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M.R. Asks 3 Questions: Jorge Torres & Adam Carrigan, Co-Founders, CEO & COO of MindsDB

By January 8, 2021Article

Determined to “democratize machine learning” – the Co-Founders of MindsDB, Jorge Torres and Adam Carrigan, are on a mission to leverage the power of machine learning, to allow organizations to ask predictive questions of their data and receive accurate answers from it. 

At the head of the table, CEO, Jorge Torres brings his academic, AI/machine learning expertise. COO Adam Carrigan delivers his experience as a Management Consultant at Deloitte and dissertation at the University of Cambridge – specializing in the use of NLP to predict equity pricing.

With significant operational experience in various industries and extensive knowledge in finance, marketing and strategy, Adam and Jorge lead this newest venture through the YCombinator accelerator in March 2020, they have recently announced their $4 million seed funding round led by OpenOceanVC.

Speaking with an additional person in our first Sand Hill feature of 2021, allowed for double the perspectvie. 


M.R. Rangaswami: How has COVID-19 changed the demands for predictive analytics and AI technology?

Jorge Torres: As companies face the challenges of supporting a more remote workforce, cutting costs, and remaining agile in the face of an unprecedented level of change, they are forced to do more with their existing resources. This created an increased demand from predictive analytics and AI technology to provide support for human and IT resources. We’re seeing this demand in everything from understaffed healthcare workers looking to leverage AI to predict treatment outcomes all the way up to CEOs who need machine learning predictions to more efficiently address industry changes.

Adam Carrigan: This year, more companies are looking to use existing data in new ways. Most companies have massive amounts of data in relational databases, data warehouses, and simple Excel files that can add significant value to the decision making process. However, the majority of data is underused or in many cases not used at all. Doing more with their existing resources means finding ways to utilize the data they already have and to use predictive analytics and machine learning to analyze this data in new ways.


M.R.: How are organizations currently making use of data and what opportunities are they leaving on the table?  What are these limitations currently holding organizations back from fully adopting machine learning technology? 

Jorge: The biggest barrier to companies fully adopting machine learning is a lack of access to the right people. There is a massive shortage for data scientists experienced in machine learning and those available are expensive. Further, most companies run all of their predictive analytics and machine learning projects through their data scientists, which creates an even greater demand and a major bottleneck for companies that aren’t able to meet this demand.

Adam: In general, traditionally implementing machine learning systems is time and resource-intensive. Even for companies with data scientists with advanced machine learning knowledge, running an ML project requires a great deal of back and forth between the data scientist and the project manager. Since the data scientist isn’t the end-user of the data, they often don’t have a full understanding of how the data will be used. This disconnect is what leaves opportunities on the table.


M.R.: What major technology trends do you expect to define 2021 in regard to predictive analytics and AI?

Adam: One thing that will help solve this disconnect between AI and the end-users is the continued democratization of machine learning. The next twelve months will see companies bringing machine learning capabilities straight to the database itself to create a simple way to create, train, and test machine learning models. With this will come a rise in citizen data scientists. By simplifying and automating the process of applying machine learning models, end-users with little ML knowledge can run simple queries. Putting machine learning capabilities into the hands of the people using the data will result in new innovations in how companies are using their data.

One of the keys for this that should improve how database users rely on machine learning models in 2021 is explainable AI. Providing insights on why the model reached its conclusion and what the prediction confidence is right along with the ML insights will help users more confidently rely on predictive analytics and AI.

Jorge: AI will become more valuable as data becomes more accurate and users can see this through explainability. 2021 should also see a shift in mindset from the belief that AI will solve all the world’s problems to a more cooperative mindset where AI enhances the human decision process. We’ll certainly see AI powering more things behind the scenes from adjusting the pricing of airline seats to predictive inventory to improve out-of-stock issues, but the real impact will be in how AI helps support humans and makes their jobs more intelligent and efficient.


M.R. Rangaswami is the Co-Founder of