Create a corporate culture that is ready to reap the rewards of AI

JOSHUA HATTON | June 24, 2019 | 167 views

A recent study of U.S. business from McKinsey and Company found that only 1% of survey respondents indicated that they saw no value from AI or a negative value. In the manufacturing sector specifically about 80% of respondents saw moderate to significant value. Organizations are still early in the adoption process of artificial intelligence. In the group surveyed nearly a quarter of companies had not developed an AI road map because one of the biggest challenges to AI adoption is strategy related. Reasons also include lack of talent siloed environments and leadership who may not value the technology.

Spotlight

SAIC

SAIC is a premier technology integrator providing full life cycle services and solutions in the technical, engineering, intelligence, and enterprise information technology markets. SAIC is Redefining Ingenuity through its deep customer and domain knowledge to enable the delivery of systems engineering and integration offerings for large, complex projects. SAIC’s approximately 15,000 employees are driven by integrity and mission focus to serve customers in the U.S. federal government. Headquartered in McLean, Virginia, SAIC has annual revenues of approximately $4.4 billion.

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FUTURE TECH

AI's Impact on Improving Customer Experience

Article | July 26, 2022

To enhance the consumer experience, businesses all over the world are experimenting with artificial intelligenace (AI), machine learning, and advanced analytics. Artificial intelligence (AI) is becoming increasingly popular among marketers and salespeople, and it has become a vital tool for businesses that want to offer their customers a hyper-personalized, outstanding experience. Customer relationship management (CRM) and customer data platform (CDP) software that has been upgraded with AI has made AI accessible to businesses without the exorbitant expenses previously associated with the technology. When AI and machine learning are used in conjunction for collecting and analyzing social, historical, and behavioral data, brands may develop a much more thorough understanding of their customers. In addition, AI can predict client behavior because it continuously learns from the data it analyzes, in contrast to traditional data analytics tools. As a result, businesses may deliver highly pertinent content, boost sales, and enhance the customer experience. Predictive Behavior Analysis and Real-time Decision Making Real-time decisioning is the capacity to act quickly and based on the most up-to-date information available, such as information from a customer's most recent encounter with a company. For instance, Precognitive's Decision-AI uses a combination of AI and machine learning to assess any event in real-time with a response time of less than 200 milliseconds. Precognitive's fraud prevention product includes Decision-AI, which can be implemented using an API on a website. Marketing to customers can be done more successfully by using real-time decisioning. For example, brands may display highly tailored, pertinent content and offer to clients by utilizing AI and real-time decisioning to discover and comprehend a customer's purpose from the data they produce in real-time. By providing deeper insights into what has already happened and what can be done to facilitate a sale through suggestions for related products and accessories, AI and predictive analytics are able to go further than historical data alone. This increases the relevance of the customer experience, increases the likelihood that a sale will be made, and increases the emotional connection that the customer has with a brand.

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SOFTWARE

The Evolution of Quantum Computing and What its Future Beholds

Article | July 13, 2022

The mechanism of quantum computers will be entirely different from anything we humans have ever created or constructed in the past. Quantum computers, like classical computers, are designed to address problems in the real world. They process data in a unique way, though, which makes them a much more effective machine than any computer in use today. Superposition and entanglement, two fundamental ideas in quantum mechanics, could be used to explain what makes quantum computers unique. The goal of quantum computing research is to find a technique to accelerate the execution of lengthy chains of computer instructions. This method of execution would take advantage of a quantum physics event that is frequently observed but does not appear to make much sense when written out. When this fundamental objective of quantum computing is accomplished, and all theorists are confident works in practice, computing will undoubtedly undergo a revolution. Quantum computing promises that it will enable us to address specific issues that current classical computers cannot resolve in a timely manner. While not a cure-all for all computer issues, quantum computing is adequate for most "needle in a haystack" search and optimization issues. Quantum Computing and Its Deployment Only the big hyperscalers and a few hardware vendors offer quantum computer emulators and limited-sized quantum computers as a cloud service. Quantum computers are used for compute-intensive, non-latency-sensitive issues. Quantum computer architectures can't handle massive data sizes yet. In many circumstances, a hybrid quantum-classical computer is used. Quantum computers don't use much electricity to compute but need cryogenic refrigerators to sustain superconducting temperatures. Networking and Quantum Software Stacks Many quantum computing software stacks virtualize the hardware and build a virtual layer of logical qubits. Software stacks provide compilers that transform high-level programming structures into low-level assembly commands that operate on logical qubits. In addition, software stack suppliers are designing domain-specific application-level templates for quantum computing. The software layer hides complexity without affecting quantum computing hardware performance or mobility.

