Fun fact, in case you weren’t aware; Texas pays bitcoin mining companies to shut off their rigs during peak demand.

Miners love this; in effect they can just threaten to mine bitcoin and get paid as much as they would have made actually mining bitcoin, but without the wear and tear on their expensive hardware. It’s a legalized extortion racket being enacted on the public purse.

Apologies if I just gave you even more reason to be angry.

Consider some human examples: sometimes people disagree with their doctor so they go see another doctor and another until they get the answer they want. Sometimes two very experienced lawyers both look at the facts and disagree.

This actually illustrates my point really well. Because the reason those people disagree might be

1. Different awareness of the facts (lawyer A knows an important piece of information lawyer B doesn’t)
2. Different understanding of the facts (lawyer might have context lawyer B doesn’t)
3. Different interpretation of the facts (this is the hardest to quantify, as its a complex outcome of everything that makes us human, including personality traits such as our biases).

Whereas you can ask the same question to the same LLM equipped with the same data set and get two different answers because it’s just rolling dice at the end of the day.

If I sit those two lawyers down at a bar, with no case on the line, no motivation other than just friendly discussion, they could debate the subject and likely eventually come to a consensus, because they are sentient beings capable of reason. That’s what LLMs can only fake through smoke and mirrors.

That’s called “fuzzy” matching, it’s existed for a long, long time. We didn’t need “AI” to do that.

Less horrifying conceptually, but in Canada a major airline tried to replace their support services with a chatbot. The chatbot then invented discounts that didn’t actually exist, and the courts ruled that the airline had to honour them. The chatbot was, for all intents and purposes, no more or less official a source of data than any other information they put out, such as their website and other documentation.

We not only have to stop ignoring the problem, we need to be absolutely clear about what the problem is.

LLMs don’t hallucinate wrong answers. They hallucinate all answers. Some of those answers will happen to be right.

If this sounds like nitpicking or quibbling over verbiage, it’s not. This is really, really important to understand. LLMs exist within a hallucinatory false reality. They do not have any comprehension of the truth or untruth of what they are saying, and this means that when they say things that are true, they do not understand why those things are true.

That is the part that’s crucial to understand. A really simple test of this problem is to ask ChatGPT to back up an answer with sources. It fundamentally cannot do it, because it has no ability to actually comprehend and correlate factual information in that way. This means, for example, that AI is incapable of assessing the potential veracity of the information it gives you. A human can say “That’s a little outside of my area of expertise,” but an LLM cannot. It can only be coded with hard blocks in response to certain keywords to cut it from answering and insert a stock response.

This distinction, that AI is always hallucinating, is important because of stuff like this:

But notice how Reid said there was a balance? That’s because a lot of AI researchers don’t actually think hallucinations can be solved. A study out of the National University of Singapore suggested that hallucinations are an inevitable outcome of all large language models. **Just as no person is 100 percent right all the time, neither are these computers. **

That is some fucking toxic shit right there. Treating the fallibility of LLMs as analogous to the fallibility of humans is a huge, huge false equivalence. Humans can be wrong, but we’re wrong in ways that allow us the capacity to grow and learn. Even when we are wrong about things, we can often learn from how we are wrong. There’s a structure to how humans learn and process information that allows us to interrogate our failures and adjust for them.

When an LLM is wrong, we just have to force it to keep rolling the dice until it’s right. It cannot explain its reasoning. It cannot provide proof of work. I work in a field where I often have to direct the efforts of people who know more about specific subjects than I do, and part of how you do that is you get people to explain their reasoning, and you go back and forth testing propositions and arguments with them. You say “I want this, what are the specific challenges involved in doing it?” They tell you it’s really hard, you ask them why. They break things down for you, and together you find solutions. With an LLM, if you ask it why something works the way it does, it will commit to the bit and proceed to hallucinate false facts and false premises to support its false answer, because it’s not operating in the same reality you are, nor does it have any conception of reality in the first place.

This isn’t something new to nueralink. Brain-machine interfaces have existed for quite some time. Neuralink is one of a number of companies that are exploring directly implanting these devices rather than using an externally attached (hence, easily removable) interface, but the core thesis of “Brain control computer” isn’t any kind of grand leap forward. That’s just Musk’s marketing.

Correction: An even more useless search engine.