Hands-on notes from building with AI since before GPT-3: machine learning and data, then LLMs, now agents.
I've been experimenting with embedding ideas into GPT-space, and using the resulting vectors to visualize. For example, you could plot different activities based on two axes: how safe they are, and how adrenaline-inducing they are. How does it work? The intuition is the following. In words, you can define an axis, e.g. "unsafe activity&
There is a massive AI overhang today. The term is analogous to snow overhang. When snow slides down a roof, sometimes a section goes over the edge and is seemingly supported by nothing. You know it will eventually fall; the tension is in the air. It's a question of time. I have the same feeling with applying AI
There's a now-famous classification of different levels of self-driving: * Level 0: No automation * Level 1: Driver assistance * Level 2: Partial automation * Level 3: Conditional automation (human fallback always available) * Level 4: High automation (can safely pull over) * Level 5: Full automation The full definition is more detailed but I won't replicate it here. Now
Probably 80% of (mainstream, non-ML) Twitter is excited about the potential of GPT and ChatGPT. The other 20% is skeptical. Few are indifferent. But both the optimists and pessimists seem to focus on the obvious set of use cases: text generation and chatbots. That is a failure of imagination. Text is just an interface to those models, and chat
I've built around 10 prototypes with GPT over the past couple weeks and it's surprisingly fun. Why is that? Obviously it's a shiny new toy. Whenever I discover a new tech capability, I get excited. I recall discovering D3.js, Bokeh, Keras... These appeal to the Technology enthusiast in me. But there's
DALL-E 2 and Imagen are the hot new models everyone is sharing: the eye candy is undeniable. But since the original release of the GPT-3 large language model, OpenAI has quietly released the GPT API and playground for anyone to try. Here's a collection of my experiments with the most recent version of the model, text-
I've driven a Tesla only once but looking at Karpathy's recent presentation [https://www.youtube.com/watch?v=NSDTZQdo6H8] at the CVPR conference, soon no human will. I think Tesla's unique data engine is what will get them there. In their self-driving stack, Tesla has always been betting on cameras and radar. This
Every classification task is linearly separable in the right feature space. This statement is a clue to building ML solutions that scale to many different use cases. Here’s a visual explanation. Say you have data points in a given two-dimensional vector space. If you can use a straight line to separate the two classes, the dataset (and prediction
Lately, Twitter has been full of the 2020 US election, which has displaced everything interesting. That means it’s a good time to blog. I’ve recently been reading James C. Scott’s Seeing Like a State, which manages to combine forestry, agriculture, and land/city planning into a history of top-down planning. One thing that stuck with me
