User intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are labeled with predefined intents. However, it is labor-intensive and time-consuming to label users' utterances as intents are diversely expressed and novel intents will continually be involved. Instead, we study the zero-shot intent detection problem, which aims to detect emerging user intents where no labeled utterances are currently available. We propose two capsule-based architectures: INTENT-CAPSNET that extracts semantic features from utterances and aggregates them to discriminate existing intents, and INTENTCAPSNET-ZSL which gives INTENTCAPSNET the zero-shot learning ability to discriminate emerging intents via knowledge transfer from existing intents. Experiments on two real-world datasets show that our model not only can better discriminate diversely expressed existing intents, but is also able to discriminate emerging intents when no labeled utterances are available.

The Army offered a $100,000 prize for a solution to an increasingly tough problem for commanders in the field: In a battlefield dense with electromagnetic signals, is there a better way to distinguish friendly transmissions from hostile attacks? There is, according to a team of eight engineers from Aerospace Corporation, based in El Segundo, Calif. They won the prize by correctly detecting and classifying the greatest number of radio frequency signals using a combination of signal processing and artificial intelligence algorithms. The competition, known as the "Blind Signal Classification Challenge," was sponsored by the Army's Rapid Capabilities Office, a small organization that looks for ways to apply commercial technology to solve military problems. When the challenge kicked off in April, the Army gave all 49 competitors a large amount of recordings of various types of radio signals to use as "training data" so they could develop their algorithms.

I recently came across an article of a Japan store using face recognition to identify possible shoplifters. My immediate hunch was that the technique they are using is the study of body pose because a person could tell the difference if a thief is being suspicious by looking at how he behaves. So I believe we can train a neural net to do the exact same thing. But I could not figure out how to represent this information in a neural net as we need to encode the information of a time unit in order to succinctly determine that a person is suspicious. Anyone knows any similar project and mind to shed a light?

Corporate earnings are set to rise more than 20%. Productivity gains in excess of 5% of global GDP are possible. And $650 billion of new industrial spending is likely. It examines how advances in information technology are tilting the landscape. Spoiler alert: Investors need to get ready.

Reuters reports that artificial intelligence (AI) will play a key role in the advancement of diagnoses and treatments across the healthcare spectrum. Siemens Heathineers (OTCPK:SIEGY)(OTCPK:SMAWF) has leveraged AI to develop a digital twin heart that mimics the electrical and physical properties of real cardiac cells enabling surgeons to run simulations before surgery, to see if a pacemaker is appropriate for a particular patient, for example. Philips sells AI-enabled heart models that convert two-dimensional ultrasound images into data that helps physicians diagnose problems or automatically analyze scans to help surgeons plan operations. A shortage of doctors in China is stoking demand for AI tools to analyze medical images. Alibaba is one of the early leaders, using its cloud and data systems to develop AI solutions to analyze CT and MRI images.

Well, what do you know, some science-fiction media and literature are starting to become realities now. If you have ever seen the movie "Minority Report" then your excitement has probably doubled. An artificial intelligence (AI) that can predict and detect crime is now in the works, by none other than Japan, of all countries. Despite the crime rate in Japan being relatively low compared to countries like the U.S. or U.K., the Asian country's military and police are still intent on developing such a technology. They also intend to apply it against maritime vessels which could pose a security threat to Japan.

HEIDELBERG, Germany (Reuters) - Armed with a mouse and computer screen instead of a scalpel and operating theater, cardiologist Benjamin Meder carefully places the electrodes of a pacemaker in a beating, digital heart. Using this "digital twin" that mimics the electrical and physical properties of the cells in patient 7497's heart, Meder runs simulations to see if the pacemaker can keep the congestive heart failure sufferer alive - before he has inserted a knife. The digital heart twin developed by Siemens Healthineers is one example of how medical device makers are using artificial intelligence (AI) to help doctors make more precise diagnoses as medicine enters an increasingly personalized age. The challenge for Siemens Healthineers and rivals such as Philips and GE Healthcare is to keep an edge over tech giants from Alphabet's Google to Alibaba that hope to use big data to grab a slice of healthcare spending. With healthcare budgets under increasing pressure, AI tools such as the digital heart twin could save tens of thousands of dollars by predicting outcomes and avoiding unnecessary surgery.

One of India's biggest names in AI, Dr. Dakshinamurthy V Kolluru, took to the stage at Rakuten CTO Summit 2018 on March 14 in Bengaluru, also known as Bangalore. Speaking in front of a rapt audience of 51 CTOs and heads of engineering from Rakuten Group's businesses across the globe, Kolluru traced the fascinating history of machine learning, deep learning and artificial intelligence, elucidating on how AI can benefit businesses and improve customer experience. Rakuten, which promotes use of AI in all of its group companies, was keen to hear the thoughts of the man described by Analytics India Mag as "a visionary, an analytics expert and a passionate educator, who has been doing highly innovative work in the field of analytics -- be it consulting, product development, corporate training or educating -- since 1999, when analytics was not part of the common lingo that it has become today." The Founder and President of International School of Engineering (INSOFE) Hyderabad, Kolluru has helped set up many data science centers of excellence (COEs) and has conducted training for multinational corporations such as Johnson and Johnson in the US and Microsoft, HP, Broadridge Financial Services and others in India. Kolluru, whose expertise lies in simplifying complex ideas and communicating them clearly, drew on landmark studies to explain where AI and deep learning fit in the spectrum of technologies like machine learning and robotic process automation (RPA) and how they can help complex businesses like Rakuten solve problems across functions and verticals.

The legal media has lately had a mania for tech headlines. Many commentators claim that tech, especially artificial intelligence (AI), will do something to Big Law. Tech more likely will do something in it: incremental change. I start with the case against disruption, then look at four headline-grabbing technologies: AI, Bots, Big Data, and Blockchain. By the late 1980s, a few law firms had most of their lawyers using PCs.

Sinitic AI-enabled system solves Asian iGaming customer support automation's 4 biggest problems TAIPEI, August, 2018 – Natural language processing (NLP) company Sinitic has announced the launch of a customized customer support automation solution for the iGaming industry in Asia. Sinitic's solution is artificial intelligence (AI)-enabled and targets the biggest pain points for customer support automation for iGaming operators. Because of unique market factors, customer support costs currently make up 50-60% of an Asian iGaming company's Costs of Goods Sold. These costs are particularly high as companies tend to centralize their operations in the few regulated markets and therefore need to import a large number of staff for multilingual customer support teams. Sinitic's AI-enabled chatbots – conversational assistants that assist human customer support staff – give businesses the power to control and reduce these operational costs while expanding profits.