Readit News> Tom is willing to pay $6 for coffee and the shop only charges him $4, the deadweight loss is $2.
That is a consumer surplus, not deadweight loss.
The idea here is that tipping enables better price discrimination, which in turn allows a higher "quantity" of coffee to be produced. With coffee shops that "quantity" will translate to things like 1) higher density or 2) more attractive placement of coffee shops, both of which increase coffee consumption by making it easier to pick one up, or 3) higher quality (think small batch) coffee being produced, which increases the quantity of labor being sold in a cup.
I still don't enjoy this tipping culture, but the argument being made makes sense when you fill in the details.
Less a functioning human and more like a mid-sized neural net trained only on making game company short-term stock evaluations increase.
He's the game dev equivalent of Nick Bostrom's paperclip maximizer thought experiment.
[1]: https://en.m.wikipedia.org/wiki/Curse_of_dimensionality
(Heavily paraphrased from a longer post I read years and years ago, no idea where)
Are there any other jobs where someone can take a month off at a time to travel to conferences, meet with international colleagues, and go on long visits to other institutions? Compare this with any tech or high powered job that has much more sensitive deadlines.
The only real deadlines for academia are: PhD graduation (4-6 years), postdoc fellowship ends (2-4 years), and tenure (5-8 years).
Between those selection cutoffs you are absolutely free to structure your life however you want.
If it doesn’t work out, oh well, you go into industry and collect double pay.
In contrast, deadlines in engineering are often even not expected to be hit.
The "freedom" to travel for conferences is an integral part of your job as a researcher: either you network and sell your ideas, or you stay in obscurity. Of course travelling on someone else's dime can be fun, but the same is true of all business travel. It stops being fun the moment it becomes a chore and you'd rather be home putting time into your hobbies or family. Then it's just more work.
If writing grant applications is your hobby and you're married to your research, academia can be great, but the freedom doesn't include a balance with all the other parts of life. I know I'm not fully contradicting you, there is indeed a lot of freedom to choose what you work on. I just think it's important that people considering academia understand what the job actually consists of.
I imagine that the simulated 3D environment and the actual control of the robot arm must have some degree of interconnection neurally.
- Stack boxes collaboratively by controlling your own arm and communicating with another agent helping you.
- First produce a plan in text that another agent has to use to predict how you're going to control the arm. You'd get rewarded for both stacking correctly and being predictable based on the stated plan.
[0]: One reason: Never once did I need to know the transformer architecture in order to be able to use these models (prompt engineering, chaining, working with local models, etc.).
I argue that the knowledge of concepts such as ROPE, Mirostat, monkeypatching, etc. is much more crucial than knowing how transformer models work.
> I tend to stick with the higher level explanation that they can predict the next word (or next sentence) based on their training text,
I think the same way, but I think it reduces LLMs into "black boxes"—many other models can also predict next tokens based on probabilities. I think we need something that at least captures the general mechanism by which LLMs predict the next token.
This bit varies a lot since the capabilities involved in prediction depend on the data. If the text is a math book and the prompt is "... three plus five apples is a total of " the crucial capability is arithmetic (plus of course NL capabilities). On the other hand, if you're completing a post from /r/relationship_advice the capabilities involved will be (vaguely) maintaining literary tone, theory of mind, psychology, etc. Within a text the capabilities needed will also vary a lot, where you might need theory of mind at some crucial inflection points, but most of the time its already clear what is going to be said and the model just has to get the wording right.
So, my take would be to really think hard about the data to understand how predictions might be made.