The last 5 years have been transformative in the AI industry, how will the next 10 look like? We've seen an explosion in IoT devices and the data flowing through them — ubiquitous computing is here to stay. How would this change the ecosystem with respect to hardware, solution development, testing, copyrights, privacy, etc.? And finally, a prediction of what all of this means for businesses, current and new, in light of advancements in deep learning.

1. https://elgeish.com
The AI Industry
in 2020-2030
Mohamed El-Geish
Director of AI at Cisco
Co-Author of Computing with Data

2. “
“My dear, here we must run as fast as
we can, just to stay in place. And if you
wish to go anywhere you must run
twice as fast as that.” ― Lewis Carroll
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3. To Predict the Future,
Let’s Revisit the Past!
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4. ResearchCompute Data
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How Far We’ve Come!

5. How Far We’ve Come!
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6. 1300 BC
How Far We’ve Come!
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• 1950: The Turing Test
• 1958: Perceptron
• 1974: Backprop
• 1985: Boltzmann Machine
• 1986: RBM, MLP, RNN
• 1990: LeNet
• 1997: LSTM
• 2012: Dropout
• 2014: GANs
• 2017: Transformers
• 2018: Muppetware
2000 2020 — we are here!1950

7. 1980s
How Far We’ve Come!
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1990s 2000s 2010s

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So Much Data!
And so Many
Connections to
Be Made!
What if I don’t want those
connections to be made?!!

9. Let’s Make
Some
Predictions!
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10. #Prediction
More Regulations Underway
◎ GDPR and CCPA are just the start
◎ We need good laws for all!
Namely, better privacy laws
◎ FDA-like agency for data?
◎ Health and legal domains
require model explicability
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11. #Prediction
Self-Supervised Learning Wins
◎ Too much data to label
◎ We already saw wins in NLP
◎ Hot research area ⇒ results
◎ “Doing this properly and
reliably is the greatest
challenge in ML and AI
of the next few years...”
- Yann LeCun
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12. #Prediction
System 2 Deep Learning
◎ Thinking, Fast and Slow
◎ Models to learn high-level
concepts and abstractions
◎ Models will need less data
◎ Meta-learning: ML modules
that just work in novel tasks
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13. #Prediction
AutoML Will Make Strides
◎ Feature engineering and manual
tuning will be less needed
◎ True end-to-end learning
◎ Opens up the door for non-
experts to build ML models;
democratizing ML in general
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14. #Prediction
NLP Will Catch up With Vision
◎ We’ll figure out better metrics
◎ Is summarization up next?
◎ GPT-3 and larger systems
◎ More cross-pollination:
learning from other fields
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15. #Prediction
Dev Ecosystem 2.0
◎ Operational data vs. fuel for AI
◎ ML is the new SQL/BI/Excel
◎ The new dev/data ecosystem
○ Hardware for AI (✓)
○ More ♡ for math & PP
○ DataHub & DataStore
○ Licensing, ratings, etc.
○ Debugging, explicability,
QA/testing, best practices, etc.
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16. We’ve, Somewhat, Mastered Code;
We’re Still Clueless about AI & Datasets
It Takes Time!
Think of all the time we’ve
collectively spent debating
the best ways to do things,
leading to thousands and
thousands of books, talks,
dissertations, courses, etc.
about software processes,
standards, and principles
which became dogma.
We’re Not There Yet!
We need a similar level of effort to
reach the same level of mastery of
traditional code. Theory needs to
catch up with practice and explain
the blessing we’ve been bestowed
and the miraculous advances in AI.
That will take many smart PhDs so
many years to fathom. Right now,
we only see the tip of the iceberg!
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Data
Compute/
Hardware
QA
Debugging
TTM
?

17. Takeaways
◎ Prepare/create for the new ecosystem
◎ Help make AI as mature as traditional software
◎ Take on a new challenge: MLSys, privacy, ethics, etc.
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18. “
It’s hard to make predictions,
especially about the future!
So let’s build it together.
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19. Thanks!
Any More Questions?
You can also find me at:
@elgeish, elgeish.com, or
computingwithdata.com
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20. Credits
Special thanks to all the people who made and released
these awesome resources for free:
◎ Presentation template by SlidesCarnival
◎ Photographs by Unsplash
◎ Icons by Icons8
All copyright, trademarks, logos, etc. referred to are the
property of their respective rightful owners.
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