implementation

When we focus on novelty, we lose sight of behavioral outcomes. And while using validated pressures to inform our designs can help guard against the natural tendency to pursue interventions that feel unique, they aren’t a guarantee; just as data can be used to justify bad business decisions, pressures can be used to justify bad interventions.

It's Not About A Spreadsheet Of Inputs

The goal of the framework is not to try and boil a decision like this down to a set of spreadsheet inputs that spits out a "right" answer. Instead, the goal of the framework is to:

• Be able to name the individual variables that are inputs into the decision.

• Evaluate each individual variable in a structured

First—and hopefully this is obvious—you have to be transparent. Put it up on the wall where everyone can see it, mention it at every town hall, orient your planning process toward it. The whole point of a good behavioral statement is that it can allow for strong decision making because you can explicitly compare available options against the

To solve the puzzle, you must first have a sense of the picture as a whole.

America is, implicitly or explicitly, making a bet that whoever wins intelligence, in the form of AI, wins the future.

China is making a different bet: that for intelligence to truly matter, it needs energy and action.

If you control energy and action, making intelligence abundant strengthens your position.

regression does not have a causal explanation.

The test procedure, the reader will note, is to use the muscle test to verify the truth or falsity of a declarative statement. Unreliable responses will be obtained if the question has not been put into this form. Nor can a reliable result be obtained from inquiry into the future; only statements regarding existent conditions or events in the past

The sources of alpha can be segmented into four key areas:

Information: The advantage of possessing data that others do not.

Processing: The capacity to interpret and analyze data more effectively than others.

Modeling: The ability to design superior mathematical models that reflect market patterns and behaviors.

Speed: The advantage of acting quicker

A key advantage of these RL advancements is their universal applicability across any open-source model. This flexibility allows organizations to future-proof their AI investments by using the best current models, and reusing the data and workflow to retrain when a better model comes up. For instance, a customer support AI could adopt newer