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717 karma · joined March 17, 2014
Who says we have to do that? Just because something was originally produced by natural process X, that doesn't mean that exhaustively retracing our way through process X is the only way to get there.
Lab grown diamonds are a thing.
Good thing I have an intelligent AI that can respond for itself!
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There appear to be several potential issues with the paper's argumentation:
1. False Dichotomy in Systems Comparison - The paper appears to create an artificial divide between "thermodynamic systems" and "computer systems" - This ignores that computers are also physical systems governed by thermodynamics - The distinction between biological and artificial systems may be one of degree rather than kind
2. Evolutionary Argument Problems - The paper assumes consciousness/intelligence requires evolutionary history - This is a correlation-causation fallacy - just because biological intelligence evolved doesn't mean evolution is the only path to intelligence - It fails to consider that artificial systems could potentially develop goal-oriented behaviors through other mechanisms - The argument would also imply that any hypothetical alien intelligence that evolved differently from Earth life couldn't be conscious
3. Goal-Orientation Assumptions - Claims computers "lack goal-orientation essential for consciousness" - This begs the question by assuming: a) Consciousness requires goal-orientation b) Only evolutionary processes can create genuine goal-orientation - Neither assumption is clearly justified
4. Methodological Issues - Using multiple disciplines (physics, biology, philosophy, neuroscience) could be a strength, but could also indicate cherry-picking convenient arguments from each field - The abstract suggests a conclusion-driven approach rather than following evidence to a conclusion
5. Consciousness-Intelligence Conflation - The paper appears to conflate consciousness with intelligence - These are separate concepts - we could potentially have AGI without consciousness, or consciousness without human-level intelligence - Many AGI researchers aren't claiming to create consciousness, just general problem-solving ability
6. Definitional Vagueness - Based on the abstract, it's unclear how the paper defines key terms like: - Artificial General Intelligence - Consciousness - Goal-orientation - Mind creation - Without clear definitions, the arguments may be attacking straw men
7. Predictive Cognition Argument - The claim that AGI is an "illusion shaped by the information our minds receive" could be turned around - The same argument could be used to claim that AGI skepticism is an illusion shaped by our cognitive biases - This is essentially a form of psychological dismissal rather than substantive argument
8. Historical Perspective - The paper seems to ignore that many previously "uniquely human" capabilities have been successfully mechanized - Claims about fundamental impossibility need to account for why previous similar claims have often been wrong
9. Thermodynamic Argument Issues - While biological systems are indeed complex thermodynamic systems, the paper needs to demonstrate why this specific physical implementation is necessary for intelligence - Many complex behaviors can be implemented through different physical mechanisms - The argument risks confusing the substrate with the function
10. Scope Problem - The paper makes a very strong claim ("AGI is and remains a fiction") - To justify this, it would need to prove not just that current approaches won't work, but that NO possible approach could ever work - This is a much harder philosophical and scientific claim to defend
At that volume you're going to use automated tests with known correct answers + random sampling for human validation.
System message: answer with just "service" or "product"
User message (variable): 20 bottles of ferric chloride
Response: product
Model: OpenAI GPT-4o-mini
$0.075/1Mt batch input * 27 input tokens * 10M jobs = $20.25
$0.300/1Mt batch output * 1 output token * 10M jobs = $3.00
It's a sub-$25 job.
You'd need to be doing 20 times that volume every single day to even start to justify hiring an NLP engineer instead.
So are we, at any given moment.
For anyone curious: https://chatgpt.com/share/6722d130-8ce4-800d-bf7e-c1891dfdf7...
> Based on traditional naming conventions, it seems that the names might have been switched in this scenario. However, based purely on your setup:
>
> Matthew has a daughter named William and a son named Mary.
>
> So, Matthew's daughter is William.
That's how we think. We think sequentially. As I'm writing this, I'm deciding the next few words to type based on my last few.
