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by simonhughes22
2168 days ago
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Short answer - no. Where is the explanation for how the predictive queries work. Is it some sort of bayesian model? It's not too hard to quickly fit some NB or regression model on some dataset on the fly given the simplicity of those models. However just throwing random features at it without consideration of bias vs variance, i.e. whether the model is either over-fitting or is not powerful enough to answer the question can easily result in a useless model. To make this useful you would need to build in all of the functionality regular data scientists use to build regular models. In doing so you would lose all of the speed and flexibility of the tool you are pushing. Also given the prevalence of deep learning models for unstructured data, and also search and recommendations, such an approach would not work given it relies on structured data. A lot of modern data science work focuses on those kind of problems as learning from structured data is mostly quick and easy with today's ML tools. I don't see how this framework would solve for these more complex and more typical business problems. |
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We have done several projects with simple machine learning problems, where e.g. semi-technical RPA developers have been able to implement ML based automation just fine. We have gotten compliments that Aito is easy to use, and one intelligent automation demo was implemented in 5 hours with some integrations by 2 RPA developers. It's worth noting, that there is an absolute abundance of ML problems (especially in domains like automation or UIs) that are simple to understand and easy as ML problems.
At the same time, we have run into many ML problems, which require data scientist to even formulate the problem and to think about it. There are also problems, where Aito's Bayesian approach is inadequate and you need a data scientist to do good amount engineering to make it possible to model the patterns and then find the right model.
So TBH: I don't think the predictive queries can fully replace the traditional models or data science work, but there are large application domains, that can be handled just fine with predictive queries and even by normal developers.
Regarding text: Aito can already handle simple texts just fine, and with representation learning based 'world modeling' approaches: I believe that we can do also more complex analysis on text.
Overall, Aito does not seek to provide the best models or solve the hardest problems, but it's value proposition is on speed and easiness. We focus on investment instead of return in the return-on-investment equation. It gives an advantage on the lower-value 'tail' of the ML market, where the importance of costs is higher, and where the traditional data science approach is economically not that attractive.