Overview of AI with applications in healthcare.

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Ivy Data Science
AI with Applications in Healthcare

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AI State of Play
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3. AI is inspired by Nature - Biological Neuron
Ivy Data Science AI with Applications v0.10 Peter Morgan
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4. Biological Neurons – Cajal, circa 1900
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5. Biological Neurons – Cortical Columns
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6. Artificial Neural Network
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7. Enterprise technology has
evolved with each wave of the
Industrial Revolution – today it is AI

8. 1st
Steam Power
3rd
Computing
2nd
Mass Production
19th Century
17th Century
21st Century
4th
Artificial Intelligence
20th Century

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The Intelligence Revolution
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10. Deep Learning hardware and algorithms
along with increasing amounts of data are
the foundation for the “fourth industrial
revolution”

11. Deep learning is a new computing model that spans
training, inference, and the billions of intelligent devices
that will take advantage of deep learning to perform
intelligent tasks

12. Deep Learning in the cloud - accelerating enterprise AI today

13. Example 1 – Microsoft cloud:
“We’ve worked with Microsoft to
create a lightning-fast AI platform
that is available from on-premises
with our DGX-1 supercomputer to
the Microsoft Azure cloud.”
- Jen-Hsun Huang
CEO at NVIDIA

15. “By working closely with
NVIDIA and harnessing the
power of GPU-accelerated
systems, we’ve made Cognitive
Toolkit and Microsoft Azure the
fastest, most versatile AI
platform.”
- Harry Shum
Executive VP of AI and
Research at Microsoft

17. “With Microsoft’s global reach,
every company around the world
can now tap the power of AI to
transform their business.”
- Jen-Hsun Huang
CEO at NVIDIA

18. Example 2 – IBM:
IBM CEO Gini Rometty saw a
two trillion dollar opportunity in
machine learning for enterprise.
This ignited their recent drive
in advancing GPU-based
cloud computing …

19. The solution runs on an
IBM server built specifically
for AI and utilizes NVLink
to optimize AI into the
enterprise
… And developed PowerAI
that accelerates machine
learning training and AI tasks

21. “PowerAI democratizes Deep
Learning and other advanced
analytics technologies by giving
enterprise data scientists and
research scientists alike an easy to
deploy platform to rapidly
advance their journey on AI.”
- Ken King
General Manager for
OpenPOWER

22. “Putting GPU’s into the IBM
system will speed up performance
for such emerging workloads as
AI, deep learning, and data
analytics.”
-eWeek

23. -Karl Freund
Moor Insights & Strategy
“With these partnerships in place, NVIDIA has aligned itself
with two of the largest suppliers of enterprise IT technology and
services to accelerate the adoption of AI in the enterprise and
help take AI into the mainstream of global businesses.”
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24. GOOGLE
Example 3 - Google Cloud: Deep Learning is available for Google Compute Engine
and Google Cloud Machine Learning users to create new AI services and applications
Tesla P100 delivers a 12x increase in neural
network training performance compared
with a previous generation offering.

25. Example 4 – AWS: Amazon has
boosted their cloud-computing
performance with new GPU-
Accelerated AWS instances that are
designed for large-scale machine
learning, deep learning, molecular
modeling, and computational
finance workloads.

26. “These (P2) instances were designed to
chew through tough large-scale
machine learning, deep learning,
computational fluid dynamics, seismic
analysis, molecular modeling,
genomics, and computational finance
workloads.”
- Jeff Barr
Chief Evangelist at AWS

27. SAP
Example 5 – SAP: Enterprise
software giant SAP is now using
DGX-1 supercomputers to build
machine learning solutions for
SAP’s 320,000 customers in 190
countries. The system is designed
to help data scientists innovate &
get to market faster with AI
applications.

28. These partnerships with the world’s largest
companies allow Deep Learning platforms to be
available for every company in the cloud today

29. AI Hardware Developments
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30. Nvidia - GPU Exponentials
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31. GPU Faster than Moore’s Law
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32. Computer Vision Accuracy
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Nvidia DGX-1

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Nvidia Pascal P100

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AMD Radeon Vega

36. Facebook Big Sur Server
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www.opencompute.org

37. The GPU in the cloud movement has quickly
accelerated AI into one of today’s biggest
enterprise technologies
Next up FPGA’s and ASICs – already happening

38. ASIC Development
Companies:
• Wave (DPU)
• Graphcore (IPU)
• KnuEdge
• Google (TPU)
• Intel/Nervana
Overviews:
http://www.nextbigfuture.com/2016/12/chips-for-deep-learning-continue-to.html
https://hackernoon.com/the-future-of-machine-learning-hardware-
c872a0448be8#.6cxtp1fux
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Google TPU

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AlphaGo – TPU Cluster

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Time to train AlexNet
Hardware Comparison – CPU v GPU v DPU (Wave ASIC)

42. AI in Healthcare
• Detection
• Diagnosis
• Prediction
• Drug discovery
• Personalized medicine
• Medical Imaging
• Genomics
• Cancer research
• Brain tumors
• Dermatology
• Mental health
• Speech patterns
• Diabetes
• Radiology
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43. Watson Tackles Cancer
• Watson was tested on 1,000 cancer diagnoses made by human
experts. In 99% of them, Watson recommended the same treatment
as the oncologists
• In 30% of the cases, Watson also found a treatment option the
human doctors missed
• Some treatments were based on research papers that the doctors
had not read — more than 160,000 cancer research papers are
published a year
• Other treatment options surfaced in new clinical trials the oncologists
had not yet seen announced on the web.
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44. AI - Accelerating the Healthcare
Transformation

45. Cancer is the
Epidemic of our Time

46. Leading to the creation of one of the most important initiatives …
On January 2016, the White House announced its aim to deliver a decade’s
worth of advances in cancer prevention, diagnosis, and treatment, in five
years, with this initiative.

