Children with autism spectrum conditions often have trouble recognizing the emotional states of people around them -- distinguishing a happy face from a fearful face, for instance. To remedy this, some therapists use a kid-friendly robot to demonstrate those emotions and to engage the children in imitating the emotions and responding to them in appropriate ways. This type of therapy works best, however, if the robot can smoothly interpret the child's own behavior -- whether he or she is interested and excited or paying attention -- during the therapy. Researchers at the MIT Media Lab have now developed a type of personalized machine learning that helps robots estimate the engagement and interest of each child during these interactions, using data that are unique to that child. Armed with this personalized "deep learning" network, the robots' perception of the children's responses agreed with assessments by human experts, with a correlation score of 60 percent, the scientists report June 27 in Science Robotics.

LAHORE: The launch of Consortium for Applied Data Science (CADS) will help the researchers at Information Technology University (ITU), Punjab, to analyse data about various sectors gathered by the Punjab Information Technology Board (PITB) to find solutions through one platform for policy-makers and to make public sector efficient and transparent. The founding Vice-Chancellor of ITU Dr Umar Saif, who is also PITB Chairman, said this in his opening remarks at the launch of a Consortium for Applied Data Science supported by Alan Turing Institute, the National Centre for Data Science and Artificial Intelligence in the United Kingdom (UK), here Thursday. Dr Saif said that with the launch of the Consortium, Pakistan has taken a leap forward in the development of the Data Science and Artificial Intelligence (AI) industry, which being a tangible field with broad spectrum would enable the stakeholders to benefit from machine learning. This Consortium for Applied Data Science would be a key enabler in improving the prosperity of the nation as well as creating a regional hub for Data Science and Artificial Intelligence advancement, he stated. He also informed that stamping project generated revenue worth USD 100 million by cutting the fake or forged stamp papers in a year.

Former Baidu fintech unit Du Xiaoman Financial says that artificial intelligence will lie at the core of upcoming fintech innovations. Speaking at the Baidu AI Developers Conference on 4 July,Du Xiaoman CEO Zhu Guang (朱光) said that finance was the most rapid channel for achieving commercialisation of AI technology. According to Zhu over the past two and half years Du Xiaoman has already applied AI technology to multiple areas including smart client canvassing, personal identification, big data risk control and smart investment and advisory. "We believe that the application of AI technology in the financial sphere has already left the laboratory phase, and officially entered the standardised application phase," said Zhu. In the area of risk control Duman is using online data in combination with central bank credit data, real-time data and conventional methods to greatly raise risk-control efficiency, while the company's facial recognition technology has achieved a success rate of over 98%, with a response time of just several dozen milliseconds.

A robot from Japan named "Pepper" is the newest edition at Tutor Me SOS in Stamford. The robot, the only one of its kind in Connecticut, is being programmed to prepare kids for the future and will interact with the 500 students enrolled at the center. Tutor Me SOS owner Mona Mitri says the robot is so impressive, with its ability to communicate by voice and touch. "It's unbelievable because no matter where you move, it follows you," she says. Pepper's battery lasts roughly 14 hours.

Autoblog recently went to Japan to drive cars, ride trains, and talk to carmakers about automotive history and the future of mobility. This video is part of a larger in a series of special reports from Japan. YOKOHAMA, Japan -- On our recent trip to Japan, we spent a day driving the new Nissan Leaf through some unfamiliar territory. Despite the challenges of driving in a foreign country on the opposite side of the road, the Leaf proved to be a calm and willing companion thanks to its smooth electric powertrain and ProPilot driver assistance system. Following our drive, we met up with Nissan's EV director, Nicholas Thomas, at the company headquarters to talk a bit about the future of electrification and automated driving, pillars of what the automaker calls "Nissan Intelligent Mobility." You can watch the interview in the video above, or, if you're more of a reader, scroll down to read the Q&A.

IMAGE: Insilico Medicine developed a novel deep-learning based model that predicts a biological age of a muscle. Thursday, July 5th, Rockville, MD - Insilico Medicine, a Rockville-based next-generation artificial intelligence company specializing in the application of deep learning for target identification, drug discovery and aging research announces the publication of a new research paper "Machine learning on human muscle transcriptomic data for biomarker discovery and tissue-specific drug target identification" in Frontiers in Genetics journal. Sarcopenia (from Greek "flesh poverty"), is one of the major age-related processes and involves the loss of skeletal muscle and its function. Age-associated muscle wasting remains an important clinical challenge that impacts hundreds of millions of older adults. It is associated with serious negative health outcomes such as falls, impaired standing balance, physical disability, and mortality.

The second annual Baidu AI Developers Conference, officially known as Baidu Create 2018, opened in Beijing today. Baidu unveiled China's first cloud-to-edge AI chip, Kunlun, and many other upgraded versions of Baidu's AI products this morning on the first day of this two-day event. Li Yanhong, known as Robin Li, the founder and CEO of Baidu, introduced Baidu's latest research achievements in artificial intelligence (AI) field. Started in 2013, the autonomous driving project was mainly lead and developed by the Baidu Research Institute. At the 2017 Baidu World Congress in November last year, Robin Li stated that Baidu's Level 4 self-driving bus "Apolong" would be mass-produced by July 2018.

Can we make a famous rap singer like Eminem sing whatever our favorite song? Singing style transfer attempts to make this possible, by replacing the vocal of a song from the source singer to the target singer. This paper presents a method that learns from unpaired data for singing style transfer using generative adversarial networks.

In the process of knowledge discovery and representation in large datasets using formal concept analysis, complexity plays a major role in identifying all the formal concepts and constructing the concept lattice(digraph of the concepts). For identifying the formal concepts and constructing the digraph from the identified concepts in very large datasets, various distributed algorithms are available in the literature. However, the existing distributed algorithms are not very well suitable for concept generation because it is an iterative process. The existing algorithms are implemented using distributed frameworks like MapReduce and Open MP, these frameworks are not appropriate for iterative applications. Hence, in this paper we proposed efficient distributed algorithms for both formal concept generation and concept lattice digraph construction in large formal contexts using Apache Spark. Various performance metrics are considered for the evaluation of the proposed work, the results of the evaluation proves that the proposed algorithms are efficient for concept generation and lattice graph construction in comparison with the existing algorithms.

Neural Machine Translation (NMT) has been widely adopted over recent years due to its advantages on various translation tasks. However, NMT systems can be error-prone due to the intractability of natural languages and the design of neural networks, bringing issues to their translations. These issues could potentially lead to information loss, wrong semantics, and low readability in translations, compromising the usefulness of NMT and leading to potential non-trivial consequences. Although there are existing approaches, such as using the BLEU score, on quality assessment and issue detection for NMT, such approaches face two serious limitations. First, such solutions require oracle translations, i.e., reference translations, which are often unavailable, e.g., in production environments. Second, such approaches cannot pinpoint the issue types and locations within translations. To address such limitations, we propose a new approach aiming to precisely detect issues in translations without requiring oracle translations. Our approach focuses on two most prominent issues in NMT translations by including two detection algorithms. Our experimental results show that our new approach could achieve high effectiveness on real-world datasets. Our successful experience on deploying the proposed algorithms in both the development and production environments of WeChat, a messenger app with over one billion of monthly active users, helps eliminate numerous defects of our NMT model, monitor the effectiveness on real-world translation tasks, and collect in-house test cases, producing high industry impact.