How Machine Learning is easing (or disrupting?) the Human life. Would machines take up ALL of the human jobs 50 years from now ? Posted at a part of course presentation at MBA Program at The University of Connecticut School of Business. #Leonar

1. Machine Learning
The Future of Computing
Team 3
Isaac Alawobu
Ankur Jain
Robert Lambrecht
Keshav Grover
Sarah Abbasi
Leonard Borriello

2. What is Machine Learning?
A type of artificial intelligence (AI) that provides computers with the ability to learn
without being specifically programmed. Machine learning focuses on the development of
computer programs that can change when exposed to new data.
-Arthur Samuel
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3. Examples of machine learning
Facebook's News Feed - personalizes each member's feed based
on scroll and like patterns
Amazon recommends a book you would like
Google predicts that you should leave now to get to your
meeting on time
Pandora magically creates your ideal playlist
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4. A little more to understand Machine Learning
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5. Evolution of Machine Learning
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1952 - The first computer learning program
to beat chess
1957 - The “nearest neighbor” – pattern
recognition
1967 - The “ Stanford card” – navigating obstacles
in a room
1985 – The “nettalk” – pronounces
words like a baby does

6. New Pace of Development
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1990's – Knowledge-driven approach Data-driven approach
Deep Blue
Brain

7. Current presence of Machine Learning
Medical and Healthcare
• Pattern learning for early detection of diseases
• Insurance claims
Home and Travel
• Smart Connected Homes
• Smart Cars
Finance and Trading
• Predict and execute trades at high speeds and high volume
• Fraud Detection
Marketing
• Customer Personalization
Security
• Data and Personal Security; Malware detection
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8. Impact on Business and Society
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• Better understanding of customers
• Better business predictability
• Automated learning – reduce manual errors, better
quality
• Largely data dependent
• What if machines go wrong? Not full-
proof!
• Risk to human jobs

9. Who are the leaders in the field?
• System that reasons like a human
• Reads through unstructured data
• Ranks news feeds and search results
• Looking to battle spam and fake news
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10. Who are the leaders in the field?
• Analyze data and improve
learning
• Impressive stock returns
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11. What do you think about Machine Learning ?
• Humanity?
• Businesses?
• Society ?
• Economy?
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12. Machine’s won’t really take ALL the jobs
https://www.youtube.com/watch?v=gWmRkYsLzB4
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13. Risk and/or Opportunities
Economic
• Save financial resources and increase efficiency
• Job loss and high unemployment
• Crash of financial markets
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Social
• More time to do other activities
• Further disconnect between members of society
• Distrust of machines
Security
• Eliminate human error in security
• Political espionage
• Control concerns, how to deal with “thinking, feeling machines”

14. Ethical Considerations
• Algorithms must be transparent to inspection
• If system fails at an assigned task, who would take the blame?
• How will we treat AI from a moral status standpoint?
• Sentience
• Sapience
• Future technology of uploading raises personal identity concerns
• Advanced AI systems could redesign themselves in a continuous cycle. How could
we ensure they won't harm humans?
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15. What's in the Future?
• Medical
• Predicting life expectancy of terminally ill patients
• Enhancing surgical procedures
• Narrowly selecting appropriate tests and treatments for ailing patients
• Military
• Machines that can "explain" themselves
• Finance
• Facial recognition for security measures
• Portfolio and Investment management
• Fraud detection
• User Technology
• Lip reading
• Voice-Language Translation
• Self-driving vehicles
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16. Gartner 2016 Technology Hype Curve
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17. Thank You
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