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IBM has brought a host of AI-powered equipment to its BI platform, Cognos Analytics, to enable more superior analytics,...
whereas on the equal time making the platform simpler for buyers to use, the company observed.
the brand new Cognos Analytics eleven.1 platform facets include an AI assistant, pattern detection, visualization strategies, improved storytelling equipment, and extra advanced analytics and predictive analytics capabilities.
The update, revealed in a Sept. 27 IBM weblog post, represents a "huge funding," pointed out Greg Adams, vp of IBM enterprise Analytics, in an interview. the brand new tools, past adding extra advanced capabilities, additionally make the platform more straightforward to make use of for nondata-oriented employees and citizen facts scientists -- a category of users that has been growing abruptly.Following a style
the new points in Cognos Analytics 11.1 make "the platform much more straightforward to use to generate insights, certainly the natural language search, which enables users to have real conversations with the data," mentioned Wayne Eckerson, founder and major consultant at Eckerson neighborhood.
IBM is [in the] middle of the pack, which is a tad staggering, given their Watson heritage. Wayne Eckerson, Eckerson community
"It automates facts preparation and information modeling tasks, so analysts can center of attention extra on insights. It additionally auto-generates insights based on a box of hobby selected through a user, using algorithms to tease out key drivers of a metric, as an example," he continued.
still, Eckerson mentioned, these are aspects that "the entire BI carriers are racing to include."
"Some are a bit bit ahead of others, but most will end up within the identical area in 18 months. IBM is [in the] core of the pack, which is a tad fantastic, given their Watson heritage," he noted.
past this year, BI software dealer Alteryx Inc. launched Alteryx 2018.3, which added superior visualization capabilities and set the stage for future updates that are referred to to boost the platform's augmented intelligence and sensible capabilities.
Tableau additionally made a move to add extra automation to its structures this 12 months with the acquisition of MIT startup Empirical methods. The startup labored on a platform to immediately mannequin data for analysis, and it provides predictive insights into that records -- aspects that Tableau mentioned it will work to add into its personal structures.Cognos Analytics eleven.1 allows quick comparisons of two visualizations in a new exploration tool. more straightforward to use
in line with Adams, at IBM, "we comprehend to in fact assist individuals discover insights, to in fact be self-service potential americans should be automatically given insights, immediately have issues dropped at their consideration, and we must have things be in reality approachable."
That strategy is mirrored within the new AI Assistant. Taking potential of new natural language processing (NLP) capabilities, the assistant in Cognos Analytics 11.1 enables users to classification in questions and commands, equivalent to a command to join records sets.
"the key with the conversation technology, it be now not anything that we wanted to bolt onto the side," Adams observed. "We really tried to inject it, so it's part of your normal circulation."advanced analytics
the new AI technologies in Cognos Analytics eleven.1 work smartly with the newly introduced superior analytics equipment, Adams referred to.
The platform now has sample detection, which can automatically find and current relationships in statistics that is being explored.
looking at a undeniable visualization, for instance, the platform will automatically discover connected visualizations that can be used, Adams stated. When picking information, the device will additionally provide recommendations for visualizations.
With a new exploration device in Cognos Analytics 11.1, users can drag and drop two visualizations for a "quick facet-through-facet evaluation" that will immediately look for relationships, Adams said.
The replace also includes more desirable predictive analytics capabilities; greater statistics storytelling alternate options, with the means to create interactive narratives with sequenced visualizations and media; and more convenient-to-use reporting equipment.atmosphere aside and looking ahead
whereas Adams acknowledged other BI systems have lately integrated equivalent elements, he claimed IBM's attention to facts governance sets Cognos Analytics 11.1 apart.
