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The software obviously poses ethical concerns as it all","og:title":"Adobe Voco","og:description":"As input the program takes approximately 20 minutes of the desired target's speech and after that it can generate sound-alike voice with even phonemes that were not present in the target example material. The software obviously poses ethical concerns as it all","og:image":"https://old.roi4cio.com/fileadmin/user_upload/Adobe_Voco.jpg"},"eventUrl":"","translationId":1041,"dealDetails":null,"roi":null,"price":null,"bonusForReference":null,"templateData":[],"testingArea":"","categories":[{"id":752,"title":"Voice Recognition","alias":"voice-recognition","description":"<span style=\"font-weight: bold;\">Voice</span> or <span style=\"font-weight: bold;\">speaker recognition </span>is the ability of a machine or program to receive and interpret dictation or to understand and carry out spoken commands. Voice recognition has gained prominence and use with the rise of AI and intelligent assistants, such as Amazon's Alexa, Apple's Siri and Microsoft's Cortana.<br />Voice recognition systems enable consumers to interact with technology simply by speaking to it, enabling hands-free requests, reminders and other simple tasks.\r\n<span style=\"font-weight: bold;\">Voice recognition technology on computers</span> requires that analog audio be converted into digital signals, known as analog-to-digital conversion. For a computer to decipher a signal, it must have a digital database, or vocabulary, of words or syllables, as well as a speedy means for comparing this data to signals. The speech patterns are stored on the hard drive and loaded into memory when the program is run. A comparator checks these stored patterns against the output of the A/D converter - an action called pattern recognition.\r\nIn practice, the size of a speech recognition system effective vocabulary is directly related to the random access memory capacity of the computer in which it is installed. A voice recognition program runs many times faster if the entire vocabulary can be loaded into RAM, as compared with searching the hard drive for some of the matches. \r\nWhile <span style=\"font-weight: bold;\">voice recognition technology </span>originated on PCs, it has gained acceptance in both business and consumer spaces <span style=\"font-weight: bold;\">on mobile devices and in home assistant products</span>. The popularity of smartphones opened up the opportunity to add voice recognition technology into consumer pockets, while home devices, like Google Home and Amazon Echo, brought voice recognition technology into living rooms and kitchens. Voice recognition, combined with the growing stable of internet of things sensors, has added a technological layer to many consumer products that previously lacked any smart capabilities.\r\nAs<span style=\"font-weight: bold;\"> uses for voice recognition technology</span> grow and more users interact with it, the companies implementing speak recognition software will have more data and information to feed into the neural networks that power voice recognition systems, thus improving the capabilities and accuracy of the automatic speech recognition products.<br />The uses for voice recognition have grown quickly as AI, machine learning and consumer acceptance have matured. In-home digital assistants from Google to Amazon to Apple have all implemented voice recognition software to interact with users. The way <span style=\"font-weight: bold;\">consumers use voice recognition technology</span> varies depending on the product, but it can include transcribing speech to text converter, setting up reminders, searching the internet, and responding to simple questions and requests, such as playing music or sharing weather or traffic information.\r\nThe government is also looking for ways to use voice recognition technology and voice identification for security purposes. The National Security Agency (the official U.S. cryptologic organization of the United States Intelligence Community under the Department of Defense) has used voice recognition systems dating back to 2004.<br /><br />","materialsDescription":"<h1 class=\"align-center\"> <span style=\"font-weight: normal;\">What is voice recognition?</span></h1>\r\n<p class=\"align-left\">Voice recognition is an alternative to typing on a keyboard. Put simply, you talk to the computer and your words appear on the screen. The software has been developed to provide a fast method of writing on a computer and can help people with a variety of disabilities. It is useful for people with physical disabilities who often find typing difficult, painful or impossible. Voice-recognition software can also help those with spelling difficulties, including users with dyslexia, because recognised words are almost always correctly spelled.