Last year, Bipin Preet Singh let go of his personal assistant. "A traditional secretary is just a messaging medium and not incredibly efficient," says the CEO and founder of MobiKwik. "Plus, there are some things that I wouldn't schedule through a secretary." For people who directly report to him, a secretary represented a layer of bureaucracy. We encourage interaction and informal conversations," Singh adds. Times have never been tougher for the secretary. Competing with virtual assistants, Google calender and mobile apps that can make bookings, take down minutes of meetings, store records and even send reminders to drink water, the secretary has lost some of his/her swag and salary. For an earlier generation of corporate leaders, having a secretary used to be a symbol of status, a measure of their professional success. But new-age managers scoff at the idea of having one. "Communication has evolved so much.

According to a 2017 University of Washington report, there are hundreds of millions of smart-home devices in more than 40 million U.S. homes. This number is expected to double by 2021. Amazon Echo, Google Home and other devices that have Alexa and Google Assistant built in, have proven to be some of the world's most promising new technologies. These AI-enabled assistants seem capable of doing everything, from turning on lights to answering simple and even complex questions. "OK Google" and "Alexa" have become common household phrases, as these smart connected speakers always have their microphones on, yet don't respond until their "wake words" are mentioned.

Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers' decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural networks (CNN features) and the scale-invariant feature transform algorithm (SIFT features), color histograms, and so on. Nevertheless, one important type of features, the \emph{aesthetic features}, is seldom considered. It plays a vital role in clothing recommendation since a users' decision depends largely on whether the clothing is in line with her aesthetics, however the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce the aesthetic information, which is highly relevant with user preference, into clothing recommender systems. To achieve this, we first present the aesthetic features extracted by a pre-trained neural network, which is a brain-inspired deep structure trained for the aesthetic assessment task. Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner. We conduct extensive experiments on real-world datasets, which demonstrate that our approach can capture the aesthetic preference of users and significantly outperform several state-of-the-art recommendation methods.

Classic supervised learning makes the closed-world assumption, meaning that classes seen in testing must have been seen in training. However, in the dynamic world, new or unseen class examples may appear constantly. A model working in such an environment must be able to reject unseen classes (not seen or used in training). If enough data is collected for the unseen classes, the system should incrementally learn to accept/classify them. This learning paradigm is called open-world learning (OWL). Existing OWL methods all need some form of re-training to accept or include the new classes in the overall model. In this paper, we propose a meta-learning approach to the problem. Its key novelty is that it only needs to train a meta-classifier, which can then continually accept new classes when they have enough labeled data for the meta-classifier to use, and also detect/reject future unseen classes. No re-training of the meta-classifier or a new overall classifier covering all old and new classes is needed. In testing, the method only uses the examples of the seen classes (including the newly added classes) on-the-fly for classification and rejection. Experimental results demonstrate the effectiveness of the new approach.

The concept of social machines is increasingly being used to characterise various socio-cognitive spaces on the Web. Social machines are human collectives using networked digital technology which initiate real-world processes and activities including human communication, interactions and knowledge creation. As such, they continuously emerge and fade on the Web. The relationship between humans and machines is made more complex by the adoption of Internet of Things (IoT) sensors and devices. The scale, automation, continuous sensing, and actuation capabilities of these devices add an extra dimension to the relationship between humans and machines making it difficult to understand their evolution at either the systemic or the conceptual level. This article describes these new socio-technical systems, which we term Cyber-Physical Social Machines, through different exemplars, and considers the associated challenges of security and privacy.

The Chart of the Week is a weekly Visual Capitalist feature on Fridays. Artificial Intelligence is transforming the way we live, and the tech giants are racing to stay ahead of the curve. AI-related funding totaled an estimated $15.2 billion in 2017, a 144% increase over the previous year. The U.S. tech industry leads with a 50% share of those investments, even with China swiftly closing the gap in terms of patents and AI research. AI itself isn't new, but boosted computing power, increased connectivity, and the sheer volume of data has paved the way for the fourth industrial revolution of AI. "The coming era will be looked back upon as the'AI era,' when AI became the defining competitive advantage for corporations, government agencies, and investment professionals," predicts David Nadler, founder of Kensho Technologies.

Artificial intelligence and machine learning are being adopted into the enterprise at a rapid clip and adoption is likely to surge in 2019. What comes next is the real business challenge: How will we manage technology that we likely don't understand? The issue is likely to bubble up in the year ahead. For now, most of us are lulled into thinking more algorithms are better and even assuming we can outsource critical thought to models. Why hurt our brains when we can trust Einstein, Watson, Alexa, Google Assistant, and other software tools to think for us?

The GLAS smart thermostat is the most beautiful smart thermostat to hit the market since Nest shook the industry out of its complacency back in 2011. And just like Nest before it, GLAS manufacturer Johnson Controls has taken an approach no other thermostat maker has to date: It has embraced Microsoft's Cortana digital assistant. Few would argue that the GLAS isn't beautiful. The device is dominated by a 5-inch translucent OLED touchscreen that's mounted to a small base housing the brains of the unit and its connections to your HVAC system. Unlike most other thermostats, which have their wiring connections on a backplate that the display half attaches to, the GLAS's wiring is accessed from the front, hidden by a removable panel.

Mattel's AI nanny, called Aristotle, recently gained the notorious distinction of being subject to a bipartisan protest in the US Congress. Plus, there was a petition against it with over 15,000 signatures. The Campaign for a Commercial-Free Childhood, which organized the petition, argued that Aristotle is a consumerist ploy. It "attempts to replace the care, judgment and companionship of loving family members with faux nurturing and conversation from a robot designed to sell products and build brand loyalty." Aristotle, designed to interact with kids, was based on the same technologies as virtual assistants such as Amazon's Alexa.

Apple's HomePod smart speaker is stealing some features from the iPhone. The tech giant announced that users will soon be able to use their HomePod to make and receive phone calls, just by asking Siri. HomePod owners will have to wait a little longer to do so, however, as the updates aren't available just yet. Many expect the updates will arrive September 17th, the same day iOS 12 will officially debut. Apple's HomePod smart speaker is stealing some features from the iPhone. The updates were snuck in by CEO Tim Cook at the end of Apple's big hardware event at the Steve Jobs Theater in Cupertino, California, after the firm revealed an entirely new lineup of smartphones, including the iPhone XR, XS and XS Max.