"one of the high-quality issues about Cognos is its business-grade, relied on ambiance," he noted, adding that IBM has additionally introduced a equipment to immediately discover AI bias.
moreover the update, IBM has released a new subscription pricing model for the platform. The mannequin units the platform into three tiers: lite, top class and business. The Cognos Analytics 11.1 replace is accessible for the highest tier as of Sept. 28, and it'll be accessible on the other tiers by means of the conclusion of the year, in keeping with IBM.
might also 19, 2016Joyce Wells
Datawatch employer has announced the provision of Datawatch Monarch for IBM Watson Analytics and IBM Cognos Analytics. previous this yr, Datawatch printed that IBM would resell Datawatch Monarch to deliver stronger and sooner records entry and self-provider facts instruction (prep) to its users. Now, the business says, those clients can leverage Monarch to prep and analyze extra statistics faster, and take skills of IBM`s cognitive computing capabilities across a wide variety of data sources.
providing the advantages of advanced analytics with out the complexity, Watson Analytics is a knowledge discovery service purchasable on the cloud that helps e-book exploration, automates predictive analytics, and makes it possible for dashboard and infographic introduction.
Cognos Analytics offers self-provider capabilities that permit business clients to create and/or customise dashboards and stories on their personal - and is available on-premise or on the cloud.
in keeping with Datawatch, statistics that provides probably the most analytical value is often locked away in inaccessible multi-structured and unstructured files, such as textual content stories, web pages, PDFs, JSON and log files. additionally, in many situations, a firm`s supply facts is distinct and infrequently items itself in an evaluation-ready layout – ensuing in the need for large amounts of time to be spent on manual facts retrieval and prep procedures.
The Datawatch Monarch self-carrier data prep answer is targeted at assisting statistics analysts and generic enterprise clients to greater unexpectedly entry, manipulate, and blend information from distinctive sources. facts can then be prepared for evaluation in Watson Analytics and Cognos Analytics more at once.
This answer is directed at the proven fact that the vast majority of strategic enterprise decisions are according to incomplete or outdated information because of the time it takes for facts retrieval and manual prep techniques, based on Michael Morrison, president and CEO of Datawatch. Monarch, he pointed out, enables Watson Analytics and Cognos Analytics clients to attain all of the information required to reap a holistic view of the enterprise, and then prepare it for analysis in order that, in its place of prepping, they could devote their time to performing analysis that accelerates choice making and provides enterprise value.
To gain knowledge of greater about Datawatch Monarch for Watson Analytics and Cognos Analytics, go to www.datawatch.com/ibm-analytics.
Have a new announcement about your enterprise? Contact firstname.lastname@example.org.
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The biggest additions to IBM Cognos Report Studio in version 10 have been Active Reports and Statistics objects. These two new ways of presenting data provide many new options for report authors to create reports that match the analytical needs of the user community. Entire classes have been created to focus on the creation of reports using these formats.
Other capabilities in IBM Cognos 10 Report Studio include the integration of external data sources and the ability to save Report Studio reports to be used directly by Business Insight Advanced authors. Using external data sources in Report Studio has been a customer request since IBM Cognos tools moved to a web-based architecture. This enhancement gives the report consumer new opportunities to process just the information needed for a specific situation. By using a common report definition between applications, users who aren't accustomed to complex reports can receive assistance to build reports that have features beyond those normally available to Business Insight Advanced users.