47. This initiative includes the building of an AI framework named CANDLE
(Cancer Distributed Learning Environment) being developed by multiple
organizations. It will help us change the way we understand cancer.

50. “Big Data and Computing Power together
provide the possibility of significant insight
into how genomics, family medical history,
lifestyles, genetic changes can trigger
cancer and how the cancers can be
treated.”
- Joe Biden
Previous Vice President of the United
States

52. The bright future of AI in healthcare only continues to
become brighter…
Enlitic
Imaging & Diagnostics
Insilico Medicine
Drug Discovery & Aging Research
Pathway Genomics
Imaging & Diagnostics

53. “A patient gets a lung CT screening. By immediately giving the clinician the most
accurate information, the patient is less likely to have a missed diagnosis or an
unneeded biopsy.”
Enlitic applies state of the art deep learning technology to medicine
- Jeremy Howard
Founder and CEO of Enlitic

54. “At Insilico we are working on a system to discover drug candidates and
looking for new approaches to treat individual patients with rare diseases.”
- Alex Zharvoronkov
CEO at Insilico Medicine
Insilico Medicine applies Deep Learning algorithms to drug discovery

55. “We are in the process of initiating disease specific studies for early
cancer detection in high risk patients, including a study in lung cancer,
and will publish the data in the appropriate forums.”
- Glenn Braunstein
Chief Medical Officer at Pathway Genomics
Pathway Genomics developed a blood test kit with IBM Watson to test
early cancer detection

56. CONCLUSION
THE WORLD OF HEALTHCARE IS BEING
TRANSFORMED BY AI

57. References
• Deep Genomics is applying GPU-based deep learning to understand how genetic
variations can lead to disease
• Arterys uses GPU-powered deep learning to speed analysis of medical images
• GE Healthcare MRI machines help to diagnose heart disease
• Atomwise – using deep learning to develop new pharmaceuticals
• Deep Learning for Healthcare
https://www.nvidia.com/en-us/deep-learning-ai/industries/healthcare/
• Deep Learning for Medical Image Analysis
https://books.google.com/books/about/Deep_Learning_for_Medical_Image_Analysis.
html?id=WVqfDAAAQBAJ&source=kp_cover
• Deep Learning for Detection of Diabetic Eye Disease
https://research.googleblog.com/2016/11/deep-learning-for-detection-of-
diabetic.html
• Retinal Assessment https://news.developer.nvidia.com/share-your-science-advanced-
retina-assessment-and-diagnostic-services/
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58. References
• MIT
http://news.mit.edu/2017/putting-data-in-the-hands-of-doctors-regina-
barzilay-0216
• waya.ai
http://waya.ai
• Startups
https://www.cbinsights.com/blog/ai-startups-fighting-cancer/
• Radiology
http://www.techrepublic.com/article/why-ai-is-about-to-make-some-of-the-
highest-paid-doctors-obsolete/
• Stem cell development
https://www.sciencedaily.com/releases/2017/02/170221081734.htm
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59. AI in Fintech
Five main areas:
• Alerting
• Reporting
• Advising
• Trading
• Investment
For Example:
• Anomaly detection
• Risk Management
• Insurance
• Algorithmic Trading
• Portfolio management
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60. Fintech – Use Cases
• Alerting - Anomaly (Fraud) Detection. Can detect and predict irregular
activity
• Reporting - systems that can instantaneously analyze data, reason about
what that analysis means and then generate natural language reports that
inform many audiences. Useful in compliance and regulation
• Advising - system has a “conceptual awareness” of client goals and needs
• E.g., Watson gives veterans financial advice related to their transition from military to
civilian life
• Deep Learning is being used in algorithmic, high frequency trading
• Ideal for creating investment portfolios
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61. The team was able to:
• Process ~11,000 payments per second
• Cut false alarm rates by 50%
• Free up resources to combat true fraud
PayPal is improving cybersecurity with deep learning
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62. General Reading on AI
• Barrat, James, Our Final Invention, St. Martin's Griffin, 2014
• Brynjolfsson, Erik and Andrew McAfee, The Second Machine Age, W.W. Norton
& Co., 2014
• Domingos, Pedro, The Master Algorithm, Basic Books, 2015
• Ford, Martin, Rise of the Robots: Technology and the Threat of a Jobless Future,
Basic Books, 2015
• Kaku, Michio, The Future of the Mind, Doubleday, 2014
• Kurzweil, Ray, The Singularity is Near, Penguin Books, 2006
• Kurzweil, Ray, How to Create a Mind, Penguin Books, 2013
• Nowak, Peter, Humans 3.0: The Upgrading of the Species, Lyons Press, 2015
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64. Training with Job Placement
• Led by Experts from Harvard, Columbia, UMass, UCL
• Real time paid projects in the last four weeks of bootcamp
• Practical hands-on training that will prepare you for a career
as a Data Scientist, Machine Learning or Deep Learning
Practitioner
• We also offer online training
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65. Network of Clients
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66. Any Questions?
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