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SOFTWARE

Language Models: Emerging Types and Why They Matter

Article | July 8, 2022

Language model systems, often known as text understanding and generation systems, are the newest trend in business. However, not every language model is made equal. A few are starting to take center stage, including massive general-purpose models like OpenAI's GPT-3 and models tailored for specific jobs. There is a third type of model at the edge that is intended to run on Internet of Things devices and workstations but is typically very compressed in size and has few functionalities. Large Language Models Large language models, which can reach tens of petabytes in size, are trained on vast volumes of text data. As a result, they rank among the models with the highest number of parameters, where a "parameter" is a value the model can alter on its own as it gains knowledge. The model's parameters, which are made of components learned from prior training data, fundamentally describe the model's aptitude for solving a particular task, like producing text. Fine-tuned Language Models Compared to their massive language model siblings, fine-tuned models are typically smaller. Examples include OpenAI's Codex, a version of GPT-3 that is specifically tailored for programming jobs. Codex is both smaller than OpenAI and more effective at creating and completing strings of computer code, although it still has billions of parameters. The performance of a model, like its capacity to generate protein sequences or respond to queries, can be improved through fine-tuning. Edge Language Models Edge models, which are intentionally small in size, occasionally take the shape of finely tuned models. To work within certain hardware limits, they are occasionally trained from scratch on modest data sets. In any event, edge models provide several advantages that massive language models simply cannot match, notwithstanding their limitations in some areas. The main factor is cost. There are no cloud usage fees with an edge approach that operates locally and offline. As significant, fine-tuned, and edge language models grow in response to new research, they are likely to encounter hurdles on their way to wider use. For example, compared to training a model from the start, fine-tuning requires less data, but fine-tuning still requires a dataset.

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SOFTWARE

Low-code and No-code: A Business' New Best Friend

Article | July 5, 2022

Businesses are starting to integrate artificial intelligence (AI) into their workflow in greater numbers as a result of the growth of digital transformation and developments in machine learning (ML). As a result, platforms that need no coding, as well as their low-code counterparts, are becoming more popular. This development is a step toward computer science's long-term objective of automating manual coding. Low-code/no-code AI platforms will be beneficial to businesses in more data-driven industries like marketing, sales, and finance. AI can assist in a variety of ways, including automating invoicing, evaluating reports, making intelligent suggestions, and anticipating churn rates. How Does an Organization Look at Low-code/No-code as the Future? Developers and other tech-related positions are in high demand, particularly in the fields of AI and data science. Organizations have the chance to close the gap with the aid of citizen data scientists who don't require an AI professional to design unique AI solutions for many scenarios, thanks to low-code and no-code AI technologies. The demand for technological solutions and AI technologies is rising significantly as the technological landscape rapidly changes. AI systems, for example, require complex software that uses a lot of code, a variety of frameworks, and the Internet of Things (IoT). One person's capacity to comprehend every technical detail is strained by the array of complicated technology. Software delivery must be timely, effective, and secure while maintaining high standards. Conclusion Low-code AI solutions offer the speed, ease of use, and adaptability of ready-made software solutions while also drastically reducing the time to market for AI solutions and the cost of recruiting software and computer vision engineers. Organizations are free to construct the architecture, functionality, or pipeline that best suits their project, the sky being the limit. However, creating such unique models may be both costly and time-consuming. Therefore, employing low-code/no-code platforms would apply to particular pipeline actions that would streamline and accelerate the processes.

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Spotlight

SAIC

SAIC is a premier technology integrator providing full life cycle services and solutions in the technical, engineering, intelligence, and enterprise information technology markets. SAIC is Redefining Ingenuity through its deep customer and domain knowledge to enable the delivery of systems engineering and integration offerings for large, complex projects. SAIC’s approximately 15,000 employees are driven by integrity and mission focus to serve customers in the U.S. federal government. Headquartered in McLean, Virginia, SAIC has annual revenues of approximately $4.4 billion.