</p>\r\n<h1 class=\"align-center\"><span style=\"font-weight: normal;\">What is voice recognition software?</span></h1>\r\n<p class=\"align-left\">Voice-recognition software programmes work by analysing sounds and converting them to text. Once correctly set up, the systems should recognise around 95% of what is said if you speak clearly. Several programmes are available that provide computer speech recognition. These systems have mostly been designed for Windows operating systems, however programmes are also available for Mac OS X. In addition to third-party software, there are also voice-recognition programmes built in to the operating systems of Windows Vista and Windows 7, 8, 10. Most specialist voice applications include the software, a microphone headset, a manual and a quick reference card. You connect the microphone to the computer, either into the soundcard or via a USB or similar connection.</p>\r\n<header><h1 class=\"align-center\"><span style=\"font-weight: normal;\">What are the types of speech recognition?</span></h1></header>\r\n<p class=\"align-left\"><span style=\"font-weight: bold;\">There are two types of speech recognition.</span> One is called speaker–dependent and the other is speaker–independent. Speaker–dependent software is commonly used for dictation software, while speaker–independent software is more commonly found in telephone applications.<span style=\"font-weight: bold;\"></span></p>\r\n<p class=\"align-left\"><span style=\"font-weight: bold;\">Speaker–dependent</span> software works by learning the unique characteristics of a single person's voice, in a way similar to voice recognition. New users must first &quot;train&quot; the voice recognition systems product by speaking to it, so the computer can analyze how the person talks. This often means users have to read a few pages of text to the computer before they can use the voice recogniser.<span style=\"font-weight: bold;\"></span></p>\r\n<p class=\"align-left\"><span style=\"font-weight: bold;\">Speaker–independent</span> software is designed to recognize anyone's voice, so no training is involved. This means it is the only real option for applications such as interactive voice response systems — where businesses can't ask callers to read pages of text before using the system. The downside is that speaker–independent software is generally less accurate than speaker–dependent software.</p>\r\n<p class=\"align-left\">Voice recognition engines that are speaker independent generally deal with this fact by limiting the grammars they use. By using a smaller list of recognized words, the speech engine is more likely to correctly recognize what a speaker said.</p>\r\n<p class=\"align-left\">This makes speaker–independent software ideal for most IVR systems, and any application where a large number of people will be using the same system. Speaker dependent software is used more widely in dictation software, where only one person will use the system and there is a need for a large grammar.</p>\r\n<h1 class=\"align-center\"><span style=\"font-weight: normal;\">What are the voice recognition applications?</span></h1>\r\n<p class=\"align-left\">The technology is gaining popularity in many areas and has been successful in the following:</p>\r\n<ul><li><span style=\"font-weight: bold;\">Device control. </span>Just saying &quot;OK Google&quot; to an Android phone fires up a system that is all ears to your voice commands.</li><li><span style=\"font-weight: bold;\">Car Bluetooth systems.</span> Many cars are equipped with a system that connects its radio mechanism to your smartphone through Bluetooth. You can then make and receive calls without touching your smartphone, and can even dial numbers by just saying them.</li><li><span style=\"font-weight: bold;\">Voice to speech transcription.</span> In areas where people have to type a lot, some intelligent software captures their spoken words and transcribe them into text. This is current in the certain word processing software. Voice transcription also works with visual voicemail.</li></ul>\r\n<h1 class=\"align-center\"><span style=\"font-weight: normal;\">What is dictation software?</span></h1>\r\n<p class=\"align-left\"></p>\r\nWith the best dictation software, you can compose memos, emails, speeches, and other writing using voice translator speech to text. Some dictation apps also give you the power to control your computer or mobile device with spoken words, too, letting you open apps and navigate the web when you aren't able to or don't want to with your fingers. \r\n<p class=\"align-left\">Dictation apps have a variety of use cases. They're well known among the accessibility community, as not everyone has full and dexterous use of their fingers and hands for typing, moving a mouse, or tapping a touchscreen. They're also quite popular with productivity enthusiasts because once you get comfortable dictating, it's typically faster than typing. Dictating also enables multitasking. You can write while walking, cooking, or even breastfeeding.