{"id":3974,"date":"2023-05-24T14:58:40","date_gmt":"2023-05-24T09:58:40","guid":{"rendered":"https:\/\/dicecamp.com\/insights\/?p=3974"},"modified":"2023-05-24T15:23:27","modified_gmt":"2023-05-24T10:23:27","slug":"4-must-know-ai-innovations-of-the-future","status":"publish","type":"post","link":"https:\/\/dicecamp.com\/insights\/4-must-know-ai-innovations-of-the-future\/","title":{"rendered":"4 \u2018must-know\u2019 AI Innovations of the Future"},"content":{"rendered":"<p><span style=\"font-weight: 400\">AI is already powerful, but the true potential of this discipline is yet to be witnessed. Recently, it\u2019s uncovered that four innovations in AI are to impact business in a transformational manner. These are to work at the level of business applications, devices and tools.<\/span><\/p>\n<p><span style=\"font-weight: 400\">This blog presents the very four innovations in AI, as explored in the Gartner\u2019s Hype Cycle 2022, that identifies topics in AI that\u2019ll gain popularity in the near-long term, 2-5 years.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Business leaders and the AI community are advised to focus on these innovative technologies and techniques in AI to enable an ever improved and expanded business decision making.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">The four innovations in AI that are to gain high attention by engineers and executives are:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Synthetic Data: Creating enhanced and enriched data for training of algorithms.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Composite AI: Fusing multiple AI techniques to get the best decision outcome.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Decision Intelligence: Creating technology that makes better business decisions.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Edge AI: Embedding AI in IoT devices, near users.\u00a0<\/span><\/li>\n<\/ul>\n<h1><span style=\"font-weight: 400\">Synthetic Data<\/span><\/h1>\n<p><span style=\"font-weight: 400\">Synthetic data is a data-centric AI innovation, which means instead of tweaking AI models for better outcomes, the focus is put on quality aspects of data. It\u2019s defined as a class of data that\u2019s artificially created and is not obtained from real world observations.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Real data is difficult to get. Often the data is expensive, contains bias and personally identifiable information, and is unapproachable. But as the innovation in synthetic data is gaining pace, it has become extremely easy to create enhanced and enriched data for training of algorithms.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">The technology is so remarkable that synthetic data appears as real as one would obtain from the real world. It can be created using many methods\u2013 for example, using generative adversarial networks (Gen AI), that\u2019s capable of creating unlimited amounts of unique data in response to user prompts.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">The following key benefits are obtained:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Reduced costs and time in ML development.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Improved performance of ML algorithms.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Creation of data for scenarios where real world data is not available\u2013 such as for training crash detection algorithms and self-driving cars.<\/span><\/li>\n<\/ol>\n<p><b>Outlook-<\/b><span style=\"font-weight: 400\"> Synthetic data has already been adopted for applications such as computer vision and NLP, and will see accelerated adoption in 3 years.<\/span><\/p>\n<p><span style=\"font-weight: 400\">In the longer term, synthetic data is predicted to completely substitute real world data, by 2030.<\/span><\/p>\n<h1><span style=\"font-weight: 400\">Composite AI<\/span><\/h1>\n<p><span style=\"font-weight: 400\">Composite AI is a model-centric innovation. It fuses many AI techniques together to improve: model performance, which is the reduction in computations it takes for learning, and solve a wider range of business problems.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">It combines the hyped AI: Machine Learning, with other traditional AI applications such as graph analytics, rule-based approach, and optimization methods, to deeply analyze data, improving the overall capability of the AI.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Note, Composite AI is not merely a combination of various techniques, rather the purpose is to improve model aspects such as: learning efficiency, and ability to deeply analyze data.<\/span><\/p>\n<p><span style=\"font-weight: 400\">While composite AI still remains a mystery, Gartner and other expert organizations state that it has the capability to create applications that rely on less training data, uses models that are more efficient in training, and solves a wider range of business problems.<\/span><\/p>\n<p><b>Outlook\u2013<\/b><span style=\"font-weight: 400\"> Composite AI will reach mainstream in 3 years and Gartner expects it\u2019ll bring transformational benefits for businesses.