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November 2019

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2Vladimir Slepnev19d (11/?) Superdense coding. Alice is told two classical bits of information and sends a qubit to Bob, who can then recover the two classical bits. Again, it relies on Alice and Bob sharing a prepared pair beforehand. It's the opposite of quantum teleportation, where Alice sends two classical bits and Bob can recover a qubit. First let's talk about bases. This is the usual basis: |00>, |01>, |10>, |11>. This is the Bell basis: (|00> + |11>)/√2, (|00> - |11>)/√2, (|10> + |01>)/√2, (|10> - |01>)/√2. Check for yourself that each two vectors are orthogonal to each other. To "measure a state in a different basis" means to apply a rotation from one basis into another, then measure. For example, if you have a state and you know that it's one of the Bell basis states, you can figure out which one, by rotating into the usual basis and measuring. One cool thing about the Bell basis is that you can change any basis vector into any other basis vector by operations on the first qubit only. For example, rotating the first qubit by 90 degrees turns (|00> + |11>)/√2 into (|10> - |01>)√2. Flipping the first qubit gives (|10> + |01>)√2, and so on. Now superdense coding is easy. Alice and Bob start by sharing two halves of a Bell state. Then depending on which two classical bits Alice needs to send, she either leaves the state alone or rotates into one of the other three, by operating only on her qubit. Then she sends her qubit to Bob, who now has both qubits and can rotate them into the usual basis and measure them, recovering the two classical bits.
1[comment deleted]16d (13/?) Global phase. Consider a quantum system that's even smaller than a qubit. Something with only one possible measurement outcome, instead of two. Let's call it "quantum nit", or "qunit", by analogy with "unit" in typed programming, which means a type with one element. A qunit has only one possible mixed state, with the density matrix ((1)). But its pure states are more complicated: a one-qunit pure state is a number whose norm is 1, so either 1 or -1 (or more if we allow complex numbers). The same is true for an N-qunit pure state - it's also a single number whose norm is 1! The reason is that the tensor product of N 1-dimensional spaces is 1-dimensional, not N-dimensional. So the joint state of all qunits in the world is a single number, which is called "global phase". Now imagine we have a system with a bunch of qubits, and take its tensor product with a qunit. We can move a scalar factor from one side of a tensor product to the other, and that shouldn't change anything. So global phase can't affect any measurement, it's just an extra degree of freedom in the theory.
1Vladimir Slepnev16d (12/?) The uncertainty principle, or something like it. When you measure a qubit cos(φ)|0> + sin(φ)|1>, the result has variance (1-cos(4φ))/4. (I'm skipping over the trig calculations here and below.) If you have a choice between either measuring the qubit, or rotating it by an angle ψ and then measuring it, then the sum of variances of the two operations is at least (1-|cos(2ψ)|)/2. In particular, if ψ=π/4, the sum of variances is at least 1/2. So no matter what state a qubit is in, and how precisely you know its state, there are two possible measurements such that the sum of variances of their results is at least 1/2. The reason is that the bases corresponding to these measurements are at an angle to each other, not aligned. Apparently in the real world, an object's "position" and "momentum" are two such measurements, so there's a limit on their joint precision. You can also carry out one measurement and then the other, but that doesn't help - after the first measurement you have variance in the second. Moreover, after the second you have fresh variance in the first. This lets you get an infinite stream of fair coinflips from a single qubit: start with |0>, rotate by π/4, measure, rotate by π/4, measure...
