Many state-of-the-art algorithms for solving hard combinatorial problems in artificial intelligence (AI) include elements of stochasticity that lead to high variations in runtime, even for a fixed problem instance. Knowledge about the resulting runtime distributions (RTDs) of algorithms on given problem instances can be exploited in various meta-algorithmic procedures, such as algorithm selection, portfolios, and randomized restarts. Previous work has shown that machine learning can be used to individually predict mean, median and variance of RTDs. To establish a new state-of-the-art in predicting RTDs, we demonstrate that the parameters of an RTD should be learned jointly and that neural networks can do this well by directly optimizing the likelihood of an RTD given runtime observations. In an empirical study involving five algorithms for SAT solving and AI planning, we show that neural networks predict the true RTDs of unseen instances better than previous methods, and can even do so when only few runtime observations are available per training instance.

A new geometric shaping method is proposed, leveraging unsupervised machine learning to optimize the constellation design. The learned constellation mitigates nonlinear effects with gains up to 0.13 bit/4D when trained with a simplified fiber channel model.

With the growth of deep learning, how to describe deep neural networks unifiedly is becoming an important issue. We first formalize neural networks mathematically with their directed graph representations, and prove a generation theorem about the induced networks of connected directed acyclic graphs. Then, we set up a unified framework for deep learning with capsule networks. This capsule framework could simplify the description of existing deep neural networks, and provide a theoretical basis of graphic designing and programming techniques for deep learning models, thus would be of great significance to the advancement of deep learning.

Modeling and verifying real-world cyber-physical systems is challenging, which is especially so for complex systems where manually modeling is infeasible. In this work, we report our experience on combining model learning and abstraction refinement to analyze a challenging system, i.e., a real-world Secure Water Treatment system (SWaT). Given a set of safety requirements, the objective is to either show that the system is safe with a high probability (so that a system shutdown is rarely triggered due to safety violation) or not. As the system is too complicated to be manually modeled, we apply latest automatic model learning techniques to construct a set of Markov chains through abstraction and refinement, based on two long system execution logs (one for training and the other for testing). For each probabilistic safety property, we either report it does not hold with a certain level of probabilistic confidence, or report that it holds by showing the evidence in the form of an abstract Markov chain. The Markov chains can subsequently be implemented as runtime monitors in SWaT.

Word embeddings have been widely used in sentiment classification because of their efficacy for semantic representations of words. Given reviews from different domains, some existing methods for word embeddings exploit sentiment information, but they cannot produce domain-sensitive embeddings. On the other hand, some other existing methods can generate domain-sensitive word embeddings, but they cannot distinguish words with similar contexts but opposite sentiment polarity. We propose a new method for learning domain-sensitive and sentiment-aware embeddings that simultaneously capture the information of sentiment semantics and domain sensitivity of individual words. Our method can automatically determine and produce domain-common embeddings and domain-specific embeddings. The differentiation of domain-common and domain-specific words enables the advantage of data augmentation of common semantics from multiple domains and capture the varied semantics of specific words from different domains at the same time. Experimental results show that our model provides an effective way to learn domain-sensitive and sentiment-aware word embeddings which benefit sentiment classification at both sentence level and lexicon term level.

Sudoku is a well-known logic-based puzzle game that was first published in 1979 under the name of "Number Place". It was popularised in Japan in 1984 by the puzzle company Nikoli, and later named "Sudoku", which roughly translates to "single digits". The puzzle gained attention in the West in 2004, after The Times published its first Sudoku grid (at the instigation of Hong Kong-based judge Wayne Gould, who first encountered the puzzle in 1997, and developed a computer program to automatically generate instances). Sudoku is now a global phenomenon, and many newspapers now carry it alongside their existing crosswords (see [4] for a general history of the puzzle). The simplest variant of Sudoku uses a 9 9 grid of cells divided into nine 3 3 subgrids (Figure 1 (left)). The aim of the puzzle is to fill the grid with digits such that each row, each column, and each 3 3 subgrid contains all of the digits 1-9 (Figure 1 (right)). An instance of Sudoku provides, at the outset, a partially-completed grid, but the difficulty of any grid derives more from the range of techniques required to solve it than the number of cell values that are provided for the player. Sudoku is an NPcomplete problem [12], as first shown in [35] (via a reduction from the Latin Square Completion problem [2]).

Machine learning is a subset of artificial intelligence that permits the computer with the ability to learn things on the go. The current level of Artificial Intelligence is achieved through years of research in Machine Learning, Deep Learning and other related fields. With a lot of hype and investments around, Deep Learning technology – a subdivision of Machine Learning is now successfully applied in our daily life from speech recognition apps in smartphones to YouTube recommendations. The machine learning is mainly used for the advancement of computer programs that can change when the new data is introduced to the picture. The factors that promote the growth of machine learning are its diverse application and its ability to learn and solve real life problems from data.

Imagine you have the chance to bring back someone you love. Someone you never thought you'd see again. But the only way you can do this is by implanting memories into a bot designed to look and speak like that person, designed to help you cope as your own memories fade away. And which memories would you opt to keep … and or forget? These are just a few of the questions raised by Jordan Harrison's Pulitzer Prize-nominated play, Marjorie Prime, a fascinating and deeply human meditation on life, loss, memory, and how technology interweaves itself between all three.

DALLAS (AP) -- US oil has shot above $70 a barrel for the first time since late 2014, foreshadowing costlier gasoline and consumer goods. It's not clear that higher crude prices will threaten economic growth, however, and stocks are moving higher. Many factors are behind the increase including the possibility that President Donald Trump will scrap the deal that eased sanctions on Iran. That could pinch exports from that key oil producer. SEATTLE (AP) -- Nestle is paying more than $7 billion to handle global retail sales of Starbucks's coffee and tea outside of its coffee shops.

The following is adapted from State of Green Business 2018, published by GreenBiz in partnership with Trucost. There is no shortage of smart people willing to offer their sometimes dire, sometimes optimistic opinions about how humankind's future will be reshaped by computers and software using some sort of artificial intelligence (AI). If there's one thing upon which the naysayers and yeasayers agree, it's that AI is already more real than many people realize. A whopping 70 percent of the companies surveyed last year by Forrester Research plan to use some form of AI by the end of this year. It's tough to think of a tech giant that isn't making AI research a priority: Alphabet (through DeepMind and Google), Amazon, Apple, Facebook, IBM and Microsoft are throwing literally millions of dollars at this opportunity.