"Alexa, what is natural language processing?" Pose that question to Alexa – or Siri, Cortana, Google Assistant, or any other voice-activated digital assistant – and it will use natural language processing (NLP) to try to answer your question about, um, natural language processing. That makes Alexa and its ilk a natural example of NLP in action: NLP is a core technology that enables virtual assistants to process your verbal queries and respond with some degree of accuracy. But that doesn't necessarily define NLP; it just points to a popular real-world application of NLP. Plus, the voice assistant example is actually too narrow: Natural language processing isn't just about speech but also written text. Moreover, NLP is already ubiquitous, and your smartphone assistant is only one common example of its everyday use.

A: There are so many applications for conversational interfaces, from commerce to customer support. The challenge isn't finding an application for conversational interfaces, it's identifying customer pain points that might best be addressed with a conversational interaction. In most cases, a conversational interface is added as an additional means of interacting with a system, and right now you're seeing a lot of interesting experimentation going on. For example, KLM Airlines' Facebook Messenger chatbot lets you find flights by having a text chat with an AI Assistant. You can obviously still search for flights using their traditional interface, but the chat experience is definitely worth checking out.

From Google Home to Amazon Echo, Home AI systems are all the rage right now. These require "Smart Speakers" and tend to function mostly with devices that are Smart Home-ready (or have apps that provided added value like Smart Home connection). While these devices are really cool, there are limitations, namely that for Echo or Home to hear you, you need to be in the house. But recently, Mark Zuckerberg showed us that you don't need this hardware. Over the past year or so (for a sum total of about 100 hours) Zuckerberg has spent his off time creating a massively intelligent Smart Home System that he controls with nothing more than his computer and his Smart Phone.

Oracle today announced availability of its AI-trained voice with Oracle Digital Assistant. Now, enterprise customers can use voice commands to communicate with their enterprise applications to drive desired actions and outcomes, enriching the user experience with conversational AI, simplifying interactions and improving productivity. "Enterprises are demanding an AI-powered voice assistant that understands their specific vocabulary and enables naturally expressive interactions for its users," said Suhas Uliyar, vice president, AI and Digital Assistant, Oracle. "Most of all though, enterprises value a highly secure AI-powered voice assistant that stores their business' sensitive data in Oracle's second generation cloud infrastructure." Built on Oracle's next-generation infrastructure, Oracle Digital Assistant applies AI with deep semantic parsing for natural language processing (NLP), natural language understanding (NLU) and custom machine learning (ML) algorithms.

The pursuit of improved accuracy in recommender systems has led to the incorporation of user context. Context-aware recommender systems typically handle large amounts of data which must be uploaded and stored on the cloud, putting the user's personal information at risk. While there have been previous studies on privacy-sensitive and context-aware recommender systems, there has not been a full-fledged system deployed in an isolated mobile environment. We propose a secure and efficient on-device mechanism to predict a user's next intention. The knowledge of the user's real-time intention can help recommender systems to provide more relevant recommendations at the right moment. Our proposed algorithm is both context and sequence aware. We embed user intentions as weighted nodes in an n-dimensional vector space where each dimension represents a specific user context factor. Through a neighborhood searching method followed by a sequence matching algorithm, we search for the most relevant node to make the prediction. An evaluation of our methodology was done on a diverse real-world dataset where it was able to address practical scenarios like behavior drifts and sequential patterns efficiently and robustly. Our system also outperformed most of the state-of-the-art methods when evaluated for a similar problem domain on standard datasets.

In machine learning we often try to optimise a decision rule that would have worked well over a historical dataset; this is the so called empirical risk minimisation principle. In the context of learning from recommender system logs, applying this principle becomes a problem because we do not have available the reward of decisions we did not do. In order to handle this "bandit-feedback" setting, several Counterfactual Risk Minimisation (CRM) methods have been proposed in recent years, that attempt to estimate the performance of different policies on historical data. Through importance sampling and various variance reduction techniques, these methods allow more robust learning and inference than classical approaches. It is difficult to accurately estimate the performance of policies that frequently perform actions that were infrequently done in the past and a number of different types of estimators have been proposed. In this paper, we review several methods, based on different off-policy estimators, for learning from bandit feedback. We discuss key differences and commonalities among existing approaches, and compare their empirical performance on the RecoGym simulation environment. To the best of our knowledge, this work is the first comparison study for bandit algorithms in a recommender system setting.

Personal assistant AI systems such as Siri, Cortana, and Alexa have become widely used as a means to accomplish tasks through natural language commands. However, components in these systems generally rely on supervised machine learning algorithms that require large amounts of hand-annotated training data, which is expensive and time consuming to collect. The ability to incorporate unsupervised, weakly supervised, or distantly supervised data holds significant promise in overcoming this bottleneck. In this paper, we describe a framework that leverages user engagement signals (user behaviors that demonstrate a positive or negative response to content) to automatically create granular entity labels for training data augmentation. Strategies such as multi-task learning and validation using an external knowledge base are employed to incorporate the engagement annotated data and to boost the model's accuracy on a sequence labeling task. Our results show that learning from data automatically labeled by user engagement signals achieves significant accuracy gains in a production deep learning system, when measured on both the sequence labeling task as well as on user facing results produced by the system end-to-end. We believe this is the first use of user engagement signals to help generate training data for a sequence labeling task on a large scale, and can be applied in practical settings to speed up new feature deployment when little human annotated data is available.

An intelligent system must be capable of performing automated reasoning as well as responding to the changing environment (for example, changing knowledge). To exhibit such an intelligent behavior, a machine needs to understand its environment as well be able to interact with it to achieve certain goals. For acting rationally, a machine must be able to obtain information and understand it. Knowledge Representation (KR) is an important step of automated reasoning, where the knowledge about the world is represented in a way such that a machine can understand and process. Also, it must be able to accommodate the changes about the world (i.e., the new or updated knowledge). Using the generated knowledge base about the world, an intelligent system should be able to do complex tasks like question-answering (QA), summarization, medical reasoning and many more.

Google has gone to new heights in order to promote its next-generation Pixel handset. Hanging atop the 49-story Marriott Marquis building in Time Square is an advertisement that gives the first official glimpse at a coral-colored Pixel 4. The promotion also encourages consumer to remind their Google Assistant about its hardware event, set to take place October 15th, where the phone will be unveiled. Hanging atop the Marriott Marquis building in Time Square is an ad that gives the first official glimpse of a coral-colored Pixel 4. The promotion also encourages consumer to remind their Google Assistant about its hardware event, where the phone will be unveiled The hardware event will take place in the Big Apple, where Google will reveal intricate details of its Pixel 4 and Pixel 4 XL – which are set to take on Apple's latest iPhone 11. Rumors have also suggested that Google could announce other devices, including new Google Home Speakers and a Pixelbook 2. Google posted cropped renders of two sleek black devices to its Twitter page. The images appear to show a square module on the back of the phone.

It seems like artificial intelligence is taking over the world, leaving many of us non-techies feeling terrified. Yet when you stop to think about it, we all use artificial intelligence every day. When we Google something, use Siri on our smartphones or ask Alexa a question, we are using AI. Hollywood has certainly featured AI in many movies from "The Terminator" series to "Robocop" and "I, Robot." In "Minority Report," algorithms predict who is going to commit a crime, and the person is arrested before the crime can be committed.