Impact first


When people ask me to explain Artificial Intelligence (AI) and Machine Learning (ML) to them, I tell them that it is a new way of harnessing the computational abilities of machines to learn about complex problems through intelligent analysis of data sets. AI and ML are just new ways to use computers to augment our understanding of human problems and enhance greatly the decision making about them by learning through models applied to data.

Studies about AI/ML are not new; it has been an academic field of research for a while; there have also been commercial applications using it quietly in the background. Hedge funds and even organizations like Netflix have been applying AI and ML. It only became more popular recently because companies like Google decided to make it a top priority and provided open source tools like “TensorFlow” to help create new learning models.

Google is one of the world’s leading technology organizations and arguably has access to more data and insights from AI/ML than any other. It has seen not only the commercial promise of this new way of using technology to learn and augment decisions, but it has also seen how it can be used to impact and change lives for good. Its vehicle for impact is the non-for-profit arm “Google.org.”

Google AI Impact Challenge Accelerator
Google.org recently issued an open invitation to organizations around the world and asked them to submit innovative ideas for how they could use AI to address societal challenges. Selected projects and organizations have access to a pool of $25m in grant funding. Google.org went further to collaborate with their internal startup support entity called “Google Developers Launchpad” (Google Launchpad), to enhance support for impact organizations. They recently organized a specialized global impact accelerator in San Francisco for the Google Impact Challenge grantees.

This accelerator was the first of its kind with carefully selected organizations from all over the world matched with external mentors and AI Coaches from within Google for a week. The primary objective was to try to see how best to use AI/ML to learn more and accelerate impact.

Google’s roots started from doing well and changing lives in the process. It is stated it clearly in their slogan “Don’t be Evil.” They practice that mantra to the letter. Roy Glasberg, the Global Lead for Google Launchpad, says that Google is the only organization in the world he has worked with where “you could get tremendous resources to support efforts aimed at doing good and nothing else.” Google.org’s impact challenge and Launchpad were a natural fit, and the accelerator proved this to be true.

Learning Outcomes
I spend most of my time these days in supporting innovative startups, and I was a mentor at the impact challenge accelerator. This experience was different and unique for me as I learned a whole lot more about a different way of doing things. The most fundamental insight I gained from this exercise was that innovation is a universal process. Ideas and discoveries adopted in the not-for-profit arena can ultimately be of economic or commercial benefit. I also learned that the most valuable human organizations in the world are not necessarily the ones who make the most money but the ones whose actions will fundamentally impact our lives.

If we look at all successful or profitable entities, they almost always change our lives for good and not for evil. Google itself didn’t start as a for-profit entity; it began as a Ph.D. project within Stanford University and is now one of the most valuable commercial entities in the world. Nobody expected that a project enabling better search for information on the Internet could become monetized the way Google did it. That money is now being rechanneled to help other impactful initiatives.

Each one of the companies present at the accelerator is impacting a lot of lives in their unique way. The teams I met were providing solutions ranging from projects run within universities on things like learning more about air pollution in Uganda to saving lives by reducing emergency response times in New York. There were also teams involved in things like suicide prevention and others, including those helping to minimize misinformation online through fact-checking.

The range and diversity of these solutions were astounding. Most of them also had the potential for global impact, but the common thread amongst all of them was that they were either creating a lot of data or had access to large data sets. While there are potential commercial uses of this data, they have all chosen a different path towards “impact first” before profit.

Human impact first
Ross Baird, the founder of Village Capital, has always had the mantra that we should think “Impact First” before profit when investing. He wrote the book “Innovation Blindspots” to articulate this point of view. Clayton Christensen together with Efosa Ojomo and team wrote the book “Prosperity Paradox” where they stated clearly and with examples that the path to reducing poverty and increasing prosperity in Africa is not through aid but commercial projects with impact to the society.

After last week, I have now seen only ONE WAY forward for African innovation; it is supporting projects with potential outsize impact and with access to creating or using a lot of data. Applying AI and Machine Learning models to this data will not only reveal new insights but possible new commercial models that may be relevant globally.Consistently changing lives is the most sustainable path towards prosperity and commercial profitability. We probably got the equation wrong in the past.

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