Machine Learning

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Greg Simpson and others discussing at the Innovation Station

The many hands of a robot

Deploying robotic process automation for modern applications.

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What a successful AI team really looks like

Forget the tech giants’ rosters of data science PhDs. As AI moves into the enterprise, blended teams with business skills become more important for driving business value.

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Matei Zaharia, creator of the Apache Spark project, on the big data framework | True Technologist Ep 2

In this episode of True Technologist, host Eric Knorr talks with Matei Zaharia, chief technologist at Databricks and an assistant professor of computer science at Stanford, about the Apache Spark and Apache Mesos projects

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The outlook for machine learning in tech: ML and AI skills in high demand

Discover the latest projections for the future of machine learning in tech, including the mainstream adoption of NLP and voice-driven interactions and the rise of innovative ML/AI talent development initiatives.

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Tech Talk: Machine learning and AI deciphered

Breaking through the hype around machine learning and artificial intelligence, our panel of Ken Mingis, Michael Simon and Serdar Yegulalp talk through the definitions and implications of the technology.

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Attainable AI, from science fiction to science fact: The reality of today’s AI

Looking into the world of artificial intelligence – the past, the present, the different types of AI and what the future holds.

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CIO Leadership Live with James Rinaldi, CIO, Director for Information Technology, NASA’s Jet Propulsion Laboratory | Ep 11

In this episode, host Maryfran Johnson talks with James Rinaldi, CIO at NASA's Jet Propulsion Lab, about opening up data silos and driving data transformation; having a seat at the table to lead change; and using technology to explore...

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Sensitivity: Important for bedside manner and algorithmic diagnostics

Deep learning algorithms hold huge potential for healthcare diagnostics, but they’re not a silver bullet. As adoption of these tools increases, it’s crucial that healthcare professionals and decision makers consider algorithmic...

What is deep learning really?

Moving toward outcome-based intelligence

Newer machine learning techniques coupled with better programming techniques and more data-driven skills should make a difference, and so should better tools to directly connect outcomes to the core models.

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How to win the recruitment war for machine learning talent

The machine learning talent you’re looking for is out there. These in-demand experts simply require a little more creativity when it comes to recruiting.

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How machine learning can be used to break down language barriers

A look at how machine learning is currently being applied to the language barrier problem, and how it might develop in the future.

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Debunking the robodoc: the pivotal role of human-machine collaboration in the future of diagnostics

AI-based diagnostic technologies will likely play a major role in the future of healthcare — but this doesn’t mean that physicians and diagnosticians need to fear for their jobs.

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Machine learning and AI are ideally suited for cryptocurrency investing

Just as institutional investors were quick to recognize the benefits of automated trading solutions, cryptocurrency investors will be the ultimate beneficiaries of the introduction of these services to the blockchain ecosystem.

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Digital disruption: software testing with machine learning

The sooner you can implement an in-house AI platform to assist you in your application testing, you will discover a more accurate and efficient deployment with reduced effort.

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Taking machine learning to new heights using Docker containers

Joining the robust capabilities of containers and the innovative technology of machine learning presents new challenges, but new opportunities as well.

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4 weather trends that will play out in 2018

No matter what Mother Nature decides to throw at us this year, the key to all weather trends is to be prepared.

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Data enrichment – a force multiplier in a big data environment

As data usage increases, data quality will only continue to evolve.

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You won’t clean all that data, so let AI clean it for you

By focusing machine learning on systematically getting smarter about how it analyzes, rates and utilizes data, we can not only reduce coding-hours but also worry less about imperfect data.

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