Data science and big data analytics are gradually making waves with advanced technologies like artificial intelligence (AI), machine learning (ML), and deep learning. Data science is essentially used to delve into huge datasets to extricate significant information. The experiences that data analysts and scientists acquire from huge volumes of data remain the mystery responsible for the swift transformation of the world around us. Companies and institutions across different divisions of the Industry are presently utilizing data science tools to control the changes driven by the recent innovations. Truth be told, about 55% of organizations have implemented huge big data analytics in 2017, which is a huge development from 18% in 2015.

Artificial Intelligence has been the center of media hype. Promises of self-driving cars, virtual assistants, and autonomy are pushed every day in headlines across the globe. Some of these headlines are legit and have real near-term possibilities, like self-driving cars. Others are greatly exaggerated with dramatic titles to drive ad revenue. A utopian future, where goods are abundant, people don't need to work, and products are manufactured by intelligent machines.

Description: We are seeking a highly motivated AI scientist to work on exciting AI, machine learning, and data science projects. Job Description Client's Software Lab is looking for an AI scientist to build the next generation AI systems. We seek candidates with a background in computer science (or a related field) and knowledge and experience with AI, machine learning, and deep learning algorithms and frameworks. Responsibilities include design and development of new data products and AI systems, design new AI algorithms, and conducting research to apply AI to solve problems and to improve the performance of existing AI systems. Desired Skills and Experience: PhD in Computer Science or related field (Electrical Engineering, Math, Physics, or Statistics) is required 5-8 years of experience in related field Strong knowledge in machine learning is required Knowledge in deep learning and experience with deep learning frameworks is required Strong programming skills with a scripting language such as Python, Perl, or shell programming are required Experience with a programming language and/or tool for machine learning, such as Python, R, TensorFlow, MXNet, or PyTorch etc. is required Experience and knowledge with reinforcement learning is a plus Experience and knowledge with distributed computing is a plus Publication on premier AI or data conferences is a plus

Meteorologists are starting to experiment with deep learning tech to predict severe weather patterns. David Gagne, a postdoctoral researcher at the US National Center for Atmospheric Research (NCAR), developed a simple convolutional neural network model to forecast the chances of hailstorms. In the last decade, severe storms caused about $14bn worth of damage and killed 94 people per year, Gagne said during a presentation at the GPU Technology Conference in San Jose, California. Meteorologists begin warning people of severe weather conditions the day before a hail event, but it's difficult to be precise. So Gagne wanted to see if deep learning could accurately identify the weather patterns leading up to hailstorms and reduce false alarms.

Complex industrial systems are continuously monitored by a large number of heterogenous sensors. The diversity of their operating conditions and the possible fault types make it impossible to collect enough data for learning all the possible fault patterns. The paper proposes an integrated automatic unsupervised feature learning approach for fault detection that uses healthy conditions data only for its training. The approach is based on stacked Extreme Learning Machines (namely Hierarchical, or HELM) and comprises stacked autoencoders performing unsupervised feature learning, and a one-class classifier monitoring the variations in the features to assess the health of the system. This study provides a comprehensive evaluation of HELM fault detection capability compared to other machine learning approaches, including Deep Belief Networks. The performance is first evaluated on a synthetic dataset with typical characteristics of condition monitoring data. Subsequently, the approach is evaluated on a real case study of a power plant fault. HELM demonstrates a better performance specifically in cases where several non-informative signals are included.

The Universal Turing Machine (TM) is a model for VonNeumann computers --- general-purpose computers. A human brain can inside-skull-automatically learn a universal TM so that he acts as a general-purpose computer and writes a computer program for any practical purposes. It is unknown whether a machine can accomplish the same. This theoretical work shows how the Developmental Network (DN) can accomplish this. Unlike a traditional TM, the TM learned by DN is a super TM --- Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, and Abstractive (GENISAMA). A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). Its learning from a teacher TM is one transition observation at a time, immediate, and error-free until all its neurons have been initialized by early observed teacher transitions. From that point on, the DN is no longer error-free but is always optimal at every time instance in the sense of maximal likelihood, conditioned on its limited computational resources and the learning experience. This letter also extends the Church-Turing thesis to automatic programming for general purposes and sketchily proved it.

