Stochastic Parrot
For what it’s worth: en.wikipedia.org/wiki/Stochastic_parrot
The term was first used in the paper “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜” by Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell (using the pseudonym “Shmargaret Shmitchell”). The paper covered the risks of very large language models, regarding their environmental and financial costs, inscrutability leading to unknown dangerous biases, the inability of the models to understand the concepts underlying what they learn, and the potential for using them to deceive people. The paper and subsequent events resulted in Gebru and Mitchell losing their jobs at Google, and a subsequent protest by Google employees.
0ops@lemm.ee 6 months ago
I feel like this is going to become the next step in science history where once again, we reluctantly accept that homo sapiens are not at the center of the universe. I’m I conscious? Am I not a sophisticated prediction algorithm, albiet with more dimensions of input and output? Please, someone prove it
I’m not saying, and I don’t believe that chatgtp is comparable to human-level consciousness yet, but honestly I think that we’re way closer than many people give us credit for. The neutral networks we’ve built so far train on very specific and particular data for a matter of hours. My nervous system been collecting data from dozens of senses 24/7 since embryo, and that doesn’t include hard-coded instinct, arguably “trained” via evolution itself. How could a llm understand an entity in terms outside of language? How can you understand an entity in terms outside of your own senses?
rambaroo@lemmy.world 6 months ago
ChatGPT is not consciousness. It’s literally just a language model that’s spent countless hours learning how to mimic human language.
lolcatnip@reddthat.com 6 months ago
The thing about saying something is or isn’t conscious is that we don’t have any good theory of what consciousness even is. It’s not something we can measure. The only way we can assure ourselves that other people are conscious is that they claim to be conscious in ways we find convincing and otherwise behave in ways we associate with our own consciousness.
I can’t think of any reason why a lump of silicon should attain consciousness because you ran the right program on it, but I also can’t see why a blob of cells should be conscious either. I also can’t think of any reason why we’d be aware of it if a lump of silicon did become conscious.
0ops@lemm.ee 6 months ago
A.) Do you have proof for all of these claims about what llm’s aren’t, with definitions for key terms? B.) Do you have proof that these claims don’t apply to yourself? We can’t base our understanding of intelligence, artificial or biological, on circular reasoning and ancient assumptions.
That’s correct, hence why I said that chatGPT isn’t there yet. What are you without input though? Is a human nervous system floating in a vacuum conscious? What could it have possibly learned? It doesn’t even have the concept of having sensations at all, let alone vision, let alone the ability to visualize anything specific. What are you without an environment to take input from and manipulate/output to in turn?
sunbeam60@lemmy.one 6 months ago
I’d give you two upvotes if I could.
We know how a neural network works in the brain. Unless you’re religious and believe in a soul, you’ve only got the reward model and any in-born setup left.
My belief is the consciousness is just the mind receiving a significant amount of constant input and reacting to it. We refuse to feel an LLM is conscious because it receives extremely little input (and probably that it isn’t simulating a neural network as large as ours, yet).
Sekoia@lemmy.blahaj.zone 6 months ago
Neural networks are named like that because they’re based on a model of neurons from the 50s, which was then adapted further to work better with computers (so it doesn’t resemble the model much anymore anyway). A more accurate term is Multi-Layer Perceptron.
We now know this model is… effectively completely wrong.
Additionally, the main part (or glue, really) of LLMs is not even an MLP, but a “self-attention” layer. You can’t say LLMs work like a brain, because they don’t. The rest is debatable but it’s important to remember that there are billions of dollars of value in selling the dream of conscious AI.
0ops@lemm.ee 6 months ago
I’m with you that LLM’s don’t work like the human brain. They were built for a very specific task. But that’s a model architecture problem (and being gimped by having only two dimension of awareness, arguably two if you count “self attention” another limiting factor in it’s depth of understanding, see my post history if you want). I wouldn’t bet against us making it to agi however we define it through incremental improvements over the next decade or two.
grabyourmotherskeys@lemmy.world 6 months ago
One of the things our sensory system and brain do is limit our input. The road to agi might involve giving it everything and finding the optimum set of filters, not selecting input and training up from that.
You’d need the baseline set of systems (“baby agi”) and then turn it loose with goal seeking.
sunbeam60@lemmy.one 6 months ago
Yup, broadly agreed. I’m not saying “give it everything”. I’m sure regions would develop to simplify processing via filtering.
0ops@lemm.ee 6 months ago
Actually, most models are already doing some form of filtering AFAIK, but I don’t know how comparable it is to our sensory system. CNN’s, for example, work the way our eyes work. The short of it is image data goes through a few layers, each node in the next layer collecting the aggregate data of several from the last (usually a 3x3) grid. Each of these layers has filters to determine the output of that node, which need to be trained to collectively recognize specific patterns in the data, like a dog. Source: lecture notes and homework from my applied neural networks class