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There is a lot of factors for the boom of AI industry. What many people miss though is the boom has mostly been in the Machine Learning part of AI. This can be attributed to various simple reasons along with their comparisons during earlier times: Mathematics: The maths behind ML algorithms are pretty simple and known for a long time (whether it would work ...


4

The ultimate goal of machine learning is to bypass the developer... When we will have a "master algorithm" that can learn how to generalize any function or algorithm from examples, it can essentially replace any developer, skip the 'development" stage, going from problem directly to algorithm. We can't know when this will happen, but as we surrounded with ...


3

Artificial intelligence and, in particular, machine learning (ML) and evolutionary algorithms are already being used to automate the task of software development and, in particular, software testing. For example, take a look at the paper An empirical evaluation of evolutionary algorithms for unit test suite generation (2018). There are other examples, such ...


2

GPUs were ideal for AI boom becouse They hit the right time AI has been researched for a LONG time. Almost half a century. However, that was all exploration of how would algorithms work and look. When NV saw that the AI is about to go mainstream, they looked at their GPUs and realized that the huge parellel processing power, with relative ease of ...


2

I think you should be fine if you are using other people's tried and tested algorithms and have a reasonable understanding of machine learning best practices - how to clean and balance your dataset and how to effectively measure performance. Maybe if you are trying to come up with new algorithms / DNN architectures you will struggle.


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