How to Build an Artificial Intelligent System (II)

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.

In the following content, I will address the rest phases and key steps during the building process.  Readers can download the keynotes here: Building an Intelligent System with Machine Learning.

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How to Build an Artificial Intelligent System (I)

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 [1].

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Increasing Transparency into What It Takes to Achieve Performance Gains of Machine Learning Algorithms

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 [1].

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 [2].

Green AI fig1
Image from the Green AI paper. 

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 [2]. In their work, they proposed the following Red AI equation:

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 [3].

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.

Please find more details in the references.

Reference:

  1. Is AI the next big climate-change threat?
  2. Green AI not Red AI
  3. AI researchers need to stop hiding the climate toll of their work

Just Got a Reviewer Certificate from Data Mining and Knowledge Discovery (WIREs)

Thanks to the Editors and Board of WIREs for supporting me. As an independent reviewer, I will be fair to everyone and never give in to the “scientific mafia” and “citation cartels”.

Data Mining and Knowledge Discovery (WIREs) (Impact Factor: 2.541)

WIREs_Reviewer_Certificate.PNG

Roads from Above: Augmenting Civil Engineering & Geospatial Workflows with Machine Learning

Road from Above is partly based on my Australia Postgraduate Intern Projects (Computer Vision and Machine Learning for Feature Extraction) within Aureon Group in Melbourne.

Aurecon’s experts, across Cape Town, Melbourne and Auckland offices, have been teamed up to develop and test approaches that capture and validate new and existing measurements of the metropolitan road network. Due to the confidentiality, we reduced the resolutions of the aerial images and only opened limited results on the public domain at https://roadsfromabove.netlify.com/. Thanks to Greg More, the design of this website got the best feedback from the workshop (Visualization for AI Explainability) of the IEEE VIS 2018 conference in Berlin, Germany

Visualization for AI Explainability: Projections and Dimensionality Reduction. The goal of this workshop is to initiate a call for “explainable” that explain how AI techniques work using visualizations. We believe the VIS community can leverage their expertise in creating visual narratives to bring new insight into the often obfuscated complexity of AI systems.

Road from Above

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MacOS X: Installing TensorFlow from Sources [TF Binary Attached]

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.

Here is the document:

If you are a Mac user, you could download the TF binary from here:

Then, you could use conda to initialize an environment with Python=3.6 and install TF by typing:

sudo pip install tensorflow-1.8.0-py2-none-any.whl

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