</p>\r\n<p class=\"align-left\">Some people also find that writing by dictating silences their internal editor. You might be more inclined to get all your thoughts out first and review them later, rather than revising ideas as you form them.</p>\r\n<p class=\"align-left\">In the last few years, dictation software has become more readily available, easier to use, and much less expensive. Also sometimes called voice-to-text apps or voice recognition apps, these tools turn your spoken words into writing on the screen quickly and accurately. </p>\r\n<p class=\"align-left\">Some best voice recognition software are standalone software programs while others are features that come inside other apps or operating systems. Take Google Docs Voice Typing, for example. It's a feature inside Google Docs, rather than a standalone app. You can use it to write in Google Docs as well as edit and format your text.<br /><br /><br /></p>","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/icon_Voice_Recognition.png"},{"id":283,"title":"Content Analytics, Discovery, and Cognitive Software","alias":"content-analytics-discovery-and-cognitive-software","description":"<span style=\"font-weight: bold; \">Content Analytics</span> comprises of range of search and reporting technologies which can provide identical levels of business intelligence and strategic value for unstructured data. Content analytics for unstructured information includes social media monitoring, reputation monitoring, and sentiment analysis. <span style=\"font-weight: bold; \">Content analytics software</span> uses natural language queries, trends analysis, predictive analytics and contextual discovery to reveal trends and patterns for the company's unstructured data.\r\n<span style=\"font-weight: bold; \">Discovery tools</span> are the search tools that analyse contents for its likely relevancy to a process, by linking names, time periods or terms used. Discovery tools can also extend to legal hold and partitioning of content for further scrutiny. Various types of discovery tools include search engines, auto-categorization, and information visualization tools.\r\n<span style=\"font-weight: bold;\">Cognitive computing software</span> makes context computable by identifying and extracting context features such as hour, location, task, history or profile in structured way for an individual or an application engaged in a specific process at definite time and place. Cognitive computing technologies and platforms includes expert assistance software.\r\n<span style=\"font-weight: bold;\">Content Analytics, Discovery and Cognitive Systems,</span> collectively covers the market and technologies that access, analyse, organize, and provides advisory services related to a range of unstructured data. Cognitive systems leverages large amounts of structured and unstructured data and content analytics tools, along with several infrastructure technologies to answer questions, provide recommendations and directions. ","materialsDescription":"<h1 class=\"align-center\">What are Content Analytics?</h1>\r\nContent analytics is one way that brands can measure the success of their digital content; it involves analyzing data from both inbound and outbound collateral to figure out what’s working and what’s not. After all, no one wants to spend hundreds or even thousands of dollars on content that isn’t resonating.\r\nIt should come as no surprise, then, that 75 percent of enterprises think content analytics can provide real business insight: What are your customers most interested in reading about? What format generates the most engagement? You can use answers to these questions to help you decide what content you should be producing, how to present it (quiz, paid ad, slideshow, blog post, etc.), and where/when to post it for the best results. If utilized correctly, content analytics can offer brands a way to build customer loyalty and boost revenue.\r\nAdvanced content analytics strategies should help you to look beyond the numbers and to focus on taking action. Content analytics is not a mirror reflecting how your company’s content is performing; rather, it is but one tool that can help steer you towards actionable solutions and more effective content choices.\r\nMetrics provided by content analytics, and the actions you take based off that data, can help you optimize conversion rates. In addition to tweaking your content strategy, you can also build a conversion optimization strategy by testing calls-to-action (CTAs), headlines, subscription forms, etc.\r\n<h1 class=\"align-center\"><span style=\"font-weight: bold; \">What are the features of a cognitive computing solution?