Amazon SageMaker is a fully managed service for developers and data scientists to quickly build, train, deploy, and manage their own machine learning models. AWS also introduced AWS DeepLens, a deep learning-enabled wireless video camera that can run real-time computer vision models to give developers hands-on experience with machine learning. And, AWS announced four new application services that allow developers to build applications that emulate human-like cognition: Amazon Transcribe for converting speech to text; Amazon Translate for translating text between languages; Amazon Comprehend for understanding natural language; and, Amazon Rekognition Video, a new computer vision service for analyzing videos in batches and in real-time. Today, implementing machine learning is complex, involves a great deal of trial and error, and requires specialized skills. Developers and data scientists must first visualize, transform, and pre-process data to get it into a format that an algorithm can use to train a model.

She's appeared on the cover of Britain's ELLE magazine and talk shows like Good Morning Britain and The Tonight Show. When Charlie Rose interviewed her on CBS's 60 Minutes this past June, the dangers of this humanoid hit me like a tidal wave. Sophia is the latest in a string of robotic humanoids. And, she is arguably the most powerful and threatening "woman" on the planet. Renowned scientist Stephen Hawking and hundreds of technology leaders are also worried about the potential risk of robots like Sophie being used as weaponry.

Long Short-Term Memory (LSTM) is a popular approach to boosting the ability of Recurrent Neural Networks to store longer term temporal information. The capacity of an LSTM network can be increased by widening and adding layers. However, usually the former introduces additional parameters, while the latter increases the runtime. As an alternative we propose the Tensorized LSTM in which the hidden states are represented by tensors and updated via a cross-layer convolution. By increasing the tensor size, the network can be widened efficiently without additional parameters since the parameters are shared across different locations in the tensor; by delaying the output, the network can be deepened implicitly with little additional runtime since deep computations for each timestep are merged into temporal computations of the sequence. Experiments conducted on five challenging sequence learning tasks show the potential of the proposed model.

In this paper, we propose a mixture of probabilistic partial canonical correlation analysis (MPPCCA) that extracts the Causal Patterns from two multivariate time series. Causal patterns refer to the signal patterns within interactions of two elements having multiple types of mutually causal relationships, rather than a mixture of simultaneous correlations or the absence of presence of a causal relationship between the elements. In multivariate statistics, partial canonical correlation analysis (PCCA) evaluates the correlation between two multivariates after subtracting the effect of the third multivariate. PCCA can calculate the Granger Causal- ity Index (which tests whether a time-series can be predicted from an- other time-series), but is not applicable to data containing multiple partial canonical correlations. After introducing the MPPCCA, we propose an expectation-maxmization (EM) algorithm that estimates the parameters and latent variables of the MPPCCA. The MPPCCA is expected to ex- tract multiple partial canonical correlations from data series without any supervised signals to split the data as clusters. The method was then eval- uated in synthetic data experiments. In the synthetic dataset, our method estimated the multiple partial canonical correlations more accurately than the existing method. To determine the types of patterns detectable by the method, experiments were also conducted on real datasets. The method estimated the communication patterns In motion-capture data. The MP- PCCA is applicable to various type of signals such as brain signals, human communication and nonlinear complex multibody systems.

Deep generative models are reported to be useful in broad applications including image generation. Repeated inference between data space and latent space in these models can denoise cluttered images and improve the quality of inferred results. However, previous studies only qualitatively evaluated image outputs in data space, and the mechanism behind the inference has not been investigated. The purpose of the current study is to numerically analyze changes in activity patterns of neurons in the latent space of a deep generative model called a "variational auto-encoder" (VAE). What kinds of inference dynamics the VAE demonstrates when noise is added to the input data are identified. The VAE embeds a dataset with clear cluster structures in the latent space and the center of each cluster of multiple correlated data points (memories) is referred as the concept. Our study demonstrated that transient dynamics of inference first approaches a concept, and then moves close to a memory. Moreover, the VAE revealed that the inference dynamics approaches a more abstract concept to the extent that the uncertainty of input data increases due to noise. It was demonstrated that by increasing the number of the latent variables, the trend of the inference dynamics to approach a concept can be enhanced, and the generalization ability of the VAE can be improved.

The dueling bandit is a learning framework wherein the feedback information in the learning process is restricted to a noisy comparison between a pair of actions. In this research, we address a dueling bandit problem based on a cost function over a continuous space. We propose a stochastic mirror descent algorithm and show that the algorithm achieves an $O(\sqrt{T\log T})$-regret bound under strong convexity and smoothness assumptions for the cost function. Subsequently, we clarify the equivalence between regret minimization in dueling bandit and convex optimization for the cost function. Moreover, when considering a lower bound in convex optimization, our algorithm is shown to achieve the optimal convergence rate in convex optimization and the optimal regret in dueling bandit except for a logarithmic factor.

What happens when you create an open AI marketplace that can learn from itself? Remember Sophia, the humanoid robot who was granted citizenship by Saudi Arabia? From the general media hubbub to the discussion surrounding the Saudi government's treatment of women's rights, the landmark event caused quite a stir across the web. But behind all that buzz, SingularityNET, whose artificial intelligence technology powers the robot built by Hanson Robotics, has been working to completely revolutionize web as we understand it. In some sense, the premise behind SingularityNET is fairly simple.

Volume 20 in the "sound tectonics" live concert series showcasing cutting-edge music and sound art from Japan and abroad is a DJ event hosted by programmer/DJ Nao Tokui's Qosmo. Having been involved in the production of AI-based works and algorithmic design, in 2016 Qosmo launched the "AIDJ PROJECT" revolving around the development of artificial intelligence DJs with the aim to establish a dialogue between humans and AI. This time's event showcases the latest AIDJ format developed in this project, with AIDJs and human DJs alternatingly spinning tracks in the so-called "back to back" style. The human performers appearing this time are tofubeats and Licaxxx, two up-and-coming DJs who have recently appeared at various music events and festivals.

Russia's next president could be an artificially intelligent robot that claims'enemies of the people will be shot'. Forty thousand Russians have nominated a piece of AI software on their phones to stand against Vladimir Putin for the 2018 Russian presidency. The AI assistant known as Alisa, similar to the Apple's voice-activated Siri, was created by Russian technology company Yandex. Russia's next president could be an artificially intelligent robot that claims'enemies of the people will be shot'. Since the AI's launch in September, Alisa has stirred controversy on social media, with users sharing a series of contentious statements from the software.

THIS is part of my ongoing series on technology and real estate. The focus is on artificial intelligence (AI), which has been casting a dark shadow on the Philippine business-process outsourcing (BPO) industry. According to the Oxford dictionary, AI is "the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making and translation between languages." Artificial intelligence has great potential to disrupt existing industries and traditional work practices across the world. And the Philippines is no exception.