I am offering a preliminary catalog of AI research idea generators distilled from at least a thousand AI research papers I have read, skimmed, or glanced at during my career. I have not seen this anywhere else. Each of these is a tried and tested formula/recipe for generating AI research ideas.
Google Colab is convenient but they shutdown after some inactivity. This is annoying if you are running some persistent process, like a server. So, how do we make them run for a while? I discovered this trick from oobabooba. First, paste the following code in a cell: When you run it, you should see an [ ]
Many folks interested in AI research may not have the luxuries of working at a top research lab or a tier-1 academic lab. Perhaps you are in a grad school with an advisor who is not meeting with you often (famous professor problem), or you are a researcher in a small school or startup without much “community” for meaningful feedback, or you are a lone warrior with no pedigree to show other than…
The recent Inflection AI fundraising news confirmed a hypothesis I had a while: for modern AI startups, the “data center is the new VC firm”. Why just rent out your silicon when you can have a nice slice of the equity?
A mental model for builders and investors building with modern AI models.
Imagine a future where Transformers become all that we need. What would that future look like? If you are a builder, a product person, or an investor, you might want to pay attention to this.
AI automation is not a dualistic experience. One of the dangers of the hype over-attributing capabilities of a system is, we lose sight of the fact that automation is a continuum as opposed to a discrete state. In addition to stoking irrational fears about automation, this kind of thinking also throws out of the window any exciting partial-automation possibilities (and products) that lie on the…
I have been exploring DeFi, and what's now broadly called Web3, for a while now. It's difficult to describe my impressions fully, but as an AI researcher, I can see a 2010 deep learning-like tech wave happening all over again, but in entirely unrelated technologies.
I advise a few founders and one general product heuristic seems to surface over and over: Any aspect of your work or life requiring discipline is usually a sign of a tooling gap or an automation gap. Many of these gaps are product/startup opportunities waiting to be taken. To bring the conversation closer to home, consider a typical deep learning researcher as a target customer. The best…
With Machine Learning increasingly looking like a software engineering discipline, and a new library coming out every other week, developers have little patience to spend months mastering terse documentation before doing something useful. How then do you capture the mind share of the talent who will go on to evangelize your precious framework or library in their workplaces?