Some argue that robots do not have free will and therefore cannot be held morally accountable for their actions. But UW psychologists are finding that people don't have such a clear-cut view of humanoid robots. The researchers' latest results show that humans apply a moderate amount of morality and other human characteristics to robots that are equipped with social capabilities and are capable of harming humans. In this case, the harm was financial, not life-threatening. But it still demonstrated how humans react to robot errors.

Using a test that can analyze the patterns of small proteins in blood serum samples in just 30 minutes, researchers were able to differentiate between samples taken from patients diagnosed with cancer and those from patients diagnosed with benign prostate disease. The technique proved effective not only in men with normal and high PSA levels, but also in those whose PSA levels were marginally elevated (4 to 10 nanograms of antigen per milliliter of fluid), in whom it is difficult to rule out cancer without a biopsy. Although the technique is still under evaluation, researchers believe the analysis of protein patterns will be a useful tool in the future for deciding whether men with marginally elevated PSA levels should undergo biopsy. PSA levels are commonly used as a preliminary screen for prostate cancer, but 70 percent to 75 percent of men who undergo biopsy because of an abnormal PSA level do not have cancer. The new proteomic approach has a higher specificity - that is, of the samples the test identifies as cancer, a large percentage are in fact cancer, rather than some other benign disease.

This book is not an introductory text. Anyone interested in a particular topic should consult the preface of the text to find out what it is about. The negative reviewers were not fair to the authors on that score. Had they read the preface they would have found out (1) how the authors define data mining, (2) that they see it as a subject with an important mix of statistical methodology and computer science and (3) that it is intended as an advanced undergraduate or first year graduate text on the topic. They also provide a very well organized structure for the text that is well described in the preface.

Many important real-world datasets come in the form of graphs or networks: social networks, knowledge graphs, protein-interaction networks, the World Wide Web, etc. (just to name a few). Yet, until recently, very little attention has been devoted to the generalization of neural network models to such structured datasets. In the last couple of years, a number of papers re-visited this problem of generalizing neural networks to work on arbitrarily structured graphs (Bruna et al., ICLR 2014; Henaff et al., 2015; Duvenaud et al., NIPS 2015; Li et al., ICLR 2016; Defferrard et al., NIPS 2016; Kipf & Welling, 2016), some of them now achieving very promising results in domains that have previously been dominated by, e.g., kernel-based methods, graph-based regularization techniques and others. In this post, I will give a brief overview of recent developments in this field and point out strengths and drawbacks of various approaches. I wrote a short comment on Ferenc's review here (at the very end of this post).

This post is authored by Kristin M. Tolle, Director of Program Management for Advanced Analytics Ecosystem Development and Training at Microsoft. Cortana Intelligence, Microsoft's end-to-end platform for Advanced Analytics, offers a suite of services to solve real world customer problems. The suite has many moving parts – Data Lake, HDInsight (Hadoop), Event Hub, Machine Learning and R – just to name a few, and we realize it may be challenging for some of you to experience first-hand how all these services work together in concert. My team, which is tasked with training our partners to use these services to address their customers' needs, is keenly aware of the breadth of that knowledge surface area. In this blog post, I outline some of the best ways for you to learn about all things Big Data and Advanced Analytics from Microsoft, including many hands-on training options, and also how to stay in the loop on our future offerings.

Job titles for data scientists, including details about the simple but powerful classifier used to categorize these job titles. This analysis provides a break down per job category, and granular reports that you can download for free (job titles broken down per company, category and level), as well as NLP (natural language processing) source code. It is based on analyzing connections from multiple LinkedIn profiles - totaling more than 10,000 professionals. The first study was published in June 2013. The table below shows the top job titles in the business analytics category.

For as long as humans have had consciousness -- two or two hundred millennia depending on who you ask -- human scholars have made great efforts to understand and define what that means. The most facile and purely conceptual description of consciousness might be that it is an awareness of the self within the context of the world. But without an understanding of the underlying mechanism, consciousness keeps chasing its tail. This is, in part, why neuroscientists have successfully interjected themselves in the ongoing conversation about consciousness by pointing to physical phenomena within the brain. But linking the metaphysical to the physical still results in the sort of quasi-scientific, quasi-philosophical overreach that gets academics laughed out of faculty lounges and labeled eccentric.

This will present what is Apache SystemML. It includes demonstration of how Data Scientist can use Apache SystemML through Jupyter notebook (or Data Science Experience Jupyter notebook) to do Machine Learning pipeline. It will have very high level information about how Apache SystemML achieves performance gain through its optimization and runtime techniques. As well it will explain how user can get Apache SystemML software and use it for doing machine learning similar to how it is demonstrated in the session. For more information about the Spark Technology Center: http://www.spark.tc/ Follow us: @apachespark_tc Location: San Francisco, CA Apache, Apache Spark, and Spark are trademarks of the Apache Software Foundation in the United States and/or other countries.

This article originally ran in Term Sheet, Fortune's newsletter about deals and dealmakers. Last year, the U.S. tech titans slowed their pace of acquisitions, but three companies bucked that trend: Google, Intel, and Salesforce.com. Of the three, Salesforce had the highest acceleration of deals. Last year the $53 billion software company did 120% more acquisitions than it did in 2015, according to CB Insights. I spoke with John Somorjai, the EVP of corporate development and Salesforce Ventures, about AI startups, startup valuations, how the company deals with coop-etition, and what's overhyped. This interview has been lightly edited for length and clarity.

Artificial Intelligence Market in the Industrial Sector research report also provides granular analysis of the market share, segmentation, revenue forecasts and geographic regions of the market. AI technologies are being developed to assist human beings in deliberating, deducing, analyzing, and inventing new technologies that can guarantee the Fourth Industrial Revolution. The availability and widespread adoption of graphical processing units due to innovation in technology, increased power capacity, and reduced costs have been an impetus for the adoption of AI technologies in sensor systems. The Artificial Intelligence Market in the Industrial Sector research report covers the present scenario and the growth prospects of the global Artificial Intelligence Market in the Industrial Sector for 2017-2021. Key Vendors of Artificial Intelligence Market in the Industrial Sector: - Amazon Web Services - IBM - Siemens - Omron Adept Technologies And many more… Artificial Intelligence Market in the Industrial Sector report provides key statistics on the market status of the Artificial Intelligence Market in the Industrial Sector manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the Artificial Intelligence Market in the Industrial Sector.