I've read some of this Universal Induction article. It seems to operate from flawed premises.

If we prescribe Occam’s razor principle [3] to select the simplest theory consistent with the training examples and assume some general bias towards structured environments, one can prove that inductive learning “works”. These assumptions are an integral part of our scientific method. Whether they admit it or not, every scientist, and in fact every person, is continuously using this implicit bias towards simplicity and structure to some degree.

Suppose the brain uses algorithms. An uncontroversial supposition. From a computational point of view, the former citation is like saying: "In order for a computer to not run a program, such as Indiana Jones and the Fate of Atlantis, the computer must be executing some command to the effect of "DoNotExecuteProgram('IndianaJonesAndTheFateOfAtlantis')".

That's not how computers operate. They just don't run the program. They don't need a special process for not running the program. Instead, not running the program is "implicitly contained" in the state of affairs that the computer is not running it. But this notion of implicit containment makes no sense for the computer. There are infinitely many programs the computer is not running at a given moment, so it can't process the state of affairs that it is not running any of them.

Likewise, the use of an implicit bias towards simplicity cannot be meaningfully conceptualized by humans. In order to know how this bias simplifies everything, one would have to know, what information regarding "everything" is omitted by the bias. But if we knew that, the bias would not exist in the sense the author intends it to exist.

Furthermore:

This is in some way a contradiction to the well-known no-free-lunch theorems which state that, when averaged over all possible data sets, all learning algorithms perform equally well, and actually, equally poorly [11]. There are several variations of the no-free-lunch theorem for particular contexts but they all rely on the assumption that for a general learner there is no underlying bias to exploit because any observations are equally possible at any point. In other words, any arbitrarily complex environments are just as likely as simple ones, or entirely random data sets are just as likely as structured data. This assumption is misguided and seems absurd when applied to any real world situations. If every raven we have ever seen has been black, does it really seem equally plausible that there is equal chance that the next raven we see will be black, or white, or half black half white, or red etc. In life it is a necessity to make general assumptions about the world and our observation sequences and these assumptions generally perform well in practice.

The author says that there are variations of the no free lunch theorem for particular contexts. But he goes on to generalize that the notion of no free lunch theorem means something independent of context. What could that possibly be? Also, such notions as "arbitrary complexity" or "randomness" seem intuitively meaningful, but what is their context?

The problem is, if there is no context, the solution cannot be proven to address the problem of induction. But if there is a context, it addresses the problem of induction only within that context. Then philosophers will say that the context was arbitrary, and formulate the problem again in another context where previous results will not apply.

In a way, this makes the problem of induction seem like a waste of time. But the real problem is about formalizing the notion of context in such a way, that it becomes possible to identify ambiguous assumptions about context. That would be what separates scientific thought from poetry. In science, ambiguity is not desired and should therefore be identified. But philosophers tend to place little emphasis on this, and rather spend time dwelling on problems they should, in my opinion, recognize as unsolvable due to ambiguity of context.