three types of quantum computer

1. Analog quantum computer (quantum annealer, adiabatic QC, direct quantum simulation) examples: D-wave
2. Noisy intermediate-scale quantum (NISQ) gate-based computer  
1. example: IBM quantum computer
3. Fully error-corrected gate-based quantum computers. 
1. not available yet

Two leading technologies for quantum computing—trapped ions and superconducting qubits—use very different strategies for embodying and operating on qubits. 

quantum computer difficuty

There are many technical challenges:

1) quits cannot intrinsically reject noise

2) error free quantum computer requires quantum error correction

3) large data inputs cannot be loaded into a quantum computer efficiently.

4) Quantum Computers Will Need a New Software Stack

5) The Intermediate State of a Quantum Computer Cannot Be Measured Directly

From "Quantum Computing: Progress and Prospects (2018)" by National Academies Press. The pdf file is http://nap.edu/25196

primer for reinforcement learning

http://adventuresinmachinelearning.com/reinforcement-learning-tutorial-python-keras/

How to build your own AlphaZero AI using Python and Keras

https://medium.com/applied-data-science/how-to-build-your-own-alphazero-ai-using-python-and-keras-7f664945c188?mkt_tok=eyJpIjoiTkRFM1lXUmlOR1poWW1JMyIsInQiOiIyMWlrbVNnOUFpNjR0QUd4RUp4ajBSUEl3aGI0bWtyVFpzcnpoK2U4Wkl1TG0yd2ZLeUlkSFVLNUVMM3Vka0ZxMVk2c2lIaUxJeEdCbjNNckk4WTFadytiZ0Yzekpva3Y1VVQ0XC90R2dQaUZtYk1iVFwvV3ZzbjhkM3RKWnNXQnpDIn0%3D

Step by step instructions on the Alpha Go prototype.

deep learning and reinforcement learning

Artificial Intelligence: What's The Difference Between Deep Learning And Reinforcement Learning?

https://www.forbes.com/sites/bernardmarr/2018/10/22/artificial-intelligence-whats-the-difference-between-deep-learning-and-reinforcement-learning/#52141bd271e1

This is a good article, easy for beginners. 

human perception

0. This is analogous to the way humans and animals perceive the world: after observ- ing a scene for a few seconds, a human can remember which abstract objects were present in it (bicycle, tree) but can’t remember the specific appearance of these objects. In fact, if you tried to draw a generic bicycle from memory, chances are you couldn’t get it even remotely right, even though you’ve seen thousands of bicycles in your lifetime (see, for example, figure 5.28). Try it right now: this effect is absolutely real. You brain has learned to completely abstract its visual input—to transform it into high-level visual concepts while filtering out irrelevant visual details—making it tre- mendously difficult to remember how things around you look.
   
   From "Deep Learning with Python"

Building your chatbot with RASA(tutorial 2)

This tutorial continues on my previous post, however, includes TensorFlow into the embedding.

Just as the page https://rasa.com/docs/core/quickstart/

Of course you want your bot to understand real language, not just structured input.An interpreter is responsible for parsing messages. It performs the Natural Language Understanding (NLU) and transforms the message into structured output. In this example we are going to use Rasa NLU for this purpose.
In Rasa NLU, we need to define the user messages our bot should be able to handle in the Rasa NLU training data format. 

1) Here we first add nlu_config.yml file as following:
language: en
pipeline: tensorflow_embedding

2) Then we add another intent bye to the following three files

trainingData.md
stories.md
domain.yml

3) We created a Makefile, including make train and make run.
    a) make train
    b) make run

  The detailed commands are :
train:
$(PYTHON) -m rasa_nlu.train -c nlu_config.yml --data trainingData.md -o models --fixed_model_name nlu --project current --verbose

run:
$(PYTHON) -m rasa_core.run -d models/dialogue -u models/current/nlu


The GitHub link is https://github.com/chaowu2009/sample_chatbot_RASA