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IoT Way of Thinking @ThingsExpo @EricssonIT @EsmeSwartz #IoT #M2M

#artificialintelligence

The buzz continues for cloud, data analytics and the Internet of Things (IoT) and their collective impact across all industries. But a new conversation is emerging - how do companies use industry disruption and technology enablers to lead in markets undergoing change, uncertainty and ambiguity? Organizations of all sizes need to evolve and transform, often under massive pressure, as industry lines blur and merge and traditional business models are assaulted and turned upside down. In this new data-driven world, marketplaces reign supreme while interoperability, APIs and applications deliver unique customer value for new go-to-market models based on a cloud and IoT way of working. Analytics, IoT service mashups, fail fast business models and contextual data streams enable data to become the new currency for digital citizens and businesses and will determine business success or failure.


Artificial Intelligence and Machine Learning in Big Data and IoT: The Market for Data Capture, Analytics, and Decision Making 2016 - 2021

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Overview: More than 50% of enterprise IT organizations are experimenting with Artificial Intelligence (AI) in various forms such as Machine Learning, Deep Learning, Computer Vision, Image Recognition, Voice Recognition, Artificial Neural Networks, and more. AI is not a single technology but a convergence of various technologies, statistical models, algorithms, and approaches. Machine Learning is a sub-field of computer science that evolved from the study of pattern recognition and computational learning theory in AI. Every large corporation collects and maintains a huge amount of human-oriented data associated with its customers including their preferences, purchases, habits, and other personal information. As the Internet of Things (IoT) progresses, there will an increasingly large amount of unstructured machine data.


FusionOps launches artificial intelligence tool for supply chain – DC Velocity

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Business analytics software provider FusionOps has launched a platform that uses artificial intelligence (AI) to identify opportunities to improve supply chain performance for customers such as manufacturers and enterprise corporations, the company said Tuesday. The new "cognitive applications" product is an extension of Mountain View, Calif.-based FusionOps' existing suite of supply chain solutions, which use database analysis and machine-learning tools to help companies improve their forecasts, control costs, and hone inventory levels. The new suite comes in three main parts, with a search feature and a cognitive application to be available in Q4 2016 and a supply chain actions tool arriving later in 2017. If you find anything in DC VELOCITY you feel is inaccurate or warrants further explanation, please?Subject Feedback -: FusionOps launches artificial intelligence tool for supply chain" contact Chief Editor David Maloney.


Registration Opens for Gigaom AI Now Conference in San Francisco

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Gigaom, the leader in emerging technology research, today announced that it will host its annual conference Gigaom AI Now in San Francisco, CA, February 15-16th. The one-and-a-half day conference, held at the Golden Gate Club, will share how companies are using AI today to drive significant ROI across every aspect of business, from administration to product development, sales and marketing, and customer experience. Attendees at this event will learn from world-leading practitioners how today's progressive businesses are applying the most innovative AI tools, platforms, and technologies in-house to drive revenue and improve operations across the enterprise. "In the last couple of years, AI has become the technology no enterprise can afford to ignore. It will change every department in almost every business.


Registration Opens for Gigaom AI Now Conference in San Francisco

#artificialintelligence

Gigaom, the leader in emerging technology research, today announced that it will host its annual conference Gigaom AI Now in San Francisco, CA, February 15-16th. The one-and-a-half day conference, held at the Golden Gate Club, will share how companies are using AI today to drive significant ROI across every aspect of business, from administration to product development, sales and marketing, and customer experience. Attendees at this event will learn from world-leading practitioners how today's progressive businesses are applying the most innovative AI tools, platforms, and technologies in-house to drive revenue and improve operations across the enterprise. "In the last couple of years, AI has become the technology no enterprise can afford to ignore. It will change every department in almost every business.


Industry expert shares 2017 data predictions

@machinelearnbot

Siummary: In 2017, AI and analytics M&A activity will accelerate, data lakes will finally become useful, and data monetization strategies will mature. These are some of the predictions Ramon Chen, CMO of data management innovator, Reltio, has for the coming year. Major players as diverse as Google, Apple, Salesforce and Microsoft to AOL, Twitter and Amazon drove the acquisition trend this year. Due to the short operating history of most of the startups being acquired, these moves are as much about acquiring the limited number of AI experts on the planet as the value of what each company has produced to date. The battle for AI enterprise mindshare has clearly been drawn between IBM Watson, Salesforce Einstein, and Oracle's Adaptive Intelligent Applications.