</span></h1>\r\nWith the present state of cognitive function computing, basic solution can play an excellent role of an assistant or virtual advisor. Siri, Google assistant, Cortana, and Alexa are good examples of personal assistants. Virtual advisor such as Dr. AI by HealthTap is a cognitive solution. It relies on individual patients’ medical profiles and knowledge gleaned from 105,000 physicians. It compiles a prioritized list of the symptoms and connects to a doctor if required. Now, experts are working on implementing cognitive solutions in enterprise systems. Some use cases are fraud detection using machine learning, predictive analytics solution, predicting oil spills in Oil and Gas production cycle etc.\r\nThe purpose of cognitive computing is the creation of computing frameworks that can solve complicated problems without constant human intervention. In order to implement cognitive function computing in commercial and widespread applications, Cognitive Computing consortium has recommended the following features for the computing systems:\r\n<span style=\"font-weight: bold; \">Adaptive.</span>This is the first step in making a machine learning based cognitive system. The solutions should mimic the ability of the human brain to learn and adapt from the surroundings. The systems can’t be programmed for an isolated task. It needs to be dynamic in data gathering, understanding goals, and requirements.\r\n<span style=\"font-weight: bold; \">Interactive.</span>Similar to brain the cognitive solution must interact with all elements in the system – processor, devices, cloud services and user. Cognitive systems should interact bi-directionally. It should understand human input and provide relevant results using natural language processing and deep learning. Some skilled intelligent chatbots such as Mitsuku have already achieved this feature.\r\n<span style=\"font-weight: bold; \">Iterative and stateful.</span>The system should “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time. It should be able to define the problem by asking questions or finding an additional source. This feature needs a careful application of the data quality and validation methodologies in order to ensure that the system is always provided with enough information and that the data sources it operates on to deliver reliable and up-to-date input.\r\n<span style=\"font-weight: bold; \">Contextual.</span>They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task, and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). ","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/Content_Analytics_Discovery_and_Cognitive_Software.png"}],"characteristics":[],"concurentProducts":[],"jobRoles":[],"organizationalFeatures":[],"complementaryCategories":[],"solutions":[],"materials":[],"useCases":[],"best_practices":[],"values":[],"implementations":[]},"grooper":{"id":5878,"logoURL":"https://old.roi4cio.com/fileadmin/content/grooper-logo.png","logo":true,"scheme":false,"title":"Grooper","vendorVerified":0,"rating":"0.00","implementationsCount":0,"suppliersCount":0,"supplierPartnersCount":0,"alias":"grooper","companyTitle":"BIS, Inc.","companyTypes":["supplier","vendor"],"companyId":8704,"companyAlias":"bis-inc","description":"<p class=\"MsoNormal\">Grooper empowers rapid innovation for organizations processing and integrating large quantities of difficult data. Created by a team of courageous developers frustrated by limitations in existing solutions, Grooper is an intelligent document and digital data integration platform. Grooper combines patented and sophisticated image processing, capture technology, machine learning, and natural language processing. Grooper &ndash; intelligent document processing; limitless, template-free data integration. <a href=\"https://www.grooper.com/\">https://www.grooper.com</a></p>","shortDescription":"Grooper software helps organizations innovate workflows by integrating difficult data.","type":null,"isRoiCalculatorAvaliable":false,"isConfiguratorAvaliable":false,"bonus":100,"usingCount":1,"sellingCount":7,"discontinued":0,"rebateForPoc":0,"rebate":0,"seo":{"title":"Grooper","keywords":"","description":"<p class=\"MsoNormal\">Grooper empowers rapid innovation for organizations processing and integrating large quantities of difficult data. Created by a team of courageous developers frustrated by limitations in existing solutions, Grooper is an intelligent docume","og:title":"Grooper","og:description":"<p class=\"MsoNormal\">Grooper empowers rapid innovation for organizations processing and integrating large quantities of difficult data. Created by a team of courageous developers frustrated by limitations in existing solutions, Grooper is an intelligent docume","og:image":"https://old.roi4cio.com/fileadmin/content/grooper-logo.png"},"eventUrl":"","translationId":5878,"dealDetails":null,"roi":null,"price":null,"bonusForReference":null,"templateData":[],"testingArea":"","categories":[{"id":283,"title":"Content