In order to understand this paper together, I am going to describe basic some concepts in Reinforcement Learning. Before you start, you need to be familiar with deep learning methods such as CNN (conventional neural network) and RNN. You can always watch Udacity’s free course in Deep Learning with Pytorch. Also, it is better if you have implemented DQN algorithm. If you do not know, do not worry about it. I am going to have a quick refresher in part 1.

This blog consists of four parts:

1, Deep Neural Network for Single-Agent: Reinforcement Review, DQN and Replay Memory

2…

What is the architecture of talent discovery? One way to view talent generation and discovery in a system such as Silicon Valley or Academia is inspired by a Generative Adversarial Network, which uses multiple child networks that interlink to build better outputs than any could alone.

An architecture is an environment — Silicon Valley, Florence, YC

An environment’s job is to discover good talent for both discriminators and generator.

This is architecture than any-frame can fit-in.

Architecture — Silicon Valley, Cambridge, YC

Goal:

  • Fosters good generators and discriminator
  • Punishes bad actors
  • Encourages diversity and avoids systemic mimicry
  • Connects compatible people

What is the architecture of talent discovery? One way to view talent generation and discovery in a system such as Silicon Valley or Academia is inspired by a Generative Adversarial Network, which uses multiple child networks that interlink to build better outputs than any could alone.

An architecture is an environment — Silicon Valley, Florence, YC

An environment’s job is to discover good talent for both discriminators and generator.

This is architecture than any-frame can fit-in.

Architecture — Silicon Valley, Cambridge, YC

Goal:

  • Fosters good generators and discriminator
  • Punishes bad actors
  • Encourages diversity and avoids systemic mimicry
  • Connects compatible people

— A world brain for problem solving

The vision of the early pioneers in computer science was to use machines solve the most challenging problems facing humanity. As Douglas Engelbart mentioned, “because the complexity of the world’s problems was increasing, and that any effort to improve the world would require the coordination of groups of people, the most effective way to solve problems was to augment human intelligence and develop ways of building collective intelligence. …

The vision of the early pioneers computer science was to use machines solve the most challenging problems facing humanity. As Douglas Engelbart mentioned “because the complexity of the world’s problems were increasing, and that any effort to improve the world would require the coordination of groups of people, the most effective way to solve problems was to augment human intelligence and develop ways of building collective intelligence. He believed that computers, which was at the time thought of only as a tool for automation, would be an essential tool for future knowledge workers to solve more complex problems.”

Knowledge workers…

Our society is continuously suffering from unequal access to the science and technology advances. At SolveCity, we believe equal access to science and technology is a universal right for tackling humanity’s challenge. While the challenges are decentralized, the human intelligence for solving them are not. At SolveCity, we believe equal access to science and technology is a universal right for tackling humanity’s challenge.

We want to maximize human capability and push their boundaries of imagination.

Last year, it was around the same time that I was at Caltrain going from San Francisco to Sunnyvale. In the middle of the way to Sunnyvale, I saw the Facebook Like graph at the abounded wall. I do not know why but this sign for me had some sort of meaning. I exactly remember what I wore at that day and what was happening in my mind. That day one of the early days of SolveCity, the day before, I bought the domain for the company. I was lost in all of my thoughts and I remembered all the…

Couple of months ago,

I start meeting lots of interesting people, with different background. I had an amazing long conversation with each of them but all lives in many different places and I did not want to stop our conversation. All conversation was bellowing my mind. So, I have started very small group at Facebook in order to continue our conversations. All of those people were visionaries and an amazing people. Those people who were thinking from majority of my Facebook friends.

One of them wants to colonize the Mars, one of them thinking about simulating human brain, the other…

Parnian Barekatain

AI Instructor @Udacity

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