The best part is the “gluten free” anti AI crowd will be left in the dust. They may think they’re being classy snobs but being against AI is going to be tantamount to saying you eat crayons and don’t read well.
923 karma · joined September 27, 2021
Agile doesn’t take much effort if you know what you’re doing. It’s just a card wall, standups and a planning meeting. This is not hard for a good lead dev. If you think it’s hard, you probably aren’t a good lead dev, or you don’t know how to do it right, which is the same thing.
ML is currently capable of the tiger case you mention. It’s generally called “few-shot” or “one-shot” learning. In the context of an image generation model, having never seen a tiger before, if you show it a few pictures of a tiger, it could immediately draw you thousands of tigers in any variation or scenario you can think of, which is way more than a child can do.
As for the need to train on millions of images for the base model, I believe you are trying to say something about “sample efficiency”, and how ML differs from the brain in this regard outside of the few/one-shot contexts (which ML is absolutely capable of). I would argue that sample efficiency of the brain is actually also quite low, much lower than people assume. It’s irrelevant to an argument that ML is not superior, because ML is clearly is capable of learning richer, more effective representations in a shorter wall time than we can, whether it is sample efficient or not. And in the sample efficient few/one-shot contexts (learning what a tiger looks like from one picture), it also outperforms humans in speed accuracy and creativity. It’s not even close.
As for classification errors, ML is capable of some errors we are not, actually by virtue of being superior at learning representations we are not even close to being capable of learning. But those are edge cases, and they are fixed by various means. In the main cases, ML outperforms humans in speed, accuracy and class complexity, all exponentially.
You said something about graph approximation but it doesn’t make a lot of sense. I’m talking about learning and you’re complaining that machine learning is not “logical reasoning”. Whether ML is currently capable of logical reasoning is another discussion. Certain models do demonstrate some types of it today.
“Graph approximation” is a type of learning task. ML is a billion times better than humans at it so it also doesn’t help you argue that ML isn’t superior (in that regard).
Where is the abuse happening?
AI, like humans, is capable of both imitation and facsimile. It is far superior at both feats.
Your fallacy is that you are noticing AI is superior at facsimile and erroneously assuming it is “not learning”. You are also ignoring the other amazing learning feats of imitation in front of you.
Parrots are lovely animals, but it’s unclear what you think you’ve accomplished by bringing them up. The fact that they are capable of more than just memorization does not differentiate them from advanced AI models, which are rapidly gaining all sorts of abilities.
Polly the parrot would have a hard time producing a picture of Elmo with a light saber in a Superman costume riding a dragon on the moon in the style of Rembrandt (in under 300ms, at least). I also know a parrot couldn’t write a 500 word story about the image.
- If you bought the book, you can read it.
- If the book is free, you can read it.
- If the painting is in a museum, or on Wikipedia, you can visit it.
- If Bozo the clown says you’re not allowed to look at drawings he posted online, it’s ok. You still can.
Same for AI.
Humans are capable of both facsimile and imitation.
The fact that ML is able to perform facsimile far better than a human can is not evidence that this is “not the same” learning. Only that ML learning is superior. ML is far superior in feats of both imitation and facsimile.
ML can do either facsimile or imitation far better than a human mind can.
You seem to be conflating both things and suggesting that ML only does facsimile, which is where the potential legal problems are.
Or it’s just too fast so let’s stop it?
I prefer a lead or senior developer to have the role rather than a dedicated non- or even former- developer.
When a good lead dev with good communication skills and experience has the role it can be awesome.
However I have noticed that when that figurehead person is missing the meetings and goals quickly disassemble.
In my experience the best leader of Agile or Scrum is always a lead developer, instead of a dedicated “project” or “product” person.
There’s a lot of gripers who don’t like feeling managed and attack it with all sorts of false criticism though. And of course, poorly-run Agile can be pretty annoying.
The key is to keep things minimal and fast: quick stand-ups, and as few meetings as needed. Who is leading it definitely matters too. Ideally it is a lead developer rather than a “product” or “project” person, IMO.
This is about miscalculating business needs and failing to create a culture of work ethic, leading to a situation where people need to be let go.
That is not a crime. It’s just business.
The author certainly seems to hate “agile” but doesn’t have any critiques for it. Instead he’s punching at ghosts.
I think you’re confused about copyright law and about what is a facsimile vs a derivative work, and you’re also wrong about learning being fundamentally different for AI. AI learning is vastly superior and much more capable of both facsimile and imitation… but what is that if not learning; that is the definition of learning, plainly.
It is also immoral in most cases.
Most importantly though, it is impossible to accomplish with a silly disclaimer.
Licenses may control the circumstances of code facsimiles and products, but trying to stop your code from simply being seen or read with a disclaimer is impossible and silly.
And a lot of people believe whatever NYT or The Hill says, when they are obviously out to get him.
If it were true I believe he would write the whole letter about that. Instead he says 5 words about it.
Combined with the rest of it being mostly whining about nonsense… it looks deranged.
There’s something about 24 hour “speakers” which sounds alarming, but if it were what you think it sounds like he would have elaborated on it a lot more. There are also a lot of people “speaking” in these places 24 hours a day which can be frustrating.
Other than that this looks like deranged whining to me.
They sell mugs that show up as dogs/birds/toasters/nothing ... shirts that are stop signs, stickers that are toasters, etc.
Great secret santa gifts or stocking stuffers for ML nerds.
No affiliation, I just think it's cool :)
You are off though. I know plenty of Meta people both inside and outside of the Reality Labs division and they love it.
No offense, but “I have no data to back this up, but … (shareholders and employees of a company I don’t have inside knowledge of think X)” is lame discourse.