🤖 AI Will Shrink Amazon’s Corporate Workforce

📦AI Will Shrink Amazon’s Corporate Workforce

🦾MIT Teaches LLMs to Self-Improve

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AI Agents are starting to change how companies perform complex business processes by acting on an employee’s behalf. They have the ability to autonomously analyze, execute, and fulfill without an employee lifting a finger.

“As we roll out more GenAI and AI Agents, it should change the way our work is done,” Mr. Jassy explained. “We’ll need fewer people doing some of the jobs that are being done today.”

“AI Agents will change how we work and live,” he added. “While many of these AI Agents have yet to be built, they’re coming, and fast.”

He continued by saying GenAI will “transform the scope and speed at which we’re able to innovate.”

Nearly 80% of companies use GenAI, yet just as many companies experience no significant financial gains, such as increased revenue, reduced costs, or improved margins, from the technology. McKinsey & Co. (McK) refers to this as the “GenAI Paradox.”

AI Agents offer a way to break out of the “GenAI Paradox” because they possess the ability to automate complex business workflows by shifting GenAI from a reactive tool to a proactive virtual co-worker.

LLMs are essentially sophisticated autocomplete machines trained on the entire Internet. So, how exactly are LLMs trained?

They’re shown millions of sentences with the last word covered up and must guess what comes next (e.g., “She poured a cup of hot {BLANK}?”).

LLMs learn to predict words like “tea” or “coffee” based on patterns they’ve seen from similar phrases within training datasets. But once training ends, the LLMs become static.

If a new fact, emerging trend, or unique task appears, the LLMs won’t know how to handle it unless developers retrain them.

SEAL solves this issue by creating “self-edits,” or instructions to help LLMs autonomously generate new training datasets and update existing Weights.

Weights help LLMs measure the meaning of words within a sentence to understand context, which is essential for generating accurate, coherent, and relevant outputs.

Consider the following sentence: “The dog barked at the stranger.” In this example, the LLMs would look at the word “barked” and determine that “dog” is important because it’s doing the barking.

LLMs are static once training ends. That means if new slang becomes popular or a recent scientific discovery occurs, they won’t know about it.

SEAL enables LLMs to self-update on the fly, so they stay current without requiring full retraining.

Context: I’m brainstorming ideas for {Insert Project},

Clarity: but my ideas feel too broad and unfocused.

Guidance: Can you help me define creative constraints to guide these ideas?

Alta raised an $11M Seed Round for an AI stylist that truly gets you.

Definely landed a $30M Series B to provide AI-based tools for lawyers.

Sword Health secured a $40M Series F to scale an AI-powered mental health platform.

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