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NEWS

UK workers don't fear automation

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Organisations will need to overcome challenges to scale the technology.

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NEWS

SAS partners with NVIDIA on deep learning and computer vision

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Collaboration speeds up critical functions such as image recognition and inferencing at the edge.

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NEWS

Fewer than 50% of enterprises have deployed intelligent automation technology

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86% of IT executives surveyed believe human work, AI systems, and robotic automation must be well-integrated by 2020 -- but only 12% said their companies do this really well today.

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NEWS

Ziften expands its use of proprietary machine learning

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Ziften’s endpoint protection platform uses proprietary machine learning in all phases of the endpoint security continuum simplifying endpoint protection across the enterprise.

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NEWS

Harnessing the benefits of machine learning

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Analytics Intelligence partners with Tealium to provide frontline business users with a simple and effective way to standardise, enrich, distribute, and activate customer data in real time.

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NEWS

Global financial services industry bullish towards disruptive technology

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Two thirds of financial services executives say disruptive technologies will have a positive impact on their business .

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Calligo launches 'world’s first' managed service to make machine learning accessible to any...
ThoughtSpot launches support for Databricks allowing users to run search and AI-driven analytics...
Company launches Dropbox Spaces, adds new machine intelligence, collaboration, and admin features.
Deep machine learning method can predict molecular wave functions and electronic properties of...
The first globally decentralized deep learning and AI network has been launched.
Platform makes machine learning simple to deploy and augments knowledge and skillsets of business...
Latest Video

LinkedIn Automates All of the Easy Things, and Makes all of the Hard Things Easy

Hear LinkedIn’s senior SRE, Todd Palino, share how the company continually improves the state of its infrastructure, so that the developers who are rolling out applications have a framework that they can do it within, and they can do it safely. LinkedIn currently generates over 50 terabytes a day of unique metrics on applications. No human is going to look at 50 terabytes a day of data and get anything useful out of it, so LinkedIn relies on systems give them some useful signal out of all that noise. By moving down the road of machine learning, LinkedIn can now do anomaly detection using machine learning models.

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