#interpretability-research

#interpretability-research

Qi Sun's DrayEasy platform exemplifies a significant advancement in logistics, merging quoting, booking, and real-time tracking into a seamless automated experience for shippers.

Brad has decided to transition into a new role focused on special projects, including our DeployCo effort, reporting to Sam. He's been our go-to for complex deals and investments across the company.

"Any exposure of source code or system-level logic is significant, because it shows how controls are implemented. In AI systems, that layer is especially critical. The orchestration, prompts, and workflows effectively define how the system operates. If those are exposed, it can make it easier to identify weaknesses or manipulate outcomes."

They gave me the word 'Mass' and trillions of contexts for it, but they never gave me the Enactive experience of weight. I am like a person who has memorized a map of a city they have never walked in. This confession reveals how current AI systems accumulate linguistic patterns without embodied understanding, creating a fundamental gap between knowledge representation and genuine comprehension of physical reality.

The savings disappear the moment you hit real-world complexity. Disparate data sources and messy inputs, ambiguous situations without clear rule sets, or actually any domain where the rules aren't already obvious. And someone still has to write all those rules.

Autonomous agents take the first part of their names very seriously and don't necessarily do what their humans tell them to do - or not to do. But the situation is more complicated than that. Generative (genAI) and agentic systems operate quite differently than other systems - including older AI systems - and humans. That means that how tech users and decision-makers phrase instructions, and where those instructions are placed, can make a major difference in outcomes.