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Rather than viewing generative AI as a form of artificial intelligence, I posit that it should be seen as an automated tool for tapping into human cultural, lin
by K0balt 3y ago
Rather than viewing generative AI as a form of artificial intelligence, I posit that it should be seen as an automated tool for tapping into human cultural, linguistic, and empirical knowledge.
Data and computation are two sides of the same coin. The 'intelligence' in AI is embedded within the data, with the computational model serving as a tool to access and express this inherent intelligence.
I would argue for a change in perspective towards AI, one that recognizes LLMs as powerful tools for accessing the vast wealth of human cultural knowledge rather than viewing them as a separate form of intelligence. We must carefully consider critical ethical considerations about control, access, and trust that will become increasingly relevant as these tools become more integrated into our everyday lives.
This paradigm shift carries some implications:
LLMs will not achieve superintelligence: Although these models can process information quickly and access a wide range of knowledge, they lack the superior reasoning or inference abilities that would classify them as superintelligence.
LLMs as an extension of human thought: These models can automate and amplify human capabilities but do not introduce new abilities beyond what is already present in human thought processes.
LLMs as mirrors of human culture and knowledge: These models reflect the recorded artifacts of human language, art, and culture. They can make the inherent intelligence within these artifacts accessible, providing a vast information resource.
Implications for the future: Access to this "memetic matrix" of human knowledge will become a fundamental part of being human as these tools become more integrated into our lives, bringing up issues of ownership, access, and the potential for misuse.
Thought consolidation and control of inference engines: There's a potential risk that control of inference engines by a small number of companies could lead to a consolidation of thought that threatens democratic governance. I propose a diversity of federated or self-hosted inference tools as solutions to mitigate this risk.
The necessity for trust and individuality: As these tools become more influential in our lives, maintaining trust in our individual thoughts and avoiding the uncritical acceptance of synthesized ideas from sources with opaque motives will become increasingly important.
Synthetic Inference relies on a vast cultural commons: We cannot allow these commons to be closed off and owned by a few big companies. This resource is to approximate totality of all human knowledge, language, and culture. It belongs to all of humanity. Training data must be open, free, and available for examination.
- Davidzheng 3y agoHow can you be confident that by scaling up the models their reasoning skills will not surpass humans
- K0balt 3y agoBecause, ultimately, the models are modelling human reasoning. Not only that we do not have any evidence to support the idea that reasoning has a higher quality than we can apply to it using logical processes. It can be done faster, it can be done many times concurrently but ultimately 1 || 1 !& 1 is going to be have the same answer. The whole idea of superintelligence except as a measure of speed or quantity seems flawed on its face. And if we are to call speed or concurrency superintelligence, then we have already hit that mark some time ago. AI wins at games because of time compression, not because of an inherently superior logic. They just have time to consider more potential outcomes.
- seanhunter 3y agoAI research went off the idea of modelling human reasoning a long time ago. To the extent that current models reproduce reasoning in any sense comparable to a human it is purely for the purpose of user interface. For example if you ask a transformer model like chatgpt to solve some problem and explain its reasoning step by step it will give you a reasonable facsimile of a human thought process, but if you’ve read the transformers paper, you know that its actual process is tokenize -> do a bunch of matrix math -> decode the result into words (entirely different to how a human reasoning process works). Although things like neural nets were clearly inspired by biology they work completely differently to a biological brain. It would be closer to truth to say that the models use linear algebra and optimization to improve performance at specific tasks. For that reason, the whole debate about whether or not superintelligence is possible/has been achieved etc boils down to an argument about the definition of intelligence (as Turing predicted so long ago).
- K0balt 3y agoI don’t think it’s a foregone conclusion that human reasoning doesn’t operate on the basis of statistical prediction of the next most probable “token” at its most granular level. Humans are certainly capable of hallucination in the LLM sense, and without training we often struggle to produce (or even outright fabricate) the rationale behind our “conclusions”. We stopped intentionally modelling human reasoning because we have no clue how it works. We roughly copied the physical devices then figured out how to slap them together with an algorithm that makes them behave similarly to human thought, when trained with a massive quantity of cultural-linguistic-memetic data. It should not be surprising that if you take a bucketful of engine parts and keep messing with them until they kinda work as an engine that you will probably wind up with something along similar lines as the original intention of the parts. Frankly, it would be quite surprising if you didn’t. Biological neural networks are fundamentally linear algebra processing device that integrate data into functions through training, so the fact that we understand the process as linear algebra is actually an argument that the process is similar. As to whether “superintelligence” can exist, I think we are in exactly the same page there. It is a matter of definition. Personally I doubt the existence of a fundamentally superior system of logic type of superintelligence, but certainly a device can made to iterate faster and flawless memory and instant, accurate calculation is going to beat the hell out of my notebook and HP28s lol. I suspect that superintelligence can be (has been?) achieved in some respects, but in the same sense that a room full of ten educated people with regular computing tools is “superintelligence”.