Artificial Intelligence / Machine Learning (AI/ML) are hot topics these days when it comes to politics, technology, and personal living. There is much discussion around the ethics, the threats, and the benefits of AI/ML in each of these circles. It comprises the newest arms race with our adversaries, and just about every industry is putting it to use to explore its benefits. AI / ML have also become critical technologies in information security, as they are able to quickly analyze millions of events and identify many different types of threats – from malware exploiting zero-day vulnerabilities to identifying risky behavior that might lead to a phishing attack or download of malicious code. As I have begun to learn more and more about the intricacies and use cases of AI/ML technologies, I have learned to respect the challenges of the MLOps life cycle. One area that seems particularly challenging is the integration of ML models with applications in a timely and efficient [i.e., Low code / No code] way, which covers the “last mile” of the MLOps life cycle. I did not have to venture far to discover a solution to this challenge as my youngest son is actually working for a company that is addressing this challenge – AI Squared. He introduced me to the CEO – Dr. Ben Harvey – who accepted my invitation for this interview with Active Cyber™ below. So read on to learn about their unique solution in this crowded AI/ML technology space.
Spotlight on Dr. Ben Harvey
» Title: CEO and Founder of AI Squared
» Website: https://squared.ai/
Read his bio below.
Chris Daly, Active Cyber™ – What is your background and how has it prepared you to lead an innovation-based AI start-up?
Dr. Ben Harvey, CEO and Founder AI Squared – My educational training includes a Cryptologic Computer Science Certificate from the University of Maryland Baltimore County, a B.S. in Pre-Medicine/Computer Science from Mississippi Valley State University, a Masters in Computer Science from Bowie State University, and a Ph.D. from Bowie State University’s Department of Computer Science.
I’ve held several positions, one with the Department of Defense/National Security Agency and served as the Chief of Operations Data Science for over a decade. Additionally, I worked as head of data science for the Edward Snowden leaks. Prior to launching AI Squared in 2019, I was an early employee of a Silicon Valley startup called Databricks where I served as an artificial intelligence and machine learning subject matter expert and architect and helped them go from $100M valuation to over $40B.
Applying my esteemed academic training and career experiences, I uncovered a problem while working at the NSA that led to a solution, which has formed the foundation of the offering of AI Squared. I decided to leave NSA and begin a journey to bring my idea to life.
Active Cyber™ – Can you provide an overview of your offering? What are some use case examples that your customers have found interesting and are adopting?
Dr. Harvey – AI Squared offers an innovative approach that makes it easy to integrate the results of artificial intelligence and machine learning (AI/ML) into any web-based application. Specifically, AI Squared empowers organizations to accelerate the integration of AI/ML models (e.g., computer vision, large language & natural language processing, conversational AI) into team workflows and business applications. In turn, organizations gain immediate insights and are empowered to share feedback on performance, impact, and value delivered through integrations in real-time. We’ve created a few use cases for a financial services company, including the following:
- Chatbot Use Case: The company’s internal chatbot was restricted to only one specific response, without additional context Solution: AI Squared executed a model in real-time over conversations which provides results for an entity or keyword that could be used to provide additional insights for the conversational AI model. AI Squared augments chatbot conversations and presents models as related categories, links to files/webpages, or topics directly embedded into the chatbot conversation, providing richer responses to questions.
- Life Event Flagging Use Case: Customer engagement was a challenge for a financial institution. Solution: AI Squared folded actionable insights into the analysts’ workflow, yielding a more efficient approach by integrating predicted next best actions into Salesforce. This life event flagging tool provided the company with the “why” behind next best actions.
Active Cyber™ – What is your market strategy and how have you adapted your architecture and market strategy since you emerged from stealth mode? What is your key differentiator and approach for the market? What are some of the key AI market trends that may impact your offering this year?
Dr. Harvey – AI Squared primarily operates in the financial services and federal government spaces. The product is targeted to enterprise companies in those industries. Marketing efforts center on sales, referrals, and plans for lead generation will be deployed in the future. Speed and agility are the key differentiators, and represent the AI Squared advantage. We remove the barriers companies encounter with AI integration processes. Instead, we make actionable insights accessible. We reduce technical debt due to integration of models and applications from eight months to eight hours, thereby helping organizations save time and money.
Active Cyber™ – Who is the intended user for your platform? What type of training or understanding is needed to fully leverage the product? What types of deployment services are available? What are some of the major tasks and dependencies for a typical install?
Dr. Harvey – The AI Squared Platform is intended for multiple users within an organization – Data Execs, Data Scientists, Business Owners, Product owners. Each user engages differently with the platform based on their job roles and business needs.
Technical users, such as Data Execs and Data Scientists would possess skills in machine learning, deep learning, and neural networks. Business Owners and Product Owners are non-technical users. A general understanding of data interpretation presented by the platform dashboard is sufficient to take advantage of the platform capabilities.
