Applications of AI, ML and Data Science in Kenya
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- Опубліковано 4 сер 2024
- Lilian Nduati (Manager, HDX Data Lab) moderated a panel at DevCraft 2018 titled "Applications of AI, ML and Data Science in Kenya". On the panel were Muthoni Wanyoike (Team Lead, InstaDeep), Chris Orwa (Data Scientist, Safaricom Alpha), and Chris Nyaga (Technical Team Lead, Andela).
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I like that guy from Andela. He sounds hands on and that is actually missing in most of kenyans self proclaimed 'data scientists'
sure
Kenyans are very talented indeed.
I think I am late because I was still in high school while you were having this conversation.😐
I don't think it's a wise decision to judge anyone for a job position, based on their level of education (entirely) overlooking other aspects such as projects, works, level of expertise and so on... This is in response to Chris Orwa (Data Scientist, Safaricom Alpha) 17:20. I know friends who built industry level autonomous vehicles using ConvNets in their high school, and now, they're great researchers in top tier institutions at their undergraduate level. We need all parties involved, from prodigies in primary schools to PhD students, to work together and learn from each other, if we want to use AI, ML & Data Science to an industrious level!
We don't necessarily have to find "The Perfect Candidate" for all positions but a "perfect match" . It's all about growing each other and that starts by having a look at all resumes not only PhD students resumes, and forming inclusive programs for undergrads other than exclusivity!
Great talk!
Well said 👏🏿
you are very right
Data Science surely needs a degree being a Math major and having interacted with people from different parts of the world while different countries have different curriculums for their high schools there's maths needed for ML which isn't accessible to high schoolers and the more ML advances the more maths you need we have some ML techniques that now use differential geometry and topology for example
The lady speaking in 45:52 has such a great insight ! .In the book "zero to one" , Peter Thiel talks about leapfrogging , how china has done leapfrogs and this is why it is almost overshadowing USA .I agree with her that we are not "behind" as we think we are !
+1 Chris for quoting the incerto
Nice one
Chris Nyaga big ups bro !
Here in 2024, how can we define Data Science by saying there is no single definition, especially for such a specific discipline?
I'm Proud of Chris Nyaga #Bigzoo
#BelieveInYou
🎯 Key Takeaways for quick navigation:
00:52 🚀 *Panel introduction and background*
- Panelists introduce themselves and share their backgrounds in artificial intelligence, machine learning, and data science.
- Each panelist discusses their journey into the field, highlighting experiences and motivations.
- The discussion sets the stage for understanding the perspectives and expertise of the panelists.
03:42 🧠 *Definitions of AI, ML, and Data Science*
- Panelists offer definitions and distinctions between artificial intelligence, machine learning, and data science.
- AI is described as creating systems that mimic human intelligence, encompassing various techniques like machine learning and reinforcement learning.
- Machine learning involves using mathematics to create predictive algorithms, while data science involves experimentation and exploration of data for insights.
07:31 🏙️ *Real-world Applications of AI and ML*
- Panelists discuss practical applications of AI and ML in everyday life, moving beyond science fiction.
- Examples include modeling traffic behavior based on ant interactions, analyzing customer behavior for business propositions, and utilizing AI in gaming for real-world problem-solving.
- The discussion emphasizes the integration of AI and ML into various industries such as e-commerce, transportation, and logistics.
14:55 🌍 *Talent Acquisition and Skill Development*
- Panelists address the challenges of talent acquisition and skill development in the field of data science.
- Perspectives on sourcing talent locally and globally are shared, with an emphasis on diversity and unconventional backgrounds.
- The discussion explores strategies for building teams with diverse skill sets and adapting recruitment processes based on data analytics and feedback.
22:35 🎓 *The importance of practical experience in data science education*
- Practical experience is crucial for understanding data science concepts effectively.
- Traditional education (college, PhD programs) is valuable but may lack hands-on experience.
- Apprenticeship or mentorship models can provide valuable practical learning opportunities.
24:36 🌍 *Opportunities for AI and ML in humanitarian efforts*
- AI and ML can significantly improve crisis response and humanitarian efforts.
- Sectors like healthcare and education stand to benefit from AI and ML implementations.
- Tailoring educational content using AI can enhance learning outcomes for students.
26:32 ⚕️ *Addressing challenges in the healthcare sector with AI*
- AI can help improve emergency response systems and streamline healthcare processes.
- Utilizing technology to enhance communication during emergencies can save lives.
- Government facilitation and collaboration with technology companies are crucial for successful implementation.
29:05 🏙️ *Solving urban planning challenges with data-driven decisions*
- Data-driven decision-making is essential for urban planning and congestion management.
- Planning infrastructure projects based on current and future data can prevent outdated solutions.
- Prioritizing basic infrastructure needs like education, healthcare, and energy access is fundamental for societal progress.
31:30 👥 *Ensuring inclusive representation and acceptance of data-driven decisions*
- Inclusive representation in data sets is crucial for equitable technological advancement.
- Encouraging acceptance of data-driven decisions requires cultural and organizational shifts.
- Addressing biases in data collection and decision-making processes is essential for fairness and accuracy.
36:04 🌍 *Fostering local innovation and research in AI and data science*
- Investing in local AI initiatives and research can contribute to global technological advancements.
- Empowering local communities to develop and utilize AI technologies addresses societal needs.
- Supporting publications and conferences to showcase local research fosters growth and collaboration in the field.
44:32 🌐 *Leveraging Technology for Education*
- Emphasizing the importance of making technology accessible and user-friendly.
- Discussing the potential of technology to address infrastructure challenges.
- Exploring innovative solutions like using flying drones for various purposes.
47:32 🎓 *Enhancing Data Science Education*
- Highlighting the need for a comprehensive curriculum in universities to produce skilled individuals.
- Advocating for investment from corporations to support education in data science.
- Discussing the importance of mastering foundational skills in data science.
49:30 🤖 *Exploring AI's Societal Impact*
- Considering the timeline and societal implications of achieving general intelligence in AI.
- Reflecting on the potential effects of AI on society, particularly in Africa.
- Emphasizing the importance of addressing infrastructure challenges before expecting significant AI integration.
52:34 🚀 *Unlocking Innovation with Data*
- Discussing the opportunity for Africa to innovate and solve problems using data-driven approaches.
- Highlighting the importance of identifying key areas where AI can drive leapfrogging.
- Addressing the challenges and limitations of AI adoption in unstructured environments.
59:45 💡 *Tangible AI Applications*
- Reflecting on concrete AI applications emerging from the market.
- Exploring examples like customer service bots and AI-powered suggestion systems.
- Considering the potential for unique African innovations in AI technology.
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no data science was not invented 5 years go , it has been there since 2003's