What to watch and why it matters
Scaling laws and the plateau question
Scaling laws say that more compute and data should improve models. But recent releases have not always met expectations. The community is debating whether we are hitting a plateau — and if so, whether new architectures will be the unlock. Just as the discovery of the Transformer architecture reshaped the entire field, a single new architectural idea could break through the current ceiling. As in Asimov's Foundation series, one individual's insight can redirect the trajectory of an entire discipline.
Multimodality makes everything better
Adding images to a text model improves its text performance. Adding audio improves its understanding of images. Each modality reinforces the others. The endpoint may be robotics, where vision, sound, language, and physical interaction all combine in one system.
Multiple learning methods in harmony
Babies learn through a mix of instinct (meta-learning), parental guidance (supervised learning), trial and error (reinforcement learning), and observation (unsupervised learning). The most capable AI systems will likely combine these methods rather than relying on any one.
The half-life of AI knowledge
Specific techniques change every few months. The breadth of understanding — knowing that prompting, RAG, agents, and evals exist and when to use each — lasts much longer. This course gave you the map; the details will keep evolving.
If a new model releases next month that is twice as capable, which techniques from this course still matter and which become less important?