The Impact of MuZero and How AI Could Change Strategy Making

Until a few years ago, the only AI agent capable of strategy making was OpenAI’s multi-agent system. OpenAI Five was capable of playing the complex strategy game Dota2 at superhuman levels. While the OpenAI Five engineers coded the various features, rules and reward functions of the game, it learned by playing against itself; 180 years per day. It was a remarkable experience in collaboration and strategy making by an AI agent.

Recently, the engineers of Deepmind went a step further in the development of a general-purpose algorithm.

Five years in the making, starting with AlphaGo in 2016, at the end of 2020, Deepmind published a paper in Nature describing a computer program that can learn to play games without knowing the rules. This ground-breaking development will most likely be as fundamental as the development of AlphaGo back in 2016. The algorithm can plan winning strategies in unknown environments and can simply learn by doing.

The algorithm, called MuZero, managed to outperform all prior algorithms in the 57 Atari games while matching the superhuman capabilities of AlphaGo in games like Go, Chess or Shogi. These prior algorithms all relied on knowledge embedded by the developers on the dynamics and rules of the environment.

Reinforcement Learning Capabilities

MuZero is a significant step forward as it can develop winning strategies in a completely unknown environment. Deepmind’s research showed that its playing strength increased as the time available to plan a move increased as it could consider more simulations per move.

The extensive reinforcement learning capabilities of MuZero shows that algorithms can also learn from messy real-life environments when the rules are not very clear. This could help organisations tackle new (strategic) challenges ranging from logistics, manufacturing, robotics to self-driving cars. The better we become at creating algorithms that can learn from their environment, the closer we get to general-purpose algorithms (which is still a far shot from Artificial General Intelligence).

The Impact of AI on Strategy

However, suppose we want to apply AI in strategy-making. In that case, it becomes a lot more difficult as the rules are often not very clear, and there are many dependencies, i.e. it is a fuzzy environment. Executives that need to make strategic decisions need to base those decisions on many information streams, ranging from customer data, changes in the market or supply chains, macroeconomic conditions, etc. With the information overload that we already experience, making the most optimal decisions can be difficult.

With the advances made by Deepmind with MuZero, applying AI within strategy-making can become more common. Although, I would avoid having AI make autonomous strategic decisions. Instead, organisations should focus on human-machine symbiosis, where AI gives recommendations, and humans make the decisions. Strategy-making is, therefore, a clear example where human-machine collaboration could thrive, resulting in better strategic decisions and outcomes for the business.

Final Thoughts

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Dr Mark van Rijmenam is The Digital Speaker and he offers inspirational (virtual) keynotes on the future of work, either in-person, as an avatar or as a hologram, bringing your event to the next level:

I think about technology & the impact on business & society. Author of 3 mgt books. Founder of Datafloq.com & fighting fake news, bots and trolls with Mavin.org