Insurance and technology are inextricably linked. The risks we seek to manage have always been connected to the tools we use. But, as Accenture's 2017 Technology Vision for Insurance points out, this relationship recently reached an inflection point. In the past, humans generally changed themselves to make use of new technologies--we learned to drive, we learned to type, we learned to code. But now modern technologies are sophisticated enough to adapt themselves to us.

Aerodynamics and physics dictate that you can't quite strap an assault rifle onto a DJI Phantom and expect it to fly and hit a target. Florida company Duke Robotics has apparently devised a way to keep a drone steady while compensating for a gun's recoil. "Though a system of flexibly connected pates, the TIKAD distributes the backward momentum in a way that keeps the vehicle stationary in the air," Defense One writes. "A ten-pound robot gimbal allows six degrees of movement freedom and the ability to rapidly re-target the weapon and camera." Supposedly the system can keep a UAV steady whether it has a grenade launcher for a payload, or a pistol -- so long as the weapon is under 22 pounds.

Theoretical neuroscientist, technologist and entrepreneur Dr Vivienne Ming looks to artificial intelligence and neuroprosthetics to augment humans. She spoke to Claire O'Connell. Artificial intelligence is a hot topic. From computers that can beat chess masters or diagnose disease, to the notion of autonomous machines putting humans out of jobs, there's a lot to think about. But, as with so many technologies, AI is just a tool, and it is how we use it that matters, according to theoretical neuroscientist and entrepreneur Dr Vivienne Ming, who was in Dublin last week.

Note: This is Part 2 of a two-part series on AI. PDF: We made a fancy PDF of this post for printing and offline viewing. We have what may be an extremely difficult problem with an unknown time to solve it, on which quite possibly the entire future of humanity depends. Welcome to Part 2 of the "Wait how is this possibly what I'm reading I don't get why everyone isn't talking about this" series. Part 1 started innocently enough, as we discussed Artificial Narrow Intelligence, or ANI (AI that specializes in one narrow task like coming up with driving routes or playing chess), and how it's all around us in the world today. We then examined why it was such a huge challenge to get from ANI to Artificial General Intelligence, or AGI (AI that's at least as intellectually capable as a human, across the board), and we discussed why the exponential rate of technological advancement we've seen in the past suggests that AGI might not be as far away as it seems. This left us staring at the screen, ...

Google has today awarded €706,000 ($800,000) to the UK's Press Association to develop robot reporters that can crank out 30,000 articles a month for local newspapers and bloggers. The cash injection is part of the advertising goliath's €150m Digital News Initiative, a three-year program that allegedly supports European journalism through technology (or, rather, helps websites get more readers and thus shift more Google ads). Now in its third and final year, the fund will bankroll 107 projects in 27 countries to the tune of €22m ($25m) in 2017. The Press Association project – codenamed RADAR, or Reporters and Data and Robots – will be a collaborative effort with Urbs Media, a UK startup that specializes in automated data journalism. The Press Association is Blighty's equivalent of America's Associated Press – a roving newswire that feeds stories to publications.

Support for the development and promotion of Artificial Intelligence is born from the deepest roots of state power in China that defined, for the new qualitative leap of that society, Innovation as its axis and Science and Technology as its sustenance. Li Keqiang, the Prime Minister, summed it up by stating that "science and technology change the world, innovation forms the future". The AI is expressly included. Xi Jinping, the President of China, described it this way: "We need to build an innovative world economy to generate new drivers of growth. Innovation holds the key to fundamentally unleashing the growth potential. The new round of scientific and industrial revolution with Internet at its core is gathering momentum, and new technologies such as artificial intelligence and virtual reality are developing by leaps and bounds. The combination of the virtual economy and the real economy will bring revolutionary changes to our way of work and way of life".

Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rely on machine learning models trained with a large number of features to make such predictions. This is inevitably requires a lot of engineering efforts to define, compute, and select the appropriate features. In this paper, we propose two novel approaches (one working at character level and the other working at word level) that use deep convolutional neural networks to predict the click-through rate of a query-advertisement pair. Specially, the proposed architectures only consider the textual content appearing in a query-advertisement pair as input, and produce as output a click-through rate prediction. By comparing the character-level model with the word-level model, we show that language representation can be learnt from scratch at character level when trained on enough data. Through extensive experiments using billions of query-advertisement pairs of a popular commercial search engine, we demonstrate that both approaches significantly outperform a baseline model built on well-selected text features and a state-of-the-art word2vec-based approach. Finally, by combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system.

We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.

The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure the cross-modality similarity. However, previous methods often establish the shared embedding space based on linear mapping functions which might not be sophisticated enough to reveal more complicated inter-modal correspondences. Additionally, current studies assume that the rankings are of equal importance, and thus all rankings are used simultaneously, or a small number of rankings are selected randomly to train the embedding space at each iteration. Such strategies, however, always suffer from outliers as well as reduced generalization capability due to their lack of insightful understanding of procedure of human cognition. In this paper, we involve the self-paced learning theory with diversity into the cross-modal learning to rank and learn an optimal multi-modal embedding space based on non-linear mapping functions. This strategy enhances the model's robustness to outliers and achieves better generalization via training the model gradually from easy rankings by diverse queries to more complex ones. An efficient alternative algorithm is exploited to solve the proposed challenging problem with fast convergence in practice. Extensive experimental results on several benchmark datasets indicate that the proposed method achieves significant improvements over the state-of-the-arts in this literature.

This paper presents a statistical method for use in music transcription that can estimate score times of note onsets and offsets from polyphonic MIDI performance signals. Because performed note durations can deviate largely from score-indicated values, previous methods had the problem of not being able to accurately estimate offset score times (or note values) and thus could only output incomplete musical scores. Based on observations that the pitch context and onset score times are influential on the configuration of note values, we construct a context-tree model that provides prior distributions of note values using these features and combine it with a performance model in the framework of Markov random fields. Evaluation results show that our method reduces the average error rate by around 40 percent compared to existing/simple methods. We also confirmed that, in our model, the score model plays a more important role than the performance model, and it automatically captures the voice structure by unsupervised learning.