Technology

"Dad, what happened to humans on this planet?"

"Well son, they used a statistical computer program predicting words and allowed that program to control their weapons of mass destruction"

"That sounds pretty stupid. Why would they do such a thing?"

"They thought they found AI, son."

"So every other species on the planet managed to not destroy it, except humans, who were supposed to be the most intelligent?"

"Yes that's the irony of humanity, son."

I wish I could upvote this comment twice! I have the same feeling about how the media and others keep trying to push this "intelligence" component for their gain. I guess you can't stir up the masses when you talk about LLMs. Just like they couldn't keep using the term quad copters, and had to start calling them drones. Fucking media.

Yup. LLMs are 90% hype and 10% useful. The challenge is finding the scenarios they're useful for while filtering out the hype.

I think the problem with the term AI is that everyone has a different definition for it. We also called fancy state machines in video games AI too. The bar for AI has never been high in the past. Let's just call autonomous algorithms AI, the current generation of AI ML, and a future thinking AI AGI.

Autocomplete but based on the last 1000 words is how I try to describe it for the people who think it's magic.

LLMs will never care about wiping out humanity. They care about writing a story the way they understand stories to be written.

Machine learning IS AI. Seriously guys, you can hate it as much as you want (and calling LLMs autocomplete is quite reductive), but Machine learning is a subfield of AI.

I see this opinion parroted a lot around here, word by word, so I guess is the new popular opinion, but still... it is a fact that it's AI.

That said, bit moronic to try an use them for military decision making, sure, at least nowadays.

So we should train a LLM with military treatise.

People need to understand that LLMs are not smart, they're just really fancy autocompletion.

These aren't exactly different things. This has been a lot of what the past year of research in LLMs has been about.

Because it turns out that when you set up a LLM to "autocomplete" a complex set of reasoning steps around a problem outside of its training set (CoT) or synthesizing multiple different skills into a combination unique and not represented in the training set (Skill-Mix), their ability to autocomplete effectively is quite 'smart.'

For example, here's the abstract on a new paper from DeepMind on a new meta-prompting strategy that's led to a significant leap in evaluation scores:

We introduce Self-Discover, a general framework for LLMs to self-discover the task-intrinsic reasoning structures to tackle complex reasoning problems that are challenging for typical prompting methods. Core to the framework is a self-discovery process where LLMs select multiple atomic reasoning modules such as critical thinking and step-by-step thinking, and compose them into an explicit reasoning structure for LLMs to follow during decoding. Self-Discover substantially improves GPT-4 and PaLM 2’s performance on challenging reasoning benchmarks such as BigBench-Hard, grounded agent reasoning, and MATH, by as much as 32% compared to Chain of Thought (CoT). Furthermore, Self-Discover outperforms inference-intensive methods such as CoT-Self-Consistency by more than 20%, while requiring 10-40x fewer inference compute. Finally, we show that the self-discovered reasoning structures are universally applicable across model families: from PaLM 2-L to GPT-4, and from GPT-4 to Llama2, and share commonalities with human reasoning patterns.

• Self-Discover: Large Language Models Self-Compose Reasoning Structures (2024)

Or here's an earlier work from DeepMind and Stanford on having LLMs develop analogies to a given problem, solve the analogies, and apply the methods used to the original problem.

At a certain point, the "it's just autocomplete" objection needs to be put to rest. If it's autocompleting analogous problem solving, mixing abstracted skills, developing world models, and combinations thereof to solve complex reasoning tasks outside the scope of the training data, then while yes - the mechanism is autocomplete - the outcome is an effective approximation of intelligence.

Notably, the OP paper is lackluster in the aforementioned techniques, particularly as it relates to alignment. So there's a wide gulf between the 'intelligence' of a LLM being used intelligently and one being used stupidly.

By now it's increasingly that often shortcomings in the capabilities of models reflect the inadequacies of the person using the tool than the tool itself - a trend that's likely to continue to grow over the near future as models improve faster than the humans using them.

Especially since how much ingested fiction is about this exact scenario.

All it knows is what humans said in its training dataset which is a lot of news, wikipedia and social media.

The thing that surprises me is people think human brains are significantly different than this. We are pattern recognition machines that build perception based on weighted neural links. We’re much better at it, but we used to be a lot better at go too.