While the features I've discussed to this point are promoted as key features, other useful options in version 10 will increase the effectiveness of reports to your users. Some of these features were previously available through complex programming by the report developer, but IBM Cognos Business Intelligence now makes this job much easier. As a report writer and instructor who can appreciate the ability to create report designs designed to tell a story, I want to highlight 10 of these new features.Alternative Text
The Alternative Text property has been added to graphical objects, such as images and charts, to allow screen readers to translate the text (see Figure 1). Another aspect of this feature that's applied in various properties is to provide localized text without using conditional formatting.Colored Regions, Plot Area Fill, Material Effects
The Colored Regions, Plot Area Fill, and Material Effects properties allow charts to have more creative designs in order to enhance the overall presentation of the charts. Mixed with other chart presentation options, these features help report developers to give new polish to the presentation of content. Figure 2 shows the additional gradient options that are now available, along with the new properties for the charts.New Gradient Fill Types
Moving beyond simple linear gradients, some new fill types provide more choices for enhanced presentations. Backgrounds behind pages, objects, and selected areas can now display a number of gradient designs. The fill types include rectangular frames and embedded circular gradients, with many parameters to customize the blending of colors (see Figure 3).Chart Combinations (Primary Axis, Secondary Axis, Primary Bottom, Secondary Bottom)
IBM Cognos 8 Business Insight had the ability to set Y1 and Y2 axes. Now combination charts can be stacked with two more axes for better analysis of related numbers. These options can be integrated into dashboard design to enhance the presentation of related measures. Figure 4 shows a chart with the additional axes selected.Query Calculations in Query Explorer Replace Calculated Member, Calculated Measure, Set Expression, and Intersection (tuple)
In IBM Cognos 8 Report Studio, different objects were created for each of the dimensional functions. Now they're all bundled into the Query Calculations (see Figure 5). This option simplifies the toolbox for report authors, while emphasizing that dimensional queries can have enhanced calculations.Summarize Small Slices and Exploded Slices
With pie charts, two improvements include summarizing small slices and exploding slices. These two options greatly improve the presentation of pie charts to emphasize the most important information (see Figure 6). In my classes, these options have been requested for years, and now they're delivered in this release.Trendlines, Category Baselines, and Numeric Baselines
Report Studio functions have now improved on the predictive ability behind charts, without having to use external functions to calculate the numbers (see Figure 7).Series Color Synchronization
With the changes made to enhance series, this feature allows report writers to simplify the legends when the series information is repeated across the combinations. By changing the Series Color property to Match (see the short arrow in Figure 8), the legend shows the different product lines and the nested measures differently (indicated by the longer arrow), increasing the effectiveness of the presentation.Custom Properties for Prompts
As a welcome addition to dimensional functions, the Set Definition option (see Figure 10) allows report authors to create complex subsets that are presented graphically. This feature can make troubleshooting complex sets much easier by providing a more modular approach to set development. This was the best new property for me, since it simplifies the development of sets that had to be full of nested functions.
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Have you ever been treated unfairly? How did it make you feel? Probably not too good. Most people generally agree that a fairer world is a better world, and our AI researchers couldn't agree more. That's why we are harnessing the power of science to create AI systems that are more fair and accurate.
Many of our recent advances in AI have produced remarkable capabilities for computers to accomplish increasingly sophisticated and important tasks, like translating speech across languages to bridge communications across cultures, improving complex interactions between people and machines, and automatically recognizing contents of video to assist in safety applications.
Much of the power of AI today comes from the use of data-driven deep learning to train increasingly accurate models by using growing amounts of data. However, the strength of these techniques can also be a weakness. The AI systems learn what they're taught, and if they are not taught with robust and diverse datasets, accuracy and fairness could be at risk. For that reason, IBM, along with AI developers and the research community, need to be thoughtful about what data we use for training. IBM remains committed to developing AI systems to make the world more fair.
The challenge in training AI is manifested in a very apparent and profound way with facial recognition technology. Today, there can be difficulties in making facial recognition systems that meet fairness expectations. The heart of the problem is not with the AI technology itself, per se, but with how the AI-powered facial recognition systems are trained. For the facial recognition systems to perform as desired – and the outcomes to become increasingly accurate – training data must be diverse and offer a breadth of coverage. For example, the training data sets must be large enough and different enough that the technology learns all the ways in which faces differ to accurately recognize those differences in a variety of situations. The images must reflect the distribution of features in faces we see in the world.Credit: IBM
How do we measure and ensure diversity for human faces? On one hand, we are familiar with how faces differ by age, gender, and skin tone, and how different faces can vary across some of these dimensions. Much of the focus on facial recognition technology has been on how well it performs within these attributes. But, as prior studies have shown, these attributes are just a piece of the puzzle and not entirely adequate for characterizing the full diversity of human faces. Dimensions like face symmetry, facial contrast, the pose the face is in, the length or width of the face's attributes (eyes, nose, forehead, etc.) are also important.
