HONG KONG--Chinese search-engine giant Baidu Inc. Senior Vice President Wang Jing said Friday that the company plans to mass produce a driverless car in five years--so that babies born today won't need a driver's license. Baidu, which is China's largest search-company with 80% market share, is already testing its model on public roads in Beijing and in Wuhu, in China's southeastern Anhui province, and in a closed testing area in Shanghai. Speaking at The Wall Street Journal's Converge technology conference, Mr. Wang said...

HONG KONG--The biggest challenges for Chinese companies making the next generation of wearables, self-driving cars and drones is having experts in cross disciplines, GGV Capital Managing Partner Jenny Lee said Friday. Speaking at the Converge technology conference hosted by The Wall Street Journal and f.ounders in Hong Kong, Ms. Lee said Chinese companies benefit from having government support and funding, and a huge market of...

Deep learning is a technique used to solve complex problems such as natural language processing and image recognition. We are now able to solve these computational problems quickly, thanks to a component called the Graphics Processing Unit (GPU). Originally used to generate high-resolution computer images at fast speeds, the GPU's computational efficiency makes it ideal for executing deep learning algorithms. Analysis which used to take weeks can now be completed in a few days. While all modern computers have a GPU, not all GPUs can be programmed for deep learning.

Note that V Y L Link set of G w ij W eight between two nodes in G G K -elite neighbor graph (K -ENG) V Node set of G . Note that V Y L Link set of G w ij W eight between two nodes in G p ij (1-step) transition probability fromy i to y j P (1-step) transition probability matrix,P { p ij } N N p T ij T -step transition probability fromy i to y j P T T -step transition probability matrix,P T { p T ij } N N p T i: The i -th row ofP T, p T i: { p T i 1,···,p T i N} PT T i Probability trajectory of a random walker starting fromnode y i with lengthT PTS ij Probability trajectory based similarity betweeny i and y j R (0) Set of the initial regions for PTA,R (0) { R (0) 1,···,R (0) R (0) } S (0) Initial similarity matrix for PTA,S (0) { s (0) ij } R (0) R (0) R ( t) Set of thet -step regions for PTA, R ( t) { R ( t) 1,···,R ( t) R ( t) } S ( t) The t -step similarity matrix for PTA,S ( t) { s ( t) ij } R ( t) R ( t) G Microcluster-cluster bipartite graph (MCBG) N Number of nodes in G V Node set of G L Link set of G w ij W eight between two nodes in G A sparse graph termedK -elite neighbor graph (K -ENG) is then constructed with only a small number of probably reliable links. The ENS strategy is a crucial step in our approach. W e argue that using a small number of probably reliable links may lead to significantly better consensus results than using all graph links regardless of their reliability . The random walk process driven by a new transition probability matrix is performed on theK -ENG to explore the global structure information.

$k$ Nearest Neighbors ($k$NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we propose a new graph-based $k$NN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data. To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by constructing an $R$-level nearest-neighbor strengthened tree over the graph, and then compute a TRW matrix for similarity measurement purposes. After this, the nearest neighbors are identified according to the TRW matrix and the class label of a query point is determined by the sum of all the TRW weights of its nearest neighbors. To deal with online situations, we also propose a new algorithm to handle sequential samples based a local neighborhood reconstruction. Comparison experiments are conducted on both synthetic data sets and real-world data sets to demonstrate the validity of the proposed new $k$NN algorithm and its improvements to other version of $k$NN algorithms. Given the widespread appearance of manifold structures in real-world problems and the popularity of the traditional $k$NN algorithm, the proposed manifold version $k$NN shows promising potential for classifying manifold-distributed data.

