In Part 1, we mentioned that since the Industrial Revolution, new jobs and industries were spawned due to automation. So how is it different this time? From the above-mentioned examples, AI has evolved from manual repetitive machines to human equivalent systems. Observational Learning is a unique trait that makes the human being unique and enables them to dominate the planet. Machine Learning, on the other hand, allows for AI to learn from data, identify patterns and make decisions, without being explicitly programmed. This allows machines to shift from being a specialised machine to a general-purpose machine. Computing was a big thing in the 1950s. However, it was costly and it only did a specific task. As the Central Processing Unit (CPU) got more efficient, its cost decreased. As it was increasingly designed for general purpose computing, the adoption rate of the personal computers increased exponentially. The same fate could be true for AI machines. As AI continues to progress in artificial general intelligence, AI would eventually be able to function over a large range of domains and dynamic situations, very much like a human.
While a human worker has increasing wages, falls sick and makes human errors, a machine robot could likely have a lower recurring cost and be more operationally efficient. In an idealistic world, the latter could be a more ideal candidate for any job – white-collar jobs such as doctors and programmers may not be spared either. Google’s AutoML system was developed as a solution to the lack of talents in the AI programming. The machine-learning codes made by the AutoML was found to better and more efficient than those made by the researchers who created the AutoML.