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AI TECH,SOFTWARE,FUTURE TECH

Amelia Leads Again in Everest Group's Assessment of Conversational AI Vendors

Amelia | September 27, 2022

Amelia, the leading Enterprise Conversational AI software company, today announced that Amelia has been recognized as a Leader in Conversational AI in Everest Group's recent report, Conversational AI – Technology Vendor Landscape with Products PEAK Matrix® Assessment 2022. This marks the second consecutive year that Amelia has been recognized by Everest Group for its leadership in the Conversational AI market. For this PEAK Matrix® assessment, Everest Group evaluated the Vision & Capability and Market Impact of 26 global Conversational AI technology vendors, and then classified each company into one of three categories: Leaders, Major Contenders and Aspirants. Of the 26 vendors assessed, Amelia is named the Leader and placed in the highest overall position in Everest Group's PEAK Matrix®. In the report, Amelia is the only vendor to be listed amongst the top technology vendors for all major business functions, and leads in all major industries – including banking, telecom, healthcare and insurance. Everest Group also highlights Amelia's considerable experience working with global clients from across all industries as a notable strength. Amelia's omnichannel capabilities, sentiment analysis, agent-assist and built-in Orchestration Services are also all recognized as key advantages of the company's Conversational AI platform. "Buyers have also highlighted the cognitive capabilities of the platform, its partnership ecosystem, and the Amelia team's transparency as strength areas of the vendor," the report states. "There is no denying the critical role that Conversational AI continues to play for successful businesses, as evidenced by the exponential increase in adoption that we're observing across industries. As pioneers of this competitive landscape, one which we have pursued for more than two decades, we are honored and proud that for two years running, Everest Group has recognized Amelia as the clear Leader in Conversational AI." Chetan Dube, CEO of Amelia About Amelia Amelia is a leading Enterprise Conversational AI software company with a long history of innovation in automation and Conversational AI. We create fulfilling human experiences through groundbreaking AI solutions, as we enable conversational experiences, streamline IT operations, and automate processes. In 2014, we launched Amelia, the Most Human AI™; then in 2018, we introduced true end-to-end, enterprise-wide automation allowing enterprises to quickly optimize back-end operations. Amelia is consistently recognized by third-party analyst firms as a market leader. Headquartered in New York City with offices in 15 countries, Amelia's roster of client success stories speaks for itself: Our technology impacts more than 200 of the world's leading brands, including global leaders in banking, insurance, telecommunications, and other industries. See how Amelia is powering the Future of Work at amelia.ai.

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AI TECH,AI APPLICATIONS,SOFTWARE

AppTek and expert.ai Announce Strategic Partnership

AppTek | September 27, 2022

AppTek and expert.ai announced today they have entered into a strategic technology partnership to bring AI-based text analytics to dynamic audio content in multiple languages. The partnership leverages AppTek's leadership in Automatic Speech Recognition (ASR) and Neural Machine Translation (NMT) technologies with expert.ai's natural language understanding (NLU) capabilities to enable organizations to leverage audio content in the unstructured data sets that they manage for improving decision making and augmenting intelligent automation. As organizations increasingly utilize language data—emails, documents, reports and other free form text— for an ever-growing range of enterprise use cases (knowledge discovery, contract analysis, policy review, email management, text summarization, classification, entity extraction, etc.), natural language capabilities will play a critical role in powering any process or application that relies on unstructured language data. The combined capabilities of AppTek and expert.ai supercharge enterprise and government NLU and NLP (natural language processing) applications, expanding the data types and sources available for analysis to provide even more informational output. "This partnership brings the full stack Human Language Technology to the federal and commercial space in both Europe and the United States. As we cover multiple sources and types of information input together, we address the full scope of recognition, cognition, interpreting and analytics. "We look forward to implementing our joint vision." Michael Veronis, Chief Revenue Officer at AppTek Using AppTek's speech-to-text technology within the expert.ai Platform, organizations can automatically transcribe audio types from different sources, including high-quality media broadcast content, podcasts, meetings, one-to-one interviews or even low-bandwidth telephone conversations. In addition, they can leverage advanced multilingual functionalities to generate highly accurate, customizable and scalable translations across hundreds of language pairs. "The challenges posed by different languages and dialects along with the constraints of speech-to-text accuracy are causing organizations to miss out on the massively unexploited value of language data, especially audio content," said Colin Matthews, Chief Revenue Officer at expert.ai. "We are thrilled for this partnership, since AppTek technologies for automating speech recognition and machine translation complement our AI-powered natural language capabilities by harnessing the potential of dynamic multilanguage audio content within the expert.ai Platform." About AppTek AppTek is a global leader in artificial intelligence (AI) and machine learning (ML) technologies for automatic speech recognition (ASR), neural machine translation (NMT), natural language processing/understanding (NLP/U) and text-to-speech (TTS) technologies. The AppTek platform delivers industry-leading, real-time streaming and batch technology solutions in the cloud or on-premises for organizations across a breadth of global markets such as media and entertainment, call centers, government, enterprise business, and more. Built by scientists and research engineers who are recognized among the best in the world, AppTek's multidimensional 4D for HLT (human language technology) solutions with slice and dice methodology cover hundreds of languages/dialects, domains, channels and demographics, and drive high impact results with speed and precision. About expert.ai Expert.ai is a leading company in AI-based natural language software. Organizations in insurance, banking and finance, publishing, media and defense all rely on expert.ai to turn language into data, analyze and understand complex documents, accelerate intelligent process automation and improve decision making. Expert.ai's purpose-built natural language platform pairs simple and powerful tools with a proven hybrid AI approach that combines symbolic and machine learning to solve real-world problems and enhance business operations at speed and scale. With offices in Europe and North America, expert.ai serves global businesses such as AXA XL, Zurich Insurance Group, Generali, The Associated Press, Bloomberg INDG, BNP Paribas, Rabobank, Gannett and EBSCO.

