>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.
The argument the article makes is indeed a bit blunt.
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.
As a crude point of comparison, you'd be buying this service at about 917 euros/hour. With a pretty lax 1600 hours/year you'd have to make almost 1.5M per year to match the rate.
This was a great read. I kept expecting it to reference the "Curse of Dimensionality" [1] as an explanation for why we see no close-to-average samples in high-dimensional spaces. I'm guessing the book this is an excerpt from might. In any case, I enjoyed this as a relatable instance of the concept!
I get the point but also fully disagree. At the point where computer cognition is good enough for humans to truly fall in love, build friendships or come to hate as an enemy, I personally think not recognizing it as AI will become a fringe belief.
I feel this description overstates the freedom. Academia is full of rather hard deadlines, as the conference you're targeting a paper for might be the only fitting one for many months. Missing these deadlines can delay the whole "pipeline" of research, as you'd like to build on your existing work and properly cite it, but publishing subsequent work might make the original result harder to publish. Grant application deadlines are similarly hard.
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.
You could also train for this kind of interconnectedness by designing tasks that are explicitly multi-modal. For example, you could:
- 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.
They definitely need a brushing after smoothies, but apart from that I'm not the most worried about bacterial growth inside them. They're not used for cooking, so as long as there isn't significant biomass inside there it doesn't feel too dangerous - bacteria multiplying in food and manufacturing toxins is the thing I'm really careful about. I imagine people routinely get similar exposure to bacteria from many other sources they don't even think about.
If you're advocating for a single tax, let it at least be LVT [1].
But seriously, the point of this Finnish tax is to internalize externalities of vehicles that contribute excessively to global warming. Even if there's some loopholes it's still shaping consumer behavior by making you jump through them... and if the jump is too easy, they can just continue making the loopholes smaller.
Thinking this tax is for meeting the government's budget is missing the point. It would serve its purpose just as well if you were required to burn the money instead of giving it to the government.
I don't think SSAO is really ever selected over more realistic methods for artistic preference. High budget animated films tend to go for ray traced methods even when they have a non-photorealistic style.
I think you misunderstood the approach. MuZero is being used to optimize the choices made in the VP9 compression. In modern video encodings there's many ways to encode the same content. As a very simple example, you can vary how often you provide a full encoding of a frame and how often you encode differences between frames. Once this off-line optimization is done, the result is still a valid VP9 encoding, just a smaller one. MuZero is not needed for decompression at all.
DPI is deep packet inspection in this context. HyperScan did some groundbreaking work for DPI, where you (1) have to match a huge number of regexes at the same time, (2) need high and consistent performance, and (3) don't need the starting points of matches.
I tried to with a Quest 2. Immersed VR was the only option I found for getting multiple virtual monitors with my non-VR-ready work laptop (it has to encode and stream the virtual monitors to the headset), but performance was not good enough for more than two screens at a reasonable resolution.
I did see the promise of it though. Input was not a problem as I'm able to touch type without looking at the keyboard. Being able to position additional screens all around your main screen was amazing -- with physical monitors I only have space for two.
Using a VR-ready machine to directly run the headset and some headless monitor emulator adapters might result in a better experience right now. With this kind of setup you can use other VR desktop software, e.g. Virtual Desktop.
>His thesis is that the US is a world power on the decline and China will soon rise to take its place.
Something I've found interesting about these kinds of theses (although I'm sure there's more to his) is that just believing that a company/country/whatever is going to be successful doesn't make it a good investment. It's all about the pricing of the asset and ultimately, for assets priced by a market mechanism, about what other people think. So a thesis for investing more in China could be "US is in decline and China will take its place faster/more fully than the market thinks."
Of course even if you don't have any special knowledge about China's future, the default position should still be to invest a part of your portfolio according to it's market weight, which in the FTSE All-World Index [1] is 3.9% currently.
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.