The document discusses emerging technologies including quantum computing, artificial intelligence, machine learning, blockchains, the internet of things, cloud computing, edge computing, and data analytics. While these technologies show promise, the document cautions against hype and notes that many technologies do not yet work as described or have practical applications. The author offers experience supporting genomics and building IT infrastructure and is available for consulting work.

1. Quantum AI Machine Learning Blockchains
on the IoT Cloud Data Ocean
Turning Hype Into Reality
Chris Dwan
May 31, 2018
chris@dwan.org
https://dwan.org
@fdmts

2. Who do you have here?
A “Cray 2” supercomputer on display
at the national cryptographic
museum. This model of system were
the fastest computers in the world
until 1990. By one measure of raw
performance, the Cray 2 is
comparable to a single iPhone
Academic background in AI / neural nets
(before they worked well)
~20 years supporting genomics for
computing, data storage, and similar topics
9 years with BioTeam (consulting company)
Designed and led implementation of IT
infrastructure for the NY Genome Center.
Directed Research Computing (and, briefly,
all of IT) at the Broad Institute.
Building my own consulting company
(https://dwan.org)

3. Data: The new oil*
Data Base: Structure, queries
Data Warehouse: All the data in one place. Limited integration.
Data Mart: Serve up warehoused data to users (Shiny counts)
Big Data: Volume, Variety, Velocity
Data Lake: Data warehouse, but designed for in-situ analytics
Data Ocean: A data lake, for cromulently embiggened data!
Data Commons: When the benefits of sharing data outweigh
the competitive instinct to horde it
Data Biosphere: A data commons, but for the cool kids
An immature ‘tyrant
flycatcher. Needs a data
mart, because it doesn’t
know R or Linux yet.
Hype-o-meter Impact-o-meter

4. Cloud
Public cloud: AWS, Azure, GCS, plus a bunch of wannabes.
Private cloud: Cloud services on gear you own.
Fog computing: On premises equipment used for cloud
stuff. It’s fog because that’s a cloud that’s close to earth.
Get it?
Enterprise cloud: IT trying desperately to align with a cloud
strategy by changing the labels on the powerpoint.
Multi cloud: Mostly using Amazon, but with Azure for
business stuff and Google for that one weird trick. Using the
right tool for the right job.
Hybrid cloud: Using your own stuff for a particular service,
but bursting to Amazon for extra capacity during peak
usage.
Look up “Pizza as a Service.”
“On premises,” or “legacy,”
carrot cake still has a
place, even in homes with a
baker-as-a-service strategy.
Hype-o-meter Impact-o-meter

5. Machine Learning (ML)
Algorithms that optimize and tune based on
large amounts of data
These have been around for a very long time
(KNN and Linear Regression are totally ML).
Algorithm innovations (deep neural nets),
plus ubiquitous big data, plus improvements
in computing, storage, network, and
software.
Killer apps everywhere in image recognition,
natural language processing, clustering,
categorization
Hype-o-meter Impact-o-meter
A ‘swan pink yellow’ columbine
flower. Identifying objects in
images is machine work now.

6. Artificial Intelligence (AI)
Distinguished (for me) by autonomous
behavior and clever-looking behavior in
the face of unanticipated situations.
No requirement that “intelligent” mean
“like a human.”
Machine learning algorithms are a great
(but not the only) way to create AI
systems.
Beware “bread machine AI.”
Hype-o-meter Impact-o-meter
Getting there!
My cat shows surprising
intelligence despite having a
brain the size of a walnut

7. Artificial Intelligence (AI)
Distinguished (for me) by autonomous
behavior and clever-looking behavior in
the face of unanticipated situations.
No requirement that intelligence be
human style.
Machine learning algorithms are a great
(but not the only) way to build AI
systems.
Beware “bread machine AI.”
Hype-o-meter Impact-o-meter
Getting there!
My cat shows surprising
intelligence despite having a
brain the size of a walnut

8. Internet of Things (IoT)
All sorts of devices on the internet, with
sensors and embedded operating systems.
Huge issues around security, privacy,
reliability: Dishwasher companies have no
idea how to patch software in the field.
Grouchy old me says: We used to call these
‘embedded systems! My phone is IoT!’ Not
quite accurate, but not fully wrong either.
Next wave source of big data.
Hype-o-meter Impact-o-meter
Lightbulbs used
in botnets!
Would this sculpture be better
if it ran Debian Linux?

9. Edge Computing
Machine Learning plus Internet of
Things = unexpected and powerful
use cases.
Example: Optical character
recognition in lightweight cameras
allows upload only of license plate
text, rather than whole images,
“preserving privacy.”
Hype-o-meter Impact-o-meter
Please stop mailing hard disks.
It’s a terrible way to move data.

10. Edge Computing
Hype-o-meter Impact-o-meter
Please stop mailing hard disks.
It’s a terrible way to move data.
Machine Learning plus Internet of
Things = unexpected and powerful
use cases.
Example: Optical character
recognition in lightweight cameras
allows upload only of license plates,
rather than whole images,
“preserving privacy.”

11. Blockchain
”The clown car of our industry in 2018”
• Distributed ledger: trustworthy data /
records without a central authority.
• Self executing contracts: Shared,
trustworthy code to operate on that
data.
• Initial Coin Offerings: massively
accelerated (and deregulated) way to
set monetary value on a data
ecosystem.
Amazing possibilities in permission /
consent management.
When I make snarky comments on
LinkedIn, people ask if they can invest.
Hype-o-meter Impact-o-meter
The angel weeps because there are
some really compelling use cases for
blockchain, but the hype is
deafening.

12. Quantum Computing
A totally different framework for computing.
Subatomic particles are ‘entangled’ and
maintained in a ‘coherent’ state while
constraints are imposed on their relationships,
allowing near-instant solutions to formerly
intractable math problems.
Going to blow up large complicated search /
optimization technology: Cryptography, comp.
chem, docking, …
Serious engineering issues remain, including bit
level error rates that are hard to even explain.
Bitcoin founder’s account is considered a
‘quantum canary.’
Hype-o-meter Impact-o-meter
Sadly, the quantum doughnuts
will not be ready for another
few years

13. Quantum AI Machine Learning Blockchains
on the IoT Cloud Data Ocean
It doesn’t
work yet
It sure looks
clever
SHOW ME
THE MONEY
Why did it do that?

14. Quantum AI Machine Learning Blockchains
on the IoT Cloud Data Ocean
It doesn’t
work yet
It sure looks
clever
Now you’re just making stuff up.
Wait, like on a thermostat?
IoT is literally the opposite of cloud.
SHOW ME
THE MONEY
Why did it do that?

15. Call to action
Don’t believe the hype
Email me if you need a hand
with anything.
chris@dwan.org
https://dwan.org
@fdmts