Artificial intelligence (AI) software and hardware advancements are resulting in a slew of smart gadgets that can detect and react to sights, sounds, and other patterns without the need for a constant internet connection.
Nowadays, researches are looking for adaptation of Machine Learning algorithms to testing processes to reduce the manual effort and improve quality. In this talk, we will discuss in detail Machine Learning practices with a case study. Testing efforts are potentially able to be minimized in all stages.
I aim to demonstrate how ML helps in all stages. In this manner, I summarize the application areas with algorithms and discuss the advantages and potential risks of AI applications in software testing.
- MNIST
- Autopilot (NVIDIA)
- Emojinator
- Malaria Detection
- Quick, Draw (Google)
So to pinpoint the techniques for minimization of CPU resources, I am going to discuss
Code formatting is an opinionated beast. It always has been a matter of taste, and it always will be a matter of taste. This is the reason, why professional formatting tools, such as Eclipse JDT, offer a gazillion number of options. Which is still not sufficient enough. After all, you can override them inline with tag-comments to make the formatter shut up. Can't we do better than that? What if we could use machine learning techniques to detect the preferred code style that was used in a codebase so far? Turned out, we can.
Teaser: Today, the development of automotive software functions based on artificial intelligence still needs a lot of different tools, languages and frameworks.
In this talk, we introduce how we built upon the Eclipse eco-system an integrated development environment called "DAISY" (Development environment for Artificial Intelligence SYstems).
