nsthorat
Joined 16 years ago
Karma
232
Submissions
42
Member since
Jan 6
Recent submissions
Comment · 3 years ago
Comment · 3 years ago
I cant tell if this is research about generative AI, or AI generated research.
The new internet sucks
Comment · 6 years ago
How could a bear population quadruple in a month of lockdown when the gestational period of a bear is 200+ days?
Comment · 7 years ago
This backend work has already begun (and can run posenet, albeit still slower than WebGL): https://github.com/tensorflow/tfjs/tree/master/tfjs-backend-...
Comment · 8 years ago
Unfortunately there is no attribution, but this tool was created by Daniel Smilkov, who also built TensorFlow Playground and who is a cocreator of TensorFlow.js.
Comment · 8 years ago
There are many reasons to do it in JavaScript:
- Many companies and projects have their entire server-side stack in JavaScript and Node.js, and often they want to simply make a prediction through a model. It's quite a lot to ask them to pull in a python runtime just to make a prediction. TensorFlow.js with node bindings to TensorFlow C enables this type of inference with minimal overhead.
- Privacy. You can make predictions locally, or send embeddings back to a server without the raw data ever leaving a client.
- Flexibility of JavaScript / TypeScript. Dynamic languages are great for scientific computing, TypeScript allows you to define your own level of type safety, from raw JS on one end, to strict typing support on the other end.
- Interactivity / education tooling. See tensorflow playground for an excellent example.
- No servers for applications. Making predictions in TensorFlow on a server can be expensive in the long run. Hosting static weights on a server is much much cheaper.
JavaScript and Python ecosystems for machine learning are not mutually exclusive -- they both have their strengths and weaknesses.
Comment · 8 years ago
This probably won't buy you anything. The API you still have is WebGL, and JS is not the bottleneck.
Comment · 8 years ago
We've done some initial tests ourselves. WASM doesn't yet support SIMD so WebGL tends to be 5-10x faster. SIMD is actively being worked on by many smart people in Chromium / other browsers, so I would expect to see huge wins in the near term future. When that happens, deeplearn.js will have a WASM backend. WASM has a much better memory management story (destructors on the C++ side) so I'm super excited about its future.
Comment · 8 years ago
Come build a WASM backend for deeplearn.js :)
Comment · 8 years ago
Comment · 9 years ago
Or you improve your algorithms and use the existing hardware (think distributed computing on cheap HDDs).
Comment · 9 years ago
Why wait?
Lilac is an open-source tool that enables AI practitioners to see and quantify their datasets.
Lilac allows users to:
- Browse datasets with unstructured data.
- Enrich unstructured fields with structured metadata using Lilac Signals, for instance near-duplicate and personal information detection. Structured metadata allows us to compute statistics, find problematic slices, and eventually measure changes over time.
- Create and refine Lilac Concepts which are customizable AI models that can be used to find and score text that matches a concept you may have in your mind.
- Download the results of the enrichment for downstream applications.
Out of the box, Lilac comes with a set of generally useful Signals and Concepts, however this list is not exhaustive and we will continue to work with the OSS community to continue to add more useful enrichments.
Check out the demo on HuggingFace: https://lilacai-lilac.hf.space/ Find us on GitHub: https://github.com/lilacai/lilac