With Artificial Intelligence making an impact in all aspects of our lives, smart homes are the next big thing. It has been operational for a while now with mobile phones and apps acting as remote controls for many of our appliances. Now, AI's involvement will be amplified as it enters our lives through audio and gesture sensing along with a rapid learning curve. There are a plethora of apps out there to turn on and turn off certain devices with audio or text commands. There are also security systems using advanced sensing techniques.

Chatbots and virtual assistants are getting a lot of attention in the AI world these days, and for good reason, as some of these tools are developing impressive skills. But there's more to the technology than conversational ability, and enterprises are finding value implementing component pieces of virtual assistants -- automated bots and natural language processing -- in other ways. "If it's hard for Siri to figure out [natural language chat], it's going to be hard for us," said Brian Canty, CEO and co-founder of Lea, a San Francisco-based company that has developed a Facebook messenger bot. Lea -- which stands for live event assistant -- delivers personalized concert recommendations to users. The original idea behind Lea was to build a conversational agent that users could chat with to explain what they're interested in, Canty said.

Collaborative filtering (CF) is the key technique for recommender systems. Pure CF approaches exploit the user-item interaction data (e.g., clicks, likes, and views) only and suffer from the sparsity issue. Items are usually associated with content information such as unstructured text (e.g., abstracts of articles and reviews of products). CF can be extended to leverage text. In this paper, we develop a unified neural framework to exploit interaction data and content information seamlessly. The proposed framework, called LCMR, is based on memory networks and consists of local and centralized memories for exploiting content information and interaction data, respectively. By modeling content information as local memories, LCMR attentively learns what to exploit with the guidance of user-item interaction. On real-world datasets, LCMR shows better performance by comparing with various baselines in terms of the hit ratio and NDCG metrics. We further conduct analyses to understand how local and centralized memories work for the proposed framework.

Developing conversational agents to engage in complex dialogues is challenging partly because the dialogue policy needs to explore a large state-action space. In this paper, we propose a divide-and-conquer approach that discovers and exploits the hidden structure of the task to enable efficient policy learning. First, given a set of successful dialogue sessions, we present a Subgoal Discovery Network (SDN) to divide a complex goal-oriented task into a set of simpler subgoals in an unsupervised fashion. We then use these subgoals to learn a hierarchical policy which consists of 1) a top-level policy that selects among subgoals, and 2) a low-level policy that selects primitive actions to accomplish the subgoal. We exemplify our method by building a dialogue agent for the composite task of travel planning. Experiments with simulated and real users show that an agent trained with automatically discovered subgoals performs competitively against an agent with human-defined subgoals, and significantly outperforms an agent without subgoals. Moreover, we show that learned subgoals are human comprehensible.

Many of us now use small doses of AI in everyday life (like Siri, Google Assistant, Alexa, and everything smart home), but hotels are putting this once-sci-fi technology to more widespread use. From concierge robots to personalized rooms to lively chatbots, your next holiday may include help from some artificially intellectualized friends. While you may miss, say, the smile or handshake you get from their human counterparts, these systems can create hyper-personalized experiences and comprehensively upgrade the level of service during your stay. Keep an eye out for these features at your next check-in. Some hotels are using robots to beef up customer service.

Retailers, struggling to connect with their customers, have been trialling new technologies to blend in-store and digital experiences. Interactive kiosks, mobile-friendly websites and transactional apps have become the norm. But shoppers are looking for deeper connections. If mobile is the glue connecting digital and physical retail, then location and voice technology are the bedrock of meaningful shopping experiences of the future. Historically, the first three rules of retail were always – "location, location, location".

One of the most important undertakings an enterprise has to deal with is finding and hiring an amazing candidate. But the fierce competition for talent, lackluster recruiting and sourcing processes and uncoordinated evaluation criteria make it an arduous task. Astoundingly, 74% of employers claim they've hired the wrong person for a position. These hires come at a cost, with organizations losing an average of nearly $15,000 on every bad hire. It's enticing to tap into artificial intelligence to help with hiring, managing and optimizing contingent workers.

Together, these technologies can provide critical data about customers' experience with products, provide information for customers to take action on, and more. As companies gather and use this data, they can provide better customer experience but also help to develop new products or features in next-generation products. Ultimately, Leary said, this new influx of product, usage and customer data is about creating a new customer experience. Leary used the example of a smart refrigerator whose temperature and be adjusted automatically, or have a broken part sent out to the customer, even before he is aware that the part is malfunctioning. It also enables companies to gather new information about product usage that can enhance customer relationships or product development.

Ask 10 CIOs how they define digital transformation and you're likely to get 10 different answers with one common theme: a laundry list of technology projects intended to foster sweeping business changes. Even so, digital transformation isn't so easily defined. Surely, digital transformation involves a radical rethinking of how an organization uses technology in pursuit of new revenue streams or new business models. And it also requires cross-departmental collaboration in pairing business-focused philosophies with rapid application development models. But for many organizations, digital transformation is really digital optimization in disguise, as new digital initiatives merely augment existing services.

Insurance agency management company In-Fi is hoping AI chatbots can streamline homeowner insurance applications and bring the process in line with customers' expectations. Edge intelligence is the newborn child of edge analytics and machine learning. In this podcast, CTO Simon Crosby explains the tech and discusses AI uses, data breaches and more. Can an AI virtual assistant in every car save the auto industry from sluggish sales and an uncertain future? Audio component manufacturer Harman argues that it can help.