1Vanessa Kosoy12d It seems useful to consider agents that reason in terms of an unobservable ontology, and may have uncertainty over what this ontology is. In particular, in Dialogic RL [] , the user's preferences are probably defined w.r.t. an ontology that is unobservable by the AI (and probably unobservable by the user too) which the AI has to learn (and the user is probably uncertain about emself). However, onotlogies are more naturally thought of as objects in a category than as elements in a set. The formalization of an "ontology" should probably be a POMDP or a suitable Bayesian network. A POMDP involves an arbitrary set of states, so it's not an element in a set, and the class of POMDPs can be naturally made into a category. Therefore, there is need for defining the notion of a probability measure over a category. Of course we can avoid this by enumerating the states, considering the set of all possible POMDPs w.r.t. this enumeration and then requiring the probability measure to be invariant w.r.t. state relabeling. However, the category theoretic point of view seems more natural, so it might be worth fleshing out. Ordinary probably measures are defined on measurable spaces. So, first we need to define the analogue of "measurable structure" (σ-algebra) for categories. Fix a category C. Denote Meas the category of measurable spaces. A measurable structure on C is then specified by providing a Grothendick fibration [] B:MFC→Meas and an equivalence E:B−1(pt)→C. Here, B−1(pt) stands for the essential fiber [] of B over the one point space pt∈Meas. The intended interpretation of MFC is, the category of families of objects in C indexed by measurable spaces. The functor B is supposed to extract the base (index space) of the family. We impose the following conditions on MFC and B: Given A∈

October 2019

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6Matthew "Vaniver" Graves20d I've been thinking a lot about 'parallel economies' recently. One of the main differences between 'slow takeoff' and 'fast takeoff' predictions is whether AI is integrated into the 'human civilization' economy or constructing a separate 'AI civilization' economy. Maybe it's worth explaining a bit more what I mean by this: you can think of 'economies' as collections of agents who trade with each other. Often it will have a hierarchical structure, and where we draw the lines are sort of arbitrary. Imagine a person who works at a company and participates in its internal economy, and the company participates in national and global economies, and the person participates in those economies as well. A better picture has a very dense graph with lots of nodes and links between groups of nodes whose heaviness depends on the number of links between nodes in those groups. As Adam Smith argues, the ability of an economy to support specialization of labor depends on its size. If you have an island with a single inhabitant, it doesn't make sense to fully employ a farmer (since a full-time farmer can generate much more food than a single person could eat), for a village with 100 inhabitants it doesn't make sense to farm more than would feed a hundred mouths, and so on. But as you make more and more of a product, investments that have a small multiplicative payoff become better and better, to the point that a planet with ten billion people will have massive investment in farming specialization that make it vastly more efficient per unit than the village farming system. So for much of history, increased wealth has been driven by this increased specialization of labor, which was driven by the increased size of the economy (both through population growth and decreased trade barriers widening the links between economies until they effectively became one economy). One reason to think economies will remain integrated is because increased size benefits all actors in the economy on net; a
5Matthew "Vaniver" Graves22d One challenge for theories of embedded agency over Cartesian theories is that the 'true dynamics' of optimization (where a function defined over a space points to a single global maximum, possibly achieved by multiple inputs) are replaced by the 'approximate dynamics'. But this means that by default we get the hassles associated with numerical approximations, like when integrating differential equations. If you tell me that you're doing Euler's Method on a particular system, I need to know lots about the system and about the particular hyperparameters you're using to know how well you'll approximate the true solution. This is the toy version of trying to figure out how a human reasons through a complicated cognitive task; you would need to know lots of details about the 'hyperparameters' of their process to replicate their final result. This makes getting guarantees hard. We might be able to establish what the 'sensible' solution range for a problem is, but establishing what algorithms can generate sensible solutions under what parameter settings seems much harder. Imagine trying to express what the set of deep neural network parameters are that will perform acceptably well on a particular task (first for a particular architecture, and then across all architectures!).
