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> Does no one remember why expert systems fell apart? Many of the current generation of AI experts mostly either did not pay great attention to the history of
by solidasparagus 2y ago
> Does no one remember why expert systems fell apart?
Many of the current generation of AI experts mostly either did not pay great attention to the history of AI or they believe this time is completely different. They would do well to spend more time learning about history.
However, your view doesn't strike me as correct either. Expert system fell apart because the world was more complex than researchers realized and enumeration was essentially discovered to be infeasible (more or less as you say). But the impossibility of enumerating the world isn't news, everyone knows "the bitter lesson". And this isn't the past - now everyone on earth carries around a computer, a video camera and a microphone. They talk to each other through the internet. Remote workers screens' are recorded. Billions of vehicles with absurd numbers of sensors are roaming around the world. More of the arenas that matter to humanity are digital and thus effective domains for automated exploration and data generation.
The information about how the world operates exists or can be generated, the only real question is how to get your hands on it.
- discreteevent 2y ago> The information about how the world operates exists or can be generated, the only real question is how to get your hands on it. I'm sure I could read all the information for an astrophysics course in a relatively short time. Understanding it is a different matter.
- solidasparagus 2y agoUnderstanding is a loaded term. But large transformers seem pretty good at learning from datasets (of more or less any modality) to they extent that they can create useful new datapoints and allow you to work with existing datapoints in useful, structured ways.
- Jensson 2y agoTransformers main usefulness is translation, including translations from technical lingo to natural languages and vice versa, or code to natural language etc. That was their targeted usecase when originally made and that is what most people use them for. This is what you use them for that doesn't require hallucinating. They don't understand so they can't replace technical experts, they can just translate a bit back and forth but you still need an expert who understands the domain since translations isn't enough for a layperson to replace an expert. The other use case is to make them hallucinate tropey stories and concepts for brainstorming, this isn't nearly as useful though due to it adding so much low quality stuff here when it hallucinates.
- zer00eyz 2y ago> The information about how the world operates exists or can be generated The hubris of mathematics. At what scale does whether prediction become 100 percent accurate? How large of a model do you need, and how big of a computer to run it? Do we thing that reducing the world to a model and feeding it through (what isnt even close to a model) of "thought" or "interaction" or ... what ever you want to bill and LLM as is going to be any more accurate than weather prediction?
- solidasparagus 2y ago100 percent accurate will never happen, nor does it need to. But think about the intelligence of an average human. Can we beat that? At least along some collection of concrete axes, enough to create a form of intelligence that can rightfully be called general? It remains to be seen, but the days where I scoffed at that idea are firmly in the past where they belong. Today we are building machines with intelligence high enough that it is forcing us to reconsider and redefine what intelligence is. And there is a huge amount of progress just sitting in front of us, waiting to be fed into models.
- sainez 2y agoEven AGI as a whole is overhyped. It's a valuable goal, but AI that beats humans on narrow metrics is still economically valuable because of scale.
- ambicapter 2y agoAre you arguing that weather prediction isn't very good right now? Within a day or two it's very, very good, no?