Reflections from the first GCA Impact Accelerator in Stockholm

Four members representing the CottonAce team at Wadhwani AI attended the GCA Impact Accelerator program from 13–17 June in Stockholm, organised by the H&M Foundation and GCA partners such as the KTH Royal Institute of Technology, Accenture, and The Mills Fabrica.
Wadhwani AI was one of the five winners of the H&M Foundation's Global Change Award in 2022.

India’s cotton farmers are, unfortunately, extremely familiar with the pink bollworm (PBW). One of the most destructive pests attacking cotton crops—its primary host—across the country, in 2017-18, an increase in farmer suicides was attributed to heavy crop loss caused by PBW. Though there are crop protection measures that farmers can adopt, the overutilisation of pesticides for cotton crops has proven to be expensive, dangerous to farmer health, and disastrous for soil and water environments in the long run. 

In 2018, we asked ourselves how we can provide timely and accurate infestation advisories to farmers so that an optimum quantity of pesticide is used, and their crops and livelihoods are protected. Four years later, our AI-powered pest management solution, CottonAce, was announced as one of the five winners of the H&M Foundation’s Global Change Award (GCA) in 2022.

CottonAce is an early warning and decision support system that equips cotton farmers with the knowledge of an agricultural expert, delivered directly on their smartphones. The solution aims to address the catastrophic impact of pest attacks on farmer livelihoods in some of the highest cotton-producing states in India.

Four members representing the CottonAce team at Wadhwani AI—Aayushi Bhotica, Jerome White, Aditya Nayan, and Janak Shah—attended the GCA Impact Accelerator program from 13–17 June in Stockholm, organised by the H&M Foundation and GCA partners such as the KTH Royal Institute of Technology, Accenture, and The Mills Fabrica.

The five-day program was planned to allow for an exchange of ideas and knowledge; the team had the opportunity to demonstrate our AI solution to stakeholders in the fashion industry, and were also invited to listen to industry experts, business leaders, and members of the GCA alumni on themes such as innovation readiness, impact leadership, and media training.

At the first GCA Impact Accelerator in Stockholm, the Wadhwani AI team had the opportunity to demonstrate our CottonAce AI solution to leaders and key stakeholders in the global fashion industry.

Day 1: Introductions and readiness training

On the first day, the Wadhwani AI team had an opportunity to explore the KTH campus and gained an understanding of the KTH Innovation Readiness Level, a framework developed by the institution for taking an idea to market, through a session conducted by Donnie Sc Lygonis, Innovation Strategist and Business Coach at KTH Innovation.

Day 2: Media training and pitch practice

GCA mentors helped the team to refine their pitch and offered tips on addressing the media, across formats. The pitch training was spearheaded by Donnie Sc Lygonis and the media training was conducted by David Callahan, international Public Information Officer (PIO) and social media content producer at the KTH Royal Institute of Technology. The team was given the opportunity to carry out mock interviews and deliver their respective pitches using professional media equipment available at KTH.

Day 3: Structured reflection

Laila Pawlak, cofounder and CEO of SingularityU Nordic, elaborated on her “​​Fundamental 4s.” The exercise was designed to understand the ways in which people can be, do, look, and feel better to drive personal and team growth. She also walked the team through the LEGO Serious Play method. The activity uses LEGO bricks to assist participants in structuring their strategic and creative thoughts with higher precision.

Additionally, there was a talk from Edwin Keh, CEO of the Hong Kong Research Institute of Textiles and Apparel.

Day 4: Network exposure and awards

In the morning, the Wadhwani AI team had the opportunity to present the CottonAce project to H&M executives, and discuss potential areas of collaboration with them.

The Foundation invited guests from the H&M Group, Accenture, and fashion VCs, among others, to an afternoon demo session. In attendance were the present and past winners of the Global Change Award, including our team. Winners were provided booths where guests could get hands-on experience with the various innovations on display.

In the evening, the team got an opportunity to interact with Melanie Hackler, Director at iCollect, Alexandra Frid Razola, Head of Circular Business Model Incubation at H&M Group, and Linda Leopold, head of Responsible AI and Data for the H&M Group, at the awards ceremony.

Over the five-day program, the Wadhwani AI team had the opportunity to present the CottonAce project to H&M executives, and discuss potential areas of collaboration with them.

Day 5: Reflection and conclusions

On the final day of the accelerator program, the Wadhwani AI team spent the morning at the H&M store at the city centre. Later in the day, at the Accenture office, they attended a constructive and fruitful discussion led by award winners, in which participants shared their experiences with fundraising.

Some thoughts and ideas the team took away from that discussion include:

1. Cultivating a synergy with the fashion world

There is huge potential to leverage the fashion industry to drive change. Leaders in the fashion industry are interested in sustainably produced cotton as it keeps their brands relevant to their customer base. 

