Customer experience chatbot developer Rulai has launched a new "low-code" chatbot development tool and raised $6.5 million to roll it out. The development team at Rulai, with offices in Beijing and Campbell, Calif., is helmed by the renowned University of California, Santa Cruz, computer science professor Yi Zhang. Professor Zhang, the company's chief technologist, and her team are launching a product that customer service managers can use to develop chatbots that will perform tasks based on customer conversations, and that can be created without a single line of code. It's an example of how software developers and artificial intelligence systems are writing themselves out of the application development process. Specifically, the Rulai tool was designed for customer service managers.

Once again, artificial intelligence triumphed over man. In the second match of a three-game series on Thursday, Google's DeepMind AlphaGo program beat the 19-year-old Chinese prodigy Ke Jie in the strategy board game Go. AlphaGo won the first game earlier in the week; the final game is scheduled for Saturday. The daily Bits newsletter will keep you updated on the latest from Silicon Valley and the technology industry, plus exclusive analysis from our reporters and editors. Please verify you're not a robot by clicking the box. You must select a newsletter to subscribe to.

PBS NewsHour full episode, May 25, 2017 Live now PBS NewsHour full episode, May 25, 2017 Show more This item has been hidden Uploads Play all 55:04 PBS NewsHour full episode May 25, 2017 - Duration: 55 minutes. PBS NewsHour full episode, May 25, 2017 4:57 Why the lessons of Mister Rogers never go away - Duration: 4 minutes, 57 seconds. Streamed 6 hours ago This item has been hidden Political analysis with Mark Shields and David Brooks Play all 12:22 Shields and Brooks on the barrage of Trump revelations - Duration: 12 minutes. This item has been hidden Brief but Spectacular Play all 3:30 Will artificial intelligence help us solve every problem? - Duration: 3 minutes, 30 seconds. This item has been hidden ScienceScope Play all 5:46 These cement-making bacteria could build the cities of the future - Duration: 5 minutes, 46 seconds.

According to a new market research report by MarketsandMarkets, the market is expected to grow from $667.1 million in 2016 to $7,988.8 million by 2022, at a CAGR of 52.68 percent during the forecast period. The growing usage of big data in the healthcare industry, ability of AI to improve patient outcomes, imbalance between health workforce and patients, reducing the healthcare costs, growing importance on precision medicine, cross-industry partnerships, and significant increase in venture capital investments are expected to drive the AI in healthcare market. Software to hold the largest share of the AI in healthcare market The AI software is used to assist the medical system in relevant insights, medical imaging and diagnostics, drug discovery, in-patient care and hospital management, virtual assistance, precision medicine, lifestyle management and monitoring, patient data and risk analysis, and research. The growing usage of smart devices, and the presence of major AI software providers such as IBM Corporation (US), Google Inc. (US) and Microsoft Corporation (US), Enlitic, Inc. (US), Next IT Corp (US) are driving the growth of the AI in healthcare market for the software offering. Deep learning technology expected to grow at the highest rate between 2017 and 2022 The deep learning technology which includes image recognition, signal recognition, and data mining-is expected to witness the highest CAGR during the forecast period.

In 1865 mining engineer Fredrik Idestam established a groundwood pulp mill on the banks of the Tammerkoski rapids in the town of Tampere, in southwestern Finland. Close to plentiful supplies of renewable forests, the company also had easy seaborne access to burgeoning European markets and the rapidly expanding US of the'Gilded Age'. The appetite for paper and other pulp based products seemed set to anchor the company's raison d'etre. The company in question was Nokia, and through a variety of monumental twists and turns, a company set to lead an entirely different market over a century later. By the end of the 20th Century Nokia dominated the global market for mobile devices.

Natural-language-facilitated human-robot cooperation (NLC), in which natural language (NL) is used to share knowledge between a human and a robot for conducting intuitive human-robot cooperation (HRC), is continuously developing in the recent decade. Currently, NLC is used in several robotic domains such as manufacturing, daily assistance and health caregiving. It is necessary to summarize current NLC-based robotic systems and discuss the future developing trends, providing helpful information for future NLC research. In this review, we first analyzed the driving forces behind the NLC research. Regarding to a robot s cognition level during the cooperation, the NLC implementations then were categorized into four types {NL-based control, NL-based robot training, NL-based task execution, NL-based social companion} for comparison and discussion. Last based on our perspective and comprehensive paper review, the future research trends were discussed.

We propose a novel adaptive approximation approach for test-time resource-constrained prediction. Given an input instance at test-time, a gating function identifies a prediction model for the input among a collection of models. Our objective is to minimize overall average cost without sacrificing accuracy. We learn gating and prediction models on fully labeled training data by means of a bottom-up strategy. Our novel bottom-up method first trains a high-accuracy complex model. Then a low-complexity gating and prediction model are subsequently learned to adaptively approximate the high-accuracy model in regions where low-cost models are capable of making highly accurate predictions. We pose an empirical loss minimization problem with cost constraints to jointly train gating and prediction models. On a number of benchmark datasets our method outperforms state-of-the-art achieving higher accuracy for the same cost.

Abstract--Data quantization learns encoding results of data with certain requirements, and provides a broad perspective of many real-world applications to data handling. Nevertheless, the results of encoder is usually limited to multivariate inputs with the random mapping, and side information of binary codes are hardly to mostly depict the original data patterns as possible. In the literature, cosine based random quantization has attracted much attentions due to its intrinsic bounded results. Nevertheless, it usually suffers from the uncertain outputs, and information of original data fails to be fully preserved in the reduced codes. In this work, a novel binary embedding method, termed adaptive training quantization (ATQ), is proposed to learn the ideal transform of random encoder, where the limitation of cosine random mapping is tackled. As an adaptive learning idea, the reduced mapping is adaptively calculated with idea of data group, while the bias of random transform is to be improved to hold most matching information. Experimental results show that the proposed method is able to obtain outstanding performance compared with other random quantization methods.

It is difficult to train a personalized task-oriented dialogue system because the data collected from each individual is often insufficient. Personalized dialogue systems trained on a small dataset can overfit and make it difficult to adapt to different user needs. One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain. By following this idea, we propose "PETAL"(PErsonalized Task-oriented diALogue), a transfer-learning framework based on POMDP to learn a personalized dialogue system. The system first learns common dialogue knowledge from the source domain and then adapts this knowledge to the target user. This framework can avoid the negative transfer problem by considering differences between source and target users. The policy in the personalized POMDP can learn to choose different actions appropriately for different users. Experimental results on a real-world coffee-shopping data and simulation data show that our personalized dialogue system can choose different optimal actions for different users, and thus effectively improve the dialogue quality under the personalized setting.

BEIJING, May 25, 2017 (GLOBE NEWSWIRE) -- Gridsum Holding Inc. ("Gridsum" or the "Company") (NASDAQ:GSUM), a leading provider of cloud-based big-data analytics, machine learning and AI solutions in China, today announced that, as a part of its strategic evolution, it has consolidated all of its artificial intelligence ("AI") activities strategically, technically and organizationally into a new division called the Gridsum Prophet. Gridsum is a first mover in China in big data intelligence. Since 2005, the Company has utilized a distributed big-data computing architecture, developed and implemented sophisticated natural language processing ("NLP"), and leveraged machine learning directed toward large enterprise clients. During that time, from serving large enterprise customers, the Company has accumulated deep domain knowledge and expertise as well as a massive amount of data that fuels its machine learning algorithms. Since this early inception, the Company has continued to stay at the forefront through focus and investment, hiring and training extraordinary engineers and architects and, importantly, playing an active and leading role in the AI academic and developer communities.