Artificial General Intelligence (AGI) is a machine’s ability to understand or learn any intellectual task that a human being would do. While the above applications demonstrate AI’s ability to execute tasks with greater efficacy than humans, they are not necessarily intelligent — they might be very good in one domain but of no use outside their niche. As a result, while an AI-powered system can be as good as a hundred highly qualified humans at one task, it may prove less capable than a ten-year-old child at another. An individual, on the other hand, can perform a wide variety of tasks compared to AI-powered systems but at a definitively lower efficacy.
Researchers have a strong belief that “transfer learning” would play a major role in the successful implementation of Artificial General Intelligence (AGI). Demis Hassabis of DeepMind, calls “transfer learning” as the key to general intelligence. Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task. The idea is that with this precedent knowledge learned from the first task, AI systems will perform better, train faster and require less labeled data than a new neural network trained from scratch on the second related task.
For a detailed view, check out: https://blogs.cisco.com/analytics-automation/is-artificial-general-intelligence-around-the-corner