Today, IBM Research is releasing a new large and diverse dataset called Diversity in Faces (DiF) to advance the study of fairness and accuracy in facial recognition technology. The first of its kind available to the global research community, DiF provides a dataset of annotations of 1 million human facial images. Using publicly available images from the YFCC-100M Creative Commons data set, we annotated the faces using 10 well-established and independent coding schemes from the scientific literature. [1-10] The coding schemes principally include objective measures of human faces, such as craniofacial features, as well as more subjective annotations, such as human-labeled predictions of age and gender. We believe by extracting and releasing these facial coding scheme annotations on a large dataset of 1 million images of faces, we will accelerate the study of diversity and coverage of data for AI facial recognition systems to ensure more fair and accurate AI systems. Today's release is simply the first step.
We believe the DiF dataset and its 10 coding schemes offer a jumping-off point for researchers around the globe studying the facial recognition technology. The 10 facial coding methods include craniofacial (e.g., head length, nose length, forehead height), facial ratios (symmetry), visual attributes (age, gender), and pose and resolution, among others. These schemes are some of the strongest identified by the scientific literature, building a solid foundation to our collective knowledge.
Our initial analysis has shown that the DiF dataset provides a more balanced distribution and broader coverage of facial images compared to previous datasets. Furthermore, the insights obtained from the statistical analysis of the 10 initial coding schemes on the DiF dataset has furthered our own understanding of what is important for characterizing human faces and enabled us to continue important research into ways to improve facial recognition technology.
The dataset is available today to the global research community upon request. IBM is proud to make this available and our goal is to help further our collective research and contribute to creating AI systems that are more fair.
While IBM Research is committed to continuing study and investigation of fairer facial recognition systems, we don't believe we can do it alone. With today's release, we urge others to contribute to the growing body of research and advance this important scientific agenda.
To request access to the DiF dataset, visit our webpage. To learn more about DiF, read our paper, "Diversity in Faces."
Explore further: Experts disagree with prior study that claimed sheep have face-recognition abilities comparable to humans
More information: L. G. Farkas, Anthropometry of the Head and Face, Raven Press, 1994.
A. Chardon I. Cretois and C. Hourseau, "Skin colour typology and suntanning pathways," International Journal of Cosmetic Science , Aug. 1991, 13(4), pp. 191-208.
Y. Liu, K. L. Schmidt, J. F. Cohn, S. Mitra, "Facial asymmetry quantification for expression invariant human identification," Computer Vision and Image Understanding, Volume 91, Issues 1–2, July–August 2003, pp. 138-159.
L. G. Farkas, et. al, "International anthropometric study of facial morphology in various ethnic groups/races," J Craniofac Surg. 2005 Jul;16(4), pp. 615-46.
N. Ramanathan, R. Chellappa, "Modeling Age Progression in Young Faces," Intl. Conf. on Computer Vision and Pattern Recognition (CVPR), 2006, pp. 387-394.
A. C. Little, B. C. Jones, L. M. DeBruine, "Facial attractiveness: evolutionary based research," Philos Trans R Soc Lond B Biol Sci. 2011 Jun 12;366(1571), pp. 1638-59.
X. Zhu, D. Ramanan, "Face Detection, Pose Estimation, and Landmark Localization in the Wild," Intl. Conf. on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 2879-2886.
A. Porcheron, E. Mauger, R. Russell, "Aspects of Facial Contrast Decrease with Age and Are Cues for Age Perception," PLoS One 8(3), Mar. 6, 2013
Z. Liu, P. Luo, X. Wang, X. Tang, "Deep Learning Face Attributes in the Wild", Intl. Conf. on Computer Vision (ICCV), 2015, pp. 3730-3738.
Journal reference: PLoS ONE
Provided by: IBM
This story is republished courtesy of IBM Research. Read the original story here.
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