Yes, we can build a better future with Artificial Intelligence – But only if we have more female leadership.
We sat together with five Women in AI who are doing inspiring work and talked about why we need more women in leadership roles, how to empower more women to join the field, and lastly what learnings and insights all five want to share with aspiring female AI engineers. Enjoy!
Yemissi B. Kifouly, Udactiy Mentor
Kulsoom Abdullah, Data Science Consultant
Ecem Yılmazhaliloğlu, President of Technoladies
Rebeca Moreno Jiménez, UNHCR Innovation Officer
Sofia Kyriazi, UNHCR AI Engineer
More about Omdena
Omdena is the collaborative platform to build innovative, ethical, and efficient AI and Data Science solutions to real-world problems.
How to learn Data Science most effectively in 2020, what goes wrong in the field, and why income is not the only relevant career metric.
In this one-hour fireside chat with Eric Weber, Data Science Influencer (40k followers) and former Senior Data Scientist at LinkedIn, we discussed strategies and tactics to learn skills, finding a balance between theory and practice, developing a mindset of learning through failure, and why everyone needs to find his or her own way.
While it is hard to wrap up all insights in a post, here are 6 learnings from the webinar. For more, you can watch the entire recording at the end of the page.
Figure 1: Data Science Skills
Finally, don`t listen too much to what others say (applies to this post as well) in your career journey. Well-known Youtuber & MIT Research Scientist Lex Fridman recently shared his own struggles with imposter syndrome and comparing himself to others. His suggestion?
Helping affected populations during a disaster most effectively through AI. A collaborative Omdena team of 34 AI experts and data scientists worked with the World Food Programme to build solutions to predict affected populations and create customized relief packages for disaster prevention.
The entire data analysis and details about the relief package tool including a live demonstration can be found in the demo day recording at the end of the article.
The problem: Quick disaster response
When a disaster strikes, the World Food Programme (WFP), as well as other humanitarian agencies, need to design comprehensive emergency operations. They need to know what to bring and in which quantity. How many shelters? How many tons of food? These needs assessments are conducted by humanitarian experts, based on the first information collected, their knowledge, and experience.
The project goal: Building a disaster relief package tool for cyclones (applicable to other use cases and disaster categories)
Use Case: Cyclones (Solution applicable to other areas)
Tropical cyclones cost about 10,000 human lives a year. Many more are injured with homes and buildings destructed, which results in financial damage of several billions of USD. Due to changes in climate and extreme weather events, the impact is growing steadily.
Long Beach after Hurricane Katrina. Estimated damage of 168 billion dollars (Source: Wikipedia).
The Omdena team gathered data from several sources:
IBTrACS – Tropical cyclone data that provides climatological speed and directions of storms (National Oceanic and Atmospheric Administration)
EmDAT – Geographical, temporal, human, and economic information on disasters at the country level. (Université Catholique de Louvain)
Socio-Economic Factors from World Bank
The Gridded Population of the World (GPW) collection – Models the distribution of the human population (counts and densities) on a continuous global raster surface
Missing data was collected manually or partially automated by scraping from Wikipedia or cyclone reports.
Data exploration: Determining affected populations
All five data set were aggregated and included more than 1000 events and 45 features characterizing cyclones and affected populations.
Impact Cyclones (Landing vs. No-landing)
Important correlation factors to determine affected populations:
Human Development Index
GDP per capita
Total hours in Land
The team mapped the correlation factors to determine which populations are most in need. As an example, below the income level is correlated with the number of people affected. Taking advantage of past data, the data model predicts affected populations.
Predicting affected populations based on income level
The tool: Calculating relief packages
Once an affected population has been identified, humanitarian actors need to design comprehensive emergency operations including how much food and what type of food is needed. The project team built a food basket tool, which facilitates calculating the needs of affected populations. The tool looks for various different features such as days to be covered, the number of affected people, pregnancies, kids, etc.
The relief package tool
The entire data analysis and details about the relief package tool including a live demonstration can be found in the video.
The team: Collaborators from 19 countries
This Omdena project hosted by the WFP Innovation Accelerator united 34 collaborators and changemakers across four continents. All team members worked together for two months on Omdena´s innovation platform to build AI solutions with the mission to improve disaster response. To learn more about the project check out our project page.
