Symbolic AI algorithms have played an important role in AI's history, but they face challenges in learning on their own. After IBM Watson used symbolic reasoning to beat Brad Rutter and Ken Jennings at Jeopardy in 2011, the technology has been eclipsed by neural networks trained by deep learning. The power of neural networks is that they help automate the process of generating models of the world. This has led to several significant milestones in artificial intelligence, giving rise to deep learning models that, for example, could beat humans in progressively complex games, including Go and StarCraft. But it can be challenging to reuse these deep learning models or extend them to new domains. Now researchers and enterprises are looking for ways to bring neural networks and symbolic AI techniques together.

In February, after more than a year consulting with a range of experts, the Department of Defense (DoD) released five principles for ethics around artificial intelligence (AI). If AI doesn't meet these standards, the Department has said, it won't be fielded. "The United States, together with our allies and partners, must accelerate the adoption of AI and lead in its national security applications to maintain our strategic position, prevail on future battlefields, and safeguard the rules-based international order," Secretary Mark Esper said in the news release. The principles, which apply to combat and non-combat functions, are that AI must be the following: responsible, equitable, traceable, reliable, and governable. Such guidelines are relatively high level, though, leaving individual departments and agencies on their own to implement what each adjective means for a specific use case.