This paper proposes a hierarchical feature extractor for non-stationary streaming time series based on the concept of switching observable Markov chain models. The slow time-scale non-stationary behaviors are considered to be a mixture of quasi-stationary fast time-scale segments that are exhibited by complex dynamical systems. The idea is to model each unique stationary characteristic without a priori knowledge (e.g., number of possible unique characteristics) at a lower logical level, and capture the transitions from one low-level model to another at a higher level. In this context, the concepts in the recently developed Symbolic Dynamic Filtering (SDF) is extended, to build an online algorithm suited for handling quasi-stationary data at a lower level and a non-stationary behavior at a higher level without a priori knowledge. A key observation made in this study is that the rate of change of data likelihood seems to be a better indicator of change in data characteristics compared to the traditional methods that mostly consider data likelihood for change detection. The algorithm minimizes model complexity and captures data likelihood. Efficacy demonstration and comparative evaluation of the proposed algorithm are performed using time series data simulated from systems that exhibit nonlinear dynamics. We discuss results that show that the proposed hierarchical SDF algorithm can identify underlying features with significantly high degree of accuracy, even under very noisy conditions. Algorithm is demonstrated to perform better than the baseline Hierarchical Dirichlet Process-Hidden Markov Models (HDP-HMM). The low computational complexity of algorithm makes it suitable for on-board, real time operations.

Machine learning has been gaining traction in recent years to meet the demand for tools that can efficiently analyze and make sense of the ever-growing databases of biomedical data in health care systems around the world. However, effectively using machine learning methods requires considerable domain expertise, which can be a barrier of entry for bioinformaticians new to computational data science methods. Therefore, off-the-shelf tools that make machine learning more accessible can prove invaluable for bioinformaticians. To this end, we have developed an open source pipeline optimization tool (TPOT-MDR) that uses genetic programming to automatically design machine learning pipelines for bioinformatics studies. In TPOT-MDR, we implement Multifactor Dimensionality Reduction (MDR) as a feature construction method for modeling higher-order feature interactions, and combine it with a new expert knowledge-guided feature selector for large biomedical data sets. We demonstrate TPOT-MDR's capabilities using a combination of simulated and real world data sets from human genetics and find that TPOT-MDR significantly outperforms modern machine learning methods such as logistic regression and eXtreme Gradient Boosting (XGBoost). We further analyze the best pipeline discovered by TPOT-MDR for a real world problem and highlight TPOT-MDR's ability to produce a high-accuracy solution that is also easily interpretable.

One of the most fundamental questions one can ask about a pair of random variables X and Y is the value of their mutual information. Unfortunately, this task is often stymied by the extremely large dimension of the variables. We might hope to replace each variable by a lower-dimensional representation that preserves the relationship with the other variable. The theoretically ideal implementation is the use of minimal sufficient statistics, where it is well-known that either X or Y can be replaced by their minimal sufficient statistic about the other while preserving the mutual information. While intuitively reasonable, it is not obvious or straightforward that both variables can be replaced simultaneously. We demonstrate that this is in fact possible: the information X's minimal sufficient statistic preserves about Y is exactly the information that Y's minimal sufficient statistic preserves about X. As an important corollary, we consider the case where one variable is a stochastic process' past and the other its future and the present is viewed as a memoryful channel. In this case, the mutual information is the channel transmission rate between the channel's effective states. That is, the past-future mutual information (the excess entropy) is the amount of information about the future that can be predicted using the past. Translating our result about minimal sufficient statistics, this is equivalent to the mutual information between the forward- and reverse-time causal states of computational mechanics. We close by discussing multivariate extensions to this use of minimal sufficient statistics.

Recognition of goals and plans using incomplete evidence from action execution can be done efficiently by using planning techniques. In many applications it is important to recognize goals and plans not only accurately, but also quickly. In this paper, we develop a heuristic approach for recognizing plans based on planning techniques that rely on ordering constraints to filter candidate goals from observations. These ordering constraints are called landmarks in the planning literature, which are facts or actions that cannot be avoided to achieve a goal. We show the applicability of planning landmarks in two settings: first, we use it directly to develop a heuristic-based plan recognition approach; second, we refine an existing planning-based plan recognition approach by pre-filtering its candidate goals. Our empirical evaluation shows that our approach is not only substantially more accurate than the state-of-the-art in all available datasets, it is also an order of magnitude faster.

