AI News, Big Data and Information Analytics 2018 artificial intelligence
Big data refers to data sets that are too large or complex for traditional data-processing application software to adequately deal with.
Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.
Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.
Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.
Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics.
Data sets grow rapidly- in part because they are increasingly gathered by cheap and numerous information- sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.
data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.
data requires a set of techniques and technologies with new forms of integration to reveal insights from datasets that are diverse, complex, and of a massive scale.
2016 definition states that 'Big data represents the information assets characterized by such a high volume, velocity and variety to require specific technology and analytical methods for its transformation into value'.
Similarly, Kaplan and Haenlein define big data as 'data sets characterized by huge amounts (volume) of frequently updated data (velocity) in various formats, such as numeric, textual, or images/videos (variety).'
2018 definition states 'Big data is where parallel computing tools are needed to handle data', and notes, 'This represents a distinct and clearly defined change in the computer science used, via parallel programming theories, and losses of some
CERN and other physics experiments have collected big data sets for many decades, usually analyzed via high performance computing (supercomputers) rather than the commodity map-reduce architectures usually meant by the current 'big data' movement.
The data lake allows an organization to shift its focus from centralized control to a shared model to respond to the changing dynamics of information management.
DARPA's Topological Data Analysis program seeks the fundamental structure of massive data sets and in 2008 the technology went public with the launch of a company called Ayasdi.[third-party source needed]
preferring direct-attached storage (DAS) in its various forms from solid state drive (SSD) to high capacity SATA disk buried inside parallel processing nodes.
Between 1990 and 2005, more than 1 billion people worldwide entered the middle class, which means more people became more literate, which in turn led to information growth.
The world's effective capacity to exchange information through telecommunication networks was 281 petabytes in 1986, 471 petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 2007
While many vendors offer off-the-shelf solutions for big data, experts recommend the development of in-house solutions custom-tailored to solve the company's problem at hand if the company has sufficient technical capabilities.
Data analysis often requires multiple parts of government (central and local) to work in collaboration and create new and innovative processes to deliver the desired outcome.
Research on the effective usage of information and communication technologies for development (also known as ICT4D) suggests that big data technology can make important contributions but also present unique challenges to International development.
Advancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas such as health care, employment, economic productivity, crime, security, and natural disaster and resource management.
However, longstanding challenges for developing regions such as inadequate technological infrastructure and economic and human resource scarcity exacerbate existing concerns with big data such as privacy, imperfect methodology, and interoperability issues.
Predictive manufacturing as an applicable approach toward near-zero downtime and transparency requires vast amount of data and advanced prediction tools for a systematic process of data into useful information.
A conceptual framework of predictive manufacturing begins with data acquisition where different type of sensory data is available to acquire such as acoustics, vibration, pressure, current, voltage and controller data.
Big data analytics has helped healthcare improve by providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries and fragmented point solutions.
This includes electronic health record data, imaging data, patient generated data, sensor data, and other forms of difficult to process data.
Human inspection at the big data scale is impossible and there is a desperate need in health service for intelligent tools for accuracy and believability control and handling of information missed.
Because one-size-fits-all analytical solutions are not desirable, business schools should prepare marketing managers to have wide knowledge on all the different techniques used in these subdomains to get a big picture and work effectively with analysts.
The industry appears to be moving away from the traditional approach of using specific media environments such as newspapers, magazines, or television shows and instead taps into consumers with technologies that reach targeted people at optimal times in optimal locations.
For example, publishing environments are increasingly tailoring messages (advertisements) and content (articles) to appeal to consumers that have been exclusively gleaned through various data-mining activities.
Health insurance providers are collecting data on social 'determinants of health' such as food and TV consumption, marital status, clothing size and purchasing habits, from which they make predictions on health costs, in order to spot health issues in their clients.
defines the Internet of Things in this quote: “If we had computers that knew everything there was to know about things—using data they gathered without any help from us—we would be able to track and count everything, and greatly reduce waste, loss and cost.
By applying big data principles into the concepts of machine intelligence and deep computing, IT departments can predict potential issues and move to provide solutions before the problems even happen.
In this time, ITOA businesses were also beginning to play a major role in systems management by offering platforms that brought individual data silos together and generated insights from the whole of the system rather than from isolated pockets of data.
Besides, using big data, race teams try to predict the time they will finish the race beforehand, based on simulations using data collected over the season.
They focused on the security of big data and the orientation of the term towards the presence of different type of data in an encrypted form at cloud interface by providing the raw definitions and real time examples within the technology.
Moreover, they proposed an approach for identifying the encoding technique to advance towards an expedited search over encrypted text leading to the security enhancements in big data.
The SDAV Institute aims to bring together the expertise of six national laboratories and seven universities to develop new tools to help scientists manage and visualize data on the Department's supercomputers.
The British government announced in March 2014 the founding of the Alan Turing Institute, named after the computer pioneer and code-breaker, which will focus on new ways to collect and analyse large data sets.
In May 2013, IMS Center held an industry advisory board meeting focusing on big data where presenters from various industrial companies discussed their concerns, issues and future goals in big data environment.
used Google Trends data to demonstrate that Internet users from countries with a higher per capita gross domestic product (GDP) are more likely to search for information about the future than information about the past.
The authors of the study examined Google queries logs made by ratio of the volume of searches for the coming year ('2011') to the volume of searches for the previous year ('2009'), which they call the 'future orientation index'.
Eugene Stanley introduced a method to identify online precursors for stock market moves, using trading strategies based on search volume data provided by Google Trends.
