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MIT aims to pry open 'black box' of machine learning systems

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The conference was a joint effort between the Massachusetts Technology Leadership Council and MIT to bring industry and academic experts together to discuss advances in artificial intelligence (AI). The computer science and artificial intelligence laboratory, aka CSAIL, at MIT wants to shed light on the black box of today's machine learning systems with a new initiative, SystemsThatLearn@CSAIL. In its quest to shed light on machine learning's black box, SystemsThatLearn@CSAIL had to break down some academic barriers. The program joins the research teams that develop algorithms at MIT with the research teams that develop the large-scale systems the algorithms run on.


Artificial intelligence in the workplace changes roles of employees - Modern Mobility

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Machine learning and data analytics are on the rise, leaving some employees to fear that computers will take over their jobs, but that is not the case. In the past, the job of professionals was to gather data and information as if they were solving a puzzle, but that changes with today's data analytics and artificial intelligence in the workplace. Employees today aren't puzzle solvers who go out and gather information, but mystery solvers who must make sense of complex information that machines gather, Gladwell said. "In the future, we are not getting rid of human judgment," Gladwell said.


ODSC Europe 2017

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Where can I contact the organizer with any questions? In summary; registrant contact information is NOT shared with third parties without your consent. Registrant information is primarily used to verify registration and notify you of similar events held by ODSC in the future. We may share your contact information with sponsors but only with your consent upon registering.


McKinsey AI research finds slender user adoption outside tech

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Few user companies and organisations are putting artificial intelligence (AI) to work at significant scale, according to a McKinsey Global Institute (MGI) discussion paper. The MGI's paper, Artificial intelligence: the next digital frontier, draws on a survey of 3,000 executives in organisations across 10 countries and 14 sectors, as well as the case studies. Just 10% reported adopting more than two technologies, and only 9% reported adopting machine learning, a type of AI that provides computers with the ability to learn without being explicitly programmed. But the paper's authors said: "Leaders' adoption is both broad and deep, using multiple technologies across multiple functions, with deployment at the core of their business.


Machine learning algorithms make life easier -- until they don't

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Perhaps they're based on some hard, rational logic leading to an unbiased and random draw, or more likely on some fancy but operationally opaque big data-based machine learning algorithm. In too many cases, poorly trained or designed machine learning algorithms end up making prejudicial decisions that can unfairly affect individuals. But with easy-to-use big data, machine learning tools like Apache Spark and the increasing streams of data from the internet of things wrapping all around us, I expect that every data-driven task will be optimized with machine learning in some important way. We often talk about how supposedly blind and fair machine learning algorithms will help the world design more optimal and efficient processes.


Machine learning algorithms make life easier -- until they don't

#artificialintelligence

Perhaps they're based on some hard, rational logic leading to an unbiased and random draw, or more likely on some fancy but operationally opaque big data-based machine learning algorithm. In too many cases, poorly trained or designed machine learning algorithms end up making prejudicial decisions that can unfairly affect individuals. But with easy-to-use big data, machine learning tools like Apache Spark and the increasing streams of data from the internet of things wrapping all around us, I expect that every data-driven task will be optimized with machine learning in some important way. We often talk about how supposedly blind and fair machine learning algorithms will help the world design more optimal and efficient processes.


Majority of Brits would use artificial intelligence, survey finds

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The latest survey of 6,000 people from six countries by Pegasystems found that 60% of UK people would use more AI if it saved them time and money. IT services giant Accenture carried out a survey of 32,715 people – 3,007 of them British – and found that more than two-thirds (68%) of UK consumers would use software robots for banking services, with a quarter attracted to the impartiality of advice given by robots. The London Borough of Enfield is using a software robot to provide customer services so it can redirect resources. In financial services, digital bank Atom Bank is offering customer support through machine learning software on its mobile app.


AI in healthcare must overcome security, interoperability concerns

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AI is also able to perform complex cognitive tasks and analyze large amounts of patient data instantly. As most AI platforms are consolidated and require extensive computing power, patient data -- or parts of it -- would likely reside in the vendor's data centers. Hospitals across the nation face the challenge of not being able to efficiently exchange patient health data across other healthcare organizations, despite the availability of data standards across the world. However, if policies are put in place that require these platforms to meet current interoperability requirements, this may help address the exchange of data right away.


Deep learning vs. machine learning: The difference starts with data

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The answer to the question of what makes deep learning different from traditional machine learning may have a lot... You forgot to provide an Email Address. For example, he pointed out that conventional machine learning algorithms often plateau on analytics performance after processing a certain amount of data. Comcast is also applying computer vision, audio analysis and closed-caption text analysis to video content to break movies and TV shows into "chapters" and automatically generate natural-language summaries for each chapter. Essa said that forward-thinking enterprises will find ways to leverage deep learning to develop new business models, while traditional machine learning is essentially relegated to helping businesses perform existing operations more efficiently.


Avoiding industrial IoT digital exhaust with machine learning - IoT Agenda

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With the Industry 4.0 factory automation trend catching on, data-driven artificial intelligence promises to create cyber-physical systems that learn as they grow, predict failures before they impact performance, and connect factories and supply chains more efficiently than we could ever have imagined. To avoid IIoT digital exhaust and preserve the potential latent value of IIoT data, enterprises need to develop long-term IIoT data retention and governance policies that will ensure they can evolve and enrich their IoT value proposition over time and harness IIoT data as a strategic asset. A practical compromise IoT architecture must first employ some centralized (cloud) aggregation and processing of raw IoT sensor data for training useful machine learning models, followed by far-edge execution and refinement of those models. A multi-tiered architecture (involving far-edge, private cloud and public cloud) can provide an excellent balance between local responsiveness and consolidated machine learning, while maintaining privacy for proprietary data sets.