Shortly after SoftBank acquired his company last October, Marc Raibert of Boston Dynamics confessed, "I happen to believe that robotics will be bigger than the Internet." Many sociologists regard the Internet as the single biggest societal invention since the dawn of the printing press in 1440. To fully understand Raibert's point of view, one needs to analyze his zoo of robots which are best know for their awe-striking gait, balance and agility. The newest creation to walk out of Boston Dynamic's lab is SpotMini, the latest evolution of mechanical canines. Big Dog, Spot's unnerving ancestor, first came to public view in 2009 and has racked up quite a YouTube following with more than six and one half million views.

The deluge of satellite imagery leaves U.S. intelligence agencies with the world's biggest case of FOMO--"fear of missing out"--because human analysts can sift through only so many images to spot a new nuclear enrichment facility or missiles being trucked to different locations. That's why U.S. intelligence officials have sponsored an artificial-intelligence challenge to automatically identify objects of interest in satellite images. Since July, competitors have trained machine-learning algorithms on one of the world's largest publicly available data sets of satellite imagery--containing 1 million labeled objects, such as buildings and facilities. The data is provided by the U.S. Intelligence Advanced Research Projects Activity (IARPA). The 10 finalists will see their AI algorithms scored against a hidden data set of satellite imagery when the challenge closes at the end of December.

We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems. Our agents explore via Thompson sampling, drawing Monte Carlo samples from a Bayes-by-Backprop neural network. Our algorithm learns much faster than common exploration strategies such as $\epsilon$-greedy, Boltzmann, bootstrapping, and intrinsic-reward-based ones. Additionally, we show that spiking the replay buffer with experiences from just a few successful episodes can make Q-learning feasible when it might otherwise fail.

Big spatio-temporal datasets, available through both open and administrative data sources, offer significant potential for social science research. The magnitude of the data allows for increased resolution and analysis at individual level. While there are recent advances in forecasting techniques for highly granular temporal data, little attention is given to segmenting the time series and finding homogeneous patterns. In this paper, it is proposed to estimate behavioral profiles of individuals' activities over time using Gaussian Process-based models. In particular, the aim is to investigate how individuals or groups may be clustered according to the model parameters. Such a Bayesian non-parametric method is then tested by looking at the predictability of the segments using a combination of models to fit different parts of the temporal profiles. Model validity is then tested on a set of holdout data. The dataset consists of half hourly energy consumption records from smart meters from more than 100,000 households in the UK and covers the period from 2015 to 2016. The methodological approach developed in the paper may be easily applied to datasets of similar structure and granularity, for example social media data, and may lead to improved accuracy in the prediction of social dynamics and behavior.

The following is a guest article from Joe Conway, founder and CEO of stable kernel. The rapid nature of technological advancement is moving us forward in ways we never predicted. The Internet of Things (IoT), in particular, has the potential to make professionals more efficient and productive through the use of enhanced machine learning (ML), augmented reality (AR) and connected devices. Companies are adding internet capability to devices that previously could only be monitored by physical access or not at all. For example, Rheem Manufacturing water heaters in customers' homes now emit performance data that provides opportunities to reduce utility bills.

Intelligence agencies have a limited number of trained human analysts looking for undeclared nuclear facilities, or secret military sites, hidden among terabytes of satellite images. But the same sort of deep learning artificial intelligence that enables Google and Facebook to automatically filter images of human faces and cats could also prove invaluable in the world of spy versus spy. An early example: US researchers have trained deep learning algorithms to identify Chinese surface-to-air missile sites--hundreds of times faster than their human counterparts. The deep learning algorithms proved capable of helping people with no prior imagery analysis experience find surface-to-air missile sites scattered across nearly 90,000 square kilometers of southeastern China. Such AI based on neural networks--layers of artificial neuron capable of filtering and learning from huge amounts of data--matched the overall 90 percent accuracy of expert human imagery analysts in locating the missile sites.

Artificial intelligence isn't just replacing previous technologies and methodologies; it is creating capabilities that weren't possible in the past. At the recent Platform Strategy Summit hosted by the MIT Initiative on the Digital Economy, SearchCIO staff sat down with Sam Ramji, the vice president of product management for the Google Cloud Platform, to discuss how to assess the value of new AI capabilities and quantify a market that isn't a typical, straightforward technology replacement or workload migration. In this video interview, he gives examples of budding AI capabilities and explains the difficulty of accurately measuring the business and societal value of such capabilities.

Epanechnikov Mean Shift is a simple yet empirically very effective algorithm for clustering. It localizes the centroids of data clusters via estimating modes of the probability distribution that generates the data points, using the `optimal' Epanechnikov kernel density estimator. However, since the procedure involves non-smooth kernel density functions, the convergence behavior of Epanechnikov mean shift lacks theoretical support as of this writing---most of the existing analyses are based on smooth functions and thus cannot be applied to Epanechnikov Mean Shift. In this work, we first show that the original Epanechnikov Mean Shift may indeed terminate at a non-critical point, due to the non-smoothness nature. Based on our analysis, we propose a simple remedy to fix it. The modified Epanechnikov Mean Shift is guaranteed to terminate at a local maximum of the estimated density, which corresponds to a cluster centroid, within a finite number of iterations. We also propose a way to avoid running the Mean Shift iterates from every data point, while maintaining good clustering accuracies under non-overlapping spherical Gaussian mixture models. This further pushes Epanechnikov Mean Shift to handle very large and high-dimensional data sets. Experiments show surprisingly good performance compared to the Lloyd's K-means algorithm and the EM algorithm.

WEST LAFAYETTE, Ind. – A system under development at Purdue University uses artificial intelligence to detect cracks captured in videos of nuclear reactors and represents a future inspection technology to help reduce accidents and maintenance costs. "Regular inspection of nuclear power plant components is important to guarantee safe operations," said Mohammad R. Jahanshahi, an assistant professor in Purdue's Lyles School of Civil Engineering. "However, current practice is time-consuming, tedious, and subjective and involves human technicians reviewing inspection videos to identify cracks on reactors." Complicating the inspection process is that nuclear reactors are submerged in water to maintain cooling. Consequently, direct manual inspection of a reactor's components is not feasible due to high temperatures and radiation hazards.

Speaking at the Abu Dhabi International Petroleum Exhibition Conference (ADIPEC) on Wednesday, Omar Saleh said technology disruptions over the past three years had been a "wake-up call" for all oil and gas firms.He said AI would be of "massive importance" over the next to five to 10 years, before adding that of any technology, AI would also have the most impact on the oil and gas sector overall.The U.S. shale revolution paved the way for a three-year oil price downturn that sent crude spiraling from more than $100 a barrel in 2014 to about $60 today. That has piled pressure on the oil-dependent economies of OPEC nations and forced a round of production cuts this year. On Tuesday, Baker Hughes GE CEO Lorenzo Simonelli said " " in the oil and gas industry should be viewed positively. Correction: This story has been updated to reflect that Omar Saleh believes AI will have the greatest technological impact on the oil and gas industry over the coming years. Speaking at the Abu Dhabi International Petroleum Exhibition Conference (ADIPEC) on Wednesday, Omar Saleh said technology disruptions over the past three years had been a "wake-up call" for all oil and gas firms.