I would say, that the answer is partially yes and partially no.
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.
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.