computational


New machine learning system can automatically identify shapes of red blood cells

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Using a computational approach known as deep learning, scientists have developed a new system to classify the shapes of red blood cells in a patient's blood. The findings, published in PLOS Computational Biology, could potentially help doctors monitor people with sickle cell disease. A person with sickle cell disease produces abnormally shaped, stiff red blood cells that can build up and block blood vessels, causing pain and sometimes death. The disease is named after sickle-shaped (crescent-like) red blood cells, but it also results in many other shapes, such as oval or elongated red blood cells. The particular shapes found in a given patient can hold clues to the severity of their disease, but it is difficult to manually classify these shapes.


Making artificial intelligence more private and portable

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The technology is a form of deep-learning artificial intelligence software developed to fit onto mobile computer chips. This allows artificial intelligence to be used in a range of devices, from smartphones to industrial robots. This portability would enable devices to operate independent of the Internet while using artificial intelligence that performs equivalent to tethered neural networks. With this, a hosting chip embedded in a smartphone could run a speech-activated virtual assistant and undertake other intelligent features, such as controlling data usage. Other applications include operating drones and surveillance cameras in remote areas.


Master of machines: the rise of artificial intelligence calls for postgrad experts

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Intelligence is no longer exclusively human. Machines can now recognise a human face, drive a car, beat a chess master and cope with uncertainty. To be as clever as a human, a system must make the right decision in complex and changing conditions – swerve to avoid someone while not knowing if it's safe, for example, or understand loosely worded commands. Expectations of what artificial intelligence (AI) can do run high, and universities are keen to meet the needs of industry. Cheaper hardware and software and an abundance of data have fuelled interest.


Learning in Brains and Machines (3): Synergistic and Modular Action

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There is a dance--precisely choreographed and executed--that we perform throughout our lives. This is the dance formed by our movements. Our movements are our actions and the final outcome of our decision making processes. Single actions are built into reusable sequences, sequences are composed into complex routines, routines are arranged into elegant choreographies, and so the complexity of human action is realised. This synergy, the composition of actions into increasingly complex units, suggests the desirability of a modular and hierarchical approach to the selection and execution of actions.


A neural algorithm for a fundamental computing problem

Science

Similarity search--for example, identifying similar images in a database or similar documents on the web--is a fundamental computing problem faced by large-scale information retrieval systems. We discovered that the fruit fly olfactory circuit solves this problem with a variant of a computer science algorithm (called locality-sensitive hashing). The fly circuit assigns similar neural activity patterns to similar odors, so that behaviors learned from one odor can be applied when a similar odor is experienced. The fly algorithm, however, uses three computational strategies that depart from traditional approaches. These strategies can be translated to improve the performance of computational similarity searches.


SC17: AI and Machine Learning are Central to Computational Attack on Cancer

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Enlisting computational technologies in the war on cancer isn't new but it has taken on an increasingly decisive role. At SC17, Eric Stahlberg, director of the HPC Initiative at Frederick National Laboratory for Cancer Research in the Data Science and Information Technology Program, and two colleagues will lead the third Computational Approaches for Cancer workshop being held the Friday, Nov. 17, at SC17. It is hard to overstate the importance of computation in today's pursuit of precision medicine. Given the diversity and size of datasets it's also not surprising that the "new kids" on the HPC cancer fighting block – AI and deep learning/machine learning – are also becoming the big kids on the block promising to significantly accelerate efforts understand and integrate biomedical data to develop and inform new treatments. In this Q&A, Stahlberg discusses the goals of the workshop, the growing importance of AI/deep learning in biomedical research, how programs such as the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) are progressing, the need for algorithm assurance and portability, as well as ongoing needs where HPC technology has perhaps fallen short.


Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics - Lispniks

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In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics. As data science is a broad discipline, I start by describing the different types of data scientists that one may encounter in any business setting: you might even discover that you are a data scientist yourself, without knowing it. As in any scientific discipline, data scientists may borrow techniques from related disciplines, though we have developed our own arsenal, especially techniques and algorithms to handle very large unstructured data sets in automated ways, even without human interactions, to perform transactions in real-time or to make predictions. To get started and gain some historical perspective, you can read my article about 9 types of data scientists, published in 2014, or my article where I compare data science with 16 analytic disciplines, also published in 2014. I also wrote about the ABCD's of business processes optimization where D stands for data science, C for computer science, B for business science, and A for analytics science.


Agenda – Johns Hopkins Mathematical Institute for Data Science

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Dean's Welcome, Ed Schlesinger, Johns Hopkins Whiting School of Engineering 9:05 a.m. Vice Provost's Welcome, Denis Wirtz, Johns Hopkins University 9:10 a.m. Director's Inaugural Address and Welcome, René Vidal, Johns Hopkins Mathematical Institute for Data Science Conditional Mean Embeddings for Reinforcement Learning, John Shawe-Taylor, University College London 11:40 a.m. Designing and Learning Representations for Visual Data in the Age of Deep Learning, Stefano Soatto, Amazon Web Services and University of California, Los Angeles 12:20 p.m. Alexa, Tell Me How Kaldi and Deep Learning Revolutionized Automatic Speech Recognition!, Sanjeev Khudanpur, Johns Hopkins Center for Language and Speech Processing Is Manifold Learning for Toy Data Only?, Marina Meila, University of Washington 5:20 p.m. Theoretical and Numerical Challenges in Medical Image Analysis and Computational Anatomy, Nicolas Charon, Johns Hopkins Center for Imaging Science


How Quantum Machine Learning will solve problems once thought out of reach

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Quantum computers will transform many fields, and the impact on optimization and machine learning (ML) will be among the most profound. In recent years, machine learning has taken off thanks partly to the acceleration in computing hardware, but there is a long list of valuable technological problems that cannot be solved simply because we are too limited by the computer power of our contemporary digital computers. Quantum machine learning sits at the intersection of quantum information processing and machine learning to solve complex problems inefficient to work on with a classical system. Quantum machine learning is a new field that has recently emerged and may have an answer to some of these problems. It is the science and technology at the intersection of quantum information processing and machine learning.


Numeric Computation and Statistical Data Analysis on the Java Platform (Advanced Information and Knowledge Processing): Sergei V. Chekanov: 9783319285290: Amazon.com: Books

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Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries.