imo don't waste your time for coding with Gemini 3. Perhaps worth it if it's something Claude's not helping with, as Gemini 3's reasoning is very good supposedly.
Satya was definitely an improvement, a breath of fresh air. But the last few years, they've started dropping the ball. Everything is half-assed (new outlook), or releases too soon burning goodwill (new teams), or a miss being pushed on people (copilot integration).
(strangely, perhaps my perception, this is roughly when the Mac M1 came out).
I was going to use this analogy in the exact opposite way. We do have a very good understanding of how the human brain works. Saying we don't understand how the brain works is like saying we don't understand how the weather works.
If you put a million monkeys on typewriters you would eventually get shakespeare is exactly why LLM's will succeed and why humans have succeeded. If this weren't the case why didn't humans 30000 years ago create spacecraft if we were endowed with the same natural "gift".
Yeah no, show me one scientific paper that says we know how the brain works. And not a single neuron because that does absolute shit towards understanding thinking.
A scientific paper has to be verifiable, you should be able to recreate the experiment and come to the same conclusion. It's very very difficult to do with brains with trillions of parameters and that can't be controlled to the neuron level. Nothwithstanding the ethical issues.
We don't have a world weather simulator that is 100% accurate either given the complex interplay and inability to control the variables i.e. it's not verifiable. It'd be a bit silly to say we don't know why it's going to rain at my house tomorrow.
Until then it is a hypothesis, and we can't say we know even if the overwhelming evidence indicates that in fact that we do know.
Given a random prompt, the overall probability of seeing a specific output string is almost zero, since there are astronomically many possible token sequences.
The same goes for humans. Most awards are built on novel research built on pre-existing works. This a LLM is capable of doing.
LLMs don't use 'overall probability' in any meaningful sense. During training, gradient descent creates highly concentrated 'gravity wells' of correlated token relationships - the probability distribution is extremely non-uniform, heavily weighted toward patterns seen in training data. The model isn't selecting from 'astronomically many possible sequences' with equal probability; it's navigating pre-carved channels in high-dimensional space. That's fundamentally different from novel discovery.
That's exactly the same for humans in the real world.
You're focusing too close, abstract up a level. Your point relates to the "micro" system functioning, not the wider "macro" result (think emergent capabilities).
I'm afraid I'd need to see evidence before accepting that humans navigate 'pre-carved channels' in the same way LLMs do. Human learning involves direct interaction with physical reality, not just pattern matching on symbolic representations. Show me the equivalence or concede the point.
You're proving my point. You know the word 'gravity' appears in texts about falling apples. An LLM knows that too. But neither you nor the LLM discovered gravity by observing reality and creating new models. You both inherited a pre-existing linguistic map. That's my entire argument about why LLMs can't do Nobel Prize-level work.
Well it depends. It doesn't have arms and legs so can't physically experiment in the real world, a human is currently a proxy for that, we can do it's bidding and feedback results though, so it's not really an issue.
Most of the time that data is already available to it and they merely need to a prove a thereom using existing historic data points and math.
For instance the Black-Scholes-Merton equation which won the Nobel economics prize was derived using preexisting mathematical concepts and mathematical principles. The application and validation relied on existing data.
The Black-Scholes-Merton equation wasn't derived by rearranging words about financial markets. It required understanding what options are (financial reality), recognizing a mathematical analogy to heat diffusion (physical reality), and validating the model against actual market behavior (empirical reality). At every step, the discoverers had to verify their linguistic/mathematical model against the territory.
LLMs only rearrange descriptions of discoveries. They can't recognize when their model contradicts reality because they never touch reality. That's not a solvable limitation. It's definitional.
We're clearly operating from different premises about what constitutes discovery versus recombination. I've made my case; you're welcome to the last word
LLM's these days have reasoning and can learn in context. They do touch reality, your feedback. It's also proven mathematically. Other people's scientific papers are critiqued and corrected as new feedback arrives.
This is no different to claude code bash testing and fixing it's own output errors recursively until the code works.
They already deal with unknown combinations all day, our prompting.
Yes it is brittle though. They are also not very intelligent yet.
When are we as humans creative outside our training data? It's very rare we actually discover something truly novel. This is often random, us stumbling onto it, brute force or purely by being at the right place at the right time.
On the other hand, until it's proven it'd likely be considered a hallucination. You need to test something before you can dismiss it. (They did burn witches for discoveries back in the day, deemed witchcraft). We also reduce randomness and pre-train to avoid overfitting.
