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The Ethics of Machine Learning/AI - Brent M. Eastwood

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Artificial Intelligence and machine learning has the potential for greater good and danger itself. Eastwood questions the ethics behind machine learning and artificial intelligence.

Brent M. Eastwood, PhD

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The Ethics of Machine Learning/AI - Brent M. Eastwood

  1. 1. The ETHICS of machine learning/AI Brent M. Eastwood, PhD
  2. 2. How do we maintain control over the machine?
  3. 3. Occam’s Razor “Other things being equal, simpler explanations are generally better than more complex ones“ Parsimony – Computer Science – use simple models with few rules and few parameters; fewer lines of code Theoretical Computer Science - you have parsimonious reductions (counting problem of solutions for Search) High R²
  4. 4. Control is easier when the machine is a 12-Year-Old It can do a repetitive simple chore very well For AI – simple is a robot vacuuming the house  But it can learn the types of dirt and stains the vacuum picks up For ML – simple control is data science
  5. 5. Explanatory Variables  The Machine as a 12-Year-old  Parsimony and Elegance in Machine Learning • Spark MLLib (code provided by Apache Spark) Occam’s Razor Parsimony Elegance
  6. 6. Controlling a complex machine This is where human morality, ethics and virtue come in The ethical human is more likely to train the machine ethically
  7. 7. Elon Musk ARTIFICIAL INTELLIGENCE IS OUR BIGGEST EXISTENTIAL THREAT
  8. 8. The Turing Test: Can a machine think? “If the output of the machine is indistinguishable from that of a human brain, then we have no meaningful reason to insist that the machine is not thinking.” The Innovators by Walter Isaacson, pg. 124
  9. 9. The Imitation Game: SQL vs. No SQL • NoSQL Cluster Data Monster • Spark • MongoDB • Kafka • Cassandra • Elasticsearch • The 4 V’s of Big Data • MLlib • MySQL Kitty Cat • Relational and Structured Database • Vertical scaling • MySQL • SparkSQL (“Dataframes”) • RMySQL • Various Cloud SQLs
  10. 10. Do you ignore certain types of data?  Room A – The NoSQl Data Monster  Room B – MySQL Kitty Cat  Room C- The Human Execute the “Big Data” Turing Test
  11. 11.  IBM Watson Health usually plays nice with doctors  Instead of basically saying “I’m correct…do this”  “There is a 40% probability that you will like this recommendation.”  Take a look at what I say and check for yourself  Human – Computer Hybrid Augmented Intelligence What if we just make the machine collegial?
  12. 12. RoboEthics: The TARS Humor Setting “Hey TARS, bring the humor setting down to 75”
  13. 13. TARS Is a Bad Boy TARS: “I have plenty of slaves for my robot colony.” TARS: “You can use it to make your way back to the ship when I blow you out of the airlock
  14. 14. TARS is a Good Boy - But is collegial AI the same as ethical AI?
  15. 15. The Ethics of The 'Singularity‘ Commentary-January 23, 2015 Alva Noe
  16. 16. Keep it simple and elegant Train the 12-year-old Human is the “Hero” (ethical and virtuous training) Is it our “biggest existential threat?” Conclusi on
  17. 17. Thank you! Brent M. Eastwood, PhD @BMEastwood BrentEastwood.com @GovBrain Washington, DC

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