Here are the backdrop slides to my recent SIFMA talk, "Advanced AI for People in a Hurry." We talk about the advent of deep learning and the rapid rise of software that can see, read, hear, speak and create... often better than humans. I end with a few examples of how people can get started today with offerings from Google.

1. AI…
for People in a Hurry
Scott Penberthy
Director of Applied AI, Google

2. Agenda
Examples How it works Start now
1 2 3

3. Examples

4. Seeing
Deep Learning has become a better
driver than humans by literally going
from pixels to steering, throttle,
distance, and more.
https://waymo.com/tech/

5. Hearing
Deep Learning now powers real time
audio speech translation for the top
languages, allowing more humans to
connect than before.

6. Speaking
Deep Learning now generates
realistic human speech, much how
we evolved as mammals.

7. Reading
Deep Learning can analyze 51
different file formats, condensing
information to tensors for search,
labeling and summarizing.
Iron Mountain InSight™
51 File Formats

8. Creating
Generative techniques are now
creating imagery and art with
humans, as well as simulated
environments for learning.
source: thispersondoesnotexist.com

9. How it works

10. Scalar Vector Matrix Tensor

11. input
input
5
3
a
b
add
mul
c
d
add
e
23
3
5
8
15
3
5
x F(x)
Tensors flow through graphs

12. Gradient Descent - find F(x)

13. “Our results are 10^5-10^6 faster with
double-digit process improvement...”
...multiple projects
F(x) v. f(x)
Universal
Approximation

14. Tensor Pods (11.5 pflops)

15. Unity (US, JP) 2010
Monet (US,
BR) 2017
Tannat (BR, UY, AR)
2017
Junior (Rio, Santos) 2017
FASTER (US, JP, TW) 2016
PLCN (HK, LA) 2019SJC (JP, HK, SG) 2013
Indigo (SG, ID, AU)
2019
Edge node locations >1000
Edge points of presence >100
Network
Network sea cable investments
The largest cloud network, comprising
>100 points of presence
25% of the World’s Internet Traffic
Tensor connectors
- 1000x our speed

16. Impact?
Product Users
Data20161950
$
Cost of
Prediction
Source: “Managing the Machines: AI is making prediction cheap, posing new challenges for managers” by
Ajay Agrawal, Joshua Gans, and Avi Goldfarb © 2016 (ajay@agrawal.ca)

17. Start now

18. Democratizing AI
https://ai.google
Normal humans
AI Nerds
ML frameworks: TensorFlow, XGBoost, Sklearn, PyTorch
Cloud ML Engine: managed service for
training & serving custom models
RPA:
Build robots for
the office
worker
Kubeflow: deploy ML pipelines for pre-processing data,
training and serving models on Kubernetes
Deep Learning VM images: spin up VMs with
popular ML frameworks pre-installed
AutoML, BQML: train & serve
no model code
ML APIs:
integrate AI into codebase

19. Process screens across SAP,
Windows, Web, Citrix at 10+ clicks per
second
AI-powered
Robotic Process
Automation

20. Enable your entire team to
automatically build and deploy
state-of-the-art ML models on
structured data at massively
increased speed and scale.
Cloud AutoML Tables Beta

21. It’s almost not fair
For each product:
● Relevant tables joined by given IDs
● Some minimal preprocessing done
to match input requirements
● Run until converge
● Benchmarks run between H2 2018
to today (as they became available)

22. http://colab.research.google.com
https://kubeflow.org
Personal
Supercomputers

23. Exponential growth AI
Because tensors … work.
Arxiv Papers
18 months
Google Directories
18 months
Model Computation
3.5 months

24. Q&A

25. Thank you!

26. Let’s connect:
sifma-ops@google.com
Learn more:
https://cloud.google.com/solutions
/financial-services/
Project X
March, 2018