An important research question that can be asked about big data sets is whether you need to look at the full data to draw certain conclusions about the properties of the data or is a sample good enough.
Even as companies invest eight- and nine-figure sums to derive insight from information streaming in from suppliers and customers, less than 40% of employees have sufficiently mature processes and skills to do so.
As a response to this critique Alemany Oliver and Vayre suggest to use 'abductive reasoning as a first step in the research process in order to bring context to consumers' digital traces and make new theories emerge'. Additionally,
Agent-based models are increasingly getting better in predicting the outcome of social complexities of even unknown future scenarios through computer simulations that are based on a collection of mutually interdependent algorithms.
Finally, use of multivariate methods that probe for the latent structure of the data, such as factor analysis and cluster analysis, have proven useful as analytic approaches that go well beyond the bi-variate approaches (cross-tabs) typically employed with smaller data sets.
new postulate is accepted now in biosciences: the information provided by the data in huge volumes (omics) without prior hypothesis is complementary and sometimes necessary to conventional approaches based on experimentation.
Large data sets have been analyzed by computing machines for well over a century, including the 1890s US census analytics performed by IBM's punch card machines which computed statistics including means and variances of populations across the whole continent.
However science experiments have tended to analyze their data using specialized custom-built high performance computing (supercomputing) clusters and grids, rather than clouds of cheap commodity computers as in the current commercial wave, implying a difference in both culture and technology stack.
Integration across heterogeneous data resources—some that might be considered big data and others not—presents formidable logistical as well as analytical challenges, but many researchers argue that such integrations are likely to represent the most promising new frontiers in science. In
the authors title big data a part of mythology: 'large data sets offer a higher form of intelligence and knowledge [...], with the aura of truth, objectivity, and accuracy'.
the other hand, big data may also introduce new problems, such as the multiple comparisons problem: simultaneously testing a large set of hypotheses is likely to produce many false results that mistakenly appear significant. Ioannidis
'There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created in every 2 days' - Eric Schmidt, Executive Chairman, Google
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few.
This Big Data offers challenge in term of storage and further analysis either by humans or automated systems. One can dig gold mine if we are able to make sense out of big data which lead to emergence of Data Science, Big Data, Analytics, Machine Learning, AI other fields.
Snapdeal, which has over 20 million subscribers and generates terabytes of data through the interactions that happen with customers in addition to a catalogue of over 5 million, churns 15 million data points (related data set like a consumer shopping on specific days for a particular thing) within two hours.
About 35% of its orders come from recommendation and personalized systems, and the conversion rate of such orders is 20-30% higher than normal orders, the company claims.
Telecommunication in association with IBM and to train the new generation of data-savvy professionals. This 11 months program provides you intensive hands-on training to develop the necessary and unique set of skills required for successful career in the fastest growing and intellectually stimulating fields of Data Science, Big Data, Business Analytics, Predictive Analytics, NLP, ML and Cognitive Computing.
Students who earn a PGP develop deep quantitative capabilities and technical expertise and are equipped to create business and social value by extracting useful insights and applying it in a various industries by playing a role of Data Scientist or modern Business Analyst.
Deep Learning and Applied AI certification from NVIDIA Aegis and NVIDIA will train and provide certification on the fundamental tenets of deep learning such as using AI for object detection or image classification, applying this to determine the best approach to cancer treatment;
Paid internship are for 2 to 3 months with various companies to give them reallife live experience which generally leads to final placement as role of Data Scientist, Manager Data Science, Business Analyst, Risk Analyst etc like Accenture, Atos, Deloitte, E&Y, PwC, Fractal, Angel Broking, Cybage, edelweiss, Teradata, HDFC, Ford Automotive, Mercedes-Benz, Bank of America, VM Ware, IBM, Aditya Birla, Suzlon, Eclerx, Aureus Analytics, Clover Infotech, Value Direct, Virtusa, Credit Vidya, Shzertech, Loginext, Persistent, L &
Diverse student profile Aegis participants' are from around the world with experience ranging from 2 year up to 25 years from diverse industry and background.
Location Analytics, Big Data, and Artificial Intelligence: The Genesis of the New Data Scientist
Big data is revolutionizing the management of large volumes of information, giving rise to new solutions capable of self-teaching without help from human beings.
On the political front, the United Arab Emirates—a leader in data technologies and the site of some of Esri’s most important projects and agreements—has announced the appointment of its first Minister of Artificial Intelligence, who will be responsible for applying AI tools in all government-managed services.
new executive post called ‘chief artificial intelligence officer’ has been created to oversee AI solutions and to address complex problems for which little information is available.
A new executive post called chief artificial intelligence officer has been created to oversee AI solutions and to address complex problems for which little information is available.
The CAIO must have scientific skills—mainly in cognitive processes, prediction, simulation, etc.—plus a vision of engineering for the application of symbolic, connectionist, and hybrid paradigms.
Codified cities: geospatial data fertilize smart territory Another new profile—the data scientist—is poised to become highly useful for local governments worldwide.
Analyses based on geoprocessing involve the use of seemingly magical tools like the ‘spatial join’ operation, which enriches a model by appending information from different layers.
By crossing and correlating data—human, natural, and climatic factors, type of road, etc.—it is possible to create a map that minimizes the risk of accidents at a particular spot and improves our understanding of the environment.
With methods and technological platforms like Esri’s, which can be applied throughout the value chain for geolocalized data—from capture and storage to analysis and display—is it possible to successfully tap into the opportunities offered by these new technologies.
- On 13. april 2021
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