LessWrong 2.0 Reader

At $1000/kW-hr it's (just barely) not worth even buying batteries to shift energy from daytime generation to night consumption, while at $700/kW-hr it definitely is worthwhile.

Doesn't this depend heavily on local utility rates, and so any discussion of crossover points should include rates? Ex: I'm at $0.33/kWh while a friend in TX is at half that.

Batteries are primarily used for intra-day time shifting, not weekly. I agree that going completely off grid costs substantially more than being able to use your own generated power for 80-90% of usage. That's why I focused on the case where home owners remain grid-connected in my top-level comment:

With smart meters and cheaper home battery systems the incentives starts to shift toward wealthier solar enthusiasts buying batteries and selling excess power to the grid at peak times (or consuming it themselves), lowering peak demand at no additional capital or maintenance cost to the grid operators.

The only mention I made regarding completely off-grid power systems was about the counterfactual scenario of $150/kW-hr battery cost, which I have not assumed anywhere else. I didn't say that it would be marginally cost effective to go completely off grid with such battery prices, just that it would be substantially more cost-effective than buying all my power from the grid. The middle option of 80-90% reduced but not completely eliminated grid use is still cheaper than either of the two extremes, and likely to remain so for any feasible home energy storage system.

That's what I was referring to regarding $700 kW/hr. At $1000/kW-hr it's (just barely) not worth even buying batteries to shift energy from daytime generation to night consumption, while at $700/kW-hr it definitely is worthwhile. Do you need the calculation for that?

On mobile we by default use a markdown editor, so you can use markdown to format things.

My sister tried TMS and said it made her ears ring. Did you experience that?

No. Normally trained networks have adversarial examples. A sort of training process is used to find the adversarial examples. 

I should have asked for clarification what you meant. Literally you said "adversarial examples", but I assumed you actually meant something like backdoors. 

In an adversarial example the AI produces wrong output. And usually that's the end of it. The output is just wrong, but not wrong in an optimized way, so not dangerous. Now, if an AI is sophisticated enough to have some kind of optimizer that's triggered in specific circumstances, like an agentic mask that came into existence because it was needed to predict agentically generated tokens in the training data, then it might be triggered inappropriately by some inputs. This case I would classify as a mask takeover.

In the case of direct optimization for token prediction (which I consider highly unlikely for anything near current-level AIs, but afaik might be possible), then adversarial examples, I suppose, might cause it to do some wrong optimization. I still don't think modeling this as an underlying different goal taking over is particularly helpful, since the "normal" goal is directed to what's rewarded in training - the deviation is essentially random. Also, unlike in the mask case where the mask might have goals about real-world state, there's no particular reason for the direct optimizer to have goals about real-world state (see below).

Is it more complicated? What ontological framework is this AI using to represent it's goal anyway?

Asking about the AI using an "ontological framework" to "represent" a goal is not the correct question in my view. The AI is a bunch of computations represented by particular weights. The computation might exhibit goal-directed behaviour. A better question, IMO, is "how much does it constrain the weights for it to exhibit this particular goal directed behaviour?" And here, I think it's pretty clear that a goal of arranging the world to cause next tokens to be predicted constrains the weights enormously more than a goal of predicting the next tokens, because in order to exhibit behaviour directed to that goal, the AI's weights need to implement computation that doesn't merely check what the next token is likely to be, but also assess what current data  says about the world state, how different next token predictions would affect that world state, and how that would affect it's ultimate goal. 

So, is the network able to tell whether or not it's in training? 

The training check has no reason to come into existence in the first place under gradient descent. Of course, if the AI were to self-modify while already exhibiting goal directed behaviour, obviously it would want to implement such a training check. But I am talking about an AI trained by gradient descent. The training process doesn't just affect the AI, it literally is what creates the AI in the first place.

Some interesting points there. The lottery ticket hypothesis does make it more plausible that side computations could persist longer if they come to exist outside the main computation.

Regarding the homomorphic encryption thing: yes, it does seem that it might be impossible to make small adjustments to the homomorphically encrypted computation without wrecking it. Technically I don't think that would be a local minimum since I'd expect the net would start memorizing the failure cases, but I suppose that the homomorphic computation combined with memorizations might be a local optimum particularly if the input and output are encrypted outside the network itself. 

So I concede the point on the possible persistence of an underlying goal if it were to come to exist, though not on it coming to exist in the first place.

And there are few ways to predict next tokens, but lots of different kinds of paperclips the AI could want. 

For most computations, there are many more ways for that computation to occur than there are ways for that computation to occur while also including anything resembling actual goals about the real world. Now, if the computation you are carrying out is such that it needs to determine how to achieve goals regarding the real world anyway (e.g. agentic mask), it only takes a small increase in complexity to have that computation apply outside the normal context. So, that's the mask takeover possibility again. Even so, no matter how small the increase in complexity, that extra step isn't likely to be reinforced in training, unless it can do self-modification or control the training environment.

Is there a way to access text formatting options when commenting on Android devices?

If you have 3 days worth of storage, even if you completely discharge it in 3 days and completely charge it in the next 3 days, you would only go through about 60 cycles per year. In reality, you might get 10 full cycles per year. With interest rates and per year depreciation, typically you would only look out around 10 years, so you might get ~100 discounted full cycles. That's why it makes more sense to calculate it based on capital cost as I have done above. If you're interested in digging deeper, you can get free off grid modeling software, such as the original version of HOMER (new versions you have to pay).

Even now at $1000/kW-hr retail it's almost cost-effective here to buy batteries to time-shift energy from solar generation to time of consumption. At $700/kW-hr it would definitely be cost-effective to do daily load-shifting with the grid as a backup only for heavily cloudy days.

Please write out the calculation. 

Have there been some recent advances in compressed air energy storage? The information I read 2-3 years ago did not look promising at any scale.

Aboveground compressed air energy storage (tanks) is a little cheaper than chemical batteries. But belowground large compressed air energy storage is much cheaper for days of storage, with estimates around $1 to $10 per kilowatt hour. Current large installations are in particularly favorable geology, but we already store huge amounts of natural gas seasonally in saline aquifers. So we can basically do the same thing with compressed air, though the cycling needs to be more frequent.

(We switched back to shipping Calibri above Gill Sans Nova pending a fix for the horrible rendering on Windows, so if Ubuntu has Calibri, it'll have reverted back to the previous font.)

From footnote 2 to The state of AI today:

GPT-2 cost an estimated $43,000 to train in 2019; today it is possible to train a 124M parameter GPT-2 for $20 in 90 minutes.

Isn't $43,000 the estimate for the 1.5B replication of GPT-2 rather than for the 124M? If so, this phrasing is somewhat misleading. We only need $250 even for the 1.5B version, but still.