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SOFTWARE,COMPUTER VISION

Deci Introduces World’s Most Advanced Semantic Segmentation Models

Deci | September 26, 2022

Deci, the deep learning company harnessing AI to build AI, today announced a new set of industry-leading semantic segmentation models, dubbed DeciSeg. Deci’s proprietary Automated Neural Architecture Construction (AutoNAC) technology automatically generated semantic segmentation models that significantly outperform the most powerful models publicly available, such as the MobileViT released by Apple, and the DeepLab family released by Google. Deci’s models deliver more than 2x lower latency, as well as 3-7% higher accuracy. Semantic segmentation is one of the most widely used computer vision tasks across many business verticals, including automotive, smart cities, healthcare, and consumer applications, and is often required for many edge AI applications. However, significant barriers exist to running semantic segmentation models directly on edge devices, such as high latency and the inability to deploy those models due to their size. With DeciSeg models, semantic segmentation tasks that previously could not be carried out at the edge because they were too resource intensive are now possible. This allows companies to develop new use cases and applications on edge devices, reduce inference costs (since AI practitioners will no longer need to run these tasks in expensive cloud environments), open new markets, and shorten development times. “DeciSegs are an example of the power of Deci’s AutoNAC engine capabilities to generate custom hardware-aware deep learning models with unparalleled performance on any hardware. AI teams can easily use DeciSegs models or leverage Deci’s AutoNAC engine to build and deploy custom models that run real-time computer vision tasks on their edge devices.” said Yonatan Geifman, PhD, co-founder and CEO of Deci. Deci’s platform has a proven-track record in enabling AI at the edge and empowering AI teams to build and deploy production grade deep learning models. Earlier this year, Deci announced the discovery of DeciNets for CPUs, which reduced the gap between a model’s inference performance on a GPU versus a CPU by half, without sacrificing the model’s accuracy, enabling AI to run on lower cost, resource constrained hardware. “In the world of automated deep neural network design and construction, Deci’s AutoNAC technology is a game changer. It uses deep learning to search vast spaces of neural networks for the model most appropriate for a particular task and particular AI chip. In this case, AutoNAC was applied to the Pascal VOC Semantic Segmentation task on NVIDIA’s Jetson Xavier NX™ chip and we are very pleased with the results.” said Ran El-Yaniv, co-founder and Chief Scientist of Deci and Professor of Computer Science at the Technion – Israel Institute of Technology. Deci’s platform is serving customers across industries in various production environments including edge, mobile, data centers and cloud. To learn more about how leading AI teams leverage Deci’s platform to build production grade models and accelerate inference performance, visit here. About Deci Deci enables deep learning to live up to its true potential by using AI to build better AI. With the company's deep learning development platform, AI developers can build, optimize, and deploy faster and more accurate models for any environment including cloud, edge, and mobile, allowing them to revolutionize industries with innovative products. The platform is powered by Deci's proprietary automated Neural Architecture Construction technology (AutoNAC), which automatically generates and optimizes deep learning models' architecture and allows teams to accelerate inference performance, enable new use cases on limited hardware, shorten development cycles and reduce computing costs. Founded by Yonatan Geifman, Jonathan Elial, and Professor Ran El-Yaniv, Deci's team of deep learning engineers and scientists are dedicated to eliminating production-related bottlenecks across the AI lifecycle.

