Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i.e. subsets of possible events are taken into account to create so-called local process models. Often such smaller models provide valuable insights into the behavior of the process, especially when no adequate and comprehensible single overall process model exists that is able to describe the traces of the process from start to end. The practical application of LPM discovery is however hindered by computational issues in the case of logs with many activities (problems may already occur when there are more than 17 unique activities). In this paper, we explore three heuristics to discover subsets of activities that lead to useful log projections with the goal of speeding up LPM discovery considerably while still finding high-quality LPMs. We found that a Markov clustering approach to create projection sets results in the largest improvement of execution time, with discovered LPMs still being better than with the use of randomly generated activity sets of the same size. Another heuristic, based on log entropy, yields a more moderate speedup, but enables the discovery of higher quality LPMs. The third heuristic, based on the relative information gain, shows unstable performance: for some data sets the speedup and LPM quality are higher than with the log entropy based method, while for other data sets there is no speedup at all.

This overview article is devoted to the study of a trend which is growing rapidly in popularity in the IT industry - chatbots, and the role of Microsoft in their development process. The article will cover the history of chatbots, peculiar properties of bots, the main, and also some unexpected spheres of their application, perspectives and technology limits. We have deliberately chosen Microsoft as the main platform for comparative research. The company does a lot of work in the field of promotion and development of intelligent bots. One of the main steps in this direction is a framework for creation of custom bots Microsoft Bot Framework platform - independent and open source; Microsoft presented it at the Build 2016 exhibition. Generally, a chatbot is a program that can imitate a meaningful dialogue with the user via text or speech in the language known to the user. The goal of such a dialogue, is often to answer the user requests and execute bot commands. Not being something substantially new, chatbots however, are positioned in the marketplace as a sort of know-how activity.

You might not be campaigning to be America's next president, or have any desire to hold such a demanding office (bless you, Hillary), but wouldn't it still be nice to be treated like POTUS when you travel? Or, at least spend a few days in the presidential suite feeling like one of the world's most … Election jokes are i Saturday Night Live' /i s bread and butter, so it should come as no surprise that the cast took aim at Donald Trump's hot mic scandal. But host Lin-Manuel Miranda also got a chance to shine in his opening monologue. Below, we've rounded up the must-see moments from last night's /b … Humans may live longer and longer, but eventually we all grow old and die. This leads to a simple question: Is there an intrinsic maximum limit to human lifespan or not?

Spookily powerful artificial intelligence (AI) systems may work so well because their structure exploits the fundamental laws of the universe, new research suggests. The new findings may help answer a longstanding mystery about a class of artificial intelligence that employ a strategy called deep learning. These deep learning or deep neural network programs, as they're called, are algorithms that have many layers in which lower-level calculations feed into higher ones. Deep neural networks often perform astonishingly well at solving problems as complex as beating the world's best player of the strategy board game Go or classifying cat photos, yet know one fully understood why. It turns out, one reason may be that they are tapping into the very special properties of the physical world, said Max Tegmark, a physicist at the Massachusetts Institute of Technology (MIT) and a co-author of the new research.

For more than a decade, engineers have been eyeing the finish line in the race to shrink the size of components in integrated circuits. They knew that the laws of physics had set a 5-nanometer threshold on the size of transistor gates among conventional semiconductors, about one-quarter the size of high-end 20-nanometer-gate transistors now on the market. A research team led by faculty scientist Ali Javey at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) has done just that by creating a transistor with a working 1-nanometer gate. Boeing CEO vows to beat Musk to Mars Boeing Co. once helped the U.S. beat the Soviet Union in the race to the moon. Now the company intends to go toe-to-toe with newcomers such as billionaire Elon Musk in the next era of space exploration and commerce.

Imagine if computers could read and interpret documents. Humans could focus their efforts on understanding what analysis results mean to make better decisions. Our interest, at Dynamic Risk, is to improve the safety and reliability of energy pipeline networks by taking full advantage of the vast amounts of data locked in cumbersome formats, handwritten documents, drawings, photographs, and in paper archives. We want to fundamentally change how we ask questions and receive answers. Today, we ask questions based on the data we have available in structured databases.

Major technology firms are racing to infuse smartphones and other internet-linked devices with software smarts that help them think like people. The effort is seen as an evolution in computing that allows users to interact with machines in natural conversation style, telling devices to tend to tasks such as ordering goods, checking traffic, making restaurant reservations or searching for information. The artificial intelligence (AI) component in these programs aims to Read More ... Tags: Computer systems Computer Software Artificial intelligence Artificial intelligence software San Francisco (AFP) - Major technology firms are racing to infuse smartphones and other internet-linked devices with software smarts that help them think like people. The effort is seen as an evolution in computing that allows users to interact with machines in natural conversation style, telling devices to tend to tasks such as ordering goods, checking traffic, making restaurant reservations or searching for information. According to PRNewswire, Arria NLG, a leader in artificial intelligence ("AI") and natural language generation ("NLG"), is pleased to announce the private beta launch of Articulator Lite, a cloud-based toolkit that allows users to build their own applications that create content from data.

Recently, we presented how to load alpha signals into a research notebook, preprocess them, and then train a Machine Learning classifier to predict future returns. This was done in a static fashion, meaning we loaded data once over a fixed period of time (using the run_pipeline() command), split into test and train, and predicted inside of the research notebook. This leaves open the question of how to move this workflow to a trading algorithm, where run_pipeline() is not available. Here we show how you can move your ML steps into a pipeline CustomFactor where the classifier gets retrained periodically on the most recent data and predicts returns. This is still not moving things into a trading algorithm, but it gets us one step closer.

New drugs typically take 12-14 years to make it to market, with a 2014 report finding that the average cost of getting a new drug to market had ballooned to a whopping 2.6 billion. It's a topic I've covered before, with a study published earlier this year highlighting how automation could be used to reduce the cost of drug discovery by approximately 70%. It's an approach that a number of companies are taking to market. For instance, London based start-up Benevolent.AI utilizes complex AI to look for patterns in the scientific literature. They have already managed to identify two potential drug targets for Alzheimer's that has already attracted the attention of pharmaceutical companies.

Our team won the second prize of the Safe Aging with SPHERE Challenge organized by SPHERE, in conjunction with ECML-PKDD and Driven Data. The goal of the competition was to recognize activities performed by humans, using sensor data. This paper presents our solution. It is based on a rich pre-processing and state of the art machine learning methods. From the raw train data, we generate a synthetic train set with the same statistical characteristics as the test set. We then perform feature engineering. The machine learning modeling part is based on stacking weak learners through a grid searched XGBoost algorithm. Finally, we use post-processing to smooth our predictions over time.