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Artificial General Intelligence is a huge topic right now even though no one has agreed what AGI really is. Some scientists think its still hundreds of years away and would need tech that we cant even begin to imagine yet, while Google DeepMind says it could be here by 2030 and its already planning safety measures. Its not uncommon for the science community to disagree on topics like this, and its good to have all of our bases covered with people planning for both the immediate future and the distant future. Still, five years is a pretty shocking number. Recommended VideosRight now, the frontier AI projects known to the public are all LLMs fancy little word guessers and image generators. ChatGPT, for example, is still terrible at math, and every model Ive ever tried is awful at listening to instructions and editing their responses accurately. Anthropics Claude still hasnt beaten Pokmon and as impressive as the language skills of these models are, theyre still trained on all the worst writers in the world and have picked up plenty of bad habits. Its hard to imagine jumping from what we have now to something that, in DeepMinds words, displays capabilities that match or exceed that of the 99th percentile of skilled adults. In other words, DeepMind thinks that AGI will be as smart or smarter than the top 1% of humans in the world. So, what kind of risks does DeepMind think an Einstein-level AGI could pose? According to the paper, we have four main categories: misuse, misalignment, mistakes, and structural risks. They were so close to four Ms, thats a shame. DeepMind considers misuse to be things like influencing political races with deepfake videos or impersonating people during scams. It mentions in the conclusion that its approach to safety centers around blocking malicious actors access to dangerous capabilities. That sounds great, but DeepMind is a part of Google and there are plenty of people who would consider the U.S. tech giant to be a potential bad actor itself. Sure, Google hopefully wont try to steal money from elderly people by impersonating their grandchildren but that doesnt mean it wont use AGI to bring itself profit while ignoring consumers best interests. It looks like misalignment is the Terminator situation, where we ask the AI for one thing and it just does something completely different. That one is a little bit uncomfortable to think about. DeepMind says the best way to counter this is to make sure we understand how our AI systems work in as much detail as possible, so we can tell when something is going wrong, where its going wrong, and how to fix it. This goes against the whole spontaneous emergence of capabilities and the concept that AGI will be so complex that we wont know how it works. Instead, if we want to stay safe, we need to make sure we do know whats going on. I dont know how hard that will be but it definitely makes sense to try. The last two categories refer to accidental harm either mistakes on the AIs part or things just getting messy when too many people are involved. For this, we need to make sure we have systems in place that approve the actions an AGI wants to take and prevent different people from pulling it in opposite directions. While DeepMinds paper is completely exploratory, it seems there are already plenty of ways we can imagine AGI going wrong. This isnt as bad as it sounds the problems we can imagine are the problems we can best prepare for. Its the problems we dont anticipate that are scarier, so lets hope were not missing anything big. Editors RecommendationsWatch Google DeepMinds robotic ping-pong player take on humans