Day: October 3, 2018

AI

Analyzing Job Applicants with AI

HireVue‘s AI technology allows companies to sort and grade video job applicants in a completely automated fashion. Ironically, companies are actively taking humans out of the hiring process, at least the initial stages. Can we still call it HR, if there are no humans in it?

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AI Data Science

67 Questions with a Lyft Data Scientist

Siraj Raval asks Brayden McLean 67 questions about life at Lyft as a Data Scientist. Lyft is a ride-sharing company very similar to Uber and is expanding globally. Brayden genuinely wants to help improve the state of the world and talks about how his work at Lyft helps him do that.

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AI Data

4 Easy Tactics for Infusing AI and Predictive Analytics Into Your Sales Processes

If you’re looking for a practical application of AI and predictive analytics, then look no further than your sales process.  Not only will it get the attention of leadership of your company, it also has serious potential to boost your bottom line. And it’s better your firm gets into it before your competition does. Here’s […]

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AI

3 Principles for Creating Safer AI

In this TED talk, Stuart Russell talks about the three rules to make AI safe for humanity.

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Azure IoT

Azure Sphere Architecture Discussion

Ed Nightingale, Partner Architect for Azure Sphere, joins Olivier on the IoT Show to discuss architecture, detailing how the silicon, the OS and the Security Service are built to allow securely connecting the billions of MCU based IoT devices that are coming.

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AI Neural Networks

Neural Networks that Empower Digital Artists

The YouTube channel “Two Minute Papers” explores how neural networks can help artists be more creative.

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AI Neural Networks

How Neural Networks Learn (Part 2)

In this video, Arxiv continues his dive into the world of adversarial examples: images specifically engineered to fool neural networks into making completely wrong decisions! This is a continuation from my previous post.

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AI Neural Networks

How Neural Networks Learn (Part 1)

Interpreting what neural networks are doing is a tricky problem. In fact, they are often referred to as a “black box.” In this video Arxiv dives into the approach of feature visualization. From simple neuron excitation to the Deep Visualization Toolbox and the Google DeepDream project. Watch to open up that black box!

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