Birago Jones is the CEO and Co-Founding father of Pienso, a no-code/low-code platform for enterprises to coach and deploy AI fashions with out the necessity for superior information science or programming abilities. At present, Birago’s clients embody the US authorities and Sky, the most important broadcaster within the UK. Pienso is predicated on Birago’s analysis from the Massachusetts Institute of Expertise (MIT), the place he and his co-founder Karthik Dinakar served as analysis assistants within the MIT Media Lab. He’s a distinguished authority within the intersection of synthetic intelligence (AI) and human-computer interplay (HCI), and an advocate for accountable AI.
Pienso‘s interactive learning interface is designed to enable users to harness AI to its fullest potential without any coding. The platform guides users through the process of training and deploying large language models (LLMs) that are imprinted with their expertise and fine-tuned to answer 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, such as 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 training machine learning models to be accessible to non-technical users originate?
My co-founder Karthik and I met in grad school while we were both conducting research in the MIT Media Lab. We had teamed up for a class project to build a tool that would help social media platforms moderate and flag bullying content. The tool was gaining lots of traction, and we were even invited to the White House to give a demonstration of the technology during a cyberbullying summit.
There was just one problem: while the model itself worked the way it was supposed to, it wasn’t educated on the proper information, so it wasn’t capable of determine dangerous content material that used teenage slang. Karthik and I have been working collectively to determine an answer, and we later realized that we may repair this situation if we discovered a means for youngsters to instantly practice the mannequin information.
This was the “Aha” second that will later encourage Pienso: subject-matter consultants, not AI engineers like us, ought to have the ability to extra simply present enter on mannequin coaching information. We ended up growing point-and-click instruments that enable non-experts to coach giant quantities of information at scale. We then took this expertise to native Cambridge, Massachusetts faculties and elicited the assistance of native youngsters to coach their algorithms, which allowed us to seize extra nuance within the algorithms than beforehand potential. With this expertise, we went to work with organizations like MTV and Brigham and Ladies’s Hospital.
May you share the genesis story of how Pienso was then spun out of MIT into its personal firm?
We at all times knew that this expertise may present worth past the use case we constructed, but it surely wasn’t till 2016 that we lastly made the soar to commercialize it, when Karthik accomplished his PhD. By that point, deep studying was exploding in reputation, but it surely was primarily AI engineers who have been placing it to make use of as a result of no person else had the experience to coach and serve these fashions.
What are the important thing improvements and algorithms that allow Pienso’s no-code interface for constructing AI fashions? How does Pienso be certain that area consultants, with out technical background, can successfully practice AI fashions?
Pienso eliminates the boundaries of “MLOps” — information cleansing, information labeling, mannequin coaching and deployment. Our platform makes use of a semi-supervised machine studying strategy, which permits customers to begin with unlabeled coaching information after which use human experience to annotate giant volumes of textual content information quickly and precisely with out having to write down any code. This course of trains deep studying fashions that are able to precisely classifying and producing new textual content.
How does Pienso supply customization in AI mannequin improvement to cater to the precise wants of various organizations?
We’re robust believers that nobody mannequin can resolve each downside for each firm. We want to have the ability to construct and practice customized fashions if we would like AI to grasp the nuances of every particular firm and use case. That’s why Pienso makes it potential to coach fashions instantly on a company’s personal information. This alleviates the privateness considerations of utilizing foundational fashions, and may ship extra correct insights.
Pienso additionally integrates with present enterprise techniques by means of APIs, permitting inference outcomes to be delivered in several codecs. Pienso may function with out counting on third-party companies or APIs, which means that information by no means must be transmitted outdoors of a safe atmosphere. It may be deployed on main cloud suppliers in addition to on-premise, making it a perfect match for industries that require robust safety and compliance practices, equivalent to authorities businesses or finance.
How do you see the platform evolving within the subsequent few years?
Within the subsequent few years, Pienso will proceed to evolve by specializing in even higher scalability and effectivity. Because the demand for high-volume textual content analytics grows, we’ll improve our means to deal with bigger datasets with sooner inference occasions and extra complicated evaluation. We’re additionally dedicated to lowering the prices related to scaling giant language fashions to make sure enterprises get worth with out compromising on pace or accuracy.
We’ll additionally push additional into democratizing AI. Pienso is already a no-code/low-code platform, however we envision increasing the accessibility of our instruments much more. We’ll constantly refine our interface so {that a} broader vary of customers, from enterprise analysts to technical groups, can proceed to coach, tune, and deploy fashions while not having deep technical experience.
As we work with extra clients throughout various industries, Pienso will adapt to supply extra tailor-made options. Whether or not it’s finance, healthcare, or authorities, our platform will evolve to include industry-specific templates and modules to assist customers fine-tune their fashions extra successfully for his or her particular use circumstances.
Pienso will develop into much more built-in throughout the broader AI ecosystem, seamlessly working alongside the options / instruments from the main cloud suppliers and on-premise options. We’ll deal with constructing stronger integrations with different information platforms and instruments, enabling a extra cohesive AI workflow that matches into present enterprise tech stacks.
Thanks for the nice interview, readers who want to be taught extra ought to go to Pienso.
