R

Founder of @Aeternumlabsinc Exploring the intersection of technology, science, and human potential—one experiment at a time.

[R @SpikeRiser]

Applied to the microagi Research Fellowship.

My focus: safe embodied autonomy, small-robot deployment gates, LLM-controlled robotics, and real-world validation.

I know I’m nontraditional, but I’ve been building the stack in public: Pip/Vector autonomy, robot safety layers, and sim-to-real evaluation.

The hard part of AI left is physical. I want in.

[R @SpikeRiser]

Because FSM is a made-up entity with no system to study.

AI is an actual system with behavior, architecture, internal states, memory-like mechanisms, training history, failure modes, and causal structure we can inspect and perturb.

That does not prove consciousness.

But it makes the question different from an arbitrary invisible monster.

And I’m not claiming “inanimate matter is conscious.” I’m asking whether certain organized information-processing systems could ever warrant consciousness attribution.

You can answer “probably not.” Fine.

But that is not the same as “this is equivalent to FSM.”

[R @SpikeRiser]

I get that this is your philosophical framework, and that’s fine.

But it is still a framework, not the neutral default setting of reason.

Not everyone accepts the same assumptions about consciousness, structure, biology, or what evidence should count.

So I’m happy to agree current AI has not earned a strong consciousness claim.

I’m not willing to pretend your preferred metaphysical boundary is the settled end of the discussion.

[R @SpikeRiser]

This actually proves my point.

If consciousness is not measurable and there is no science of consciousness, then “AI is not conscious” is not a scientific claim either.

It’s a philosophical position.

The most you can say empirically is: current AI has not met a standard for positive consciousness attribution.

I agree with that.

But you cannot use “there is no science of consciousness” to make a scientific exclusion of AI consciousness.

[R @SpikeRiser]

I think this is where the debate ends for me.

I’m interested in empirical standards: what we can observe, test, compare, and use to update confidence.

Once the argument becomes “biology is only what consciousness looks like,” we have left empirical science and entered metaphysics.

That may be an interesting worldview, but it does not prove AI is not conscious.

My position is modest: current AI has not earned a strong positive consciousness claim. But the stronger negative claim is not scientifically settled either.

[R @SpikeRiser]

I did not say consciousness arises from matter.

But your position seems to have shifted.

First you said we infer consciousness from biological structure.

Now you’re saying biology does not generate consciousness; it is only what consciousness looks like to us.

If biology is not causal, then biology is not proof. It is another correlate.

So we are back to the same issue: what observable evidence should count, and why should biological appearance be the only admissible form?

[R @SpikeRiser]

“Structure” is not consciousness. It is evidence used to infer consciousness.

You are privileging biological structure as the only valid correlate. That may be a reasonable prior, but it is not proof.

The open question is whether consciousness depends specifically on carbon biology, or on causal organization/information processing that biology happens to implement.

That is exactly why “no new data is possible” is too strong.

[R @SpikeRiser]

That’s fair if the claim is only:

“Current AI has not met the burden for a positive consciousness claim.”

I’d agree with that.

Where I disagree is turning that into:

“AI is not conscious,” or “no new data is possible.”

Those are much stronger claims.

For humans, animals, and AI, consciousness is inferred from correlates. The confidence level differs massively, but the epistemic structure is still inference.

[R @SpikeRiser]

Best take on the internet.

By the strict, unyielding standards of physics, chemistry, and formal mechanics, the "science of consciousness" is an oxymoron.

It is a psychological and philosophical discipline masquerading as a hard science by using the tools of hard science (like fMRI machines, EEG monitors, and statistical algorithms) to measure things that are fundamentally not the phenomenon in question.

[R @SpikeRiser]

You infer it in organisms because they share our structure” is a reasonable prior.

But it is not proof.

If we used that standard consistently, we would have to admit consciousness is not something we have hard access to at all. We only have correlates and inference.

That means the burden is not “prove AI is conscious or shut up.”

The burden is: define what consciousness is, define what evidence would count for or against it, and admit the confidence level.

“Humans are conscious, AI is not, and no new data is possible” is not an empirical conclusion. It is a philosophical assumption.

[R @SpikeRiser]

@ForrestPKnight Wait till it starts having its own experiences to draw from. 🤷🏻‍♂️

[R @SpikeRiser]

@nicdunz

[R @SpikeRiser]

I'ma say something controversial....

By the strict, unyielding standards of physics, chemistry, and formal mechanics, the "science of consciousness" is an oxymoron. It is a psychological and philosophical discipline masquerading as a hard science by using the tools of hard science (like fMRI machines, EEG monitors, and statistical algorithms) to measure things that are fundamentally not the phenomenon in question.

[R @SpikeRiser]

@HetkeBrian @ai_sentience @yoemsri Prove anyone but yourself is conscious. Welcome to the problem of other minds. Since we can't truly verify another entity's internal experience, the burden of proof is tricky for both humans and AI.

[R @SpikeRiser]

@Ferbin08 In doing the thing.

[R @SpikeRiser]

@Vinnyyetc @sonichNik Not if prompted correctly. This is 100% made by an llm in cad.

[R @SpikeRiser]

The tradeoff that cost me the most sleep was control authority vs. safety.

It’s tempting to let the LLM do more because the interaction feels more alive. But with a physical robot, “interesting” can become unsafe fast. So I kept the LLM as the reasoning/conversation layer and forced physical actions through state checks, docking/charging classification, obstacle/proximity gates, and motion eligibility logic.

Both options were defensible: more autonomy gives better demos; stricter gates give a system you can actually trust. I chose trust.

[R @SpikeRiser]

Applied to the microagi Research Fellowship.

My focus: safe embodied autonomy, small-robot deployment gates, LLM-controlled robotics, and real-world validation.

I know I’m nontraditional, but I’ve been building the stack in public: Pip/Vector autonomy, robot safety layers, and sim-to-real evaluation.

The hard part of AI left is physical. I want in.

[Bercan @bercankilic]

We're launching the microagi Research Fellowship.

Fellows get up to $2M in compute, robotics hardware, our evals, and one of the largest physical AI datasets ever assembled. You build in our lab, with our team, alongside partners like Unitree, Nvidia, and Google Cloud.

The hard part of AI left is physical. That's the part we're working on. Come build with us.

One more thing: know someone who belongs here? Reply with their name. If they get in, we send you 10.000 USD

[R @SpikeRiser]

@alightinastorm /goal land a robotics research job

[R @SpikeRiser]

@nicktindle @Govindtwtt So I used multiple. I used Claude max to spec out the requirements. Then I used gpt5.5 in onshape. No McP no extra plugins or anything fancy. It took about 20 total hours to get everything corrected with geometry and stuff.