The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent years. There are mainly two aspects of limitations in the existing clustering ensemble approaches. Firstly, many approaches lack the ability to weight the base clusterings without access to the original data and can be affected significantly by the low-quality, or even ill clusterings. Secondly, they generally focus on the instance level or cluster level in the ensemble system and fail to integrate multi-granularity cues into a unified model. To address these two limitations, this paper proposes to solve the clustering ensemble problem via crowd agreement estimation and multigranularity link analysis. We present the normalized crowd agreement index (NCAI) to evaluate the quality of base clusterings in an unsupervised manner and thus weight the base clusterings in accordance with their clustering validity. To explore the relationship between clusters, the source aware connected triple (SACT) similarity is introduced with regard to their common neighbors and the source reliability. Present address: School of Information Science and Technology, Sun Yat-sen University, Guangzhou Higher Education Mega Center, Panyu District, Guangzhou, Guangdong, 510006, P. R. China. The experiments are conducted on eight real-world datasets. The experimental results demonstrate the effectiveness and robustness of the proposed methods. Keywords: Clustering ensemble, Clustering aggregation, Weighted evidence accumulation clustering, Graph partitioning with multi-granularity link analysis 1. Introduction Data clustering is a fundamental and very challenging problem in data mining and machine learning.

Driving styles have a great influence on vehicle fuel economy, active safety, and drivability. To recognize driving styles of path-tracking behaviors for different divers, a statistical pattern-recognition method is developed to deal with the uncertainty of driving styles or characteristics based on probability density estimation. First, to describe driver path-tracking styles, vehicle speed and throttle opening are selected as the discriminative parameters, and a conditional kernel density function of vehicle speed and throttle opening is built, respectively, to describe the uncertainty and probability of two representative driving styles, e.g., aggressive and normal. Meanwhile, a posterior probability of each element in feature vector is obtained using full Bayesian theory. Second, a Euclidean distance method is involved to decide to which class the driver should be subject instead of calculating the complex covariance between every two elements of feature vectors. By comparing the Euclidean distance between every elements in feature vector, driving styles are classified into seven levels ranging from low normal to high aggressive. Subsequently, to show benefits of the proposed pattern-recognition method, a cross-validated method is used, compared with a fuzzy logic-based pattern-recognition method. The experiment results show that the proposed statistical pattern-recognition method for driving styles based on kernel density estimation is more efficient and stable than the fuzzy logic-based method.

If you still think Minecraft is a PC game--well, you're flat wrong. According to new numbers released by Mojang and Microsoft, the original version for the PC is the least popular platform, in almost every region worldwide. Microsoft said Thursday that Minecraft has sold more than 106,859,714 copies to date across all platforms--which would represent the twelfth most populous nation in the world, right behind Japan. Four copies have even been sold into Antarctica. But if you dig into Microsoft's numbers, they reveal that far, far more users are buying Minecraft on platforms other than the PC.

Robots play football in a demonstration of artificial intelligence at the stand of the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum fuer Kuenstliche Intelligenz GmbH) at the CeBIT Technology Fair on March 2, 2010 in Hannover, Germany. China's National Development and Reform Commission (NDRC) has announced a three-year guidance program in which the country plans to increase the advancement of the nation's artificial intelligence (AI) sector. According to the NDRC, the plan - which was devised together with China's Ministry of Science and Technology, the Ministry of Industry and Information Technology, and the Cyberspace Administration of China - is expected to create new AI industries and economic growth which will result in a market value of over 15.26 million (100 billion Yuan) in the next three years. The three-year program for "Internet Plus" AI indicated that the move will eventually see China developing "platforms for fundamental AI resources and innovation" by the year 2018. In the same year the country is expected to be at about the same level of the world's AI industry and technology, according to the NDRC website.

The fact that most insects (except for the really freaky ones) are very small doesn't stop them from getting everywhere they want to, especially all of those places that you try to keep them out of. Roboticists have been experimenting with bug-sized robots, but they're still pretty large, about the size of giant beetles or moths. Most insects are far smaller than that, which means that they experience the world much differently, and that can be a hard thing to study effectively. At ICRA last month, Ryan St. Pierre and Professor Sarah Bergbreiter from the University of Maryland presented a paper on the gait characteristics of magnetically actuated legged robots weighing less than 2 grams, which was very cool to see. It's only the beginning, though: robots like these are about to get way, way smaller.