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Leaving the underground
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NIGERIA IS NOT SAFE
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Q&A Sessions with bright African minds across the globe.
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Recent Sessions with some of Africa's brightest minds.
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HR Manager @ Pernod Ricard Western Africa
Doctoral Student @ MIT
Senior Writer @ TechCabal
Creative Director @ Thalia Bespoke Nigeria
Senior Digital Communications Analyst @ Oando Plc
Educator @ Covenant University
Managing Director & Computer Science PhD Student @ The Diasporic Group & Cornell University
International Criminal and Human Rights Lawyer
Senior Lecturer @ The Technical University of Kenya
Personal Brand Therapist | Bus Consultant | Relationship Counsellor | Content Creator @ NEST Consolidated
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Top answers from some of our sessions.
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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
My biggest advice to women starting out their careers in tech is to be confident. Often times, I feel that women are discouraged from pursuing highly technical careers because of male dominance or the lack of confidence in their ability to make it happen. One thing that I've realized is that men are not better at women inherently at programming. Removing humility for a moment, I'm probably better than the majority of men in my program at software development. This is because of the confidence that my family and mentors have instilled in me along the way that has allowed me to push through the difficult situations, including failing classes, internship/scholarship/fellowship rejections, and the general abuse that is almost unavoidable as a woman in this field, while developing my skills to a professional level. My other big piece of advice is to understand that tech is difficult for the majority of people, and to find a community of supporters in your early days. When I first began programming academically 10 years ago, it was nothing like I had ever done before - I had a little experience with web programming and computer architecture, but let me tell you, that introductory C programming course made me cry on several occasions. I actually vowed at one point to never program again. However, I had a community of women around me in computer science and other engineering fields, and they really built me up, helping me learn the concepts in ways that made sense to me, reviewing my problem sets for bugs that I wouldn't have noticed, and giving me confidence that I probably had no business having with my skill level at the time. I wouldn't have had the balls to take some of the risks that I did without the confidence they instilled in me.;It is so important to find a community, and they do exist outside of academia. For example, baddies in tech is a community I'm part of that is provides to support, recruitment and mentorship for black women in their early tech careers.

Ifueko Igbinedion
Doctoral Student @ MIT
This does not apply to everyone. People react to things differently .

Priscilla Ofosu Mensah
Bridal Makeup Artist, Makeup Educator, Makeup Special Effects (Sfx) @ Vogue by Prylla
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
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Picking the minds of Africa's finest.
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