Strong vs Weak AI Battle

Strong versus Weak Artificial Intelligence Battle of Nov 28!!!

This special edition attempts to register the traces left by the referred battle. So, here there is one interpretation of the main questions discussed that day.

Strong AI

1. ¿Is the project feasible in the near time? Ans.: This is the starting point, and we are actively working in researching.
2. ¿How a machine can be better than a real teacher? Ans.: The strong point here is the human improvement in the robot, since the machine is adapting through the interaction with the students. Consequently, we are not trying to replace a teacher.
3. ¿How do you plan to achieve empathy with this machine? Ans.: We are actively working in researching to clarify that point. Nowadays, we cannot assure 100% certainty since we perform under an uncertainty environment.
4. ¿How can you persuade people if your project is reliable with such uncertainty? Ans.: There are not advertisements, since we are making researching. We are far from the idea of marketing.
5. ¿Is this outcome software or hardware? Ans.: A robot.
6. ¿How can you build an affordable solution in comparison to a personal tutor? If the tutor is expensive as you said. Ans.: We reaffirm that a tutor per student is much more expensive than one robot for many people.

Weak AI

1. ¿What is your sustainability plan? ¿Why to invest in your project? Ans.: The niche is health industry and our value is accuracy.
2. ¿What is the originality or novelty contribution behind your idea? Ans.: We apply predictive algorithms and we innovate over the existing technology.
3. ¿How do you think to become strong AI in the near future? Ans.: Currently, we cannot anticipate external conditions. The focus of our sensors is gathering real time data instead an adaptive approach.
4. ¿What about liability considering a mistake in a medical diagnostic? Ans.: There are sensors measuring real time information in the form of smart watches. Gathering health conditions like heartbeats, pressure, etc. Through continual improvement we can get more precise results reducing false negative and false positives.
5. ¿How do you handle the legal aspects in order to control the privacy of the data sets? Ans.: The data is anonymized. Additionally, we consider non disclosure agreement clauses in the contract.
6. ¿Is the model considering inputs from “relatives/family” to make relations to anticipate inherited diseases? Ans.: We do not use such criteria.
7. ¿How are you handling your budget? Ans.: We have a business model based on our smart watch sensor.

Hope this helps! All the best,

Berioshka, Notekeepers Team

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