But that is not the standard for current GNU projects in large part because of all the easily avoidable friction. "If it was good enough for Richard Stallman in 1987, it's good enough for Microsoft in 2024" is just a dumb argument.
Not to mention you're conflating apples with oranges, since a software standard is very different from an application. POSIX wasn't just one Bell Labs employee working by himself.
From the article:
> The LSP should be an open standard, like HTTP, with an open committee that represents the large community which is invested in LSP, and can offer their insight in how to evolve it.
At least for me, Monty Hall makes more intuitive sense to me when there are 10 doors than when there are 3, even though the benefit of the optimal strategy is quite small: there's a 1/10 chance you picked the car and shouldn't have switched, but a 9/10 chance that you can play the game again with 1:9 odds. Without doing the math, it makes sense to me that the optimal strategy actually does improve your odds.
Yet for some reason a 2/3 chance you can play the game with 50-50 odds is harder to accept - in particular I have to consider the 10 door case to understand why the 3 door case makes sense. I suspect it has to do with the psychology of loss aversion: a 1/3rd chance that you incorrectly switched "feels like" a reckless risk.
Look at who the tools are marketed towards. Writing software involves a lot of tedium, eye strain, and frustration, even for experts who have put in a lot of hours practicing, so LLMs are marketed to help developers make their jobs easier.
This is not the case for art or music generators: they are marketed towards (and created by) laypeople with who want generic content and don't care about human artists. These systems are a significant burden on productivity (and fatal burden on creativity) if you are an honest illustrator or musician.
Another perspective: a lot of the most useful LLM codegen is not asking the LLM to solve a tricky problem, but rather to translate and refine a somewhat loose English-language solution into a more precise JavaScript solution (or whatever), including a large bag of memorized tricks around sorting, regexes, etc. It is more "science than art," and for a sufficiently precise English prompt there is even a plausible set of optimal solutions. The LLM does not have to "understand" the prompt or rely on plagiarism to give a good answer. (Although GPT-3.5 was a horrific F# plagiarist... I don't like LLM codegen but it is far more defensible than music generation)
This is not the case with art or music generators: it makes no sense to describe them as "English to song" translators, and the only "optimal" solutions are the plagiarized / interpolated stuff the human raters most preferred. They clearly don't understand what they are drawing, nor do they understand what melodies are. Their output is either depressing content slop or suspiciously familiar. And their creators have filled the tech community with insultingly stupid propaganda like "they learn art just like human artists do." No wonder artists are mad!
> Might there be certain laws of physics that are also “necessary” in the same way? In his paper, Molinini argues that the principle of conservation may be one such law. In physics, some properties of a system, such as energy or momentum, can’t change. A bicyclist freewheeling down a hill, for example, is converting her gravitational potential energy into movement energy, but the total amount of energy she and her bike have stays the same.
Arithmetic itself is a consequence of physical conservation: if you have a collection of four acorns, another collection of three acorns, then combine them without dropping an acorn, then you must have a collection of seven acorns. It is our deep physical understanding of space and causality which leads to simple arithmetic being intuitively true to most (if not all) vertebrates. (If the squirrel only got six acorns after combining then there must be a causal explanation for the quantitative discrepancy; another squirrel stole an acorn from the older stash, or maybe it fell in a hole.)
Isn't "plasticity is not necessary for intelligence" just defining intelligence downwards? It seems like you want to restrict "intelligence" to static knowledge and (apparent) short-term cleverness, but being able to make long-term observation and judgements about a changing world is a necessary component of intelligence in vertebrates. Why exclude that from consideration?
More specifically: it is highly implausible that an AI system could learn to improve itself beyond human capability if it does not have long-term plasticity: how would it be able to reflect upon and extend its discoveries if it's not able to learn new things during its operation?
I have not used LLMs since 2023, when GPT-4 routinely failed almost every counting problem I could think of. I am sure the performance has improved since then, though "write an essay with 250 words" still seems unsolved.
The real problem is that LLM providers have to play a stupid game of whack-a-mole where an enormous number of trivial variations on a counting problem need to be specifically taught to the system. If the system was capable of true quantitative reasoning that wouldn't be necessary for basic problems.
There is also a deception is that "chain of thought" prompting makes LLMs much better at counting. But that's cheating: if the LLM had quantitative reasoning it wouldn't need a human to indicate which problems were amenable to step-by-step thinking. (And this only works for O(n) counting problems, like "count the number of words in the sentence." CoT prompting fails to solve O(nm) counting problems like "count the number of words in this sentence which contain the letter 'e'" For this you need a more specific prompt, like "First, go step-by-step and select the words which contain 'e.' Then go step-by-step to count the selected words." It is worth emphasizing over and over that rats are not nearly this stupid, they can combine tasks to solve complex problems without a human holding their hand.)
I don't know what you mean by "10 years ago" other than a desire to make an ad hominem attack about me being "stuck." My point is that these "capabilities" don't include "understands what a number is in the same way that rats and toddlers understand what numbers are." I suspect that level of AI is decades away.
Modern ANN architectures are not actually capable of long-term learning in the same way animals are, even stodgy old dogs that don't learn new tricks. ANNs are not a plausible model for the brain, even if they emulate certain parts of the brain (the cerebellum, but not the cortex)
I will add that transformers are not capable of recursion, so it's impossible for them to realistically emulate a pigeon's brain. (you would need millions of layers that "unlink chains of thought" purely by exhaustion)
In 2022 Ilya Sutskever claimed there wasn't a distinction:
> It may look—on the surface—that we are just learning statistical correlations in text. But it turns out that to ‘just learn’ the statistical correlations in text, to compress them really well, what the neural network learns is some representation of the process that produced the text. This text is actually a projection of the world.
