IEEE Future Directions
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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.
