The human brain didn't design itself either - it's the product of millions of years of evolution via natural selection. But this fact is irrelevant to the topic of whether the approach of DeepMind is fundamentally different from the approach of Deep Blue.

To help you appreciate the difference, try answering this question: Were experts players of Pong, Space Invaders or any other video games consulted to contribute strategies for winning those particular games? Was a redesign required to play a new, different game?

If not, you'll need to tell me where the knowledge of how to win at those games came from. I hope you'll appreciate that the ability to autonomously obtain that knowledge by trial and error rather than design and programming constitutes the fundamental difference.

Any actual chess knowledge in Deep Blue was an optimization. The fundamental algorithm is alpha-beta search, which can learn to play many, many games of strategy. Not Go, as we know now, but the exceptions are few. This is an algorithm that is very adaptable and very good at learning to play games based on a heuristic for whether you are winning.

The DeepMind implementation learns how to play Atari games. You could even say that it's learning how to play one game, where your opponent chooses the game cartridge and your goal is to increment the score counter.

You would absolutely need to redesign DeepMind to play Super Mario Bros.

When you give DeepMind credit for designing itself, you are glossing over the fact that it was programmed by people, most likely for long hours.