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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
Personal Brand Therapist | Bus Consultant | Relationship Counsellor | Content Creator @ NEST Consolidated
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How many fishers will be ready to tell you, fishing at large scale is very profitable? How many even in your environment will consistently encourage and mentor you to gain more. The role of an engineer, beyond his analytical sense and political ambitions is that of developing substantial growth even low scale. As a computer engineer (that's how you ought to be addressed even by yourself: the consciousness of who you are) no one hinders you from developing programs and tools to solve issues in your community. Renewable energies utilisation could be part of it. Have you ever thought of how in coaction with other engineers around you, you could mobilise waste resources in Buea to make it active? What of developing plants which will assist the women along the Muea market to easily wash their fish for example? What of extension to use streams, to generate electricity for Student hostels or automation of water plants in student hostels. Your question shows you have interest in solving Education and Health issues? What plans have you taken towards that? If you do consistently, the same funding agencies will come to you, then we could assess your loyalty to solve Africa's problems with your skills. The earlier we stop the blame game, the better. You might also want to host a session which people who share interest in your field. This particular session comes from a public demand to elucidate with persons having interest in renewable energies.

Loic Bethel Dje
Youth and Apex Performance Mentor | Initiator @ PureBreedSbe
To your first question, I believe they somewhat already have. If you look purely at the numbers, I believe they already lead the world in newly published AI research or are quite close to that. In my opinion, its because the K-12 education system is much more tech focused there, and until the US competes at that level of education as well (which seems somewhat unlikely, considering the state of public education in the US government), we will surely be not just outpaced, but completely dominated in the next few decades. Also at the doctoral level at many Ivy+ schools, almost half of PhDs in tech come from international students, so you can draw whatever conclusions you'd like from that.To your second question, none of them. They will likely purchase another company doing such h things. One thing that we have to realize is that cutting-edge B2C AI generally doesn't usually make money in short term timelines. Companies that do often end up being unicorns or acquired by one. If you were to take a look at how FAANG companies make the majority of their money, I would bet a lot of money that its not the jaw dropping consumer AI systems. From the B2B sense, however, probably Amazon, as they dominate the B2B computing market already.The third question: hard work rarely yields personal financial success. This may be a jaded opinion, but the majority of wealth made in the US isn't made by the hard work of the person who benefits. The most profound book that I've read in the past year is a book of poetry, called the sun and her flowers, by Rupi Kaur. For me, poetry helps me find solace and meaning, and speaks when I cannot come up with words myself. Lastly, the startup that excites me the most is the one I'm working on in secret.

Ifueko Igbinedion
Doctoral Student @ MIT
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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