DeepMind AI’s new way of sorting objects could speed up global computation

Sorting algorithms are a vital part of computer science


An algorithm used trillions of times a day around the world could work up to 70% faster, thanks to an artificial intelligence created by the British company DeepMind. He found a better way for computers to sort data that has been overlooked by human programmers for decades.

“We honestly didn’t expect to get anything better. It’s a very short program. These types of programs have been studied for decades,” he says. Daniel Mankowitz at DeepMind.

Known as sorting algorithms, they are one of the workhorses of computation, used to organize data by alphabetizing words or ranking numbers from smallest to largest. There are many different sorting algorithms, but innovations are limited as they have been highly optimized over the decades.

Now, DeepMind has created an AI model called AlphaDev designed to discover new algorithms for completing a given task, in hopes of beating our existing efforts. Rather than modifying current algorithms, AlphaDev starts from scratch.

It uses assembly code, which is the intermediate computer language between human-written code and binary instruction sequences encoded in 0s and 1s. Assembly code can be laboriously read and understood by humans, but most of the software is written in a higher level language that is more intuitive before being translated, or “compiled”, into assembly code. DeepMind says that the assembly code gives AlphaDev more leeway to create more efficient algorithms.

The AI ​​is told to do so build an algorithm one statement at a time and test its output against a known good solution to make sure it’s building an effective method. It is also told to make the algorithm as short as possible. DeepMind says the task quickly becomes more difficult with larger problems, as the number of possible combinations of instructions can quickly approach the number of particles in the universe.

When asked to create a sorting algorithm, AlphaDev came up with one that was 70% faster than best for five-data lists and 1.7% faster for lists over 250,000 items.

“Initially we thought it was a bug or a bug or something, but as we analyzed the program, we realized that AlphaDev had actually figured out something faster,” Mankowitz says.

Since sorting algorithms are used in many common software, this improvement could have a significant cumulative effect globally. Such algorithms are so vital that they are written in code libraries that anyone can use, rather than writing their own. DeepMind has made its new algorithms open-source and included them in the commonly used Libc++ library, which means people can already use them today. This is the first change to this part of the sort algorithm library in over a decade, says DeepMind.

Mankowitz says Moore’s law — the idea that the amount of computing power on a single chip doubles at regular intervals — is coming to an end because miniaturization is reaching immutable physical limits, but that AlphaDev may be able to compensate for that improving efficiency.

“Today these algorithms are being mined [run in software] we estimate trillions of times every day and [are] capable of being used by millions of developers and businesses around the world,” says Mankowitz. try to do even more of these functions and have this as a path to unblock this bottleneck [of Moore’s law slowing].”

Mark Lee of the University of Birmingham in the UK says that AlphaDev is interesting and that a 1.7% speedup is also useful. But he says that even if similar efficiencies are found in other common algorithms, he’s skeptical that this approach will compensate for violating Moore’s Law, as he won’t be able to achieve the same benefits in more esoteric software.

“I think they’ll be able to do that with things like sorting algorithms and standard types of computation algorithms. But it won’t apply to…complex bits of code,” he says. “I think hardware augments will continue to outpace that.”


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