The document outlines Africa's Artificial Intelligence Initiative called Cirrus. It discusses impediments to AI development like technical debt, energy use, and lack of data infrastructure. It proposes addressing these by bringing together specialized expertise in areas like hardware, software, and data engineering. Cirrus would consist of various components including infrastructure, research programs, and the Cirrus FOUNDRY to help startups. The Cirrus FOUNDRY Fund is described as providing capital to support startup portfolios. An example application of using AI in materials science is discussed through collaborations that could accelerate discovery.

1. Africa’s
Artificial Intelligence
Initiative

2. Outline
Rest of the world
Impediments
Addressing impediments
Cirrus overview
Cirrus FOUNDRY overview
Cirrus FOUNDRY Fund overview
Example application
Going forward

3. Rest of the world

4. United States
AI-IA
(Artificial Intelligence Initiative Act)
- 5 Artificial Intelligence Research Centres
- 300 million USD per centre over 5 years (60 million USD a
year)
- Higher education, industry and national laboratories
Link:
https://bit.ly/2I0KEbE

5. UK/Europe
CLAIRE:
Confederation of Laboratories for Artificial Intelligence Research in Europe
- ~600 top scientists (visiting for periods of time)
- +~400 world-class support staff (permanent)
- ~1 billion EUR a year budget (hub + network)
- https://claire-ai.org
ELLIS:
European Laboratory for Learning and Intelligent Systems
- ELLIS Institutes which need at least 200 million EUR each for the first ten
years
- 10 million EUR a year for co-funding about 200 students / postdocs initially
- https://ellis.eu

6. Africa
Cirrus:
- > 50 million USD over 6 years (excluding revenue,
grants, gifts and sponsorships)
- Brings together academia and industry

7. Impediments

8. Technical Debt
“If you are anything like me, 99 percent of the time you
will be in a situation where your code is not working…”
– Jeremy Howard, Fast.ai
https://dawn.cs.stanford.edu/assets/dawn-overview.pdf
https://xkcd.com/1319/

9. “The biggest lesson that can be read from 70 years of
AI research is that general methods that leverage
computation are ultimately the most effective, and by
a large margin.”
The Bitter Lesson
Rich Sutton
March 13, 2019
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
A New Golden Age for Computer Architecture
John L. Hennessy, David A. Patterson
February, 2019
https://cacm.acm.org/magazines/2019/2/234352-a-new-golden-age-for-computer-architecture/fulltext
Compute Infrastructure and Engineering
The twilight of Moore’s Law and the impact of computing resources on the performance of AI
applications requires optimal hardware and software deployment

10. Energy
Since 2012, the amount of compute used in the largest AI
training runs has been increasing exponentially with a 3.5
month doubling time (by comparison, Moore’s Law had an 18
month doubling period).
Dario Amodei and Danny Hernandez
https://openai.com/blog/ai-and-compute
The total energy cost of producing a model increases linearly
with each of three variables:
- the size of its training data set
- the number of hyperparameter experiments
- the cost of executing the model on a single example.
https://arxiv.org/abs/1907.10597
The amount of compute used to train deep learning
models has increased 300,000x in 6 years.
https://openai.com/blog/ai-and-compute

11. Data Infrastructure and Engineering
https://wallpaperstock.net

12. Addressing impediments

13. Bring together the necessary specialised expertise
Distinct tones symbolize the distinct possible tasks for an individual or team to perform. A. Separating distinct types of tasks, specialists can address more conditions
than one individual; the numbers add. What is shown is that two specialists can do twice as many tasks as one. For example, if each one can do 10,000 tasks,
together they can do 20,000. B. Teams can address an even more diverse set of conditions because the numbers multiply. For a two member team it would be
10,000 x 10,000 = 100,000,000.
Teams
http://necsi.edu/research/overview/why-teams

14. Graphcore Rackscale IPU-POD
https://www.graphcore.ai/posts/introducing-the-graphcore-rackscale-ipu-pod
NVIDIA DGX-2
https://www.nvidia.com/content/apac/gtc/ja/pdf/2018/1050.pdf
Compute

