This post is following upgrade with respect to the early post How to Build an Artificial Intelligent System (I) The last one is focused on introducing the six phases of the building an intelligent system, and explaining the details of the Problem Assesment phase.
Phase 1: Problem assessment – Determine the problem’s characteristics.
What is an intelligent system?
The process of building Intelligent knowledge-based system has been called knowledge engineering since the 80s. It usually contains six phases: 1. Problem assessment; 2. Data and knowledge acquisition; 3. Development of a prototype system; 4. Development of a complete system; 5. Evaluation and revision of the system; 6. Integration and maintenance of the system .
Generative models, like Generative Adversarial Networks (GAN), are a rapidly advancing area of research for computer science and machine intelligence nowadays. It’s hard to keep track of them all, not to mention the incredibly creative ways in which researchers have achieved and been working on.
The following figures demonstrate some results of the current works ( Images from https://blog.openai.com/generative-models/).
I think it is necessary to understand the basic pros and cons of it, and it may be very helpful to your own research. I have not fully reviewed the theory and papers, but after skimmed a few papers, I got the impression that the training process of GAN models is very tricky as well as any neural networks model. Thus, there must be a huge improving space for people to make.
Thanks to the internet! There are papers and codes everywhere and nobody will be left behind in these days unless he/she wants to. So working hard and to be a better man (or women or anything good for humanity), cheers!
Here are some papers and blogs that summarized the literature very well.
The computations required for Deep Learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018. AI could account for as much as one-tenth of the world’s electricity use by 2025 according to this article .
AI papers tend to target accuracy rather than efficiency. This following figure shows the proportion of papers that target accuracy, efficiency, both or other from a sample of 60 papers from top AI conferences .
The Allen Institute for Artificial Intelligence (AI2) is proposing a new way to incentivize energy-efficient machine learning. Researchers of AI2 have proposed a new way to mitigate this trend. They recommend that AI researchers always publish the financial and computational costs of training their models along with their performance results . In their work, they proposed the following Red AIequation:
Cost(R) ∝ E·D·H
The cost of an AI (R)esult grows linearly with the cost of processing a single (E)xample, the size of the training (D)ataset and the number of (H)yperparameter experiments.
Even though this equation ignores other factors, it illustrates three quantities that are each an important factor in the total cost of generating a result. Below,
The authors hope that increasing transparency into what it takes to achieve performance gains will motivate more investment in the development of efficient machine-learning algorithms .
The vision of Green AI raises many exciting research directions that help to overcome the inclusiveness challenges of Red AI. Progress will reduce the computational expense with a minimal reduction in performance, or even improve performance as more efficient methods are discovered.
When I am using TensorFlow on my MacBook Air, I always get annoyed by the warnings comes from nowhere, so I followed the documentation below to build TensorFlow sources into a TensorFlow binary and installed it successfully. In theory, this will make the TF running faster on my machine.