AI Squared provides clients with a cloud formation template, or equivalent for non-AWS cloud customers, enabling the installation of the AI Squared platform within their virtual private cloud. For clients who want to solely make use of the AI Squared extension without the platform backend, we can distribute local-only versions of the AI Squared extension. In all cases, use of AI Squared at this point requires the use of Chromium-based browsers (e.g. Chrome, Edge, etc.), although this will not be required for all use cases as the technology matures. We have an initial deployment package that we offer as an addition to the contract. We also offer upgrade packages where we will update the latest version of the platform in the customer’s environment.
Currently we offer a VPC (virtual private cloud) deployment where we install our software in the customer’s cloud environment, but we also offer an on-premise solution where we install in a customer’s facility. The customer will need to download and install the AI Squared browser extension and either have the AI Squared platform deployed in their VPC or their on-premise environment. AI Squared provides a framework that empowers their users to integrate AI models into applications. The framework provides both mechanism for integrations where they can use either the low code python package or the no code configuration editor inside of the platform. For organizations using the python package, they must download and install it in their environment.
Active Cyber™ – What are some of the innovations that are necessary for greater robustness, security, and safety of AI/ML systems in general? How is your offering poised to address these innovations?
Dr. Harvey – It is common practice to test and evaluate AI/ML systems prior to productization, but that practice is frequently found to be insufficient when the system is released into the real world and asked to predict on noisy, distributionally drifting, or even adversarial inputs. AI Squared is poised to assist organizations by empowering their internal subject matter experts to provide human feedback on the performance of AI/ML systems within their workflow and provide corrections to the system. This enables the organization to (1) monitor the performance of their AI/ML systems in real time on real-world data, and (2) aggregate expert-curated labels for further tuning of their AI/ML systems.
Active Cyber™ – What do you see as the toughest cybersecurity challenges facing organizations today? How can AI/ML address these challenges? How will the use of machine learning evolve in the next 5 years in the cybersecurity industry?
Dr. Harvey – The stakes can be high in machine learning. If a machine learning model is not sufficiently accurate then it can bring down a business’ critical infrastructure, either by missing a cyber-attack or by mistakenly interfering with normal operations. A single incorrect prediction can have major impacts. As the transfer of control from human to computer moves forward with AI systems, there will likely be a shift from human error to programmer error.
Active Cyber™ – What types of new sensors, protocols, safety and security algorithms, metrics and testing approaches are needed to tolerate intermittent failures in systems and to ensure safety- and security-resilient AI systems? How will this affect your offering?
Dr. Harvey – Approaches to ensuring the resilience of AI/ML systems runs the gamut from traditional information security approaches, homomorphic encryption, data and object provenance, et cetera to multi-sensor fusion, ensemble approaches to ML, and adversarial robustness. AI Squared is currently in talks with Fortanix to identify approaches to leverage their information security technology for AI Squared customers and is working with the National Security Agency under a Cooperative Research and Development Agreement (CRADA) to identify specific product enhancements related to ensuring robust and trustworthy AI/ML systems.
Active Cyber™ – What are your top 3 priorities for the remainder of this year?
Dr. Harvey – Pilots, federal contracts, and annual recurring revenue. Currently our ACV (annual contract value) is anywhere from $400-600,000 annually.
Thank you Dr. Harvey for these insights to your AI integration platform and your plans for this fast-moving AI/ML start-up! I sense that your focus on the “last mile” of the AI/ML operations pipeline along with a low code/no code platform puts AI Squared in a unique position to succeed in this congested AI/ML space. I can see that 2023 will be a very busy year for you and your company as you continue to grow. I look forward to following your progress in 2023 and beyond!
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About Dr. Ben Harvey. Dr. Benjamin Harvey is the Founder and CEO of AI Squared. He has a BS in Computer Science from Mississippi Valley State University and a Masters and Doctor of Science in Computer Science from Bowie State University. He worked for over a decade for the Department of Defense at the National Security Agency where his last position was the Chief of Operations Data Science. He is a research professor at George Washington University, Bowie State University and also a Research Scientist at Johns Hopkins University.
Dr. Harvey leads a diverse team of 20 at AI Squared and has secured contracts with NSA, NGA, DoD and Air Force in the federal space, and with Fortune 500 companies in financial services and the beverage industry. AI Squared has achieved the following accolades:
• DC Startup To Watch in 2023 – Washington Business Journal
• Ranked 5th as DC Startup to Watch in 2023 – Technical.ly
• Top 10 Startup to Watch in 2022 – AI Magazine
• 10 Hottest AI Startup Companies Of 2022 – CRN
• Fire Awardee – DC Inno/Washington Business Journal
AI Squared has also been recognized as a Community Partner for the Partnership for Child Health.