<\/span><\/p>\n<h1><span style=\"font-weight: 400\">Decision Intelligence<\/span><\/h1>\n<p><span style=\"font-weight: 400\">Decision making in business has become tough over time. With the evolving landscape\u2013 changing customer needs, technology disruption and competition among enterprises, leaders today find decision making more difficult than 2 years ago. Moreover, there\u2019s more pressure to explain how a particular decision is reached.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Decision Intelligence is the class of AI technology that understands and explains how effective decisions are made.<\/span><\/p>\n<p><span style=\"font-weight: 400\">It\u2019s an application-centric AI, which means that it creates complete AI systems\u2013 starting from designing, to modeling, aligning, executing, monitoring and tuning of the decision models and processes.<\/span><\/p>\n<p><span style=\"font-weight: 400\">In the designing stage, DI specifically focuses on the understanding of how decisions are made, evaluated, and improved via feedback. It then uses this knowledge to create AI applications that are powerful in outcome.<\/span><\/p>\n<p><span style=\"font-weight: 400\">DI also brings multiple traditional and advanced techniques together, to power AI applications. This includes, for example, decision management ( that uses non-deterministic techniques such as agent based systems), and decision support techniques (such as descriptive, diagnostic, prescriptive and predictive analytics).\u00a0<\/span><\/p>\n<p><b>Outlook\u2013<\/b><span style=\"font-weight: 400\"> According to Gartner prediction, work on decision intelligence has already accelerated and will reach its maximum level in 3 years.<\/span><\/p>\n<h1><span style=\"font-weight: 400\">Edge AI<\/span><\/h1>\n<p><span style=\"font-weight: 400\">IoT sensors or edge devices (as they are commonly called) essentially need cloud access to perform processing and decision making on data. However, as AI and devices have advanced, IoT finds a better opportunity in AI.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Edge AI brings AI technology in IoT devices, making the processes faster and better.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">The application of AI in IoT devices has many benefits that were earlier not possible in the absence of AI.<\/span><\/p>\n<p><span style=\"font-weight: 400\">These benefits include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Access to analytics is faster with Edge AI, as AI is installed within the IoT device, there\u2019s no need to transmit data to the cloud server, saving users time.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Networking costs are reduced as the IoT devices perform analytics within them, without having to leave and connect with the centralized cloud processing.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Model accuracy is enhanced as newer data arrives at the IoT sensors and consumed by the AI model, to make it learn and make more effective decisions.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">High availability is achieved from an offline edge device that doesn\u2019t need network connectivity to perform data analytics.<\/span><\/li>\n<\/ul>\n<p><b>Outlook\u2013<\/b><span style=\"font-weight: 400\"> Edge AI is expected to reach mainstream in 3 years.<\/span><\/p>\n<h1><span style=\"font-weight: 400\">Conclusion<\/span><\/h1>\n<p><span style=\"font-weight: 400\">Gartner\u2019s Hype Cycle 2022 analyzes the AI landscape for the near-term future. The results show four areas will come to the mainstream AI-scape in 3 years. These include: Synthetic Data, Composite AI, Decision Intelligence and Edge AI.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">If you are an AI engineer, or a business leader, keep these AI innovations at the top of your AI strategies so that you can help yourself in creating more powerful AI than what we see today.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI is already powerful, but the true potential of this discipline is yet to be witnessed. Recently, it\u2019s uncovered that four innovations in AI are to impact business in a transformational manner. These are to work at the level of business applications, devices and tools. This blog presents the very four innovations in AI, as [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":3975,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,8],"tags":[],"class_list":{"0":"post-3974","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai","8":"category-data-analytics"},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.14 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>4 \u2018must-know\u2019 AI Innovations of the Future - Dicecamp Insights<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dicecamp.com\/insights\/4-must-know-ai-innovations-of-the-future\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"4 \u2018must-know\u2019 AI Innovations of the Future - Dicecamp Insights\" \/>\n<meta property=\"og:description\" content=\"AI is already powerful, but the true potential of this discipline is yet to be witnessed. 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