to change the competitive landscape for many companies. At the same time, talented professionals will continue to demand more from their jobs through increased calls for transparency around pay and fairness and more flexibility in work-life balance. It's a lot for companies to navigate, and they're struggling with it: an analysis by Korn Ferry of more than 150,000 leadership profiles shows that only 15% of business leaders today have the right blend of skills to be the leaders of tomorrow. But such disruption can be a boon to workers who are agile and forward thinking.

Over the last few decades, research, activity, and funding have been devoted to improving the recruitment, retention, and advancement of women in the fields of science, engineering, and medicine. In recent years the diversity of those participating in these fields, particularly the participation of women, has improved and there are significantly more women entering careers and studying science, engineering, and medicine than ever before. However, as women increasingly enter these fields they face biases and barriers and it is not surprising that sexual harassment is one of these barriers. According to the National Academies of Sciences, Engineering, and Medicine 2018, report, the count of women in science is decreasing since 1990. The report also revealed that till 2015, women made up only 18% of computer science majors in the US -- a decline from a high of 37% in 1984.

You can be addicted to your Artificial Intelligence (AI) software as much as your favored fortune. And you'll feel rewarding being addicted to your AI. Because they replace the extravagance, inefficiency, and endangerment associated with business operations. Tech Oracle if you ask? Employing AI will lessen human error, mundane tasks, in turn, more time for innovation. This means you print money while remaining effortless.

This research paper explores the performance of Machine Learning (ML) algorithms and techniques that can be used for financial asset price forecasting. The prediction and forecasting of asset prices and returns remains one of the most challenging and exciting problems for quantitative finance and practitioners alike. The massive increase in data generated and captured in recent years presents an opportunity to leverage Machine Learning algorithms. This study directly compares and contrasts state-of-the-art implementations of modern Machine Learning algorithms on high performance computing (HPC) infrastructures versus the traditional and highly popular Capital Asset Pricing Model (CAPM) on U.S equities data. The implemented Machine Learning models - trained on time series data for an entire stock universe (in addition to exogenous macroeconomic variables) significantly outperform the CAPM on out-of-sample (OOS) test data.

In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. Different from existing approaches, our algorithm considers contextual correlation among words, described in domain knowledge ontologies, to generate semantic explanations. To narrow down the search space for explanations, which is a major problem of long and complicated text data, we design a learnable anchor algorithm, to better extract explanations locally. A set of regulations is further introduced, regarding combining learned interpretable representations with anchors to generate comprehensible semantic explanations. An extensive experiment conducted on two real-world datasets shows that our approach generates more precise and insightful explanations compared with baseline approaches.

New machine translations (MT) technologies are emerging rapidly and with them, bold claims of achieving human parity such as: (i) the results produced approach "accuracy achieved by average bilingual human translators" (Wu et al., 2017b) or (ii) the "translation quality is at human parity when compared to professional human translators" (Hassan et al., 2018) have seen the light of day (Laubli et al., 2018). Aside from the fact that many of these papers craft their own definition of human parity, these sensational claims are often not supported by a complete analysis of all aspects involved in translation. Establishing the discrepancies between the strengths of statistical approaches to MT and the way humans translate has been the starting point of our research. By looking at MT output and linguistic theory, we were able to identify some remaining issues. The problems range from simple number and gender agreement errors to more complex phenomena such as the correct translation of aspectual values and tenses. Our experiments confirm, along with other studies (Bentivogli et al., 2016), that neural MT has surpassed statistical MT in many aspects. However, some problems remain and others have emerged. We cover a series of problems related to the integration of specific linguistic features into statistical and neural MT, aiming to analyse and provide a solution to some of them. Our work focuses on addressing three main research questions that revolve around the complex relationship between linguistics and MT in general. We identify linguistic information that is lacking in order for automatic translation systems to produce more accurate translations and integrate additional features into the existing pipelines. We identify overgeneralization or 'algorithmic bias' as a potential drawback of neural MT and link it to many of the remaining linguistic issues.

Our tech world is fraught with troubling trends when it comes to gender inequality. A recent UN report "I'd blush if I could" warns that embodied AIs like the primarily female voice assistants can actually reinforce harmful gender stereotypes. Dag Kittlaus, who co-founded Siri before its acquisition by Apple, spoke out on Twitter against the accusation on Siri's sexism: It is important to acknowledge that the gender of Siri, unlike that of other voice assistants, was configurable early on. But the product's position becomes harder to define when you notice that Siri's response to the highly inappropriate comment "You're a slut" is in fact the title of the UN report: "I'd blush if I could." Therefore, in this article I'd like to discuss the social and cultural aspects of voice assistants, and specifically, why they are designed with gender, what ethical concerns this causes, and how we can fix this issue.