Ai+ Training

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AI Engineering Accelerator - 9 Jun - 23 Jul

6-Week | 8-Course AI Accelerator | Virtual

Begin your accelerator journey with Live and on-demand training to build confidence in the fundamentals. Steadily progress through the Accelerator week with our hands-on training and expert-led sessions and workshops.

Why you start here: Build your unshakeable core. This phase guarantees success, ensuring you confidently grasp essential AI concepts and tools, from basic literacy to advanced AI building blocks.

What you gain: Master AI/Data Literacy, Python, Data Wrangling, Generative AI fundamentals, LLMs, RAG, and the crucial progression to AI Agents, plus Machine Learning basics, through 8 live virtual courses and on-demand training. Gain foundational fluency to accelerate your entire AI journey.

8 VIRTUAL LIVE TRAINING | SESSIONS LENGTH: 2.5 HOURS

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Course of the Week

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Talk

Jagged Intelligence: Limits, Reasoning, and Safety in LLMs

LLMs can ace the bar exam but fail at trivial tasks – a phenomenon known as jagged intelligence. While their peak capabilities are undeniable, their real-world reliability remains brittle.

In this talk, we’ll explore three critical pillars for moving past simple pattern matching toward truly robust AI:

- Mapping the Limits: Exploring where and why LLM reasoning fails on out-of-distribution and algorithmic tasks.

- Supercharging Reasoning with RL: How structured rewards unlock entirely new model capabilities rather than just refining existing ones.

- Scaling Safety Systems: Integrating safety as a core design constraint using co-evolving safeguards like WildTeaming and WildGuard.

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Video of the Month

Featuring 6-Week AI Accelerator Sessions, Personal AI Agents Summit + MORE

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Conveying Tasks to Computers: How Machine Learning Can Help

with Michael Littman, PhD, Professor at Brown University

It is immensely empowering to delegate information processing work to machines and have them carry out difficult tasks on our behalf. But programming computers is hard. The traditional approach to this problem is to try to fix people: They should work harder to learn to code. In this talk, I argue that a promising alternative is to meet people partway. Specifically, powerful new approaches to machine learning provide ways to infer intent from disparate signals and could help make it easier for everyone to get computational help with their vexing problems.

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