Well, it's a real challenge. One of the problems with AI systems is their intelligence can be very narrow, and so they can be very good at a task that they were trained to do, but if the environment or their context for use changes ever so slightly, their performance can drop off fairly dramatically. So one of the earlier versions of Alpha Go reportedly, if you slightly change the size of the board, its performance will drop off. It didn't have the ability to generalize its knowledge about Go even to just a differently sized board. That's a real problem when you think about, for example, a military environment where we don't get to control where we're going to operate or who we're going to fight against, the enemy's not going to give you a preview of their tactics and the enemy's adaptive. The final battlefield is also an area ripe for reform. Ultimately, it is not the technology itself that will determine success; it depends on institutions that adapt their processes, metrics, and structures to best apply AI and machine learning. The author suggests we have a ways to go if we want to move past hardware and platforms and accept data and algorithms as units of combat power. More critically, he excoriates the bureaucratic processes that retard agile development of AI capabilities. The greatest barrier to adoption is not computing power or creative new algorithms. The most significant hurdle is the government’s own acquisition bureaucracy and red tape. An encrusted system designed to eradicate risk and curtail budgetary fraud extends the proverbial “valley of death” for startup companies and strangles them in the cradle as they try to scale up. To Scharre, the government’s own system is more lethal to our success at innovation than any “pacing threat.” Artificial intelligence has already brought us killer robots, chat bots that can pen government speeches, programs that can process data faster than our mammalian minds, and software that can make apparently original art upon request.

This book will likely help the military keep from falling too far behind in its understanding of AI. Military units almost always have a leader directly commanding less than 18 subordinates. AIs will likely have the capacity to coordinate a much larger set of units, which will presumably enable new tactics. How significant is that? My intuition says it will change war in some important way, but the book left me without any vision of that impact.What's the connection between Orwellian developments in China and the future of war? Scharre's patterns here suggest that he's mostly focused on convincing the US to go to war with China. In particular, head-to-head gunshots are actually banned in training by human pilots because there's a high risk of collision if the pilot is trying to maneuver the aircraft when you're racing each other hundreds of miles an hour. And it's extremely difficult to do in any case and requires superhuman levels of precision, but none of that was a problem for the AI agent. It can achieve these split-second shots while also avoiding a collision. And the AI agent learned to do this all on its own — it wasn't trained to do that. The AI system that won was trained in a simulator and had over 30 years of simulated flying time and this was one of the things that it simply learned on its own from all of these years of simulated dogfights. It’s probably a relatively level playing field between the U.S. and China, and what’s going to matter more is whether companies or government institutions have the ability to refine the data that they have and apply it toward machine learning applications. The title's battlegrounds refer to data, compute, talent, and institutions. Those seem like important resources that will influence military outcomes. But it seems odd to label them as battlegrounds. Wouldn't resources be a better description?

Over time, regulation in some fashion of AI technology; probably much of which will be sector-specific. The regulations for AI in medical applications will fall within the sort of the broader paradigm of how we regulate tools and safety for medical devices, and the way that we regulate AI in finance applications will fall from how we regulate trading and other financial things. But I do think that AI doesn't get a pass on regulations and it's worth reflecting on the fact that we only have clean air and water, and safe highways, and safe air travel, and safe food to eat in America because of government regulation of industry. An award-winning defense expert tells the story of today’s great power rivalry—the struggle to control artificial intelligence.** Four Battlegrounds argues that four key elements define this struggle: data, computing power, talent, and institutions. Data is a vital resource like coal or oil, but it must be collected and refined. Advanced computer chips are the essence of computing power—control over chip supply chains grants leverage over rivals. Talent is about people: which country attracts the best researchers and most advanced technology companies? The fourth “battlefield” is maybe the most critical: the ultimate global leader in AI will have institutions that effectively incorporate AI into their economy, society, and especially their military. An award-winning defense expert tells the story of today’s great power rivalry—the struggle to control artificial intelligence. It’s very possible that we end up in a place where countries are building and deploying quite dangerous AI weapons, and I think that’s something we need to think about and guard against.” Paul ScharreA bigger concern is that the best tech companies in the world don’t know how to make these systems safe. It’s not necessarily that these chatbots are claiming to be in love with people or arguing with them. The problem is that some of the best AI scientists don’t know how to make it stop doing that. The solution that Microsoft has put in place right now, which is basically to cut off the conversation after a few replies, is very much a band aid solution. But if countries are not interested in going down the surveillance road the way the Chinese have, it's less important to be at the cutting edge of AI in this space. Correct?