Alibaba is teaming up with SenseTime, the world's highest-valued AI startup, to launch a not-for-profit artificial intelligence lab in Hong Kong in a bid to make the city a global hub for artificial intelligence. Alibaba, which is SenseTime's largest single investor thanks to a recent $600 million round at a valuation of $4.5 billion, is providing financing for the "HKAI Lab" through its Hong Kong entrepreneurship fund. SenseTime said it will contribute too, although the total amount of capital backing the initiative hasn't been revealed. The partners of the project -- which also includes the Hong Kong Science and Technology Parks Corporation (HKSTP) -- said the aim is to "advance the frontiers of AI," which includes helping startups commercialize their technology, develop ideas and promote knowledge sharing in the AI field. That's all fairly general -- Alibaba has a track record of politicking through technology investment schemes in Greater China and Southeast Asia -- but one tangible project is a six-month accelerator program planned for September which will welcome AI startups to the HKAI Lab.

Before diving into global investment trends in Artificial Intelligence ("AI") and Machine Learning ("ML"), it may be worth quickly defining them. To quote John McCarthy, widely recognized as one of the pre-imminent leaders in this space, AI is defined as "The science and engineering of making intelligent machines." AI systems can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Machine Learning is a subset of AI. The main characteristic that separates Machine Learning from most AI is its ability to modify itself when exposed to more data; i.e.

India plans to roll out state-of-the-art 5G telecom services in the next four years, a senior official said, as the nation rushes to catch up with its Asian peers. "We are not there yet," Telecom Secretary Aruna Sundararajan said in an interview in New Delhi, adding that the complete rollout of 5G will be done by 2022. "5G won't be driven by supply, it'll be driven by demand and the rest of industry needs to wake up to this." The South Asian nation, traditionally a laggard in embracing the latest technology in telecommunications, will follow South Korea, Japan and China, countries where 5G service will be offered within the next two years. The high-speed and low-latency service will help Prime Minister Narendra Modi's Digital India plan, which seeks to broaden Internet access.

Tokyo 2020 Olympic and Paralympic organizers are confident that security for the games will run smoothly and effectively after unveiling a pioneering face-recognition ID system Tuesday. The technology, which will be provided by NEC Corp. and used at the Olympics and Paralympics for the first time, allows athletes, officials and others accredited for the games access to restricted areas by identifying their faces, based on images previously collected and stored in a database. Accredited individuals must hold a card containing a chip with their facial data up to a terminal at each security check point, while also looking into a camera to verify their identity. NEC says that the technology, which will not be used for spectators, performs facial recognition "immediately" and has an accuracy rate of more than 99 percent. Organizers believe the system will speed up a process that could otherwise see long lines of people waiting in the sweltering summer heat, and will "drastically increase security levels" by detecting forgeries and attempted misuse of access.

Local organizers said Tokyo will be the first Olympic host to introduce the face recognition technology at all venues. The system is expected to effectively eliminate entry with forged IDs, reduce congestion at accredited waiting lines and reduce athletes' stress under hot weather.

Artificial intelligence is being credited for helping Hong Kong customs officials increase seizures of fake goods sold online by about one-third in the first six months of this year, resulting in a haul of counterfeit items worth HK$1.96 million (US$247,000). A new supercomputer they began using last December scoured websites 24 hours a day and detected close to 2,000 of the 5,200 items seized by the Customs & Excise Department. Over the same period last year, officers netted 11,800 pieces of counterfeit goods worth HK$1.47 million. A source said the department might look into expanding the capacity of the computer, which gathers important information during investigations, but he stressed it would complement rather than replace manual enforcement work by customs officers. "The analytics tool saves us a lot of time screening online platforms manually," the source said.

Researchers at the Advanced Telecommunications Research Institute International in Japan have created a brain-machine interface for the manipulation of a robotic arm. Researchers at the Advanced Telecommunications Research Institute International in Japan have created a brain-machine interface (BMI) for manipulating a robotic arm without requiring the use of hands. Their system enables the user to control three limbs at once, including two natural arms and the robotic arm. The researchers demonstrated the system by having volunteers balance a ball on a flat surface while the system recorded their brain wave activity; afterward, when a volunteer would think about balancing the ball, the system would recognize the brain wave pattern and move the robot arm to move the ball in a similar way. The researchers found some volunteers were more successful than others in having the robot arm balance the ball, which the researchers said probably was due to the volunteers, not the system.

Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and research applications. How- ever, acquiring quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at odds with high-resolution in MRI, especially in a single rapid sequence. In this paper, we demonstrate how super-resolution can be utilized to maintain adequate SNR for accurate quantification of the T2 relaxation time biomarker, while simultaneously generating high- resolution images. We compare the efficacy of resolution enhancement using metrics such as peak SNR and structural similarity. We assess accuracy of cartilage T2 relaxation times by comparing against a standard reference method. Our evaluation suggests that SR can successfully maintain high-resolution and generate accurate biomarkers for accelerating MRI scans and enhancing the value of clinical and research MRI.

Despite great advances, molecular cancer pathology is often limited to use a small number of biomarkers rather than the whole transcriptome, partly due to the computational challenges. Here, we introduce a novel architecture of DNNs that is capable of simultaneous inference of various properties of biological samples, through multi-task and transfer learning. We employed this architecture on mRNA transcription profiles of 10787 clinical samples from 34 classes (one healthy and 33 different types of cancer) from 27 tissues. Our system significantly outperforms prior works and classical machine learning approaches in predicting tissue-of-origin, normal or disease state and cancer type of each sample. Furthermore, it can predict miRNA transcription profile of each sample, which enables performing miRNA expression research when only mRNA transcriptome data are available. We also show this system is very robust against noise and missing values. Collectively, our results highlight applications of artificial intelligence in molecular cancer pathology and oncological research.

Conventional approaches used supervised learning to estimate off-line writer identifications. In this study, we improved the off-line writer identifica- tions by semi-supervised feature learning pipeline, which trained the extra unla- beled data and the original labeled data simultaneously. In specific, we proposed a weighted label smoothing regularization (WLSR) method, which assigned the weighted uniform label distribution to the extra unlabeled data. We regularized the convolutional neural network (CNN) baseline, which allows learning more discriminative features to represent the properties of different writing styles. Based on experiments on ICDAR2013, CVL and IAM benchmark datasets, our results showed that semi-supervised feature learning improved the baseline meas- urement and achieved better performance compared with existing writer identifications approaches.