All changemakers: Ali El-Kassas, Alolaywi Ahmed Sami, Anel Nurkayeva, Arnab Saha, Beata Baczynska, Begoña Echavarren Sánchez, Chinmay Krishnan, Dev Bharti, Devika Bhatia, Erick Almaraz, Fabiana Castiblanco, Francis Onyango, Geethanjali Battula, Grivine Ochieng, Jeremiah Kamama, Joseph Itopa Abubakar, Juber Rahman, Krysztof Ausgustowski, Madhurya Shivaram, Onassis Nottage, Pratibha Gupta, Raghuram Nandepu, Rishab Balakrishnan, Rohit Nagotkar, Rosana de Oliveira Gomes, Sagar Devkate, Sijuade Oguntayyo, Susanne Brockmann, Tefy Lucky Rakotomahefa, Tiago Cunha Montenegro, Vamsi Krishna Gutta, Xavier Torres, Yousof Mardoukhi
10 tips to deal with Impostor Syndrome in Data Science. A Data Scientist, Junior ML engineer, and a Ph.D. in Physics and career mover share their experiences.
Below you can find the key points from the webinar and for further insights, check out the entire session at the end of the blog post.
But first, let us start with a definition. According to Maria Klawe, the President of Harvey Mudd College, impostor syndrome can be described as:
The frequent feeling of not deserving one’s success and of being a failure despite a sustained record of achievements.
Indeed, no matter your knowledge or expertise, Imposter Syndrome can still make you feel like a complete failure.
At its roots, are several factors such as previous failures, inherited fears, social biases, culture, education, and more. Being a minority in one’s domain, or working in an active field of research such as data science, can also trigger and worsen impostor syndrome.
10 Tips on Overcoming Impostor Syndrome
Now let’s dive into it, the following points stem from our vibrant discussion with more than 70 participants.
1. Understand that you will never know everything
Data Science is an ever-changing field where new technologies are constantly needed. If you want to excel in Data Science (and if you’re reading this, you do), you need to face the fact that your learning curve will get steeper over time. While you need to learn a lot, you also need to realize that you’ll never know everything, and this is ok! In fact, it is more important that you are able to work in a team where each member can complement the other.
2. Break the silence and speak about it
In Psychology, this is referred to as “Name it to tame it”. It is a phrase coined by author and psychiatrist Dr. Daniel Siegel. By putting this simple tool to work, your emotions can inform you and not overwhelm you. Once you notice you are having a strong emotional reaction, the next step is to describe, or name it – whether to yourself or out loud. In the case of an impostor, you need to label what you feel in order to be able to deal with it, for example by speaking with people you trust about it. When you speak to your data science peers, you’ll be surprised how many are struggling with it. Just as we talked about it in the webinar below!
3. Become good at asking questions (to your team)
Asking questions can sometimes seem scary. No one wants to appear “silly.” But I assure you:
You’re not silly.
It’s way scarier if you’re not asking questions.
Data Science is a constant collaboration with the business, the team, your student friends, and a series of questions and answers allows you to deliver the analysis/model/data product that is needed.
As Junior Machine Learning Engineer Joseph Itopa A. put it in the webinar:
At first, everything seems to be difficult but the more we learn, the better we become, and asking questions is a big part of this.
Questions are required to fully understand what the business wants and not find yourself making assumptions about what others are thinking.
4. Separate feelings from facts
This connects back to tip 2 “Name it to tame it”. Once you experience a certain emotion like self-doubt, put it out there and analyze if what you are feeling is based on facts or based on something else.
In 36:30 min of the webinar below Panelist, Rosana de Oliveira Gomes shares a mental exercise to deal with unhelpful thought patterns.
5. Avoid toxic environments and join helpful ones
Kulsoom Abdullah recalls from graduate school and industry: “My Imposter Syndrome was triggered and worsened by arrogant people. If they perceived you as lacking some knowledge, their reaction towards you was either mocking, aggressive, or belittling. When one already feels vulnerable and not confident, this can make it worse.”
Toxic environments can have devastating effects. If your work environment is unhealthy, change it. Otherwise, change your attitude about it, but don’t remain passive. Then, consider joining an environment that is conducive to growth. Why not join us at Omdena? With the very diverse groups of contributors working collaboratively on our challenges, you are bound to feel at home.
6. Change your mindset from failure to learning
Failure, as much as it hurts, is an important part of life. … Without failure, we’d be less capable of compassion, empathy, kindness, and great achievement; we would be less likely to reach for the moon and the stars. It’s through failure that we learn the greatest lessons that life could teach us. Meaning failure just equals learning. And the learning never stops, which makes life exciting, doesn’t it?
