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PS. See also http://norvig.com/21-days.html (notice who is the author!) and this Bismon draft report (developing programming techniques, in a GPLv3+ alpha-stage software prototype, notably frame-based or semantic-network related ones, with reflection and introspection, reusable and relevant to AI).

PS. See also http://norvig.com/21-days.html (notice who is the author!) and this Bismon draft report (developing programming techniques, in a GPLv3+ alpha-stage software prototype, notably frame-based or semantic-network related ones, with reflection and introspection, reusable and relevant to AI), and the RefPerSys project.

For examples, recent IJCAI2016 papers such as Coco: Runtime Reasoning About Conflicting Commitments, Interdependent Scheduling Games, Control of Fair Division, Verifying Pushdown Multi-Agent Systems against Strategy Logics, etc.... don't mention any costly computation. Actually, it is likely that most recent papers don't use and don't need large scale costly cloud computing. And some of that research might not have been implemented (perhaps by some intern) in anything more than a toy prototype.

^{Here are some recent research publications, they all explicitly mention the needed computer equipement, follow the link to read them:}

Event recent IJCAI experimental papers like A Multicore Tool for Constraint Solving, Compiling Constraint Networks into Multivalued Decomposable Decision Graphs, External Memory Bidirectional Search, Multiple Constraint Acquisition, Completion of Disjunctive Logic Programs, Eliminating Disjunctions in Answer Set Programming by Restricted Unfolding and On the Empirical Time Complexity of Random 3-SAT at the Phase Transition mention at most a multi-core workstation (e.g. at most a dual Xeon socket workstation, often a cheaper laptop or desktop). I guess that most authors need their GPGU only as a graphics card, to use their display screen. BTW papers in other journals, such as JAIR (like Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning), when they mention computer equipement, gives similar kind of machines (laptop or at most high-end desktop).

^{(actually I don't remember having read an AI paper mentionning costly computing equipement; and I believe the reason for that is that in the academic community access to supercomputers is nearly reserved to research in other domains: physics, bioinformatics, etc... For an AI researcher gaining such access is difficult and uncommon; BTW in H2020 European research grants computing cost above 15% of the labor cost needs to be dully justified, so is exceptional)}

However, you'll better publish your software as free software or open source, and you need a lot of time (preferably full-time, or at least half-time) to do the research work, publish it, and follow outside progress in your area. BTW, contacting a nearby university could be helpful (you could attend some seminars, etc...)

So most research in AI (even in Machine Learning, read papers from JMLR) are done by researchers working on a laptop or desktop. I am not even sure you need a powerful GPU to do the research. You certainly can do interesting research with a desktop computer (probably running Linux, with 32Gb RAM, some AMD Ryzen or Intel i5 or i7) costing in 2017 less than 1500€ or maybe 2500€ (and sometimes a low end laptop is enough). In rare cases (a small minority of papers) you might need a dual-socket workstation (something costing perhaps 5000€).

PS. See also http://norvig.com/21-days.html (notice who is the author!) and this Bismon draft report (developing programming techniques, in a GPLv3+ alpha-stage software prototype, notably frame-based or semantic-network related ones, with reflection and introspection, reusable and relevant to AI).

For examples, recent IJCAI2016 papers such as Coco: Runtime Reasoning About Conflicting Commitments, Interdependent Scheduling Games, Control of Fair Division, Verifying Pushdown Multi-Agent Systems against Strategy Logics, etc. don't mention any costly computation. Actually, it is likely that most recent papers don't use and don't need large scale costly cloud computing. And some of that research might not have been implemented (perhaps by some intern) in anything more than a toy prototype.

^{Here are some recent research publications, they all explicitly mention the needed computer equipment, follow the link to read them:}

Event recent IJCAI experimental papers like A Multicore Tool for Constraint Solving, Compiling Constraint Networks into Multivalued Decomposable Decision Graphs, External Memory Bidirectional Search, Multiple Constraint Acquisition, Completion of Disjunctive Logic Programs, Eliminating Disjunctions in Answer Set Programming by Restricted Unfolding and On the Empirical Time Complexity of Random 3-SAT at the Phase Transition mention at most a multi-core workstation (e.g. at most a dual Xeon socket workstation, often a cheaper laptop or desktop). I guess that most authors need their GPU only as a graphics card, to use their display screen. BTW, papers in other journals, such as JAIR (like Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning), when they mention computer equipment, gives similar kind of machines (laptop or at most high-end desktop).

^{(Actually, I don't remember having read an AI paper mentioning costly computing equipment; and I believe the reason for that is that in the academic community access to supercomputers is nearly reserved to research in other domains: physics, bioinformatics, etc. For an AI researcher gaining such access is difficult and uncommon; BTW, in H2020 European research grants computing cost above 15% of the labor cost needs to be dully justified, so is exceptional)}

However, you'll better publish your software as free software or open-source, and you need a lot of time (preferably full-time, or at least half-time) to do the research work, publish it, and follow outside progress in your area. BTW, contacting a nearby university could be helpful (you could attend some seminars, etc.)

So, most research in AI (even in Machine Learning, read papers from JMLR) are done by researchers working on a laptop or desktop. I am not even sure you need a powerful GPU to do the research. You certainly can do interesting research with a desktop computer (probably running Linux, with 32Gb RAM, some AMD Ryzen or Intel i5 or i7) costing in 2017 less than 1500€ or maybe 2500€ (and sometimes a low end laptop is enough). In rare cases (a small minority of papers), you might need a dual-socket workstation (something costing perhaps 5000€).

PS. See also http://norvig.com/21-days.html (notice who is the author!) and this Bismon draft report (developing programming techniques, in a GPLv3+ alpha-stage software prototype, notably frame-based or semantic-network related ones, with reflection and introspection, reusable and relevant to AI).

PS. See also http://norvig.com/21-days.html (notice who is the author!) and this Bismon draft report (developing programming techniques, in a GPLv3+ alpha-stage software prototype, notably frame-based or semantic-network related ones, reusable and relevant to AI).

PS. See also http://norvig.com/21-days.html (notice who is the author!) and this Bismon draft report (developing programming techniques, in a GPLv3+ alpha-stage software prototype, notably frame-based or semantic-network related ones, with reflection and introspection, reusable and relevant to AI).