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AI TECH,SOFTWARE,FUTURE TECH

Amelia Leads Again in Everest Group's Assessment of Conversational AI Vendors

Amelia | September 27, 2022

Amelia, the leading Enterprise Conversational AI software company, today announced that Amelia has been recognized as a Leader in Conversational AI in Everest Group's recent report, Conversational AI – Technology Vendor Landscape with Products PEAK Matrix® Assessment 2022. This marks the second consecutive year that Amelia has been recognized by Everest Group for its leadership in the Conversational AI market. For this PEAK Matrix® assessment, Everest Group evaluated the Vision & Capability and Market Impact of 26 global Conversational AI technology vendors, and then classified each company into one of three categories: Leaders, Major Contenders and Aspirants. Of the 26 vendors assessed, Amelia is named the Leader and placed in the highest overall position in Everest Group's PEAK Matrix®. In the report, Amelia is the only vendor to be listed amongst the top technology vendors for all major business functions, and leads in all major industries – including banking, telecom, healthcare and insurance. Everest Group also highlights Amelia's considerable experience working with global clients from across all industries as a notable strength. Amelia's omnichannel capabilities, sentiment analysis, agent-assist and built-in Orchestration Services are also all recognized as key advantages of the company's Conversational AI platform. "Buyers have also highlighted the cognitive capabilities of the platform, its partnership ecosystem, and the Amelia team's transparency as strength areas of the vendor," the report states. "There is no denying the critical role that Conversational AI continues to play for successful businesses, as evidenced by the exponential increase in adoption that we're observing across industries. As pioneers of this competitive landscape, one which we have pursued for more than two decades, we are honored and proud that for two years running, Everest Group has recognized Amelia as the clear Leader in Conversational AI." Chetan Dube, CEO of Amelia About Amelia Amelia is a leading Enterprise Conversational AI software company with a long history of innovation in automation and Conversational AI. We create fulfilling human experiences through groundbreaking AI solutions, as we enable conversational experiences, streamline IT operations, and automate processes. In 2014, we launched Amelia, the Most Human AI™; then in 2018, we introduced true end-to-end, enterprise-wide automation allowing enterprises to quickly optimize back-end operations. Amelia is consistently recognized by third-party analyst firms as a market leader. Headquartered in New York City with offices in 15 countries, Amelia's roster of client success stories speaks for itself: Our technology impacts more than 200 of the world's leading brands, including global leaders in banking, insurance, telecommunications, and other industries. See how Amelia is powering the Future of Work at amelia.ai.

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AI TECH,AI APPLICATIONS,SOFTWARE

AppTek and expert.ai Announce Strategic Partnership

AppTek | September 27, 2022

AppTek and expert.ai announced today they have entered into a strategic technology partnership to bring AI-based text analytics to dynamic audio content in multiple languages. The partnership leverages AppTek's leadership in Automatic Speech Recognition (ASR) and Neural Machine Translation (NMT) technologies with expert.ai's natural language understanding (NLU) capabilities to enable organizations to leverage audio content in the unstructured data sets that they manage for improving decision making and augmenting intelligent automation. As organizations increasingly utilize language data—emails, documents, reports and other free form text— for an ever-growing range of enterprise use cases (knowledge discovery, contract analysis, policy review, email management, text summarization, classification, entity extraction, etc.), natural language capabilities will play a critical role in powering any process or application that relies on unstructured language data. The combined capabilities of AppTek and expert.ai supercharge enterprise and government NLU and NLP (natural language processing) applications, expanding the data types and sources available for analysis to provide even more informational output. "This partnership brings the full stack Human Language Technology to the federal and commercial space in both Europe and the United States. As we cover multiple sources and types of information input together, we address the full scope of recognition, cognition, interpreting and analytics. "We look forward to implementing our joint vision." Michael Veronis, Chief Revenue Officer at AppTek Using AppTek's speech-to-text technology within the expert.ai Platform, organizations can automatically transcribe audio types from different sources, including high-quality media broadcast content, podcasts, meetings, one-to-one interviews or even low-bandwidth telephone conversations. In addition, they can leverage advanced multilingual functionalities to generate highly accurate, customizable and scalable translations across hundreds of language pairs. "The challenges posed by different languages and dialects along with the constraints of speech-to-text accuracy are causing organizations to miss out on the massively unexploited value of language data, especially audio content," said Colin Matthews, Chief Revenue Officer at expert.ai. "We are thrilled for this partnership, since AppTek technologies for automating speech recognition and machine translation complement our AI-powered natural language capabilities by harnessing the potential of dynamic multilanguage audio content within the expert.ai Platform." About AppTek AppTek is a global leader in artificial intelligence (AI) and machine learning (ML) technologies for automatic speech recognition (ASR), neural machine translation (NMT), natural language processing/understanding (NLP/U) and text-to-speech (TTS) technologies. The AppTek platform delivers industry-leading, real-time streaming and batch technology solutions in the cloud or on-premises for organizations across a breadth of global markets such as media and entertainment, call centers, government, enterprise business, and more. Built by scientists and research engineers who are recognized among the best in the world, AppTek's multidimensional 4D for HLT (human language technology) solutions with slice and dice methodology cover hundreds of languages/dialects, domains, channels and demographics, and drive high impact results with speed and precision. About expert.ai Expert.ai is a leading company in AI-based natural language software. Organizations in insurance, banking and finance, publishing, media and defense all rely on expert.ai to turn language into data, analyze and understand complex documents, accelerate intelligent process automation and improve decision making. Expert.ai's purpose-built natural language platform pairs simple and powerful tools with a proven hybrid AI approach that combines symbolic and machine learning to solve real-world problems and enhance business operations at speed and scale. With offices in Europe and North America, expert.ai serves global businesses such as AXA XL, Zurich Insurance Group, Generali, The Associated Press, Bloomberg INDG, BNP Paribas, Rabobank, Gannett and EBSCO.

