Birago Jones, Co-Founder and CEO of Pienso – Interview Series

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Birago Jones is the CEO and Co-Founding father of Pienso, a no-code/low-code platform for enterprises to coach and deploy AI models without the necessity for advanced data science or programming skills. Today, Birago’s customers include the US government and Sky, the most important broadcaster within the UK. Pienso is predicated on Birago’s research from the Massachusetts Institute of Technology (MIT), where he and his co-founder Karthik Dinakar served as research assistants within the MIT Media Lab. He’s a distinguished authority within the intersection of artificial intelligence (AI) and human-computer interaction (HCI), and an advocate for responsible AI.

Pienso‘s interactive learning interface is designed to enable users to harness AI to its fullest potential with none coding. The platform guides users through the technique of training and deploying large language models (LLMs) which can be imprinted with their expertise and fine-tuned to reply their specific questions.

What initially attracted you to pursue your studies in AI, HCI (Human Computer Interaction) and user experience?

I had already been developing personal projects focused on creating accessibility tools and applications for the blind, corresponding to a haptic digital braille reader using a smartphone and an indoor wayfinding system (digital cane). I believed AI could enhance and support these efforts.

Pienso was initially conceived during your time at MIT, how did the concept of coaching machine learning models to be accessible to non-technical users originate?

My co-founder Karthik and I met in grad school while we were each conducting research within the MIT Media Lab. We had teamed up for a category project to construct a tool that might help social media platforms moderate and flag bullying content. The tool was gaining a number of traction, and we were even invited to the White House to provide an illustration of the technology during a cyberbullying summit.

There was only one problem: while the model itself worked the way in which it was imagined to, it wasn’t trained on the appropriate data, so it wasn’t in a position to discover harmful content that used teenage slang. Karthik and I were working together to determine an answer, and we later realized that we could fix this issue if we found a way for teenagers to directly train the model data.

This was the “Aha” moment that might later encourage Pienso: subject-matter experts, not AI engineers like us, should have the option to more easily provide input on model training data. We ended up developing point-and-click tools that allow non-experts to coach large amounts of information at scale. We then took this technology to local Cambridge, Massachusetts schools and elicited the assistance of local teenagers to coach their algorithms, which allowed us to capture more nuance within the algorithms than previously possible. With this technology, we went to work with organizations like MTV and Brigham and Women’s Hospital.

Could you share the genesis story of how Pienso was then spun out of MIT into its own company?

We all the time knew that this technology could provide value beyond the use case we built, however it wasn’t until 2016 that we finally made the jump to commercialize it, when Karthik accomplished his PhD. By that point, deep learning was exploding in popularity, however it was mainly AI engineers who were putting it to make use of because no one else had the expertise to coach and serve these models.

What are the important thing innovations and algorithms that enable Pienso’s no-code interface for constructing AI models? How does Pienso make sure that domain experts, without technical background, can effectively train AI models?

Pienso eliminates the barriers of “MLOps” — data cleansing, data labeling, model training and deployment. Our platform uses a semi-supervised machine learning approach, which allows users to begin with unlabeled training data after which use human expertise to annotate large volumes of text data rapidly and accurately without having to write down any code. This process trains deep learning models that are able to accurately classifying and generating latest text.

How does Pienso offer customization in AI model development to cater to the precise needs of various organizations?

We’re strong believers that nobody model can solve every problem for each company. We’d like to have the option to construct and train custom models if we wish AI to grasp the nuances of every specific company and use case. That’s why Pienso makes it possible to coach models directly on a corporation’s own data. This alleviates the privacy concerns of using foundational models, and may deliver more accurate insights.

Pienso also integrates with existing enterprise systems through APIs, allowing inference results to be delivered in several formats. Pienso may operate without counting on third-party services or APIs, meaning that data never must be transmitted outside of a secure environment. It will probably be deployed on major cloud providers in addition to on-premise, making it a really perfect fit for industries that require strong security and compliance practices, corresponding to government agencies or finance.

How do you see the platform evolving in the following few years?

In the following few years, Pienso will proceed to evolve by specializing in even greater scalability and efficiency. Because the demand for high-volume text analytics grows, we’ll enhance our ability to handle larger datasets with faster inference times and more complex evaluation. We’re also committed to reducing the prices related to scaling large language models to make sure enterprises get value without compromising on speed or accuracy.

We’ll also push further into democratizing AI. Pienso is already a no-code/low-code platform, but we envision expanding the accessibility of our tools much more. We’ll repeatedly refine our interface in order that a broader range of users, from business analysts to technical teams, can proceed to coach, tune, and deploy models without having deep technical expertise.

As we work with more customers across diverse industries, Pienso will adapt to supply more tailored solutions. Whether it’s finance, healthcare, or government, our platform will evolve to include industry-specific templates and modules to assist users fine-tune their models more effectively for his or her specific use cases.

Pienso will turn out to be much more integrated throughout the broader AI ecosystem, seamlessly working alongside the solutions / tools from the foremost cloud providers and on-premise solutions. We’ll deal with constructing stronger integrations with other data platforms and tools, enabling a more cohesive AI workflow that matches into existing enterprise tech stacks.

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