Microsoft speaks to the ethics of AI

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To showcase the latest in artificial intelligence, Microsoft recently hosted an "underground" tour, two days' worth of virtual reality demos, product prototypes, programming and platform innovation, research news and philosophical musings on the future of AI from technological, social and business perspectives. AI progress can be attributed to a number of factors, including advancements in processing power, powerful new algorithms, data availability, cloud computing, and machine and deep learning capabilities. One of the more compelling milestones that furthered the cause for many applications was Microsoft's achievement late last year of error rates that are on par with, if not better than, human benchmarks – under 5.9 percent for speech recognition and 3.5 percent for image recognition. Autonomous cars, smart homes, automated assistants, translation apps, virtual and augmented reality were all represented over the course of the event as part of the AI spectrum. But the most compelling discussions were those that went beyond technical wizardry (which was impressive in itself) to explore the social and cultural impacts of AI.


Global Bigdata Conference

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News concerning Artificial Intelligence (AI) abounds again. The progress with Deep Learning techniques are quite remarkable with such demonstrations of self-driving cars, Watson on Jeopardy, and beating human Go players. This rate of progress has led some notable scientists and business people to warn about the potential dangers of AI as it approaches a human level. Exascale computers are being considered that would approach what many believe is this level. However, there are many questions yet unanswered on how the human brain works, and specifically the hard problem of consciousness with its integrated subjective experiences.


The Real Potential of AI (hint: it's not robots)

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This week Stanford was the center of attention in the artificial intelligence community after it published news that it trained a deep learning model that diagnoses skin cancer as accurately as a dermatologist. The algorithm apparently can identify a cancerous mole with nothing more than a picture, meaning it could be put into the hands of anyone with a simple smartphone -- otherwise known as a pocket supercomputer. Deep learning is revolutionizing the way innovators can apply AI and data science to solve real-world problems. Image classification, facial recognition, computational linguistics, translation, augmented reality, self-driving cars -- all of these fields have made huge leaps in the last several years as computer scientists apply the rapidly-developing machine learning models that empower them. With all the excitement around these developments, one starts to wonder…what does a future with advanced AI look like?


Future of Medical Diagnostics Industry using AI and Deep learning MarkTechPost

#artificialintelligence

Who thought in 1950's that AI and deep learning will make self-driving cars and impossible missions like Mission Mars almost possible. While these innovations are not only getting possible but also the future predictions are getting quite interesting as well. While everyone is predicting future of AI mostly in the Software sector, I believe the most influential application of AI-based Nanochip will be in the medical diagnostics industry. These bot chips can be implanted in human brain just like currently a female can implant a birth control rod in her arm and can avoid taking pills. This nano biochip NBC will be biocompatible and will be programmed.


A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks

arXiv.org Machine Learning

Abstract--We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportatio n networks. The proposed EVT -LSTM model is derived from the popular LSTM (Long Short-T erm Memory) network and adopts an objective function that is based on fundamental results f rom EVT (Extreme V alue Theory). We compare the EVT -LSTM model with some established statistical, machine learning, and hybrid deep learning baselines. Experiments on seven diver se real-world data sets demonstrate the superior anomaly dete ction performance of our proposed model over the other models considered in the comparison study. The increasing availability of large-scale traffic data set s provides an opportunity to explore them for knowledge discovery in ITS (Intelligent Transportation Systems). The av - enues for exploration are numerous, ranging from uncoverin g traffic patterns [1], city dynamics [2], driving directions [3], discovering hot spots in a city [4], finding vacant taxis arou nd a city [5], predicting taxi demand [6], taxi operation patte rns [7], to detecting anomalies [8], among others. V arious verticals of ITS have always received active research attention in the past. However, the recent emergence of deep learning techniques and their applicability in tran s-portation systems has resulted in a heightened interest in t his area [9]. Consequently, traditional machine learning mode ls in many applications are now being replaced by deep learning techniques, which is reshaping the landscape of intelligen t transport networks. Out of the several applications of ITS, the area of anomaly detection has benefited significantly from th e application of deep learning-based techniques [10]. Anoma ly detection aims to find those patterns which are not normally expected from the data. Typical observations from traffic da ta demonstrate strong spatiotemporal patterns, showing per iod-icity and strong correlations between adjacent observatio ns.