As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction. Although extremely effective, most works are based only on quantitative characteristics of the convolutional filters, and highly overlook the qualitative interpretation of individual filter's specific functionality. In this work, we interpreted the functionality and redundancy of the convolutional filters from different perspectives, and proposed a functionality-oriented filter pruning method. With extensive experiment results, we proved the convolutional filters' qualitative significance regardless of magnitude, demonstrated significant neural network redundancy due to repetitive filter functions, and analyzed the filter functionality defection under inappropriate retraining process. Such an interpretable pruning approach not only offers outstanding computation cost optimization over previous filter pruning methods, but also interprets filter pruning process. 1 I NTRODUCTION The great success of Convolutional Neural Network (CNN) is benefited from its advanced algorithm and architecture, which utilize interconnected multi-layer network structure to hierarchically abstract the data feature for recognition tasks (Krizhevsky et al. (2012)). However, this complex mechanism offers CNN outstanding performance at the price of intensive computation cost. Therefore, many CNN optimization works have been proposed to alleviate this computation cost (Han et al. (2015), Jaderberg et al. (2014), etc).

Computer simulation provides an automatic and safe way for training robotic control policies to achieve complex tasks such as locomotion. However, a policy trained in simulation usually does not transfer directly to the real hardware due to the differences between the two environments. Transfer learning using domain randomization is a promising approach, but it usually assumes that the target environment is close to the distribution of the training environments, thus relying heavily on accurate system identification. In this paper, we present a different approach that leverages domain randomization for transferring control policies to unknown environments. The key idea that, instead of learning a single policy in the simulation, we simultaneously learn a family of policies that exhibit different behaviors. When tested in the target environment, we directly search for the best policy in the family based on the task performance, without the need to identify the dynamic parameters. We evaluate our method on five simulated robotic control problems with different discrepancies in the training and testing environment and demonstrate that our method can overcome larger modeling errors compared to training a robust policy or an adaptive policy. Recent developments in Deep Reinforcement Learning (DRL) have shown the potential to learn complex robotic controllers in an automatic way with minimal human intervention. However, due to the high sample complexity of DRL algorithms, directly training control policies on the hardware still remains largely impractical for agile tasks such as locomotion. A promising direction to address this issue is to use the idea of transfer learning which learns a model in a source environment and transfers it to a target environment of interest. In the context of learning robotic control policies, we can consider the real world the target environment and the computer simulation the source environment.

Neural architecture search (NAS) automatically finds the best task-specific neural network topology, outperforming many manual architecture designs. However, it can be prohibitively expensive as the search requires training thousands of different networks, while each can last for hours. In this work, we propose the Graph HyperNetwork (GHN) to amortize the search cost: given an architecture, it directly generates the weights by running inference on a graph neural network. GHNs model the topology of an architecture and therefore can predict network performance more accurately than regular hypernetworks and premature early stopping. To perform NAS, we randomly sample architectures and use the validation accuracy of networks with GHN generated weights as the surrogate search signal. GHNs are fast - they can search nearly 10 faster than other random search methods on CIFAR-10 and ImageNet. GHNs can be further extended to the anytime prediction setting, where they have found networks with better speed-accuracy tradeoff than the state-of-the-art manual designs. The success of deep learning marks the transition from manual feature engineering to automated feature learning. However, designing effective neural network architectures requires expert domain knowledge and repetitive trial and error.

Understanding how a learned black box works is of crucial interest for the future of Machine Learning. In this paper, we pioneer the question of the global interpretability of learned black box models that assign numerical values to symbolic sequential data. To tackle that task, we propose a spectral algorithm for the extraction of weighted automata (WA) from such black boxes. This algorithm does not require the access to a dataset or to the inner representation of the black box: the inferred model can be obtained solely by querying the black box, feeding it with inputs and analyzing its outputs. Experiments using Recurrent Neural Networks (RNN) trained on a wide collection of 48 synthetic datasets and 2 real datasets show that the obtained approximation is of great quality.