2. Human beings make decisions emotionally

Products need to focus on connecting with the user emotionally. Even if one tries to provide rational reasons to use the product, if the user does not connect with it emotionally, they will not use it. Therefore, it is imperative to invest in features that may not always make sense with regard to AI, but will help users adopt the product. 

3. In-person workshops/sprints

In order to understand perspectives better, it is important to have interactive sessions where team members can bounce ideas. In-person workshops can be conducted where external stakeholders can participate and help the team reflect on decisions. This will be instrumental in creating agile teams.

We are eagerly looking forward to attending the second GCA accelerator program in New York City, coming up in October 2022.

ML Engineer

ROLES AND RESPONSIBILITIES

An ML Engineer at Wadhwani AI will be responsible for building robust machine learning solutions to problems of societal importance; usually under the guidance of senior ML scientists, and in collaboration with dedicated software engineers. To our partners, a Wadhwani AI solution is generally a decision making tool that requires some piece of data to engage. It will be your responsibility to ensure that the information provided using that piece of data is sound. This not only requires robust learned models, but pipelines over which those models can be built, tweaked, tested, and monitored. The following subsections provide details from the perspective of solution design:

Early stage of proof of concept (PoC)

  • Setup and structure code bases that support an interactive ML experimentation process, as well as quick initial deployments
  • Develop and maintain toolsets and processes for ensuring the reproducibility of results
  • Code reviews with other technical team members at various stages of the PoC
  • Develop, extend, adopt a reliable, colab-like environment for ML

Late PoC

This is early to mid-stage of AI product development

  • Develop ETL pipelines. These can also be shared and/or owned by data engineers
  • Setup and maintain feature stores, databases, and data catalogs. Ensuring data veracity and lineage of on-demand pulls
  • Develop and support model health metrics

Post PoC

Responsibilities during production deployment

  • Develop and support A/B testing. Setup continuous integration and development (CI/CD) processes and pipelines for models
  • Develop and support continuous model monitoring
  • Define and publish service-level agreements (SLAs) for model serving. Such agreements include model latency, throughput, and reliability
  • L1/L2/L3 support for model debugging
  • Develop and support model serving environments
  • Model compression and distillation

We realize this list is broad and extensive. While the ideal candidate has some exposure to each of these topics, we also envision great candidates being experts at some subset. If either of those cases happens to be you, please apply.

DESIRED QUALIFICATIONS

Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.

Programming

  • Expert level Python programmer
  • Hands-on experience with Python libraries
    • Popular neural network libraries
    • Popular data science libraries (Pandas, numpy)
  • Knowledge of systems-level programming. Under the hood knowledge of C or C++
  • Experience and knowledge of various tools that fit into the model building pipeline. There are several – you should be able to speak to the pluses and minuses of a variety of tools given some challenge within the ML development pipeline
  • Database concepts; SQL
  • Experience with cloud platforms is a plus
mle

ML Scientist

ROLES AND RESPONSIBILITIES

As an ML Scientist at Wadhwani AI, you will be responsible for building robust machine learning solutions to problems of societal importance, usually under the guidance of senior ML scientists. You will participate in translating a problem in the social sector to a well-defined AI problem, in the development and execution of algorithms and solutions to the problem, in the successful and scaled deployment of the AI solution, and in defining appropriate metrics to evaluate the effectiveness of the deployed solution.

In order to apply machine learning for social good, you will need to understand user challenges and their context, curate and transform data, train and validate models, run simulations, and broadly derive insights from data. In doing so, you will work in cross-functional teams spanning ML modeling, engineering, product, and domain experts. You will also interface with social sector organizations as appropriate.  

REQUIREMENTS

Associate ML scientists will have a strong academic background in a quantitative field (see below) at the Bachelor’s or Master’s level, with project experience in applied machine learning. They will possess demonstrable skills in coding, data mining and analysis, and building and implementing ML or statistical models. Where needed, they will have to learn and adapt to the requirements imposed by real-life, scaled deployments. 

Candidates should have excellent communication skills and a willingness to adapt to the challenges of doing applied work for social good. 

DESIRED QUALIFICATIONS

  • B.Tech./B.E./B.S./M.Tech./M.E./M.S./M.Sc. or equivalent in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics, or a relevant quantitative field. Work experience beyond the terminal degree will determine the appropriate seniority level.
  • Solid software engineering skills across one or multiple languages including Python, C++, Java.
  • Interest in applying software engineering practices to ML projects.
  • Track record of project work in applied machine learning. Experience in applying AI models to concrete real-world problems is a plus.
  • Strong verbal and written communication skills in English.
mls