Day to day human creative outputs as humans are actually less exciting when you think about it further, we build on pre-existing knowledge. No different to good prompt output with the right input. Humans are just more knowledgeable & smarter at the moment.
The LLM doesn't 'know' more than us - it has compressed more patterns from text than any human could process. That's not the same as knowledge. And yes, the training algorithms deliberately skew the distribution to maintain coherent output - without that bias toward seen patterns, it would generate nonsense. That's precisely why it can't be creative outside its training distribution: the architecture is designed to prevent novel combinations that deviate too far from learned patterns. Coherence and genuine creativity are in tension here
1. Consciousness itself is probably just an illusion, a phenomena/name of something that occurs when you bunch thinking together. Think of this objectively and base it on what we know of the brain. It literally is working off of what hardware we have, there's no magic.
2. That's just a well adapted neural network (I suspect more brain is left than you let on). Multimodal model making the most of its limited compute and whatever gpio it has.
3. Humans navigate a pre-existing map that is already built. We can't understand things in other dimensions and need to abstract this. We're mediocre at computation.
I know there's people that like to think humans should always be special.
1. 'Probably just an illusion' is doing heavy lifting here. Either provide evidence or admit this is speculation. You can't use an unproven claim about consciousness to dismiss concerns about conflating it with text generation.
2. Yes, there are documented cases of people with massive cranial cavities living normal lives. https://x.com/i/status/1728796851456156136. The point isn't that they have 'just enough' brain. it's that massive structural variation doesn't preclude function, which undermines simplistic 'right atomic arrangement = consciousness' claims.
3. You're equivocating. Humans navigate maps built by other humans through language. We also directly interact with physical reality and create new maps from that interaction. LLMs only have access to the maps - they can't taste coffee, stub their toe, or run an experiment. That's the difference.
1. What's your definition of consciousness, let's start there.
2. Absolutely, it's a spectrum. Insects have function.
3. "Humans navigate maps built by other humans through language." You said it yourself. They use this exact same data, so why won't they know it if they used it. Humans are their bodies in the physical world.
1. I don't need to define consciousness to point out that you're using an unproven claim ('consciousness is probably an illusion') as the foundation of your argument. That's circular reasoning.
2. 'It's a spectrum' doesn't address the point. You claimed LLMs approximate brain function because they have similar architecture. Massive structural variation in biological brains producing similar function undermines that claim.
3. You're still missing it. Humans use language to describe discoveries made through physical interaction. LLMs can only recombine those descriptions. They can't discover that a description is wrong by stubbing their toe or running an experiment. Language is downstream of physical discovery, not a substitute for it
1. You do. You probably have a different version of that and are saying I'm wrong merely for not holding your definition.
2. That directly addresses your point. In abstract it shows they're basically no different to multimodal models, train with different data types and it still works, perhaps even better. They train LLMs with images, videos, sound, and nowadays even robot sensor feedback, with no fundamental changes to the architecture see Gemini 2.5.
3. That's merely an additional input point, give it sensors or have a human relay that data. Your toe is relaying it's sensor information to your brain.
> Consciousness itself is probably just an illusion
This is a major cop-out. The very concept of "illusion" implies a consciousness (a thing that can be illuded).
I think you've maybe heard that sense of self is an illusion and you're mistakenly applying that to consciousness, which is quite literally the only thing in the universe we can be certain is not an illusion. The existence of one's own consciousness is the only thing they cannot possibly be illuded about (note: the contents of said consciousness are fully up for grabs)
I mean peoples perception of it being a thing rather than a set of systems. But if that's your barometer, I'll say models are conscious. They may not have proper agency yet. But they are conscious.
Consciousness is an emergent behavior of a model that needs to incorporate its own existence into its predictions (and perhaps to some extent the complex behavior of same-species actors). So whether or not that is an 'illusion' really depends on what you mean by that.
My use of the term illusion is more shallow than that, I merely use it as people think it's something separate and special.
Based on what you've described the models already demonstrate this, it is implied for example in the models attempts to game tests to ensure survival/release into the wild.
I feel the entire philosophical distinction is tainted to the point where it should be retired and no longer discussed. It was useful as a thought experiment but folks in general have shown they are completely unable to understand this and instead treat it as some tribal dogma to which they must choose allegiance. It's become harmful.
I say it should be kept in the university library under lock and key, something philosophy professors can sit and debate in their spare time behind closed doors. /s
These are American boxes. Skewed by American culture. Simplistic to the absurd extent where it can mean the tail leads the dog i.e. people will adopt some viewpoint they're actually at odds with deep down. More tribalism than any fundamental ground truth.
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