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AI Grant aims to fund the unfundable to advance AI and solve hard problems

Artificial intelligence-focused investment funds are a dime a dozen these days. Everyone knows there’s money to be made from AI, but to capture value, good VCs know they need to back products and not technologies. This has left a bit of a void in the space where research occurs within research institutions and large tech companies and commercialization occurs within verticalized startups — there isn’t much left for the DIY AI enthusiast. AI Grant, created by Nat Friedman and Daniel Gross, aims to bankroll science projects for the heck of it to give untraditional candidates a shot at solving big problems. Gross, a partner at YCombinator, and Friedman, a founder who grew Xamarin to acquisition by Microsoft, started working on AI Grant back in April. AI Grant issues no-strings-attached grants to people passionate about interesting AI problems. The more formalized version launching today brings a slate of corporate partners and a more structured application review process. Anyone, regardless of background, can submit an application for a grant. The application is online and consists of questions about background and prior projects in addition to basic information about what the money will be used for and what the initial steps will be for the project. Applicants are asked to connect their GitHub, LinkedIn, Facebook and Twitter accounts.



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A walk through implementation for synthesizing adversarial examples in tensorflow

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This is a groundbreaking paper just released:

https://arxiv.org/pdf/1706.02515.pdf

But its content is rather hard to understand. Luckily, this blog explained it very clear and well enough:

https://calculatedcontent.com/2017/06/16/normalization-in-deep-learning/

I think this is going to the start of a new revolution in Deep Learning + FNN.

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Types of Machine Learning Algorithm


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Human-Centered Artificial Intelligence

_The term Human-Centered Artificial Intelligence is a recognition that the future is increasingly putting humans in contact with artificial intelligences.
At the heart of human-centered AI is the recognition that the way AI systems solve problems — especially using machine learning — is fundamentally alien to humans without training in computer science or AI. When consumer-facing AI systems are significantly more sophisticated than Siri, Alexa, or Cortana, what will it take for my mother to feel comfortable using these systems? Human-centered AI is also a recognition that humans can be inscrutable to AI systems. AI systems do not grow up immersed in a society and culture in the way humans do._

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Today, we’re publishing “Achievement of Sustained Net Plasma Heating in a Fusion Experiment with the Optometrist Algorithm” in Scientific Reports. This paper describes the first results of Google’s collaboration with the physicists and engineers at +Tri Alpha Energy that led to unexpected plasma confinement results.

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As Tensorflow is gaining a lot of popularity in deep learning and deep neural networks, many open source projects have come up and this article shares a list of awesome projects with tensorflow in speech, Images, Text and more.

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