Industry expert shares 2017 data predictions

@machinelearnbot

Major players as diverse as Google, Apple, Salesforce and Microsoft to AOL, Twitter and Amazon drove the acquisition trend this year. Due to the short operating history of most of the startups being acquired, these moves are as much about acquiring the limited number of AI experts on the planet as the value of what each company has produced to date. The battle for AI enterprise mindshare has clearly been drawn between IBM Watson, Salesforce Einstein, and Oracle's Adaptive Intelligent Applications. What's well understood is that AI needs a consistent foundation of reliable data upon which to operate. With a limited number of startups offering these integrated capabilities, the quest for relevant insights and ultimately recommended actions that can help with predictive and more efficient forecasting and decision-making will lead to even more aggressive M&A activity in 2017.


RAGE Frameworks' Artificial Intelligence Solution Automates Financial Data Processing for Major Financial Institution

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DEDHAM, MA--(Marketwired - Dec 7, 2016) - RAGE Frameworks, a provider of artificial intelligence (AI) for the Enterprise, today announced that a leading diversified investment and financial services company has deployed RAGE LiveSpread to automate the extraction, interpretation and processing of financial statements and other documents needed for credit analysis. At this leading investment and financial services company, RAGE's solution completely automates the ingestion, extraction, interpretation of investment reports, bank statements and Income Tax returns. RAGE-AI offers the market something that was previously unavailable, high accuracy automated extraction and interpretation of data from various types of financial documents, including the interpretation of unstructured text found in footnotes," said Aashish Mehta, SVP Business Banking at RAGE Frameworks. RAGE's LiveSpread has been deployed in several leading global financial institutions resulting in higher quality, improved customer satisfaction, reduced costs, and improved responsiveness to regulatory changes and faster turnaround times.


Ayasdi wins RiskTech100 Artificial Intelligence Category

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"In 2015 Chartis's continuous market scan of end-user organizations identified Ayasdi as one of the most innovative AI players in the risk and compliance space," said Peyman Mestchian, Managing Partner at Chartis. "We have been particularly impressed by the practical use-cases and proof points provided by Ayasdi and how its large-scale machine intelligence-based applications and overall platform strategy help some of the world's largest financial services firms to reduce the cost and risk of compliance." Ayasdi is well positioned globally to help financial services firms leverage AI to address pressing challenging including CCAR/stress testing, risk management, internal surveillance, client intelligence, market intelligence, fraud detection and anti-money laundering. Ayasdi's enterprise machine intelligence platform ingests and processes large volumes of internal data, market data, and/or third party data and then applies multiple machine learning, statistical and geometric algorithms to gain insight and predict the future. For some applications, such as anti-money laundering, Ayasdi operates autonomously behind the scenes to help reduce false positives in existing rule-base AML systems.


NVIDIA and Microsoft Accelerate AI Together

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This jointly optimized platform runs the new Microsoft Cognitive Toolkit (formerly CNTK) on NVIDIA GPUs, including the NVIDIA DGX-1 supercomputer, which uses Pascal architecture GPUs with NVLink interconnect technology, and on Azure N-Series virtual machines, currently in preview. Faster performance: When compared to running on CPUs, the GPU-accelerated Cognitive Toolkit performs deep learning training and inference much faster on NVIDIA GPUs available in Azure N-Series servers and on premises. Faster performance: When compared to running on CPUs, the GPU-accelerated Cognitive Toolkit performs deep learning training and inference much faster on NVIDIA GPUs available in Azure N-Series servers and on premises. Certain statements in this press release including, but not limited to the impact and benefits of NVIDIA's and Microsoft's AI acceleration collaboration, Tesla GPUs, DGX-1, the Pascal architecture, NVLink interconnect technology and the Microsoft Cognitive Toolkit; the availability of Azure N-Series virtual machines; and the continuation of NVIDIA's and Microsoft's collaboration are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations.