Data driven decision making takes courage and teamwork; business interpretation of AI will unlock the technology’s true potential. However, interpreting machine-produced outputs can be difficult for non-technical business users. In order for organizations to unlock the technology’s true potential they need to be able to pair AI analysis with human-focused interpretation. The interface for that pairing are interactive smart applications that take AI analysis and turn it into digestible insights. H2O.ai has assembled a rock star team of visual designers and UX engineers in order to help organizations develop smart applications.
Leland is renowned in the data visualization community as the author of “The Grammar of Graphics,” which presents a unique foundation for producing almost every quantitative graphic. He also wrote the SYSTAT statistical package and founded SYSTAT Inc. in 1984.
Prithvi is a visual analytics pro and serves as chief of technology, applications. Before H2O.ai, he built Platfora’s exploratory visualization environment and founded Plot.io.
“Vertical is the new horizontal”
“Our largest customers are transforming their businesses with data and AI and nurturing their communities with beautiful data products. Visual experience and the interpretation of AI is crucial for further democratizing algorithms and making them easily accessible,” said H2O.ai CEO Sri Ambati. “We’re incredibly excited to have such a profoundly creative and talented team to bring design thinking to our customers and community to unlock the true power of AI through visualization.”
“Several of my colleagues at Stanford University’s Department of Statistics are closely involved with H2O.ai and pointed me in the direction of the company,” said Wilkinson. “I was excited to find that H2O.ai is the real deal. This is a company that’s deeply focused on developing powerful algorithims that deliver tangible business results. Today Fortune 100 companies deal with terabytes of data every single day and it takes them too long to get results – they need a better way to explore their data. I look forward to building out a statistical basis for big data visualization, with no need to sample.”