Analytics, Discovery, and Cognitive Software","alias":"content-analytics-discovery-and-cognitive-software","description":"<span style=\"font-weight: bold; \">Content Analytics</span> comprises of range of search and reporting technologies which can provide identical levels of business intelligence and strategic value for unstructured data. Content analytics for unstructured information includes social media monitoring, reputation monitoring, and sentiment analysis. <span style=\"font-weight: bold; \">Content analytics software</span> uses natural language queries, trends analysis, predictive analytics and contextual discovery to reveal trends and patterns for the company's unstructured data.\r\n<span style=\"font-weight: bold; \">Discovery tools</span> are the search tools that analyse contents for its likely relevancy to a process, by linking names, time periods or terms used. Discovery tools can also extend to legal hold and partitioning of content for further scrutiny. Various types of discovery tools include search engines, auto-categorization, and information visualization tools.\r\n<span style=\"font-weight: bold;\">Cognitive computing software</span> makes context computable by identifying and extracting context features such as hour, location, task, history or profile in structured way for an individual or an application engaged in a specific process at definite time and place. Cognitive computing technologies and platforms includes expert assistance software.\r\n<span style=\"font-weight: bold;\">Content Analytics, Discovery and Cognitive Systems,</span> collectively covers the market and technologies that access, analyse, organize, and provides advisory services related to a range of unstructured data. Cognitive systems leverages large amounts of structured and unstructured data and content analytics tools, along with several infrastructure technologies to answer questions, provide recommendations and directions. ","materialsDescription":"<h1 class=\"align-center\">What are Content Analytics?</h1>\r\nContent analytics is one way that brands can measure the success of their digital content; it involves analyzing data from both inbound and outbound collateral to figure out what’s working and what’s not. After all, no one wants to spend hundreds or even thousands of dollars on content that isn’t resonating.\r\nIt should come as no surprise, then, that 75 percent of enterprises think content analytics can provide real business insight: What are your customers most interested in reading about? What format generates the most engagement? You can use answers to these questions to help you decide what content you should be producing, how to present it (quiz, paid ad, slideshow, blog post, etc.), and where/when to post it for the best results. If utilized correctly, content analytics can offer brands a way to build customer loyalty and boost revenue.\r\nAdvanced content analytics strategies should help you to look beyond the numbers and to focus on taking action. Content analytics is not a mirror reflecting how your company’s content is performing; rather, it is but one tool that can help steer you towards actionable solutions and more effective content choices.\r\nMetrics provided by content analytics, and the actions you take based off that data, can help you optimize conversion rates. In addition to tweaking your content strategy, you can also build a conversion optimization strategy by testing calls-to-action (CTAs), headlines, subscription forms, etc.\r\n<h1 class=\"align-center\"><span style=\"font-weight: bold; \">What are the features of a cognitive computing solution?</span></h1>\r\nWith the present state of cognitive function computing, basic solution can play an excellent role of an assistant or virtual advisor. Siri, Google assistant, Cortana, and Alexa are good examples of personal assistants. Virtual advisor such as Dr. AI by HealthTap is a cognitive solution. It relies on individual patients’ medical profiles and knowledge gleaned from 105,000 physicians. It compiles a prioritized list of the symptoms and connects to a doctor if required. Now, experts are working on implementing cognitive solutions in enterprise systems. Some use cases are fraud detection using machine learning, predictive analytics solution, predicting oil spills in Oil and Gas production cycle etc.\r\nThe purpose of cognitive computing is the creation of computing frameworks that can solve complicated problems without constant human intervention. In order to implement cognitive function computing in commercial and widespread applications, Cognitive Computing consortium has recommended the following features for the computing systems:\r\n<span style=\"font-weight: bold; \">Adaptive.