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Leaving the underground
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NIGERIA IS NOT SAFE
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Recent Sessions with some of Africa's brightest minds.
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Doctoral Student @ MIT
HR Manager @ Pernod Ricard Western Africa
Senior Digital Communications Analyst @ Oando Plc
Creative Director @ Thalia Bespoke Nigeria
Senior Writer @ TechCabal
Managing Director & Computer Science PhD Student @ The Diasporic Group & Cornell University
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Senior Lecturer @ The Technical University of Kenya
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Top answers from some of our sessions.
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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
I think that often times people assume that one's identity as a black women is by default interwoven into one's academic experiences, and while that may true, I never believed that the marginalization I face based on my identity meant that there was something I could not achieve. Quite frankly, someones racism or sexism towards me is their own problem, and over time I've become immune to it. Additionally I believe the glass ceiling is a metaphor people put on us that inadvertently gives some people imposter syndrome. To me, there are no glass ceilings. I know that if I'm able to build technology solve problems for many people, and advocate for myself while doing it, I'll be able to have whatever impact I want on the world and gain recognition for doing it. Obviously I've faced misogynior in academia, and you'd be hard pressed to find any black woman at an institution with billions of dollars at their disposal to to have been shielded from that. But numbers can't be denied, and when you walk into interviews, proposals or pitches with compelling data, concise arguments and confidence, it's often hard to be ignored. That being said, we have to acknowledge that there are very few people in general in this field. Our lack of representation as black women doesn't mean that we aren't capable, just that we haven't been shown what we can do. Consequently I believe that helping others see their potential is the greatest way I can share and create value with my knowledge, whether that be through building human-centric technology that focuses on bringing value to a user personally, or inspiring others to see their own skill solve important problems themselves.

Ifueko Igbinedion
Doctoral Student @ MIT
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