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Machine/Human Integrated Learning Technologies III

An overview of approaches to Machine Learning, an area in rapid evolution. Notice the structuring into Supervised, Unsupervised and Reinforced Learning. Associated to each of these a few areas of application. Image credit: TechLeer

There is also a learning that requires the “building” of knowledge. You learn something that -basically- did not existed before you thought about it. Research is an example, finding the demonstration to a new theorem is another example.

It is a time consuming process as we know very well. Autonomous systems, equipped with deep learning technology, are able to explore new ways and create knowledge and can do that faster than humans. We have software that can demonstrate theorems that have not been demonstrated before, software that can play a game (like Go) creating new strategies that it has not “learned” from any book (or observing any other entity doing it).

An autonomous system “brain” can learn by “arguing” with itself, like AlphaGo did to get better at Go. It started with the “normal” learning process, by looking at what good players do, then it started to play against itself thousands of games learning from their outcome and getting smarter and smarter through a process of “deep reinforced learning”. AlphaGo neural networks were trained on over 30 million moves actually made by Go players, becoming able to predict with a 57% accuracy the move a player would execute. This is also an interesting capability for an autonomous system: predicting what may happen next. Then it started playing thousands of games against itself trying new strategies and reinforcing the ones that proved successful.

The possibility for an autonomous system to “autonomously” learn opens up the issue of losing control on the system itself, i.e. in a while the system may learn and therefore act in ways that have not been “designed”, nor, potentially, envisaged.

Collective learning, also called “ensemble learning”, will become more and more common. It is already a reality with Tesla cars. The autopilot system on a Tesla car has been programmed to learn as it gets more and more experience. In addition, since 2016, each Tesla car reports on a daily bases its “experience” and this creates a collective experience that greatly increases the learning speed of each car. The collective experience is processed centrally and emerging “lessons” are then distributed to all cars. It is like each car, every day, would drive over 1 million miles (the Tesla “fleet” is driving every day over 1.6 million miles. Clearly several cars are driving along the same road. Still, they are driving it at different times so they will acquire different experiences), clearly harvesting a huge experience.

There are a host of technologies that are being used and experimented in the autonomous systems learning, and that are contributing to this area, including:

Hitting the market

  • Ensemble learning
  • Convolutional networks
  • Video Image analytics (learning from image analyses)
  • Simulation

Peak of expectation

  • Deep learning
  • Cognitive computing
  • Prescriptive analytics
  • Augmented data discovery
  • Graph analytics
  • Predictive analyses
  • Data lakes

On the rise

  • Human in the loop crowdsourcing
  • Artificial general intelligence
  • Conversational analytics
  • Embedded analytics
  • IoT Edge analytics
  • Advanced anomaly detection
  • Citizen data science

Notice among the technologies on the rise the “human in the loop crowdsourcing” that directly connects to the learning of symbiotic autonomous systems.

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. He's currently the Chair of the Symbiotic Autonomous Systems Initiative of IEEE-FDC. Until April 2017 he led the EIT Digital Italian Node and up to September 2018 he was the Head of the EIT Digital Industrial Doctoral School. Previously, up to December 2011, he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the Industry Advisory Board within the Future Directions Committee. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books. He writes a daily blog,  http://sites.ieee.org/futuredirections/category/blog/, with commentary on innovation in various technology and market areas.

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