</span>This is the first step in making a machine learning based cognitive system. The solutions should mimic the ability of the human brain to learn and adapt from the surroundings. The systems can’t be programmed for an isolated task. It needs to be dynamic in data gathering, understanding goals, and requirements.\r\n<span style=\"font-weight: bold; \">Interactive.</span>Similar to brain the cognitive solution must interact with all elements in the system – processor, devices, cloud services and user. Cognitive systems should interact bi-directionally. It should understand human input and provide relevant results using natural language processing and deep learning. Some skilled intelligent chatbots such as Mitsuku have already achieved this feature.\r\n<span style=\"font-weight: bold; \">Iterative and stateful.</span>The system should “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time. It should be able to define the problem by asking questions or finding an additional source. This feature needs a careful application of the data quality and validation methodologies in order to ensure that the system is always provided with enough information and that the data sources it operates on to deliver reliable and up-to-date input.\r\n<span style=\"font-weight: bold; \">Contextual.</span>They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task, and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). ","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/Content_Analytics_Discovery_and_Cognitive_Software.png"},{"id":709,"title":"Data Entry","alias":"data-entry","description":" Data entry, a person-based process, is &quot;one of the important basic&quot; tasks needed when no machine-readable version of the information for planned computer-based analysis or processing is readily available.\r\nSometimes what is needed is &quot;information about information (that) can be greater than the value of the information itself.&quot; It can also involve filling in required information which is then &quot;data-entered&quot; from what was written on the research document, such as the growth in available items in a category. This is a higher level of abstraction than Metadata, &quot;information about data.&quot;\r\nData entry is often done with a keyboard and at times also using a mouse, although a manually-fed scanner may be involved.\r\nHistorically, devices lacking any pre-processing capabilities were used.\r\nKeypunching. Data entry using keypunches was related to the concept of Batch processing - there was no immediate feedback.\r\nComputer keyboards. Computer keyboards and online data-entry provide the ability to give feedback to the data entry clerk doing the work.\r\nNumeric keypads. The addition of numeric keypads to computer keyboards introduced quicker and often also the less error-prone entry of numeric data.\r\nComputer mouse. The use of a computer mouse, typically on a personal computer, opened up another option for doing data entry.\r\nTouch screens. Touch screens introduced even more options, including the ability to stand and do data entry, especially given &quot;a proper height of work surface when performing data entry.&quot;","materialsDescription":" <span style=\"font-weight: bold;\">What does Data Entry mean?</span>\r\nData entry is the process of transcribing information into an electronic medium such as a computer or other electronic device. It can either be performed manually or automatically by using a machine or computer. Most data entry tasks are time consuming in nature, however data entry is considered a basic, necessary task for most organizations.\r\nData entry is considered a non–core process for most organizations and is usually performed on data forms such as spreadsheets, handwritten or scanned documents, audio or video. Addition, modification and deletion are the three modes of operation in data entry.\r\nData entry jobs do not require any special qualifications, knowledge or talent, and only require accuracy and fast turnaround. As such, data entry jobs are frequently outsourced in order to lower costs. Computers are also used in automated data entry, as they are highly accurate and can be programmed to fetch and transcribe data into the required medium.\r\nAccurately keyed data is the base upon which the organization can perform analyses and make plans.\r\nManual data entry often requires good concentration and focus over a long duration of time, and this can prove physically and mentally challenging for data entry workers.