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Discover 2 Women Transforming AI Ecosystems in Africa




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In this series of profiles we interview Innovative AI in the first lines – the work that has been offered to improve the human life situation through technological advances.

Muthoni Wanyoike and Jade Abbott.© 2019 NVIDIA CORPORATION. ALL RIGHTS RESERVED.

Discover Muthoni Wanyoike and Jade Abbott, two women in the amazing African AI ecosystem. Abbott is an old software engineer and machine engineer in Retro Rabbit, South Africa software company, and Wanyoike brings the InstaDeep team: AI launcher with Tunisia, Nigeria, Kenya, England and France.

Both Abbott and Wanyoike also take part in building a developer community on the continent.

Recent conferences on AI and new technologies in Africa in recent years. Wanyoike is a member of the organizing committee, Deep Learning Indaba, and co-organizer of the Nairobi Women's Machine Studies and Data Science community. Abbott, an organizer of Women in Tech ZA, had a paper about the neural translation of African-American supported languages ​​in 2018 at a worldwide workshop at the AI ​​Congress of Neurips.

In this interview, Wanyoike and Abbott share the views of the community of AI community that achieve traction in Africa and explore their vision of developing different regional talent technologies.

Why did you participate in the promotion of the machine learning community in Africa?

Abbott: In Africa, although I came from a privileged background, I still do not like global games. Plan the people outside of this privileged location, that is, the majority of the continent. What is it for me to attract people interested in machine learning, as attractive to people and not to be excluded, as well as to all international players. They can help not only within their communities, but also in the global scientific community.

Wanyoike: My vision is to provide opportunities for learning opportunities for women interested in accessing the AI. In my opinion, my vision and why I have continued to work on AI, I see myself as AI and as an opportunity for solving African problems, solving everyday challenges, health care, congestion, or planning.

What is your vision to advance in AI Africa?

Wanyoike: Today Kenya is a really young space. It's just 50 years in my country, so we start from an interesting place, starting with a timely information, because we get a sense of the data we collect. I think this is a great opportunity for us. Africa has the chance to define the next 100 years. So, my point of view is the meaning of our continent, the data we use to collect our lives and our spaces wisely, as well as addressing the challenges of product development and solutions we face.

Abbott: And, in addition, the path I see in Africa becomes an axis of innovation, not only in our problems, but also in a theoretical basic level. I do not know why they learn basic research that can not be extracted from African, African-American, low-grade, technical, machine-learning theory. We can apply a special point of view of problems, which apply directly to our environment, or apply to the world.

Africa, when younger, is lighter and will be able to innovate very quickly with some international players. For example, Kenya's main banks are younger than banks in America and Europe. And that does not allow the load of the heritage they will carry with them, which allows it to move considerably faster. However, after five or ten years of floating money, M-Pesak filtered through Kenya and East Africa.

What are some of the African AI applications, most proud of?

Abbott: Honestly, there is so much to choose from. If you asked me two years ago, which application I was most proud of, I would not know it in AIn Africa. That's the beauty of events like Deep Learning, which brings together amazing people. In Africa the visibility of AI has been very little in the continent until now.

Some of the favorites Justine Nasebiet's work is the analysis of survival, and wants to direct our continent to the deep challenges of our public health. Raesetje Sefala uses satellite imagery and computer vision to analyze the evolution and effects of South Africa's spatial apartheid. Or more in government space, Cape Town, data science and machine learning, to predict the problems of water crisis. The MomConnect Praekelt Foundation has enhanced the ability to connect and advise African-American women in pregnancy, creating chatbots to work with African-language variants.

How have you seen that the AI ​​ecosystem in Africa is changing over time?

Wanyoike: I think that Kenya has changed a lot in the last year. Thus, for example, in Machine Learning and Data Science, we went from almost 500 to almost 2,000 in one year. We also have a lot of new AI communities. AI Kenya and Nairobi.AI. We work with amazing, incredible work groups, sharing knowledge, inviting new sources or speakers, and streamlining information. So we are seeing a lot of changes, many of which can be attributed to community initiatives on the continent.

What has caused these conferences and community events held on the continent for women interested in AI?

Wanyoike: There are challenges to machine studies and data science in women. It is primary education. There are many gaps in the field. And aside from the gaps, there are many gaps in information and networks. Who connects to you if you're trying to solve a language processing issue, for example? The community offers space for the public as well as studying, collaborating and working together. We have seen a lot of projects coming from people who come to the people, and they do not have the available job opportunities.

Abbott: It is a very different layer. But as for the first time, it is very difficult to change the scale in this phase, it is a perception. Let's say we came across some traditional backdrop or backdrop; in South Africa, it's a definitive case for most of the population, if you are a woman and go to the business, parents will pay you a dentist, accountant, doctor or lawyer. Have you not been very tech-savvy at a young age?

What we have found is the challenge at multiple levels. Schedule days or STEM days work in schools. When you enter the women's delegation, students see: "Better look, that's what I could do." I often say that the reason they ended up in technology was that my mother was a programmer. It was always a woman to see. So I think they are basic issues.

It allows counselors and people to talk to you or talk to you. It does not seem to be a model that needs to be female, but that's it. We do. He also shows that we can do it.