7. Stop comparing to others
Remind yourself that other people’s “outsides” can’t be compared to your “insides”.
This is such a helpful habit to cultivate. Unless you’re really close to someone, you can’t use their outward appearance to judge the reality of their life. People carefully curate the social media versions of their lives, and do the same with the lives they live out publicly.
If you took the strengths of others and compared them to your weaknesses, how do you think you’d size up? And do you think this would make you feel good? Compare yourself to the person or the data scientist that you were yesterday, a year ago, and so on.
8. Pave your own path
Well-known Youtuber & MIT Research Scientist Lex Fridman recently shared his own struggles with the impostor syndrome in data science and suggested to “Pave your own path”, which very much connects to tip 7.
9. Develop a healthy inner dialogue
Take all of the previous tips into account and be kind to yourself. Self-criticism does not work as well as self-compassion. We are all a work in progress, no matter if it is data science, art, business, or life in general.
10. Celebrate each little milestone
The way is the goal.
You might have heard this before. And it is true, we cant make our well-being dependent on a future outcome. In the end, the data science career path consists of hundreds of little steps and each step to be taken is worth it a smile and a moment of gratitude for having just having progressed a bit further.
Some final words. Define your own journey and enjoy it. Get more insights and tips in the entire session below!
Panelists: Rosana de Oliveira Gomes, Joseph Itopa A., Kulsoom Abdullah, Michael Burkhardt
A vibrant and insightful discussion on how to define an impactful AI use case (framework below), how AI technology can help during COVID19, why we might be losing our voice during lockdowns, and how to overcome this. Most importantly we discussed how we as citizens and communities can take action to build a better future. Make sure to read the key points below and check out the entire recording at the end of the blog post.
Master Inventor Neil Sahota takes the stage
Our fireside guest Neil Sahota is a sought-after global expert who has worked with Global Fortune 500 companies, world government leaders, and startups around the world. He has been on panels with world-known business leaders like Gary Vaynerchuk and he creates value as a United Nations AI Expert, Master Inventor, and best-selling author.
What constitutes an impactful AI use case?
Focus on solving a problem and creating value, then ROI will follow.
In the first part of the webinar, we addressed the question of what value creation means in the context of AI.
Creating tangible results requires experimentation and breaking with old and conventional thinking habits that are deeply rooted in many organizations. AI is a disruptive technology and with all disruptive technologies, there is an exploration phase and maturity phase. It is the decision of the company to be part of it or miss the accelerating AI train.
Everyone is talking about automation while the real value lies in building innovative solutions where technology is paired with human creativity.
Copyright: Neil Sahota
The magic formula for impactful AI use cases
AI is not the solution to all problems. Building products does not start with thinking about AI but finding a meaningful problem that once solved adds value for the customer or user. Many organizations fall into the trap of adding AI to their strategy without first defining the problem in detail.
According to Neil defining a use case successfully comes with two key ingredients. The right mindset of entrepreneurial thinking, stepping away from “why it won’t work” to “how to make something work”. Next, we need to dive into what are the real problems and root causes to derive a clear problem statement. Only after doing this, we can explore further through knowledge of the space, Rules/regulations, adoption challenges to identify if there is an opportunity or not. Next, the technical implementation starts to build an actual solution. If you are interested, you can find real-world use cases here.
Use cases of AI for COVID19
We addressed the following use cases in the webinar:
AI-driven monitoring tools for social distancing
Cracking COVID-19 genetic signatures through Machine Learning
Looking at the effects of policies for vulnerable populations
As Omdena Founder Rudradeb Mitra points out the current crisis shows who is affected the most or in other words who is most vulnerable. This can also be applied to customers, users, and even employees where organizations can use AI technology like sentiment analysis to understand which subgroups are most affected by a regulation or policy.
How can we build a better future
Lastly, we discussed how each one of us can make a difference by using his or her voice. There are no experts of the future and the only way to predict the future is to create it. What can each one of us do to create a better future is a question we all need to ask ourselves!
Here is the entire outline of the webinar:
Min 3:00: Introduction by Neil Sahota: How to build an impactful AI use case
Min 13:15: Opportunities for organizations, increase of domestic violence
Min 23:05: Fireside chat: Use cases, how to overcome AI bias, how to build solutions as communities rather than relying on governments and large organizations, etc.