<br /><br />","iconURL":"https://old.roi4cio.com/fileadmin/user_upload/icon_Data_Entry.png"}],"characteristics":[],"concurentProducts":[],"jobRoles":[],"organizationalFeatures":[],"complementaryCategories":[],"solutions":[],"materials":[],"useCases":[],"best_practices":[],"values":[],"implementations":[]},"oracle-adaptive-intelligent-apps":{"id":974,"logoURL":"https://old.roi4cio.com/fileadmin/user_upload/Oracle_Adaptive_Intelligent_Apps.jpg","logo":true,"scheme":false,"title":"Oracle Adaptive Intelligent Apps","vendorVerified":0,"rating":"2.00","implementationsCount":0,"suppliersCount":0,"supplierPartnersCount":150,"alias":"oracle-adaptive-intelligent-apps","companyTitle":"Oracle","companyTypes":["supplier","vendor"],"companyId":164,"companyAlias":"oracle","description":"<span style=\"font-weight: bold;\">Adaptive Intelligent Apps Characteristics</span>\r\n<span style=\"font-weight: bold;\">Data and Context-Driven</span>\r\nProcess enormous amounts of web-scale data in real-time and synthesizes context to deliver the best and most personalized outcomes.\r\n<span style=\"font-weight: bold;\">Action-Oriented and Omni-Channel</span>\r\nAnticipate user behaviors and deliver actionable and individualized outcomes. Data and events are tracked from everywhere so recommendations are coordinated across channels.\r\n<span style=\"font-weight: bold;\">Continuously Adaptive and Self-Learning</span>\r\nContinuously improves outcomes as the system reacts, learns and adapts. There is a value exchange between the end user and the adaptive intelligent app—the more the end user interacts, the more value they receive.\r\n<span style=\"font-weight: bold;\">Built for Business Users</span>\r\nImmediate value out of the box, with no need for a legion of data scientists!\r\n<span style=\"font-weight: bold;\">Adaptive</span>\r\nLearn from your behavior and &quot;others like you&quot; in the moment.\r\nReact, change, and adapt based on continued use—click by click.\r\nDeliver increasingly relevant and optimized outcomes in real-time.\r\n<span style=\"font-weight: bold;\">Intelligent</span>\r\nProcess and derive insights from web-scale 3rd-party data.\r\nSynthesize context: weather, life events, real-time actions, social activity, and much more.\r\nIdentify and serve up—in real-time—the best individualized outcomes.","shortDescription":"Oracle Adaptive Intelligent Apps Add New Business Value to CX, HCM, SCM, and ERP\r\nAdaptive Intelligent Apps are uniquely powered by enormous amounts of digital consumer and business data from Oracle’s Data Cloud, sophisticated decision science/machine learning, and a scalable cloud infrastructure","type":null,"isRoiCalculatorAvaliable":false,"isConfiguratorAvaliable":false,"bonus":100,"usingCount":10,"sellingCount":2,"discontinued":0,"rebateForPoc":0,"rebate":0,"seo":{"title":"Oracle Adaptive Intelligent Apps","keywords":"outcomes, Adaptive, from, data, value, more, user, real-time","description":"<span style=\"font-weight: bold;\">Adaptive Intelligent Apps Characteristics</span>\r\n<span style=\"font-weight: bold;\">Data and Context-Driven</span>\r\nProcess enormous amounts of web-scale data in real-time and synthesizes context to deliver the best and most per","og:title":"Oracle Adaptive Intelligent Apps","og:description":"<span style=\"font-weight: bold;\">Adaptive Intelligent Apps Characteristics</span>\r\n<span style=\"font-weight: bold;\">Data and Context-Driven</span>\r\nProcess enormous amounts of web-scale data in real-time and synthesizes context to deliver the best and most per","og:image":"https://old.roi4cio.com/fileadmin/user_upload/Oracle_Adaptive_Intelligent_Apps.jpg"},"eventUrl":"","translationId":975,"dealDetails":null,"roi":null,"price":null,"bonusForReference":null,"templateData":[],"testingArea":"","categories":[{"id":283,"title":"Content Analytics, Discovery, and Cognitive Software","alias":"content-analytics-discovery-and-cognitive-software","description":"<span style=\"font-weight: bold; \">Content Analytics</span> comprises of range of search and reporting technologies which can provide identical levels of business intelligence and strategic value for unstructured data. Content analytics for unstructured information includes social media monitoring, reputation monitoring, and sentiment analysis. <span style=\"font-weight: bold; \">Content analytics software</span> uses natural language queries, trends analysis, predictive analytics and contextual discovery to reveal trends and patterns for the company's unstructured data.\r\n<span style=\"font-weight: bold; \">Discovery tools</span> are the search tools that analyse contents for its likely relevancy to a process, by linking names, time periods or terms used. Discovery tools can also extend to legal hold and partitioning of content for further scrutiny. Various types of discovery tools include search engines, auto-categorization, and information visualization tools.\r\n<span style=\"font-weight: bold;\">Cognitive computing software</span> makes context computable by identifying and extracting context features such as hour, location, task, history or profile in structured way for an individual or an application engaged in a specific process at definite time and place. Cognitive computing technologies and platforms includes expert assistance software.