Conditional generation enhances synthetic data by allowing data scientists to generate targeted, realistic datasets with specific characteristics, helping improve model training, testing, fairness, risk management and trustworthy AI.
The post Beyond balancing data: How conditional generation makes synthetic data more useful appeared first on SAS Blogs.
Organizations are investing heavily to make AI more trustworthy: building governance programs, establishing AI policies, documenting models and investing in explainability tools. These are all important steps, but what if one of the biggest barriers to trustworthy AI lies somewhere else entirely? What if it's the data? The Data and [...]
The post The AI trust gap might actually be a...
A new approach known as “reverse game theory” aims to control powerful AI models by designing systems that incentivize them to stick to the rules. As AI models become more capable, safety researchers are scrambling to ensure they are “aligned” with human desires—to make sure, in other words, that rogue agents don’t break the law,…
Proprietary models are amazing! But sometimes what is of importance is configurability rather than raw power. This has led to the emergence of locally hosted models. Running AI locally gives users more control over their setup. With suitable hardware and tools like Ollama and LM Studio, capable models can run entirely on-device. But which models are worth running, […]
The post Top 5...
New Pulse of AI Podcast. Season 8, Ep 173
Pulse of AI Podcast Host Jason Stoughton is joined by Michael Davies.
As Senior Lecturer at the Massachusetts Institute of Technology, Michael teaches Breakthrough Innovation: Harnessing AI to Create Value, and Ethics of AI: Building Responsible AI, Machine Learning, and GPTs and on the Technology Leadership Program and the CXO programs...
Technology modernization is disruptive even under ideal circumstances. But what happens when the business itself is already undergoing massive change? Recently, I spoke with a SAS account team that supported a global organization through a significant corporate transition. The company was separating from its parent organization and needed to establish [...]
The post When business...
これまでの連載では、同じデータ、同じ定義を使うことの大切さを書いてきました。今回の話は、その延長にあります。AIが作る判断材料の質は、元のデータと、「何を異常と呼ぶか」という定義の質で決まります。データがばらばらで、定義が人によって違えば、AIは速く、大量に、間違った判断材料を作ります。どこまで任せるかを考える前に、判断材料の質を揃えておく必要があります。 ※第1回:「経営KGIと現場KPIをつなぐ」、第2回:「同じデータ・同じ定義」を誰がつくるのか、第3回:「同じ判断材料の共有」についてお話ししました。 「AIが判断したので」は会議で通るか 「それはAIが判断したことなので」。この言葉を、経営会議で口にできる人はいないはずです。...
When we joined the SAS Data for Good project supporting the Kay Yow Cancer Fund, we expected to contribute our technical expertise. What we didn’t anticipate was how much we’d learn ourselves Throughout this series, we've explored how SAS and the Kay Yow Cancer Fund are using data to better [...]
The post We came to give back. The Kay Yow Cancer Fund gave us a new perspective. appeared...
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. At the end of September, President Trump signed an executive order that contained just one directive, ordering government agencies to rebrand what they had been calling artificial intelligence as “Super Intelligence.” That…
Customer-facing AI agents often stall between pilot and production, held back by governance overhead and run costs that break the business case. In this episode, Yogesh Bhagat, Vice President of Digital and Technology at CVS Health, explains how autonomous agents earn the right to operate without human oversight by proving consistent, measurable behavior. He discusses validating a shared...