15. Data Management Platform
Conventional ML workflow and data silos. MLdp system architecture showing control plane and data plane and hybrid
storage model.
https://dl.acm.org/citation.cfm?doid=3299869.3314050

17. The Cirrus Components
Cirrus
Cirrus FOUNDRY
Cirrus
FOUNDRY
Fund

18. Cirrus: Capability
Cirrus
Cirrus FOUNDRY
Cirrus
FOUNDRY
Fund
Infrastructure:
- Hardware infrastructure
- Software infrastructure
- Data infrastructure
Focus areas:
- AI research
- AI application
- Particularly the “hard sciences”
External collaboration:
- Cooperation agreements with universities and
industry
Revenue generation:
- Partner and Affiliate programs with industry
- In person seasonal programs
Open Learning:
- Open and free access to learning products
- Tea time sessions and salons
Technology collaboration
- Co-development program
- Hardware
- Software
Research to Communication (RtC) program
- Program to aid in the communication of
research results

19. Cirrus: Resources
Cirrus
Cirrus FOUNDRY
Cirrus
FOUNDRY
Fund
Engineering and research core:
- Hardware engineers
- Software engineers
- Data engineers
- Machine learning engineers

20. Academically Speaking
https://www.thetimes.co.uk/article/is-the-new-dyson-institute-
the-answer-to-the-university-funding-crisis-55qcdblhs

21. Cirrus: Resources
Cirrus
Cirrus FOUNDRY
Cirrus
FOUNDRY
Fund
Engineering and research core:
- Hardware engineers
- Software engineers
- Data engineers
- Machine learning engineers
Cirrus Residency program:
- 12 month program
- Open to undergrad, masters and PhD
- Paid program
Cirrus PhD internship program:
- 5 month program
- PhD’s in their penultimate or final year of study
- Paid program
Cirrus Postdoc program:
- Minimum 12 months
- PhD’s
- Paid program
Cirrus Assistantship program:
- Duration varies
- Primarily masters and PhD’s
- Paid program

22. Cirrus: Revenue
Cirrus
Cirrus FOUNDRY
Cirrus
FOUNDRY
Fund
Revenue from:
- Partner program
- Affiliate program
- In person Summer and Winter programs
- Grants
- Gifts
- Sponsorships

24. Cirrus FOUNDRY
Cirrus
Cirrus FOUNDRY
Cirrus
FOUNDRY
Fund
Bridges the “Valley of Death”
- Challenge of turning a start-up idea or scientific
research into large-scale commercial application
Build and foster the ecosystem of:
- Serial entrepreneurs
- Financiers
- Coaches
- Academic bridges
- Infrastructure builders
Building businesses around Cirrus
- Ideation
- Product creation
Monthly classes, events and workshops
Cirrus FOUNDRY Residency program:
- 12 month program
- Open to undergrad, masters and PhD
- Paid program
Cirrus FOUNDRY PhD internship program:
- 5 month program
- PhD’s in their penultimate or final year of study
- Paid program
Results:
- Ability to deploy machine learning
products with significantly more ease and
affordability
- Foster new start-up opportunities for the
region and beyond

25. Cirrus FOUNDRY: Revenue
Cirrus
Cirrus FOUNDRY
Cirrus
FOUNDRY
Fund
Revenue options
- Successful exits of start-up business
- Run the product as a revenue generating
project
- Sell the code/IP without spinning it out
Opportunity to generate a return on the
investment and sustain the FOUNDRY fund

26. Stage of Business
Stage of Technology
Start-up Company
Product/Market Fit
Discovery
No Business Activity
No tech
R or D
Scholarly
Research
Translational
Research
New Product
R & D
Commercial Product
(1.0)
Cirrus
Cirrus
FOUNDRY

27. Where The Money Goes
2017 Early Stage Venture Deals
Online/Software Other Life Sciences Physical Sciences
Online/Software
81%
Other
7%
Physical Sciences
3%
Life Sciences
9%
www.pitchbook.com