\r\n<span style=\"font-weight: bold;\">Content Analytics, Discovery and Cognitive Systems,</span> collectively covers the market and technologies that access, analyse, organize, and provides advisory services related to a range of unstructured data. Cognitive systems leverages large amounts of structured and unstructured data and content analytics tools, along with several infrastructure technologies to answer questions, provide recommendations and directions. ","materialsDescription":"<h1 class=\"align-center\">What are Content Analytics?</h1>\r\nContent analytics is one way that brands can measure the success of their digital content; it involves analyzing data from both inbound and outbound collateral to figure out what’s working and what’s not. After all, no one wants to spend hundreds or even thousands of dollars on content that isn’t resonating.\r\nIt should come as no surprise, then, that 75 percent of enterprises think content analytics can provide real business insight: What are your customers most interested in reading about? What format generates the most engagement? You can use answers to these questions to help you decide what content you should be producing, how to present it (quiz, paid ad, slideshow, blog post, etc.), and where/when to post it for the best results. If utilized correctly, content analytics can offer brands a way to build customer loyalty and boost revenue.\r\nAdvanced content analytics strategies should help you to look beyond the numbers and to focus on taking action. Content analytics is not a mirror reflecting how your company’s content is performing; rather, it is but one tool that can help steer you towards actionable solutions and more effective content choices.\r\nMetrics provided by content analytics, and the actions you take based off that data, can help you optimize conversion rates. In addition to tweaking your content strategy, you can also build a conversion optimization strategy by testing calls-to-action (CTAs), headlines, subscription forms, etc.\r\n<h1 class=\"align-center\"><span style=\"font-weight: bold; \">What are the features of a cognitive computing solution?</span></h1>\r\nWith the present state of cognitive function computing, basic solution can play an excellent role of an assistant or virtual advisor. Siri, Google assistant, Cortana, and Alexa are good examples of personal assistants. Virtual advisor such as Dr. AI by HealthTap is a cognitive solution. It relies on individual patients’ medical profiles and knowledge gleaned from 105,000 physicians. It compiles a prioritized list of the symptoms and connects to a doctor if required. Now, experts are working on implementing cognitive solutions in enterprise systems. Some use cases are fraud detection using machine learning, predictive analytics solution, predicting oil spills in Oil and Gas production cycle etc.\r\nThe purpose of cognitive computing is the creation of computing frameworks that can solve complicated problems without constant human intervention. In order to implement cognitive function computing in commercial and widespread applications, Cognitive Computing consortium has recommended the following features for the computing systems:\r\n<span style=\"font-weight: bold; \">Adaptive.</span>This is the first step in making a machine learning based cognitive system. The solutions should mimic the ability of the human brain to learn and adapt from the surroundings. The systems can’t be programmed for an isolated task. It needs to be dynamic in data gathering, understanding goals, and requirements.\r\n<span style=\"font-weight: bold; \">Interactive.</span>Similar to brain the cognitive solution must interact with all elements in the system – processor, devices, cloud services and user. Cognitive systems should interact bi-directionally. It should understand human input and provide relevant results using natural language processing and deep learning. Some skilled intelligent chatbots such as Mitsuku have already achieved this feature.\r\n<span style=\"font-weight: bold; \">Iterative and stateful.</span>The system should “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time. It should be able to define the problem by asking questions or finding an additional source. This feature needs a careful application of the data quality and validation methodologies in order to ensure that the system is always provided with enough information and that the data sources it operates on to deliver reliable and up-to-date input.\r\n<span style=\"font-weight: bold; \">Contextual.</span>They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task, and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). 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