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Advancements in automation: why AI won't replace the human touch - Data Matters

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Customer Relationship Management (CRM) systems have not followed Moore's prediction. It has evolved from somewhat a cumbersome platform to a productivity enabler, providing relationship intelligence and bringing in data from outside source sand it's something we've been proud to bring to the market in our own recent launch of Sugar Hint. Ultimately, it's about automating mundane tasks, allowing humans to focus on tasks that only humans at present can do. In the context of AI and chatbots managing customer interactions, there are some benefits in using computer power to handle high volume tasks – like in e-commerce.


HSBC uses IBM to automate the processing of 100 million document pages

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HSBC is using IBM artificial intelligence (AI) technology to process documents related to international trade. Currently around 100 million pages of documents, such as invoices and insurance documents, are manually reviewed and processed by HSBC staff. Using optical character recognition and robotics technology from IBM, HSBC's Global Trade and Receivables Finance (GTRF) is automating the review of documents and sending them automatically to the bank's transaction processing systems. For example, a report from financial services management consultancy Opimas predicted that in 2017, discounting acquisitions of startups, finance firms in the investment sector would spend $1.5bn on robotic process automation, machine learning, deep learning and cognitive analytics, increasing by 75% to $2.8bn in 2021.


Automated decision making shows worrying signs of limitation

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The service also uses data analysis to target fire safety advice, and has found correlations between high risks of accidental home fires and single-person households, social renting, unemployment, smoking and black and Afro-Caribbean ethnicity. The US Supreme Court recently declined to review the Wisconsin supreme court's ruling in favour of Compas' use in Loomis' case, although the Electronic Privacy Information Centre is involved in several other legal challenges it calls a lack of algorithmic transparency. In the 1990s, Rich Caruana, then a graduate student at Carnegie Mellon University, worked on training a neural net machine learning system to predict the probability of death for pneumonia patients. More recent research found this data similarly suggested that chest pain and heart disease patients were less vulnerable to pneumonia.


Tuning and maintaining algorithms: The Achilles' heel of AI projects

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People love to talk about artificial intelligence tools' potential to make content management and CRM... You forgot to provide an Email Address. AI projects require continuous human monitoring, or its results degrade into something at best irrelevant to the business and at worst detrimental to business goals and harmful to customers. In content management systems, AI can police network activity in both sales and service data stores for potential fraud and report suspicious customer activity to experts who can take a closer look. "That's where the hard part is," said Thomas Dong, OpenText's vice president of product marketing, acknowledging that product marketers sometimes do simplify AI project requirements because the processes around advanced analytics aren't always easy to explain.


Deep learning technologies evolving beyond human capacities - IoT Agenda

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Biologists at Tufts University made a system that combined genetic algorithms and genetic pathway simulations. The technologies employ a convolutional neural network (CNN) to instantly recognize specific image features. CNNs' complex processing capabilities enable deep learning algorithms employed in IoT technologies that don't just identify images, but also speeches, behaviors and patterns. The computer vision field is also improving vastly through deep learning technologies that also offer user-friendly programming tools and reasonably priced computing.


Google applies gamification technique to neural nets - and optimizes its data center - TotalCIO

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Prodded by gamification techniques, artificial intelligent (AI) systems are quickly becoming game masters: There's IBM Watson and Jeopardy!, Google DeepMind and Go, and Carnegie Mellon University's Libratus and No Limit Texas Hold'Em Poker champion -- the latter a landmark victory in the annals of AI gaming because poker involves bluffing and guesswork. The Google DeepMind team has trained computational AI systems known as neural nets to play Atari video games such Breakout, which was released in 1976. Here's the real punchline: Researchers at DeepMind then took the process of training neural nets on how to win at Atari video games and turned it into a gamification technique for energy efficiency. Researchers trained a system of neural nets on operating scenarios, historical data on energy consumption as well as prediction data and gave it access to all of the gauges and dials; this time, the objective of "the game" was to maximize energy efficiency, a huge cost center for the internet search giant.


IT leaders' pragmatism will be the antidote to AI scaremongering - Computer Weekly Editor's Blog

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You forgot to provide an Email Address. By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers. Where once we were told that mainframe computers would destroy back-office admin jobs causing mass unemployment, now we're told AI will destroy front-office jobs causing mass unemployment. But the same ingenuity that is being applied to developing AI, will also be applied to learning how to manage it, to understand the risks and mitigate them.


ODSC West 2017

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Using artificial intelligence for forgery: Fake could be eerily real - IoT Agenda

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The researchers envision the future version automatically producing realistic sound effects good enough for use in movies and television. A startup called Deep Art uses a technique known as style transfer -- which uses neural networks to apply the characteristics of one image to another -- to produce realistic paintings. Other work being done on multimedia manipulation using artificial intelligence includes the creation of 3D face models from a single 2D image, changing the facial expressions of someone on video using the Face2Face app in real time and changing the light source and shadows in any picture. Secret agents control millions of botnet social media accounts that tweet about politics in order to shape national discourse.


What's behind Google Hire?

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It builds on the company's previous Google Jobs and Google Cloud Jobs application programming interfaces (APIs), which use machine learning and artificial intelligence (AI) for the talent marketplace. In a blog post announcing Hire, Berit Johnson, senior product manager for Google Cloud, said Hire was the latest product offering from Google to address the talent marketplace. Matt Moore, product manager at Google Cloud, discussed the application programming interface at the Google Cloud Next 17 conference in San Francisco. Although Hire is only available in the US, and primarily targets small and medium-sized businesses that do not require the full recruitment capabilities built into leading human capital management suites, Hire enables Google to test how well the algorithm matches people to jobs.