## 8 Conclusions  
**The flavor of the open questions. ** While most of the key questions about universal sequence prediction have been solved, many key questions about universal AI remain open to date. The questions in Sections 4.-7. are centered around the AIT approach to induction and AI, but many require interdisciplinary working. A more detailed account with technical details can be found in the book [27] and paper [31]. Most questions are amenable to a _rigorous mathematical_ treatment, including the more philosophically or vaguely sounding ones. Progress on the latter can achieved in the usual way by cycling through \((i)\) craft or improve mathematical definitions that resemble the intuitive concepts to be studied (e.g. \"natural\", \"generalization\", \"optimal\"), \((ii)\) formulate or adapt a mathematical conjecture resembling the informal question, \((iii)\) (dis)prove the conjecture. Some questions are about approximating, implementing, and testing various ideas and concepts. Technically, many questions are on (the _interface_ between) and exploit _techniques_ used in _(algorithmic) information theory_, _machine learning_, _Bayesian statistics_, _(adaptive) control theory_, and _reinforcement learning_. **Feasibility, difficulty, and interestingness of the open questions. ** I concentrated on questions whose answers probably help to develop the foundations of universal induction and UAI. Some problems are very hard, and their satisfactory solution worth a Nobel prize or Turing award, e.g. problem 5.j. I included those questions that looked promising and interesting at the time of writing this article. In the following I try to estimate their relative feasibility, difficulty, and interestingness:  
* Problems roughly sorted from most important or interesting to least: 5.j,4.h,7.b,5.a,7.c,4.b,4.f,4.1,5.d,5.f,5.i,4.a,4.c,4.d,4.e,4.g,5.c,6.a,6.b,5.g,5.h,4.j,5.b,6.c,7.a,4.i,4.k,6.d. * Problems roughly sorted from most to least time consuming: 4.b,4.h,5.d,5.j,7.c,4.c,5.b,5.c,6.c,6.a,5.g,5.i,7.b,4.i,4.j,4.1,5.a,6.d,6.b,5.f,5.h,4.d,4.f,4.g,4.k,7.a,4.a,4.e. * Problems roughly sorted from hard to easy: 4.h,6.c,5.j,4.b,4.i,4.1,5.b,6.a,7.b,4.c,4.g,5.a,5.c,6.b,5.f,5.i,4.j,5.h,7.a,4.d,4.f,4.k,5.d,6.d,7.c,4.a,4.e,5.g. These rankings hopefully do not mislead but give the interested reader some guidance where (not) to start. The final paragraphs of this article are devoted to the role UAI plays in the grand goal of AI. **Other approaches to AI. ** There are many fields that try to understand the phenomenon of intelligence and whose insights help in creating intelligent systems: _Cognitive psychology_ and _behaviorism_[181], _philosophy_ of mind [182, 183], _neuroscience_[184], _linguistics_[185, 186], _anthropology_[187], _machine learning_[59, 188], _logic_[189, 190], _computer science_[25, 191], _biological evolution_[192], and others. In computer science, most AI research is _bottom-up_; extending and improving existing or developing new _algorithms_ and increasing their range of applicability; an interplay between experimentation on toy problems and theory, with occasional real-world applications. The agent perspective of AI [25] brings some order and unification in the large variety of problems the fields wants to address, but it is only a framework rather than a complete theory. In the absence of a perfect (stochastic) model of the environment, machine learning techniques are needed and employed. Apart from AIXI, there is no general theory for learning agents. This resulted in an ever increasing number of _limited models and algorithms_ in the past. **The information-theoretic approach to AI. ** Solomonoff induction and AIXI are mathematical _top-down_ approaches. The price for this generality is that the full models are computationally intractable, and investigations have to be mostly theoretical at this stage. From a different perspective, UAI strictly _separates the conceptual and algorithmic AI questions_.