NTT Corp. and Toyota Motor Corp. announced Monday the launch of a joint research project to promote the development and use of "partner robots" tasked with helping people in their everyday lives. NTT will provide its Corevo artificial intelligence technology that enhances nonverbal interactions between humans and robots as well as improving voice recognition and dialogue control techniques. They will be incorporated in an existing Human Support Robot developed by Toyota. That robot is capable of basic functions crucial to daily life, including picking up objects from the floor and communicating with families and caregivers. Partner robots that operate in the home helping with tasks such as housework and nursing have been garnering attention amid the increasing labor shortage in quickly graying Japan, Toyota said.

Alexander the Great's'lost city' was a magical place where people drank wine and naked philosophers imparted wisdom, ancient accounts claim. Now, nearly 2,000 years after the great warrior's death, archaeologists believe this illusive city may have finally been discovered in Iraq. Experts first noticed apparently ancient remains in the Iraqi settlement, known as Qalatga Darband after looking at declassified American spy footage from the 1960s. The images were made public in 1996 but, due to political instability, archaeologists were not able to explore the site properly for years. Using more recent drone footage, experts have now established there was a city during the first and second centuries BC which had strong Greek and Roman influences.

Lighting technology will be at the heart of urban life in 2030, helping deliver more sustainable and better-connected smart cities.

From a sutra-chanting humanoid robot to automated cemeteries, technological innovation is changing how Japanese deal with funerary rituals. As traditional family ties erode, people are opting for more affordable and convenient alternatives to conventional ceremonies and burial services. Here's a peek into how mourning the dead is going high-tech in Japan: What is driving the trend? Various factors are behind the ongoing transformation of the funeral business, but demographics lie at its core. Japan's low birthrate has diminished the size of family units while the aging population has given rise to elderly single-person households.

University of Montreal professor Yoshua Bengio is one of three members of what they have self-titled the "deep learning conspiracy." This "Canadian Mafia" of artificial intelligence visionaries is largely responsible for the tech industry's leap into machine learning. Bengio and his colleagues consult for the companies to which he is referring: Bengio for IBM, with Geoffrey Hinton and Yann LeCun for Google and Facebook, respectively. These are the companies that can use AI to create even better AI, embarrass China at their oldest game, police Twitter, or give your next iPhone a brain. In responding to a question from an Axios reporter as to whether the tech giants should be broken up, Bengio openly scoffed.

Sentient also released a new solution to AB testing called Sentient Ascend. Right now, the conversion rate optimization, CRO, industry, is based on AB testing, where you test a new design against an old design. "We decided we could transform this industry by completely dissolving the concept of AB testing, by thinking of the website no longer being a static property, but a dynamic property that is always evolving based on the way it interacts with your audience. We wanted to enable the marketer to make changes to quickly see market and conversion rate improvements. Our solution points to the fallacy of AB testing, where you come with a defined preconception of what to test against. We want to open the floodgates and show people that they don't have to limit themselves to a test. The number of possible combinations is so large that it's impossible to explore the surface exhaustively or comprehensively, except with an intelligent system that learns from every interaction, and understands from that learning what matters, what doesn't, and progressively builds solutions," says Blondeau.

Number of companies: As of June 31, 2017, there were 2,542 AI companies worldwide, of which 41%, or 1,078, were located in the U.S. However, the difference between the U.S. and China – 486 companies – is huge. Capital investment: Based on existing data, U.S. – based AI companies have raised a total of 97.8 billion RMB (50.1% of global AI investment) compared to China-based companies that have raised 63.5 billion RMB (33.2% of global AI investment) Human capital: China has 39,200 AI specialists compared to the U.S.' 78,700. Training and retaining talent has become a critical impediment to the development of China's AI industry. Number of investment firms: The U.S. has three times the number of AI investment firms as China does.

Mizuho Financial Group Inc. will start artificial-intelligence trading to bolster its Japanese equity business, according to people with knowledge of the plan. Japan's third-biggest lender will begin offering an algorithm-based AI trading service to some large institutional clients in Japan and elsewhere in Asia, the people said, asking not to be identified because the plan is private. The product attempts to predict how stock prices in Japan will change over an hour and find the best time to trade, they said. Global banks are seeking to improve the quality of their trading execution and equity research before the European Union's revision of the Markets in Financial Instruments Directive, or MiFID II, comes into force in January. Under the new rules, brokerages will be required to separate research fees from trading commissions to ensure investors pay fair prices for the services, a move that may spur competition.

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.

In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squared Error. The result shows that the LSTM model has excellent properties with respect to predict time series. Also, stateful LSTM networks and stack LSTM networks are employed to further study the improvement of accuracy of the house prediction model.