Failing to Understand the Exponential, Again

Most LLMs use GeGLU or SwiGLU.

> I thought the original article included the strongest objective data point on this: recent progress on the METR long task benchmark isn't just on the historical "task length doubling every 7 months" best fit, but is trending above it.

There is selection bias in that paper. For example, they chose to measure “AI performance in terms of the length of tasks the system can complete (as measured by how long the tasks take humans)”, but didn’t include calculation tasks in the set of tasks, and that’s a field in which machines have been able to reliably do tasks for years that humans would take centuries or more to perform, but at which modern LLM-based AIs are worse than, say, Python.

I think leaving out such taks is at least somewhat defensible, but have to wonder whether there are other tasks at which LLMs do not become better as rapidly they also leave out.

Maybe it is a matter of posing different questions, with the article being discussed being more interested in “(When) can we (ever) expect LLMs to do jobs that now require humans to do?” than in “(How fast) do LLMs get smarter over time?”

The human brain has many systems that adapt on multiple time-frames which could loosely be called “memory”.

But here I’m specifically interested in real-time updates to medium/long term memory, and the episodic/consciously accessible systems that are used in human reasoning/intelligence.

Eg if I’m working on a big task I can think through previous solutions I learned, remember the salient/surprising lessons, recall recent conversations that may indirectly affect requirements, etc. The brain is clearly doing an associative compression and indexing operation atop the raw memory traces. I feel the current LLM “memory” implementations are very weak compared to what the human brain does.

I suppose there is a sense in which you could say the weights “remember” the training data, but it’s read-only and I think this lack of real-time updating is a crucial gap.

To expand on my hunch about scaffolding - it may be that you can construct an MCP module that can let the LLM retrieve or ruminate on associative memories in such a way as to allow the LLM to not make the same mistake twice and be steerable on a longer timeframe.

I think the best argument against my hunch is that human brains have systems which update the synaptic weights themselves over a timeframe of days-to-months, and so if neural plasticity is the optimal solution here then we may not be able to efficiently solve the problem with “application layer” memory plugins.

But again, there is a lot of solution-space to explore; maybe some LoRA-like algorithm can allow an LLM instance to efficiently update its own weights at test-time, and persist those deltas for efficient inference, thus implementing the required neural plasticity algorithms?

The progression is much less clear when you don't view it anthropocentrically. For instance, we see an explosion in intelligible information: information that is formatted in human language or human-made formats. But this is concomitant with a crash in natural spaces and biodiversity, and nothing we make is as information-rich as natural environments, so from a global perspective, what we have is actually an information crash. Or hell, take something like agriculture. Cultured environments are far, far simpler than wild ones. Again: an information crash.

I'm not saying anything about the future, mind you. Just that if we manage to stop sniffing our own farts for a damn second and look at it from the outside, current human civilization is a regression on several metrics. We didn't achieve dominion over nature by being more subtle or complex than it. We achieved that by smashing nature with a metaphorical club and building upon its ruins. Sure, it's impressive. But it's also brutish. Intelligence requires intelligible environments to function, and that is almost invariably done at the expense of complexity and diversity. Do not confuse success for sophistication.

> last 1 year - even the basic improvements to AI models in the last 12 months are an unprecedented level of change, per time, looking back.

Are they? What changed, exactly? What improvements in, say, standards of living? In the rate of resource exploitation? In energy efficiency? What delta in our dominion over Earth? I'll tell you what I think: I think we're making tremendous progress in simulating aspects of humanity that don't matter nearly as much as we think they do. The Internet, smartphones, AI, speak to our brains in an incredible way. Almost like it was by design. However, they matter far more to humans within humanity than they do in the relationship of humanity with the rest of the universe. Unlike, say, agriculture or coal, which positively defaced the planet. Could we leverage AI to unlock fusion energy or other things that actually matter, just so we can cook the rest of the Earth with it? Perhaps! But let's not count our chickens before they hatch. As of right now, in the grand scheme of things, AI doesn't matter. Except, of course, in the currency of vibes.

Well, the shapes look very seasonal... Do you know something about epidemiological curves?!

The wave 2020 in Europe was often smaller than 2018. And the data was perfectly seasonal. If you know people working in nursing homes and hospitals, you can ask them what happened later in 2021...

I heard a lot of stories - from first hand... They parked old ladies in the cold in front of open windows for fresh air - until they were blue... They vaccinated old people right into an ongoing wave and of course they had more problems caused from a wrongly trained vulnerable immune system - sane doctors don't vaccinate into an ongoing wave. What was going on in hospitals and nursing homes was a crime for money. Just ask the people that were there. A combat medic I know that now works in a hospital called 2021 a crime.

And still - solid Epidemiological data - wherever you could find it - was still perfectly seasonal. You could see some perfect mathematical curves. Just very high because they actively killed people. Even pupils in school spent all day in front of open windows in the cold... To remain healthy... How stupid is that...

Not all places are equal, but I've taken a look at German all cause mortality. 2020 was not special. In 2021 it started rising synchronous with vaccinations.