Forbes / This AI Asks Questions, Finds Answers And Suggests Actions, All At Scale

The Covid-19 pandemic has revealed to us a very inconvenient truth: Regardless of all the technological advances of the last century and a half, our lives can be stopped and destroyed suddenly and unexpectedly by an invisible plague. The excitement over the most recent technological breakthrough—artificial intelligence or deep learning—has met the grim reality of our inability to adequately prepare for, manage, and overcome society’s most important and consequential challenges.

While Google’s DeepMind set itself up to “solve intelligence,” i.e., to advance the state of AI and then use the hoped-for “superior intelligence” to solve humanity’s challenges, Sagie Davidovich and Ron Karidi co-founded SparkBeyond 7 years ago “to harness the world’s collective intelligence in order to solve the world’s toughest challenges.” They aimed to use existing AI, algorithms, and knowledge to advance humanity’s problem-solving capabilities.

Most important, SparkBeyond wanted to go beyond the typical use of AI which is basically an extension and an upgrade of what has been called “predictive analytics” before the 2010s. “We focus on coming up with an idea that can change a system, solve a problem, rather than reacting through better predictive analytics,” says Davidovich, SparkBeyond’s CEO. And he adds: “Instead of predicting where the next lightning strike will hit, we can try to invent the lightning rod.”

To use AI as a tool for coming up with new ideas and new solutions, SparkBeyond first established a comprehensive library of algorithms. That gave it what Davidovich calls “a hypothesis engine,” producing ideas at scale, a number of possible solutions which is much larger than what an individual (e.g., a data scientist defining a few models for data analysis) or a group of people can come up with. The hypothesis engine delivers “entirely new and possibly crazy ideas,” says Davidovich, and then proceeds to autonomously tests and rank the ideas, discovering which are better supported by the data.  

For SparkBeyond, “data” means all available data and the most up-to-date data. The second pillar of the SparkBeyond platform is the “knowledge engine,” a data library of more than 400 billion web pages. Using natural language understanding software, the SparkBeyond engine scans and reads these pages to find possible answers to complex questions. Davidovich sees this as the essence of creativity: “We want to optimize for creativity. We want the machine to discover a new signal, new ideas, we don’t want to be constrained by ‘here’s the problem, here’s what makes sense for this problem.’”

As an example of SparkBeyond’s creativity and how is goes beyond typical predictive analytics solutions, Davidovich relates the question facing a Japanese convenience store chain of where to establish new retail locations. It turns out the profitability of these convenience stores is highly correlated with proximity to laundromats. (Possibly because people living in small apartments without laundry machines, especially young people, do their convenience store shopping during the time their clothes enjoy automated cleaning and drying). Where SparkBeyond went, in this case, beyond generating a “heat map” of the right locations for new stores, was to suggest a strategic action: Acquiring a laundromat chain, to facilitate co-locating laundromats and convenience stores.

So SparkBeyond re-invents, re-engineers, re-thinks the age-old idea (and widespread business process) of “strategic planning.” It transforms it to a “science project” with an AI-driven dynamic research engine, a continuously updated library of algorithms and linked data, helping business executives emulate scientists. Articulating questions and hypotheses, testing against available data and finding answers, even identifying the type of data that could refute their theories and challenge existing practices. Unlike research scientists, however, managers use the research to support their business decisions and to take specific actions. The successful ones believe that the best way to predict the future is to create it and, just like the SparkBeyond platform, don’t stop at finding correlations but create new combinations, new markets, new ways to compete.

SparkBeyond is currently working on the third pillar of their idea-generation platform, looking for direct augmentation of machine and web intelligence with human intelligence. Davidovich envisions “a self-organizing network of agents, humans and machines,” training together to improve their research methods and real-world impact.

“We are not domain experts so we partner with the best,” explains Davidovich their approach to developing special applications of the SparkBeyond platform. These include a leading management consulting firm, energy companies, and Baker McKenzie, a leading law firm.

Baker McKenzie is collaborating with SparkBeyond to develop and provide new services to its clients and improving the insight, speed, and accuracy of the law firm’s own machine learning-enabled decision-making. As both Baker McKenzie and SparkBeyond strongly advocate “social impact at scale,” they recently announced that their first social impact project is nearing completion of its first stage, aiming to demonstrate “the links between child detention and unintended negative consequences for detained children and the authorities detaining them.”

Will combining machine, web, and human intelligence help address society’s challenges? “The current pandemic is an example of how horrible we are in solving problems. Collective intelligence may help us come up with better approaches and accelerate the time to solutions,” concludes Davidovich.

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