sorry you can’t keep up

Anyone who thinks LLMs have not come a long way in approximating human linguistic capabilities (and associated thinking) are in fact, engaging in (delusional) wishful thinking regarding human exceptionalism.

With respect to consciousness, you are doing nothing more than asserting a special domain inside the brain that, unlike the rest of the mechanisms of the brain, has special "magic" that creates qualia where classical mechanisms cannot. You are saying that there is possibly a different explanation for intelligence as consciousness, when it would be much simpler to say the same mechanisms explain both. Furthermore, you have no explanation for why this quantum "magic", even if it was there, would solve the hard problem of consciousness - you are just saying that it does. Why should quanta lend themselves anymore to the possibility of subjective experience/qualia than classical systems? Finally, a brain operates at 98.6° and we can't even create verifiable quantum computing effects at near absolute zero, the only place where theory and experiment both agree is the place quantum effects start to dominate. The burden of proof is on you and Penrose as what you are both saying is wildly at odds with both physics, experimental and theoretical, and recent advancements in computing. Penrose is a very smart guy but I fear on these questions he's gone pretty rogue scientifically.

In just 2-3 years we've gone from primitive LLMs to LLMs reaching Graduate PhD-level knowledge and intelligence in multiple domains. LLMs can complete almost any code I write with high accuracy given sufficient context. I can have a naturalistic dialog with an LLM that goes on for hours in multiple languages. Frankly (and humblingly, and frighteningly) they have already surpassed my own knowledge and intelligence in many, probably most, domains. Obviously they aren't perfect and make a lot of errors - but so do most humans.

The fact that it takes decades to master such a mundane task may mean the entire approach is wrong. The article hand-waves a lot of the complexity of "automating as much as possible."

In my opinion, the solution lies in append-only software as dependencies. Append-only means you never break an existing contract in a new version. If you need to do a traditional "breaking change" you instead add a new API, but ship all old APIs with the software. In other words - enable teams to upgrade to the latest of anything without risking breaking anything and then updating their API contracts as necessary. This creates the least friction. Of course, it's a long way for every dependency and every transitive dependency to adopt such a model.

"Doing them swiftly, efficiently, and -- most of all -- completely is one of the most critical skills you can develop as a team."

That all sounds great. However, I'd like to understand what teams are actually able to do this, because it seems like a complete fantasy. Nobody I've seen is doing migrations swiftly and efficiently. They are giant time-sucks for every company I've ever worked for and any company anyone I know has ever worked for.

Not talking about just monitoring outputs though. I'm talking about monitoring the internals of the model as it reaches its output. The entire issue around interpretability / observability inside the LLM's model is the hard problem, one for which considerable resources are being dedicated to solve - not simply hooking the public-facing APIs up to observability tools like any other service API. This is just conventional telemetry. Calling this LLM observability implies there is something special about it and unique to LLMs in particular that enhances introspection into the AI model itself, which is not true. The title is highly misleading, classic startup-bro fake-it-til-you-make-it hustling crap, and deserves to be called out.

Correct- the summary is misleading marketing. This is just normal system / service observability. What people mean by observability in the LLM context is specific.