Machine Learning 101

Presenters

  • Greg Corrado, Senior Research Scientist, Google
  • Vincent Nestler, Professor & Assistant Director of Cybersecurity, CSU San Bernardino
  • David Vasilia, Enterprise Network Administrator & Faculty, CSU, San Bernardino
  • Internet2 & GCP: internet2.edu/gcp
  • CS edu grants: cloud.google.com/edu

Machine Learning 101

  • Already in everyday products: photos, inbox, maps
  • 2 disciplines: AI and machine learning
  • Traditional AI systems are programmed to be clever
  • ML-based AI systems are designed to learn to be clever
  • Classic AI works on rules and contingencies; ML AI learns from examples and data.
  • Machines learn by example: models (which have parameters) feed predictions, which feeds a learner, which in turn feeds the parameters. This is surprisingly simple and generic.
  • Need 4 things: computational resources, good tools & algorithms, training examples, creativity and ingenuity of people.
  • Effective, but very gradual process that takes millions or billions of examples for it to work. It needs to cycle many many times.
  • ML coming of age in this decade because the computational power is exists now and it’s cheap and plentiful enough, i.e. CPU, GPU, Google TPU.
  • tensorflow.org a toolkit for machine learning
    • Open standard
    • Next gen deep learning tools built in
    • One system flexible enough for ML research
    • Robust enough for use in real products
    • Same software Google researchers use
  • Deep learning not one function, but a set of composable subfunctions for model building.
  • Distributing ML Tech Globally
    • Shared Tools: TensorFlow + CloudML
    • Ready-made ML systems (Cloud Vision API, Cloud Speech API, Cloud Translate API, etc.)
    • Use our tools to build your own system!
    • Example: TensorFlow cucumber sorting tool (really!)
    • Shared knowledge: open research publication at intl conferences; global direct community education; funding academic research and education.
  • Google published 90+ papers in the last 4 years
  • Takeaways:
    • Differentiation between AI vs. ML vs. Robotics
    • It isn’t magic, just a tool
    • Machines learn best from examples
    • Why now? fast computation
    • Make ML work requires creativity/ingenuity, cheap/fast computation/examples to learn from (data), tools & algorithms, TensorFlow makes ML software available for free.
    • Google Cloud makes hardware available.

Cloud for Higher Ed

  • Programming a campus rover: students are given a sensor, a raspberry pi, and Python. Then, they need to figure out how to integrate it.
  • Hacking now means hacking things together. You don’t have to be an engineer and you don’t need to know everything.
  • How can I level the playing field for my students? Be able to connect to Chrome and a Google compute engine. Everyone can look at and work with this environment, and they can explore from there.
  • A project we worked on in class: Android mapping for WiFi signal strength on campus. War driving took signal strength and using mapping API to literally map it to a real topographical map. Now we can “see our WiFi.”
  • We used Intermapper software to map the Internet, specifically the CENIC network from Los Angeles. The students loved this.

Panel

  • What is the difference between deep learning and machine learning? ML is the larger field of making machines that learn. DL is a small subset of this.
  • How far is Google taking cultural sensitivity into account with ML? Take translate as an example: you can dig into what the algorithm did to come up with its response.
  • If we use a Google tool, does this tool report what it learns back to Google? NO. What is pricing model for Google Cloud for Google Apps customers? It is independent of G-Suite.

Next Steps

  • Google is now a member of Internet2.
  • Will work with universities across the US to explore how Google Cloud Platform can better serve higher education
  • Help students build what’s next!
  • GCP Education Grants are available to: faculty in US, teaching university courses in CS or related fields in 2016-17 academic year. Examples: general CS, Cybersecurity, systems administration, networking.