very general definition. This is easier to do if we abstract over the internal workings of the agent and define intelligence only in terms of external communications. Practically, what matters is how well something works. By definition, if an agent has a high value of Υ, then it must work well over a wide range of environments.

Block attacks this perspective by describing a machine that appears to be intelligent as it is able to pass the Turing test, but is in fact no more than just a big look-up table of questions and answers [Blo81] (for a related argument see [Gun71]). Although such a look-up table based machine would be unfeasibly large, the fact that a finite machine could in theory consistently pass the Turing test, seemingly without any real intelligence, is worrisome. Our formal measure of machine intelligence could be challenged in the same way, as could any test of intelligence that relies only on an agent's external behaviour.

Our response to this is very simple: If an agent has a very high value of Υ then it is, by definition, able to successfully operate in a wide range of environments. We simply do not care whether the agent is efficient, due to some very clever algorithm, or absurdly inefficient, for example by using an unfeasibly gigantic look-up table of precomputed answers. The important point for us is that the machine has an amazing ability to solve a huge range of problems in a wide variety of environments. How do you respond to Searle's "Chinese room" argument? Searle's Chinese room argument attacks our functional position in a similar way by arguing that a system may appear to be intelligent without really understanding anything [Sea80]. From our perspective, whether or not an agent understands what it is doing is only important to the extent that it affects the measurable performance of the agent. If the performance is identical, as Searle seems to suggest, then whether or not the room with Searle inside understands the meaning of what is going on is of no practical concern; indeed it is not even clear to us how to define "understanding" if its presence has no measurable effects. So long as the system as a whole has the powerful properties required for universal machine intelligence, then we have the kind of extremely general and powerful machine that we desire. On the other hand, if "understanding" does have a measurable impact on an agent's performance in some well defined situations, then it is of interest to us. In which case, because Υ measures performance in all well defined situations, it follows that Υ is in part a measure of how much understanding an agent has.

But you don't deal with consciousness (or creativity, imagination, freewill, emotion, love, soul, etc.) We apply the same argument to consciousness, emotions, freewill, creativity, the soul and other such things. Our goal is to build powerful and flexible machines and thus these somewhat vague properties are only relevant to our goal to the extent to which they have some measurable effect on performance in some well defined environment. If no such measurable effect exists, then they are not relevant to our objective. Of course this is not the same as saying that these things do not exist. The question is whether they are relevant or not. We would consider creativity, appropriately defined, to have a significant impact on an agent's ability to adapt to challenging environments. Perhaps the same is also true of emotions, freewill and other qualities.

Universal intelligence is impossible due to the No-Free-Lunch theorem. Some, such as Edmonds [Edm06], argue that universal intelligence is impossible due to Wolpert's