Book review 2/2 on Robot Proof: Higher Education in the Age of AI

2019/08/02

(Part 1 is written elsewhere, I’ll add a link here to it later)

I finished the book by Joseph Aoun a little while ago, and I’ve been sitting on my notes letting them stir. I think i have a fairly safe conclusion for its second half. That said, I would expect those with an understanding and empathetic relationship with their CS students and their families will have been at the cusp of some similar conclusions drawn by Aoun in Robot Proof in 2017.

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Open Source Code for Light Stage Capture Sequences

Today I’m posting updates (1/n) to the Light Stage open source project codebase.

The updates mark improvements for integrating experimental result data and 3d geometry data with light and camera-trigger hardware controllers (3). Included are two new lighting sequence improvements (1) and (2) and a way to get started, no matter your stage design and target capture application (4). These changes contribute towards standardised capture sequences and integrated 3d reconstruction pipeline processing, while supporting stage design tools and retaining visualisations, measurable evaluations and optimisations at each step.

Altogether, this work takes a step towards the vision of a comprehensive open source framework for open hardware light stages, find more details at the Build a Light Stage website.

These recent updates to the LightStage-Repo on github include:

  1. Spherical gradient” lighting sequence.
  2. Balanced lighting baseline”.
  3. Local web service (on port 8080) to return data requested by an HTTP client, such as a hardware controller with Ethernet/Wifi module.
  4. Configuration file designed for each Light Stage, to easily get the web service responding with correct sequence data.
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Collaboration Platforms for Data Scientists

News from April 10th 2019 is the release of Google’s collaborative AI platform for Data Science teams, for execution on cloud or on premises. Google’s platform joins Alibaba‘s similar platform called PAI 2.0 announced in March 29th 2017. While comprehensive information on Alibaba’s platform is sparse in non-Chinese, the Google AI Platform does give samples and tutorials. Two others ClusterOne for the DevOps of data science and DeterminedAI for collaboration each had funding announcements earlier this year. Google and Alibaba’s platforms give a clear separation for team roles to collaborate at each stage of the process (as is indicated for the two yet-to-be-released others). The concept is well worth a mention because they are collaborative frameworks pushing forward the methodologies of data science, engineering and in essence, social intelligence..

 

cloud-ai-platform2
AI Platform announced by Google April 10th 2019: Process pipeline of data-driven application stages.

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