MEPs have called for the adoption of comprehensive rules for how humans will interact with artificial intelligence and robots. The report makes it clear that it believes the world is on the cusp of a "new industrial" robot revolution. It looks at whether to give robots legal status as "electronic persons". Designers should make sure any robots have a kill switch, which would allow functions to be shut down if necessary, the report recommends. Meanwhile users should be able to use robots "without risk or fear of physical or psychological harm", it states.

Public sector agencies must adopt emerging technologies – including machine learning, artificial intelligence, and biometrics – to attract and retain more technically adept employees, a new report from Accenture recommends. It said this is critical to addressing a widening skills gap and strong competition from a better financed private sector. According to the report, "Emerging Technologies in Public Service," the need to attract technically proficient employees is becoming even more urgent as the existing workforce continues to age, creating an irrevocable loss of institutional knowledge unless action is taken now. The report emphasized that hiring and developing people with the necessary skills, including the need for emerging technology specialists, is one of the top three challenges across all industries and countries today," the report noted. "The very concept of work is being redefined as different generations enter and exit the workforce in a rapidly changing technological landscape," said Terry Hemken, who leads Accenture's Health & Public Service Analytics Insights for Government business. "Government leaders must make every effort to reskill their people to be relevant in the future and ready to adapt to change." Survey respondents said emerging technologies will augment existing roles rather than replace them. Automating tasks, whether through artificial intelligence, machine learning or other technologies, frees up employees to focus on activities that are more critical and more closely aligned with citizen needs, according to the research. In fact, eight in 10 respondents said that implementing emerging technologies will improve job satisfaction and can aid staff retention, partly by automating certain repetitive tasks and making others more aligned with citizens' direct needs. Nearly 60 percent of respondents also said that being able to implement projects using emerging technologies would require significant investment in reskilling existing staff. "Responsive and responsible leaders must ensure that their people are relevant and adaptable to keep pace with technology," Hemken said. "Creating the future workforce now is the responsibility of the very highest levels of an organization.

Chess enthusiasts watch World Chess champion Garry Kasparov on a television monitor as he holds his head in his hands at the start of the sixth and final match 11 May 1997 against IBM's Deep Blue computer in New York. Kasparov lost this match in just 19 moves giving overall victory to Deep Blue with a score of 2.5-3.5 (STAN HONDA/AFP/Getty Images) This week's milestones in the history of technology include the invention of the integrated circuit, the first singing telegram, and the first widely-publicized triumph of the machines over humans. Jack Kilby of Texas Instruments (TI) files for a patent on the integrated circuit. For this invention he received the 2000 Nobel Prize for Physics. The notion of an integrated circuit was there.

The days of a depending on a human doctor may soon be numbered, as the future of the health industry looks increasingly like an AI-assisted scenario. Researchers and startups are developing artificially intelligent systems that are capable of diagnosing disease using a patient's breath and even from the emotional inflection of their voice. Someday, your smartphone may help you and your doctor determine whether a strange-looking lesion on your skin is cancerous or not, thanks to a team of Stanford University scientists that have developed a deep learning algorithm tailored just for the task. Led by Sebastian Thrun, an adjunct professor at the Stanford Artificial Intelligence Laboratory, the team found that their diagnostic tool, which builds upon the same classification technique used by Google to differentiate between images of cats and dogs, performed as well or better than 21 board-certified dermatologists. Their findings were detailed in a recent paper published in Nature.

Machine learning gives you unimaginably powerful insights into data. Today, implementations of machine learning have been adopted throughout Industry and its concepts are numerous. This video is a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. The video will cover concepts such as classification, regression, clustering, and more, all the while working with different kinds of databases.

Artificial Intelligence: How far have we reached since the term was coined? Artificial intelligence holds the key to a new era of innovation, one where computers begin to work intelligently on our behalf rather than under our command. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider "smart". Artificial Intelligence has been around for a long time – the Greek myths contain stories of mechanical men designed to mimic our own behavior. It's an era where technology will become more intuitive, more conversational, and more intelligent, will enable businesses to better know and serve their customers in ways previously unimaginable, and ultimately help solve some of the planet's biggest challenges.