8Vanessa Kosoy25d Game theory is widely considered the correct description of rational behavior in multi-agent scenarios. However, real world agents have to learn, whereas game theory assumes perfect knowledge, which can be only achieved in the limit at best. Bridging this gap requires using multi-agent learning theory to justify game theory, a problem that is mostly open (but some results exist). In particular, we would like to prove that learning agents converge to game theoretic solutions such as Nash equilibria (putting superrationality aside: I think that superrationality should manifest via modifying the game rather than abandoning the notion of Nash equilibrium). The simplest setup in (non-cooperative) game theory is normal form games. Learning happens by accumulating evidence over time, so a normal form game is not, in itself, a meaningful setting for learning. One way to solve this is replacing the normal form game by a repeated version. This, however, requires deciding on a time discount. For sufficiently steep time discounts, the repeated game is essentially equivalent to the normal form game (from the perspective of game theory). However, the full-fledged theory of intelligent agents requires considering shallow time discounts, otherwise there is no notion of long-term planning. For shallow time discounts, the game theory of a repeated game is very different from the game theory of the original normal form game. In fact, the folk theorem asserts that any payoff vector above the maximin of each player is a possible Nash payoff. So, proving convergence to a Nash equilibrium amounts (more or less) to proving converges to at least the maximin payoff. This is possible using incomplete models [] , but doesn't seem very interesting: to receive the maximin payoff, the agents only have to learn the rules of the game, they need not learn the reward functions of the othe
6Vanessa Kosoy1mo This is preliminary description of what I dubbed Dialogic Reinforcement Learning (credit for the name goes to tumblr user @di--es---can-ic-ul-ar--es): the alignment scheme I currently find most promising. It seems that the natural formal criterion for alignment (or at least the main criterion) is having a "subjective regret bound": that is, the AI has to converge (in the long term planning limit, γ→1 limit) to achieving optimal expected user!utility with respect to the knowledge state of the user. In order to achieve this, we need to establish a communication protocol between the AI and the user that will allow transmitting this knowledge state to the AI (including knowledge about the user's values). Dialogic RL attacks this problem in the manner which seems the most straightforward and powerful: allowing the AI to ask the user questions in some highly expressive formal language, which we will denote F. F allows making formal statements about a formal model M of the world, as seen from the AI's perspective. M includes such elements as observations, actions, rewards and corruption. That is, M reflects (i) the dynamics of the environment (ii) the values of the user (iii) processes that either manipulate the user, or damage the ability to obtain reliable information from the user. Here, we can use different models of values: a traditional "perceptible" reward function, an instrumental reward function [] , a semi-instrumental reward functions, dynamically-inconsistent rewards [] , rewards with Knightian uncertainty etc. Moreover, the setup is self-referential in the sense that, M also reflects the question-answer interface and the user's behavior. A single question can consist, for example, of asking for the probability of some sentence in F or the expected
2Vladimir Slepnev20d (10/?) Quantum teleportation. It's not a very good name, maybe we should call it "sending qubits over a classical channel using a quantum one-time pad". The idea is that Alice and Bob are far apart from each other and have a prepared pair of qubits, and Alice also has some qubit in unknown state. She can measure it in a clever way and send some classical bits to Bob, which allows him to recreate the unknown qubit. Both qubits of the prepared pair are spent in the process. Let's say the unknown qubit is a|0>+b|1>, and the prepared pair is (|00> + |11>)/√2. So the whole state is (a|0> + b|1>) ⊗ (|00> + |11>)/√2 = (a|000> + a|011> + b|100> + b|111>)/√2. Alice can access the first two qubits and Bob can access the third. 1. Alice flips the second qubit depending on the first, leading to (a|000> + a|011> + b|110> + b|101>)/√2. 2. Alice applies a combined reflection and rotation to the first qubit, which is known as Hadamard gate: |0> becomes (|0> + |1>)/√2, while |1> becomes (|0> - |1>)/√2. So the whole state becomes (a|000> + a|100> + a|011> + a|111> + b|010> - b|110> + b|001> - b|101>)/2. 3. We can rewrite that as ((|00> ⊗ (a|0> + b|1>)) + (|01> ⊗ (a|1> + b|0>)) + (|10> ⊗ (a|0> - b|1>)) + (|11> ⊗ (a|1> - b|0>)))/2. 4. Notice something funny? In the above state, measuring the first two qubits gives you exactly enough information to know which operation to apply to the third qubit to get a|0> + b|1>. So if Alice measures both her qubits and sends the two classical bits to Bob, he can reconstruct the unknown qubit on his end. And if Carol eavesdrops on the message without having access to Bob's qubit, she won't learn anything, because the four possible messages have probability 1/4 each, regardless of a and b. If the unknown qubit was entangled with something else, that also survives the transfer, though proving that requires a bit more calculation.
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