Boosting Interactive Evolution Using Human Computation Markets

Lehman, Joel; Miikkulainen, Risto

doi:10.1007/978-3-642-45008-2_1

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Technically, Ascend is related to approaches to interactive evolution (Takagi 2001; Secretan et al 2011) and crowdsourcing (Brabham 2013; Lehman and Miikkulainen 2013a) in that evaluations of candidates are done online by human users. The usual interactive evolution paradigm, however, employs a relatively small number of human evaluators, and their task is to select good candidates or evaluate the fitness of a pool of candidates explicitly.…”

Section: Introductionmentioning

confidence: 99%

Ascend by Evolv: Artificial Intelligence‐Based Massively Multivariate Conversion Rate Optimization

Miikulainen

Brundage²,

Epstein³

et al. 2020

AI Magazine

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Conversion rate optimization (CRO) means designing an e-commerce web interface so that as many users as possible take a desired action such as registering for an account, requesting a contact, or making a purchase. Such design is usually done by hand, evaluating one change at a time through A/B testing, evaluating all combinations of two or three variables through multivariate testing, or evaluating multiple variables independently. Traditional CRO is thus limited to a small fraction of the design space only, and often misses important interactions between the design variables. This article describes Ascend by Evolv,1 an automatic CRO system that uses evolutionary search to discover effective web interfaces given a human-designed search space. Design candidates are evaluated in parallel online with real users, making it possible to discover and use interactions between the design elements that are difficult to identify otherwise. A commercial product since September 2016, Ascend has been applied to numerous web interfaces across industries and search space sizes, with up to fourfold improvements over human design. Ascend can therefore be seen as massively multivariate CRO made possible by artificial intelligence.

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Section: Introductionmentioning

confidence: 99%

Ascend by Evolv: Artificial Intelligence‐Based Massively Multivariate Conversion Rate Optimization

Miikulainen

Brundage²,

Epstein³

et al. 2020

AI Magazine

Self Cite

View full text Add to dashboard Cite

show abstract

“…Technically, Ascend is related to approaches to interactive evolution [12,15] and crowdsourcing [2,9] in that evaluations of candidates are done online by human users.…”

Section: Introductionmentioning

confidence: 99%

Conversion rate optimization through evolutionary computation

Miikkulainen

Iscoe

Shagrin

et al. 2017

Proceedings of the Genetic and Evolutionary Computation Conference

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In the last decade or so we have seen tremendous progress in Arti cial Intelligence (AI). AI is now in the real world, powering applications that have a large practical impact. Most of it is based on modeling, i.e. machine learning of statistical models that make it possible to predict what the right decision might be in future situations. For example, we now have object recognition, speech recognition, game playing, language understanding, and machine translation systems that rival human performance, and in many cases exceed it [8,9,20]. In each of these cases, massive amounts of supervised data exists, specifying the right answer to each input case. With the massive amounts of computation that is now available, it is possible to train neural networks to take advantage of the data. erefore, AI works great in tasks where we already know what needs to be done. e next step for AI is machine creativity. Beyond modeling there is a large number of tasks where the correct, or even good, solutions are not known, but need to be discovered. For instance designing engineering solutions that perform well at low costs, or web pages that serve the users well, or even growth recipes for agriculture in controlled greenhouses are all tasks where human expertise is scarce and good solutions di cult to come by [4,7,11,12,18]. Methods for machine creativity have existed for decades. I believe we are now in a similar situation as deep learning was a few years ago: with the million-fold increase in computational power, those methods can now be used to scale up to real-world tasks.Evolutionary computation is in a unique position to take advantage of that power, and become the next deep learning. To see why, let us consider how humans tackle a creative task, such as engineering design. A typical process starts with an existing design, perhaps an earlier one that needs to be improved or extended, or a design for a related task. e designer then makes changes to this solution and evaluates them. S/he keeps those changes that work well and discards those that do not, and iterates. It terminates when a desired level of performance is met, or when no be er solutions can be found-at which point the process may be started again from a di erent initial solution. Such a process can be described as a hill-climbing process (Figure 1a). With good initial insight it is possible to nd good solutions, but much of the space remains unexplored and many good solutions may be missed.Interestingly, current machine learning methods are also based on hill-climbing. Neural networks and deep learning follow a gradient that is computed based on known examples of desired behavior [14,22]. e gradient speci es how the neural network should be adjusted to make it perform slightly be er, but it also does not have a global view of the landscape, i.e. where to start and which hill to climb. Similarly, reinforcement learning starts with an individual solution and then explores modi cations around that solution, in order to estimate the gradient [21,25]. With large enou...

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Sentient Ascend: AI-Based Massively Multivariate Conversion Rate Optimization

Miikkulainen

Iscoe

Shagrin

et al. 2018

AAAI

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Conversion rate optimization (CRO) means designing an e-commerce web interface so that as many users as possible take a desired action such as registering for an account, requesting a contact, or making a purchase. Such design is usually done by hand, evaluating one change at a time through A/B testing, or evaluating all combinations of two or three variables through multivariate testing. Traditional CRO is thus limited to a small fraction of the design space only. This paper describes Sentient Ascend, an automatic CRO system that uses evolutionary search to discover effective web interfaces given a human-designed search space. Design candidates are evaluated in parallel on line with real users, making it possible to discover and utilize interactions between the design elements that are difficult to identify otherwise. A commercial product since September 2016, Ascend has been applied to numerous web interfaces across industries and search space sizes, with up to four-fold improvements over human design. Ascend can therefore be seen as massively multivariate CRO made possible by AI.

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Boosting Interactive Evolution Using Human Computation Markets

Cited by 3 publications

References 17 publications

Ascend by Evolv: Artificial Intelligence‐Based Massively Multivariate Conversion Rate Optimization

Ascend by Evolv: Artificial Intelligence‐Based Massively Multivariate Conversion Rate Optimization

Conversion rate optimization through evolutionary computation

Sentient Ascend: AI-Based Massively Multivariate Conversion Rate Optimization

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