SAS

Understanding where programs are already making an impact is one part of the picture. The next step is looking ahead to where additional outreach and resources could make a difference. For our analytics initiative supporting the Kay Yow Cancer Fund, we brought together public health data from various sources to identify locations for community outreach, education and cancer screening programs.

Delivering timely access to benefits and services. Managing growing demand with limited resources. Supporting staff overwhelmed with heavy caseloads. Modernizing aging systems and fragmented data. Detecting increasingly sophisticated fraud schemes. Improving payment accuracy while reducing fraud, waste and abuse. Social benefits organizations are expected to expand access to services, protect public funds and improve program integrity and operational efficiency, all while maintaining public trust.

Part 2 in the Demystifying Recent AI Advances Series In Part 1 of this series, we toured the model zoo. We covered what separates LLMs, SLMs, VLMs, reasoning models, and diffusion models, and the deeper split between discriminative and generative approaches. Knowing which model to reach for is a good start. But a model on its own is inert. It answers when spoken to, and that is the whole of the interaction.

A convolution is a mathematical operation on two discrete sequences (or continuous functions) that produces a new sequence (or function).

AI agents AI agents in production: Improve quality, cost and latency That means looking beyond a successful output. Could the agent reach the same result in fewer steps? Could it respond faster or cost less to run? And as the system changes, how can we tell whether performance is truly improving? We encountered these questions while developing an industrial asset maintenance agent with SAS® Retrieval Agent Manager (RAM). To answer them, we had to look at the agent as a complete system.

Claims range from a greater than 10% chance that AI “could kill all humans” within the next decade to those who consider the technology “AI snake oil.” The latter claim is backed by a world-renowned researcher and empirical data showing models cannot reason or replace your job. Whatever you believe, the truth likely falls somewhere between these opposing viewpoints.

Reliable solar power is not just an infrastructure improvement. It becomes part of safer care, greater confidence and dignity for both the mother and the clinic team. Before solar electrification, darkness could mean limited services, difficult working conditions and barriers to care. Today, reliable solar lighting powers clinics and keeps them open, supports safer deliveries and serves communities after dark.

Surprisingly, this question first came up around our Thanksgiving dinner table last year. My sister-in-law, Paula, works for the Kay Yow Cancer Fund, an organization dedicated to supporting underserved women facing cancer through research, education and access to care. I work at SAS, where I have a front-row seat to how data and AI help organizations make better decisions and create meaningful outcomes.

Maintaining that control is at the heart of digital sovereignty. Government organizations must remain accountable for how data is collected, used and protected; establish guardrails for AI systems; strengthen cybersecurity resilience; ensure business continuity; and manage supply chain dependencies.

Align incentives. Improve outcomes. Reduce unnecessary utilization. Create a better experience for patients and providers. But in the U.S. commercial health care market, agreement on the promise has not always produced agreement on the proof. Health plans, employers, and provider organizations can look at the same contract and reach different conclusions about whether it worked. One sees a downward trend in medical costs. Another sees higher shared-savings payments.