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SOFTWARE,COMPUTER VISION

Deci Introduces World’s Most Advanced Semantic Segmentation Models

Deci | September 26, 2022

Deci, the deep learning company harnessing AI to build AI, today announced a new set of industry-leading semantic segmentation models, dubbed DeciSeg. Deci’s proprietary Automated Neural Architecture Construction (AutoNAC) technology automatically generated semantic segmentation models that significantly outperform the most powerful models publicly available, such as the MobileViT released by Apple, and the DeepLab family released by Google. Deci’s models deliver more than 2x lower latency, as well as 3-7% higher accuracy. Semantic segmentation is one of the most widely used computer vision tasks across many business verticals, including automotive, smart cities, healthcare, and consumer applications, and is often required for many edge AI applications. However, significant barriers exist to running semantic segmentation models directly on edge devices, such as high latency and the inability to deploy those models due to their size. With DeciSeg models, semantic segmentation tasks that previously could not be carried out at the edge because they were too resource intensive are now possible. This allows companies to develop new use cases and applications on edge devices, reduce inference costs (since AI practitioners will no longer need to run these tasks in expensive cloud environments), open new markets, and shorten development times. “DeciSegs are an example of the power of Deci’s AutoNAC engine capabilities to generate custom hardware-aware deep learning models with unparalleled performance on any hardware. AI teams can easily use DeciSegs models or leverage Deci’s AutoNAC engine to build and deploy custom models that run real-time computer vision tasks on their edge devices.” said Yonatan Geifman, PhD, co-founder and CEO of Deci. Deci’s platform has a proven-track record in enabling AI at the edge and empowering AI teams to build and deploy production grade deep learning models. Earlier this year, Deci announced the discovery of DeciNets for CPUs, which reduced the gap between a model’s inference performance on a GPU versus a CPU by half, without sacrificing the model’s accuracy, enabling AI to run on lower cost, resource constrained hardware. “In the world of automated deep neural network design and construction, Deci’s AutoNAC technology is a game changer. It uses deep learning to search vast spaces of neural networks for the model most appropriate for a particular task and particular AI chip. In this case, AutoNAC was applied to the Pascal VOC Semantic Segmentation task on NVIDIA’s Jetson Xavier NX™ chip and we are very pleased with the results.” said Ran El-Yaniv, co-founder and Chief Scientist of Deci and Professor of Computer Science at the Technion – Israel Institute of Technology. Deci’s platform is serving customers across industries in various production environments including edge, mobile, data centers and cloud. To learn more about how leading AI teams leverage Deci’s platform to build production grade models and accelerate inference performance, visit here. About Deci Deci enables deep learning to live up to its true potential by using AI to build better AI. With the company's deep learning development platform, AI developers can build, optimize, and deploy faster and more accurate models for any environment including cloud, edge, and mobile, allowing them to revolutionize industries with innovative products. The platform is powered by Deci's proprietary automated Neural Architecture Construction technology (AutoNAC), which automatically generates and optimizes deep learning models' architecture and allows teams to accelerate inference performance, enable new use cases on limited hardware, shorten development cycles and reduce computing costs. Founded by Yonatan Geifman, Jonathan Elial, and Professor Ran El-Yaniv, Deci's team of deep learning engineers and scientists are dedicated to eliminating production-related bottlenecks across the AI lifecycle.

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