28. FOUNDRY Advantage
Research:
Knowing what facilities and programs exist and where and how to leverage them.
Innovation:
Knowing what innovations are in the pipeline and what industries are most likely to be affected.
Industry:
Knowing what types of innovation industry is looking for and what constitutes technical validation - to create technology
pathway maps to success.
https://static1.squarespace.com/static/543fdfece4b0faf7175a91ec/t/5d1169950985c600011d354e/1561422231115/Hardtech+Investing+White+Paper.pdf

29. Cirrus FOUNDRY Fund

30. Cirrus FOUNDRY Fund
Cirrus
Cirrus FOUNDRY
Cirrus
FOUNDRY
Fund
Cirrus FOUNDRY FUND
- Provides the Cirrus FOUNDRY with the capital to
support the start-up portfolio
- ~ R100 million in capital
- No management fees
- No performance fees
Assists the Cirrus FOUNDRY to:
- Maximise its ownership stake in the start-up
company
- Maximise the valuation of the start-up
Ensures the Cirrus FOUNDRY start-ups:
- Are not wholly dependent on outside capital
- Increases the short term success of the start-up

31. Summary
Cirrus
Cirrus FOUNDRY
Cirrus
FOUNDRY
Fund
Global Engagement:
- Full time persons responsible for establishing
and managing collaborative engagements in:
- North America
- UK/Europe
- Asia Pacific
- Africa and South America
Collaborative engagements include:
- Partner and Affiliate programs
- Academic collaboration
- Cirrus FOUNDRY Fund capital raising
- Technology co-development
Governance
- Equity owned by:
- Staff
- Funders
- Board of Directors
- Advisory Council
Program managers:
- Partner and Affiliate program
- RtC program
- Open Learning Program
- Residency, PhD Internships, Postdocs
and Assistantships programs
- Engineering projects
- Cirrus FOUNDRY
- Grants, Gifts and Sponsorships

32. Example Application

33. Matter Still Matters…
https://www.bloomberg.com/features/2019-periodic-table-elements-issue

34. Illustration of Early ML Approaches in Materials Science
https://doi.org/10.1016/j.jmat.2017.08.002

35. Machine Learning Matters in Matter…
- Structure determination and imaging
- Drug design, drug screening, chemical and fuels production, discovery of new scientifically designed materials.
- ML is already being applied to a large suite of scientific techniques such as:
- phase identification via the ICSD database
- Rietveld refinement, and
- pair distribution function analysis [1] of total scattering experiments
- Density functional theory [2]
- Modelling of new chemically relevant systems
- Materials discovery and design
- Energy storage, space-age materials, environmental solutions
- Automating synchrotron data-analysis [3]
- Detectors in synchrotrons generate 9 Gbps each (over 100 detectors running simultaneously)
- Simulation and prediction of properties of materials [4]
- Massive savings on hit-and-miss type R&D
- Processing of research papers in experimental materials science to extract structural data on huge classes of novel materials[5]
- Extraction of rules from known manufacturing processes to guide synthesis of materials, fuels, drugs

36. [1]
Using a machine learning approach to determine the space group of a structure from the atomic pair
distribution function (PDF)
Chia-Hao Liu, Yunzhe Tao, Daniel Hsu, Qiang Du, Simon J.L. Billinge
https://arxiv.org/abs/1902.00594
[2]
A density functional theory parameterised neural network model of zirconia
Chen Wang, Akshay Tharval, John R. Kitchin
https://doi.org/10.1080/08927022.2017.1420185
Deep Learning and Density Functional Theory
Kevin Ryczko, David Strubbe, Isaac Tamblyn
https://arxiv.org/abs/1811.08928
[3]
Automated Data Analysis Strategy for Synchrotron Experiments
Lifen Yan
http://www.imedpub.com/articles/automated-data-analysis-strategy-for-synchrotron-experiments.pdf
Researchers get most comprehensive view yet of lithium-ion battery electrode damage
https://www6.slac.stanford.edu/news/2019-06-03-researchers-get-most-comprehensive-view-yet-
lithium-ion-battery-electrode-damage
[4]
Scientists Use Artificial Intelligence To Discover New Materials
https://www.forbes.com/sites/meriameberboucha/2018/04/22/scientists-use-artificial-
intelligence-to-discover-new-materials/#1b0b6cf138c4
New Lithium-Rich Battery Could Last Much Longer
https://www.mccormick.northwestern.edu/news/articles/2018/01/new-lithium-rich-battery-
could-last-much-longer.html
Generative models for fast simulation
Sofia Vallecorsa
https://indico.cern.ch/event/567550/papers/2656673/files/5841-
SofiaVallecorsa_Plenary.pdf
Accelerated discovery of metallic glasses through iteration of machine learning and high-
throughput experiments
Fang Ren, Logan Ward, Travis Williams, Kevin J. Laws, Christopher Wolverton, Jason Hattrick-
Simpers, and Apurva Mehta
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5898831
[5]
With little training, machine-learning algorithms can uncover hidden scientific knowledge
https://techxplore.com/news/2019-07-machine-learning-algorithms-uncover-hidden-
scientific.html

