Size of the AI/ML global opportunity
a. It is believed that artificial intelligence (AI) and machine learning (ML) will contribute $13 billion to the global economy by 2030.
b. The following six companies have collectedly invested over $7.7 billion in AI/ML research and development: Google ($3.9 billion), Amazon ($871 million), Apple ($786 million), Intel ($776 million), Microsoft ($690 million), and Uber ($680 million).
Deficit of trained data scientists’ vs. open opportunities
a. According to the August 2018 LinkedIn Workforce Report, there is a national shortage of 151,717 people with data science skills.
b. IBM predicts that by 2020, the number of jobs for all U.S. data professionals will increase by 364,000 openings to 2,720,000.
Development of tools to make it easier for non-data scientists to do AI/ML
a. Six of the most common ML use cases are as follows: Customer Lifetime Value Modeling, Churn Modeling, Dynamic Pricing, Customer Segmentation, Image Classification, and Recommendation Engines.
b. Talk about ML APIs, Keras, and AutoML as enablers of addressing the “turnkey ML” use cases
Introduction to Use Case: Autonomous Inspections of Aircraft
a. Market opportunity for infrastructure inspection is over $45.2 Billion, with predictive maintenance specifically growing 400% by 2022.
b. Similar solutions have seen the following results: 10x reduction in time spent in inspections, 20% to 30% reduction in maintenance costs, and 15% to 20% reduction in unplanned downtime.
Breakout of the Google tools used in the demo and what each one does
a. Pub/Sub
b. Cloud Storage
c. Machine Learning APIs/AutoML
d. App Engine
e. Google Street View API
f. Google Earth API
See the slides.Watch the video.
