Demand for artificial intelligence and machine learning solutions continues to surge. At the same time, agencies must secure private and confidential data. Synthetic data creation helps protect data while meeting the demand for AI/ML. However, traditional rule-based or statistical approaches offer limited control, lack realism, and may perpetuate bias.
In recent years, there has been an increased focus on the role of data and analytics in government. As the use of technology has become more ubiquitous, the need for agencies to establish a Center of Excellence (CoE) has become more apparent. A CoE is a cross-functional team that is responsible for developing and maintaining best practices around a specific area or technology.
Every student has been there—the teacher’s eyes lock on you. They ask a question that makes your stomach sink and heart rate quicken—because you really don’t know the answer. You could say “I don’t know (IDK),” but that won’t look good. Instead, with a slight stammer, you begin to make something up, hoping to fake it well enough to get off the hook.
In a strategic move to accelerate growth and enhance employee experience, Unissant Inc. (Unissant) today announced the appointment of Laila Salguero as Senior Vice President and Chief People Officer. In this new role, Salguero will expand Unissant’s corporate culture initiatives and align its people priorities with the company’s ambitious growth objectives.
Artificial intelligence and machine learning (AI/ML) hold the power to rapidly transform healthcare and improve health outcomes. However, the success of AI/ML solutions depends on the accessibility of diverse and representative data. Scarcity of data for specific socioeconomic or ethnic groups, though, can introduce bias, skewing AI/ML models.
Every day, countless systems work in unison behind the scenes, powering our lives. From the electricity that lights our homes to the internet that provides global connectivity, our critical infrastructure is integral to our economy, our security, and our way of life.
For federal missions, the stakes are high. Sure, an inaccurate weather forecast can disrupt your day or maybe ruin your favorite pair of shoes. But for national security missions, an unreliable AI model can introduce false positives or negatives, leading to the misidentification of threats. Similarly, for federal health missions, an unreliable AI model could perpetuate bias, negatively impacting research and care.
The demand for solutions leveraging artificial intelligence (AI) and machine learning (ML) continues to surge. Core to every AI or ML solution is a robust security model that protects data privacy. These more advanced data solutions demand a data security and privacy paradigm that evolves beyond approaches such as data suppression and data masking that, frankly, miss the mark when it comes to robust model security.
One of the biggest challenges associated with AI algorithms is the idea that they require vast amounts of data. While data is essential for development, it presents several challenges, particularly in the areas of Data Privacy and Data Security.
Unissant Inc. (Unissant) today announced that Hitesh “Tesh” Vashistha has joined the company as Chief Growth Officer (CGO). Vashistha’s addition reflects Unissant’s continued growth trajectory as it expands work supporting federal national security and healthcare missions.
Fall is synonymous with football. The roar of the crowd, the crunch of cleats on the turf, the whistle of the referee—it’s a cacophony of athleticism and excitement. Ever-focused on the sidelines—the head coach, meticulously analyzing player strengths and weaknesses, evaluating opponent strategies, and making decisions on the fly.
Mature infrastructure automation serves as an enterprise enabler, empowering agencies to manage complex configurations and processes with precision and efficiency. Infrastructure automation unlocks opportunities for streamlined workflows, accelerated deployments, and optimized resource utilization.
In the age of TikTok dance crazes and meme-able moments, marketers revel in viral sensations that sweep the globe. Over the decades, we've seen several marketing campaigns gain virality, from Wendy’s “Where’s the Beef,” to Nike’s “Just Do It,” and Staples’ “Easy” button.
Imagine a doctor who gives you a diagnosis but can't explain how they reached that conclusion. Doesn’t instill a lot of confidence, right? That's how some AI systems operate: their inner working are mysterious, and it’s hard to pinpoint why they make a specific prediction.
