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.
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.