A team of researchers at the University of Tokyo has created what appears to be the most advanced humanoid robot yet--actually two of them, one called Kenshiro the other Kengoro. In their paper published in the journal Science Robotics, the team describes working to make robots that are as similar to humans as possible and demonstrates what their two latest models can do. As robotics has advanced, most robots have evolved to become near single-purpose machines. Some are designed to paint cars, for example, others to carry supplies for soldiers. But some are of another class altogether, and are called humanoids because their purpose is not necessarily to accomplish a task, but to mimic the abilities of human beings.

For the past 90 years, TIME magazine has named a'Person of the Year'. The result is based on who has had the most influence on the news, leading to some controversial but entirely justified selections including Adolf Hitler and Josef Stalin. Last year, Donald Trump received the title over resounding favourite Narendra Modi. This year the Crown Prince of Saudi Arabia, Mohammed bin Salman, topped the reader's poll with a clear majority. Despite this, history repeated itself yet again when the #MeToo Campaign was announced as TIME's final choice. So, despite various online polls and predictions, it's notoriously difficult to know who will be picked.

Andrew Ng thinks a lot about getting machines ... to think for us. He was the founder of the artificial intelligence research project Google Brain. And his latest company is called Landing.AI. The startup has partnered with the electronics manufacturing giant Foxconn, as well as other companies in Japan, Taiwan, and mainland China to bring more AI into the manufacturing space. Ng says that AI has the potential to revitalize the factory floor.

AlphaGo has been going from strength to strength. In January, it emerged that DeepMind's Go-playing AI had been lurking incognito in online Go tournaments and secretly beating some of the world's top human players. And in May it beat Ke Jie, the world's number one player, in Wuzhen, China. Finally, in October, DeepMind unveiled a new version that hones its considerable skills by playing against itself. Three days and 4.9 million games later, AlphaGo Zero is unbeatable.

Abstract--The diversification (generating slightly varying separating discriminators) of Support V ector Machines (SVMs) for boosting has proven to be a challenge due to the strong learning nature of SVMs. Based on the insight that perturbing the SVM kernel may help in diversifying SVMs, we propose two kernel perturbation based boosting schemes where the kernel is modified in each round so as to increase the resolution of the kernel-induced Reimannian metric in the vicinity of the datapoints misclassified in the previous round. We propose a method for identifying the disjuncts in a dataset, dispelling the dependence on rule-based learning methods for identifying the disjuncts. We also present a new performance measure called Geometric Small Disjunct Index (GSDI) to quantify the performance on small disjuncts for balanced as well as class imbalanced datasets. Experimental comparison with a variety of state-of-the-art algorithms is carried out using the best classifiers of each type selected by a new approach inspired by multi-criteria decision making. The proposed method is found to outperform the contending state-of-the-art methods on different datasets (ranging from mildly imbalanced to highly imbalanced and characterized by varying number of disjuncts) in terms of three different performance indices (including the proposed GSDI). UPPORT V ector Machines (SVMs) [1] are a family of popular classifiers having elegant mathematical basis that can be used to model both linear and nonlinear (using the kernel trick) decision boundaries. The kernel trick is used to map the data to a higher dimensional feature space in order to facilitate linear separability between classes not linearly separable in the native input space. Shounak Datta, Sankha Subhra Mullick, and Swagatam Das are with the Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India. Sayak Nag is with the Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, India. While being highly effective for non-overlapping classes, the performance of SVMs suffers in case of overlapping classes, due to the presence of data irregularities such as class imbalance (under-represented classes) [2]-[4] and small disjuncts (under-represented sub-concepts within classes) [5]-[7]. Class imbalanced often results in greater misclassification from the minority class.

From human crowds to cells in tissue, the detection and efficient tracking of multiple objects in dense configurations is an important and unsolved problem. In the past, limitations of image analysis have restricted studies of dense groups to tracking a single or subset of marked individuals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here, we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive to automatically recognize all individuals in a dense group from raw image data. We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. We additionally exploit temporal regularities of the video recording in a recurrent manner and achieve near human-level performance while reducing the network size by 94% compared to the original U-Net architecture. Given our novel application of CNNs, we generate extensive problem-specific image data in which labeled examples are produced through a custom interface with Amazon Mechanical Turk. This dataset contains over 375,000 labeled bee instances across 720 video frames at 2 FPS, representing an extensive resource for the development and testing of tracking methods. We correctly detect 96% of individuals with a location error of ~7% of a typical body dimension, and orientation error of 12 degrees, approximating the variability of human raters. Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently within one network architecture.

December 21, 2017: Business Wire India NVIDIA brought together the best minds in research, academia and industry across Hyderabad, Chennai, Mumbai, Pune, Delhi and Bangalore 42 speaker sessions from leading experts in fields such as computer vision, sensor fusion, software development, regulation and HD mapping provide expertise NVIDIA today completed its first edition of Developer Connect 2017 in Bangalore. The six-city developer roadshow witnessed over 5,000 attendees who experienced some of the highest quality workshops and demonstrations of AI and deep learning tools, designed to meet the challenges big data presents. Attendees got a closer look at NVIDIA's DGX systems, as well as the opportunity to learn more about its new Volta architecture. Both the DGX-1 and DGX Station were on display to demonstrate the full power of these AI supercomputers. The concluding segment witnessed prominent speakers from organizations such as Ola, Cognitive Computing, Microsoft, Hewlett Packard Enterprise Labs, Shell India, Sony India and Aditya Imaging Information Technologies provide their views.

After 10 months in detention, on Nov. 27, the People's Court of Ha Tinh Province found Hoa guilty of inciting social unrest and promoting anti-state propaganda. He was sentenced to seven years in prison with a subsequent three years of house arrest for "sharing and disseminating articles, videos, images with negative content, inciting, distorting the truth," according to the court.

The Assistant SDK is the key to enabling Google's AI helper in third-party devices. Google has expanded the developer toolkit's support to several new countries, including Australia, Canada (both English and French), Germany, Japan and the UK. While Assistant-enabled devices have certainly been available in other countries, this makes it easier for hardware companies in those countries to get the ball rolling and cater to local audiences. On top of this, the SDK itself is becoming more powerful. If a device is using the kit, you can now customize its location (either through its latitude and longitude or a street address) to get area-specific results.

If 2017 left you breathless, exhausted by unexpected headlines, then brace yourself. The coming year may bring even more turbulent change, according to the CEOs, academics, economists and other bold thinkers we consulted for our annual peek at the year ahead.