Resources:

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In this series of profiles we interview Innovative AI in the first lines – the work that has been offered to improve the human life situation through technological advances.

Muthoni Wanyoike and Jade Abbott.© 2019 NVIDIA CORPORATION. ALL RIGHTS RESERVED.

Discover Muthoni Wanyoike and Jade Abbott, two women in the amazing African AI ecosystem. Abbott is an old software engineer and machine engineer in Retro Rabbit, South Africa software company, and Wanyoike brings the InstaDeep team: AI launcher with Tunisia, Nigeria, Kenya, England and France.

Both Abbott and Wanyoike also take part in building a developer community on the continent.

Recent conferences on AI and new technologies in Africa in recent years. Wanyoike is a member of the organizing committee, Deep Learning Indaba, and co-organizer of the Nairobi Women's Machine Studies and Data Science community. Abbott, an organizer of Women in Tech ZA, had a paper about the neural translation of African-American supported languages ​​in 2018 at a worldwide workshop at the AI ​​Congress of Neurips.

In this interview, Wanyoike and Abbott share the views of the community of AI community that achieve traction in Africa and explore their vision of developing different regional talent technologies.

Why did you participate in the promotion of the machine learning community in Africa?

Abbott: In Africa, although I came from a privileged background, I still do not like global games. Plan the people outside of this privileged location, that is, the majority of the continent. What is it for me to attract people interested in machine learning, as attractive to people and not to be excluded, as well as to all international players. They can help not only within their communities, but also in the global scientific community.

Wanyoike: My vision is to provide opportunities for learning opportunities for women interested in accessing the AI. In my opinion, my vision and why I have continued to work on AI, I see myself as AI and as an opportunity for solving African problems, solving everyday challenges, health care, congestion, or planning.

What is your vision to advance in AI Africa?

Wanyoike: Today Kenya is a really young space. It's just 50 years in my country, so we start from an interesting place, starting with a timely information, because we get a sense of the data we collect. I think this is a great opportunity for us. Africa has the chance to define the next 100 years. So, my point of view is the meaning of our continent, the data we use to collect our lives and our spaces wisely, as well as addressing the challenges of product development and solutions we face.

Abbott: And, in addition, the path I see in Africa becomes an axis of innovation, not only in our problems, but also in a theoretical basic level. I do not know why they learn basic research that can not be extracted from African, African-American, low-grade, technical, machine-learning theory. We can apply a special point of view of problems, which apply directly to our environment, or apply to the world.

Africa, when younger, is lighter and will be able to innovate very quickly with some international players. For example, Kenya's main banks are younger than banks in America and Europe. And that does not allow the load of the heritage they will carry with them, which allows it to move considerably faster. However, after five or ten years of floating money, M-Pesak filtered through Kenya and East Africa.

What are some of the African AI applications, most proud of?

Abbott: Honestly, there is so much to choose from. If you asked me two years ago, which application I was most proud of, I would not know it in AIn Africa. That's the beauty of events like Deep Learning, which brings together amazing people. In Africa the visibility of AI has been very little in the continent until now.

Some of the favorites Justine Nasebiet's work is the analysis of survival, and wants to direct our continent to the deep challenges of our public health. Raesetje Sefala uses satellite imagery and computer vision to analyze the evolution and effects of South Africa's spatial apartheid. Or more in government space, Cape Town, data science and machine learning, to predict the problems of water crisis. The MomConnect Praekelt Foundation has enhanced the ability to connect and advise African-American women in pregnancy, creating chatbots to work with African-language variants.

How have you seen that the AI ​​ecosystem in Africa is changing over time?

Wanyoike: I think that Kenya has changed a lot in the last year. Thus, for example, in Machine Learning and Data Science, we went from almost 500 to almost 2,000 in one year. We also have a lot of new AI communities. AI Kenya and Nairobi.AI. We work with amazing, incredible work groups, sharing knowledge, inviting new sources or speakers, and streamlining information. So we are seeing a lot of changes, many of which can be attributed to community initiatives on the continent.

What has caused these conferences and community events held on the continent for women interested in AI?

Wanyoike: There are challenges to machine studies and data science in women. It is primary education. There are many gaps in the field. And aside from the gaps, there are many gaps in information and networks. Who connects to you if you're trying to solve a language processing issue, for example? The community offers space for the public as well as studying, collaborating and working together. We have seen a lot of projects coming from people who come to the people, and they do not have the available job opportunities.

Abbott: It is a very different layer. But as for the first time, it is very difficult to change the scale in this phase, it is a perception. Let's say we came across some traditional backdrop or backdrop; in South Africa, it's a definitive case for most of the population, if you are a woman and go to the business, parents will pay you a dentist, accountant, doctor or lawyer. Have you not been very tech-savvy at a young age?

What we have found is the challenge at multiple levels. Schedule days or STEM days work in schools. When you enter the women's delegation, students see: "Better look, that's what I could do." I often say that the reason they ended up in technology was that my mother was a programmer. It was always a woman to see. So I think they are basic issues.

It allows counselors and people to talk to you or talk to you. It does not seem to be a model that needs to be female, but that's it. We do. He also shows that we can do it.

Resources:


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