Software engineer and small time DS/ML practitioner.
Thank you for this summary! It is nice to see someone covering these topics here, I personally rarely have enough nerves left after 2+ years of this hell. Victory to Ukraine and peaceful skies to us all!
This is what happens when the min wage is too high
... These automated kiosks existed for years and were used in Mac for years. And in places they were set Mac had better employment, not worse - there was exactly the same number of staff members on the same-ish salary but with decreased load on each member, while, as stated, leading to slightly bigger orders and less awkwardness.
So far I have been highly underwhelmed by what has been done with newly public domain properties
Some can argue it's quite an argument in favor of lowering the length of protected period. We can observe first hand that things going public doesn't cause any problem for previous owners at all and my opinion is that we are cutting it too far. If we want proper balance between ownership and creativity we need to put the threshold somewhere where it is at least a mild inconvenience for the owners, maybe more.
Oh, there are absolutely correct places to use the phrase and correct places to benefit from reliable simplicity! My main argument is against mindless usage that I unfortunately witness nowadays a lot. Understanding why and when we need to solve for the equilibrium in evaluation replaced by the simple belief in a rule that we should - always and for everything.
Depends on what you include in the definition of LLM. NN itself? Sure, it can. With the caveat of hardware and software limitations - we aren't dealing with EXACT math here, floating points operations rounding, non-deterministic order of completion in parallel computation will also introduce slight differences from run to run even though the underlying math would stay the same.
The system that preprocess information, feeds into the NN and postprocess NN output into readable form? That is trickier, given that these usually involve some form of randomness, otherwise LLM output would be exactly the same, given exactly the same inputs and that generally is frowned upon, not very AI-like behavior. But if the system uses pseudo-random generators for that - those also can be described in math terms, if you know the random generator seed.
If they use non-deterministic source for their randomness - no. But that is rarely required and makes system really difficult to debug, so I doubt it.
Both Gemini and GPT-4 also provide quite interesting answers on the very same prompt.
Adam Grant suggests: “I’m giving you these comments because I have very high expectations for you, and I’m confident you can reach them. I’m trying to coach you. I’m trying to help you.” Then you give them the feedback. Love it.
These are great, but unfortunately only work if the person is ready to accept your authority as a coach. If they don't - they work in an opposite direction.
California Fatburger manager trims hours, eliminates vacation days and raises menu prices in anticipation of $20/hour fast food minimum wage. That seems like a best case...
That's not how any of this works. You don't do that beforehand because there will be 20$/h. If you actually need this - you prepare plans conditional on wages becoming 20$/h. If you do this now, that's because of greed. And because of greed you'll also repeat it when the wages will rise.
Writers and artists say it’s against the rules to use their copyrighted content to build a competing AI model
The main difference is they say it NOW, after the fact that this happened, and OpenAI said so beforehand. There's long history of bad things happening when trying to retroactively introduce laws and rules.
I wonder at which point we'll start seeing LLM-on-a-chip.
One big reason for the current ML/AI systems inefficiencies is just abstraction layering overhead, our pay for the flexibility. We currently run hardware that runs binary calculations that run software that run other software that runs other software (many many layers here, OS/drivers/programming language stacks/NN frameworks etc) that finally runs the part we're actually interested in - bunch of matrix calculations representing the neural network. If we collapse all the unnecessary layers between, burning the calculations directly to hardware, running particular model should be extremely fast and cheap.