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Linearizing attention. A team of Standford researchers has achieved what was thought almost impossible. They have retrained standard Llama 3.1 models to behave like linear attention models, which require much less compute while preserving most of their capabilities and using 2,500 times fewer tokens than would have been required in other methods.

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The 10 Pillars of A Successful AI Leader

As AI slowly becomes omnipresent in our work and lives, even if you’re an employer or an employee, you will have to adapt to some of the most dramatic changes in your career.

And whether these changes are positive or negative for your prospects will depend on a series of traits and approaches based on ten pillars regarding technical intuition, Operational capacity, and Personal/Hiring Traits.

By the end of the article, you will have a clear set of guidelines I call ‘pillars’ of what I believe are necessary traits of a successful leader in the age of machines.

Let’s dive in!

Technical Intuition

Hold your horses one second. I’m not saying you must be an AI engineer, know how to code a script to call APIs or anything like that. With technical intuition, I’m referring to having a clear notion of how AI works, what it can or can’t do, and being aware of the different alternatives at your disposal when engaging in an AI use case.

There’s no way around this. Even if you’re the Senior vice president of Human Resources, AI will still massively affect you. You will have to make educated decisions about it, as the idea of AI agents or copilots becoming omnipresent across all parts of the organization is a matter of time, so the idea of having IT making these decisions for you is nonsensical. Bosses make the decisions regarding their teams, and your teams will be filled with semi-autonomous AI agents.

Therefore, in the age of machines, all Leaders must understand AI.

Consequently, I am going to provide you with some high-level guidelines that clearly define AI limits, how to regard products intuitively, the importance of open-source as a fundamental weapon in your arsenal, and proof that, luckily, the first step in the process is always the same.

A Simple Guide to AIs Limits

As we have explained several times before, AI’s essence has remained unchanged for 70+ years since Alan Turing described his view of AI as ‘the imitation game.’ To this day, AI largely remains the capability of machines to imitate our intelligence.

This is extremely powerful already because AIs act intelligently. However, that doesn’t mean they embody that intelligence. Sadly, this entails that an AI model’s performance is largely bounded by its own experience through the training data, something we discussed in full detail last week, despite consistent efforts from Silicon Valley to ensure this limitation goes unnoticed.

The fact your training data contains the task or knowledge isn’t the only factor. You also must consider the frequency; the more a fact or experience…

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