1,119 karma · joined June 20, 2011
- lactose tolerance
- immunity and disease resistance
- lighter skin at northern latitudes
- metabolism and vitamin D processing changes in response to changes in diet after the rise of agriculture
All these traits go beyond just increasing the odds of survival, they improve the life of the individual directly. I.e. they confer fitness. Individuals carrying those traits will, on average, in that ecosystem they are inhabiting, be more healthy than those who don't.Yes, if it's not redundant software. The ultimate utility is to a human. Sure, at some point humans stopped writing assembly language and employed a compiler instead, so the abstraction level and interfaces change, but it's all still there to serve humans.
To use your example, do you think humans will want to interact with AI agents using a chat interface only? For most tasks humans use computers today, that would be very unwieldy. So the UI will migrate from the website to the AI agent interface. It all transforms, becoming more powerful (hopefully!), but won't go away. And just how the advent of compilers led to an increase of programmers in the world, so will AI agents. This is connected with Javon's paradox as well.
As long as you build software for humans (and all software we build is for humans, ultimately), you'll need humans at the helm to steer the ship towards a human-friendly solution.
Yeah, for those you can just relax and trust the vibes. It's for complex software projects you need those software engineering chops, otherwise you end up with a intractable mess.
That doesn't mean language ability is a natural outcome of crossing a certain threshold of brain complexity; if anything it's more likely the other way around: this complexity being be driven by highly social behavior and communication.
1. https://en.wikipedia.org/wiki/List_of_animals_by_number_of_n...
Abstract:
Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such systems can improve. The Darwin Gödel Machine (DGM) demonstrates open-ended self-improvement in coding by repeatedly generating and evaluating self-modified variants. Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. However, this alignment does not generally hold beyond coding domains. We introduce \textbf{hyperagents}, self-referential agents that integrate a task agent (which solves the target task) and a meta agent (which modifies itself and the task agent) into a single editable program. Crucially, the meta-level modification procedure is itself editable, enabling metacognitive self-modification, improving not only the task-solving behavior, but also the mechanism that generates future improvements. We instantiate this framework by extending DGM to create DGM-Hyperagents (DGM-H), eliminating the assumption of domain-specific alignment between task performance and self-modification skill to potentially support self-accelerating progress on any computable task. Across diverse domains, the DGM-H improves performance over time and outperforms baselines without self-improvement or open-ended exploration, as well as prior self-improving systems. Furthermore, the DGM-H improves the process by which it generates new agents (e.g., persistent memory, performance tracking), and these meta-level improvements transfer across domains and accumulate across runs. DGM-Hyperagents offer a glimpse of open-ended AI systems that do not merely search for better solutions, but continually improve their search for how to improve.
There's an acronym for this: POSSE (Publish [on your] Own Site, Syndicate Elsewhere). Part of the IndieWeb movement, for those who want to explore this worthwhile idea further.
That sounds very neutral, but wouldn't this, by removing the human element and flexibility from business transactions, be a further step along a general enshittification trend?
"The behavioural components of happiness are less easily characterised but particular facial expressions such as 'smiling' have been noted"
"Certainly, if television soap operas in any way reflect real life, happiness is a very rare phenomenon indeed in places as far apart as Manchester, the East End of London and Australia. Interestingly, despite all the uncertainty about the epidemiology of happiness, there is some evidence that it is unevenly distributed amongst the social classes: individuals in the higher socio-economic groupings generally report greater positive affect which may reflect the fact that they are more frequently exposed to environmental risk factors for happiness."
...pretty hilarious if you ask me :)
If we never try, we'll never know. I wouldn't be surprised if there is something to gain from a form of deterministic computation which is still integrated with the NN architecture. After all, tool calls have their own non-trivial overhead.
Starting with that absurd first paragraph offering proof for the otherwise inconceivable idea that there are are indeed topics that you aren't qualified to comment on - on one hand, and on the other insinuating that you surely must be more qualified than me to comment on semantics; continuing with the second, totally uncalled for given that I prefaced my comment with "to my ears", yet you didn't; the third, again redundant since I already mentioned that "received" is more general than "understood", so of course the meaning is different - that's the whole point, using a tool to find more fitting meanings, if they would be the same what would be the point?? The assumption is whoever uses the tool keeps the one they feel comes closer to what they had in mind, discarding the rest, no?
Let's stick to this particular example. Why is "understood" a better fit in that context (beyond the original comment suggesting it was closer to their intended meaning)? Because that's as much as we can hope for - to convey the desired understanding. (And yes, that includes connotations and the like, at least if you want to stick to a reasonable, not tendentiously restricted understanding of the word.) How the meaning is received depends indeed on other context, like maturity and generally life experience. For example, you were probably hoping that your message would be received with awe and newfound respect on my part for your wit and depth of insight. But instead, I found you comment merely tedious and vacuous. Consequently, I don't plan to check back on whatever you might scribble in response.
Introducing "because" also adds to the clarity without weighing down things or changing the meaning. "Improved" instead of the bland "better" again is an... improvement.
I imagine GP didn't sneak in the tendentious "to fit with and be well received in the hacker news community" in his instructions.
Overall this was a worthwhile assist. I believe (totally understandable) anti-AI animus is coloring a lot of these replies. These tools can be useful when applied sparingly and targeted la GP did. It's true and very unfortunate that often they are used as the proverbial hammer in search of a nail, flattening everything in the process.
Does it? I don't see it. If anything, it is more direct and clear, not less, i.e. "to help me find wording that conveys my thoughts the way I want them to be understood by the reader" instead of the more convoluted "to search for a way to formulate my thoughts like I intend them to be received by the reader". How is it pandering? And how exactly does it remove "injection points"?
It basically chose more precise words where that was possible, resulting in a net improvement, AFAICS.