This is transparently false - newer LLMs appear to be great at arithmetic, but they still fail basic counting tests. Computers can memorize a bunch of symbolic times tables without the slightest bit of quantitative reasoning. Transformer networks are dramatically dumber than lizards, and multimodal LLMs based on transformers are not capable of understanding what numbers are. (And if Claude/GPT/Llama aren't capable of understanding the concept of "three," it is hard to believe they are capable of understanding anything.)
Sutskever is not actually as stupid as that quote suggests, and I am assuming he has since changed his mind.... but maybe not. For a long time I thought OpenAI was pathologically dishonest and didn't consider that in many cases they aren't "lying," they blinded by arrogance and high on their own marketing.
I learn best via writing things out by hand in my own words, and almost never read the notes afterwards. I am also profoundly disorganized :) Before I got a reMarkable I had accumulated (and thrown out) dozens of bulky paper notebooks. Now those are all digital.
Despite reMarkable's marketing around high-quality hand-drawn professional notes, I suspect crappy "transient" notes to aid memory and mental organization are the most common use case. For me it's really a thinking device rather than a writing device.
If I actually need to reference or organize my notes I will
type something out in emacs.
My issue with this line of argument is that people always want to compare "do it with Copilot" to "do it completely by scratch" when they should be comparing it to "do it by ignorantly copy-pasting from one of the many similar projects on GitHub then tweaking a few things." There are quite a few open-source GLSL implementations of marching squares, maybe copy-pasting would have been faster and higher-quality.
> But isn't the ability to learn also a major component of IQ?
Not sure what you mean by this - it's certainly not something a single-day IQ test could possibly measure! The primary reason IQ is a discredited measure of intelligence is that people are perfectly able to learn how to perform better on IQ tests - any supposed influence
of "intelligence" on IQ scores is hopelessly confounded with how much you've practiced similar tests / trivial logic puzzles / etc.
This stuff about "attempts to broaden the definition of intelligence to something that is more inclusive" is backwards. The whole problem is that nobody has managed to scientifically define (let alone measure) intelligence in any vertebrate species. IQ is dangerously misleading precisely because it is so narrow: its precision makes claims about IQ seem quantitatively rigorous when they are qualitatively meaningless.
If you care about music: at least 100 years, almost certainly much longer. I don't think any of us in this comment section will live to see an AI that truly understands human music.
If you care about money and don't mind making the world a more terrible place: humiliate a few dozen human classical pianists by making them record hundreds of hours of motion capture, invest in engineering a good robo-arm, and I would guess in 5 years you'd have something passable.
I think this is a mischaracterization. If you read the original post[1] it's clear he's not at all sympathetic to the cause, though given recent news he is more sympathetic to the people who have the cause (even if he thinks it's misguided).
> it's not in a way that would be particularly helpful here.
Why would you say that? This claim seems completely unsupported. What if the human directed the Waymo to drive more slowly and carefully than it would otherwise? That sort of instruction seems entirely consistent with the blog post and would be critical to the impressive behavior we see in the video.
> You may be under the common misunderstanding that remote operators can directly control the vehicle.
I think you are under the misunderstanding that since humans don't control the vehicle, they must have only a second-order impact on safety. But I am not concerned about self-driving's technical ability to dodge obstacles, I am concerned about its judgment around passing school buses or not slowing down when there are lots of pedestrians. It's this sort of human-level reasoning that I suspect is critical for Waymo's safety record. Nothing in Waymo's blog post / infomercial suggests otherwise.
Waymo's numbers aren't dishonest and their accomplishment is real, but this data should be taken in its proper context.
1) The comparison lumps in a lot of humans which cynically and selfishly break the law. Currently Waymos attempt to obey the laws of the road, but Tesla FSD shows that self-driving companies can and will market towards people who want to speed, run red lights, etc. So I am deeply concerned that a formal comparison of law-abiding self-driving cars will be used to encourage development of autotaxis which compete with human Ubers on travel time by speeding or rolling through stop signs like a human would. It would ultimately be a correlation/causation error: Waymo's training and development correlates with better safety records when the causative variable is ultimately risk-adversity, with better engineering being a secondary factor. In particular, I suspect Waymos will eventually be safer than humans at legal driving but dramatically more dangerous at illegal driving, and Waymo executives using motivated reasoning to dispel the concerns. Again, FSD literally offered different options for how much you wanted to speed or roll stop signs. This isn't hypothetical.
2) This is less concerning than reckless driving, but these stats include a lot of human accidents due to mechanical failure. This is something I expect self-driving to handle very poorly without human intervention. The most cognitively demanding moment of my entire life was having a rear tire blowout while driving a heavily-loaded truck with little experience - as the end started to fishtail I quickly realized what happened and carefully applied the brakes, hit the emergency lights, closely paying attention to the tactile feedback in my feet and hands, then pulled over as soon as I possibly could. If I were an AI and this was an edge case the engineers forgot to train on, Robo-Me would have easily flipped the truck. Mechanical failures aren't occurring in Waymos now because the cars are still new. Eventually it might be a real problem.
The thing I wish we better understood about Waymo is how much the remote human operators are actually intervening on a daily basis - maybe I don’t know where to look, but I’ve never gotten a clear answer here. AFAICT the success of Waymo means Tesla needs to have a similar level of human oversight for its FSD vehicles. But since Waymo’s PR is all about the autonomy, way too many people have the impression that Waymo’s advantage is solely about better algorithms along with humility around limited geographical range. These are probably important factors! But I strongly suspect we don’t appreciate the human oversight.
There's a huge difference between "the AI receives synthetic data as if from an omniscient being and proceeds as if it were true" and "the animal brain creates synthetic data and assesses its plausibility and downstream consequences."
Synthetic data in AI training in fact has nothing to do with dreaming or armchair theorizing. It's a ridiculous comparison.
