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Some historical perspective:Tom.G said:
How often will the number of elements being sorted be known at compile time?.Scott said:if you are sorting exactly 7 elements
I am writing assembly code at the moment because I demand time critical control of external signals while all interrupts are disabled, and exceptions impossible..Scott said:Coding at the Assembly level was once a common optimization practice.
Sometimes it is very, very stable. The number of hours in a day hasn't changed since the invention of computers. The number of states in the US has been stable for more than half a century. Even the number of schools in the Big Ten is constant on the time scale of compilimg.PeterDonis said:How often will the number of elements being sorted be known at compile time?
An easy example that answers your question, but not the general point:PeterDonis said:How often will the number of elements being sorted be known at compile time?
And sometimes it does.Vanadium 50 said:It does no good to spend an hour trying to speed up a sort by 30 minutes. It may not even make sense to hire a team of programmers to speed up a sort by 30 minutes compared to just getting a faster computer.
DeepMind AI develops efficient sorting algorithms through a process known as reinforcement learning. The AI is trained on a large dataset of examples and learns to optimize its sorting algorithms based on feedback received during training.
The benefits of using DeepMind AI for developing sorting algorithms include the ability to quickly and efficiently optimize algorithms, leading to faster and more accurate sorting results. Additionally, the AI can adapt to different types of data and sorting tasks, making it versatile and adaptable.
Yes, DeepMind AI has been shown to outperform traditional sorting algorithms in terms of efficiency and speed. The AI is able to learn and adapt to different types of data and sorting tasks, allowing it to optimize its algorithms for specific scenarios.
DeepMind AI is known for its advanced capabilities in developing sorting algorithms, outperforming many other AI systems in terms of efficiency and accuracy. The AI's use of reinforcement learning allows it to quickly adapt and optimize its algorithms for various sorting tasks.
The potential applications of DeepMind AI-developed sorting algorithms are vast and varied, including data processing, image recognition, natural language processing, and more. These algorithms can be used to optimize and streamline a wide range of tasks that involve sorting and organizing data.