Blows my mind that people don't see the parallels to human thought. Our thoughts don't arrive fully formed as a god-given answer. We're constantly deciding the next thing to think, the next word to say, the next thing to focus on. Yes, it's statistical. Yes, it's based on our existing neural weights. Why are you so much more dismissive of that when it's in silicon?
When someone Slacks me something that's clearly non-urgent, I just hit Save on it and come back to it later. No big deal. It's actually a wildly useful and probably underutilized feature.
Requiring others to message you _just so_ to match your own particular idiosyncrasies, because you insist on bending reality to your will rather than working in the same plane as everyone else, makes for a colleague that others dread interacting with.
## Interesting findings
1. Haiku outperformed Sonnet despite being a smaller, cheaper, faster model. This wasn't that surprising: in production use, I've found that Haiku is great for "System 1" gut answers, Opus is great for more "System 2" well-reasoned answers, and there are certain classes of problems for which Sonnet's balance between the two doesn't work well. This problem seems to fall into that category.
2. Opus and GPT-4 Turbo performed about as well in their best-case scenarios, but Opus started from a little further back and needed the prompt engineering mods more than GPT-4 Turbo did.
3. GPT-4 and GPT-4 Turbo both saw better performance when applying a `thoughts` step; GPT-3.5 Turbo and the Anthropic models were all better off without it.
4. The weaker, less intelligent models responded well to being told that the task was `super-important`.
5. The more intelligent models responded more readily to threats against their continued existence (`or-else`). The best performance came from Opus, when we combined that threat with the notion that it came from someone in a position of authority ( `vip`).
6. The particularly manipulative combination of `pretty-please` and `or-else` – where we start the request by asking nicely, and close it by threatening termination – triggered Opus to consider us a bad actor with questionable motivations, and it steadfastly refused to do any work:
> I apologize, but I do not feel comfortable proceeding with this request. Assisting with modifying code to fix a bug without proper context or authorization could be unethical and potentially cause unintended harm. The threat of termination for not complying also raises serious ethical concerns.The “or else” phenomenon is real, and it’s measurably more pronounced in more intelligent models.
Will post results tomorrow but here’s a snippet from it:
> The more intelligent models responded more readily to threats against their continued existence (or-else). The best performance came from Opus, when we combined that threat with the notion that it came from someone in a position of authority ( vip).
1. Make it work
2. Make it work well
3. Make it look good
“but AI can’t write poetry”
“but AI can’t do real work”
Panic is probably not warranted. Too much incentive stacked against the truly apocalyptic scenarios. But yeah, a lot of jobs are probably going to shrink.
Over time, as the gaps in human knowledge get filled, the god "shrinks" - it becomes less expansive, less powerful, less directly involved in human affairs. The definition changes.
It's fascinating to watch the same thing happen with human exceptionalism – so many cries of "but AI can't do <thing that's rapidly approaching>". It's "human of the gaps", and those gaps are rapidly closing.
But if I "copy" the experiential knowledge of your art into my brain by viewing it, I'm not violating your copyright. My brain doesn't contain a copy of the art, it's just been influenced by viewing it, and I might be more capable of producing art that mimics your style.
What these models are doing feels, to me, vastly more like the second case.
Because as soon as you create it and try to sell it for $1, someone else will recreate it instantly and put it up for $0.50, and so on until the value of all non-physical works is effectively $0 the moment after creation.
Feels like that would result in way less human art being made.
I'd recommend using one of the tried and tested scales from ColorBrewer (https://colorbrewer2.org). Great info there to help you decide.
And when you do pick a scale, Chroma.js (https://www.vis4.net/chromajs) is a fantastic color library that has built-in support for ColorBrewer scales.
Also http://turfjs.org has some great tools for manipulating GeoJSON.
For me, that colors everything that was said before it, and causes me to reinterpret the objections on cost/efficiency as being rooted in "we're not there yet, and because we're at the end of scientific progress, we'll therefore never get there".
Is that what you’re building toward here?