Understanding Intelligence: Ml vs. AI
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The document provides an overview of machine learning and artificial intelligence. It defines machine learning as a technique used to develop artificial intelligence by having computer programs learn from examples without being explicitly programmed. The key elements of machine learning/AI are data, algorithms, and computations. Machine learning is used across many industries and in products/services like healthcare applications that can detect diseases from medical images. Machine learning tools are being democratized through APIs to expand their use. AI will be applied to more areas as machines learn and adapt based on data.
![0.95
Algorithm
0.91Ophthalmologist
(median)
F-score
The study by Gulshan and colleagues truly
represents the brave new world in medicine.
– Dr. Andrew Beam, Dr. Isaac Kohane
Harvard Medical School
“
”
[...] It actually lives up to the hype.
– Dr. Luke Oakden-Rayner
University of Adelaide
“
”
JAMA | Original Investigation | INNOVATIONS IN HEALTHCARE DELIVERY
Development and Validation of a Deep Learning
Algorithm for Detection of Diabetic Retinopathy in
Retinal Fundus Photographs
The Journal of the
American Medical Association
n=9,963 images
AUC 99.1% [98.8, 99.3
Sensitivity,%
1- Specificity, %
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Understanding Intelligence: Ml vs. AI
- 1. Understanding Intelligence: Machine Learning & Artificial Intelligence
- 2. Machine learning is a core, transformative way by which we’re rethinking how we’re doing everything. – Sundar Pichai “ ”
- 3. AI and Machine Learning A.I. is the science of making things smart Machine learning is a technique used to develop A.I.
- 4. A branch of artificial intelligence A way to solve problems without explicitly codifying the solution A way to build systems that improve themselves over time Machine learning is...
- 5. A different way of doing things Write a computer program to learn from examples try to classify some emails; change self to reduce errors; repeat; Write a computer program with explicit rules to follow if email contains “You won a Cruise” then mark is-spam; if email contains … if email contains …
- 6. Machine learning is learning from examples and experience
- 7. data algorithmscomputations Key Elements of ML/AI
- 8. Deep Neural Networks Step 1: training
- 9. Deep Neural Networks Step 2: testing
- 11. ML in Products
- 12. Healthy Diseased Hemorrhages Proliferative DRSevere DRModerate DRMild DRNo DR 54321
- 13. INDIA Shortage of 127,000 eye doctors 45% of patients suffer vision loss before diagnosis
- 14. 0.95 Algorithm 0.91Ophthalmologist (median) F-score The study by Gulshan and colleagues truly represents the brave new world in medicine. – Dr. Andrew Beam, Dr. Isaac Kohane Harvard Medical School “ ” [...] It actually lives up to the hype. – Dr. Luke Oakden-Rayner University of Adelaide “ ” JAMA | Original Investigation | INNOVATIONS IN HEALTHCARE DELIVERY Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs The Journal of the American Medical Association n=9,963 images AUC 99.1% [98.8, 99.3 Sensitivity,% 1- Specificity, % 100 80 60 40 20 0 0 20 40 60 80 100
- 15. ML for everyone
- 16. TensorFlow released in Nov. 2015 #1 Repository for machine learning on GitHub Sharing ML tools with people around the world
- 17. APIsAI
- 18. Cloud Vision API Cloud Translate API Cloud Natural Language API Cloud Speech API Democratization of AI -- How? Partner’s AI Capability Corporate Secret AI Capability
- 19. Myriads of Applications will go from Transact To Adapt Democratization of AI will happen through APIs Partner’s AI Capability Cloud Vision API Cloud Translate API Cloud Natural Language API Cloud Speech API Corporate Secret AI Capability
- 20. Use of AI In reality, every application will become a learning application
- 21. Manufacturing ● Predictive maintenance ● Condition monitoring ● Warranty reserve estimation ● Demand forecasting ● Process optimisation Travel & hospitality ● Aircraft scheduling Dynamic pricing ● Consumer interaction analysis ● Customer complaint resolution ● Traffic patterns ● Congestion management ● Recommendation engines Retail ● Predictive inventory planning ● Recommendation engines ● Upsell & cross-channel marketing ● Market segmentation & targeting ● ROI & lifetime value modelling Financial services ● Risk analytics ● Customer segmentation ● Cross-selling and upselling ● Marketing campaign management ● Creditworthiness evaluation ● Trading desk bid management Health & life sciences ● Real-time alerts and diagnosis ● Disease identification ● Patient triage optimisation ● Proactive health management ● Healthcare provider sentiment analysis Energy & utilities ● Power usage analytics ● Seismic data processing ● Carbon emissions & trading ● Customer-specific pricing ● Smart grid management ● Energy demand and supply optimisation Wherever there are large data sets and complex problems
- 22. BI + Reporting Machine Learning + AI Machine Learning and AI thrive on predictive and prescriptive analytics Low HighSophistication Descriptive -> Predictive Analytics & Diagnostic -> Prescriptive Analytics with the help of ML/AI Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics
- 23. AI for Good ● Establish best practices ● Advance public understanding ● Open discussion of AI and its influences on people and society http://www.partnershiponai.or g/ est. September 2016
- 24. “AI is turning science fiction into non-fiction, but some of its most meaningful applications haven’t been imagined yet.”
- 25. Thank you