AI 101

These subjects often turn into buzzwords with little clear explanation. No worries, we will provide straightforward insights into these complex areas in plain English and help you learn how to implement it.

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AI 101AI 101
AI 101: What is Continual Learning?

AI 101: What is Continual Learning?

Can models add new knowledge without wiping out what they already know? We look at why continual learning is becoming important right now and explore the new methods emerging for it, including Google’s Nested Learning and Meta’s Sparse Memory Fine-tuning

Alyona Vert.
Alyona Vert.
AI 101AI 101
AI 101: What is LeJEPA? The Theory Upgrade JEPA Has Been Missing

AI 101: What is LeJEPA? The Theory Upgrade JEPA Has Been Missing

We discuss how Yann LeCun and Randall Balestriero's latest research drives JEPA foundations and puts it into solid, meaningful practice – and what it means for the community. Time to work on world models!

Alyona Vert.
Ksenia Se
Alyona Vert., +1
AI 101AI 101
AI 101: What is so special about Kimi K2 Thinking?

AI 101: What is so special about Kimi K2 Thinking?

Tracing the rise of China’s agentic intelligence strategy – from Kimi’s early vision to today’s open-source breakthrough

Alyona Vert.
Ksenia Se
Alyona Vert., +1
AI 101AI 101
AI 101: What matters for RL? Precision! Switching BF16 → FP16

AI 101: What matters for RL? Precision! Switching BF16 → FP16

How this switch in precision influences the reinforcement learning accuracy and stability and why everyone, including Andrej Karpathy and Nathan Lambert, paid attention to this method

Ksenia Se
Alyona Vert.
Ksenia Se, +1
AI 101AI 101
AI 101: Inside Robotics

AI 101: Inside Robotics

Building the body of AI: How Physical AI is trained and powered – from Figure 03, Neo, Unitree robots to NVIDIA freshest updates

Alyona Vert.
Alyona Vert.
AI 101AI 101
AI 101: What is Neuro-Symbolic AI?

AI 101: What is Neuro-Symbolic AI?

Everything you need to know about hybrid neuro-symbolic AI, how it blends strict logic and rules with neural networks, and why it shouldn't be overlooked

Alyona Vert.
Alyona Vert.
AI 101AI 101
AI 101: What are Modular Manifolds?

AI 101: What are Modular Manifolds?

How Thinking Machines Lab is redefining neural network optimization through geometry-awareness

Alyona Vert.
Alyona Vert.
AI 101AI 101
AI 101: What's New in World Models?

AI 101: What's New in World Models?

A glimpse at Code World Model, PSI, and others – redefining how models catch the world in their nets

Alyona Vert.
Alyona Vert.
AI 101AI 101
AI 101: Reinforcement Learning: The Ultimate Guide to Past, Present, and Future

AI 101: Reinforcement Learning: The Ultimate Guide to Past, Present, and Future

From the early trial-and-error concepts to today’s breakthroughs with RLHF, PPO, and GRPO, and where to go next, according to Andrej Karpathy and Richard Sutton.

Alyona Vert.
Alyona Vert.
AI 101AI 101
AI 101: Rethinking Causal Attention

AI 101: Rethinking Causal Attention

How Causal Attention with Lookahead Keys (CASTLE) and future-aware casual masks reshape the strict left-and-right order of autoregressive models, plus attention in cause-effect relationships.

Alyona Vert.
Alyona Vert.
AI 101AI 101
AI 101: What are Guardian Models?

AI 101: What are Guardian Models?

Everything you need to know about models that defend AI today

Ksenia Se
Alyona Vert.
Ksenia Se, +1
AI 101AI 101
AI 101: Intelligence Processing Unit (IPU) and other alternatives to GPU/TPU/CPU

AI 101: Intelligence Processing Unit (IPU) and other alternatives to GPU/TPU/CPU

Everything you need to know about CPU, GPU, TPU, ASICs, APU, NPU and others, unpacking the meaning behind these abbreviations

Alyona Vert.
Alyona Vert.