When we program morality into robots, are we doomed to disappoint them with our very human ethical inconsistency? In his newest novel, Machines Like Me, the British writer Ian McEwan takes on the rise of artificial intelligence, exploring how humans would react to the creation of perfectly human-like robots. But he does it in his particularly McEwan-esque fashion. Rather than creating a work of speculative fiction set in a near future where general artificial intelligence has become a reality, McEwan sets Machines Like Me in an alternate past, a 1980s London where the internet already exists and the English A.I. pioneer Alan Turing -- who in our world was effectively hounded to death by government authorities in 1954 because of his homosexuality -- instead lived and went on to create the first true A.I.: a collection of androids for purchase called Adams and Eves. Why paranoia about digital voice assistants is overblown.

Are you interested in machine learning and its applications? If you're looking to learn something new and great, how about a machine learning recommendation system? Well, don't be scared or blown away with those massive terms and words, they just tell someone what exactly some code does. Machine learning has always been in the news, whether for taking away jobs or making a robot understand and reply to a human. But what exactly can we do with machine learning?

Are you interested in machine learning and its applications? If you're looking to learn something new and great, how about a machine learning recommendation system? Well, don't be scared or blown away with those massive terms and words, they just tell someone what exactly some code does. Machine learning has always been in the news, whether for taking away jobs or making a robot understand and reply to a human. But what exactly can we do with machine learning?

"While such phrasings may be natural for a user, current speech processing systems are not configured to handle commands that are not preceded by a wakeword," wrote the patent application's authors, Kurt Wesley Piersol and Gabriel Beddingfield. "Offered is a system to correct this problem." In a statement, an Amazon spokesperson told BuzzFeed News, "The technology in this patent is not in use, and referring to the potential use of patents is highly speculative." The spokesperson added that Amazon files many patent applications that are not ultimately implemented into consumer-facing products, and that patents do not necessarily reflect "current or near-future states of products and services." According to the patent application, after a wake word is detected, Alexa may "look backwards" to determine if the command came before the wake word, and use pauses in speech to identify the beginning of the command.

Content-based news recommendation systems need to recommend news articles based on the topics and content of articles without using user specific information. Many news articles describe the occurrence of specific events and named entities including people, places or objects. In this paper, we propose a graph traversal algorithm as well as a novel weighting scheme for cold-start content based news recommendation utilizing these named entities. Seeking to create a higher degree of user-specific relevance, our algorithm computes the shortest distance between named entities, across news articles, over a large knowledge graph. Moreover, we have created a new human annotated data set for evaluating content based news recommendation systems. Experimental results show our method is suitable to tackle the hard cold-start problem and it produces stronger Pearson correlation to human similarity scores than other cold-start methods. Our method is also complementary and a combination with the conventional cold-start recommendation methods may yield significant performance gains. The dataset, CNRec, is available at: https://github.com/kevinj22/CNRec

With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet-of-Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions Bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new inter-discipline, edge AI or edge intelligence, is beginning to receive a tremendous amount of interest. However, research on edge intelligence is still in its infancy stage, and a dedicated venue for exchanging the recent advances of edge intelligence is highly desired by both the computer system and artificial intelligence communities. To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence. Specifically, we first review the background and motivation for artificial intelligence running at the network edge. We then provide an overview of the overarching architectures, frameworks and emerging key technologies for deep learning model towards training/inference at the network edge. Finally, we discuss future research opportunities on edge intelligence. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions and inspire further research ideas on edge intelligence.

Corporate payments technology firm Bill.com is rolling out a new platform designed to deploy artificial intelligence (AI) for automated workflows. In an announcement on Wednesday (May 22), Bill.com said it has launched the Intelligent Business Payments Platform for small and medium-sized businesses that need a more efficient business payments solution. Bill.com pointed to its own research, which found the payments platform saves SMB users an average of 5.5 hours every week, or 35 business days a year, by automating processes that professionals had previously completed manually. The platform includes an Intelligent Virtual Assistant, which automates invoice processing and approval, and uses machine learning to automatically capture data from those invoices and identify potential errors. It can also recognize bill approval routing and payment thresholds.

The Echo Wall Clock looks classic but acts futuristic. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. If you own an Amazon Echo smart speaker, you need to keep reading. Last last year, Amazon started teasing the Echo Wall Clock, an Alexa-connected device that syncs with your Echo to display visual timers and alarms.

When women are over-represented in the workforce, it tends be in industries of assistance – cleaning, nursing, secretarial work and, now, the world of virtual assistants. Research by Unesco has shown that using default female voices in AI – as Microsoft has done with Cortana, Amazon with Alexa, Google with Google Assistant and Apple with Siri – is furthering the belief that women exist merely to help men to get on with more important things. There is no real reason for AI technologies to be gendered at all, but we are at the mercy of tech companies "staffed by overwhelmingly male engineering teams", fixated on living out a Captain Kirk fantasy and delegating to the subservient, silky-voiced computers of Star Trek. These systems are unapologetically built by men, for men. They can even struggle to understand the "breathy" voices of women as software is often developed with male voice samples.

More people will speak to a voice assistance machine than to their partners in the next five years, the U.N. says, so it matters what they have to say. The numbers are eye-popping: 85% of Americans use at least one product with artificial intelligence (AI), and global use will reach 1.8 billion by 2021, so the impact of these "robot overlords" is unparalleled. But (AI) voice assistants, including Apple's Siri, Amazon's Alexa, Microsoft's Cortana, and Google's Assistant are inflaming gender stereotypes and teaching sexism to a generation of millennials by creating a model of "docile and eager-to-please helpers," with acceptance of sexual harassment and verbal abuse, a new U.N. study says. A 145-page U.N. report published this week by the educational, scientific and cultural organization UNESCO concludes that the voices we speak to are programmed to be submissive and accept abuse as a norm. The report is titled, "I'd blush if I could: Closing Gender Divides in Digital Skills Through Education."