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Leaving the underground
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Doctoral Student @ MIT
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I think this is a bit dangerous. Attempting to ascertain sentimental correlations and apply them to huge financial decisions may work in certain contexts and you could definitely train a model with 99% training accuracy on this task, but future situations that are dependent on complex human action can never be adequately represented by a numerical parameterization and a finite state machine. If the model is not large enough, we will not learn all the possible combinations of interactions. If it is too large, then we only learn the context of our training dataset. That being said, you could do both and get good results during training. Personally, I do not have extensive NLP experience or Bayesian experience in production, but their fundamentals suggest that they would learn this type of model well independently or in conjunction. Naïve Bayes is good for state estimation-based decision making, and NLP can be used to model language and extract sentiment. However, these models depend completely on the input dataset that one utilizes, and the chosen labels (if using a supervised method) that are often subjective. Using data from the internet is also dangerous because it is next to impossible to have humans annotate every piece of training data without spending a large amount of money, and learning from problematic input data can lead to problematic situations.To make this less vague, take the 2016 example where Tay, a chatbot made by Microsoft and trained on Twitter data, became extremely racist in less than a day of online training (https://twitter.com/geraldmellor/status/712880710328139776). Attempting to determine causation in a data driven sense is a slippery slope, and until AI solves the data-driven generalization problem (which I believe may be never) I wouldn't build a system like this in production until I could guarantee significant human supervision and have looked at the ethical implications on those who do not financially benefit from the proposed system.

Ifueko Igbinedion
Doctoral Student @ MIT
The use or existence of renewable energy is not directly related to carbon emissions. Ghana had a 3.54% carbon footprint in 2016, while Africa's global compounded carbon footprint is expected to be 3.8% by 2021. This means that Africa's contribution to the total of 76% of CO2 emissions is extremely small. Renewable energy sources are more of a convenience factor than a global concern. In remote villages, using a solar-paneled lamp at night is easier and safer than using a kerosene lamp. How much more expensive is kerosene today compared to 20 years ago? Renewable energy will not be able to replace conventional energy. Take note that the amount of fossil fuels used to manufacture an electric car's battery is greater than the amount used to drive 60 (average fuel consumption) cars over a 100km road. For a family, having a well dug within their property is safer and easier than having the kids run to the streams at night out of a rampant fear of watercraft. Renewable energy sources play an important role. How many years do we need to wait for geothermal sources? Is electricity, by the way, not derived from a renewable source? Dams are used to generate electricity from naturally flowing streams that are fed by an inland flow of water. The more living things die, the more biomass there is. Renewables will always be a question, as old as recycling and as new as 7th generation electric vehicles. I am ready to learn more from you.

Loic Bethel Dje
Youth and Apex Performance Mentor | Initiator @ PureBreedSbe
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