Plasma: Scalable Autonomous Smart Contracts

I actually saw Vitalik speak last week or so here in Shenzhen came away less convinced than ever of their theoretical utility for anything but a tiny fraction of users.

In short, adding a decentralized network of paid/incentivized actors to any existing potential cryptographic problem space (a fair rough summary of the smart contract notion?), in particular the subset relevant for most businesses, doesn't seem to solve anything particularly well and typically decreases critical measures of engineering elegance such as simplicity, comprehensibility, predictability, etc. while increasing negative measures such as technical lock-in to piles of rapidly evolving technologies for which hiring and building is expensive and error-prone.

This seems to be the direction that blockchain and smart contract tech needs to go if it aims to be scalable. Conduct the vast majority of your business off-chain, and resort to the blockchain in the event of a dispute.

Kind of like how two parties in a business relationship will generally act on good faith, but may choose the nuclear option of legally enforcing a contract if the relationship sours.

Imagine a 10,000-node network, where each node has to handle 1 billion messages per second. Firstly, if we assume a single VM can handle 1 million messages per second, it would require that each node rent ~1000 VMs (for a total of 10 million VMs for the entire P2P network), which they obviously can’t do without compensation. And if you introduce compensation, you need to pay the same fee to each node, which makes it inherently 10,000 times as expensive as a centralized network.

So the question is not whether it’s possible, the question is whether it’s worth it: in how many cases would a user prefer a system that’s 10,000 times cheaper, albeit requiring some trust?

Merchants all over the world are willing to accept VISA credit in exchange for goods and services, but they could — in theory — demand trustless payments in the form of international bank transfers, which are less than 10,000 times as expensive as a VISA transaction.

Looking forward to seeing results

Vitalik has created Ethereum of course, and he's not asking for investment, so this is better than your garden variety ICO scam. But right now this is more of a wishlist of desired features than a roadmap to implementation.

- Medieval barber, Theodoric of York

http://blog.stephenwolfram.com/2016/10/computational-law-sym...

And so it is with bitcoin, Ethereum, etc. The idea is that some particular thing that happened (“X paid Y such-and-such” or whatever) is shared and recorded in so many places that there can’t be any doubt about it. Yes, it’s in principle possible that all the few thousand places that actually participate in something like bitcoin today could collude to give a fake result. But the idea is that it’s like with gas molecules in a room: the probability is inconceivably small. (As it happens, my Principle of Computational Equivalence suggests that there’s more than an analogy with the gas molecules, and that actually the underlying principles at work are basically exactly the same. And, yes, there are lots of interesting technical details about the operation of distributed blockchain ledgers, distributed consensus protocols, etc., but I’m not going to get into them here.) It’s popular these days to talk about “smart contracts”. When I’ve been talking about “computational contracts” I mean contracts that can be expressed computationally. But by “smart contracts” people usually mean contracts that can both be expressed computationally and execute automatically. Most often the idea is to set up a smart contract in a distributed computation environment like Ethereum, and then to have the code in the contract evaluate based on inputs from the computation environment. Sometimes the input is intrinsic—like the passage of time (who could possibly tamper with the clock of the whole internet?), or physically generated random numbers. And in cases like this, one has fairly pure smart contracts, say for paying subscriptions, or for running distributed lotteries. But more often there has to be some input from the outside—from something that happens in the world. Sometimes one just needs public information: the price of a stock, the temperature at a weather station, or a seismic event like a nuclear explosion. But somehow the smart contract needs access to an “oracle” that can give it this information. And conveniently enough, there is one good such oracle available in the world: Wolfram|Alpha. And indeed Wolfram|Alpha is becoming widely used as an oracle for smart contracts. (Yes, our general public terms of service say you currently just shouldn’t rely on Wolfram|Alpha for anything you consider critical—though hopefully soon those terms of service will get more sophisticated, and computational.) But what about non-public information from the outside world? The current thinking for smart contracts tends to be that one has to get humans in the loop to verify the information: that in effect one has to have a jury (or a democracy) to decide whether something is true. But is that really the best one can do? I tend to suspect there’s another path, that’s like using machine learning to inject human-like judgment into things. Yes, one can use people, with all their inscrutable and hard-to-systematically-influence behavior. But what if one replaces those people in effect by AIs—or even a collection of today’s machine-learning systems? One can think of a machine-learning system as being a bit like a cryptosystem. To attack it and spoof its input one has to do something like inverting how it works. Well, given a single machine-learning system there’s a certain effort needed to achieve this. But if one has a whole collection of sufficiently independent systems, the effort goes up. It won’t be good enough just to change a few parameters in the system. But if one just goes out into the computational universe and picks systems at random then I think one can expect to have the same kind of independence as by having different people. (To be fair, I don’t yet quite know how to apply the mining of the computational universe that I’ve done for programs like cellular automata to the case of systems like neural nets.) There’s another point as well: if one has a sufficiently dense net of sensors in the world, then it becomes increasingly easy to be sure about what’s happened. If there’s just one motion sensor in a room, it might be easy to cover it. And maybe even if there are several sensors, it’s still possible to avoid them, Mission Impossible-style. But if there are enough sensors, then by synthesizing information from them one can inevitably build up an understanding of what actually happened. In effect, one has a model of how the world works, and with enough sensors one can validate that the model is correct. It’s not surprising, but it always helps to have redundancy. More nodes to ensure the computation isn’t tampered with. More machine-learning algorithms to make sure they aren’t spoofed. More sensors to make sure they’re not fooled. But in the end, there has to be something that says what should happen—what the contract is. And the contract has to be expressed in some language in which there are definite concepts. So somehow from the various redundant systems one has in the world, one has to make a definite conclusion—one has to turn the world into something symbolic, on which the contract can operate.