Mind-reading AI can translate brainwaves into written text: Using only a sensor-filled helmet combined with artificial intelligence, a team of scientists has announced they can turn a person’s thou…::A system that records the brain’s electrical activity through the scalp can turn thoughts into words with help from a large language model – but the results are far from perfect
I think your mixing up sentience / consciousness with intelligence. What is consciousness doesn’t have a good answer right now and like you said philosophers, computer scientists and neurologist can’t come to a clear answer but most think llms aren’t conscious.
Intelligence on the other hand does have more concrete definitions that at least computer scientists use that usually revolve around the ability to solve diverse problems and answer questions outside of the entities original training set / database. Yes doing an SAT test with the answer key isn’t intelligent because that’s in your “database” and is just a matter of copying over the answers. LLMs don’t do this though, it doesn’t do a lookup of past SAT questions it’s seen and answer it, it uses some process of “reasoning” to do it. If you gave an LLM an SAT question that was not in it’s original training set it would probably still answer it correctly.
That isn’t to say that LLMs are the be all and end all of intelligence, there are different types of intelligence corresponding to the set of problems that intelligence is solving. A plant identification A.I. is intelligent for being able to identify various plants in different scenarios but it completely lacks any emotional, conversational intelligence, etc. The same can be said of a botanist who also may be able to identify plants but may lack some artistic intelligence to depict them. Intelligence comes in many forms.
Different tests can measure different forms of intelligence. The SAT measures a couple like reasoning, rhetoric, scientific etc. The turing test measures conversational intelligence , and the article you showed doesn’t seem to show a quote from him saying that it doesn’t measure intelligence, but turing would probably agree it doesn’t measure some sort of general intelligence, just one facet.
The “reasoning” in LLM is literally statistical probability of which word would follow which word. It has no real concept of what it talks about beyond the pre-built relationship matrices between words and language rules. That’s why LLMs confidently hallucinate obvious bullshit time to time - to them there’s no meaning to either truthful or absolute bonkers text, it’s just words that should probably follow each other.
All inference is just statistical probability. Every answer you give outside of your direct experience is just you infering what might be the answer. Even things we hold as verifiable truth that we haven’t experienced is just a guess that the person who told it to us isn’t lying or has some sort of proof to there statement.
Take some piece of knowledge like “Biden won the 2020 election” me and you would probably agree this is the truth, but we can’t possibly “know” it’s the truth or connect it to some verifiable experience, we never counted every ballot or were at every polling station. We “know” it’s the truth because more people, and more respectable people, told us it was and our brain makes a statistical guess that their answer is right based on their weight. Just like an LLM other people will hallucinate or bullshit and come on the other side of that guess and assert the opposite and even make up stuff to go along with that story.
This in essence is what reasoning is, you weigh the possibilities of either side being correct, and pick the one that has more weight. That’s why science, an epistemological application of reason, is so heavily reliant on statistics…
You’ve now reduced the “process of reasoning” to hitting the autocomplete button until your keyboard spits out an answer from a database of prior conversations. It might be cleverly designed, but generative models are no more intelligent than an answer key or a library’s card catalog. Any “intelligence” they appear to encode actually comes from the people who did the work to assemble the training database.
This is not how LLMs work, they are not a database nor do they have access to one. They are a trained neural net with a set of weights on matrices that we don’t fully understand. We do know that it can’t possibly have all the information from its training set since the training sets (measured in tb or pb) are orders of magnitude bigger than the models (measured in gb). The llm itself is just what it learned from reading all the training data, just like how you don’t memorize every passage in a book you read, just core concepts, relationships and lessons. So if I ask you " who was gatsbys love interest?" You don’t remember the line and page of the text that says he loves Daisy, your brain just has a strong connection of neurons between Gatsby, Daisy , love, longing etc. that produces the response “Daisy”. The same is true in an LLM, it doesn’t have the whole of the great Gatsby in its model but it too would have a strong connection somewhere between Gatsby, Daisy, love etc. to answer the question.
What your thinking of are older chatbots like Siri or Google assistant which do have a set of preset responses mixed in with some information from a structured database.
Please do explain how you think they make LLMs without a database of training examples to build a statistical model from.
I.e. “a model that encodes a database”.
I.e., “we applied a very lossy compression algorithm to this database”.
Check out the demoscene sometime, you’ll be surprised how much complexity can be generated from a very small set of instructions. I’ve seen entire first person shooter video games less than 100kb in size that algorithmically generate hundreds of megabytes of texture data at runtime. The idea that a mere 1,000x non-lossless compression of text would be impossible is laughable, especially when lossless text compression using neural network techniques achieved a 250x compression ratio years ago.
If LLMs were just lossy encodings of their database they wouldn’t be able to answer any questions outside of there training set. They can though, and quite well as shown by the fact you can give it completely made up information that it can’t possibly have “seen” and it will go along with it and give plausible answers. That is where it’s intelligence lyes and what separates it from older chatbots like Siri that cannot infer and are bound by the database they pull from.
How do you explain the hallucinations if the llm is just a complex lookup engine? You can’t lookup something you’ve never seen.
Of course they could, in the same way that hitting the autocomplete key can finish a half-completed sentence you’ve never written before.
The fact that models can produce useful outputs from novel inputs is the whole reason why we build models. Your argument is functionally equivalent to the claim that wind tunnels are intelligent because they can characterise the aerodynamics of both old and new kinds of planes.
For the same reason that a random number generator is capable of producing never-before-seen strings of digits. LLM inference engines have a property called “temperature” that governs how much randomness is injected into their responses:
Auto complete is not a lossy encoding of a database either, it’s a product of a dataset, just like you are a product of your experiences, but it is not wholly representative of that dataset.
A wind tunnel is not intelligent because it doesn’t answer questions or process knowledge/data it just creates data. A wind tunnel will not answer the question “is this aerodynamic” but you can observe a wind tunnel and use your intelligence to process that and answer the question.
Temperature and randomness don’t explain hallucinations, they are a product of inference. If you turned the temperature down to 0 and asked it the question " what happened in the great Christmas fire of 1934" it will give it’s best guess of what happened then even though that question is not in it’s dataset and it can’t look up the answer. The temperature would just mean that between runs it would consistently give the same story, the one that is most statistically probable, as opposed to another one that may be less probable but was pushed up due to randomness. Hallucinations are a product of inference, of taking something at face value then trying to explain it. People will do this too, if you tell someone a lie confidently then ask them about it they will use there intelligence to rationalize a story about what happened.
If LLMs don’t encode their training data, then why are they proving susceptible to data exfiltration techniques where they output the content of their training dataset verbatim? https://m.youtube.com/watch?v=L_1plTXF-FE
I’m not saying it doesn’t encode some of its training data, I’m saying it’s not just encoding its training data. It probably does “memorize” a bunch of trivial facts from its training data and regurgitate them when asked. I’m saying that’s not all they are and that’s not what makes the intelligent, their ability to also answer questions outside their training data is.