Usually home to the area’s beloved Tampa Bay Lightning hockey team, this week Amalie Arena hosted the Synapse Summit, an annual gathering of entrepreneurs, innovators and community leaders.
No Zamboni Here: Just 6,000+ Innovators and Entrepreneurs
Although the ice was covered, organ shuttered and the Zamboni comfortably parked in its garage, the 6,000 people gathered at the arena held the same passion as those Lightning fans.
But instead of cheering for a shot on goal, the applause in the arena resulted from inspiring speeches, panel discussions and exhibits highlighting the latest tech and innovation in the area.
One of those panel discussions focused on what might seem to be two divergent concepts: corporate culture and data.
Corporate Culture and Data
Lumina’s Co-Founder and Chief Data Scientist, Dr. Morten Middelfart, joined Dr. Balaji Padmanabhan, the Director of the Center for Analytics & Creativity at the University of South Florida and Synapses’ Vice President of Corporate Engagement, Bob Dixon on the panel.
The idea was this: companies not only need to find the right data scientists or programmers, but also need to create a culture where everyone in the organization has access to the right information to make the best decisions at every level.
Insisting on Data-Informed Decisions
As Dixon put it, “Decision-makers must insist on proper evidence to inform their decision, and organizational leaders must routinely insist on timely, accurate insights from the organization’s data. Creating this culture takes time and education. Senior leaders who understand the value and potential of their data ask better questions and insist on better, data-informed answers. They insist that their people make data-informed decisions and that their systems provide access to the right data to all levels.”
Middelfart noted that artificial intelligence (AI) and machine learning (ML) are increasingly powerful tools to help companies interpret data, find patterns in that data as well as unlikely correlations that can ultimately predict behavior.
Four Key Elements to Culture of Analytics
Padmanabhan outlined four key elements in building a culture of analytics:
- Investment – invest in people, data and infrastructure;
- Inspiration – imagine what can be down in the future is better and different than the past;
- Values – embrace cross-functional data sharing as a core value;
- Practice – do the right things with data and be willing to learn from mistakes.
In inspiring companies to imagine the power of data, Padmanabhan paraphrased the famous Albert Einstein quote: “Imagination is more important than knowledge. For knowledge is limited, while imagination embraces the entire world….”
Imagination is More Important than Knowledge
Middelfart further added to the power of imagination in the world of AI. He cautioned that while most data scientists are using deep learning algorithms today, the algorithm is only as good as the data and the size of the data being used.
“I like the idea of training the algorithm on data,” Middelfart said. “And then, what the algorithm holds makes it proprietary. At Lumina, we optimize our algorithms again the data.”
Solving the Problem of Chinese Influence in Academia
He explained a project he has been working on since recent revelations of Chinese influence with U.S. university professors, like the high-profile incident at Harvard University.
Not surprisingly, the open sourced internet provides a significant amount of publicly available information about people.
And that is where Middelfart began.
He trained algorithms to look at the online signature of those who have been influenced by China – in other words, the “knowns.” Then the algorithms seek to find what sets those who have been influenced apart from those who haven’t. Ultimately, as the algorithm learns and evolves, it can review all the data on the open web and identify those who should be scrutinized for potential influence and eliminate those who those who don’t show the patterns of influence.
Proprietary AI will Create Organizational Value
As Lumina continues to seek to solve the issue of potential Chinese influence in academia, Middelfart reinforced the importance of not relying on off-the-shelf AI tools.
“The companies that work deep learning at this type of level will beat out the commodity tools and drive value to their customers’ and organizations,” Middelfart said.