The last few months have witnessed a rise in the attention given to Artificial Intelligence (AI) and robotics. The fact is that robots have already become a part of society; in fact, it is now an integral part. Big data is also definitely a buzzword today. Enterprises worldwide generate a huge amount of data. The data doesn't have a specified format.
Business metaphors often contain biological references. For example, we refer to "product families" and talk about the "next generation." We talk about businesses "evolving" and "product lifecycles." We find some companies "on the bleeding edge" of new technologies. In the Digital Age, we find data running through veins of companies and the Internet of Things providing the nervous system of the digital enterprise.
Julia is a free open source, high-level, high-performance, dynamic programming language for numerical computing. It has the development convenience of a dynamic language with the performance of a compiled statically typed language, thanks in part to a JIT-compiler based on LLVM that generates native machine code, and in part to a design that implements type stability through specialization via multiple dispatch, which makes it easy to compile to efficient code. In the blog post announcing the initial release of Julia in 2012, the authors of the language--Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman--stated that they spent three years creating Julia because they were greedy. They were tired of the trade-offs among Matlab, Lisp, Python, Ruby, Perl, Mathematica, R, and C, and wanted a single language that would be good for scientific computing, machine learning, data mining, large-scale linear algebra, parallel computing, and distributed computing. In addition to being attractive to research scientists and engineers, Julia is also attractive to data scientists and to financial analysts and quants.
Industry 4.0 is characterized by applying cloud and cognitive computing to current automated and computerized industrial systems resulting in the ability to create smart factories that monitor physical processes, identify issues or optimizations, and perform iterative refinement or proactive maintenance and updates. A recent study was released by Emory University and Presenso called The Future of IIoT Predictive Maintenance. The study is focused on predictive maintenance current state, implementation, resulting impact, and future needs identified within smart factories. Over 100 operations and maintenance professionals across Europe, North America, and Asia Pacific participated. The results showed that while there was good satisfaction with existing predictive maintenance environments, the modeling and machine learning aspects are lagging behind where spreadsheet based statistical modeling has not been replaced by more advanced capabilities.
Whether you call it cognitive computing, machine learning, deep learning or artificial intelligence (AI), the era of collaborative human-machine intelligence has begun, and the implications for healthcare are enormous. Without the leverage of AI, there's just simply no other way to turn the massive volumes of data coming from diverse and rapidly growing sources into the meaningful insights so critically needed to move into the new age of precision medicine and rise of healthcare consumerism. In fact, according to a PwC report, 54 percent of healthcare consumers worldwide are already open to receiving AI-enabled healthcare. Humans are critical to this next wave. The humans of healthcare--physicians, caregivers, researchers, administrators, policy makers--will increasingly rely on thinking machines to uncover patterns and inform decisions that benefit patients, populations, health systems and society at large.
Is Big Data vs. artificial intelligence even a fair comparison? To some degree it is, but first let's cut through the confusion. Those are two buzzwords you are hearing an awful lot lately, perhaps to the point of confusion. What are the similarities and differences between artificial intelligence and Big Data? Do they have anything in common?
This edition of ITCC TOE provides a snapshot of the emerging ICT led innovations in machine learning, blockchain, cloud computing, and artificial intelligence. This issue focuses on the application of information and communication technologies in alleviating the challenges faced across industry sectors in areas such as brick & mortar retail, e-commerce, data labelling, 5G, photo and video editing, manufacturing, talent and business intelligence, amongst others. ITCC TechVision Opportunity Engine (TOE)'s mission is to investigate emerging wireless communication and computing technology areas including 3G, 4G, Wi-Fi, Bluetooth, Big Data, cloud computing, augmented reality, virtual reality, artificial intelligence, virtualization and the Internet of Things and their new applications; unearth new products and service offerings; highlight trends in the wireless networking, data management and computing spaces; provide updates on technology funding; evaluate intellectual property; follow technology transfer and solution deployment/integration; track development of standards and software; and report on legislative and policy issues and many more. The Information & Communication Technology cluster provides global industry analysis, technology competitive analysis, and insights into game-changing technologies in the wireless communication and computing space. Innovations in ICT have deeply permeated various applications and markets.
This article will cover a brief introduction to these topics and show how to implement them, using Google Colaboratory to do automated machine learning on the cloud in Python. Originally, all computing was done on a mainframe. You logged in via a terminal, and connected to a central machine where users simultaneously shared a single large computer. Then, along came microprocessors and the personal computer revolution and everyone got their own machine. Laptops and desktops work fine for routine tasks, but with the recent increase in size of datasets and computing power needed to run machine learning models, taking advantage of cloud resources is a necessity for data science.
TOPOLOGY SHOWS US THAT ALL DATA HAS a underlying shape.TOPOLOGY STUDIES the shape of data. THE DEGREE OF SIMLARITY BETWEEN TWO SHAPES (PATTERN) CAN BE EXPRESSED AS THE AMOUNT OF STRETCHING. TOPOLOGICAL DATA ANALYSIS PROVIDES A GENERAL FRAMEWORK TO EXTRACT INFORMATION FROM DATA-SETS WHICH ARE HIGHDIMENSIONAL, INCOMPLETE NOIS NOISY. TDA PROVIDES A GENERAL FRAMEWORK TO ANALYZE SUCH DATA IN A MANNER THAT IS INSENSITIVE TO THE METRIC CHOSEN AND PROVIDES DIMENSIONALITY REDUCTION AND ROBUSTNESS TO NOISE.
Many businesses are beginning to rely on large scale data analytics for greater insights into their customers' behavior and their business requirements. Simplifying the process so that a wider range of employees can make conclusions from the massive amounts of data is important and can lead to more profits and better customer service. Harp-DAAL is a framework developed at Indiana University that brings together the capabilities of big data (Hadoop) and techniques that have previously been adopted for high performance computing. Together, employees can become more productive and gain deeper insights to massive amounts of data. Modern analytics systems are clusters of independent systems which need to be synchronized in order to make sense of all of the data.