I see nothing in my library that can be abbreviated to MK1.

I dream of a world where people can follow their passion without having to worry about where their next meal will come from.

Who enforces these laws on the international stage?

I think mine already figured out by 2. This kid keeps making the animal noises of any animal we happen to be eating. They’re also doing “woof woof” at hot dogs.

I think we should be looking at all the marketing and data collection used to that end.

If it involves heating food on the stove top or the oven, I’d call it cooking. Find me a child that can make either the bread or jelly without that. I’ll wait.

What you want is a distribution-aware contextual binary search. With whatever information you have (appearance, personality, vocabulary, etc), you can come up with a probability distribution in the space of possible ages and start your guess with the value at the 50th percentile. Then depending on whether the true age is higher or lower, your next guess will be either the 25th or 75th percentile. Rinse and repeat.

In reality, the way most people intuitively do agree guessing is already an approximation of this procedure.

Going to disagree with the responses you have so far. You need to consider both the cost and benefit of taking precautions. The cost of sending someone else in your place is low. The benefit is that you negate that small chance of getting assaulted. The cost of not driving in a car-centric society? It’s way higher. It could mean not being able to go to the store to buy groceries, or not being able to get a job. For most people, that cost is much higher compared to the chance of dying in a car crash.

You can also tell me that someone out there won the lottery this week and have it be true. It’s not the same as seeing this person’s live reaction to learning about it. It wouldn’t be the same if you watched that person act out the scene exactly as it happened. AI generated is so much further removed from all that.

The info provided is that there exists another happy dog out there doing happy dog things and I briefly connected with it, which made me happy. This information would be incorrect if it was AI generated.

If sexual pleasure is the only thing going great in a couple […] one should probably reconsider if said relationship is still a loving one.

This makes sense if you omit the second condition.

You’re basically telling me that asexual people can’t love one another.

And seriously, if the friction is the only thing that make you enjoy sex, and not with whom you have sex with, you seriously should reconsider your relationship.

Why would you choose to do anything for fun that isn’t mutually enjoyable? You can enjoy both your partner’s company and also the activity you do together at the same time. It’s not one or the other. Don’t like condomed sex? Then don’t do it. There’s more to a romantic relationship than sex.

That’s why you always specify the base

10₁₀

The main difficulty is in how many hyperparameters are involved in training an RL agent, high sensitivity of RL algorithms to those hyperparameters, and not having a good understanding of how to select them based on the properties of your task. This problem is exacerbated by the high sample complexity of RL. If something doesn’t work out, you don’t know if it’s because you chose the wrong set of hyperparameters or if you just haven’t trained for long enough.

I don’t know much about game design, but I do know that it’s a much more mature field than RL, so surely they have better tools than guessing and praying.

It is expensive, but it does work. We’ve already seen things work to a limited extent on StarCraft 2, Dota, and Gran Turismo, and those are all multiplayer games. The article seems to be talking about single player games, which simplified things a lot.

Game playing is not LLM. They’re game-specific reinforcement learning models. It’s not easy, but definitely doable with existing tech. Sony’s GT Sophy is a good demonstration on what they’re capable of.

I don’t know if you can describe it as “can’t be arsed” when their proposed solution is so much harder to implement.

There’s high demand for both RAM and GPUs coming from datacenters. Us regular consumers are just a tiny blip on their radar.