37. Collaboration
DeepMind: AlphaFold: Using AI for scientific discovery
https://deepmind.com/blog/alphafold
Google AI: Healthcare and biosciences
https://ai.google/healthcare
Toyota Research Institute (TRI): Researchers Use AI to Accurately Predict the Useful Life of Batteries
https://www.tri.global/news/researchers-use-ai-to-accurately-predict-the-usefu-2019-3-25
Machine learning collaborations accelerate materials discovery:
https://physicsworld.com/a/machine-learning-collaborations-accelerate-materials-discovery

38. Where We Are Headed
AI and Robotics in the Lab: Accelerating Materials Discovery
https://events.technologyreview.com/video/watch/jill-becker-accelerating-materials-discovery
Jill Becker, Kebotix

39. Going Forward
Get in touch
- Contact details on the website
www.CirrusAI.net
Letters of support
- Individuals
- Research groups
- CoE’s
- Institutes
- Universities
- Corporations
Cooperation Agreement
- Announcements coming soon

41. Slide 3:
NASA. (1969). Apollo 11 mission image - earth view over Africa, the Mediterranean and the Middle East. [image]. Retrieved from
https://www.flickr.com/photos/nasacommons/5052744618
Slide 8:
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Retrieved from https://dawn.cs.stanford.edu/assets/dawn-overview.pdf
Slide 9:
Hennessy, J. Patterson, D. (2019). A new golden age for computer architecture. [figure]. Retrieved from Communications of the ACM, Vol. 62 No. 2, Pages 48-60.
Retrieved from https://cacm.acm.org/magazines/2019/2/234352-a-new-golden-age-for-computer-architecture/fulltext
Sutton, R. (2019). The bitter lesson. [blog]. Retrieved from http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Slide 10:
Amodei, D. Hernandez, D. (2019). AI and compute. [blog]. Retrieved from https://openai.com/blog/ai-and-compute
Schwartz, R. Dodge, J. Smith, N. Etzioni, O. (2019). Green AI. [pdf]. Retrieved from https://arxiv.org/abs/1907.10597
Slide 13:
Bar-Yam, Y. (2017). Fig. 2. [figure]. Retrieved from NECSI. (2017). Why teams?. [webpage]. Retrieved from http://necsi.edu/research/overview/why-teams
Reference

42. Slide 14:
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Slide 15:
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Slide 20:
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dyson-institute-the-answer-to-the-university-funding-crisis-55qcdblhs
Slide 26:
Adapted from:
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Slide 27:
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https://static1.squarespace.com/static/543fdfece4b0faf7175a91ec/t/5d1169950985c600011d354e/1561422231115/Hardtech+Investing+White+Paper.pdf
Reference

43. Slide 28
U.S. Department of Energy. (2019). A New Mechanism to Fund Hardtech Innovation. [pdf[. Retrieved from
https://static1.squarespace.com/static/543fdfece4b0faf7175a91ec/t/5d1169950985c600011d354e/1561422231115/Hardtech+Investing+White+Paper.pdf
Slide 34:
Liu, Y. Zhao, T. Ju, W. Shi, S. (2017). Graphical abstract. [figure]. Retrieved from Materials discovery and design using machine learning. [webpage]. Retrieved from
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